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POLITECNICODIMILANO
DIPARTAMENTODIMECCANICA
Ph.D.CourseonMechanicalEngineering–XXVIIICycle
10.2012‐10.2016
Improving ideas novelty based on OTSM-TRIZ model of
contradiction
Candidate:
MehdiParvin
Supervisor:
Prof.GaetanoCascini
Tutor:
Prof.TullioTolio
PhD. Coordinator:
Prof.BiancaMariaColosimo
Abstract Smallandmedium‐sizedenterprises(SMEs)aremajorcontributorsto
industrialeconomies.Therefore,therehasbeenalong‐standinginterestintheempiricalliteraturetosupportR&Dengineers.TheobjectiveofthisresearchisbasedwithintosupporttheR&Dengineerstoimprovethepatentability(non‐obviousnovelty)ofideasduringideagenerationsession.BasedontheresultsofpreviousresearchinthefieldofPatentMappingandpatentanalysis,IdeationtechniquesandDesignbyAnalogy,andTRIZ,TechnicalContradictionMapwasdeveloped as the enrichment of Problem‐Solution Patent Map by thecontradiction concept. In addition, the procedure of building the map wasproposedbasedontheOTSM‐TRIZcontradictionmodel,theDesignbyAnalogymodelandPatentMapping.
Two experiments were planned and performed to study the map’susabilityandeffectiveness,andrepeatabilityof the map‐building process. Toperform the studies, the suggested map was built for Walker, as a sampletechnicalsystemthroughfollowingthedevelopedprocedure.
Inthefirstexperiment,fourdifferentmethods(Brainstorming,Problem‐SolutionMatrixMap,TechnicalContradictionMapandPatentTextFar‐Field)wereappliedandcompareeachoftheireffectiveness.Bycollectingdatafromexperiments, the efficiency of each method was estimated and evaluated inimprovingtheideationnoveltyofR&Dengineers.Inparticular,ithasreliedonthree variables; Novelty, Quantity, and Variety to assess the effect of eachmethod and these variables. First, it was estimated the model based on thecollected data in the first experiment: Usability of the map. The estimatesshowed that among four methods, introducing Technical Contradiction Mapprovidesthehighesteffectiveness in ideation ofNovelty,Quantity,aswellasVariety.
Also to be able to introduce the Technical Contradiction Map as abenchmark to the literature of ideation novelty, it was analyzed therepeatability of building the map. R&D engineers were involved in buildingthreeotherversionofthemapsbyfollowingtheproposedprocedure,andtheresultsofusageofthesemapswerecomparedtogetherwiththefirstmapbuiltby the researcher. As one would expect estimated results based on therepeatability experiment was very close across the groups applied the fourdifferentversionsofthemap.Infact,thisexperimentverifiestherepeatabilityof building the map, and if one uses the same method, conditions, andequipmentexplainedinthisresearch,willobtainthesameresults.
Acknowledgements
TheresearchworkdescribedinthisthesiswouldnothavebeenpossiblewithoutthehelpandguidanceofsomepeopletowhomIwouldliketoexpressmygratitude.
IwouldliketoexpressmythankstomysupervisorProf.GaetanoCasciniforhisimmenseknowledgeandintroducemeaninterestingresearchtopic.Alsoforallowing me to do this research in the KAEMART research group, whichconsistedofdifferentPh.D.studentsfrommethodsandtoolsforproductdesigngroup and the other groups in the mechanical engineering department ofPOLIMI.Itgavemetheopportunitytoprolongmyexchangestudieswhichhavebeenagreatexperience.
IamdeeplyindebtedtosomeofmypreviouscolleaguesandfriendsinIranianInstituteofInnovationandTechnologicalStudies(IIITS)forparticipatinginmyexperimentsandgivingmetheopportunitytogothroughtheircompaniestoperformmytests.
I would also like to express my sincere gratitude to my lovely family. Myparticularheartfeltthankstomylatefather,Prof.AmirParvin;mydearmotherNahidMobasserifortheirencouragementofmystudyprogressduringmylife;mybelovedwifeSaraSaliminaminforherunmeasurablehelpandsupport,andmychildrenAtenaandAmirhosseinformakingmeaquicklearnerofcertaingeneralmanagementskillsandnaturallyahappyfather.
Tableofcontent
5
Table of Content: [1] Introduction ........................................................................................................................ 9
1.1 Problem background................................................................................................................... 10
1.1.1 The role of Patent analysis in idea patentability .............................................................. 10
1.1.2 The importance of idea patentability for Iranian SMEs ................................................... 11
1.2 Research framework and contribution ....................................................................................... 15
1.3 Research objective and questions .............................................................................................. 17
[2] State of the Art .................................................................................................................. 18
2.1 Patent Analysis ............................................................................................................................ 19
2.1.1 Patentability ..................................................................................................................... 20
2.1.2 Patent Analysis techniques .............................................................................................. 21
2.1.3 Patent Maps ..................................................................................................................... 27
2.2 Idea Generation .......................................................................................................................... 30
2.2.1 Idea characteristics and ideation Metrics ........................................................................ 32
2.2.2 Ideation Methods ............................................................................................................. 37
2.3 TRIZ and OTSM-TRIZ model of contradiction .............................................................................. 43
[3] Research Methodology ..................................................................................................... 48
3.1 Methodological proposal to improve the Novelty of design proposals ..................................... 49
3.1.1 Research contribution ...................................................................................................... 49
3.1.2 Developed model for the target contribution of the research ........................................ 51
3.1.3 Developed procedure for building Technical Contradiction map .................................... 57
3.2 Designing empirical study ........................................................................................................... 63
3.2.1 Research contribution sample ......................................................................................... 63
3.2.2 Plan of empirical studies .................................................................................................. 78
[4] Empirical Study ................................................................................................................. 86
4.1 Experiment I: Usability and effectiveness of proposed map ...................................................... 87
4.1.1 Ideation metrics measurement ........................................................................................ 88
4.1.2 Estimated results .............................................................................................................. 93
4.1.3 Data analysis .................................................................................................................... 97
4.2 Experiment II: Repeatability of the building the map ............................................................... 108
4.2.1 Ideation metrics measurement ...................................................................................... 110
4.2.2 Estimated results ............................................................................................................ 110
4.2.3 Data analysis .................................................................................................................. 113
4.3 Conclusion of Experiment I and Experiment II .......................................................................... 121
[5] Discussions and Conclusions ........................................................................................... 123
5.1 Summary ................................................................................................................................... 124
5.2 Research results and Discussion ............................................................................................... 126
5.3 Limitations and future developments ...................................................................................... 130
[6] References ...................................................................................................................... 132
6.1 References ................................................................................................................................ 133
[7] Appendix ......................................................................................................................... 145 7.1 Appendix A - Patent analysis survey in Iranian SMEs ............................................................... 146 7.2 Appendix B - Characteristics of Patent Map methods .............................................................. 161 7.3 Appendix C - The instruction of providing the Technical Contradiction Map ........................... 163 7.4 Appendix D - Walker Patents Profile......................................................................................... 164 7.5 Appendix E - Technical Contradiction Map ............................................................................... 169 7.6 Appendix F - Generated ideas (Experiment I&II) ...................................................................... 174
Listoffigures
6
List of Figures: Figure1‐Stepsinhumanreasoningbyanalogy. ................................................................................... 40
Figure2‐TheformulateContradictionaccordingtoOTSM‐TRIZ. ....................................................... 46
Figure3‐RelationsofResearchmodel,Designprocesssteps,andoriginalcontribution. ................. 50
Figure4‐Proposedmodelofresearch. .................................................................................................. 52
Figure5‐AconceptualdiagramofaTechnicalContradictionMap. .................................................... 56
Figure6‐SimplifiedproceduresforbuildingaTechnicalContradictionMap. ................................... 57
Figure7‐Asimplewalkingframe. .......................................................................................................... 64
Figure8‐Walkerforimprovedstairwaymobility. ................................................................................ 70
Figure9‐The30patentmatrixinformation. ......................................................................................... 74
Figure10‐Problem‐Solutionmatrixmap. ............................................................................................. 75
Figure11 ‐ Proposedcontradictionmap. ............................................................................................... 76
Figure12‐TheproposedthreeDimensionalTechnicalContradictionMap. ...................................... 77
Figure13‐Theresultsofgenealogytreeanalysisforateam‐ExperimentI. ..................................... 92
Figure14‐Graphicalrepresentationofassessingcriteriaoftwosessions‐ExperimentI. ............... 95
Figure15‐Theideatimelineofallgroup‐ExperimentI. ..................................................................... 96
Figure16‐NASAtaskloadIndexresultsofparticipants‐ExperimentI. ............................................ 96
Figure17‐NormalityoftheData‐ExperimentI. .................................................................................. 97
Figure18‐EstimatedresidualsfortheNoveltyregression:Homoscedasticitytest‐ExperimentI. .. 98
Figure19‐EstimatedresidualsfortheNoveltyregression:Normalitytest‐ExperimentI. .............. 99
Figure20‐EstimatedresidualsfortheNoveltyregression:Homoscedasticitytest‐ExperimentI. ............................................................................................................................................................ 102
Figure21‐EstimatedresidualsfortheQuantityregression:Normalitytest‐ExperimentI. .......... 103
Figure22‐EstimatedresidualsfortheVarietyregression:Homoscedasticitytest‐ExperimentI. 105
Figure23‐EstimatedresidualsfortheVarietyregression:Normalitytest‐ExperimentI. ............ 106
Figure24‐Graphicalrepresentationofassessingcriteriaoftwosessions‐ExperimentII. ............. 112
Figure25‐NormalityoftheData‐ExperimentII. ............................................................................... 113
Figure26 ‐ EstimatedresidualsfortheNoveltyregression:Homoscedasticitytest‐ExperimentII. ............................................................................................................................................................ 114
Figure27‐EstimatedresidualsfortheNoveltyregression:Normalitytest‐ExperimentII. .......... 115
Figure28‐EstimatedresidualsfortheQuantityregression:Homoscedasticitytest‐ExperimentII. ............................................................................................................................................................ 117
Figure29‐EstimatedresidualsfortheQuantityregression:Normalitytest‐ExperimentII. ......... 117
Figure30‐EstimatedresidualsfortheVarietyregression:Homoscedasticitytest‐ExperimentII. ............................................................................................................................................................ 119
Figure31‐EstimatedresidualsfortheVarietyregression:Normalitytest‐ExperimentII. ........... 120
Listoftables
7
List of Tables: Table1‐ThreelevelsofthebenefitsofpatentanalysisforIranianindustries. .................................. 12
Table2‐TheInterviewwithresponsibleorganizationsforIranianinnovationsandinventions. .... 13
Table3‐SummaryofQuestionsofthesurveywithinIranianSMEsaboutpatentanalysis. .............. 14
Table4‐SummaryofR&Dengineers’knowledgeonpatentanalysis. ................................................. 15
Table5‐TypesofdesignresearchandDRMframework. ..................................................................... 16
Table6‐ThelogicbehindeachofTextMiningtechniques. .................................................................. 23
Table7‐RepresentativeExamplesofPatentMap. ................................................................................ 28
Table8‐Assessingmethodsofideas. ..................................................................................................... 34
Table9‐TheformulaforassessingNovelty,Variety,QualityandQuantity. ....................................... 36
Table10‐Examplesofnature‐basedandnon‐nature‐basedmethods. ............................................... 41
Table11‐ComponentsofClassicalTRIZandOTSM‐TRIZtheories. .................................................... 44
Table12‐ConceptsforappropriateAnalogs. ........................................................................................ 52
Table13‐DetailedprocedureforbuildingaTechnicalContradictionMap. ....................................... 58
Table14 ‐ Technicalsystemsclassifiedbydegreeofcomplexity. ........................................................ 64
Table15‐Theresultsofsearchingandrefiningthepatents. ............................................................... 65
Table16‐Resultsofextractedinformationfromapatent. ................................................................... 66
Table17‐Eightcategoriesofproblems. ................................................................................................. 70
Table18‐Sixcategoriesofsolutions. ..................................................................................................... 72
Table19‐DedicatetimeforbuildingtheTechnicalContradictionMapofaWalker. ......................... 78
Table20‐Similarpartsofexperiments. ................................................................................................. 80
Table21‐Theappliedformulaincurrentresearch. ............................................................................. 82
Table22‐Usabilityofproposedmapplan‐ExperimentI. ................................................................... 87
Table23‐ParticipantsProfile‐ExperimentI. ....................................................................................... 88
Table24‐TheNoveltyattributewithweightsandrelatedFBSLevels‐ExperimentI. ...................... 89
Table25‐The���scoresof4Groups‐ExperimentI. ............................................................................ 90
Table26–ThecalculationofthedegreeofNoveltyofoneoftheteams‐ExperimentI. ................... 90
Table27‐ThetemplatetableforassessingthedegreeofVarietyofteams‐ExperimentI. .............. 91
Table28‐ThefilledtemplatetableofassessingthedegreeofVarietyforateam‐ExperimentI..... 92
Table29–ThescoresofQuantity,Novelty,andVarietyforallteams‐ExperimentI. ....................... 93
Table30‐ThescoresofQuantity,Novelty,andVarietyrespecttothegroupwithdifferentstimuli‐ExperimentI. ....................................................................................................................................... 94
Table31‐EstimatedresultsofeffectsofdifferentmethodsonNovelty‐ExperimentI................... 100
Table32‐Estimatedresultsofimprovingideasfordifferentmethods:Novelty(clustering)‐ExperimentI. ..................................................................................................................................... 101
Table33‐Estimatedresultsofimprovingideasbasedondifferentmethods:Quantity‐ExperimentI. .......................................................................................................................................................... 103
Table34‐Estimatedresultsofimprovingideasfordifferentntmethods:Quantity(clustering)‐ExperimentI. ..................................................................................................................................... 104
Table35‐Estimatedresultsofimprovingideasfordifferentmethods:Variety‐ExperimentI...... 106
Table36‐Estimatedresultsofimprovingideasfordifferentmethods:Variety(clustering)‐ExperimentI. ..................................................................................................................................... 107
Table37‐RepeatabilityofbuildingthemapPlan‐ExperimentII(PartI). ....................................... 108
Table38‐Repeatabilityofbuildingthemapplan‐ExperimentII(PartII). ...................................... 109
Table39‐ParticipantsProfile‐ExperimentII. .................................................................................... 109
Listoftables
8
Table40‐The���scoresof4Groupsofexperiment‐ExperimentII. ................................................ 110
Table41‐ThescoresofQuantity,Novelty,andVarietyforallteams‐ExperimentII. ...................... 111
Table42–ThescoresofQuantity,NoveltyandVarietyrespecttothegroupwithdifferentstimuli‐ExperimentII. .................................................................................................................................... 112
Table43‐Estimatedresultsofimprovingideasforthesamemethods:Novelty‐ExperimentII. .. 116
Table44‐Estimatedresultsofimprovingideasforthesamemethods:Quantity‐ExperimentII. 118
Table45‐Estimatedresultsofimprovingideasforthesamemethods:Variety‐ExperimentII. ... 121
Table46–Summaryofresultsandreflectingonexistingtheories. ................................................... 127
Chapter1:Introduction
9
Chapter1
[1] Introduction
Chapter1:Introduction
10
1.1 Problem background
Theproblembackgrounddiscusses themotivations fortheresearch.The
motivationbehindcurrentresearchcanbediscussedbothasalivescientificissueand also a critical issue for companies. Supportive methods and tools for ideapatentability and similar research issues can be considered as the scientificdimension,andtheimportanceofideapatentabilityforindustriescanbeconsideredasapplicationdimension.Inthissection,thesetwoscopesarementioned.
1.1.1 The role of Patent analysis in idea patentability
PatentabilityofaninventionorasolutiongeneratedbyR&Ddepartmentsisacrucialissueforindustriesasitletsthemprotecttheirinvestigationsandcompetemoreactivelyinthemarket.Patentabilityisoneofcurrentresearchissuesinpatentanalysis domain that patent analysis, itself, is known as a tool for supportinginnovationandinventionandconsequentlyengineeringdesign(Reitzig,2005).Aninventioncanbeacceptedandregisteredasapatentwhenitsnoveltyisnon‐obviousfortheexpertsinthefield,anditsindustrialapplicationisvisible(Franzosi,2000).Everyinventionincludesatleastanovelsolutionforaproblemwhichitisnotmostlynon‐obviousfortheexpertsinthefield.Thiskindofinventionsmostlyusesmoreresourcesprovidedthetargetanddesiredexpectationsandperformances.Ontheotherhand,non‐obviousnovelideasaremostlythesolutionswhichprovidemorereturnswithsameorevenlessusageofthesamekindofresourcesorapplyingnewsort of resources. This kind of inventions exploits new physical principles andbehaviors.
Thepatentabilityofaninventionisstudiedasataskbasedontheexpertiseof theexperts in the fieldandworkthat can becomputerizedandperformedbysupportive software. The literature shows Non‐obviousness of novelty is a mostcriticalpartofapatentabilityofaninventionwhichmustbestudiedbytheexpertsinthefield,andthesoftwarecannotsubstitutetheirexpertise.Inotherwords,theliteratureshowswhileconsideringthenoveltyisknownasanexpertise‐basedtask;itcanbesupportedpartiallybysoftware(Pimenteletal.,2014),Butthestudiesforclarifying the non‐obviousness novelty is followed mostly by an expertise‐basedtaskwhichtheycannotbecomputerized.Thesupportivesoftwareforhighlightingthe newness and novelty of an invention use the solved problem, systems or itselements proposed by the solution or some characteristics of a solution such asfunctionandbehavioraskeywordsforpatentminingandtherearetoofewstudiestodiscussthekeywordsforhighlightingtheNon‐obviousnessofasolution.PatentMapsandnoveltydetectionaresomemethodsforhighlightingandsupportingthenewness and novelty of the patents. Some TRIZ‐based patent mining researches,approachthenon‐obviousnessofanoveltybyfocusingonsearchingandclarifyingthecontradiction(s)resolvedbyapatent(CasciniandRusso,2006).TRIZ(TheoryofInventiveProblem‐Solving)isknownanideationtechniqueofDesignbyAnalogy,uses the characteristics of solutions. Specifically, principles applied for resolvingcontradictions. This methodology discusses the inventions in five different levels
Chapter1:Introduction
11
whichthreeofthemcanbeconsideredasthenon‐obviousinventionsfordesignersinthefield.Also,therearefewtypesofresearchtodiscusstheeffectsofexpertiseinsearching and clarifying resolved contradictions of patents and consequently therelationsof levelsofresolvedcontradictionsontheNon‐obviousnessofapatent.Therefore, it isworthtostudythe roleofexpertise insearchingandhighlightinglevelsofresolvedcontradictionsandrelationofthemonthenon‐obviousnessofthenoveltyofpatents.
1.1.2 The importance of idea patentability for Iranian SMEs
Studying the patentability of an invention become a bigger issue whenthinkingaboutthescopeofSMEs(SmallandMediumEnterprises)whichareoneofthe critical sectors of industries. Nowadays the role of the SMEs in industrialdevelopment and the immense potential for growth are well known, so thegovernmentsaretryingtosupporttheSMEsindevelopingtheleadingindustriesbyconsideringdifferentencouragement(BennettandRobson,2003).SMEsaremostlyclassifiedbasedontheirnumberofemployeeswhichshowtheirlevelofaffordancesoninvestigatingformanyprofessionalmulti‐tasksandexpertisewhiletheyhavetocompete in the market with other sorts of companies. SMEs in the Europeancountriesaredefinedlessthan250employeesclassifiedintothreecategories;0‐9employees,10‐49employees,and50‐249employees(UNIDO,2003).AccordingtotheIranianStatisticalYearbookfor1999,whichisthescopeofthisresearch,IranianSMEscategoriesintofourclasses;1to9employees,10to49employees,50to99employees, and more than 100‐249 employees. The main part of manufacturingcompaniesinIranareintheSMEsector,andabout75%aresmallbusinesses.Also,approximately 63% of the human resources in the industrial enterprises isemployedintheSMEs(UNIDO,2003).
TheIraniantechnicalsectionhasmetvariousdifficultiesinrecentyears,butthemost importantone is thelow levelsofactivities in innovationandthesmallinvestmentfordoingR&Dtasks.StatisticsshowthetotalnumberofIranianpatentsinlocalofficeuptotheyear2013were47262patents(www.ip.ssaa.ir),whileonly677 patents registered in the international patent office information(www.orbit.com)andonly264ofthem(www.orbit.com)weregrantedintheworld.These results show only 0.55% of Iranian patents are granted in the world.According to this problem, two goals are defined; “upgrading productivity andhumanresourceefficiency,”and“upgradingtechnicalandprofessionalknow‐howandtheskilllevelofthelaborforce”(UNIDO,2003).Thesegoalsarepursuedintwoprimary policies; “reorganizing the training of labor to increase technical andprofessionalcompetenciesandtherebyachieveincreasedlevelsofproductivityandefficiency,”and“providingfacilitiesfornewindustrialSMEs”(UNIDO,2003).
PatentanalysisisamongtheissueswhichareconsideredasnecessaryskillsforengineerswhoareresponsiblefordifferenttechnicaltasksincludingR&DtaskssuchasQualitycontrolandnewproductdevelopment.ThemainbenefitsofpatentanalysisinIranianindustrieshavebeenclassifiedintothreecategories(Bagherietal.,2009).Table1showstheseadvantages.
Chapter1:Introduction
12
Table1‐ThreelevelsofthebenefitsofpatentanalysisforIranianindustries.
Competitive-level
information
Technical-level
information
Strategic-level
information
Identificationof
competitors Acquiringtechnical
informationfrom
patents
TechnologytrajectoryIdentificationofkey
inventor(s)
Avoidanceofinfringing
others’patents
Usingunprotected
technologies
Technological
orientationofmajor
companies
Identificationofpotential
licensorsAvoidingduplication
Tracinginfringements
IncreasingBargaining
powerintransferof
technology Source of ideas
Selectionofsuitable
partnersforstrategic
R&DalliancesCurrentawareness
Despiteconsideringpatentanalysisasoneoftherequiredskillsforengineersof SMEs, there is no empirical research to show the level of knowledge andapplicationofthistoolsintheIranianSMEs.Therefore,asurveywasperformedastheinitialentrancetotheissueinthescopeofthecurrentresearch.ThisstudywasconductedtoclarifymoretheexpectationofpatentanalysismethodsandtoolsforIranian industries and correspondingly the level of an acquaintance of R&DengineersinIranianSMEsonpatentanalysis.Thisreviewincludesdifferentparts(Details are available in Appendix A); the first section with the responsibleorganizationsforinnovationandinvention,thesecondpartwiththeIranianSMEs,and the third part with individual R&D engineers. The results of each part arediscussedinmoredetailinfollowing.
1. Preferred sector for using patent analysis (results of the survey within responsible organizations for innovation and inventions in Iran): Thisparthasconsistedofaninterviewwith20relatedresponsibleinfollowingorganizations:
ElitesFoundation;
TechcommitteeinExpediencyDiscernmentCouncil;
SciTechpark;MinistryofScienceandresearchandtechnology;
ResearchcenterofTehranpolytechnicuniversity;
IncubatorCentreofIranUniversityofScienceandTechnology;
Chapter1:Introduction
13
TehranUniversitySciTechparkandNANOCommittee;
MinistryofScienceandresearchandtechnology.
Thisinterview(Table2)withthesubjectofinventionandpatentshastaken40hours intotal,andtheresultsshow,despitegovernment financialsupport forgrantingpatentsandtheneedtothepatentinformationinIranianSMEs,thereisalow level of the acquaintance and usage on patent analysis. Table 2, shows thequestionsofthispartandalsoclarifiesthemostmeaningfulandconsensusresponsetothem.
Table2‐TheInterviewwithresponsibleorganizationsforIranianinnovationsandinventions.
Questions Results
1.TheleveloffinancialsupportsofgovernmentforgrantingapatentwithinIranianIndustry?
Mediumlevel
2.Thelevelofusageofpatentinformation(National/International)inIranianIndustry?
Lowlevel
3.ThemostpriorandpreferredsectorforusingpatentinformationamongIranianIndustry?
SmallandMediumEnterprises
4.ThelevelofthenecessityofpatentanalysisinIranianIndustry? Highlevel
5.ThelevelofusageofpatentanalysisinIranianIndustry? Lowlevel
6.ThelevelofanacquaintanceonthepatentanalysisinIranianIndustry? Lowlevel
TheTableshowsthat theresponsibleareawareof thenecessityofpatentanalysis for Iranian industries, but the usage and awareness are low in them.Moreover, theymostlyagree that IranianSMEsarethemostpreferredsector forapplyingpatentinformationtosolvetheirproblems.
2. The position of Iranian SMEs in using patent analysis (Result of the survey within Iranian SMEs):AfterclarifyingSMEsectoragainasthepreferredareaforusingpatentanalysisinthefirstpartofthesurvey,thesecondpartwasdonetoclarifythepositionofIranianSMEsinusingpatentanalysis.Thispartconsistedofaquestionnairewithtenquestionsinfoursections;generalcompanyinformation,thelevelofawarenessandusageofpatentanalysis,patentanalysispurposeandusingdatabases.25R&Dengineerscompletedthequestionnaire in 25 separate SMEs (or the engineers that they alsoconsideredresponsibleforR&Dtasksbesidestheirotherduties).Table3showsthesummaryofthequestionnaireanditsresults.
Chapter1:Introduction
14
Table3‐SummaryofQuestionsofthesurveywithinIranianSMEsaboutpatentanalysis.
Questions Results
1.ThenumberofemployeeinSMEcompany?‐10to49employees‐50to99employees
2.ThenumberofpatentsinIranianSMEcompany? 1‐5patent
3.Theresponsibledepartmentfortheinventions,newproductdevelopment,andpatentsinSMEcompany?
‐R&Ddepartment‐EngineeringdepartmentwithresponsibilityforR&Dtasksbesidesotherresponsibilities
4.TheLevelofanacquaintanceonthepatentanalysisinSMEcompany?
Lowlevel
5.RequestedandinterestedlevelforexploitingpatentinformationforSMEcompany?
Technicallevel(amongstrategic,Juridicalandcompetitive)
6.AnystandardoraspecificprocessforpatentanalysisprojectsinSMEcompany?
Withoutanystandardprocess
7.ThemainbenefitandexpectationsofpatentanalysisprojectinSMEcompany?
Twomainadvantages:‐ProposingaNovelpatentablesolution;‐Studyingpastresearchandfindingsolutionstoproblems
8.Forwhichstepoftheinventionprocess,thepatentanalysisisexpectedtobeused?
Ideagenerationbybecomingawareofexistingpossiblesolutions
9.TheprimarypurposeforusingpatentanalysisinSMEcompany?
RealizingNoveltiesofpatents
10.Themostuseddatabaseinthecompany? ‐USPTOandEPO
TheresultofthesurveyshowstheparticipatedSMEsintheinquiryhasatleastonepatent.TheyusuallyusedUSPTOandEPOdatabasesatthetechnicallevelforanalyzingthepatents.Thelevelofanacquaintanceonpatentanalysisislow,andthecompanieshavenotanystandardprocessforpatentanalysisprojects. Inthiscompanies, the principal purpose of the patent analysis is the to identify thenovelties in patented inventions, to exploit them for generating new patentablesolutionsandideas.
3. The knowledge and skills of R&D engineers (Result of a survey with R&D engineers):
Consideringtheresultsof thesecondpartof thesurvey, thethirdpartwas planned to observe the overall knowledge and skills of R&Dengineersinpatentanalysis.Thethirdpartwasdoneasaworkshop;thisworkshopwasperformedwith15R&DEngineersfromdifferentIranianSMEs.Someinformation(Table4)aboutthepatentanalysisknowledgeofparticipantswasgathered,andthenthepatentanalysiswaspresentedanddiscussedingroups.
Chapter1:Introduction
15
Table4‐SummaryofR&Dengineers’knowledgeonpatentanalysis.
Questions Results
1.Thenumberofpatentshasyoueverstudieduntilnow? Between1‐10
2.Thenumberofpatentanalysisprojectshaveyoueverparticipated?
Between1‐2
3.Thenumberofpatentdatabaseshasyoueverused? Between1‐2
4.Thenumberofpatentanalysissoftwareortoolshaveyoueverused?
0
As the Table shows in overall R&D engineers are not skillful in patentanalysis,andtheyevenhavereadverylessnumberofpatents.RespecttothegeneralpoliciesforIranianindustries(UNIDO,2003)fromonehandandtheresultsofthesurvey, it is logical to propose a contribution supporting R&D engineers inintroducingpatentable ideasusingpatentanalysistools.Also,asdiscussedintheprevious section, the scientific studies show few studies in searching andhighlightingthenon‐Obviousnessnoveltiesofpatents.Therefore, theobjectiveofthisinvestigationis,tosupportR&Dengineerstoproducemorenon‐obviousnovelideasbyusingpatentanalysis.
1.2 Research framework and contribution
ImprovingthepatentabilityofaninventiongeneratedbyR&Dengineersis
considered as the objective of this investigation. Patent analysis and ideationtechniquesarethetworelatedresearchfields.Problem‐SolutionPatentMapisoneofthetoolsofpatentanalysisatthetechnicallevelforimprovingthenoveltyofideasgeneratedbyR&Dengineers.TRIZisoneoftheknownmethodsbasedonDesignbyAnalogy model for improving the performances of engineers in solving inventiveproblemswhichincludingthecontradictorysituation.Literatureshow:
1. Problem‐SolutionPatentMapcanbeusedforsupportingR&Dengineersingeneratingnovelideas(Suzuki,2011),whileitwasnotdevelopedtohelpR&Dengineerstoproducenon‐obviousnovelideastobepotentialpatents.
2. Providing the previous solution of a technical system as stimuli toengineersinadesignsessioncanincreasetheQuantityofgeneratedideas(Simonton, 2010) and reduce the creativity (Jansson and Smith, 1991;Smithetal.,1993;DoboliandUmbarkar,2014).
3. Morethansources,analogsareusefulinqualityandappropriatenessofgenerated ideas by analogy for a problem (Casakin and Goldschmidt,1999;Casakin,2004).
Chapter1:Introduction
16
4. TRIZasamethodandtechniqueswasdevelopedtosupportengineersforresolvingthecontradictorysituationsintheevolutionpathofatechnicalsystem (Altshuller, 1984) which can result to non‐obvious novelinventions,butitsprocessforproblemdefinitionandProblem‐Solvingistoo systematic and time‐consuming to follow in every situation anddesignsessionbyengineers(Fricke,1993;Fricke,1996).
Accordingtothesummarymentionedabove,inthescopeofthisresearch,itisconsideredtoenrichtheProblem‐SolutionPatentMapofaparticulartechnicalsystemfor increasingthegenerationofnon‐obviousnovel ideasbyanewanalogaccording to the observed abstract patterns for resolving the contradictorysituations.
To address the objective of the current research and study the proposedcontribution,atype3studiesinthescopeofDRMframeworkisdefined.TheDRM(Design Research Methodology) consists of four phases (Blessing & Chakrabarti,2009),andthetypesofresearchprojectsinthiscontextaredefinedbasedonthekind of research activities in the different phases. Research clarification, theDescriptivestudyI,PrescriptiveStudy,andDescriptiveStudyIIarethefourphasesofresearchprojectsinDRM.ThedesignactivitiescanbeReview‐basedstudy,Initialstudy, or Comprehensive study in each of these four stages. The ResearchClarification phase points to formulate a realistic and alive research goal,Descriptive Study I clarifies the description of the existing situation, PrescriptiveStudyproposessolutionsforimprovingthecurrentsituationtowardsthedesiredsituation,andDescriptiveStudyIIassessestheeffectsofdevelopedsolutionrespectto the desiredsituation.Table5 (Blessing&Chakrabarti,2009)shows thescopeofcurrentresearch(Type3)respecttotheothersixtypesofstudiesinthisframework.
Table5‐TypesofdesignresearchandDRMframework.
In the scope of the current research, research clarification was studiedliterature‐based to clarify the necessity to approach patentability of an idea as a
Research Phases
Types Research
clarification
Descriptive
study I
Prescriptive
study
Descriptive
study II
1 Review‐based Comprehensive ‐ ‐
2 Review‐based Comprehensive Initial ‐
3 Review-based Review-based Comprehensive Initial
4 Review‐based Review‐basedReview‐based
Initial/ComprehensiveComprehensive
5 Review‐based Comprehensive Comprehensive Initial
6 Review‐based Review‐based Comprehensive Comprehensive
7 Review‐based Comprehensive Comprehensive Comprehensive
Chapter1:Introduction
17
vividresearchgoal.ThereviewedliteratureshowedthatthesupportingtoolsarenotcapableenoughtoleadR&Dengineerstogeneratenovelideaswhichhavethehighpotentialitytobeacceptedaspatents.ThedescriptivestudyI,asthesecondphase, was performed literature‐based too, to compare the results and effects ofdifferentappliedtoolsandmethodsforsupportingthepatentabilityof ideas.ThereviewedliteratureshowedFull‐TextPatentandProblem‐SolutionPatentMapareusedforimprovingthenoveltyofideaswhilethereisnotdirectresearchoneffectsof them on the patentability of novel ideas. The prescriptive study, as the thirdphase,wasdonecomprehensivelythroughproposinganoveltoolforimprovingthepatentabilityofideas.ATechnicalContradictionMapandprocedureofbuildingthemapweretheproposedsolutionsinthisphase.Finally,theimpactsofthedevelopedmaprespecttothefirstsituationwerestudiedinthefourthphase,DescriptiveStudyII.
Theresultsofthetwofirstphases,researchclarification,andthedescriptivestudy I, were presented as state of the art in Chapter 2. The third phase, theprescriptivestudywasperusedcomprehensivelyinChapter3wherethetheoreticalandempiricalstructureoftheproposedsolutionwerediscussed.InChapter4,theempiricalperformancevaliditywasdiscussedastheresultsofthefourthphase,thedescriptive study II. Finally, Chapter 5, discusses some limitations and futurecorrespondingstudies.
1.3 Research objective and questions
This study is interested in finding and using non‐obvious novel ideas of
patentsofaparticularsystemforimprovingthepatentabilityofaninventionforthesame system. Therefore, the primary objective of this research is consideredimprovingthepatentabilityofaninventiongeneratedbyR&DengineersinIranianSMEs.Toapproachthisgoal,‘TechnicalContradictionMap”isdeveloped,andtwomain research questions are defined through a quantitative study and statisticalanalysis:
1. Can R&D engineers in Iranian SMEs improve Novelty within their ideas, through the use of an enriched Problem-Solution Patent Map by the ‘contradiction concept’?
2. Can Iranian R&D engineers build the proposed enriched Patent Map by following the developed procedure?
Twoexperimentsareplannedandperformed–theusabilityofthemapandthe repeatability of building the map – to answer and analyze these questions.SeveralhypothesestestsareusedtodeterminewhetherthereisenoughevidenceinpresentedsampleofcollecteddatafromtwoexperimentstoinferthattheusabilityoftheTechnicalContradictionMapisrightfortheentirepopulation.
Chapter2:Stateoftheart
18
Chapter2
[2] State of the Art
Chapter2:Stateoftheart
19
This chapter presents the results of the two first phases of the
research, research clarification and the descriptive study I, in the form ofa
summaryofthepreviousresearchthatthecurrentresearchbenefitsofthem.
Thisresearchisdefinedasanadvancementinoneoftheapplicationsofpatent
analysis by exploiting an enriched Design by Analogy model in solving
problems.Therefore,thischapterispresentedinthreeparts.Part1illustrates
thebasicterminologyandconceptsofapatent,patentanalysis,itsapplications
whichfollowbystudyingthedevelopedtoolsforsupportingthepatentability
of an invention. Part 2 concerns idea generation methods and its
advancements to get an idea for improving the patent analysis tools
specialized for supporting the patentability of an invention. Part 3, reviews
OTSM‐TRIZmodelofcontradictioninresolvinginventiveproblemstoreach
non‐obviousnoveltieswhichcanbeappliedtoimprovingthetargettool.
2.1 Patent Analysis
A patentisanagreementbetweenthegovernmentoritsresponsible
agency and patent owner; patent owner discloses and exposes the new
knowledge, technology, and relevant engineering science behind its patent,
andthegovernmentprotectstheexploitingrightofthepatentfortheowner
foracertainperiod(HufkerandAlpert,1994;Ernst,2003).EuropeanPatent
Office (EPO), the United States Patent and Trademark Office (USPTO), the
JapanPatentOffice(JPO)aresomeofthepublicoffices,andthepatentsare
accessible through different integrated, up‐to‐date sources of this office
(Abbasetal.,2014).
Patentsprovidevariousinformationsuchasthecontentofanexclusive
rightoranintellectualattributeright,andtechnicalinformationofbroadsort
of state‐of‐the‐artistic creation technology, which is used by companies to
knowthecompetitor'stechnologicaldevelopmentschemeorglobalstrategies
toplanR&Dprojects.However,usingpatentinformationisnoteasy,because
patentinformationintentionallyincludesexplicitexpressions;relatedtothe
natureofpatentsanditsspecificterminology,andalsorelatedtotherights
(Suzuki,2011).Also,thereiscontradictorydiscussionaboutadvantagesofthe
vastamountofpatentinformationwhichcanbehelpfulforgettingnewsights
andknowledgebutmustbereviewedandconsideredtoavoidanyproblemfor
newclaims(Cotropia,2005).
Patent analysis is a term to refer a set of task including searching
relevantpatents,extractingandanalyzingpatents’informationandpreparing
themtorespecttothebroadrangeofdecisionsinthetechnicalorstrategic
Chapter2:Stateoftheart
20
levels.AwideVarietyofapplicationsisdiscussedintheliteratureforpatent
analysis. Checking the patentability and Novelty of a new invention of the
company (Bonino et al., 2010), analysis of the competitors (Abraham and
Moitra,2001),studyingthepossiblegrowthofacompanyinaspecificperiod
(Ernst, 2003), studying the relation of the technological developments and
economic growth (Coussement and Van den Poel, 2008), recognizing the
trendsoffuturetechnologyinaparticularareaoftechnology(YoonandKim,
2011),clarifyingtheinfringements(Leeetal.,2013),studyingthetheproduct
evolutionandmarketopportunitiesrespecttothetechnologicaldevelopments
(Phaaletal.,2003),highlightingthepromisingpatentsfortechnologytransfer
(Trappey et al., 2013; Du and Ai, 2008) are some of the applications and
purposesofpatentanalysismentionedintheliterature.Inaddition,analysis
ofthepatentscanclarifytechnologicalfeatureandconnection,revealmarket
trends,showdirectionsfornoveltechnicalsolutionsandinfringementrisks,
highlight competitive positions, and support investment policies (Liu and
Shyu,1997;AbrahamandMorita,2001;Campbell,1983;Jung,2003;Daimet
al.,2006).
Checking the patentability of an invention is one of the technical
applications of patent analysis that is the interest of R&D engineers and
departments.DespitethebenefitofR&Ddepartmentstothisapplication,the
researchesinthisfieldshowsomelimitationswhicharemostlyrelatedtothe
methodsofextractingtheusefulandrelevantpatentsandtheir information
and analyzing and presenting the extracted information in a usable way.
Therefore, in following, the previous researches related to each one is
reviewedinmoredetaillevel.
2.1.1 Patentability
Respect to the rapid technology advancements in Industrial and
InformationTechnologyage,mostinventionsaredevelopedandbuiltonprior
patents.Therefore,thestudyisneededtoensurethateachnewpatentisnota
smallderivativeimprovementtoanexistingtechnologyorapriorpatentand
consequentlyitdoesnotdeprivethecurrentpatentholderofhis/herprofits
(GreenandScotchmer,1995).Therefore,patentsarealegaltitleforaperiod
that must cover three primary requirements by the experts in the field;
Novelty, Non‐obviousness, and Usefulness according to the patent law
(Samuelson,2004).
Legalimportanceofapatent,makethedefinitionandmeasurement
ofthecharacteristicsofapatent,specificallyNoveltyandNon‐obviousness,
averycriticalissue.Despitethisimportance,thereisnoagreementonthe
Chapter2:Stateoftheart
21
definition of these characteristics and their corresponding assessing
methods.
Optimal configuration of Novelty and Non‐obviousness and the
benefits of this configuration on overall welfare for various types of
industries are studied by theoretical economists (Scotchmer and Green,
1990; Green and Scotchmer, 1995). To determine whether a proposal
satisfiestheNoveltyandNon‐obviousnesscriteria,fourclassesoftheprior
art,aredefined(Franzosi,2000);Commonknowledge(alreadyknowntothe
expertsinthefield),Enhancedknowledge(canbeaccessedbyagoodexpert
whenconfrontedwithanewproblem),Hiddenknowledge(notknowntothe
mostexpertsinthefield),andfinallyPriorapplications.Noveltyisachieved
whentheinventionisnotintheclassesofCommonandEnhancedknowledge
in thetarget field.Similarly, todeterminewhetheraproposalsatisfiesthe
Non‐obviousnesscriterionanditissufficientlydifferentfromthepriorart,
the expert must conclude that the idea is not a simple derivation or
combinationofthepriorartwhichcanbelogicaltoanaverageexpertinthe
field(Franzosi,2000).Non‐obviousnessisachievedwhentheNoveltyisnot
in the classes of Common and Enhanced knowledge. The corresponding
assessmentconsidersCommonandEnhancedknowledgethroughchecking
the prior art in the field. It is worth to mention Common or Enhanced
awareness of another area of the art, can be used in a Non‐obviousness
NoveltyanditisnottobecheckedfortheNon‐obviousnessofaninvention
intargetfieldofart.TheoreticallycheckingtheNoveltyandNon‐obviousness
ofaninventiontobebeyondCommonandEnhancedknowledgeinatarget
fieldoftheart,doesnotseemacomplextask.Thedifficultybecomesevident,
consideringtheincreasingvolumeofpatentsinacertainfieldoftechnology
and not supportive computerized tool for effective and efficient search
(Bonino et al., 2010; Hunt et al., 2012; Wanner et al., 2008) and analysis
whicharediscussedinthefollowingsection.
2.1.2 Patent Analysis techniques
As mentioned, the patent analysis starts from searching relevant
patentsandcontinuesbyprovidingtherequiredinformationfortargetusers
atdifferent levelssuchasR&Dengineersandmanagers invarious formsof
text and graphs to let them decide in the strategic or technical level. The
generalprocessofpatentanalysiscomprisesoffourfollowingsteps(Liuetal.,
2011;Tsengetal.,2007):
1. Identificationofcandidateexistingpatentstoanalyze;2. Extractionofinformationfromthesecandidates;
Chapter2:Stateoftheart
22
3. Analysisoftheextractedinformationandassessingrelevance;4. Determining and presenting conclusions on whether and how this
informationaffectstheresearchconclusions.
Eachoftheabovestagesofpatentanalysisisataskwhichispursuedin
somemoredetaillevelsteps,whereasliteraturehasdiscusseddifficultiesof
each stage respect to the expertise, skills, and capabilities needed even by
experts in the field (Kostoff, 1998). Patent analysts with the various skill
required patent analysis tools with a different ability (Bonino et al., 2010).
Usingpatentanalysisautomatedtoolsassistandrelievethepatentanalysis
experts of the labour‐intensive and time‐consuming tasks of manually
searchingandanalyzingthepatents,andalsoacceleratetheanalysisprocess
(YoonandPark,2004).
Thetwofirststagesarecriticaltasksfromtheviewpointofsearching
andextractingrelevantpatentsorinformation;thefirststepaimsatreaching
tothemostappropriatepatentsandthesecondphaseaimsatretrievingthe
mostpromisinginformationofeachpatentrespecttotherequestedtargets.
Also,thetwolaststagesareconsideredascomplextasksfromtheperspective
of transferring the searched and extracted data and information to useful
knowledge for users. Previous research in the field of the patent analysis
showsthetextminingtechniquesaremostlyusedtoobtaintheinformation
forthetwofirststages,andthevisualizationtechniquesaredevelopedtohelp
thetwolaststagesindescribingthepatentinformationvisuallyfordecision
makersortechnologyexperts.Infollowing,thepatentanalysistechniquesare
reviewed respectively into text mining techniques and visualization based
techniques.
Text mining techniques
Asmentionedthepatentanalysisisperformedinfourmainstages,and
theautomatedtoolsaredevelopedtoreducethelimitationofperformingeach
stage. Identification of candidate patents to be analyzed and Extraction of
information from these candidates are the two first stages that their
boundariesarestudiedrespecttothestructureofthecontentsofpatents.The
contentsofapatentarecategorizedinstructuredandunstructureddata.The
structured data contains determined and accurate information such as the
inventor,assignee,andcitationinformation.Thepartswhichmustbenarrated
and described such as title, abstract, claims, and description is the
unstructureddata(Liuetal.,2011;Tsengetal.,2007).Most limitationsare
relatedtothesearchingandretrievingunstructureddatawhicharethemain
bodyofthefirststages;searchingtherelevantpatentsandsearchingdataofa
Chapter2:Stateoftheart
23
patent through unstructured data. In other words, although some required
analysis of patents (aims of stages of 3 and 4 of patent analysis) can be
followed by searching and extracting patents or their accurate information
through structured data, most of the advanced requested analysis must be
donethroughsearchingandretrievingunstructureddata.
Text mining tools are developed for mining both structured and
unstructureddatatosupportthetwofirststages.Extractthestructureddata
fromthepatentreportiseasierthanunstructureddata(Tsengetal.,2007).
Respectively,threeapproachesareobservedindevelopingthecorresponding
tools;mappingbetweenthestructureddataandrequestedanalysisasmuch
aspossible,substitutingtheunstructureddatabysomeofthestructuredones
(when their relations are studied respect to the target), and finally using
naturallanguageprocessingtobepossibletodofurtheranalysisbyautomated
tools.Searchingpatentsthroughasetofpre‐definedcodessuchasIPC‐codes
insteadofsearchingbykeywordsisanexampleofdevelopedmethodsinthe
secondapproach.Textminingtoolsaremostlydevelopedinthedirectionof
thethirdapproach.
Textminingisaknowledge‐basedmethodforobtainingtheusefuldata
from the natural language text by using logical tools while recognizing
meaningful patterns from unknown textual data (Tseng et al., 2007;
Ghazinoory et al., 2013). Text mining tools are developed to be capable of
miningtextfrombothstructuredandunstructureddata(Tsengetal.,2007).
Textminingtoolsaredevelopedthroughfivegroupsoftechniques.Thelogic
behindeachofthesecategoriesofmethodsissummarizedinTable6.
Table6‐ThelogicbehindeachofTextMiningtechniques.
No. Technique Characteristics
1
Natural language
processing (NLP)
based techniques
‐Semantictextminingapproachbyusingcomputational
mechanismsandstructures;
PAT‐Analyzer for Identifying the resolved
contradictionsthroughthepatents(Casciniand
Russo,2006);
‐ Transforming the technical data in an easy word
compositionbyselectingthegrammaticalselectionofthe
textual data and producing the structural relation
between the elements (Masiakowski and Wang, 2013)
basedontwofollowingapproaches(YoonandKim,2011;
Parketal.,2013):
Chapter2:Stateoftheart
24
keyword based approaches; involving
predefinedkeywordsandkeyphrasesthatneed
proficientunderstanding;
Subject‐Action‐Object (SAO) based approaches;
analyzing unstructured data through
relationshipsofkeytechnologicalelements;
‐AdvantagesofNLPtechniques:
Powerfulforprocessingbigtextincludinglarge
amountoftextualdata;
‐Keywordbasedapproachessufferof:
Lexicalandgrammaticalobscurities;
Needtorealizethemeaningfulrelationbetween
thegrammaticalconstruction;
‐Developedversionstofollowtherelationsofnovelties
ofpatents:
Presenting the structural relationships among
components of patents by using semantic SAO
structures(Parketal.,2011);
Identifying the inventions that are extremely
novelbyusingsemanticSAOstructuresthrough
determining the gap with the current patents
andanewpatent(GerkenandMoehrle,2012);
TechTreeforshowingsimilaritiesofpatentsina
treeofpatentsandmappingtheirsimilaritiesby
usingsemanticSAOstructures(Choietal.2012);
Constructing a patent similarity and
dissimilarity matrix by measuring statistical
semanticSAOstructures(Yoonetal.,2013);
TechPerceptor for mapping similarities of
functions of patents through extracting the
function of patents and mapping their
similarities by using semantic SAO structures
(Parketal.,2013);
Product‐Function‐Technology (PFT) map for
mappingtechnologyroadbyusingsemanticSAO
structures(Choietal.,2013);
Extractingtheevolutiontrendsthroughranking
and classifying patents similarities based on
TRIZtrendsofevolutionbyusingSAOstructures
(Parketal.,2013).
2 Property function
based techniques
‐Grammaticaltextminingapproach;
‐Usingapropertytoexpressaparticularcomponentofa
systemandusingafunctiontorepresentaproperaction
ofthesystem(Dewulf,2011);
‐Advantagesofpropertyfunctionbasedtechniques:
Eliminatingtheneedtopredefinethekeywords
throughnaturallanguageprocessing
‐Somedevelopedversions:
Chapter2:Stateoftheart
25
TrendPerceptor for presenting invention
conceptsandtechnologicaltrendsbycomparing
thesimilaritiesofproperties,functionsandtheir
co‐occurrencesamongpatentsinanetwork(not
usable for a new patent having different
technological foundations) (Yoon and Kim,
2012).
3 Rule-based
techniques
‐Metadatatextminingapproach;
‐Capturingthedifferenceintrendsofpatentwithoutthe
need of expert information by using rules such as
associationrulemininganddegreeofchange
‐Somedevelopedversions:
PatentTrendChangeMining(PTCM)tocompute
the similarity and dissimilarity of trends of
patent for two statute in two various period,
which uses patent fetcher to get International
PatentClassificationCode(IPC)foreachselected
keyword, patent transformer to transform the
PatentTextofHTMLformintextformandfilter
out irrelevant information, patent indicator
calculator module to determine the patent
values, and finally change detection module
definethetrendsofpatentchangebyusingrule
mining and discarding frequently mined
patterns(Shihetal.,2010).
Fuzzy Inference System (FIS) as a strategy
planningmethodbyusingfuzzyIF‐THENrulesof
learningalgorithmofKohonen(Kohonen,2012)
and primary related heuristic (Lin and Lee,
1991)torefinethestrategicrulesbyconsidering
indicators containing Patent Quantity (PQ),
Revealed Patent Advantage (RPA), Patent
Activity(PA),BeCitedRate(BCA),andRelative
CitationIndex(RCI)(YuandLo,2009).
4 Semantic analysis
based techniques
‐Grammaticaltextminingapproach;
‐Creatingrelationshipsamongdomainspecificconcepts
(Boninoetal., 2010)byrecognizing therelation within
patentsanddefiningthecomingtechnicaltrendsthrough
logicalcorrelatedparsedgrammaticalcomposition;
‐Dealingwithsemanticsinsteadoftechnicalkeywords;
‐Somedevelopedversions:
Identifying the infringement by capturing the
occurrenceof dependencyrelationshipsamong
theelementsofclaimsectionsthroughmapping
hierarchical keyword vectors (utilizing
correspondence sign to recognize the relation
between the structured claim component and
unstructured text data), and a tree matching
Chapter2:Stateoftheart
26
algorithm to compare the component of claim‐
by‐claimbasis(Leeetal.,2013);
Semantic Intellectual Property Management
System (SIPMS) by discovering and mapping
relations among main concepts of the patent
data by transferring the texts to a semi‐
structured format and classifying them by an
indexing agent in three main processes,
including pre‐processing, patent analysis, and
inventionsupport(WangandCheung,2011).
A knowledge‐based software structure to help
theextractionofrelevantinformationofpatent
from repetitious, various, and un‐coordinated
data sources through a patentsystem ontology
using external information sources such as the
ontologyfield(Tadurietal.,2011,2012).
5 Neural networks
based techniques
‐Rule‐basedtextminingapproach;
‐ Making a trained patent network for checking the
Quality of patents with rule‐based approaches in
conjunction
‐Usingforpatenttaxonomyandtechnologyforecasting
(Cheetal.,2010).
‐Somedevelopedversions:
UsingBackpropagationneuralnetworkstrained
to recognize the patents that are particular to
technology,throughdevelopedmodelalongwith
the identification of indicators including
InternationalPatentClassification(IPC)andthe
patentcitationsnumber(Trappeyetal.,2013).
As Table 6 shows the five groups of techniques, differ regarding the
issuetobesearchedandanalyzedinminingstructuredandunstructureddata
together; technical keywords and patterns, grammatical parsing, indicator
calculators and rules, and semantic concepts. It is also worth to take into
consideration, the most of the techniques developed for distinguishing
Novelty of patents and supporting patentability are in thegroup of Natural
languageprocessing(NLP)basedmethodsbyusingSAOstructureforfurther
analysis.DespitethelimitationsofNLP‐basedtextminingmethodsandtools,
the researches show they are extremely effective in processing large
documents containing huge volumes of textual data (Abbas et al., 2014). In
general,theNLP‐basedmethodsstillcannotcopecompletelywithlexicaland
grammatical ambiguities, and also lack in representing the semantic
relationshipsamongthegrammaticalstructures.TextminingbasedonNLPis
widely classified into two main approaches; keyword based and Subject‐
Action‐Object(SAO)based(Parketal.,2013).TheSAOmethodcananalyzethe
unstructured data by describing the link between the main technological
Chapter2:Stateoftheart
27
elements(YoonandKim,2011).IntheSAOstructure,informationisextracted
directlyfromthePatentText(Parketal.,2011).ByusingtheSAOstructures,
information can visualize the concepts in the Problem‐Solution format and
usuallybasedonTRIZ(Choietal.,2013).
Property function based techniques is the second group of the text
mining techniques. These techniques are also used to find and clarify the
inventionconceptbyusingapropertytoexpressaparticularcomponentofa
systemandusingafunctiontorepresentaproperactionofthesystem.This
technique is more preferable by eliminating the need to predefine the
keywordsthroughnaturallanguageprocessing(Dewulf,2011).
ConsideringtheresearchesindevelopingNLP‐basedtechniques(both
keywordbasedandSAObased)andProperty‐Functionbasedtechniquesfor
extractingthetechnicalinformationofpatentsandhighlightingtheirnovelty
and resolved the contradiction, a developing method for supporting
patentabilitycanbeexploitedofthesetechniques.
Visualization techniques
As mentioned the patent analysis is performed in four main stages
whichautomatedtoolsaredevelopedforeachstagetoreducethelimitation
ofperformingeachstage.Analysisoftheextractedinformationandassessing
therelevance,andDeterminingandpresentingconclusionsonwhetherand
howthisinformationaffectstheresearchoutcomes,arethestages3and4that
visualizationtechniquesareappliedtofacilitatethesetwostages.PatentMaps
and patent networks are the most used and known techniques for the
visualization purpose. These techniques exploited clustering methods,
bibliometric methods, quantitative frequency techniques, and ranking and
weighting approaches by distance, size and density indexes for nodes and
vectors.SomeofthetechniquesmentionedinTable6arepatentanalysistools
whicharedevelopedforallfourstagessuchasthedevelopedtoolsinSemantic
analysisbasedtechniquesandNeuralnetworksbasedtechnique.
2.1.3 Patent Maps
AsmentionedPatentMapsareresultsofsomeofthetextminingtools
topresentthefindingsandresultsofanalysisinamannerappropriatefor
thefinaluse;atitssimplestversion,ahumanexpertorautomatedtoolmay
use raw text, whereas, more powerful patent‐mapping approaches use
visualizations such as tables, trees, or graphs to illustrate relationships,
trends,andpossibleduplication,andsoguidetheoperatortonextstepsin
R&Dpolicy,training,businessstrategy,competitorresearch,or IPdefence
Chapter2:Stateoftheart
28
(Suzuki, 2011). There is more attention to implementing text mining
methodsforsupportingthetaskofpatentanalysisandPatentMapping(Lent
etal.,1997;Fattorietal.,2003;YoonandPark,2004).
ThemainpurposeofPatentMappingistohelpengineersrecognition
ofatextimmediatelyandeasily.PatentMapsintheformofgraphs,tables,
charts,andnetworks,providethecombinedinformationtorecognizethem
quicklyandmoreefficiently(Yoon,2010).PatentMapsarefocusedonthe
technological analysis, and they are presented in a single, two and more
dimensional matrix. The duty of summary involves quickly detecting the
topicandclassification(Tsengetal.,2007).APatentMapisusedtoreflect
the correlation between the patents by building the maps within the
keywordsandthekeyphrases(Changetal.,2010).Followingfeaturesare
listedasleastcharacteristicsofPatentMaps:
1. Usingpatentinformation;
2. Havingaclearpurposeofuse;
3. Consistingappropriatepatentinformationrespecttothepurposeofuse;
4. Containingorganizedpatentinformation;
5. Presentinginformationvisually.
The patent‐maps are built through using analytic, qualitative,
quantitative,andindexanalysisandmethods.Varioustechniquesandtools
havebeenintroducedforPatentMapping;patentvacuummaps(Yoonetal.,
2002),patentvacancymaps(Leeetal.,2009),GTM‐basedPatentMaps(Son
et al., 2012), and semantic Patent Maps (Bergman et al., 2008). Table 7
summarizesthemostusedPatentMapsaccordingtotheirform,analytical
method,andpurposes(Suzuki,2011).
Table7‐RepresentativeExamplesofPatentMap.
Representative Examples of Patent Map
No. Name Commonly
used form of presentation
Major analytical
method Overview Brief benefit
1 Element-Based
Map Illustration
Qualitative analysis
Showingthepositionofpatentsrespecttodifferentelementsofaproductonitsillustrationbyconsideringtechnicalandfunctionalinformationofthepatents.
Positioningofapatentanditsdifferencesfromexistingpatentsbyshowingonlypatents’numbersor/andholdernamesonthecorrespondingelements
Chapter2:Stateoftheart
29
2 Diagram of
Technological Development
Tree-structured
form
Qualitative analysis
Showingthepasttechnicaladvancementinaspecifictechnologicalfieldandthepresenceofmainpatentsincludingexpiredpatents.
Checkingtheexistenceofpioneerpatentsandachievingthespillovereffectsofdevelopmentresults
3 Interpatent
Relation Map (Citation Map)
Tree-structured
form
Qualitative analysis
Showingtherelationsamongcitationinformationofpatents.
Consideringtheusage(mechanism,function,…)oftheinformationofpriorpatents.
4 Matrix Map Matrix/graph
Qualitative analysis
Quantitative analysis
Showingthevastnessofpatentnetworksbyacombinationofmultipleaspects;manufacturingpurpose,industrialcomponent,thefunctionalcomponent,theproblems,thesolutions,etc.
Positioningtherelevantkeypatentsrespectstoeachother,withthecorrespondingpatentinformationsuchasapatentholder,number,forasetofproblemsinincorporationwithasetofsolutions.
5 Systematized Art Diagram
Illustration Quantitative
analysis
Showingthesystemofartsbasedonpatentinformationconsideringgrantedpatentsincludingtechnicalelements,andreportnumberforaprimarypatenttocompletethetechnologicalcontents.
Presentingtheentireamountofpatentsdescribetoaparticularsetofartwhereas,summarizingIPrelevantactionatnationalinstituteandacademy.
6 Time Series
Map Graph
Quantitative analysis
Showingpatentdocumentsforaparticularright‐holderinorderbasedoftheyearsofthefilingofthepatentapplication.
Analyzingthenumberofpatentapplicationsfiledandtrendsofinventors.
7 Twin Peaks
Analysis Map Graph
Quantitative analysis
Showingagroupofthepatentcommunityaccordingtosomeaspecttodisclosesomenewaspects.
Showingtheprecedingorlaggingnatureoftechnologicaldevelopment,andalsodelayingingainingacompetitiveedgeinaspecificartinthescopeofacompanyunderthecorporatestrategyorinthescopeofacountry.
8 Maturation
Map Graph
Quantitative analysis
Showingtheinterestleveloftheappropriatetechnologyintheindustrybyconsideringinventorsandtheirapplicationsfilednumberandtheyearfilingofpatentapplications.
Detectingsignsofchangeinthenumberofapplicationsfiledorthenumberofapplicants.
9 Ranking Map List/graph Quantitative
analysis
ShowingthestrengthleveloftechnologicaloftheapplicantortheinfluenceofIPintherelevanttechnicalfieldbyrankingthenumberofpatentsfiledbyelementorbytheapplicant.
Presentingtrendsoftechnologicaladvancementinatechnicalfieldbyleadingcompanies.
10 Share Map List/Graph Quantitative
analysis
Showingthedistributionofapplicationsfiledbyatechnologicalcomponent.
Presentingtheoneswhofiledanapplicationforapatentassociatetoaparticulartechnology.
Chapter2:Stateoftheart
30
11 Skeleton Map Tree-
structured form
Quantitative analysis
Qualitative analysis
Showinganumberofapplicationsfiledintheyearofthetimeofdivergencewithoutdisclosingtheyear.
Gainingacomprehensiveunderstandingofthespreadoftechnologicaldevelopment.
12 Radar Map Graph Quantitative
analysis
ShowinginIPpolicyamongdifferentorganizationandthedifferencesinthethemeoftechnicaldevelopmentacrosstime.
Comparingtheglobalcompetitivenessofenterprisesbyshowingthetechnicalfieldorganization.
Table7summarizestheinformationof12mostusedPatentMaps.Literaturedid
notdiscusstheresultsofempiricalstudiesoneffectsofvariousPatentMapson
theirultimatepurpose.AmongthemostusedPatentMaps,TheMatrixMapis
used for showing the technical information of patents for a target system by
using unstructured data of patents and analytical methods. The Problem‐
Solution Matrix Map is one kind of Matrix Maps which presents classes of
problems and solutions of patents to facilitate studying the Novelty of an
invention respect to its similar patents positioning on the same point of the
matrix.Itreviewspreciseproblemsdiscussedbythepatentandsimilarsolutions
(Bonino et al., 2010). (see detailed information on different Patent Maps in
AppendixB).
2.2 Idea Generation
As mentioned in the previous section, checking and supporting the
patentabilityofaninventionisoneoftheapplicationsofpatentanalysisinthe
industries,whichisthefocusofcurrentresearch.Toimprovethepatentability
ofideasandinventionsofacompany,thefocuscanbedefinedonsupporting
R&Dengineerstogeneratenon‐obviousnovel ideas.Thenon‐obviousnovel
ideaistheaimofsomeotherfieldsofresearchsuchaspsychologyanddesign
and reviewing the relevant investigations in this area can reveal some
directions for improving patent analysis respects to the application of
patentability.Fromtheotherhand,thepatentanalysisisdoneinfourstages
ofidentificationofcandidatepatentstobeanalyzed,extractionofdatafrom
the candidates, analysis of the extracted data, and preparing the research
conclusions. Text mining and visualization are two group of techniques are
studied to support patent analysis; text mining techniques are mostly
supportivetechniques fortwopreviousstages,andvisualizationtechniques
are mostly supportive techniques for two last stages. Therefore, the new
directions can be used to improve any of the four mentioned steps or the
relatedtechniques.
Chapter2:Stateoftheart
31
Ideasorsolutionsareproposedtosolveaparticularproblemorto
improve the current situation.Problems, ingeneral,aredivided intowell‐
defined and ill‐defined problems. These two kinds of problems are
distinguished by their characteristics such as wholly or partially specified
problem space, and feasibility and infeasibility of known and existing
solutions for solving them (Shelly and Bryan, 1964; Schön, 1983;
Goldschmidt,1997;Coyne,2005;Darlington andCulley,2004).Respect to
thesetwocategoriesofproblems, twomainapproachesaredevelopedfor
solvingproblems;typicalproblemsolvingbysearchingasolutiontofitthe
problem among existing and available solutions, and creative problem
solving to generate solution correctly respect to the requirements of the
problem.Problemsofdesignarerecognizedas ill‐definedproblemswhich
are open‐ended and must be approached by Creative Problem‐Solving in
regardstotheknownconceptsandlanguageofcognitionscience.Problems
whichareapproachedandsolvedinR&Ddepartmentsaredesignissuesin
thedirectionofachievingNewProductResearch,NewProductDevelopment,
ExistingProductUpdates,QualityChecksandInnovationwhichareknown
asprimary functionsofR&Ddepartmenttasks. It isreportedthatwithout
creativity in design, there is no potential for innovation (Mumford and
Gustafson,1988;Amabile,1996).
Ideagenerationmethodsaredevelopedinthefieldofpsychologyand
design simultaneously while these two areas have been influenced each
other. Psychology ismore interested insupporting individuals inproblem
solving and idea generation whereas the design is more interested in
characteristicsofresultsofideagenerationmethodsandsessions.Inamore
detailed level, the methods are developed to support generating ideas
respect to the required characteristics of solutions. An organization who
wants to survive and thrive in a challenging environment must develop
innovative solutions for existing and new problems. The required
components of a solution are defined respect to the ultimate purpose of
Problem‐Solving.Apatent,aninvention,anadvancementanddevelopment
ofanexistingproduct,oranextensionofanexistinginnovationintoanew
application,aretheexpectedresultsofProblem‐Solvingandideageneration
inthecompaniestoreachinnovations(Zhuangetal.,1999).
In this section, the relevant studies are reviewed respect to the
expectedcharacteristicsofideasastheresultsofideagenerationmethods,
andalsotheideationmethods.
Chapter2:Stateoftheart
32
2.2.1 Idea characteristics and ideation Metrics
Inthefieldofengineeringdesign,ideasorsolutionsaredefinedasthe
results and outputs of solving design problems. In this domain, design is
studiedthroughconsideringitsbroadsectionsusingtheterms,thedesign
problem/ task, the design process, the design type/output/proposal/
solution/ idea, the design activity/ move/ action, and the design
organization/team/personnel (Pahl and Beitz, 1984; Ulrich and Eppinger,
1995;Ullman,2002).Therefore,ideasorproposalsaretheoutputofadesign
processrespecttothedesignproblemortaskbyadesignteam.Toassess
design process, first, the characteristics of required design proposals and
ideasaredefined.
Somecommoncriteriaforassessingthegeneratedideasanddesign
proposals are discussed in the literature. In most research, the group
performanceisdefinedbyevaluatingtheproposalsregardingthenumberof
ideas (Nijstad et al. 2002; Shah et al. 2003; Perttula and Sipila 2007) and
Qualityofideas(Wierenga,1998,Shahetal.2003).Consequently,theQuality
ofanideaisdeterminedbyappropriatenessandoriginalityonthetargettask
(Massetti, 1996; Runco and Jaeger, 2012) and some situations
unexpectedness (Gero, 1996) and Non‐obviousness (Howard et al. 2006;
Howardetal.,2008).Someexaminationsinengineeringcharacterizethese
criteriabythelevelofmeetinggoals(Shahetal.,2003)andinventiveness
and orderliness (Sternberg, 1985). The four criteria of Novelty, Variety,
QualityandQuantityofideasanddesignproposalsareoneofthewell‐known
criteria for characterizing a design project through exploration and
expansion of design space (Shah et al., 2003). In this scope, Novelty is an
approach for highlighting the unusualness or unexpectedness an idea
comparestoasetoftargetideas(Shahetal.,2003)andregardingadesign
space, Novelty shows the well‐travelled or little‐travelled identification of
ideas in the design area (Nelson et al., 2009). Variety is a criterion for
studyingdissimilarityanddistanceofanideafromotherideasinasetunder
analysis(Shahetal.,2003)anditshowsthedegreeofexplorationinsolution
spacebyanidea.Quantityreferstothenumberofdifferentideasgenerated
(Shahetal.,2003).Qualityisacriterionforstudyingthedegreeoffeasibility
of an idea and the level of satisfying the design requirements which are
discussedasrelevanceorappropriatenessinotherinvestigation(Shahetal.,
2003).
It is expected design proposals solve design problems while the
solutioncanbeembodiedasaninnovationincludingapatentoratleastan
Chapter2:Stateoftheart
33
invention.Innovationisdefinedastheintendedapplicationofcreativityto
achieveaspecificgoal(Omanetal.,2013).Relevance(solutionaddressing
the problem), Novelty (solution different from past solutions), elegance
(solution capable of the pleasing observer), and generalizability (solution
applicable to other domains) are four criteria for assessing an innovation
(Cropley and Cropley, 2005). In engineering design, most innovations are
including inventions which can be granted whether as patents or not.
Innovationbasedonagrantedpatentismorepromisingforcompaniesdue
tothepossibilitytobemoreprotectedinthemarketfromcompetitors.From
this viewpoint, it is expected the characteristics mentioned above can be
representativeforapotentialideatobegrantedasapatent.
Apatentisalegaltitleforaperiodforaninventionthatmustcover
three primary requirements by the experts in the field; Novelty, Non‐
obviousness,andUsefulness(Industrialusage)accordingtothepatentlaw
(Samuelson, 2004). A patent is assessed through the position of the idea
respect to the prior art. Optimal configuration of Novelty and Non‐
obviousnessandthepositiveeffectsofthisconfigurationonoverallwelfare
for various types of industries are studied by theoretical economists
(Scotchmer and Green, 1990; Green and Scotchmer, 1995). To determine
whetheraproposalsatisfiestheNoveltycriteriontobedifferentfromprior
art, four classes of prior art are defined (Franzosi, 2000); Common
knowledge(alreadyknowntotheexpertsinthefield),Enhancedknowledge
(canbeaccessedbyagoodexpertwhenconfrontedwithanewproblem),
Hiddenknowledge(notknowntomostexpertsinthefield),andfinallyPrior
applications.Noveltyisachievedwhentheinventionisnotintheclassesof
CommonandEnhancedknowledgeinthetargetfield.Similarly,todetermine
whetheraproposalsatisfiestheNon‐obviousnesscriteriontobesufficiently
differentfrompriorart,theexpertmustconcludethattheideaisnotasimple
derivationorcombinationofpriorart that wouldbe logical toanaverage
expertinthefield(Franzosi,2000).Non‐obviousnessisachievedwhenthe
Novelty is not in the classes of Common and Enhanced knowledge. The
corresponding assessment considers Common and Enhanced knowledge
throughcheckingthepriorartinthefield.ItisworthtomentionCommonor
Enhanced awareness of another field of art, can be used in a Non‐
ObviousnessNoveltyanditisnottobecheckedfortheNon‐obviousnessof
aninventionintargetfieldofart.
Respecttothecharacteristicsofapatentmentionedabove,Engineering
and design fields, have proposed and applied different definitions and
assessing methods for Novelty and Non‐obviousness. The developed
Chapter2:Stateoftheart
34
definitionsandmethodsinthisfieldaimtosupporttheR&Dactivitiestowards
theacceptablepatentsandpromisinginnovations.Non‐obviousNoveltyand
Usefulness,areconsideredasthemaincomponentswhichmustbecoveredby
thecriteriadiscussedintheliterature.Non‐obviousNoveltycanbepresented
respecttothedefinitionsoftheNoveltyandVarietycriteriainthefourcriteria
framework(Quantity,Quality,Novelty,Variety)ofbecausethesetwocriteria
show unexpectedness and more exploration in design space, while the
UsefulnesscanbediscussedrespecttotheQualityinthescopeoffourcriteria
mentionedabove.Therefore,mostly inengineering design, the four criteria
areusedforassessingadesignproposaloradesignprocess.Respecttothe
different characteristics of design proposals mentioned in the literature,
variousmethodsalsoproposedforevaluatingthem.Table8summarizessome
ofthemostusedmethods.
Table8‐Assessingmethodsofideas.
Source Purpose Evaluation method Criteria
(Redelinghuys,1997)Assessingboth
theideasandthedesigner
Evaluatingdesignerrespectstoexpertsandassessingdesignfor
valuescomparetoengineeringrequirements
‐ProductQuality,‐Designerexpertise,‐Designercreativeeffort
(Besemer,1998;BesemerandO’Quin,
1999;O’QuinandBesemer,2006)
Assessingcreativity
UsingLikert‐typescalesystem
‐Novelty(originalandsurprise)‐ElaborationandSynthesis(organic,elegant,andwell‐crafted)‐Resolution(valuable,logical,useful,andunderstandable)
(SarkarandChakrabarti,2003)
Assessingcreativity
UsingSAPPhIREmodelandFBSFramework
‐Novelty‐Usefulness
(Shahetal.,2003)Assessinggroups
ofideas
Thesatisfactionassessingoffunctionalrequirementsbythe
numberofideas
‐Novelty‐Variety‐Quality‐Quantity
(VanDerLugt,2000;Vidaletal.,2004)
Assessinggroupsofideas
Determiningrelationsoffunctionsolutionsinagraphicallinkchart
‐Thelinksnumberamongideas‐Thelinktype‐Thedesigner'snumberassociatedperidea
(Vidaletal.,2004) Assessinggroups
ofideasDeterminingrelationsoffunction
solutionsbasedonlinkdensity
‐Ideasnumber‐Validideasnumber‐Denyideasnumber‐Notrelatedideas‐GlobalIdeasnumber
(Kaufmanetal.,2008)Assessinggroups
ofideasConceptsassessingfacingeachother
(byLikert‐typescalesystem)
‐Novelty‐Appropriateness‐Technicality‐Harmony‐ArtisticQuality
(Nelson,2009)Assessinggroups
ofideas
AssessingnewcombinationofVarietyandNovelty(Newapproach
ofShah’smetrics)
‐Novelty‐Variety‐Quality‐Quantity
(Srivathsavaietal.,2010)
Assessingproduct
Analyzingtheinterraterreliabilityandrepeatability
‐Novelty,‐Technicalfeasibility‐Originality
(Chulvietal.,2011)Assessingcreativity
Using0–3scaleratingsbyjudges‐Elementimportance‐Satisfactiondegreeofeachelement‐ThesuggesteddesignNovelty
Chapter2:Stateoftheart
35
AstheTable8shows,themostmethodsareusedforassessingthe
ideaswhilesomeofthemalsousedforevaluatingthedesigners.Someofthe
methodsareusedfordeterminingindividual ideaandsomeforagroupof
ideas which can be the outcome a design session. Besides, some of the
methodsusing simple scaling (veryhigh,high,medium, low,very low) by
experts,somemethodsrankideasbasedonsomemodelofdesign,andsome
methodsrankideasbasedonbothscalinganddesignmodels.Designmodels
suchasSAPPhIRE(statechange,action,parts,phenomenon, input,organs,
andeffect)andFBS(function–behaviour–structure)areusedforrankingthe
degree of criteria based on the covering concepts. The function (F) of a
technicalsystemis themotivation/purposeof itsexistencewhichsatisfies
the requirements, (i.e. what it is for) (Gero and Rosenman, 1990). The
behavior(B)isasequentialchangeofstates(Umedaetal.,1995),whatthe
systemdoestofulfilltheintentexpressedbythefunction(F).Thebehavioral
levelisbasedonthenetworkofalternativebehaviors(B)alldrivingfromthe
samefunctionalconcept.Thestructure(S)describesthecomponentsofthe
objectandtheirrelationships(GeroandKannengiesser,2004).
ThethreecriteriaofNovelty,Usefulness,andFeasibilityaremostly
commononthemetricspresented,butthemeaningofNoveltyaredifferent
in these methods. Most of the methods concentrate principally on the
identificationofNoveltyoftheideasandverylittlepartsproposeamethod
forassessingthedegreeofNovelty(ChakrabartiandKhadilkar,2003).
The method proposed by Shah is one of the methods which rather
covers the expected characteristics for a patent. This method proposed a
framework for defining the Novelty, Variety, and Quality based on the
functionofthesystemunderinvestigation,andsetofformulaforassessing
thedegreeofNovelty,Variety,andQualityofgroupsofideasbothpriorior
posteriori.Inotherwords,thismethodlettorankthedegreeofeachcriterion
basedonthedesignmodels.TheNoveltyofanideagenerationsessioninthis
methodisassessedconsideringthreemainissues;settingthereferenceideas
or solutions for comparison, defining the degree of Novelty of each idea
comparetotheotherideas,andmeasuringthetotaldegreeofNoveltyofall
proposedideasofanideagenerationsession.Noveltyisassessedrespectto
asetofideaswhichcanbepersonal(newnesscomparetotheotherideasof
thatperson),societal(newnessrespecttoideasorknowledgeofexpertsina
specific society), and historical (the first of its kind in the history of all
relevantsocieties)(Shahetal.,2003).Also,theNoveltyassessmentcanbe
approachedprioriorposteriori(Shahetal.,2003).Inprioriview,theentire
set of ideas is collected for evaluation by determining the unusualness or
Chapter2:Stateoftheart
36
expectedness, before examining any information for avoiding any bias. In
posterioriperspective, the keyattributesand theoccurrencesof themare
definedrespecttotheallgeneratedideasincorrespondingdesignsessions.
Finally,themeasurementoftotalNoveltyofanideagenerationsessionisa
combination of degree of Novelty of ideas and the numbers of their
occurrences.
RespectivelytoassessingthedegreeofNoveltyofanideageneration
session, the Variety is assessed considering three main issues; setting the
categoriesofVarietyofsolutions,definingthevalueoffordifferentlevelof
categories of Variety in a hierarchical structure, and measuring the total
degree of Variety of all proposed ideas of an idea generation session. The
AssessingQualityissimilartotheNoveltyandVariety.
BasedonShah’sassessingmetricand formula, the definitionof the
degreeofNovelty,Variety,andQualitystartsbydefinitionoffunctionsofthe
system under investigation. These criteria are then assessed based on the
weightsconsideredforthepre‐definedlevels.ThefinaldegreeofNoveltyand
VarietyofideagenerationsessionisachievedthroughShah’sformula(Shah
etal.,2003)whicharepresentedinTable9.
Table9‐TheformulaforassessingNovelty,Variety,QualityandQuantity.
No. The criteria Formula Description
1
Novelty (inposteriori
approach)
�� = ���
�
���
�������
�
���
M1:TotalNoveltyscorem:Numberoffunctionsorattributesn:Numberofstagesfj:Weightsassignedtothevalueoffunctionorcharacteristictocalculateatotalscorepk:Weightsallocatedtotheimportanceofstages.
���� =��� ���
���× 10
Cjk:TheideasnumberforfunctionjinstagekTjk:Theideasnumberforfunctionjforallstagesk
2 Variety �� = 10 ���
�
���
�����/�����
�
���
M3:Varietyscoreb�:BranchesnumberatlevelkS�:Levelscorek(10,6,3,1)m :TotalnumberoffunctionsM ����:MaximumVarietyscore
3 Quality �� = ���
�
���
�������/(� × ���
�
���
)
MQ:QualitygradeSQ:NumbersofQualityscale(1‐10)fjandpk:Weightsoffunctionandstepm:Totalnumberoffunctionsn:Variableofthetotalnumberofideasproducedformeasurement
4 Quantity ‐ anumberofconceptsgenerated.
Chapter2:Stateoftheart
37
The Table 9 shows that higher Novelty can be achieved when the
occurrenceofinstancesofnewnessintheideasarelow,andthedegreeofthe
Noveltyishighaccordingtotheweights.HigherVarietycanalsobereached
whentheideascovermorecategoriesoffunctions,atahigherlevelofdegree
of Variety. Both final scores of Novelty and Variety show the portion of
NoveltyandVarietyonthesetof reference ideas; thesearethegenerated
ideasbyallteamsincurrentresearch.
NotconsideringallaspectsofdefinitionofnoveltyinNoveltycriterion
and the corresponding assessing method, no relation among Novelty and
timeline of an invention, not considering the different levels of value for
Novelty respect to the design abstract models (instead of bi‐situational of
yes‐no),aresomeoftheflawsofassessingmetricsforNovelty(Sarkarand
Chakrabarti, 2011). Among different assessing metrics presented and the
flaws,Shah’smetricwasselectedforthisresearch.Itisselectedbecauseof
theadequateandrelatedmeaningofbothNoveltyandVariety,thepossibility
ofimprovingwithFBSframework,andevaluationofgroupsofideas.
2.2.2 Ideation Methods
Inengineeringdesign,achievinganinnovationisfollowedbycreative
problem solving process including five main steps; identification of the
problem to be addressed, information gathering, idea generation, idea
evaluation and screening by alignment with strategy and feasibility, and
finally communication of the selected solution, implementation, and
commercialization(Amabile,1996;Finkeetal.,1992;MumfordandConnelly,
1991; Stain, 1967). In other words, one of the main stages of creative
problem solving is the ideation which is important to a Problem‐Solving
process(Majaro,1988;McAdam,2004).
When encountering a problem, people typically fall back on their
individual knowledge and experience, which is limited and subject to
cognitive biases (Parnes, 1988). It is believed that for individuals the
potentialityofcreativity,ideagenerationandProblem‐Solvingdependsonto
Domain‐Relevant knowledge and skills, personality variables, cognitive
factors,creativityskills,andtaskmotivation(Amabile,1983;Woodmanetal.,
1993; Ford, 1996). Therefore, different directions are studied in the
literature to improve the results of idea generation. These studies can be
classifiedintwomainapproaches;transmissiontogeneratingideas inthe
teams and developing supportive techniques and methods for idea
generation.
Chapter2:Stateoftheart
38
Thelogicbehindthefirstapproach,generatingideaingroups,isthat
by involving more people in the teams, the domain knowledge and skills,
creativityskillsandmotivationsareincreasedintermsofbothvolumeand
diversity, so the results will be improved (Nemeth, 1986; Amabile, 1988;
Kanter, 2000; Payne, 1990; Ancona and Caldwell, 1992; Woodman et al.,
1993; Wilson, 2000; Heinstrom, 2003). Mutual interactions for idea, goal,
strategy, and knowledge sharing in teams is a driver for idea generation
(Quinn,1985;Amabile,1998).Themorevariousgroup(genderandexpertise
Variety)resultstomorechaosintheimageofproblemandsituation,which
canbereachedtoideasifthechaosconvergestoconsistencyandconsensus
(Gilson,2001;Mumfordetal.,2001;Reiter‐PalmonandIllies,2004;Runco,
1986).
The logic behind the second approach, developing supportive
techniquesandmethods for idea generation, is thatexploiting thedomain
knowledge and skills, creativity skills, and other useful factors in idea
generationcanbeimprovedbyfollowingsomemethodsandtechniques.Itis
worthtakingintoaccountthatthesemethodsarealsodevelopedforusage
byindividualsandteams.Also,thescopeofsupportiveskillsisdifferentin
thesetechniquesrespecttothefivementionedstepsforCreativeProblem‐
Solvingatthebeginningofthissection.Sometechniquesaredevelopedto
supportthelevelofideagenerationwhilesomearedesignedtocover3or4
firststepsbysupportingdefiningorredefiningtheproblem,gatheringthe
necessaryinformationandthengeneratingideas(Colinetal.,2015).
Ideationmethodsaimtoexplicitlybroadenthesearchareaandguide
theteamtowardsmorecreativesolutions(Runco&Okuda,1988;Shalley&
Gilson, 2004) while providing an indication of progress towards an
acceptable solution (Yamamoto & Nakakoji, 2005). They achieve this by
providingaformalscaffoldandrulesaroundtheideagenerationprocess,and
theyrequire ideas tobe frequentlycaptured—orexternalized—ateach
stepthroughsketchesorsimilarmechanisms(Shahetal.,2001).Over300
ideationmethodsarelisted(Takahashi,1993),thoughonlyasmallsubsetof
thesearewidelyappliedinpractice.Thesemethodscanbeclassifiedinthe
followingapproaches(Shahetal.,2001):
Intuitive,whichaimatstimulatingsubconsciousthought:
o Germinal:aimtoeasebootstrappingasolution.Examplesare
Morphological Analysis (Zwicky, 1969), Brainstorming
(Osborn,1953),andtheK‐JMethod(Hogarth,1980).
Chapter2:Stateoftheart
39
o Transformational:adaptexistingsolutionstogeneratefresh
ideas. Examples are Checklists (Osborn, 1979), Random
Stimuli(DeBono,1970),andPMIMethod(DeBono,1970).
o Progressive: promote idea generation by repeated
application of a step of steps. Examples are Method 6‐3‐5
(Rohrbach, 1969), C‐Sketch (Shah, 1993), and the Gallery
Method(VanGundy,1988).
o Organizational: provide a meaningful structure around
generated.ExamplesareTheAffinityMethod(Mizuno,1988),
Storyboarding (Van Gundy, 1988), and Fishbone Diagrams
(FoglerandLeBlanc,1995).
Logical,whichfocusonsystematicallydecomposingtheprobleminto
components that can bebetter understoodand for whichsolutions
canbemoreeasilyderived.Theseinclude:
o History‐based methods that point designers to study past
solutions; examples are Design Catalogues (Pahl and Beitz,
1996)andTRIZ(Altshuller,1984).
o Analytical methods that guide them to explore variations of
theirstartingsolution;examplesareSIT,ForwardSteps,and
Inversion(Shahetal.,2001).
Success in Problem‐Solving is closely tied to the ability of the
participantstoengageindivergentthinking("thinkoutsidethebox");avoid
overly focusing on history, assumptions, and constraints (Cropley and
Cropley,2005),definedivergentthinkingas"branchingoutfromthegiven
toenvisagepreviously unknown possibilities,andarriveat unexpectedor
evensurprisinganswers,andthusgeneratingnovelty".
The logic behind most of the ideation methods and techniques is
discussedas“DesignbyAnalogy”inengineeringdesign.Extensiveresearch
inpsychologyhasattemptedtoclarifyhowpeoplegeneratesolutionsfornew
problemsandthemechanismisreportedasreasoningbyanalogy(Casakin
andGoldschmidt,1999;Eckertetal.,2005;ChristensenandSchunn,2007;
KelleyandLittman,2001).Theanalogyisknownasacomparisonbetween
two items in when they can be distinguished as similar, dissimilar, and
opposite in some traits or properties in a relational or causal structure
(Falkenhainer et al., 1989; Gentner and Markman, 1997; Hummel and
Holyoak, 1997; Blanchette and Dunbar, 2001; Gentner et al., 2001). The
people generate solutions for new problems through comparing the new
issuebytheirpreviousknowledgeandsolutions.DesignbyAnalogyusesthe
analogyfordesigning.Theproblemfieldistheprimarytargetoftheanalogy,
Chapter2:Stateoftheart
40
and the prior knowledge and experiences are the sources for searching a
potentialsolutiontotheproblem.TheprocessofDesignbyAnalogycanbe
pursuedwhensomeAnalogsaredecodedas similarities,dissimilaritiesor
oppositesindesigner’smemory.RetrievingtheappropriateAnalogsfromthe
previousknowledgeandexperiencesisthemostdifficultstepinDesignby
Analogy(Falkenhainer et al.,1989;HolyoakandThagard,1989;Markman
andGentner,1993;GentnerandMarkman,1997).Next,thedesignermust
map between the design problem and the source analog. Inferences and
designsolutionsarethengenerated.ThesestepsareillustratedinFigure1.
Figure1‐Stepsinhumanreasoningbyanalogy.
Accordingtothestepsasmentionedearlier,itcanbeconcludedthat
Design by Analogy is the mental attempt of a designer to infer the design
solutionaftermappingbetweenthedesigntarget(designproblem/task)and
thesourcesofanalogy,throughoneorsomeAnalogs.
FindingtheappropriateAnalogsandsourcesonthedesigntargetis
oneof the criticalaspectsofDesign by Analogy; the designer’s memory is
limited,butalso,mappingamongavailablesourcesinthedesigner’smemory
andthetargetproblemisataskwhichneedspreviousexperience.Sources
aremostlyextractedfrompriorknowledgeandexperiencesofthedesigner
storedinthememory,butexternalresourcessuchasdatabasescanbeused
too.Evenprofessionaldesignershavelimitedexperience,anditislogicalto
support them by resources available in various databases (Linsey et al.,
2012). The analogous domain may be from nature (Moreno et al., 2014).
Table10showssomeofthemethodsofDesignbyAnalogyintwocategories
ofnature‐basedandnon‐nature‐basedmethods.
Chapter2:Stateoftheart
41
Table10‐Examplesofnature‐basedandnon‐nature‐basedmethods.
BioX analogies Non-BioX analogies
BiomimicryandAskNature WordTreeMethod
IDEA‐INSPIRE SCAMPERMethod
BiomimeticDesignThroughNaturalLanguageAnalysis
Synectics
Engineering‐to‐BiologyThesaurusandFunction‐BasedBiologicallyInspiredDesign
SearchenginesandAlgorithm‐BasedMethods
DesignbyAnalogytoNatureEngine(DANE)(ComputationalTool)
Visual‐BasedMethods
Bio‐TRIZTRIZ‐BasedMethods
Among the techniques are mentioned in the Table 10, TRIZ‐Based
methodsareknownasthemethodswhichareenrichedmethodsintermsof
usingthepreviousknowledgeandexperiencesstored inpatents inavery
abstract levelwhichmakeitpossible tobeusedforanalogyforVarietyof
problems whereas some predefined Analogs are also developed in these
methods forsolving inventive problemsand proposing non‐obvious novel
solutions.Duetotherelevanceofthesemodelstothecurrentresearch,they
arediscussedmoreinthefollowingsection.
The Quality and appropriateness of design solutions in Design by
Analogydependonthenatureof theAnalogs,thesourceandthemapping
among found information and the design target. To facilitate Design by
Analogy,differentAnalogs,sourcesandmappingmethodsarestudiedinthe
literature.Thestudiesaremorefocusedonthedomainofsources,typeof
sources,methodsfordefiningAnalogs.Theproblem,source,Analogsarethe
central concepts of models of Design by Analogy which are completed by
methods of extracting Analogs form the sources and methods of mapping
betweenproblemandsourcesthroughAnalogs.
Someresearchersstudiedtheroleofguidanceofanalogretrievalon
the Design by Analogy. The researches show the leadership can enhance
DesignbyAnalogy(Clementetal.,1994;Clement,1994;Linsey,2007;Linsey
et al., 2007; Linsey and Markman, 2008). Visual analogies improve design
solutionsforbothnovicesandengineeringexpertswhileshowhigherimpact
for learners (Casakin and Goldschmidt, 1999). The novice designers using
sketches of example designs generate more Novel, and higher Quality
solution compares to the other designer with text‐based example designs
(McKoyetal.,2001).Case‐DrivenAnalogyisusedmorerespecttoSchema‐
DrivenAnalogybynovices(Balletal.,2004).ACase‐DrivenAnalogyisknown
Chapter2:Stateoftheart
42
an analogy where a specific example is used to develop a new solution. A
Schema‐DrivenAnalogyisconsideredananalogywherethecharacteristics
of solutions are derived from some cases in an abstract level. The FBS
framework is one of the frameworks which is proposed to be used as
Schema‐Driven Analogy whereas the designer thinks about Function,
Behaviour, and structure of the system during designing (Gero &
Kannengiesser,2004).
Someresearchhasdiscussedtheeffectsofdifferentresourcesonthe
Quality of design solutions (Casakin and Goldschmidt, 1999; Leclercq and
Heylighen,2002;Casakin,2004;Eckertetal.,2005;ChristensenandSchunn,
2007;Tsengetal.,2008a;Chanetal.,2011;Linseyetal.,2012).Engineers
mostlyapplyCross‐Domainanalogiesinideagenerationprocesses(Casakin
and Goldschmidt, 1999; Leclercq and Heylighen, 2002; Christensen and
Schunn,2007).Close‐Domainanalogiesandexploitingtheknowledgeofthe
past solutions are often used in cost estimation, process planning and
evaluation of new product concepts (Eckert et al., 2005). Cross‐Domain
SpecificallyFar‐FieldAnalogyincreasesthenoveltyofsolutions(Chanetal.,
2011).ACross‐DomainAnalogyisappliedmorewhenthedesignersarenot
capable of solving the design problem (Tseng et al., 2008b; Linsey et al.,
2012).
Also, empirical studies demonstrated that professional designers
oftenuseanalogies(BlanchetteandDunbar,2001;LeclercqandHeylighen,
2002;Balletal.,2004;Eckertetal.,2005;ChristensenandSchunn,2007).
Experts apply analogies more significantly respects to novices whereas
novices tend to apply case‐driven analogies compare to Schema‐Driven
Analogies (Ball et al., 2004). To support the patent analysis tools for
application of patentability, rather than selecting just one or some special
method,itisworthtoconsiderDesignbyAnalogyastheprimarymodel.In
otherwords,improvingPatentMapwhichisthefocusofthecurrentresearch
forgeneratingpatentable ideasthroughsupporting thegenerationofnon‐
obvious novel ideas can be pursued by the main concepts of Design by
Analogy. As discussed Design by Analogy is the logic behind most of the
ideationmethods.Also,theTRIZ‐Basedmodelisrelevanttothescopeofthis
researchwhichisdiscussedinthefollowingsection.
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2.3 TRIZ and OTSM-TRIZ model of contradiction
TRIZisanacronymof theLatintitle"TeoriyaResheniyaIzobretatelskikh
Zadach" of the Russian name "теория решения изобретательских задач,"meaning"Theoryoftheresolutionofinvention‐relatedtasks,"commonlystatedas"Theory of inventive Problem‐Solving." TRIZ is a methodology first proposed byGenrich Altshuller to identify patterns that repeatedly feature in the Problem‐Solving as evidenced by patent literature (Altshuller, 1984). As discussed before,TRIZ‐BasedmethodsareknownasatypeofDesignbyAnalogymethodswhichareenrichedregardingusingthepreviousknowledgeandexperiencesstoredinpatentsin a very abstract level whereas some predefined Analogs are also developed inthese methods for solving inventive problems and proposing non‐obvious novelsolutions.TRIZasatheory,abstractedobservedpatternsinpatentsinthreemainpostulates: the existence of Objective Laws of Engineering system evolution, thedynamicsofContradictionandtheconceptofResourcescharacterizingtheSpecificSituation (Khomenko and Ashtiani, 2007). Each engineering system evolves tosatisfythedesireofindividualidealityrequirements(characteristics).SomelawsofNatureconstituteanobstacletosystemevolution:inTRIZterms,thisisrepresentedbytheactionofananti‐systemthatsharessomepartswiththesystem‐of‐interest.The opposition of the anti‐system gets manifested through contradiction(s). Forcontinuingtheevolutiontowardsideality,thecontradictionsbetweensystemandanti‐system(eithertakenasawholeorjustassharedparts)mustbeovercomebytheavailableresourcesintheparticularsituation(Cascinietal.,2015).
OTSM‐TRIZextendsTRIZformodelingmorecomplexandmultidisciplinaryproblems(CavallucciandKhomenko,2007).OTSMisanacronymoftheLatinform"GeneralTheoryofpotentcogitate"oftheRussianname"Общаятеориясильногомышления,"commonlystatedas"GeneralTheoryofPowerfulThinking."Insomerecent researches, TRIZ and OTSM‐TRIZ are known as applied scientific theorieswhichareevolvedfromoriginalpatternsinthefieldofProblem‐Solving,andtheysupport users tosolvetechnicaland interdisciplinaryproblemsrespectively (SeeTable11,Cascinietal.,2015).Somenecessarycomponentofthebodyofknowledgeof these two applied scientific theories, such as assumptions, models, andinstruments/toolsaresummarizedinTable11(KhomenkoandAshtiani,2007).
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Table11‐ComponentsofClassicalTRIZandOTSM‐TRIZtheories.
Topic Classical TRIZ OTSM-TRIZ
Assumptions
ObjectiveLawsofEngineeringsystemevolutiondoexistandcouldbeusedforproblem‐solving;Contradictionshowstherootoftheproblem.Itshouldbedisclosedandresolved;SpecificSituationprovidesresourcesthatshouldbeusedtosolveaproblem.
AxiomsofModelsareusedduringthinkingprocess.Theproblemariseswhentypical,traditionalmodelscouldnotbeusedandshouldbechanged:
Group1:AxiomsonThinkingProcess((1)AxiomofImpossibility;(2)Axiomofrootofproblems;(3)Axiomofreflection;(4)Axiomofprocess.);Group2:AxiomsonworldVision((1)AxiomofUnity;(2)AxiomofDisunity;(3)AxiomofConnectedness.).
Models
SystemOperatormodelofsystemthinking;ClassicalTRIZModelsofProblemSolvingProcessdedicatedtodevelopingandorganizeotherproblem‐solvinginstrumentsintowholesystemefficientforsolvingtheproblemanddevelopthinkingskillsfurther:
‐TongsModel;‐HillModel;‐FunnelModel;‐ParallelModel.
ENVFractalModelisageneralandformalizedlanguagetodescribeproblemsandsolutions,realandimaginaryfactsandobjectsOTSMFractalModelofProblemSolvingProcessdedicatedtomanagingaproblem‐solvingprocessandharmonizetheapplicationofvariousinstrumentsevenoutofClassicalTRIZ.
Instruments
ForTypicalProblems:
‐Standards;‐PointersofEffects;‐MechanismofConvergence;‐…
ForNon‐TypicalProblems:‐ARIZ.
ForSmallProblemsituation(adozenofsub‐problems):
‐NewProblemTechnology;‐TypicalSolutionTechnology;‐ContradictionTechnology;‐ProblemFlowTechnology.
ForComplexProblems(hundredsofsub‐problems):
‐ProblemFlowNetworkApproach.
A necessary first step to Problem‐Solving is to analyze the problem, thedesiredgoal,andtheknowledgeaboutfillingthegapbetweentheproblemandthegoal.Specifically,aproblemmaybewell‐defined,byaclearperceptionofthecurrentsituation,thegoal,andhowtogetthere.Problemsthatarenotwell‐definedinallthreeaspectsareconsideredill‐structured(Jonassen,1997)andrequireexplorationandideation.Morethanclassifyingproblemstoowell/ill‐structuredproblems,TRIZandOTSM‐TRIZtheoriesrankproblemstotypical/non‐typicalandinventive/non‐inventiveproblems(Simon,1973;Khomenkoetal.,2007).Typical/non‐typicalandinventive/ non‐inventive are the classifications focused on the availability ofsolutions.Non‐typicalproblemsarethetypeofill‐structuredproblemsthatthereisnotypicalsolutionmodelforthataccordingtotheusualtypesofProblem‐Solvinginthe corresponding field of knowledge and technology. Inventive problems areknown as kind of non‐typical problems which the solution must cope with acontradictory situation for solving them. As patents are the non‐obvious novelsolutionsforproblems,theywereconsideredaspotentialsourcesforexploringthepatternsofsolutionsforinventiveproblemsoratleastnon‐typicalproblems.
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Modelingthecontradictorysituationofaninventiveproblemispursuedinthree levels; administrative, technical, and physical (Jonassen, 1997).Administrative,wherecontradictorydesiredoutcomesare identified but withnoknownwaytoachieve it; technical,wherebothsimultaneous useful andharmfuleffectsofanactionareidentifiedbutwithnoknownwaytoovercomethesituation;and Physical, where a property of an element make both desired and undesiredoutcomes but with no known way to cope with the situation (Altshuller, 1984;Terninkoetal.,1998;Salamatov,1999).
TRIZ and OTSM‐TRIZ models and instruments let the problem‐solversidentify and explicitly state non‐typical and inventive problems to propose non‐obvious novel solutions for them. The instruments propose models for firstmodellingandthenovercomingorbypassingthecontradictorysituations;OTSM‐TRIZ Model of contradiction is a model for formulating a contradictory situationsimultaneously in both technical and physical levels, and Separation principles(Separationinspace,intime,betweenthewholeandtheparts,uponConditions,…)areatoolforbypassingcontradictorysituations.AsmentionedOTSM‐TRIZmodelofcontradictionisamodelformodelingthecontradictorysituationofaninventiveproblemwhichisdevelopedbasedonENVmodel.ENVmodelisasimplemodelfordescribing a situation, an event, an element or a goal/ requirement precisely; EstandsforElement,NstandsforNameofthefeature,andVstandsforthevalueofthe feature. A four‐step process is proposed for formulating the contradictorysituation of an inventive problem simultaneously in both levels of technical andphysicalasmuchaspossibleclearbasedonENVmodel(CavallucciandKhomenko,2007).OTSM‐TRIZmodelofcontradictionformulatetheproblemconsideringinitialsituationA,achoicesituationB,andcontradiction;theinitialsituationAshowstheproblemandthechoicesituationBisconsideredasthesolutionthatisusuallyusedtoresolvethesituationAbutresultsfromanewproblemandcontradiction.Thesethreeconceptsshowtheparametershavetobeusedtomodelthesolution.Initialsituation A aims to clearly define the main requirement which is expected to besatisfied. This requirement is called Evaluation Parameter (EP1) (Cavallucci andKhomenko,2007),anditrepresentstheimprovingparameter.NewsituationBaimstoidentifyanewsituationBwhereEP1issatisfiedbyapplyingoneofalreadyknownsolutions/systems.ProblemsderivingfromsituationBaimstofindnewproblems(EP2)representsworseningparameter.Finally,ContradictionFormulation,aimstoselectonlysomeof(EP2)amongallrequirements/problems(EP2)extractedfromsituation B, the ones which are in conflict with the requirement (EP1) of thesituationA.BasedonOTSM‐TRIZmodelofcontradiction,acontradictorysituationcanbeformulatedasbelow(Figure2,BecattiniandCascini,2013):
<Control Parameter> of Element X should assume Value 1 in order to improve Evaluation
Parameter 1 of Element Y, but then Evaluation Parameter 2 of Element Z worsens. <Control Parameter> of Element X should assume Value 2 in order to improve Evaluation
Parameter 2 of Element Z, but then Evaluation Parameter 1 of Element Y worsens.
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Figure2‐TheformulateContradictionaccordingtoOTSM‐TRIZ.
Thecontradictioninthetechnicallevelisformulatedontherightsideofthemodelwhereasitsformulationatthephysicallevelisillustratedontheleftside.Inthismodel,animprovingparameterisapropertyofoneofthesystemcomponentsthat isexpected tobe improved by thesolution, andaworsening parameter is aproperty of a system component that prevents the improving parameter fromachievingthedesiredvalue.Theworseningandimprovingparametervalueshaveaninverserelationship.Acontrolparameterisacomponentsystempropertythatallowstrade‐offsbetweenimprovingandworseningparameters,anditispossibletocontrolvaluesofimprovingandworseningparametersthroughit.Resolvingthecontradictioncanbepursued inrightsideor leftsidebyapplyingtheseparationprinciples.
Literaturediscussesmore theclassicalTRIZ andOTSM‐TRIZmethodologyandtheircorrespondingmodelsofthinkingandtheirtools.ItisclaimedthatTRIZisgrowth and developed based on analyzing and classifying the level of resolvedcontradictionsinpatents.Thismainclaimwasnotissuedscientificallyyet.However,therearethereportsofsuccessfulcasestudiesbyusingTRIZforCreativeProblem‐Solving.ItisreportedthatTRIZradicallyenhancestheQualityandQuantityofidea‐generation(Souchkov,2007).TRIZprovidesusefulandnon‐obviousnovelsolutionsinshortertimewhereasincreaseseffectiveteamisworkingwhenwasappliedinagroup (Ilevbare, 2013). TRIZ was identified applicable technique when the ideageneration process is characterized by knowledge background of participants,opportunityoftryanderror,thoroughnessofideas,orelaborationofideaswhereasitwasobservedasuitabletechniqueforcontextscharacterizedbyhighknowledgebackgroundofparticipants,needforthoroughnessofideas,orelaborationofideas(Linetal.,2006).Inotherwords,oneofthedifficultiesofusingTRIZisassociatedwithitslearningandapplication(Ilevbare,2013).
Infurtherstudiesrespecttothemodelofcontradiction,theliteratureshowsthat there are studies for improving the various models for formulatingcontradictionsand theprocess formodeling the inventiveproblems (CasciniandRusso,2006;MaandTAN,2007;Cavalluccietal.,2008;MontecchiandRusso,2015).The contradiction model and matrix were used for idea generation (Kobayashi,2003;Dalyetal.,2012),buttheliterature isnotrichinreportingtheeffectof its
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usageontheNovelty,Quality,andappropriatenessofdesignandProblem‐Solving(Dalyetal.,2012).ArecentpaperclaimedthatusingOTSM‐TRIZgamesspecificallythe one which is based on OTSM‐TRIZ model of contradiction, increases thecreativityandVarietyofideas.Therefore,itcanbepromisingthatusingOTSM‐TRIZmodelofcontradictioncanincreasetheNoveltyandVarietyofideas(Belski,2011;Cascinietal.,2015;Dumasetal.,2016).
The OTSM‐TRIZ model of contradiction let the problem‐solvers model acontradictorysituationofaninventiveproblem.Fromtheotherhand,patentscanbethesolutionsforthecontradictorysituationofaninventiveproblem.ExploitingthismodelinexploringandhighlightingtheresolvedcontradictionsbyapatentcanenrichaPatentMapforreachingmorepatentableideasasaresultofatoolbasedontheDesignbyAnalogymodelofideageneration.
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Chapter3
[3] Research Methodology
Chapter3:Researchmethodology
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In this chapter, the prescriptive study as the third phase of research was
perused comprehensively, and the research contribution for increasing thepatentability of ideas is proposed. Then the set of required empirical studies forinvestigatingtheproposedcontributionareplannedanddiscussed.
3.1 Methodological proposal to improve the Novelty of design proposals
Increasingthepatentabilityofideagenerationanddesignsessionsareone
of theapplicationsmentioned for patent analysis,as identifiedas arequest fromIranian SMEs through a survey. Therefore, improving the patentability of ideagenerationanddesignsessionsthroughpatentanalysisisconsideredastheultimategoalofthisresearch.Patentsarealegaltitleforacertainperiodthatmustbecoverthree main requirements: Novelty, Non‐obviousness, and Usefulness. As such,increasing the patentability of ideas means increasing both the Usefulness andNovelty of ideas. However, the proposed Novelty should not be obvious for theexpertsinthefield.Noveltyandindustrialusagearethetwomainissuesthatmustbecoveredsimultaneously,whileNon‐obviousnessisconsideredasacharacteristicofaNovelidea.ThisresearchisfocusedontheNoveltyofideas.
3.1.1 Research contribution
The contribution of the current research is an improved Patent Map forsupportingR&Dengineersforimprovingthepatentabilityoftheirgeneratedideas.Todescribethiscontribution,variousdesignconceptsandmodelssuchasdesignprocess,characteristicsofdesignproposals,DesignbyAnalogy,patentanalysis,andPatentMap,andideationtechniquesmustbeconsideredinabigpicturealtogether.Figure 3 Shows the position of the contribution of this research respect to thementionedconceptsandmodels.
As the picture shows, the TC Map is the particular contribution of theresearch which is developed as one of Patent Map tools to be used as a designstimulus in part of the design process to increase the patentability of designproposals.
AsreviewedinChapter2,inengineeringdesign,achievinganinnovationisfollowed by creative problem solving process including five main steps;identification of the problem to be addressed, information gathering, ideageneration,ideaevaluationandscreeningbyalignmentwithstrategyandfeasibility,and finally communication of the selected solution, implementation, andcommercialization(Buhl,1960;Svensson,1974;Wilson,1980;Crawford,1984;Ray,1985;Cooper,1986;AndreasenandHein,1987;Cougeretal.,1993;Amabile,1996;Finkeetal.,1992;MumfordandConnelly,1991;Stain,1967).
Designproposalsaretheresultsofthefourfirststepswhichareknownasthestepsfordesigningtheconceptofsolution.Manydesigntechniquesandmethodshave been developed and applied in these four steps to increase the requiredcharacteristics of design proposals. The improved ideation and Problem‐Solving
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techniquesandmethodsaredevelopedtobeusedasadesignstimulusintheformofadesignprecedentoradesignprocedure.
Figure3‐RelationsofResearchmodel,Designprocesssteps,andoriginalcontribution.
Chapter 2 reviewed the characteristics of design proposals in three maingroups;Usefulness,Feasibility,andNovelty.Usefulnessshowstheappropriatenessandrelevanceoftheconceptofsolutionrespecttothedesigntaskandsolvingthetargetproblem.Feasibilityhighlightsthepossibilityandplausibilityofgeneratingthe developed concept as a concrete solution. Novelty clarifies the newness,unusualness, and unexpectedness of the developed concept respect to the set ofreference for the target users of the solution. As picture shows, respect to theultimate purpose of discussing the characteristics of the design proposals, thedifferentviewpoint,terms,meaning,andmeasuringmethodsaredeveloped,appliedandreviewedintheliterature.Patentabilityofanideacanbediscussedasaspecificlevel for these three groups of characteristics. Shah metric which is discussed inChapter 2, can be considered as precise meaning for these three groups ofcharacteristics with another term of Quality, Novelty, and Variety with insistingdifferentlyontherequiredconcepts,andasmuchasapossibleobjectiveformulaforassessingthem.
Inthescopeofthisresearchamongmanymetricsfordefiningandevaluatingthesecharacteristics,ShahmetricisselectedasthemeaningofNovelty,andsimilar
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objectiveformulaforassessingitaremorecompatiblewiththemeaningsbehindpatentabilityrequirementsregardingnon‐obviousnovelty,asdiscussedinChapter2.Noveltyisthemainconceptforpatentabilityandthereforethemostmetrics,andalsoShahmetricisfocusingonthiscriterion.Anon‐obviousnovelinventionhasachancetoberegisteredasapatent.NoveltymustbeprovedintheHiddenandPriorapplicationlevelofknowledgeofallfieldoftechnologiesamongthefourthlevelofCommon, Enhanced, Hidden, and Prior application (Franzosi, 2000). Non‐obviousnessmustbeprovedinthesamelevelsbut just inthespecificandtargetdomainoftechnology(Franzosi,2000).ThesetwoexpectedlevelsforNoveltyandNon‐obviousness, is discussed in the engineering design as Novelty and well‐travellinginbothproblemandsolutionspacesasNoveltyandVariety(Shahetal.,2003) which are mostly results of resolving contradiction by new resources in acontradictorysituationforcoveringtherequiredanddesiredvalueofrequirements(Altshuller, 1984). In other words, non‐obvious novelty can be represented byresolvingcontradictionforacombinationofnewproblemandnewsolution.
Respect to the essential and desired value of characteristics of a designproposal to be accepted as the patent regarding non‐obvious novelty, it seemsamongmanydesigntoolsandprecedents,Problem‐SolutionPatentMap(PS.Map)and OTSM‐TRIZ model of contradiction can be focused more on developing asupportivetool.Problem‐SolutionPatentMapprovideallproblemsandsolutionsfortechnicalsystemsbasedonallcorrespondingpreviouspatentsandconsequentlyincrease the possibility of absorbing the attention of problem‐solver to a newcombination of Problem‐Solution for a target system. Also, OTSM‐TRIZ model ofcontradictioncanhighlighttheresolvedcontradictionofthepatentsforeachpairofProblem‐Solution,andconsequently,increasethepossibilityofproposingthenon‐obviousconceptofsolutionforthecorrespondingpair.
Technical Contradiction Map, as the original contribution of currentresearch,isthecombinationoftheProblem‐SolutionPatentMapandOTSM‐TRIZmodel of contradiction for increasing the patentability of ideas regarding non‐obviousnoveltygeneratedbyR&Dengineers.Ontheotherhand,theTC.Amapisanewtool in thedomainofDesign by Analogymethods,andtherefore, itmust bediscussed based on the components of Design by Analogy model. Next sectiondiscussestheproposedcontributionbasedontheDesignbyAnalogymodel.
3.1.2 Developed model for the target contribution of the research
Asmentionedinchapter2,Increasingthepatentabilityofthedesignsolutionbysupportingdesigners inproposingnon‐obviousnovelsolutions istheultimategoalofthisinvestigation.ItislogicaltodeveloptheDesignbyAnalogymodelaboutthisaim. It isexpected that thedevelopedmodelconsiders themostappropriateAnalogs,source,andmappingmethod.Table12showstheconceptsforappropriateAnalogs,withthesourceandmappingmethodtoenrichDesignbyAnalogyfornon‐obviousnoveldesignsolutions.
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Table12‐ConceptsforappropriateAnalogs.
Fundamentals of
Design by Analogy
The developed concept for enriched
Design by Analogy for Novel, non-
obvious design solutions
Supportive logic
Analogs
Problem
Solution
Resolvedcontradiction
TosupportbothNoveltyand
Non‐obviousness
Source Relatedpatentstotargetdesignproblem TosupportNoveltyandnewness
Mappingbetween
analoganddesign
target
TechnicalContradictionMap
Toimprovepossibilityofcovering
newproblemsandalsoNoveltyand
Non‐obviousnesssolutionsfor
existingproblems
ExtractingAnalogs
fromthesourceOTSM‐TRIZmodelofcontradiction
Tosupportextractingproblems,
solutionsandcontradictionsfrom
patents
Novelty can be reached by proposing new solutions for existing or newproblemswhicharecoveredinthepatentsofatargetsystem,whilenon‐obviousnoveltycanbeconsideredasresolvinganewcontradictionorexistingcontradictionbynewsolutions.AstheTable12showstosupportnon‐obviousnovel ideas,thecontradiction is considered as one of the Analogs and therefore retrieving theresolvedcontradictionsandmappingthemarecoveredinthefundamentalconceptsofthemodel.Figure4showstheabove‐mentionedconceptsinthedevelopedmodel.
Figure4‐Proposedmodelofresearch.
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DesignbyAnalogyisdonewhenadesignermaponeorsetofAnalogsamongdesignproblemandsourcesofanalogy,andthentheideaswillbegeneratedthroughinference.Figure4suggeststhepreviouspatentsinafieldasasourcewhereastheproblem, solution and resolved contradictions of them as Analogs for proposingnon‐obviousnovelsolutions forthe targetdesignproblem.TosupportextractingtheproposedAnalogs,theOTSM‐TRIZmodelofcontradictionissuggestedandalsoforsupportingthemapping,PatentMappingisused.Eachcomponentoftheabove‐developedmodelisdiscussedinmoredetail.
The source: PreviouspatentsofatargetsystemareconsideredasthesourceforDesign
by Analogy in enriching the Patent Map for achieving more non‐obvious novelsolutions by designers. As discussed in Chapter 2, the patents are sources ofinformationconsistofbothtechnicalinformationsuchastheproblem,themeanstosolvetheproblem,andthecitedpatentsandinformationaboutthemarketsuchasthenameofholders,andtimeofregistration(Parketal.,2005).Therefore,theyhavecommonlystudiedinR&Dplanning;inthemacro‐levelstrategicanalysis,andinthemicro‐levelmodelingofspecificemergingtechnologies(LiuandShyu,1997;Wangetal.,1998;AbrahamandMorita,2001;Watanabeetal.,2001).
Theresearchshowsdesignersusethepreviousknowledgeandexperiences(Casakin and Goldschmidt, 1999; Leclercq and Heylighen, 2002; Casakin, 2004;Eckertetal.,2005;ChristensenandSchunn,2007;Tsengetal.,2008a;Chanetal.,2011;Linseyetal.,2012);relyingtheirpreviousknowledgeandexperiencesstoredin their memory, and external knowledge which are provided for them(Falkenhainer et al., 1989; Holyoak and Thagard, 1989; Markman and Gentner,1993; Gentner and Markman, 1997; Linsey et al., 2012). Patents are the type ofexternalknowledgefordesignerswhichcanbeprovidedforthemintwodifferentformsanddistance.
Theinformationofpatentscanbepresentedasrawinformationtodesignersas some individual sources for Case‐Driven Analogy or can be presented ascharacteristics of solutions by abstracting the information of the patents as thesourceforSchema‐DrivenAnalogy(Balletal.,2004;GeroandKannengiesser,2004).LiteratureshowsnovicestendtoapplyCase‐DrivenAnalogy(Balletal.,2004)whileSchema‐Driven Analogy is more effective on increasing Quantity, Quality andNoveltyandreducingfixation(Marslen‐Wilson&Tyler,1980;Oxman1990;Lane&Jensen,1993;Liikkanen&Perttula,2006;Zahneretal.,2010;Goldschmidt,2011;Howardetal.,2013).
Inaddition,theprovidedpatentscanbeselectedforClose‐DomainAnalogyby focusing on previous patents of a target system, or can be chosen for Cross‐Domain Analogy by focusing on more effective patents on the purpose of designproblem(CasakinandGoldschmidt,1999;LeclercqandHeylighen,2002;Eckertetal., 2005; Christensen and Schunn, 2007; Tseng et al., 2008b; Chan et al., 2011;Linsey et al., 2012). Engineers mostly apply Cross‐Domain analogies in ideageneration processes (Casakin and Goldschmidt, 1999; Leclercq and Heylighen,
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2002;ChristensenandSchunn,2007).Close‐Domainanalogiesandexploitingtheknowledgeofthepastsolutionsareoftenusedincostestimation,processplanningand evaluation of new product concepts (Eckert et al., 2005). Cross‐DomainSpecificallyFar‐FieldAnalogyincreasestheNoveltyofsolutions(Chanetal.,2011).A Cross‐Domain Analogy is applied more when the designers are not capable ofsolvingthedesignproblem(Tsengetal.,2008b;Linseyetal.,2012).
AlthoughSchema‐DrivenandCross‐Domainanalogies seemmoreefficienton increasing the Noveltyof ideas, in thescopeof thecurrent research,Schema‐Driven of Close‐Domain sources are considered as the primary source for thedevelopedcontribution.Inotherwords,previouspatentsofatargetsystem(Close‐DomainAnalogy)asatypeofPatentMap(Schema‐DrivenAnalogy)areconsideredasthesourceforthisresearch.
The Analog: Three Analogs are selected for the study; the problem, solution, and
contradiction.Problemspacewhichcoversthecharacteristicsofbothproblemandsolutionmustbeexploredforcreativeproblem‐solving.Problemspaceistheareawheretheproblemsolvingbeaccomplishedanditincludesboththecurrentpartialsolutionsandpotentialsolutions.Theproblem(s)whichisaddressedbyeachpatentand the proposed solution(s) by the same patent can help designers to exploreproblemspaceofthetargetsystem.Consequently,thewholepreviouspatentsofatargetsystemcanrevealtheproblemareainabroaderscope.TheproblemofeachpatentismostlymentionedintheBackgroundsectionofthepatent.ThesolutionofeachpatentisdefinedintheClaimsectionofeachpatent,andtheconceptofthatismentionedintheSummarysection.
The contradiction is the third considered Analog for supporting thegenerationofnon‐obviousnovelsolutions.Theresolvedcontradiction,ifsearchedandfindinthepatents,cansupportdesignerstoexploretheproblemspacemoretechnicallybyconsideringtherelationsamongthecomponentsofthetargetsystem.Thecontradictioncannotbeseenexplicitinthepatentsandtheymustbeinferredbytheexpertiseofapatentanalystwhichismentionedinthefollowingsection.
The method of extracting Analogs from the sources: Althoughitisexpectedthattheinformationofproblemsandsolutionofthe
inventionaredescribedinapatent,noneofthemareincludedinthebibliographicalinformation. This data is only contained in the narrated parts which are un‐structureddataofthepatents.Anengineerorpatentanalystwhointendtoextractthis information must study all related patent documents for organizing the“problems correctly to be resolved by the invention” and “means for solving theproblem.”Usually,theproblemsarementionedintheBackgroundofeachpatentandcanbesearchedbykeywordssuchasProvide,Need,Improve.Furthermore,thegeneralsolutionconceptofapatentisusuallymentionedintheSummaryofpatentsandcanbesearchedbyInclude,Supportkeywordswithinthepatents.
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The resolved contradiction is not mentioned in the patents too, andextracting it fromthe patent information is notaneasy task. In the scopeof thisresearch, extracting the related information is going to be searched through theconceptualstepsbyR&DengineersbasedontheOTSM‐TRIZmodelofcontradiction.TheOTSM‐TRIZcontradictionisusedforformulatingaproblemtobesolved.OTSM‐TRIZmodelofcontradictionformulatetheproblemconsideringinitialsituationA,achoicesituationB,andcontradiction;theinitialsituationAshowstheproblemandthechoicesituationBisconsideredasthesolutionthatisusedusuallytoresolvethesituationAbutresultsinanewproblemandcontradiction.Thesethreeconceptsshowtheparametershavetobeusedtomodelthesolution.InitialsituationAaimsto clearly define the main requirement which is expected to be satisfied. Thisrequirement is called Evaluation Parameter (EP1) (Cavallucci and Khomenko,2007),anditrepresentstheimprovingparameter.NewsituationBaimstoidentifya new situation B where EP1 is satisfied by applying one of already knownsolutions/systems.ProblemsderivingfromsituationBaimstofindnewproblems(EP2)representsworseningparameter.Finally,ContradictionFormulation,aimstoselectonlysomeof(EP2)amongallrequirements/problems(EP2)extractedfromsituation B, the ones which are in conflict with the requirement (EP1) of thesituationA.BasedonOTSM‐TRIZmodelofcontradiction,acontradictorysituationcanbeformulated(BecattiniandCascini,2013)asmentionedinchapter2.
Patentsaremostlytheresultsofresolvedcontradictionsbutformulatingtheresolvedcontradictionfromtheinformationofapatentdocumentmustbeslightlydifferent from the above‐mentioned model. In the next part, the developedprocedure for extracting Analogs from patents are proposed with somemodificationsofthesesteps.
ReviewedliteratureinChapter2showed(Table6),themostofthedevelopedtext mining techniques for distinguishing Novelty of patents and specifically theresolved contradiction are in the group of SAO based NLP techniques whereaskeywordbasedNLPtechniquesandProperty‐FunctionbasedtechniquesarealsousedforminingtheinventionandNovelty.Inthescopeofthecurrentresearch,theconceptsbehindthesetechniquesareusedforextractingthecontradictionbyR&Dengineersmanually.
Method of analogy between the source and the Analog As mentioned in Chapter 2, analysis of the technological information of
patentdocumentsandpresentingthemvisuallyinakindofPatentMapsupportsunderstandingthepatentinformation(WIPO,2003).APatentMapcollectspatentdata through considering several aspects, and it is regarded as a type sources ofSchema‐DrivenAnalogy.AMatrixMapaskindofPatentMap,clusterspatentsbyconsideringaspectssuchasthefieldofmanufacturingpurpose,use,technologicalcomponent,a functionalcomponent,theproblemsandsolutions.BothstructureddataliketheapplicantnameandfilingdateandunstructureddatasuchasproblemandsolutionareusedastheaspectsforaMatrixMap.Therefore,aPatentMapis
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builtbybothtextminingandvisualizationtechniques.Usually,theMatrixMapsbuiltinthetwo‐dimensionalgraphonpaperorscreen.
In this research, a three‐dimensional Matrix Maps which deals with threeaspectofproblem,solution,andcontradictionisproposed.Problemsandsolutionsare considered as the two first dimensions for clustering the patents. In general,“problemstobesolvedbytheinvention”canbeclassifiedbasedontherequestedmainandsecondaryfunctionsofthesystems.The“solutionsofproblems”canbegrouped into different categories, covering the improvement of a new system;developmentofprocessapplicationofnewelementorchangingofnewmaterials;theincreaseofsupportingmembers;improvementofnewformation,etc.(Suzuki,2011). These two dimensions are displayed on the main paper. The resolvedcontradictionforthesetofpatentsineachcrossamongtheclustersofproblemsandsolutions is considered as the third dimension which is displayed on othersupportivepapers.Figure5showstheoverallschemaofdevelopedmapconsistsofabubblegraphicalmatrixmapinmainpaperandContradictionMapsforeachcrossofthematrixmap.
Figure5‐AconceptualdiagramofaTechnicalContradictionMap.
AstheleftpartoftheFigure5shows,themainmapclassifiestherelevantpatents by combinations of classes of problems as the first aspect and classes ofsolutionsasthesecondaspect.Thenumberofpatentsineachcrossareintroducedby the size of a bubble. This type of quantitative Matrix‐Map supports theinterpretertorecognizeatalook,theproblems,andtechnicalcomponentandtheconcentration of means for solving problems. This kind of map also presentinformationfornewartwhenthelargestnumberofpatentapplicationswerefiledinsomecrosses,withalargevolumeofinformationdisclosed,whichsupportsR&Dstoreachrelevantinformation(Suzuki,2011).Itisalsopossibletogetinsightabout
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57
thecompetitionsamongthecompanies in thecrosses witha big bubble.Thebigbubble also proposes the art is simple to improve technically in such crosses,althoughtheintellectualpropertymustbeconsideredmoreprecisely.Adversely,inthebubbleswithveryfewpatentapplications,itseemslesschancefornewartformoreimprovement.
TherightpartofFigure5showsthegraphsofresolvedcontradictionofthecrosses of the matrix map. Each contradiction graph consists of the worsening,improvingandcontrol parameters inthecomponents(orelements)of thetargetsystem.Acontrolparameterisacomponentsystempropertythatallowstrade‐offsbetween improvingandworseningparameters; it ispossible tocontrolvaluesofimprovingandworseningparametersthroughit.Improving,worseningandcontrolparameters together help engineers to understand the inventive problem. ThesesupportivepaperslettheR&Dengineersknowthetechnicalandinventiveproblemsofeachcrosstoresolvethemandgeneratenon‐obviousnovelsolutions.
To support understanding and to exploit theTechnical Contradiction Map(T.C Map) by R&D engineers for the first time, an instruction is developed. Theinstruction defines the main concepts and information provided in the Map andsomegeneraldirectionsforgeneratingnewideas(SeeAppendixC).
3.1.3 Developed procedure for building Technical Contradiction map
Based on the developed model of Design by Analogy specialized for non‐obvious novel ideas, a “Technical Contradiction Map” is proposed to facilitatemapping between Analogs and the design target by covering the information ofproblems,solutions,andresolvedcontradictionsofthepatentsrelatedtothetargetsystem.
Figure6‐SimplifiedproceduresforbuildingaTechnicalContradictionMap.
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ATechnicalContradictionMapisathree‐dimensionalMatrixMapsconsistsproblem,solution,andresolvedthecontradictionofpatentsasitsthreedimensions.Similar toeachMatrixmap, threemainstages must be plannedand followed forbuildingtheTechnicalContradictionMap:(i)preparingtheresourcebyselectingthe appropriate patents, (ii) gathering target patent information by extractingAnalogsfromselectedpatents,andfinally(iii)buildingthemap.Tocoverallthesethree stages, a simplified procedure is developed that is shown in Figure 6. ThisFigure shows the three stages, their inputs, interventions, and outputs. Table 13showsindetailthestepsofeachofthestages.
Table13‐DetailedprocedureforbuildingaTechnicalContradictionMap.
Input Main
stages Main steps Detail steps Output
Target system
1-
Preparing
the
resource
Step 1:
Identifying
related
keywords to
the system
Considering“system’sname”;
Determiningthemainkeyword;
o (Nameofthesystem)+(Purposeofthesystem).
Target patents
Step 2:
Identifying
the CPC code
of the system
(Espacenet)
FindingouttheCPCcodeofthesysteminEspacenet;
o SearchingKeywordinthe“Classificationsearchpart”ofEspacenet.
o Checkingthedescriptionofcodetoensuretherelevanceofcodetothesystem.
Step 3:
Extracting
the related
patents
(Orbit)
Considering“Orbit”asthedatabaseforsearch;
Limitedpatentsearch;
o Bytitle,abstract,claims,descriptions,objectoftheinvention.
o Bykeyword.
o Alive&Granted.
o Between20years(validityofapatent).
Step 4: Refine
the patents
Eliminatingincompleteorwithoutdatapatents;
Eliminatingpatentsinotherlanguages,exceptEnglishpatents.;
Eliminatingnotrelatedpatentsbyreadingthetitle,abstractandseethefigures.
Step 5:
Selecting the
patents
Sortingthepatentsbythedateofpatents(newertoolder);
Selectingrandomly(onefromolder,onefromnewerandonefrommiddle);
Studyingthepatentsandcategorizetheproblemandsolution;
Intheproblemandsolutioncategories,wehavetherepetitionofcategories(redundant);
Studyingsomepatentsafterredundantformorecertainty.
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Target patents
2-
Extracting
the
Analogs
Step 1:
Identify
Problem of
the patent
Definition:Theproblemdefinitionistheintendedimprovedbehaviorofthesystemunderinvestigationtobeachievedthroughtheproposedpatent.
Questionstolookfor:
o Whatistheultimatebenefitofapplyingforthepatentinthesystem?
o Whatneedorrequirementdoesthepatentsatisfy?
Comments:
o UsualLocationtolookfor:The“Background,”“Summary”and“Abstract”sectionsofthepatentapplication.
o Theusualformatinthepatent:Wordsthatcommonlyappearinproximitytotheproblemstatement:
Provide,Support,Need,Improve,Include,...
o Samplepattern:
Actionofchange(Preventing/Improving/…)
+Affectedfactors(InsufficientUsefulFunction/HarmfulFunction/Consumptionofexcessiveresources)
+Forreason(Ultimategoals). Contradiction formulation
Step 2:
Identify
Solution of
the patent
Definition:Thesolutionisthecombinationofthemeansproposedandappliedbypatentforsolvingtheproblem.
Questionstolookfor:
o Whatelementsorfunctionsareaddedorchangedinthesystemtosatisfytheproblem?
o Howhastheneedbeensatisfiedbythispatent?
o Howaretheinsufficient,harmful,orexcessivefunctionsofthesystemmodifiedbythispatent?
Comments:
o UsualLocationtolookfor:The“Summary,”“Background,”“Abstract”and"DetailedDescription"sectionsofthepatentapplication.
o Theusualformatinthepatent:Wordsthatcommonlyappearinproximitytothesolutionstatement:
Support,Include,Provide,Need,…
o Samplepattern:
Theactionofchange(Propose/Add/Develop/…)
+Somecomponent.
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Step 3: Define
Undesired
outcome of
the patent
Definition:Aworseningparameterisapropertyofasystemcomponentthatpreventstheimprovingparameterfromachievingthedesiredvalue(asmuchasitisexpectedtopreventundesiredoutcome);theworseningandimprovingparametervalueshaveaninverserelationship.
Questionstolookfor:
o Whichsystemcomponentpropertyislimitingtheimprovingparameter?
o Whentheimprovingparameterprogresses,whichsystemcomponentpropertyregresses?(Itcouldbetheimprovingcomponentitself).
Comments:
o UsualLocationtolookfor:The“Background,""Detaileddescription"and"Claim"sectionofthepatentapplication.
o Theusualformatinthepatent:Wordsthatappearinproximitytotheworseningparameter:
Limited,Provide,Help,Reduce,…
o Samplepattern:
Nameoffeatureorpropertyofacomponentofsystem
+ofthe +nameofthecomponent.
Step 4:
Identify
Improving
Parameters &
elements of
the patent
Definition:Animprovingparameterisapropertyofoneofthesystemcomponentsthatisexpectedtopreventtheundesiredoutcomeinthesystem,butisnotsuccessful.
Questionstolookfor:
o Withoutconsideringthesolutionofthepatent,whichcomponentofthesystem,andwhichpropertyofthatcomponent,wereexpectedtopreventtheundesiredoutcome?
Comments:
o UsualLocationtolookfor:The"Summary,""DetailedDescription,""Background,""Abstract"and“Claims”sectionsofthepatentapplication.
o Theusualformatinthepatent:Wordsthatappearinproximitytotheimprovingparameter:
Support,Provide,Need,Stability,Include,...
o Samplepattern:
Nameoffeatureorpropertyofacomponentofsystem
+ofthe +nameofthecomponent.
Step 5:
Identify
Worsening
Definition:Aworseningparameterisapropertyofasystemcomponentthatpreventstheimprovingparameterfromachievingthedesiredvalue(as
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Parameter &
elements of
the patent
muchasitisexpectedtopreventundesiredoutcome);theworseningandimprovingparametervalueshaveaninverserelationship.
Questionstolookfor:
o Whichsystemcomponentpropertyislimitingtheimprovingparameter?
o Whentheimprovingparameterprogresses,whichsystemcomponentpropertyregresses?(Itcouldbetheimprovingcomponentitself).
Comments:
o UsualLocationtolookfor:The“Background,""Detaileddescription"and"Claim"sectionofthepatentapplication.
o Theusualformatinthepatent:Wordsthatappearinproximitytotheworseningparameter:
Limited,Provide,Help,Reduce,…
o Samplepattern:
Nameoffeatureorpropertyofacomponentofsystem
+ofthe +nameofthecomponent.
Step 6:
Identify
Control
Parameter of
the patent
Definition:Acontrolparameterisacomponentsystempropertythatallowstrade‐offsbetweenimprovingandworseningparameters,anditispossibletocontrolvaluesofimprovingandworseningparametersthroughit.
Questionstolookfor:
o Whichsystemcomponentpropertyaffectsbothimprovingandworseningparameterswithaninverserelationshipbetweenthetwoparameters?
Comments:
o UsualLocationtolookfor:The"DetailedDescription,“Summary”and“Background”sectionsofthepatentapplication.
o Theusualformatinthepatent:Wordsthatappearinproximitytothecontrolparameter:
Adjust,Provide,Support,…
o Samplepattern:
Nameoffeatureorpropertyofacomponentofsystem
+ofthe +nameofthecomponent.
Step 7:
Formulate
Contradiction
of the patent
Comments:
o Filltheshapeaccordingtothecorrectnessoffollowingsentences:
<ControlParameter>ofComponentXshouldassumeValueinordertoimproveEvaluationparameterof
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componentZ(improvingparameter),butthenEvaluationparameterofcomponentY(worseningparameter)worsens
<ControlParameter>ofComponentXshouldassumeAnti‐valueinordertoimproveEvaluationparameterofElementY,butthenEvaluationParameterofElementZworsens.
o Giverealvaluestovalueandanti‐value.
o Filltheblankboxesofthetemplatefigureofthecontradiction.
o Addoneofthemainpicturesofthepatentsbesidesthedrawngraphofthecontradiction.
Contradiction formulation
3-
Building
the map
Step 1:
Categorize
the extracted
problems of
patents
Quantitativeanalysisofextractedproblem
Classifythesimilarproblemsinoneclass
Classifythesimilarclasses
Technical Contradiction
Map
Step 2:
Categorize
the extracted
solutions of
patents
Quantitativeanalysisofextractedproblem
Classifythesimilarproblemsinoneclass
Classifythesimilarclasses
Step 3: Build
the matrix
map of
problems
and solutions
DedicateX‐axistoclassesofproblems
DedicateY‐axistoclassesofsolutions
WritetheQuantityofpatentsofeachcrossamongtheclassesofproblemandsolutionsinthecross
Step 4: Build
the
contradiction
graphs of
each cross
Positiontheresolvedcontradictionofpatentsofeachcrossonaseparatesheetofpaperasasupportivegraph
Mergeandtrimthecontradictionsofeachgraphbysimilarelements
As Table 13 shows, the three‐stage procedure for building the map isproposed in 16 steps together: preparing the resource in 5 steps, extracting theAnalogsin7steps,andbuildingthemapin4steps.Itisworthconsideringthatallthreestagesneedengineerexpertise,whilethemostprofessionalstageisthesecondstage.
The time needed for following the procedure and building the map is notconstantforanytechnicalsystem,anditdependsontheleastandenoughnumberofpatents forbuildingtherepresentativemap.Inotherwords,althoughthemapcanbebuiltbasedontheallretrievedrelevantpatents,itislogicaltostartwitharepresentativeone.Therepresentativemapcanbeconsideredasthemapwithleastandenoughnumberofpatentswhichcoverstheclassesofproblemsandsolutions.Thisaimiscoveredinstep5ofthefirststageoftheprocedurewhichreferredasselectingthepatentsafterredundancyintheclassesofproblemsandsolutions.
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Therefore,thetimeneededforfulfillingtheproceduredependsontheleastand enough number of patents respect to the all relevant patents for the targettechnicalsystem.
Thestudytheefficiencyofdedicatedtime,thetimeneededforpreparingthemapcanbecomparedtothetimewhichisdedicatedtocheckingthepatentabilityofanewideaafterdesignsessioninR&Ddepartments.Astheexpertiseissocrucialforthesecondstageoftheprocedure, it is logicalthat themainemphasisfortheempiricalstudieswillbeconcentrateduponthisstage.
3.2 Designing empirical study
Theultimateobjectiveofthisresearchistoimprovethepatentabilityofidea
generationsessionsbyincreasingthegenerationofnon‐obviousnovelsolutionsforinventive problems of a target system. A Technical Contradiction Map is thesuggestedcontributionofthisresearchforthetargetobjective.Asetofempiricalstudies is needed to study the map’s usability and effectiveness and easiness ofrepeatingthemap‐buildingprocess.Toperformthestudies,thesuggestedmapwasbuiltforasampletechnicalsystemthroughfollowingthedevelopedprocedure.Thepreparedmapwasthenusedforstudyingtheusabilityandeffectivenessofthemap.Therefore, the prepared map for a sample technical system and the performedstructurearepresented,inadditiontothestructureofasetofstudiesforexaminingthemap’susabilityandeffectiveness,andtheeaseofrepeating themap‐buildingprocess.
3.2.1 Research contribution sample
Sample technical system The first step in preparing a Technical Contradiction Map is selecting a
technical system as a sample. Technical systems are classified into four levelsaccordingtotheircomplexity.Inthescopeofthisresearch,asimplesysteminlevelthreewaschosenasasampletechnicalsystem,inordertoshowtheusabilityoftheproposedcontributionforacompletetechnicalsystem.Table14showsthelevelofcomplexityoftechnicalsystems(HubkaandEder,2002);complexityisknownasthe required information to define a system (Kolmogorov, 1983), understand,predict,manage,design,and/orchangeit.
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Table14 ‐ Technicalsystemsclassifiedbydegreeofcomplexity.
Complexity
Level System Feature Sample
1 Part,
Component
Theprimarysystemmanufacturedwithout
assemblyprocessBolt,BearingSleeve
2
Group
mechanism,
Sub‐assembly
Thesimplesystemthataccomplishessome
extrapurposes
Gearbox,Hydraulic
drive,Spindlehead
3 Apparatus,
Device
Thesystemwithsomepartsforclosed
function
Lathe,MotorVehicle,
Electricmotor
4
Plant,
Equipment,
Complex
machineunit
Thecomplexsystemthatperformssome
functions(containmachineandcomponent
thatorganizeafunctionalandspatialunity)
Hardeningplant,
Machiningtransferline,
Factoryequipment
A Walker (or walking frame) is a simple system in Level three of thecomplexityoftechnicalsystems;thisisconsideredasthesampletechnicalsystemofthisresearch.AWalkerisadeviceforhandicappedoroldpeoplewhoneedmoresupport to keep balance or stability during walking. On the requirementsof thisresearch’sempiricalstudies(describedinthefollowingsection),thesamplesystemmust be known and familiar with a wide range of engineers, so the Walker isconsideredasanappropriateoption.Figure7showsthestandardframe,alsoknownasthesimpleWalker.
Figure7‐Asimplewalkingframe.
Performed procedure for building Technical Contradiction Map for the sample technical system
As mentioned in the previous section, the procedure is proposed in threemainstages.Table15showstheresultsofthefirststageinsearchingandgatheringtherelativeandappropriatedpatents.
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65
Table15‐Theresultsofsearchingandrefiningthepatents.
Preparing the
resource
Step 1: Identifying
related keywords to the system
Considering“system’sname”;
Determiningthemainkeyword;o (Nameofthesystem)+
(Purposeofthesystem).
Considering“Walker”asatechnicalsystem;
Determiningthemainkeyword;
Walker(Nameofthesystem)+Disability(Purposeofthesystem).
Step 2: Identifying the CPC code of the
system (Espacenet)
FindingouttheCPCcodeofthesysteminEspacenet;o SearchingKeywordin
the“Classificationsearchpart”ofEspacenet.
o Checkingthedescriptionofcodetoensuretherelevanceofcodetothesystem.
FindingouttheCPCcodeofthesysteminEspacenet;o SearchingKeyword(Walkerdisability)
inthe“Classificationsearchpart”ofEspacenet(A61H3/00).
o Checkingthedescriptionofcodetoensuretherelevanceofcodetothesystem(Appliancesforaidingpatientsordisabledpersonstowalkabout(apparatusforhelpingbabiestowalkA47D13/04;{orthopaedicdevicesforcorrectingdeformitiesof,orsupporting,limbsA61F5/0102};exercisingapparatusforthefeetortoesA63B23/10;{stairwaysorrampsE04F11/00}).
Step 3: Extracting the
related patents (Orbit)
Considering“Orbit”asthedatabaseforsearch;
Limitedpatentsearch;o Bytitle,abstract,
claims,descriptions,objectoftheinvention.
o Bykeyword.o Alive&Granted.o Between20years
(validityofapatent).
Considering“Orbit”asthedatabaseforsearch;o Limitedpatentsearch;o Bytitle,abstract,claims,descriptions,
objectoftheinvention.o Bykeyword“Walker2Ddisable+”.o Alive&Granted.o Between1994‐2014(20years’validity
ofapatent).
Step 4: Refine the patents
Eliminatingincompleteorwithoutdatapatents;
Eliminatingpatentsinotherlanguages,exceptEnglishpatents.;
Eliminatingnotrelatedpatentsbyreadingthetitle,abstractandseethefigures.
Eliminatingincompleteorwithoutdatapatents(4patents~%4);
Eliminatingpatentsinotherlanguages,exceptEnglishpatents(33patents~%33);
Eliminatingnotrelatedpatentsbyreadingthetitle,abstractandseethefigures(10patents~%10).
Selecting 54 out of 101 patents
Step 5: Selecting the
target patents
Sortingthepatentsbythedateofpatents(newertoolder);
Selectingrandomly(onefromolder,onefromnewerandonefrommiddle);
Studyingthepatentsandcategorizetheproblemandsolution;
Intheproblemandsolutioncategories,wehavetherepetitionofcategories(redundant);
Studyingsomepatentsafterredundantformorecertainty.
Sortingthe54patentsbythedateofpatents(newertoolder);
Selectingrandomly(onefromolder,onefromnewerandonefrommiddle);
Studyingthepatentsandcategorizetheproblemandsolution;
Intheproblemcategory(after19thpatent)andthesolutioncategory(after10thpatent),wehavetherepetitionofcategories(redundant);
Studying11patentsafter19thuntil30thpatent.
Selecting 30 out of 54 patents
AstheTable,15shows,among101foundpatentsbysearchingintheOrbit
database,around50%ofthem(54patents)couldbestudiedforbuildingthemap.Reading and extracting the required information from all 54 patents is a time‐consumingactivity,thereforeinthescopeofthissample,itisintendedtodecreaseanumberofpatentsforfurtherstudies.Theclassesofproblemsandsolutionsare
Chapter3:Researchmethodology
66
considered as concepts for reducing the Quantity of patents for the study. Thepatentswerefirstsorted,andthenthestudyselectedonepatentfromtheearliest,onefromthelatestandonefromthemiddle.Afterreducingtheclassesofproblemsand solutions, the analysis of more patents was completed. This supportive sub‐procedurereducedtheQuantityofpatentstobestudiedfrom54to30.
The second main stage of the procedure for extracting Analogs was thencompletedforthe30selectedpatents.Table16showstheperformedprocedureforone of the 30 selected patents; see detailed information of the 30 patents inAppendixD.
Table16‐Resultsofextractedinformationfromapatent.
Extracting the Analogs
Step 1: Identify Problem of the
patent
Definition:Theproblemdefinitionistheintendedimprovedbehaviorofthesystemunderinvestigationtobeachievedthroughtheproposedpatent.
Questionstolookfor:
o Whatistheultimatebenefitofapplyingforthepatentinthesystem?
o Whatneedorrequirementdoesthepatentsatisfy?
Comments:
o UsualLocationtolookfor:The“Background,”“Summary”and“Abstract”sectionsofthepatentapplication.
o Theusualformatinthepatent:Wordsthatcommonlyappearinproximitytotheproblemstatement:
Provide,Support,Need,Improve,Include,...
o Samplepattern:
Actionofchange(Preventing/Improving/…)
+Affectedfactors(InsufficientUsefulFunction/HarmfulFunction/Consumptionofexcessiveresources)
+Forreason(Ultimategoals).
Improvinguserstability,control,andeaseofusefor
navigatinginclinedsurfacesandstairways
(wasfoundinBackground,ClaimandSummary
sections)
Step 2: Identify Solution of the
patent
Definition:Thesolutionisthecombinationofthemeansproposedandappliedbythepatentforsolvingtheproblem.
Questionstolookfor:
o Whatelementsorfunctionsareaddedorchangedinthesystemtosatisfytheproblem?
o Howhastheneedbeensatisfiedbythispatent?
Addmechanically‐drivenfrontlegs
(wasfoundinSummarysection)
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67
o Howaretheinsufficient,harmful,orexcessivefunctionsofthesystemmodifiedbythispatent?
Comments:
o UsualLocationtolookfor:The“Summary,”“Background,”“Abstract”and"DetailedDescription"sectionsofthepatentapplication.
o Theusualformatinthepatent:
Wordsthatcommonlyappearinproximitytothesolutionstatement:
Support,Include,Provide,Need,…
o Samplepattern:
Theactionofchange(Propose/Add/Develop/…)
+Somecomponent.
Step 3: Define Undesired outcome
of the patent
Definition:Aworseningparameterisapropertyofasystemcomponentthatpreventstheimprovingparameterfromachievingthedesiredvalue(asmuchasitisexpectedtopreventundesiredoutcome);theworseningandimprovingparametervalueshaveaninverserelationship.
Questionstolookfor:
o Whichsystemcomponentpropertyislimitingtheimprovingparameter?
o Whentheimprovingparameterprogresses,whichsystemcomponentpropertyregresses?(Itcouldbetheimprovingcomponentitself).
Comments:
o UsualLocationtolookfor:The“Background","Detaileddescription"and"Claim"sectionofthepatentapplication.
o Theusualformatinthepatent:Wordsthatappearinproximitytotheworseningparameter:
Limited,Provide,Help,Reduce,…
o Samplepattern:
Nameoffeatureorpropertyofacomponentofsystem
+ofthe +nameofthecomponent.
InabilityofWalkertoproperlybalanceoninclined
surfaces
(wasfoundinBackgroundsection)
Step 4: Identify Improving
Parameters &
Definition:Animprovingparameterisapropertyofoneofthesystemcomponentsthatisexpectedtopreventtheundesired
Stabilityoninclined/stairwayssurfaces
ofWalkerframe
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elements of the patent
outcomeinthesystem,butisnotsuccessful.
Questionstolookfor:
o Withoutconsideringthesolutionofthepatent,whichcomponentofthesystem,andwhichpropertyofthatcomponent,wereexpectedtopreventtheundesiredoutcome?
Comments:
o UsualLocationtolookfor:The"Summary,""DetailedDescription,""Background,""Abstract"and“Claims”sectionsofthepatentapplication.
o Theusualformatinthepatent:Wordsthatappearinproximitytotheimprovingparameter:
Support,Provide,Need,Stability,Include,...
o Samplepattern:
Nameoffeatureorpropertyofacomponentofsystem
+ofthe+nameofthecomponent.
(wasfoundinBackgroundandSummarysections)
Step 5: Identify Worsening
Parameter & elements of the
patent
Definition:Aworseningparameterisapropertyofasystemcomponentthatpreventstheimprovingparameterfromachievingthedesiredvalue(asmuchasitisexpectedtopreventundesiredoutcome);theworseningandimprovingparametervalueshaveaninverserelationship.
Questionstolookfor:
o Whichsystemcomponentpropertyislimitingtheimprovingparameter?
o Whentheimprovingparameterprogresses,whichsystemcomponentpropertyregresses?(Itcouldbetheimprovingcomponentitself).
Comments:
o UsualLocationtolookfor:The“Background,""Detaileddescription"and"Claim"sectionofthepatentapplication.
o Theusualformatinthepatent:Wordsthatappearinproximitytotheworseningparameter:
Limited,Provide,Help,Reduce,…
o Samplepattern:
StabilityonnormalsurfacesofWalkerframe
(definedconceptuallybyresearcher)
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Nameoffeatureorpropertyofacomponentofsystem
+ofthe +nameofthecomponent.
Step 6: Identify Control Parameter
of the patent
Definition:Acontrolparameterisacomponentsystempropertythatallowstrade‐offsbetweenimprovingandworseningparameters,anditispossibletocontrolvaluesofimprovingandworseningparametersthroughit.
Questionstolookfor:o Whichsystemcomponentproperty
affectsbothimprovingandworseningparameterswithaninverserelationshipbetweenthetwoparameters?
Comments:o UsualLocationtolookfor:The
"DetailedDescription,“Summary”and“Background”sectionsofthepatentapplication.
o Theusualformatinthepatent:Wordsthatappearinproximitytothecontrolparameter: Adjust,Provide,Support,…
o Samplepattern: Nameoffeatureorpropertyof
acomponentofsystem +ofthe +nameofthecomponent.
HeightofWalkerlegs(frontlegs)
(wasfoundinDetailed
descriptionsection)
Step 7: Formulate Contradiction of
the patent
Comments:o Filltheshapeaccordingtothe
correctnessoffollowingsentences: <ControlParameter>of
ComponentXshouldassumeValueinordertoimproveEvaluationparameterofcomponentZ(improvingparameter),butthenEvaluationparameterofcomponentY(worseningparameter)worsens
<ControlParameter>ofComponentXshouldassumeAnti‐valueinordertoimproveEvaluationparameterofElementY,butthenEvaluationParameterofElementZworsens.
o Giverealvaluestovalueandanti‐value.
o Filltheblankboxesofthetemplatefigureofthecontradiction.
o Addoneofthemainpicturesofthepatentsbesidesthedrawngraphofthecontradiction.
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ThelastcolumnofTable16highlightstheresultsofapatent:“Walker forimprovedstairwaymobility.”ThisWalkerwasadaptedtonavigatestairwaysandinclinedsurfaces;Figure8showsaperspectiveviewofanexemplaryembodimentoftheinvention.
Figure8‐Walkerforimprovedstairwaymobility.
Thethirdmainstageoftheprocedureformap‐buildingwasundertakenafteranalyzing all 30 patents on the extracted Analogs of problems, solutions andresolved contradictions of each patent. As mentioned before, the map is a three‐dimensional map. The first two dimensions are presented in the main paper bypositioningthepatentsrespecttotheclassesofproblemsandsolutions.Thenforeach cross occupied by at least one patent, the third dimension is provided bypresentingtheresolvedcontradictionsofpatentsonthecross.
Toproceed.First,theproblemsandsolutionsextractedforeachpatentareclassified,andthentheProblem‐SolutionMapofthemainpaperisbuilt.Tables17and 18 show the classes of extracted problems and solutions of the patents,respectively.
Table17‐Eightcategoriesofproblems.
No. Problem group name Problems
1
Reducing volume of Walker for non-using
period (4 patents)
ImprovingafoldableWalker(storageconfigurationandfoldableinallthreedimensionsandfold/unfoldbyuserindependently)
ImprovingaWalkerapparatuswithafoldingmechanism(thatallowstheWalkertobefoldedlaterallyinacompactmanner,andthatminimizesthenumberofrequiredpartswhileoptimizingrobustnessandlateralsupport)forusers
Improvingfold‐ability(configurebetweenfoldedstorageandunfoldedpositions)Walkerbyhandicappedpersonsoflimiteddexterity
ImprovingaWalkerforpatientswithdexterityproblemstoliftuptoastandingpositionandtoopenandcloseindividually
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2 Supporting user for
normal walking (9 patents)
Preventingfallinginanydirectionswhilewalkingforaphysicallychallengedperson
Preventingcollapsingforuserwheresupportingweightisnecessaryduringnormalwalking
Preventinghealthproblems(stressontheback,shoulders,arms,wristsandhands)andimprovingsafetyissues(balanceandstabilitythroughthearmsandhandstrengthwhileleaningovertheWalker)ofthepatienthavingdifficultywithself‐sustainedwalking
Preventingcollapsingpatientandimprovingmobilityassistancetoprovidebalancedsupportforelderlyindividualsorotherdisabledpopulationsneedingwalkingassistance
Improvingcontrolofbalanceandbodyorientationduringhands‐freestandingandwalkingtoindividualswithcompromisedphysicalabilitiescausedbyinjuryordiseaseofthecentralnervoussystemorotherreasons
ImprovingaWalker/deviceforgaittrainingandwalkingimpairedpatient;canbeadjustedwideenoughtofitovertreadmillsorwheelchairs
Preventingthecollapseofhandicappedandphysicallyimpaired,withinsufficientlegstrengthtostandorwalkindependentlyduringambulation
ImprovingWalkermaneuverability(difficultorimpossiblegetcloseenoughtoobjectstotouchthem)fordisabledchildpatientsintheindoorenvironment(homeandschool)
ImprovingaWalkerforsupportthewalkingofamputees
3
Preventing collisions to obstacles during
walking (4 patents)
Preventingfallinguserwithpoorvision,orinlowlevelofilluminationintheenvironmentforusers
Improvingmobilityinpoorlylitareasbythepersonwithlimitedorpooreyesight
Improvinganambulatorydeviceforassistingphysicallychallengedusers(youngchildrenlearningtowalk,thosewhosufferlastingeffectsofinjuryandphysicalchallengesandtheelderly)inwalking,exerciseorotherwisegoingonfoot
Preventingfallingpatientwithdiminishedhearing/eyesightcapacityduringwalking
4
Supporting user for necessary sitting
motions (2 patents)
ImprovingaWalkertohaveaccesstoallpartsofthepatient'sbody(disabledand/orelderlytohaveashowerforadequatecleansing)toassurecompletebathing
Preventingfallingandriskingfurtherinjurybythepersonwhoneedsawalkingaidandneedstoswitchbetweenwalkingandsitting
5
Supporting user for walking on various
surfaces (4 patents)
Improvinguserstability,control,andeaseofusefornavigatinginclinedsurfacesandstairways
Improvingwalkingonthesoftsurfacessuchassandanddirt
Preventingpersonwhohasphysicaldifficultyinambulation(illness,injury,etc.)fallingandreducesnoise
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Improvingtraversingstairsandsubstantiallylevelsurfacesforhandicappedpersons
6 Setting size of Walker
for user (2 patents)
Improvingfold‐ability(facilitatestorageandtransport)andstructuralstabilityofWalkerforbothchildandadultusers
ImprovingaWalkerthatsupportsthesizeofobesepersons(accommodateseveraldifferentsizedpersons),butcanbewellstoredandtransported
7
Walker’s Aesthetics and Ability to be
repaired (2 patents)
ImprovinggriphandlesofWalkerwhichiscostlyandtime‐consumingtoreplaceforsubsequentusers
ImprovingappearanceofWalker;morefriendlyforusers
8
Supporting user to stand for using
Walker and to sit after walking
(3 patents)
Improving(facilitating)disabledpersonrisingfromaseatedpositionorreturningtoaseatedposition
Improvingwalkingaidsandassistingapersonneedinghelptorisetoastandingposition
Preventingtheriskoffrequentfallsandinjuriesofliftedperson (elderly,personsrecoveringfromsicknessorsurgicalprocedures,personswithbalanceproblems)
Table 17 shows the problems of all 30 patents being classified into eightgroups (see the column labeled ‘problem group name’). Among the 8 groups, 3groupsofproblemsarerelatedtothemainfunctionoftheWalker(i.e.supportingusersinwalkingandusingtheWalker),2arerelatedtosupportiveotherfunctionswhileusingtheWalker,1isrelatedtothecustomizingtheWalkerfortheuser,andfinally,the2othergroupsarerelatedtoissuessurroundingabilitytoberepairedandtheWalker’saesthetics.
Table18‐Sixcategoriesofsolutions.
No. Solution group name Solutions
1 Customizing lower end of
Walkers’ legs (3 patents)
Addmechanically‐drivenfrontlegs
Addtheadapterwithameshthatincludesarunningsurface
AddaglideballintheformofaresilientballprovidedwithapluralityofholesforinstallingoverthelowerendofaWalker'sleg
2
Applying motion sensors (add illumination, alarm
system, signal device) (4 patents)
Addilluminationsourcetofocusonatargetarea,alocationemitter,aglobalpositioningsystem,atactilesignalemitterorasensordevice
Addtheintegratedilluminationmeans;alarmwiresanalarmandlights
Addamotionsensorapparatusandasignaldeviceoperatively
AsafetyWalkerwithanautomaticalertdevicecomprisingabatterypoweredlampassemblyandanaudiblealarmsystem
3 Applying new materials
(2 patents)
ProposeareplaceableandsanitarygriphandleforWalker,mobility,supportandseatingdevices
ProposeimproveddesignforanorthopedicWalkerthatpermitstheWalkertobeformedsubstantiallyfromapolymer
4
Applying body support Addaremovablemeshseatandalatchabledoorandmanynovelfeatures
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73
devices (seat, belt, frames, handle,
...) (13 patents)
Addahandrailextension(Includesaclamp,aprimarysupport,andanangularsupport,andfoot)
Addanup‐rightWalker(includessideframeshavingabacklegframeandafrontlegframe)forsupportingapatientwithup‐rightpostureandcouplingwheels
Designashowerchair/WalkercombinationandfoldingWalkerhavingapivotedseat(asplittoiletseatwithagapatitsfrontend)
Addarisingsupportintegratedintoawalkingaid;havingapartthatisverticallyadjustableconnectingaslingorharness
DevelopamobilityapparatusorWalkerthatfacilitatesbothnormalgaitvelocityandanuprightposturewithalumbarandcervicalbelt
Proposeaphysicalassistancedeviceisconfiguredasawalkingaidandtosupportauserinaseatedposition
Proposeamodularandadaptiveapparatusforstabilityandbodyadjustmentaidwithsensoryinability
Proposealightweight,foldabledevicethatcanpartiallysupporttheweightofapatientduringrehabilitation
Proposeanadaptiveassistivewalkingdevice(aweight‐relievingWalker)insupportingtheuser'sbodyinspecificvariableamountsduringambulationwithouttheneedforbeinghandheldforpropulsion
ProposeaWalkerandaliftingarmattachedtotheWalkerthatextendsinanapproximatelyverticaldirectionfromtheWalkerforassistingaseatedpersontostand
Proposeanambulationaidwhichhasasupportstructurethatbothsupportsthepatient'sweightandismovablelaterallyontheframetoaccommodatesidewayshipmovementofthepatient'sgait
Proposeastand‐upWalkerforassistingtheweightinanuprightpositionwiththehandlesandliftspringmeans
5 Applying telescopic
structure (3 patents)
ProposeadjustableheightWalkerincludestwoassistingpartsandatransverselypartconnectingtotheassistingpartsatthefrontsides
ProposeadjustablewidthWalkerandfoldingtoacompactstateforstoragepurposesortravel
Proposeawalkingaiddevicehavingspring‐loadedandseparatelyadjustablerearlegs
6
Improvement folding mechanism of Walker
(pivot and joint) (5 patents)
ProposeaspecificsystemormethodforanarticulatingWalkermotionofarmsandlegs
Add telescoping legs, foldable handles, and body with a storageconfigurationthathaslessheight,depth,andwidththanitsfullydeployedconfiguration
Propose a foldable Walker apparatus having a Variety of optimizedfeatures relating to its folding mechanism, braking pad mechanismandbrakehousing,brakerodassembly,frameshape,andcollapsiblebasket
Propose a locking assembly for use with a Walker having foldable sidemembers
Propose a foldable Walker with a release mechanism (a paddle‐shapedleverarrangedtointeractwithlockingpinsbylateralmovementineitherdirection)forsaferandeasieroperation
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Thesixcategories of solutionsareshown inTable 18;seecolumn labeled‘solutiongroupname.'Theclassesofthesolutionsareverycommonsolutionsforsuchengineeringsystemhoweverthedetailsmustbestudied.
Preparing the matrix of problems and solutions, and determining theQuantityofpatentsineachcrossoftheclassofproblemandsolution,wasthenextstepforbuildingthemap.Figure9showsthepreparedmatrixforthe30analyzedpatents.
Figure9‐The30patentmatrixinformation.
AsFigure9shows,inthecrossofeachclassofproblemsandsolution,the
Quantityofrelevantpatentsandthecodeof themarementioned.Forinstance,8
patents are distinguished relevant to the cross of the first class of the problems
“supporting user for normal walking” and fifth class of solutions “applying body
supportdevices”.Thepatentswithcodes3,8,13,16,20,21,24and26arethecodes
ofthese8patents.Itisworthmentioning;eachpatentappearsonceonthematrix.
ThebubblegraphicalschemaoftheProblem‐SolutionMatrixMapwaspreparedby
usingtheFigure9byExcelsoftware.Figure10demonstratesthedrawnmatrix.
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Figure10‐Problem‐Solutionmatrixmap.
Figure 10 is a graphical representation of Figure 9, and it shows the
distributionofpatentsrespecttotheclassesofproblemsandsolutionsgraphically.
The third dimension as the other supporting graphs for each cross of the
Technical Contradiction Map was prepared by bringing all the analyzed resolved
contradictionofpatentsofeachcrossinapapertogether.Thereare10bubbleson
theMainmatrixoftheTechnicalContradictionMap,andrespectively,10supportive
graphsareexpectedforthismap(SeeAppendixEfordetailsofallgraphs).Figure
11showsoneofthesupportivegraphsforthecrossof‘supportinguserfornormal
walkingasaproblem’and‘applyingbodysupportdevicesasasolution.'Thiscross
consistsof8patentsandconsequently8contradictions.
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Figure11 ‐ Proposedcontradictionmap.
AsseeninFigure11,tosimplifythegraphs,similarpartsofcontradictions
aremergedtosupportmoredesignersinanalyzingtheinventiveproblemsoftarget
technical systems and propose non‐obvious novel ideas by resolving the
contradictions.Thepositionof patentscan be followedby lookingat thegeneral
pictureofthecorrespondingpatentonthemergedmodelsofcontradiction.
Figure12showsthefinalpreparedschemaofTechnicalContradictionMap
fortheWalkerasthesampleofthefollowingempiricalstudies.Themainbubble
graph and the 10 graphical representations of contradictions of the patents
correspondingtoeachbubblearetogetherthe11pagesofTechnicalContradiction
MapofthesystemofWalker.
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TheproposedmappresentedinfollowingFigures:
Figure12‐TheproposedthreeDimensionalTechnicalContradictionMap.
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Dedicated time for following the procedure and building Technical Contradiction Map for the sample technical system
As mentioned Technical Contradiction Map is built by a three stages
procedure;Preparingtheresources,extractingtheanalogs,andbuildingthemap.
Following the procedure took around 35‐40 hours for the researcher. Table 19
showsthededicatedtimeapproximatelyforeachstage.
Table19‐DedicatetimeforbuildingtheTechnicalContradictionMapofaWalker.
1-Preparing the resource 2-Extracting the analogs 3- Building the map
Step 1: Identifying related
keywordstothesystem
Step2:IdentifyingtheCPCcodeof
thesystem(Espacenet)
Step 3: Extracting the related
patents(Orbit)
Step4:Refinethepatents
Step5:Selectingthepatents
Step 1: Identify Problem of the
patent
Step 2: Identify Solution of the
patent
Step3:DefineUndesiredoutcome
ofthepatent
Step 4: Identify Improving
Parameters & elements of the
patent
Step 5: Identify Worsening
Parameter & elements of the
patent
Step6:IdentifyControlParameter
ofthepatent
Step7:FormulateContradictionof
thepatent
Step 1: Categorize the extracted
problemsofpatents
Step 2: Categorize the extracted
solutionsofpatents
Step 3: Build the matrix map of
problemsandsolutions
Step 4: Build the contradiction
graphsofeachcross
10 Hrs. 20 Hrs. 5 Hrs.
35 – 40 Hrs.
It is worth considering the researcher is familiar with the procedure and
thereforethededicatedtimecanbeconsideredasthetimewhichisneededforan
expert.Inaddition,astheneededmentaltaskloadishigh,this40‐hourscanbedone
atleastin2weeksinsteadof1week(40hoursis1workingweek).
3.2.2 Plan of empirical studies
Research questions Improvingthepatentabilityofideagenerationsessions,throughincreasing
the generation of non‐obvious novel solutions for inventive problems of a targetsystem, is consideredas the objectiveof this research.ATechnicalContradictionMapisthesuggestedcontributionofthisresearchforthetargetobjective.Accordingto the considered objective and original contribution, the following researchquestionscanbepursued:
1. Research Question 1: Can R&D engineers in Iranian SMEs improve Novelty within their ideas, through the use of an enriched Problem-Solution Patent Map by the ‘contradiction concept’?
ForRQ1,itisworthmentioningthelackofanassessingsystemormatrixinthe literature for assessing non‐obvious novel ideas, which can be used by an
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individual engineer and analyzer. Non‐obvious novelty is mostly evaluated byexpertsinthefieldsubjectivelyconsideringsomethepositionoftheidearespecttothe fourth level of knowledge. Therefore, it is considered to apply a degree ofNoveltyVarietyofideas,insteadofassessingnon‐obviousnovelties,inthescopeofthe empirical study. These degrees together can be used for showing the non‐obviousnoveltiesiftheassessingcriteriaandthecorrespondinglevelsaredefinedforthisaim.Thedetailsoftheassessingcriteriaarementionedinfollowingsections.
2. Research Question 2: Can Iranian R&D engineers build the proposed enriched Patent Map by following the developed procedure?
ThemapcanbeusefulforcompaniesifR&Dengineerscanbuilditforanytargetsystembyfollowingtheprocedure.Therefore,itisworthstudyingiftheycanfollowtheproposedstepsofproceduresformap‐building.
Proposed structure for the studies
GivenRQ1andRQ2,twoempiricalstudiesareplanned.Thefirstempiricalstudy investigates the usability and effectiveness of the Technical ContradictionMap;thesecondempiricalstudyinvestigatesrepeatabilityoftheprocessofbuildingthemap.
TostudytheusabilityandeffectivenessoftheTechnicalContradictionMap,theresultsofusingthemaparecomparedtosomeothermethodsusedforthesamepurpose in design and idea generation sessions. Idea generation usingbrainstormingasatechniqueisconsideredasthecontrolgroup,asmostdesignandideagenerationsessionsusethismethod(Lewisetal.,1975).TheProblem‐SolutionMatrixMapandPatentText(Far‐Field)areconsideredastheotherinterventionsfor comparison. The Far‐Field Analogy is considered as another intervention forcomparison, as literature discusses the effectiveness of Far‐Field Analogy onincreasingtheNoveltyandQuantityofideas(Chanetal.,2011).
TheProblem‐SolutionMatrixMapisconsideredasoneoftheinterventionsfor comparison as it is basic for a developed Technical Contradiction Map; it is,therefore,usefultostudytheeffectivenessofthedevelopedmapinrespecttothat.Intotal,theresultsoftheideagenerationsessionwiththeTechnicalContradictionMapwillbecomparedtothethreeotherinterventions,inordertostudythemap’susability and effectiveness: (i) idea generation session with Problem‐SolutionMatrixMap(ii) ideagenerationsessionwithPatent Text(Far‐Field)of thetargetsystem,and(ii)ideagenerationsessionwithbrainstorming.
Tocomparetheresultsofthefourconsideredinterventions,fourgroupsof7teams are planned, each consisting of 2 R&D engineers. The teams are asked togeneratepatentableideasintwosessions,each30minuteslongwitha15‐minutebreak in between. In the first 30 minutes, all teams generate ideas by applyingbrainstorming,however inthesecondsession;eachgroupwillgenerate ideasbyone of the considered interventions. Comparing the results of two sessions, theeffectivenessoftheTechnicalContradictionMapwillbestudied.
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Tostudy the repeatability, themap‐building process, thesame30 patentsused for sample study, are given to different R&D engineers to analyze themaccording to the developed procedure. The second main stage of procedure forextractinganalogsfromthepatentstakesaround1hour;itisnotexpectedthereforethattheparticipantswilldedicatetheirtimetoanalyzemorethanonepatent.Itisdesignedtoinvite90R&Dengineerstopursuetheprocedureforonlyonepatent.TheresultsofthefollowedprocedurebyR&Dengineersaregatheredasthreenewmaps.RepeatabilityisthencheckedbyanalyzingthesimilarityinresultsofusingthemapsaboutthedegreeofNoveltyoftheideas.Therefore,thesecondexperimentconsistsof2mainparts;extractingtheanalogsbyR&Dengineersasthefirstpart,andapplyingthebuiltmapsbasedontheresultsofpartoneasthesecondpart.Eachnewmapisgivento7newteamsof2R&Dengineerstoallowforcomparisonwiththe results from the seven first teams, which applied the built sample TechnicalContradictionMapbytheresearcherinthefirstexperiment.
Inthisexperiment,theteamsarealsoaskedtogeneratetheirideasintwo30‐minutesessions,likethefirstexperiment,toallowforcomparison.It isworthmentioning,beforerunningthefirstpartofthesecondexperiment,theobviousnessofthesentenceswascheckedinasampletext,byparticipating6R&Dengineers,andthesentencessimplifiedbasedontheiropinionsandsuggestions.Table20showstheschemaofcommonandsimilarpartsofexperimentsoneandtwo.
Table20‐Similarpartsofexperiments.
Participants of the studies For the designed empirical study, it is expected in total 194 R&D
engineers to participate in theexperiments. 56R&Dengineers in the firstexperiment(4groupseachoneconsistsof7teamsof2engineers),6R&DengineersfamiliarwithTRIZand90R&Dengineersforthefirstsessionofthesecondexperiment,and42R&Dengineersinthesecondsessionofthesecondexperiment(3groupseachoneconsistsof7teamsof2engineers).Itis worth mentioning the teams are selected randomly after the engineersacceptedtoparticipateintheexperiments.
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Inrespectstotheconsideredproblemandtheultimateobjectiveofthe whole research, the R&D engineers are going to be invited for theexperimentsfromIranianSMEs.ToinvitetherequiredQuantityofengineerstotheexperiments,10Iraniancompaniesareco‐operatingwiththestudy.Itis expected that the target R&D engineers have around nine years’ workexperiencesindifferentpartsofindustriesincludingR&Ddepartments.
Idea generation task Thetaskispresentedasanoralpresentationinlessthan5minutesas
following: “In general the patentable idea is considered as a novel(/completely new) idea that is not obvious to the experts in the field andindustrialusageispredictedforthat.ConsiderWalkerasthetargetsystemand please propose as much as you can non‐obvious novel ideas that youthinktheyhavethepotentialtobeacceptedaspatent”.Inaddition,Figure7isgiventoparticipantsasawell‐knownFigureofaWalker.
Different stimuli Atthebeginningof thesecondpartof thedesignsession, the three
followingstimulipresentedtoteamsrandomly:‐Fulltextof5patentsofdomain/for7teams;‐Problem‐SolutionMapof30Far‐FieldpatentsofWalker/for7teams;‐TechnicalContradictionMapof30patentsofWalker/for7teams;‐Brainstorming/for7teams.
Data collection Inbothexperiments,participantsareaskedtofillthetablesofideas
onthesheetofpaperspreparedfordatacollection.Adescriptionoftheideaand its simple picture are the data asked to be filled by participants.Therefore, it can be considered that the data collection is done byparticipantsoftheexperiments.Inthiskindofdatacollection,theresearchertrusts the final report of the participants, and it is obvious that manymediatoryandnotcompletedideasaremissed.
Data processing Dataprocessingofdesignedempiricalstudiescanbedividedintotwo
parts;assessingtheresultsofeachideagenerationsessionbyeachteam,andstatisticalstudiesincomparingtheresultsofgroupstogether.Infollowingeachoneisdescribedinmoredetails.
The current research aims at increasing the patentability of ideasgenerated by R&D engineers in idea generation sessions. The researchfollows this aim through increasing the generation of non‐obvious novelideas.Itismentionedinadvancethattherearenospecificassessingmetricsfornon‐obviousnovelideasintheliterature.Ontheotherhand,theliteratureshowsresearchonmetricsystemsandcriteriaforassessingtheQualityandQuantityofideagenerationsessions(Chapter2).
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Non‐obvious novelty, which is the focus of this research, can beapproachedbyconsideringthetwocriteriaofNoveltyandVarietybecauseaccording to their definitions, they show unexpectedness and moreexplorationindesignspace.Quantityisalsoconsideredasanothercriterionfor assessing the results of different proposed interventions on ideageneration sessions to allow comparison of the results with relatedliterature.
Theposterioriapproachisselectedforthereference ideastostudytheNoveltyofideas.TobestrongerintheassessmentofNon‐obviousness,thedegreeofNoveltyofthenewideasarerankedaccordingtotheirnewness,inrespecttotheFBSframeworkandthegradegivenforthedegreeofVarietyof ideas on the more detail version of FBS (physical principle, workingprinciple, embodiment, and detail). To follow Shah’s assessing metric andformula, the functions of the sample target system (Walker) are defined,which is similar to the problems extracted from the patents. Novelty andVarietyarethenassessedbasedontheweightsconsideredforthelevelsofNovelty and Variety of ideas in respect to the FBS framework (details arementioned in Chapter 4). The final degree of Novelty and Variety of ideageneration of each team is achieved through Shah’s formula (Shah et al.,2003).Table21showstheappliedformulaincurrentresearch.
Table21‐Theappliedformulaincurrentresearch.
No. The criteria Formula Description
1
Novelty (inposteriori
approach)
�� = ���
�
���
�������
�
���
M1:TotalNoveltyscorem:Numberoffunctionsorattributesn:Numberofstagesfj:Weightsassignedtothevalueoffunctionorcharacteristictocalculateatotalscorepk:Weightsallocatedtotheimportanceofstages.
���� =��� ���
���× 10
Cjk:TheideasnumberforfunctionjinstagekTjk:Theideasnumberforfunctionjforallstagesk
2 Variety �� = 10 ���
�
���
� ����/�����
�
���
M3:Varietyscoreb�:BranchesnumberatlevelkS�:Levelscorek(10,6,3,1)m :TotalnumberoffunctionsM ����:MaximumVarietyscore
ItshowsthathigherNoveltyisachievedforateamwhentheoccurrenceofinstancesofnewnessintheirideasarelow,andthedegreeoftheirNoveltyishighaccordingtotheweightsintheFBSframework.HigherVarietyisalsoreachedforateamwhentheideascovermorecategoriesoffunctions,atahigherlevelofdegreeofVarietyinrespecttothedetailversionoftheFBSframework.BothfinalscoresofNoveltyandVarietyshowtheportionofNoveltyandVarietywithrespecttothesetofreferenceideas;thesearethegeneratedideasbyallteamsincurrentresearch.
After the measuring the degree of Novelty and Variety of each team,statistical studies are needed. Statistical studies are pursued through the linear
Chapter3:Researchmethodology
83
regressionmodel.TheLinearregressionmodelisusedtoestimatetheimpactofanindependent variable on the value of a dependent variable. One of the simplestmethodsofthelinearregressionmodelisOrdinaryLeastSquares(OLS)thatweuseinthisproject.OLSmethodisbasedonthefittingastraightlinetoasampleofdatabyminimizingthesumofthesquaresofthedeviationsofthedatafromtheline.Thisequationhastheform:
� = � + ���� + ���� + ���� + ���� + �
whereYontheleft‐hand‐sideisourdependentvariablethatwearegoingtopredict;X�,X�moreover,soonareindeedourindependentvariablesusedtopredictit and ��,�� and so on are the coefficients that describe the impact of theseindependentvariablesonourdependentvariable�,andfinally�istheresiduals.
Novelty, Quantity, and Variety are the three dependent variables areconsideredforthisstudy,sothethreeseparatelinearregressionmodelsareappliedto estimate the impact of each intervention (/method) on these dependentvariables.Infact,inthefirstregression,�isNovelty;inthesecondone,itisQuantity;andfinallyinthelastregressionmodel�isVariety.Inallthreeregressionmodels,��,��, �� and�� are dummy variables corresponding to the presence of eachmethod.Fourdifferentgroupsareinvolvedinthefirstexperiment;eachoneappliedone different method for idea generation, therefore three dummy variables areimposedtoestimatetheimpactofeachmethodtoimproveideationeffectivenessofR&D engineers. In particular, three dummy variables are put, instead of four,because one group or indeed one method is considered as a control group. Theestimates for each coefficient, ��, shows the contribution of the correspondingmethodwithrespecttothemethodthatisusedasthecontrolgroup.Eachdummyusesthevalue0or1,respectively,theabsenceorpresenceofsomecategoricaleffectorevent.Basedonabovementionedgeneraldefinition,theequationforNoveltyisasfollow:
Novelty = � + ���� + ���� + ���� + �
Astheequationshowstheregressionfor'Novelty'consistsofthreedummyvariables,'ga,''gb'and'gc.'Infact,'Novelty'isthedataobservedfromthe1stideasevaluationacross fourrandomgroups(A,B,CandD),witheachgroupincludingsevenrandomteams(datahasbeenshowninChapter4)whereDstands forthegroup applying the ‘Brainstorming’ method, A stands for the group applying the‘Problem‐Solution Matrix Map’, B stands for the group applying the ‘TechnicalContradiction Map’ which is the target group, and finally C stands for the groupapplying‘PatentTextFar‐Field’.GroupD(brainstormingmethod)isconsideredasacontrol(reference)group.Therefore,threedummyvariables,'ga','gb'and'gc',aredefinedinwhichgatakes1forgroupAand0otherwise,andsimilarlyfor'gb'and'gc'.Infact,thesethreedummyvariablesmeasuretheexistenceofeachmethodwithrespecttogroupD.Forexample,'ga'takes1forgroupAandzeroforothergroups,
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84
andthenmeasuresthedifferencebetweentheNoveltyofgroupAandD,andsoonfortheothergroups.��showsthecontributionof ‘Problem‐SolutionMatrixMap’techniquewithrespecttothecontrolgroup(Brainstorming)inimprovingideationeffectivenessofR&DengineerstogenerateNovelty.Analogously,��and��show,respectively, the contributions of the ‘Problem‐Solution Matrix Map’ and ‘PatentTextFar‐Field’ingeneratingNoveltywithrespecttothecontrolgroup.Asexpected,theestimatedcoefficientsfor��,��,and��shouldbepositiveandalsothemainhypothesisofthisresearchwhichis�� > ��andalso�� > ��.Moreformally,thetwofollowinggroupsofhypothesesaretestedforeachregressionmodel:
FirstGroupofhypotheses:H�: �� ≤ 0againstH�: �� > 0H�: �� ≤ 0againstH�: �� > 0H�: �� ≤ 0againstH�: �� > 0
SecondGroupofhypotheses:H�: �� ≤ ��againstH�: �� > ��H�: �� ≤ ��againstH�: �� > ��
whereH�istheNullHypothesisandH�istheAlternativeHypothesis,andtherejectionofNullHypothesisisexpected.
TheSTATAsoftwarewhichisadataanalysisandstatisticalsoftwareisusedtoestimatetheregressionmodelsandreportedoutputconsistsoffourmainpartsofinformation:(a)theR2value("R‐squared"row)whichrepresentstheproportionof variancein the dependent variable that can be explained by the independentvariable(technicallyitistheproportionofvariationaccountedforbytheregressionmodelaboveandbeyondthemeanmodel).However,R2isbasedonthesampleandisapositivelybiasedestimateoftheproportionofthevarianceof thedependentvariable, accounted for by the regression model (i.e., it is too large); (b) anadjustedR2value("AdjR‐squared"row),whichcorrectspositivebiastoprovideavaluethatwouldbeexpectedinthepopulation;(c)theFvalue,degreesoffreedom("F(3,24)")andstatisticalsignificanceoftheregressionmodel("Prob>F"row);and(d)thecoefficientsfortheconstantandindependentvariable("Coef."column),which is the necessary information to predict the dependent variable, using theindependentvariables,'ga','gb'and'gc'.
Theterms“significancelevel”or“levelofsignificance”refertothelikelihoodthatthechosenrandomsampleisnotrepresentativeofthepopulation.Thelowerthe significance level, the more confident one can be in replicating the results.Significancelevelsmostcommonlyusedineducationalresearcharethe.05and.01levels. If it helps, think of .05 as another way of saying 95/100 times that onesamples fromthepopulation, thisresultwillbeachieved. Similarly, .01suggeststhat 99/100times that onesamples from thepopulation, the sameresultwillbeachieved.ThesenumbersandsignscomefromSignificanceTesting,whichbeginswiththeNullHypothesis.
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Forthefirstgroupofhypothesis,t‐testandforthesecondgroupofF‐testwillbe bone. Thet‐statisticis defined as theratio of the estimated coefficientanditsstandarderror.Thestandarderrorisanestimateofthestandarddeviationofthecoefficient.This test isameasureof the precisionof theregressioncoefficient. Itmeans that, ifacoefficient is largecompared to its standarderror,and then it isprobablydifferentfrom0.STATAreportsanaccompanyingp‐valueforthetestinsimpleregression.Theresultsarereportedtothreedecimalplacesofaccuracyforthep‐value.Therefore,avalueof0.000meansthep‐valueislessthan0.0005.Thisleadstothedecisionthatthenullhypothesisofazerocoefficientcanberejectedatanyreasonablesignificancelevel.Similarly,thenullhypothesiscanberejectedbyalargeF‐testresult.
Beforerunningtheestimatesandtestingthehypothesis,theassumptionsoftheclassicallinearregressionmodelmustbeclarifiedinordertogetreliableresultsandtest.Thefirstsetofassumptionsisconsideredastheso‐calledGauss–Markovassumptions.AssumptionsoftheClassicallinearregressionmodelareasfollows;theyarecheckedtobesuretheestimatedcoefficientsresultsareBLUE(BestLinearUnbiasedEstimators).
A.1: Linearity:� = � + ���� + ���� + ���� + ���� + �. There is a linear relationbetweenthedependentvariableandtheregressors.Thisassumptionisnotneededtobecheckedbecausebasedonthedefinitionofthemodel,itisalreadylinear.A.2:Fullrank:Thematrix�hasfullcolumnrank.Nooneofthekregressorcanbeexpressedasalinearfunctionfortheremainingk 1regressors.Basedonequation(2),thisassumptionisalsosatisfiedbecausedummyvariablesareused.A.3:Exogeneityoftheregressors: �[��|���. ���. ���. ��� = 0].Theexpectedvalueof
therandomdisturbanceatobservationiisnotafunctionoftheregressorsobservedat anyobservation j (includingobservation i).Thismeans that in theerror term,somethinguncorrelatedwiththe���andcannotbepredictedbythe��� isshown.
Durbin–Wu–Hausman is the formal test tocheckExogeneity, but again, since theexplanatoryvariablesaredummy,thereisnoneedtocheckthisassumption.A.4:Homoscedasticity:Eachdisturbancehasafinitevariance��whichisconstantacrossobservations.Thedisturbance��isnotcorrelatedwiththedisturbance�� .In
fact, the error term has constant variance: ��� (��)= �� for every i. To testHomoscedasticity for each regression, a graphical approach is used and also theBreusch‐Pagan/Cook‐Weisberg test. In case of heteroscedasticity, Huber/Whiteestimatorsorsandwichestimatorsofvariance(robuststandarderrors)todealwiththisissueareused.A.5: Normal distribution: The disturbances are normally distributed. Thisassumptionisnotstrictlynecessarysince,inlargesamples,theconditionsofCentralLimit Theorem will apply, and there is normality in the distribution of the mainstatistics.However,especiallyinasmallsample(inpresentedcase),itisnecessaryto check that the error terms are normally distributed: �|� ~�(0. ��) so thisassumptionischeckedforeachregression.
Chapter4:EmpiricalStudy
86
Chapter4
[4] Empirical Study
Chapter4:EmpiricalStudy
87
In this chapter, the descriptive study II was studied, and the empirical
performancevaliditywasdiscussedastheresultsofthefourthphaseofDRM.Theempiricalstudywasplannedandperformedthroughtwoexperimentsthateachoneispresentedindetailinthechapter.
4.1 Experiment I: Usability and effectiveness of proposed map
Experiment I is planned and performed to study the usability and
effectivenessofTechnicalContradictionMap(T.CMap)whichisthecontributionofcurrentresearchforimprovingthenon‐obviousnoveltyofgeneratingideasbyR&DengineersofSMEs.Table22presentstheperformedworkplan.
Table22‐Usabilityofproposedmapplan‐ExperimentI.
Groups First session (30 min) Break Second session (30 min)
A IdeaGeneration
(BrainstormingSession)7Teams,
2R&DEngineers
15min
IdeaGeneration(Problem‐SolutionMatrixMap)
7Teams,2R&DEngineers
B IdeaGeneration
(BrainstormingSession)7Teams,
2R&DEngineersIdeaGeneration
(TechnicalContradictionMap)7Teams,
2R&DEngineers
C IdeaGeneration
(BrainstormingSession)7Teams,
2R&DEngineersIdeaGeneration
(PatentTextCross‐Domain)7Teams,
2R&DEngineers
D IdeaGeneration
(BrainstormingSession)7Teams,
2R&DEngineersIdeaGeneration
(BrainstormingSession)7Teams,
2R&DEngineers
Total 56 R&D Engineers (28 teams, 2 R&D Engineers)
undergo the same treatment _
56 R&D Engineers (28 teams, 2 R&D Engineers) undergo the 4 different treatment
Asthetableshows,theexperimentwasintwomainparts.Inthefirstsection,allgroupstriedtogenerateasmanynewideasaspossibleaboutatargettechnicalsystem, Walker, by using the brainstorming method. In the second session, eachteam were asked to apply a stimulus except the teams were selected as controlgroup;teamsinGroupA,generatedideasbyusingregularProblem‐SolutionMatrixMap;teamsinGroupB,generatedideasbyusingtheTechnicalContradictionMap;teamsinGroupC,generatedideasbyusingdifferentPatentTextfromanotherfieldsuchasskateboard,unicycle,scooter,rollerskate,etc.;andfinallyteamsinGroupD,generated ideasabout theWalkerwithoutany involvement,as thecontrolgroup(seegeneratedideasofallgroupsinAppendixF).
56R&D Engineerswereasked toparticipate in the firstexperiment. Theywere randomly divided into four groups. Each group had 14 R&D Engineer in 7random teams; two individuals for each team. The allocating processes for eachgroupandtheteamshavebeendonecompletelyatrandom,andthereforethereisno systematic selection regarding their gender, age, education, degree and their
Chapter4:EmpiricalStudy
88
knowledgeinpatentanalysis.Table23showstheparticipantprofiles.From56totalmembers,45weremen,and11werewomen.Theywerebetween27to43yearsoldwhichcanbecategorizedasyoungandmiddle‐agedadults.Theycamefromseveralfields of study, but they were all engineers including Mechanical, Industrial,Electronics, Computer, Chemical, Aerospace, Marine, Metallurgical, and MaterialsEngineers.Someothercharacteristicsarementionedinthetable.
Table23‐ParticipantsProfile‐ExperimentI.
Participants Sex Average
age (year) Degree Field of study
Experiences in the field (year)
Knowledge in patent and patent
analysis
56
45M
35.4(27‐43)STD:4.4
41Master
13MechanicalEng.
9.2(7‐13)
STD:1.5
2High
12IndustrialEng.
12Medium8ElectronicEng.
5ComputerEng.
4ChemicalEng.
42Low11F
9Bachelor4AerospaceEng.
4MarineEng.
6PhD3MetallurgicalEng.
3MaterialsEng.
In followingafterreviewingtheappliedmethodandformulaforassessingtheexperimentresult, theobserveddataandstatisticalstudiesarepresentedformoreformalevidence.
4.1.1 Ideation metrics measurement
AsmentionedinChapter3,toassesstheresultsofeachteamandcomparetheresultsamongthegroups,thecombinationofShah’smetricandFBSframeworkisusedinthescopeofthisresearch.Followingthefinalappliedformulaisexplainedindetail.
Novelty: To calculate the degree of Novelty of each team, according to the Shah’s
formula,first,thecriticalortargetfunctions(orattributes)mustbelisted,andthenthe expected stages of a novelty for desired functions (or attributes) must bedefined.Theformulaneedstheweightsofthelistedfunctions(orattributes)basedontheir importance forthestudy,andalsothescores forthedefinedstagesofanoveltyforeachfunctionorattribute.Thescoresforeachstageofeachfunction(orattribute) is defined priori or posteriori. In priori view, the entire set of ideas iscollectedforevaluationbydeterminingtheexpectingunusualnessorexpectedness,beforeexamininganyinformationforavoidinganybias.Inposterioriperspective,the key attributes and the occurrences of them are defined respect to the allgeneratedideasincorrespondingdesignsessions.Basedonthepreparingnumbers,Noveltyofeachteamcanbecalculatedbasedonfollowingformula:
Chapter4:EmpiricalStudy
89
�� = ���
�
���
�������
�
���
(1)
���� =��� ���
���× 10 (2)
Where:M1:TotalNoveltyscorem:Numberoffunctionsorattributesn:Numberofstagesfj:Weightsassignedtothevalueoffunctionorcharacteristictocalculateatotalscorepk:WeightsallocatedtotheimportanceofstagesCjk:TheideasnumberforfunctionjinstagekTjk:Theideasnumberforfunctionjforallstagesk
Inthescopeofthisresearch,sixtargetattributesforexpectingnoveltiesofideasweredefinedandaccordingtotheirimportanceandexpectednovelties,theirweightsweredefined.Thenthestagesofexpectednoveltyforeachattributeweredefined in three levels of Function, Behavior, and Structure respect to the FBSframework.Table24,showsthedefinedattributesandstages.
Table24‐TheNoveltyattributewithweightsandrelatedFBSLevels‐ExperimentI.
No. Novelty
Attribute Wt. (fj)
Level 1 New Structure
Level 2 New Behavior
Level 3 New Function
Same Function-Change field Another Function
1 Support user
unbalance 0.25
Supportunbalanceduserbyusingmodular
partsmanually
Supportunbalanceduserbyactivatingmodularparts(electrically)
Supportuserbalancebyself‐
settingtothedisordersigns(smartsystem)
2 Support Walker
unbalance 0.25
Supportunbalanced
Walkerbyusingmodularlegsfitto
thesurfaces
SupportunbalancedWalker
byactivating(electrically)
modularlegsfitthesurfaces
SupportWalkerbalancebyself‐
settingtothesurfaceseffects(smartsystem)
3 Support Walker
storage & transport
0.15
SupportWalkerstorage&
transportbyusingfoldingparts
manually
SupportWalkerstorage&transport
byfoldingpartselectrically,etc.
SupportWalkerstorage&transportbyself‐settingtothe
locations(smartsystem)
4 Support user
body non-ergonomic
0.15
Supportnon‐ergonomicuserbodybysettingWalkerframe
manually
Supportnon‐ergonomicuserbodybysettingWalkerframe
electrically,etc.
Supportergonomicuserbodybyself‐
settingtothepositions
(smartsystem)
5 Supply Walker
propulsion 0.1
SupplyWalkerpropulsionby
liftinguser
SupplyWalkerpropulsionbypushinguser
SupplyWalkerpropulsionbyusing
engine
6
Support user routine
activities and user accessories
0.1
Supportroutineuseractivitiesby
holdinguseraccessories
manually
Supportroutineuseractivitiesby
holdinguseraccessorieselectrically
Supportroutineuseractivitiesbyself‐
settingtotheneeds(smartsystem)
Total 1
Asthetableshows,tobeabletodedicatetheideastoeachcellforassessingthedegreeofNoveltyofeachteam,accordingtotheconceptsofFunction,Behavior,
Chapter4:EmpiricalStudy
90
andStructure,themaincriterionforeachstageofeachattributeiswritteninthecorresponding cell in the table. In the level of Structure, the same Function isansweredbysameBehaviorbutnovelchangesinthestructureofthesystem.InthelevelofBehavior,thesameFunctionissatisfiedbynewprinciples.InthelevelsofStructureandBehavior,theusers’requirementsofsystemsarenotchangedwhilein the Function level, there is a new requirement or new value for samerequirements. In the scope of this research, the score of eachwas cell calculatedbasedonposterioriapproachbyapplyingFormula2.Table25showsthecalculatedscoresforeachcell.
Table25‐The���scoresof4Groups‐ExperimentI.
No. Novelty Attribute Wt. (fj)
Level 1 (New
Structure)
Level 2 (New
Behavior)
Level 3 (New
Function)
1 Support user unbalance 0.25 6.2 5.3 8.5
2 Support Walker unbalance 0.25 3.9 6.6 9.5
3 Support Walker storage & transport 0.15 1.5 8.8 9.7
4 Support user body non-ergonomic 0.15 4.0 6.4 9.5
5 Supply Walker propulsion 0.1 5.1 7.9 6.9
6 Support user routine activities and user accessories 0.1 5.5 5.0 9.5
Total 1
Table25wasappliedtocalculatethedegreeofNoveltyofeachteambasedontheFormula1.Table26showsthecalculationforateamasanexample.
Table26–ThecalculationofthedegreeofNoveltyofoneoftheteams‐ExperimentI.
no. Novelty Attribute Wt. (fj)
Level 1 Level 2 Level 3 Novelty Scores
1 Support user unbalance 0.25 0 0 0 0.25*((0*6.2)+(0*5.3)+(0*8.5))=0
2 Support Walker unbalance 0.25 1 1 0 0.25*((1*3.9)+(1*6.6)+(0*95))=2.6
3 Support Walker storage & transport 0.15 1 1 0 0.15*((1*1.5)+(1*8.8)+(0*9.7))=1.5
4 Support user body non-ergonomic 0.15 4 1 0 0.15*((4*4)+(1*6.4)+((0*9.5))=3.4
5 Supply Walker propulsion 0.1 1 0 1 0.1*((1*5.1)+(0*7.9)+((1*6.9))=1.2
6 Support user routine activities and
user accessories 0.1 2 3 0 0.1*((2*5.5)+(3*5)+((0*9.5))=2.6
Total 1 9 6 1 11.4
The number of each cell in Table 26 shows the corresponding number ofnovelattributes for that cell in thewholesetof solutions of the team. In the lastcolumn,thetotalscoreofNoveltyiscalculated.Similarly,byapplyingtheTable25,thedegreeofNoveltyofallteamswerecalculated.Theresultsareshowninnextsection.
Variety: To calculate the degree of Variety of each team, according to the Shah’s
formula, first,all ideasareanalyzed,and theircorresponding Functions,Physical
Chapter4:EmpiricalStudy
91
principle, Working principle, Embodiment, and details are drawn in a genealogytree.Inotherwords,eachideaisanalyzedin5levelswhichthenewsuggestionsinhigherlevels(meansFunctionandPhysicalprinciple),showthemorevarietyofidearespecttotheexistingandknownversionofthesystem.Therefore,theweightsforthelevelsmustshowthemorevarietyforhigherlevels.Mostly,literatureuses10,6,3and1forthelevelsofPhysicalprincipletodetail forassessingthedegreeofVarietyof ideas.Innextstep,theweightsmustbededicatedtoeachFunction.ByconsideringtheweightsfortheFunctionswerereferredinthetotalidea,thedegreeofVarietyofeachteamiscalculatedbasedonFormula3.
�� = 10 ∑ ������ ∑ ����/�����
���� (3)
M3:Varietyscoreb�:BranchesnumberatlevelkS�:Levelscorek(10,6,3,1)m :TotalnumberoffunctionsM ����:MaximumVarietyscore
InthescopeofExperimentI,afteranalyzingallideas,thelistoffunctionwasprepared, and then the template table for assessing the degree of Variety wasprovided.Table27showstheprovidedtemplateconsideringallrequiredweights.
Table27‐ThetemplatetableforassessingthedegreeofVarietyofteams‐ExperimentI.
Function
1-Support
user to Transport
2-Support walking in
any
surfaces
3-Support
user to sit
and stand
4-Support normal
walking
5-Prevent user
collision
& falling
6-Ergonomics
balance
7-Accessories
holder
8-Information
support
Weight
Sk
0.1 0.2 0.1 0.2 0.2 0.1 0.05 0.05
Physical Principles
10
Working Principles
6
Embodiment 3
Detail 1
Table27wasfilledforeachteamonce.Table28showsthefilledtableforoneoftheteamsasanexample.
Chapter4:EmpiricalStudy
92
Table28‐ThefilledtemplatetableofassessingthedegreeofVarietyforateam‐ExperimentI.
Function
1-
Support user to
Transport
2-Support walking in any surfaces
3-
Support user to sit
and stand
4-Suppo
rt norma
l walkin
g
5-Prevent user collision & falling
6-Ergonomics balance
7-Accessories holder
8-Informati
on
support
Weig
ht
Sk
0.1 0.2 0.1 0.2 0.2 0.1 0.05 0.05
Physical Principles
10 Mechanic
alMechanic
alMechanic
al_ _
Electrical
Chemical
_Mechanica
l_
Working
Principles 6 Portable Move Move _ _ Lighting Protect _ Protect _
Embodiment
3 Walkerframe
Walkerlegs
Walkerlegs
_ _Walkerframe
Walkerframe
_Walkerframe
_
Detail 1
Usingcomposit
ematerials
Walker
withfourwheels
Addtwo
wheelstofrontlegs
_ _
Contactled
lightsto
illuminatethe
pathatnight
Usingreflectorontheframe
_
Addtheumbrella
orroofduring
rain
_
Beforefollowingthecalculation,theresultsoftable28,mustbeshownintheformofthegenealogytreetomakepossibletocalculatethemaxquantityofvariety(M3)tobeusedinthecorrespondingformula.TheresultsofthegenealogytreeforateamisshowninFigure13.
Figure13‐Theresultsofgenealogytreeanalysisforateam‐ExperimentI.
BaseonFigure13andFormula3,thedegreeofVarietyoftheexamplewascalculatedas:
��= (10)*[(0.1)*((10*1)+(6*1)+(3*1)+(1*1))+(0.2)*((10*1)+(6*1)+(3*1)+(1*2))+(0.2)*((10*2)+(6*2)+(3*2)+(1*2))+(0.05)*((10*1)+(6*1)+(3*1)+(1*2))]/2=76.25
Similarly,thedegreeofVarietyofeachteaminbothsessionswascalculated.
Chapter4:EmpiricalStudy
93
4.1.2 Estimated results
The data were collected by team members during design session throughfillingthesolutionpapers,wheneverthemembersagreedonproposingsolutions.Adescription,aschema,andthetimeofappearanceofthesolutionswerethecollecteddatainsolutionpapers.Thedatacollectedforeachsessionseparately.Attheendofbothtwosessions,NASAsurveywascompletedbyparticipantsoftheteamstoo.ThedescriptionandschemaofeachsolutionwereanalyzedbyresearcherandthetotalscoreofNoveltyandVarietywerecalculatedbasedontheTables25and28.Table29 shows the results of calculation of Novelty and Variety for each 28 teamsorganizedbasedontheinterventioninthesecondsessiontoletfurtherstudies.
Table29–ThescoresofQuantity,Novelty,andVarietyforallteams‐ExperimentI.
Name Team
First session (Brainstorming)
Second sessions (various stimuli)
Two sessions
Quantity Novelty Variety Quantity Novelty Variety Quantity Novelty Variety
Group A (P.S)
1 16 11.4 67.8 7 7.2 70 23 18.6 137.8
2 8 13.4 29.2 4 5.3 80 12 18.7 109.2
3 12 8.7 28.2 5 7.6 50 17 16.3 78.2
4 17 12 59.5 5 4 42.5 22 16 102
5 7 4.9 60.5 4 3.6 40.5 11 8.5 101
6 22 5.6 77.3 11 6.9 67.3 33 12.5 144.6
7 6 15.5 51 3 3.4 60 9 18.9 111
Group B (T.C)
1 10 6.3 65.7 8 7.7 34 18 14 98.7
2 17 13.6 49.2 11 8.4 57.7 28 22 106.9
3 11 9 67 4 3.6 80 15 12.6 147.0
4 17 12 55 10 12.8 46.2 27 24.8 101.2
5 5 3.6 26 5 4.1 51 10 7.7 77.0
6 9 5.8 34.8 7 4.7 37.6 16 10.5 72.4
7 19 21 49.3 9 10.9 27.6 28 31.9 76.2
Group C (P.T)
1 10 6.7 49.3 3 4.3 30.5 13 11 79.8
2 10 6.9 49 8 7.2 70.5 18 14.1 119.5
3 10 9.6 39.3 2 1.9 40 12 11.5 79.3
4 18 11.9 35.3 6 5.3 34 24 17.2 69.3
5 5 4.8 31 4 3.3 31.5 9 8.1 62.5
6 15 14.6 56.5 6 6.1 21.3 21 20.7 77.8
7 11 7.5 42.3 5 5.9 50.5 16 13.4 92.8
Group D (B.S)
(Control Group)
1 5 3.8 21 4 3.7 40.5 9 7.5 61.5
2 17 12 49 3 2.7 30 20 14.7 79.0
3 8 6.7 47.3 3 2.1 30.5 11 8.8 77.8
4 12 7.6 64 3 3.3 60 15 10.9 124.0
5 10 9.1 61.3 4 3.8 40.5 14 12.9 101.8
6 16 15.1 59.8 3 2.5 30.5 19 17.6 90.3
7 12 8.4 62 2 1.6 10.5 14 10 72.5
Chapter4:EmpiricalStudy
94
ThetableshowsthescoreofQuantity,Novelty,andVarietyofeachteaminthreeconditions;firstsession,secondsession,andtwosessionstogether.Intotal,formostoftheteams,despitetheappliedinterventions,thescoresfortheallthreecriteriainthesecondsessionarelessthanthefirstsession.Furtherstudiescanbefollowedbytwoapproaches;investigationontheeffectofeachinterventionrespectto the control group in the second session, and investigating the effect of eachinterventionrespecttothecontrolgroupbyconsideringthetotalscoresforthetwosessionstogether.Two‐partdesignsessionwhereasapplyingbrainstormingforthefirstsession,reducestheeffectsofparticipants’expertiseintothetestastheytrytogenerate as much as possible ideas by first reflection on their mind in the firstsessionandtheeffectsofinterventionsandstimulicanbeobservedinthesecondsession. Also, the literature shows, without any intervention, the number ofgeneratedideasinabrainstormingsessiondecreaseafterhalfanhour,whilethebestideasaregeneratedinfirst15minutes(Howardetal.,2010).Moreover,stimuliwhich are prepared and applied during the early design stages, or when theparticipanthasbeenunabletosolvethedesignproblemforadifficultopen‐endeddesign problem (Tseng et al., 2008). Therefore, in the scope of Experiment I,Brainstormingisusedonlyatthebeginningoftheideationprocess,andformoreimprovement,thestimuliwerepresentedatthebeginningofthesecondsession.
To follow further studies, the scores of Quantity, Novelty and Variety are
calculatedfortheallteamsofagroupwiththesamestimulitogether.Table30showstheestimatedresultsforeachcriterionforthefourgroupsoftheexperimenttomakepossible comparison among the groups in the second approach for all sessionstogether.
Table30‐ThescoresofQuantity,Novelty,andVarietyrespecttothegroupwithdifferentstimuli‐ExperimentI.
Name First session Second sessions Two sessions
Quantity Variety Novelty Quantity Variety Novelty Quantity Variety Novelty
Group A (P.S)
Mean 12.6 53.3
Sco
re
71.4Mean 5.6 58.6
Sco
re
38Mean 18.1 112
Sco
re
109.4STD 6 18.7 STD 2.7 14.9 STD 8.5 22.7
Group B (T.C)
Mean 12.6 49.471.2
Mean 7.7 47.752.2
Mean 20.3 97.1123.4
STD 5.2 14.9 STD 2.6 17.6 STD 7.3 25.9
Group C (P.T)
Mean 11.3 43.262.2
Mean 4.9 39.834
Mean 16.1 8396.2
STD 4.2 8.9 STD 2 16.3 STD 5.3 18.7
Group D (B.S)
(Control Group)
Mean 11.4 52.1
62.8
Mean 3.1 34.6
19.5
Mean 14.6 86.7
82.3STD 4.2 15.2 STD 0.7 15 STD 4 20.8
Table 30 shows, almost the calculated average of Quantity, Novelty, andVarietyoftheteamsofallgroupinthefirstsessionaresimilar,andthereisnotabigdifferenceamongthem.However,thecorrespondingvalueforthesecondsessionwithvariousstimuliisdifferent.ThescoresofQuantityandNoveltyarehighestforthe group received Technical Contradiction Map (T.C Map) after the group with
Chapter4:EmpiricalStudy
95
Problem‐SolutionMap(P.SMap)whilethedegreeofVarietyofthesetwogroupsareso close but higher for the P.S Map. Considering the expecting meaning for non‐obvious novelty and the Shah metrics for Novelty and Variety, discussed inChapter3, it seems that T.C Map is more effective on the ultimate target of theresearchwhileitmustbestudiedthroughstatisticalstudies.Itisworthconsideringthatthelessvalueinthesecondsessionforallthreecriteriaareforthecontrolgroupwhichappliedbrainstormingforgeneratingideasinthesecondsessiontoo.
Respectivelytheanalysiscanbefollowedforthethirdcolumn,twosessionstogether.AgainthescoresofQuantityandNoveltyarehighestforthegroupreceivedT.C Map while the Variety is highest for the P.S Map group. Figure 14 showsgraphicallytheassessingcriteriaforthelastcolumn.
Figure14‐Graphicalrepresentationofassessingcriteriaoftwosessions‐ExperimentI.
Chapter4:EmpiricalStudy
96
Before performing statistical analysis, it is worth to look at the otherinformationgatheredduringandafterdesignsessions;thetimeofappearanceofsolutions,andtheNASAsurvey.
Figure15showstheperiodofemergingsolutionsinthesecondsession.Thetotaltimeisdividedintosixsessions.
Figure15‐Theideatimelineofallgroup‐ExperimentI.
ThefigureshowsT.CMapgrouprespecttotheothergroups,generatedthehigherpercentageoftheirideasamong6to20minute.Inotherwords,TechnicalContradictionMapincreasedthespeedof ideagenerationatthebeginningofthesessionwhilethememberscontinuegeneratingideauptotheend.
According to the NASA task load Index, the team members are asked torespondtosixquestions.Thequestionsaskabouttheattemptsandthefeelingofparticipants;demandingmentaltask,physicaldemandingtask,appropriatenessofthededicated time,success in fulfilling the taskrequirements, theharnessof thetask,andstressfulness.Figure16showstheresultsofthesurveyrespecttothefourvariousinterventions.
Figure16‐NASAtaskloadIndexresultsofparticipants‐ExperimentI.
0
5
10
15
20
0-5 6-10 11-15 16-20 21-25 26-30
Ide
as
Time
Idea Timeline
Problem-Solution Map Technical contradiction Map
Patent Text (Cross-field) Brainstorming (Control)
Chapter4:EmpiricalStudy
97
The best results for the T.C Map can be seen for the forth question which is thefeelingoftheparticipantsabouttheirsuccessesinfulfillingthetask.ThehardnessofT.CMapislessthanFullPatentText,anditishigherthanP.SMap.Inaddition,T.CMapisrankedaslessmentaldemandingrespecttoPatentText,whichcanbemeantthatT.CMapprovidedinformationmoreaccessible.
4.1.3 Data analysis
Statisticalanalysisusuallystartsbychecking thenormalityofdata.Thegathereddatainthefirstsessionwhereastheconditionofdesignsessionwasthesameforallthe 28 teams. The normality of data was studied through all scores of Quantity,Novelty,andVariety.Figure17showstheresultsofnormalitystudies.
Figure17‐NormalityoftheData‐ExperimentI.
As mentioned in Chapter 3, the statistical studies for usability andeffectivenessoftheproposedmap,TechnicalContradictionMap,isstudiedthroughan OLS model for the calculated scores of Novelty, Quantity, and Variety in theprevioussectionforeachteam.
0.0
00.2
50
.50
0.7
51
.00
Norm
al F
[(N
oveltyP
1-m
)/s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
Novelty - Part 10.0
00.2
50
.50
0.7
51
.00
Norm
al F
[(Q
uantity
P1
-m)/
s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
Quantity - Part 1
0.0
00.2
50
.50
0.7
51
.00
Norm
al F
[(V
ari
ety
P1-m
)/s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
Variety - Part 1
0.0
00
.25
0.5
00.7
51.0
0N
orm
al F
[(N
oveltyP
2-m
)/s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
Novelty - Part 2
0.0
00
.25
0.5
00
.75
1.0
0N
orm
al F
[(Q
ua
ntity
P2
-m)/
s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
Quantity - Part 20
.00
0.2
50
.50
0.7
51
.00
No
rma
l F
[(V
arie
tyP
2-m
)/s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
Variety - Part 2
0.0
00
.25
0.5
00
.75
1.0
0N
orm
al F
[(N
oveltyT
-m)/
s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
Novelty - Sum
0.0
00.2
50.5
00.7
51.0
0N
orm
al F
[(Q
uantity
T-m
)/s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
Quantity - Sum
0.0
00.2
50.5
00.7
51.0
0N
orm
al F
[(V
ari
ety
T-m
)/s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
Variety - Sum
Chapter4:EmpiricalStudy
98
Novelty: AsmentionedinChapter3,tostudytheeffectsofTechnicalContradictionMap
on the degree of Novelty of ideas of corresponding teams respect to the othergroups,anOLSmodelconsideringthreevariablesarerepresentingthethreeappliedmethodsrespecttothecontrolgroup,isused.ThemodelforestimatingNoveltyisasfollow:
Novelty = � + ���� + ���� + ���� + �
where ��, �� and �� present, respectively, the contribution of ‘Problem‐Solution Matrix Map’, ‘Technical Contradiction Map’ and ‘Patent Text Far‐Field’methods with respect to the control group (Brainstorming). Since group B (whoreceivedtheTechnicalContradictionMapmethod)isthetargetgroup,theestimatedresultfor��willbeonthefocus.Thestudyispursuedthroughsetofhypothesesafterstudyingthevalidityofassumptionsofclassicallinearregressionmodelonthedataset.
1. Possibility of applying LOS model for statistical analysis: Novelty
2ofthe5assumptionsofaclassicallinearregressionmodelontheresidualsneeded to be tested in order to ensure a reliable interpretation of the testedhypotheses; Homoscedasticity and the normal distribution of the residuals weretested.
First,HomoscedasticityoftheresidualsoftheNoveltyregressionwastestedusingthegraphicalapproachandalsoBreusch‐Pagan/Cook‐Weisbergtest.Figure18 shows a graphical representation of the estimated residuals of the Noveltyregression.Itisclearthatthereisnosystematictrendfortheestimatedresiduals,indicatingthattheHomoscedasticityassumptionissatisfiedforthisvariable.
Figure18‐EstimatedresidualsfortheNoveltyregression:Homoscedasticitytest‐
ExperimentI.
The Breusch‐Pagan/Cook‐Weisberg test is also used to make sure theHomoscedasticity. This test is designed to detect any linear form of
-10
-50
510
15R
esid
uals
0 10 20 30group
Chapter4:EmpiricalStudy
99
heteroskedasticity. Breusch‐Pagan/Cook‐Weisberg tests the null hypothesis thatthe error variances of the regressions are all equal which indicatesheteroskedasticity,versusthealternativehypothesissayingthattheerrorvariancesareamultiplicativefunctionofoneormorevariables.Therefore,largevaluesofchi‐squareindicatepresentingoftheheteroskedasticity.Inourexample,thechi‐squarevalue is small enough to make sure that heteroskedasticity is not a problem, asshown in the graph. In this example, the chi‐square value is small, indicatingheteroskedasticityisprobablynotaproblem,asshowninthegraph.
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of Novelty chi2(1) = 2.20 Prob > chi2 = 0.1384
TheNormalityof theresiduals thenneededtobechecked.Bothgraphical(Figure19)andSkewness/KurtosistestsconfirmthenormalityoftheresidualfortheNoveltyregression.
Figure19‐EstimatedresidualsfortheNoveltyregression:Normalitytest‐ExperimentI.
Skewness/Kurtosis tests for Normality ------ joint ------ Variable | Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 -------------+--------------------------------------------------------------- resNormal1 | 28 0.2368 0.6552 1.74 0.4199
2. First Group of hypotheses: Novelty
ThefirstgroupofhypothesesstudiestheimpactofeachmethodontheOLSmodel respect to the control group by expecting positive coefficient for effectivemethodsontheOLSmodelofNovelty.Therefore,threehypothesesarestudied:
1. [H�: �� ≤ 0againstH�: �� > 0]2. [H�: �� ≤ 0againstH�: �� > 0]3. [H�: �� ≤ 0againstH�: �� > 0]
0.0
00
.25
0.5
00
.75
1.0
0N
orm
al F
[(re
sNorm
al1
-m)/
s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
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Theabove‐mentionedhypothesesarestudiedthrought‐testonthebasisofOLS model by STATA software. Therefore, first, the both simple regression andregression with clustering analysis are done. Table 31 shows STATA output ofsimpleregressionanalysisfortheNoveltyacrossdifferentgroups.
Table31‐EstimatedresultsofeffectsofdifferentmethodsonNovelty‐ExperimentI.
Source | SS df MS Number of obs = 28 -------------+---------------------------------- F(3, 24) = 1.16
Model | 112.229643 3 37.409881 Prob > F = 0.3469 Residual | 776.502857 24 32.3542857 R-squared = 0.1263
-------------+---------------------------------- Adj R-squared = 0.0171 Total | 888.7325 27 32.9160185 Root MSE = 5.6881
------------------------------------------------------------------------------ Novelty | Coef. Std. Err. t P>|t| [95% Conf. Interval]
ga | 2.585714 3.040408 0.85 0.403 -3.68938 8.860808 gb | 5.571429* 3.040408 1.83 0.079 -.7036654 11.84652
gc | 1.914286 3.040408 0.63 0.535 -4.360808 8.18938 cons | 12.45714 2.149893 5.79 0.000 8.019981 16.8943
AsthetableshowsR2=0.126andAdjustedR2=0.017,whichmeansthatthethree dummy variables, explain 12.6% of the variability of the dependentvariable,'Novelty,' in the population. However, normally it isR2, not theadjustedR2,thatisreportedinresults.Inthisexample,F(3,24)=1.16andp=.35;thismeansthattheregressionmodelisnotstatisticallysignificantat90%levelofsignificance.Thisissuewillbedealtwithusingclusteringissue.Inaddition,onlythecoefficientofthedummyvariableforgroupB(T.Cmapastargetgroup)issignificantat90%levelofsignificance;butallofthemarepositiveandbiggerthanone.2.59forthedummy'ga'meansthatthemethodappliedforgroupAgenerates2.59moreNoveltiesthangroupD;however,itisnotsignificant.Also,5.57for'gb'meansthatthe method used for group B has the highest effect between other methods andproduces 5.57 more ideas than group D; hopefully this is the only significantcoefficient.Thisdifferenceis1.91forgroupCascomparedtogroupD,butagain,itisnotsignificant.Thepresenceofnosignificantcoefficientswillalsobeaddressedbyusingtheclusteringmethod.
Clusteringisamethodofgroupingasetofobjectssothatobjectsinthesamegroupas aclusteraremoresimilar toeachother, rather than to thoseobjects inother groups or clusters. Since the groups are truly random, regression can beestimatedbyclusteringwithrespecttoeachgroup.Infact,clusteringwithrespecttotheidvariabletakesadifferentnumberforeachgroup.Table32showstheSTATAoutputforthesameregressionasabove,butwithclustering.Itisclearthatallthreedummyvariablesaresignificantatthe99%confidencelevel.However,clusteringdoesnotchangetheestimatedcoefficients,andgroupBstillgenerated5.57moreNovelty than the control group (Brainstorming) and also more than the othermethodsappliedtogroupsAandC.
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101
Table32‐Estimatedresultsofimprovingideasfordifferentmethods:Novelty(clustering)
‐ExperimentI.
Linear regression Number of obs = 28 F(0, 3) = . Prob > F = . R-squared = 0.1263 Root MSE = 5.6881
(Std. Err. adjusted for 4 clusters in id)
Robust NoveltyT | Coef. Std. Err. t P>|t| [95% Conf. Interval]
ga | 2.585714*** 3.47e-16 7.4e+15 0.000 2.585714 2.585714 gb | 5.571429*** 1.13e-15 4.9e+15 0.000 5.571429 5.571429 gc | 1.914286*** 1.28e-15 1.5e+15 0.000 1.914286 1.914286
cons | 12.45714*** 3.11e-16 4.0e+16 0.000 12.45714 12.45714 Tostudythefirstgroupofhypotheses,at‐testasasignificancetest,isdone.
Theteststatisticgiveninthetcolumnisatestthatthecoefficientissignificantlydifferentfromzero.STATAreportsanaccompanyingp‐valueforatwo‐tailtestinsimpleregression.Thatis,fortheslopecoefficient,thet‐statisticisatestof:[H�: β =0againstH�: β ≠ 0].Asthet‐valuesshowinTable31,only'gb'and'constant'term
coefficients are significant at, respectively, 90% and 99% confidence levels.Therefore, the null hypothesis is rejected (H�: �� ≤ 0 against H�: �� > 0) withextremelyhighconfidenceforthedifferencebetweengroupBandgroupD‐above90%infact.Butthenullhypothesisforgroups'ga'and'gc'cannotberejected.ThisimpliesthatthereisnosignificantdifferencebetweengroupsAandCwithgroupD.This indicates that only the Technical Contradiction Map is the effective method,comparedtotheothermethods.
3. Second Group of hypotheses: Novelty
Thesecondgroupofhypothesesstudiesthemagnitudeofeffectsof targetmethodrespecttotheothermethodsthroughfollowinghypotheses:
4. [H�: β� ≤ β�againstH�: β� > β�]5. [H�: β� ≤ β�againstH�: β� > β�]
Thesecondtypeofhypotheseswasapplied intwodifferentstyles;testinghypotheses4and5separately,andajointtestconsidering4and5together.Bothtypesoftestsarereportedasbelow:
Single test (4) ga - gb = 0 F( 1, 24) = 0.96 Prob > F = 0.3359
Single test (5) - gb + gc = 0 F( 1, 24) = 1.45 Prob > F = 0.2408
Joint test (4&5) ga - gb = 0 & - gb + gc = 0 F( 2, 24) = 0.82 Prob > F = 0.4524
AlargeF‐testwouldindicatethatthenullhypothesiscanberejectedwhichmeansthattheimpactoftheTechnicalContradictionMapissignificantlylargerthantheothermethods.Runninganotherregressionwithclusteringcanbeundertaken.
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Quantity: Similar to the Novelty part, the different assumptions and hypothesis for
QuantityareestimatedandtestedthroughanOLSmodel:
Quantity = � + ���� + ���� + ���� + �
where ��, �� and �� present, respectively, the contribution of ‘Problem‐Solution Matrix Map’, ‘Technical Contradiction Map’ and ‘Patent Text Far‐Field’methods with respect to the control group (Brainstorming). Since group B (whoreceivedtheTechnicalContradictionMapmethod)isthetargetgroup,theestimatedresultfor��willbeonthefocus.Thestudyispursuedthroughsetofhypothesesafterstudyingthevalidityofassumptionsofclassicallinearregressionmodelonthedataset.
1. Possibility of applying LOS model for statistical analysis: Quantity
Beforetestingthehypothesis,twoassumptionsregardingHomoscedasticityandthenormaldistributionoftheresidualsneedtobechecked.
Figure20showsthegraphicalviewoftheestimatedresidualsoftheQuantityregression; it is clear that it is distributed randomly over the groups andHeteroskedasticityisnotpresentinthisregression.
Figure20‐EstimatedresidualsfortheNoveltyregression:Homoscedasticitytest‐
ExperimentI.
TheBreusch‐Pagan/Cook‐Weisbergtestfollowssincethechi‐squarevalueissmall,indicatingthatheteroskedasticityisprobablynotaproblem,asshowninthegraphabove.
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of QuantityT chi2(1) = 2.34 Prob > chi2 = 0.1260
TheNormalityoftheresidualsoftheQuantityregressionandbothgraphical(Figure21)andSkewness/Kurtosistests,confirmnormalityoftheresidualfortheregression;normality,therefore,isnotofconcernforthisexperience.
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esid
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Figure21‐EstimatedresidualsfortheQuantityregression:Normalitytest‐ExperimentI.
Skewness/Kurtosis tests for Normality ------ joint ------ Variable | Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 -------------+--------------------------------------------------------------- resQuantity1 | 28 0.3767 0.7803 0.91 0.6337
2. First Group of hypotheses: Quantity
ThethreehypothesesarestudiedforinvestigatingtheimpactofeachmethodontheOLSmodelrespecttothecontrolgroupbyexpectingpositivecoefficientforeffectivemethodsontheOLSmodelofQuantity:
1. [H�: �� ≤ 0againstH�: �� > 0]2. [H�: �� ≤ 0againstH�: �� > 0]3. [H�: �� ≤ 0againstH�: �� > 0]
Thet‐testisdonethrougharegressionmodel.Table33showstheestimatedresultsoftheanalysis.
Table33‐Estimatedresultsofimprovingideasbasedondifferentmethods:Quantity‐
ExperimentI.
Source | SS df MS Number of obs = 28 -------------+---------------------------------- F(3, 24) = 1.02
Model | 128.857143 3 42.952381 Prob > F = 0.4030 Residual | 1014.85714 24 42.2857143 R-squared = 0.1127 -------------+---------------------------------- Adj R-squared = 0.0017
Total | 1143.71429 27 42.3597884 Root MSE = 6.5027
QuantityT | Coef. Std. Err. t P>|t| [95% Conf. Interval]
ga | 3.571429 3.475864 1.03 0.314 -3.602403 10.74526 gb | 5.714286 3.475864 1.64 0.113 -1.459546 12.88812 gc | 1.571429 3.475864 0.45 0.655 -5.602403 8.74526
_cons | 14.57143 2.457807 5.93 0.000 9.498764 19.64409
Table 33 shows the corresponding coefficient for Technical ContradictionMapis5.71morecomparedtocontrolgroup,D;Furthermore,itisalsolargerthantheothergroupsthatare3.57and1.57,respectively,forgroupsAandC.Inthiscase,noneoftheestimatedparametersaresignificantateventhe90%confidencelevel
0.00
0.25
0.50
0.75
1.00
Nor
mal
F[(
resQ
uant
ity1-
m)/
s]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
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andsoclusteringisneededforthisvariable.R2is0.11;thisspecifiedregressiononlyexplains11%ofthevariationinQuantity,though.Table34showstheresultsoftheclustering, and as previously discussed, clustering corrects non‐significantcoefficients,butestimatedcoefficientsarenotaffectedbyclustering;groupBstillhasthehighestbetacomparedtoothergroups.
Table34‐Estimatedresultsofimprovingideasfordifferentntmethods:Quantity
(clustering)‐ExperimentI.
Linear regression Number of obs = 28 F(0, 3) = . Prob > F = . R-squared = 0.1127 Root MSE = 6.5027
(Std. Err. Adjusted for 4 clusters in id)
Robust QuantityT | Coef. Std. Err. t P>|t| [95% Conf. Interval]
ga | 3.571429 1.97e-15 1.8e+15 0.000 3.571429 3.571429 gb | 5.714286 2.24e-15 2.5e+15 0.000 5.714286 5.714286 gc | 1.571429 1.97e-15 8.0e+14 0.000 1.571429 1.571429
_cons | 14.57143 1.86e-15 7.8e+15 0.000 14.57143 14.57143
As t‐values show in Table 33, 'ga', 'gb', 'gc', and 'constant' term are allsignificant; the null hypothesis of the first group of hypotheses is rejected. ThisindicatesthatallthreemethodsaremoreeffectivethanBrainstormingandapplyingthesemethodsimprovetheQuantitymorethanBrainstorming.
3. Second Group of hypotheses: Quantity
AnF‐testwasundertakentoseewhethertheTechnicalContradictionMapwasmoreeffectivethantheothermethodsthroughtwohypotheses:
4. [H�: �� ≤ ��againstH�: �� > ��]5. [H�: �� ≤ ��againstH�: �� > ��]
Twodifferenttestswererun,thattestedhypotheses4and5separately,andajointtestthatconsidered4and5together.Bothtypesoftestsarereportedas:
Single test (4) ga - gb = 0 F (1, 24) = 0.38 Prob > F = 0.5434 Single test (5) - gb + gc = 0 F (1, 24) = 1.42 Prob > F = 0.2450 Joint test (4&5) ga - gb = 0 & - gb + gc = 0 F (2,24) = 0.71 Prob > F = 0.5014
A largeF‐testwould indicatethat thenull hypothesiscanberejected; thismeansthattheimpactoftheTechnical Contradiction Mapissignificantlylargerthantheothermethods.
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105
Variety: Finally, the same approach was repeated to test the effectiveness of each
methodthroughVariety.
1. Possibility of applying LOS model for statistical analysis: Variety
Beforetestingthehypothesis,twoassumptionsregardingHomoscedasticityandNormaldistributionoftheresidualsneededtobechecked.Figure22showsthegraphicalviewoftheestimatedresidualsoftheVarietyregression;it'sobviousitisdistributedrandomlyoverthegroupsandHeteroskedasticityisnotpresentinthisregression.
Figure22‐EstimatedresidualsfortheVarietyregression:Homoscedasticitytest‐
ExperimentI.
The Breusch‐Pagan/Cook‐Weisberg test followed; the chi‐square value issmall,indicatingthatheteroskedasticityisprobablynotaproblem,asshowninthegraphabove.
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of Variety chi2(1) = 0.26 Prob > chi2 = 0.6111
Normality of the residuals of the Variety regression, and both graphical(Figure23)andSkewness/Kurtosistests,confirmnormalityoftheresidualfortheregression;however,normalityisnottheresearchconcernforthisexperience.
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Figure23‐EstimatedresidualsfortheVarietyregression:Normalitytest‐ExperimentI.
Skewness/Kurtosis tests for Normality ------ joint ------ Variable | Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 -------------+--------------------------------------------------------------- resVariety1 | 28 0.0790 0.7472 3.52 0.1718
2. First Group of hypotheses: Variety
Theeffectsofeachappliedmethodrespecttothecontrolgroup,arestudiedthrough t‐test by estimating a simple dummy regression model consideringfollowinghypotheses:
1. [H�: �� ≤ 0againstH�: �� > 0]2. [H�: �� ≤ 0againstH�: �� > 0]3. [H�: �� ≤ 0againstH�: �� > 0]
Table35showstheresults;surprisingly,inthiscase,theestimatedbetaforgroupAisalmosttwiceasbigastheoneestimatedforgroupB.ThisimpliesthattheProblem‐Solution Matrix Map is more effective than the Technical ContradictionMapregardingVariety.Itisalsotheonlycoefficientthatissignificantatthe95%significant level. Other betas were not significant therefore clustering wasundertakenforthisvariablebeforetestingthehypothesis.However,R2is0.23;thismeansthatthespecifiedregressiononlyexplains23%ofthevariationinVariety.
Table35‐Estimatedresultsofimprovingideasfordifferentmethods:Variety‐
ExperimentI.
Source | SS df MS Number of obs = 28 -------------+---------------------------------- F(3, 24) = 2.39
Model | 3532.74717 3 1177.58239 Prob > F = 0.0937 Residual | 11821.8125 24 492.575522 R-squared = 0.2301 -------------+---------------------------------- Adj R-squared = 0.1338 Total | 15354.5597 27 568.687397 Root MSE = 22.194
VarietyT | Coef. Std. Err. t P>|t| [95% Conf. Interval]
ga | 25.25714 11.86321 2.13 0.044 .7726711 49.74161 gb | 10.44524 11.86321 0.88 0.387 -14.03923 34.92971 gc | -3.7 11.86321 -0.31 0.758 -28.18447 20.78447
_cons | 86.7 8.38856 10.34 0.000 69.38686 104.0131
0.00
0.25
0.50
0.75
1.00
Nor
mal
F[(r
esV
arie
ty1-
m)/s
]
0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)
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107
SincetheestimatedcoefficientsarenotsignificantforbothgroupsBandD,before testing hypothesis, clustering was undertaken. Based on the clusteringresults,thehypothesiswastestedinTable36.
Table36‐Estimatedresultsofimprovingideasfordifferentmethods:Variety(clustering)
‐ExperimentI.
Linear regression Number of obs = 28 F(0, 3) = . Prob > F = . R-squared = 0.2301 Root MSE = 22.194 (Std. Err. adjusted for 4 clusters in id)
Robust VarietyT | Coef. Std. Err. t P>|t| [95% Conf. Interval]
ga | 25.25714 2.14e-14 1.2e+15 0.000 25.25714 25.25714 gb | 10.44524 1.34e-14 7.8e+14 0.000 10.44524 10.44524 gc | -3.7 1.27e-14 -2.9e+14 0.000 -3.7 -3.7 cons | 86.7 1.24e-14 7.0e+15 0.000 86.7 86.7
AsTable36shows,GroupAstillhasthehighestbetacomparedtotheothergroups.Ast‐valuesshowinTable36,'ga','gb'and'constant'termareallsignificant;thenullhypothesisofthefirstgroupofthehypothesiscanberejected,exceptforgroup C. As shown, the estimated beta for group C is negative, thus the nullhypothesiscannotberejected(Hypothesis3).ItindicatesthatinregardstoVariety,Patent Text Far‐Field is less effective than even Brainstorming. A test wasundertakentoseewhethertheTechnicalContradictionMapwasmoreeffectivethantheothermethods.
3. Second Group of hypotheses: Variety
AnF‐testwasundertakentoseewhethertheTechnicalContradictionMapwasmoreeffectivethantheothermethodsthroughtwohypotheses:
4. [H�: �� ≤ ��againstH�: �� > ��]5. [H�: �� ≤ ��againstH�: �� > ��]
Twodifferenttestswererunagain,testinghypotheses4and5separately,and then a joint test that considered 4 and 5 together. Both types of tests arereportedas:
Single test (4) ga - gb = 0 F( 1, 24) = 1.56 Prob > F = 0.2239 Single test (5) - gb + gc = 0 F( 1, 24) = 1.42 Prob > F = 0.2448 Joint test (4&5) ga - gb = 0 & - gb + gc = 0 F( 2, 24) = 2.98 Prob > F = 0.0699
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A large F‐test would indicate that the null hypothesis should be rejected,however,inthiscase,itisnotpossibletoshowthattheTechnicalContradictionMapismoreeffectivethanothermethods.Basedonthistest,itcanonlybeinterpretedthattwobetasforgroupsAandBarenotthesame,butitcannotbesaidthatthebetaforgroupBislargerthanbetaofgroupA.However,asshowninTable36,thebetaforgroupAistwiceasbigthangroupB.
4.2 Experiment II: Repeatability of the building the map
Experiment II is planned and performed to study the repeatability of
buildingtheTechnicalContradictionMap.Therepeatabilityofbuildingthemapisstudied through the existence of significant differences among the effects ofdifferentmapsdevelopedbydifferentengineers.AsmentionedinChapter3,exceptthemapwhichwasdevelopedbytheresearcher,3othermapsweregenerated,eachoneby30otherR&Dengineers.Topursuethestudy,theeffectsofthreenewmapsarestudiedrespecttothemapwhichwasdevelopedbytheresearcherforthefirstexperiment. It is worth considering; every 30 engineers developed a map byfollowingprocedure.Theprocedure,alsodevelopedinthescopeofthisresearchforextractingthemaincontradictionwasresolvedbythatpatent.Everyengineerbyfollowing procedure analyzed a patent and formulated the main contradiction.Therefore, the Experiment II consists of two part; first part for studying andsimplifying the following the procedure, and the second part for analyzing thedifferencesamongtheusageoffourdevelopedmaps.Table37showsthetotalplanofExperimentII.
Table37‐RepeatabilityofbuildingthemapPlan‐ExperimentII(PartI).
Steps First session
(45 min) Second sessions
(45 min)
Groups 6patentsrandomlyselectedandexaminedbyTRIZ
experts(6Groups,6R&DEngineers)
30patentsexaminedformappingtheinformation
(3Groups)
30R&DEngineers,30Patent
30R&DEngineers,30Patent
30R&DEngineers,30Patent
Total 6 R&D Engineers undergo the same treatment 90 R&D Engineers undergo the same treatment
Tomakepossibletocomparetheresultsofusageofthe3newmapswiththepreviousmap,thestructureofExperimentIIwasplannedas thestructureof theExperimentI.Thetestwasdoneintwo‐sessiondesigneachone30minutes.Theparticipantsgenerated their ideas throughBrainstorming in the first sessionandfollowedthedesigningbyapplyingoneofthefourdevelopedmapswhichdedicatedtothemrandomly.Inaddition,thequantityoftheteamsandthemembersofeach
Chapter4:EmpiricalStudy
109
teamwereplannedlikewisethefirstexperiment.Table38showstheplanofsecondpartofExperimentII.
Table38‐Repeatabilityofbuildingthemapplan‐ExperimentII(PartII).
First session (Brainstorming/ 30 min)
Break Second sessions
(various T.C Map/ 30 min)
Group A
IdeaGeneration(BrainstormingSession)
7Teams,2R&DEngineers
15min
IdeaGeneration(T.CMatrixMap)
7Teams,2R&DEngineers
Group B
IdeaGeneration(BrainstormingSession)
7Teams,2R&DEngineersIdeaGeneration(T.CMatrixMap)
7Teams,2R&DEngineers
Group C
IdeaGeneration(BrainstormingSession)
7Teams,2R&DEngineersIdeaGeneration(T.CMatrixMap)
7Teams,2R&DEngineers
Group D
IdeaGeneration (BrainstormingSession)
Controlgroup7Teams,2R&DEngineers
IdeaGeneration(T.CMatrixMap)
Controlgroup7Teams,2R&DEngineers
56 R&D Engineers (28 teams, 2 R&D Engineers) undergo the same treatment
_ 56 R&D Engineers (28 teams, 2 R&D
Engineers) undergo the same treatment
The allocating processes for each group and teams have been donecompletelyat random; therewasnosystematicselectionregarding theirgender,age,education,degreeandtheirknowledgeinpatentanalysis.Table39showstheparticipants’profilesforthispartoftheresearch;inordertocomparewithpreviousgroups,thesamedistributionofpeoplewasalmostused,asinthefirstexperiment.In total, there were 56 participants (40 men and 16 women). Participants werecategorizedbyageintoyoungandmiddle‐agedadults(25‐44)andalsoclassifiedinthreedegreesofeducation(master,bachelor,andPh.D.).Followingtheliterature,participantsfromseveralfieldsofstudywereused,butallwereengineersincludingMechanical, Industrial, Electronics, Computer, Chemical, Aerospace, Marine,MetallurgicalandMaterialsEngineers.
Table39‐ParticipantsProfile‐ExperimentII.
In followingafterreviewingtheappliedmethodandformulaforassessingtheexperimentresult, theobserveddataandstatisticalstudiesarepresentedformoreformalevidence.
Participants Sex Average
age (year) Degree Field of study
Experiences in the field (year)
Knowledge in patent and patent
analysis
56
40M
36.2(25‐44)STD:4.5
40Master
14MechanicalEng.
9(5‐13)
STD:1.9
4High
10ElectronicEng.
15Medium9IndustrialEng.
6CivilEng.
5AerospaceEng.
37Low16F
8Bachelor4MaterialsEng.
3ChemicalEng.
8PhD3ComputerEng.
2MetallurgicalEng.
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110
4.2.1 Ideation metrics measurement
As reviewed in Chapter 3 and similarly to the measurement metrics wereappliedinExperimentI,ShahmetricsempoweredbyFBSframeworkisused.ThemainstructureandtheformulaforassessingtheNovelty,VarietyareapproximatelythesameforExperimentIandExperimentII.TheonlydifferenceistheprovidedscoresforthelevelsofNovelty.Table40showsthepreparedscoresintheposterioriapproachforassessingthedegreeoftheNoveltyofteams.
Table40‐The���scoresof4Groupsofexperiment‐ExperimentII.
No. Novelty Attribute Wt. Level 1
(New Structure) Level 2
(New Behavior) Level 3
(New Function)
1 Support user balance 0.25 3.1 7.1 9.8
2 Support Walker balance 0.25 3.8 6.9 9.3
3 Support Walker storage & transport 0.15 0.6 9.4 10
4 Support user body ergonomic 0.15 3.8 7 9.2
5 Supply Walker propulsion 0.1 5 8 7
6 Support user routine activities
and user accessories 0.1 4.7 5.4 9.9
Total 1
4.2.2 Estimated results
Likewise, the first experiment, the data were collected by team membersduringdesignsessionthroughfillingthesolutionpapers,wheneverthemembersagreedonproposingsolutions.Adescription,aschema,andthetimeofappearanceofthesolutionswerethecollecteddatainsolutionpapers.
ThedescriptionandschemaofeachsolutionwereanalyzedbyresearcherandthetotalscoreofNoveltyandVarietywerecalculatedbasedonthepreparedtableandformula.TheQuantity,Novelty,andVarietyscoreswerecalculatedforfirstandseconddesignsessionsseparatelyandthese twosessions together.Table41showsthecalculatedscores.Todofurtherstudies,thegroupwhichappliedthemapproducedbyaresearcherinExperimentIisconsideredasthecontrolgroup.ThisgroupisconsideredasGroupDinthetable.
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Table41‐ThescoresofQuantity,Novelty,andVarietyforallteams‐ExperimentII.
Name Team
First session (Brainstorming)
Second sessions (various T.C Map)
Two sessions
Quantity Novelty Variety Quantity Novelty Variety Quantity Novelty Variety
Group A
1 18 10.4 38.0 7 5.5 52.0 25 16 90
2 11 12.5 65.0 9 5.8 38.0 20 18 103
3 9 6 50.7 5 4.1 50.0 14 10 101
4 17 9.9 44.0 11 12.4 48.0 28 22 92
5 10 10.1 53.7 6 7.1 34.0 16 17 88
6 13 7.6 44.3 10 10.9 47.0 23 19 91
7 14 9.6 48.5 8 8.1 55.5 22 18 104
Group B
1 13 8.3 43.3 9 7.9 46.7 22 16.2 89.9
2 8 5.8 65.5 7 6.3 38.3 15 12.1 103.8
3 12 9.1 64.0 9 10.5 40.3 21 19.6 104.3
4 13 8 71.0 7 6.1 61.0 20 14.1 132.0
5 9 8.6 51.3 4 4.4 80.0 13 13 131.3
6 14 13.1 43.2 5 3.4 50.0 19 16.5 93.2
7 17 12.4 45.0 10 11.1 53.7 27 23.5 98.7
Group C
1 10 8.1 43.8 6 5.2 52.5 16 13.3 96.3
2 14 12.6 60.3 8 8.8 50.3 22 21.4 110.6
3 11 8.3 47.7 7 5.4 71.0 18 13.7 118.7
4 8 6.7 44.7 4 4.2 35.0 12 10.9 79.7
5 18 12.5 62.3 8 7.4 31.0 26 19.9 93.3
6 14 10 56.5 10 13.2 53.7 24 23.2 110.2
7 9 4.4 47.7 5 8.6 50.5 14 13 98.2
Group D (Control Group)
1 10 6.3 65.7 8 7.7 34 18 14 98.7
2 17 13.6 49.2 11 8.4 57.7 28 22 106.9
3 11 9 67 4 3.6 80 15 12.6 147.0
4 17 12 55 10 12.8 46.2 27 24.8 101.2
5 5 3.6 26 5 4.1 51 10 7.7 77.0
6 9 5.8 34.8 7 4.7 37.6 16 10.5 72.4
7 19 21 49.3 9 10.9 27.6 28 31.9 76.9
Like the shows, the scores for the first session are close to the calculatedscoresof the firstdesignsessionof theExperiment I.Alsotheresultofusingthethreenewdevelopedmapsseemsimilartotheresultsofthecontrolgroup.Table42summarizesmeanandstandarddeviationoftheobserveddataasinitialdescriptivestatisticsregardingthesecondexperiment.
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112
Table42–ThescoresofQuantity,NoveltyandVarietyrespecttothegroupwithdifferent
stimuli‐ExperimentII.
Name First session Second sessions Two sessions
Quantity Variety Novelty Quantity Variety Novelty Quantity Variety Novelty
Group A (T.C))
Mean 13.1 49.2
Sco
re
65.8Mean 8 46.4
Sco
re
53.9Mean 21.1 95.5
Sco
re
119.7STD 3.4 8.6 STD 2.2 7.7 STD 4.9 6.8
Group B (T.C)
Mean 12.3 54.862.2
Mean 7.3 52.949.6
Mean 19.6 107.6114.8
STD 3 11.8 STD 2.2 14.2 STD 4.6 17.2
Group C (T.C)
Mean 12 51.862.5
Mean 6.9 49.152.9
Mean 18.9 101115.4
STD 3.5 7.7 STD 2 13.2 STD 5.3 13.1
Group D (T.C)
(Control Group)
Mean 12.6 49.4
66.6
Mean 7.7 47.7
51.1
Mean 20.3 97.2
117.7STD 5.2 14.9 STD 2.6 17.6 STD 7.3 25.9
Asonecanseefromthecolumn,itseemsthereisnosignificantdifferencebetweenthemregardingNovelty,Quantity,andVarietyinthesecondsessionandalso the two sessions together; statistical studies needed for any further claim.Figure 24 also shows the graphical representation of the data for each variableacrossgroups.
Figure24‐Graphicalrepresentationofassessingcriteriaoftwosessions‐
ExperimentII.
The figure shows there is no significance difference between groupsobservedinthesecondexperiment.
Chapter4:EmpiricalStudy
113
4.2.3 Data analysis
In this section, a linear regression method is applied, including dummyvariables. The estimated results, testing the assumptions of the classical linearregression model and finally, hypothesis tests regarding each variable arepresented.
The normality of gathered data is studied through the data of the first sessionwhereas the condition of design session was the same for all the 28 teams. ThenormalityofdatawasstudiedthroughallscoresofQuantity,Novelty,andVariety.Figure25showstheresultsofnormalitystudies.
Figure25‐NormalityoftheData‐ExperimentII.
Figure25showsthedistributionalplotofthedataforastandardizednormaldistribution; it demonstrates that the data are normally distributed. Therefore,furtherstatisticalstudiesaredonebycheckingthesetsofhypothesesforallCriteriaNovelty,Quantity,andVariety.
0.0
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ariety
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orm
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ove
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orm
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uantity
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Novelty-
Novelty-
Novelty-
Quantity-
Quantity-
Quantity-
Variety-
Variety-
Variety-
Chapter4:EmpiricalStudy
114
Novelty: The novelty is estimated through regression model with the explanatory
variablesbeingthethreedummyvariablesregardinggroupsA,B,andCandgroupD(thecontrolgroup):
Novelty = � + ���� + ���� + ���� + �
As each group used the same method (T.C Map), the smaller number forcoefficientsβ�,β�andβ�isexpected,becausethesecoefficientsshowthedifferencebetween groups A, B, and C with respect to group D. Before discussing thehypotheses,checkingtheassumptionsofregressionisneeded.
1. Possibility of applying LOS model for statistical analysis: Novelty
HomoscedasticityandNormaldistributionoftheresidualsaretestedastwoassumptionsoffivepossibleones.HomoscedasticityoftheresidualsoftheNoveltyregression was tested using a graphical approach and also Breusch‐Pagan/Cook‐Weisbergtest.Figure26belowshowsagraphicalrepresentationoftheestimatedresidualsoftheNoveltyregression.Itisclearthatthereisnosystematictrendforthe estimated residuals, indicating that the Homoscedasticity assumption issatisfiedforthisvariable.
Figure26 ‐ EstimatedresidualsfortheNoveltyregression:Homoscedasticitytest‐
ExperimentII.
Asbefore,theBreusch‐Pagan/Cook‐Weisbergtestwasusedtomakesureofthe Homoscedasticity. A large value for chi‐square indicates presenting ofheteroskedasticity.Since in thisexercise, the chi‐squarevalue issmall.Thereforeheteroskedasticitywouldnotprobablybeaproblem(asseeninthegraph).
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of NoveltyT chi2(1) = 0.00 Prob > chi2 = 0.9714
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TheNormalityoftheresidualswillnowbeexamined.Bothgraphical(Figure27) and Skewness/Kurtosis tests confirmed the normality of the residual for theNoveltyregression.
Figure27‐EstimatedresidualsfortheNoveltyregression:Normalitytest‐Experiment
II.
Skewness/Kurtosis tests for Normality ------ joint ------ Variable | Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 -------------+--------------------------------------------------------------- resNovelty2 | 28 0.9530 0.0318 4.65 0.0976
2. Group of hypotheses: Novelty
Thefollowinggroupofhypothesesstudiesthesimilarityamongtheresultsofapplyingdifferentmapsfortherepeatabilityofbuildingthemap:
1. [H�: �� ≠ 0againstH�: �� = 0]2. [H�: �� ≠ 0againstH�: �� = 0]3. [H�: �� ≠ 0againstH�: �� = 0]
4. [H�:�� ≠ �� ≠ ��andagainstH�: �� = �� = �� = 0]
Hypotheses1to3aresingletests;eachparameterisdifferentfromzero,andsincetheestimatedparametershouldbeclosetozero,thenullhypothesiscanberejectedandthereforethealternativehypothesiscanbeaccepted.Inotherwords,thereneedstobenodifferencebetweencontrolgroupDandothergroups,A,B,andC.Inaddition,Hypothesis4testsusethejointtestforallthecoefficientstogether,and represents that they are jointly different from group D. However, it is notnecessarytostudythehypothesis4,butitisonlyforbeingmoreaccurate.
Table43showstheresultsofaregressionmodelofNoveltyacrossdifferentgroupsandcomparesthiswithgroupD.
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orm
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m)/
s]
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Chapter4:EmpiricalStudy
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Table43‐Estimatedresultsofimprovingideasforthesamemethods:Novelty‐
ExperimentII.
Source | SS df MS Number of obs = 28 -------------+---------------------------------- F(3, 24) = 0.16
Model | 11.6014286 3 3.86714286 Prob > F = 0.9198 Residual | 567.017143 24 23.6257143 R-squared = 0.0201
-------------+---------------------------------- Adj R-squared = -0.1024 Total | 578.618571 27 21.4303175 Root MSE = 4.8606
NoveltyT | Coef. Std. Err. t P>|t| [95% Conf. Interval]
ga | 1.328571 2.598115 0.51 0.614 -4.033675 6.690818 gb | .4714286 2.598115 0.18 0.858 -4.890818 5.833675 gc | -.4 2.598115 -0.15 0.879 -5.762247 4.962247
_cons | 17.55714 1.837145 9.56 0.000 13.76546 21.34882
Thereisnobigdifferencebetweentheestimatedcoefficients,thusitcanbeinterpreted that those coefficients are not significantly different from zero; thisindicates that there is no significant difference between groups A, B, and C withgroupD.Thesignificanceoftheresultsdoesnotneedtobecheckedbecauseitisonlythevalueofthecoefficientsbeingsmallwhichistheconcernoftheresearch.
Fromrunningfourtypesofhypothesis,theresultsarereportedasbelow:
Single test (1) ga = 0 F( 1, 24) = 0.26 Prob > F = 0.6138 Single test (2) gb = 0 F( 1, 24) = 0.03 Prob > F = 0.8575 Single test (3) gc = 0 F( 1, 24) = 0.02 Prob > F = 0.8789 Joint test (4) ga = gb = gc = 0 F( 2, 24) = 0.16 Prob > F = 0.9198
AlargeF‐testforeachhypothesisindicatesthatitispossibletorejectthenullhypothesiswhichmeansthatthereisnosignificantdifferencebetweengroupsA,B,andCwithD.Asexplainedbefore,asthereisnointerestonthesignificantresults,anotherregressionwithclusteringdoesnotneedtobeundertaken.TheestimatedresultsbasedonTable43areenoughforthesecondexperiment.
Quantity: AsintheNoveltypart,thesignificancedifferenceamongthefourdeveloped
mapsisstudiedthroughestimatingaregressionmodelforQuantityaftercheckingthevalidityofassumptionsforregressionanalysis.
1. Possibility of applying LOS model for statistical analysis: Quantity
Similar to the previous experiment, before testing the hypotheses, twoassumptionsregardingHomoscedasticityandNormaldistributionoftheresidualsneedtobechecked.Figure28showsthegraphicalviewoftheestimatedresiduals
Chapter4:EmpiricalStudy
117
oftheQuantityregression;it'sobviousitisdistributedrandomlyoverthegroups,with no kind of Heteroskedasticity in this regression being observed. Breusch‐Pagan/Cook‐Weisberg test also shows that heteroskedasticity is probably not aproblemsincethechi‐squarevalueissmall.
Figure28‐EstimatedresidualsfortheQuantityregression:Homoscedasticitytest‐
ExperimentII.
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of QuantityT chi2(1) = 0.05 Prob > chi2 = 0.8204
TheNormalityoftheresidualsoftheQuantityregressionandbothgraphical(Figure29),andSkewness/Kurtosistestsconfirmnormalityoftheresidualfortheregression;normalityisnottheresearchconcernforthisexperience.
Figure29‐EstimatedresidualsfortheQuantityregression:Normalitytest‐ExperimentII.
Skewness/Kurtosis tests for Normality ------ joint ------ Variable | Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 -------------+--------------------------------------------------------------- resQuantity2 | 28 0.9475 0.0580 3.87 0.1441
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Chapter4:EmpiricalStudy
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2. Group of hypotheses: Quantity
ThesamehypothesesasintheNoveltypartasfollowsweretested:
1. [H�: �� ≠ 0againstH�: �� = 0]2. [H�: �� ≠ 0againstH�: �� = 0]3. [H�: �� ≠ 0againstH�: �� = 0]
4. [H�:�� ≠ �� ≠ �� andagainstH�: �� = �� = �� = 0]
Hypotheses1to3aresingletests;thetestlookedatwhethereachparameterisdifferentfromzero.Givenitwasneededtoshowthattheestimatedparametershouldbeclosetozero,theinterestwastorejectthenullhypothesisandtoacceptthealternativehypothesis.Thismeansthat,inthecaseofrepeatabilityofbuildingthemap,nodifferencewillbeobservedbetweencontrolgroupDandothergroups,A, B, and C. In addition, Hypothesis 4 tested the use of the joint test for all thecoefficientstogetherandrepresentedthattheyarejointlydifferentfromgroupD.
Table44showstheestimatedresults,andasonewouldexpect,thereisnosignificantdifferencebetweengroups.Moreover,inthiscase,noneoftheestimatedparameters are significant at even the 90% confidence level. Clustering can beappliedtothisvariable,althoughitisnotnecessary,becausetheresearchismostlyinterested in the size of the coefficients, and not theirsignificant level. However,R2is0.026whichisverylow;themainreasonisthatbecausethosemethodsarealmostthesame,andwhencomparingagainstthecontrolgroup,onlyaminorpartofthevariationinQuantityhasbeenexplained.
Table44‐Estimatedresultsofimprovingideasforthesamemethods:Quantity‐
ExperimentII.
Source | SS df MS Number of obs = 28 -------------+---------------------------------- F(3, 24) = 0.21
Model | 20.1071429 3 6.70238095 Prob > F = 0.8875 Residual | 760.857143 24 31.702381 R-squared = 0.0257
-------------+---------------------------------- Adj R-squared = -0.0960 Total | 780.964286 27 28.9246032 Root MSE = 5.6305 ----------------------------------------------------------------------------- QuantityT | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+---------------------------------------------------------------- ga | 1.571429 3.009622 0.52 0.606 -4.640125 7.782983 gb | .7142857 3.009622 0.24 0.814 -5.497268 6.92584 gc | -.7142857 3.009622 -0.24 0.814 -6.92584 5.497268
_cons | 19.57143 2.128124 9.20 0.000 15.1792 23.96366
Fourtypesofthehypothesisaretested,andtheresultsarereportedbelow.AlargeF‐testforeachhypothesisindicatesthatthenullhypothesiscanberejected,meaningthereisnosignificantdifferencebetweengroupsA,B,andCwithD.
Chapter4:EmpiricalStudy
119
Single test (1) ga = 0 F( 1, 24) = 0.27 Prob > F = 0.6064 Single test (2) gb = 0 F( 1, 24) = 0.06 Prob > F = 0.8144 Single test (3) gc = 0 F( 1, 24) = 0.06 Prob > F = 0.8144 Joint test (4) ga = gb = gc = 0 F( 2, 24) = 0.21 Prob > F = 0.8875
Variety: AsintheNoveltyandQuantityparts,thesignificancedifferenceamongthe
fourdevelopedmapsisstudiedthroughestimatingaregressionmodelforVarietyaftercheckingthevalidityofassumptionsforregressionanalysis.
1. Possibility of applying LOS model for statistical analysis: Variety
Here also two assumptions regarding Homoscedasticity and Normaldistributionoftheresidualsneededtobecheckedasbefore.Figure30showsthegraphicalviewoftheestimatedresidualsoftheVarietyregression;itisclearthatitis distributed randomly over the groups and no kind of Heteroskedasticity isobserved in this regression. The same results obtained from the Breusch‐Pagan/Cook‐Weisbergtestshowsthatheteroskedasticityisprobablynotaproblemsincethechi‐squarevalueissmall.
Figure30‐EstimatedresidualsfortheVarietyregression:Homoscedasticitytest‐
ExperimentII.
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity Ho: Constant variance Variables: fitted values of VarietyT chi2(1) = 0.03 Prob > chi2 = 0.8870
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esi
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Chapter4:EmpiricalStudy
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TheNormalityoftheresidualsoftheVarietyregressionandbothgraphical(Figure31)andSkewness/Kurtosistestsconfirmnormalityoftheresidualfortheregression;normalityisnottheresearchconcernforthisexperienceaswell.
Figure31‐EstimatedresidualsfortheVarietyregression:Normalitytest‐ExperimentII.
Skewness/Kurtosis tests for Normality ------ joint ------ Variable | Obs Pr(Skewness) Pr(Kurtosis) adj chi2(2) Prob>chi2 -------------+--------------------------------------------------------------- resVariety2 | 28 0.0260 0.0609 7.42 0.0244
2. Group of hypotheses: Variety
Finally,fourhypothesesweretestedfortheestimatedcoefficientsasfollows:
1. [H�: �� ≠ 0againstH�: �� = 0]2. [H�: �� ≠ 0againstH�: �� = 0]3. [H�: �� ≠ 0againstH�: �� = 0]
4. [H�:�� ≠ �� ≠ �� andagainstH�: �� = �� = �� = 0]
Hypotheses 1 to 3 are single tests; the research tested whether eachparameter is different from zero. It was needed to show that the estimatedparametershouldbeclosetozero,withthenullhypothesisbeingrejectedandthealternativehypothesisbeingaccepted.Thismeansthat,givenabeliefthatthesameresults for each group will occur, for the repeatability of building the map, nodifferencebetweencontrolgroupDandothergroups,A,B,andCshouldbeseen.Inaddition,Hypothesis4testedtheuseofthejointtestforallthecoefficientstogether,and represents that they are jointly different from group D. Table 45 shows theSTATAreportforregressionanalysisforVariety.
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Chapter4:EmpiricalStudy
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Table45‐Estimatedresultsofimprovingideasforthesamemethods:Variety‐ExperimentII.
Source | SS df MS Number of obs = 28 -------------+---------------------------------- F(3, 24) = 0.68
Model | 606.368294 3 202.122765 Prob > F = 0.5720 Residual | 7118.93802 24 296.622417 R-squared = 0.0785
-------------+---------------------------------- Adj R-squared = -0.0367 Total | 7725.30631 27 286.122456 Root MSE = 17.223
------------------------------------------------------------------------------ VarietyT | Coef. Std. Err. t P>|t| [95% Conf. Interval]
ga | -12.0881 9.205936 -1.31 0.202 -31.08821 6.912023 gb | -10.45952 9.205936 -1.14 0.267 -29.45964 8.540595 gc | -6.647619 9.205936 -0.72 0.477 -25.64774 12.3525
_cons | 107.6119 6.50958 16.53 0.000 94.17679 121.047
ResultsforVarietyarequitedifferentfromNoveltyandQuantity.AsseeninTable 45, there are larger numbers of all the groups compared to the estimatedresultsofNoveltyandQuantity.Inparticular,itis‐12forgroupA,meaningthatintermsofVariety,12morevarietiesforgroupAarepresentincomparisontogroupD,andsimilarlyforgroupBandD;however,noneofthemaresignificant.
Fourtypesofhypothesisweretestedwiththeresultsreportedbelow.AlargeF‐test foreach hypothesis indicates that the null hypothesiscanbe rejected; thismeansthatthereisnosignificantdifferencebetweengroupsA,B,andCwithD.
Single test (1) ga = 0 F( 1, 24) = 1.72 Prob > F = 0.2016 Single test (2) gb = 0 F( 1, 24) = 1.29 Prob > F = 0.2671 Single test (3) gc = 0 F( 1, 24) = 0.52 Prob > F = 0.4772 Joint test (4) ga = gb = gc = 0 F( 2, 24) = 0.68 Prob > F = 0.5720
4.3 Conclusion of Experiment I and Experiment II
The ultimateobjective of this research is considered improving the
patentability of an invention generated by R&D engineers in Iranian SMEs. Toapproach this aim, ‘Technical Contradiction Map’ is developed, and two mainresearch questions are defined. Respectively two sets of empirical studies areplannedandperformedtostudytheresearchquestions.
Experiment I, compared the effects of four interventions on the designsession;Brainstorming+Problem‐SolutionMatrixMap(GroupA),Brainstorming+TechnicalContradictionMap(GroupB/Targetgroup),Brainstorming+PatentTextFar‐Field(GroupC),andBrainstorming+Brainstorming(GroupD/Controlgroup).Among the four methods, Technical Contradiction Map provided the highest
Chapter4:EmpiricalStudy
122
effectiveness in Novelty and Quantity. Also, Problem‐Solution Map provided thehighesteffectivenessinVariety.Specifically,theestimatedcoefficient forgroupB(Brainstorming + Technical Contradiction Map) was 5.57; this means that thiscomposition of methods provides more ideas than group D for Novelty. Thecorresponding estimated coefficients for group A (Brainstorming + Problem‐SolutionMatrixMap)and groupC(Brainstorming +Patent Text Far‐Field)were,respectively, 2.59 and 1.91. Besides the highest effectiveness of the TechnicalContradiction Map in comparison to other methods, the estimated coefficient forthisoneistheonlysignificantcoefficientinthisregression.
Additionally, twogroupsofhypothesesweretested todetermine whetherthereisenoughevidenceinthesampleofcollecteddatabasedontheexperimentsandestimations,toinferthattheusabilityoftheTechnicalContradictionMapistruefortheentirepopulation.ThetestsbasedontheF‐teststatisticalsoconfirmedthehighesteffectivenessoftheimprovedversionofTechnicalContradictionMap.TheF‐test showed the impact of the Technical Contradiction Map was significantlylarger than the other methods. This study’s estimates and hypotheses testssupportedtheeffectivenessoftheTechnicalContradictionMapcomparedtoothermethodsregardingNoveltyandQuantity.
Experiment II studied the repeatability of building the TechnicalContradiction Map through investigating some maps produced by many R&Dengineers discarding their level of expertise on building the map whereas theyfulfilled their task by following procedure. As it can be expected, the estimatedresults were very close across the different groups for Novelty and quantity andvariedonVariety.Thismakessensebecausethesamemethodswereappliedacrossdifferentgroups,andthereforetheestimatedresultsforeachgroupwerealmostthesame.Infact,thisexperimentverifiesthereplicationcapabilityofbuildingthemap;ifoneusesthesamemethod,conditionsandequipmentexplainedinthisresearch,thesameresultswillbeobtained.
Chapter5:Discussionsandconclusions
123
Chapter5
[5] Discussions and Conclusions
Chapter5:Discussionsandconclusions
124
Small and medium‐sized enterprises (SMEs) are main contributors to
industrial economies. Therefore, therehas beena long‐standinginterest in theempirical literature to support R&D engineers. Improving the patentability of aninventiongeneratedbyR&Dengineersisoneofresearchissuesinthisfield,whichisconsideredastheobjectiveofthisresearchtoo.BasedontheresultsofpreviousresearchdiscussedinChapter2inthefieldofPatentMappingandpatentanalysis,IdeationtechniquesandDesignbyAnalogy,andTRIZ,itisconsideredtoenrichtheProblem‐Solution Patent Map of a specific technical system for increasing thegenerationof non‐obvious novel ideasbyanewanalogaccordingtotheabstractobservedpatternsforresolvingthecontradictorysituations.TheproposedmapandtheprocedureforprovidingitwerediscussedinChapter3.Chapter4discussedtheeffects of the developed map on the performances of R&D engineers in terms ofgenerating ideas with a higher degree of Novelty and variety, simultaneouslystudied the performance of R&D engineers in building the developed map bythemselves. This chapter discusses some limitations and future correspondingstudies.
5.1 Summary
The ultimateobjective of this research is considered improving the
patentability of an invention generated by R&D engineers in Iranian SMEs. Toapproachthisobjective,‘TechnicalContradictionMap’isdeveloped,andtwomainresearchquestionsaredefinedthroughaquantitativestudyandstatisticalanalysis:
1. Can R&D engineers in Iranian SMEs improve Novelty within their ideas, through the use of an enriched Problem-Solution Patent Map by the ‘contradiction concept’?
2. Can Iranian R&D engineers build the proposed enriched Patent Map by following the developed procedure?
Twoexperimentswereplannedandperformedtostudythemap’susabilityandeffectivenessandrepeatabilityofthemap‐buildingprocess.
Toperformthestudies,thesuggestedmapwasbuiltforWalker,asasampletechnicalsystemthroughfollowingthedevelopedprocedure.Astheresultsofthefirstmainstageoftheprocedureofbuildingthemap,among101foundpatentsbysearchingintheOrbitdatabase,around50%ofthem(54patents)wereconsideredasnot‐noisypatentsforbuildingthemap.Astheresearchisthetypeofexploratoryresearch,thisnumberisalsoreducedbasedonthesufficientpatentsineachclassofproblemsandsolutions.Thepatentswerefirstsorted,andthenthestudyselectedone patent from the earliest, one from the latest and one from the middle. Afterreducingtheclassesofproblemsandsolutions, theanalysisofmorepatentswascompleted. This supportive sub‐procedure reduced the Quantity of patents to bestudied from 54 to 30. The second and third main stages of the procedure forextractingAnalogsandbuildingthemapwerethencompletedforthe30selectedpatents.Itisworthmentioningintotalthesolvedproblemswereclassifiedinto8
Chapter5:Discussionsandconclusions
125
groups,thesolutionswereclassifiedin6categorieswhereasthe30patentsweredistributedin10crossesof48possiblecrosses.Therefore,thefinalmapconsistsofthemainpapertoshowthepositionsofpatentsonthecrossesand10supportivegraphstoillustratetheresolvedcontradictions.Thepreparedmapwasthenusedforstudyingtheusabilityandeffectivenessofthemap.
TostudytheusabilityandeffectivenessoftheTechnicalContradictionMap,theresultsofusingthemapwerecomparedtosomeothermethodsusedforthesame purpose in design and idea generation sessions. Idea generation usingBrainstormingasatechniqueisconsideredasthecontrolgroup,asmostdesignandidea generation sessions use this method (Howard et al., 2010). The Problem‐Solution Matrix Map and Patent Text (Far‐Field) were considered as the otherinterventions for comparison. The Far‐Field Analogy was considered as anotherintervention forcomparison,as literaturediscusses theeffectivenessofFar‐FieldAnalogy on increasing the Novelty and Quantity of ideas (Chan et al., 2011).Engineers mostly apply Cross‐Domain analogies in idea generation processes(Casakin and Goldschmidt, 1999; Leclercq and Heylighen, 2002; Christensen andSchunn,2007).Cross‐DomainSpecificallyFar‐FieldAnalogyincreasesthenoveltyofsolutions (Chan et al., 2011). A Cross‐Domain Analogy is applied more when thedesignersarenotcapableofsolvingthedesignproblem(Tsengetal.,2008b;Linseyet al., 2012). The Problem‐Solution Matrix Map was considered as one of theinterventionsforcomparisonasitisbasicforadevelopedTechnicalContradictionMap. In total, the results of the idea generation session with the TechnicalContradiction Map were compared to the three other interventions, in order tostudy the map’s usability and effectiveness: (i) idea generation session withBrainstorming,(ii)ideagenerationsessionwithProblem‐SolutionMatrixMap,and(ii)ideagenerationsessionwithPatentText(Far‐Field)ofthetargetsystem.
Tocomparetheresultsofthefourconsideredinterventions,fourgroupsof7teamswereplanned,eachconsistingof2R&Dengineers.Theteamswereaskedtogeneratepatentableideasintwosessions,each30minuteslongwitha15‐minutebreak in between. In the first 30 minutes, all teams generated ideas by applyingBrainstorming,howeverinthesecondsession;eachgroupgeneratedideasbyoneof the considered interventions. Comparing the results of two sessions, theeffectiveness of the Technical Contradiction Map was studied. Specifically, thisresearch tests the hypothesis that using a Technical Contradiction Map afterBrainstormingismoreeffectiveingeneratingideasthanothertechniques,suchastheProblem‐SolutionMatrixMap,PatentTextFar‐FieldandalsoBrainstorming.Bycollecting data based on an experiment and using a statistical model, theeffectiveness of each technique in improving the ideation of R&D engineers inIranianSMEsintermsofNovelty,VarietyandQuantitywasexamined.
Tostudytherepeatabilityofthemap‐buildingprocess,thesame30patentsused for sample study, were given to different R&D engineers to analyze themaccordingtothedevelopedprocedure.Respecttothetimeneededforanalyzing1patentandalsothetimelimitationforinvolvingtheR&Dengineersinthestudy,onepatent was given to one R&D engineer to follow the second main stage of the
Chapter5:Discussionsandconclusions
126
procedureandextractingtheAnalogs.Therefore,90R&Dengineerswereinvolvedinthestudytopursuetheprocedureforonlyonepatent.TheresultsofthefollowedprocedurebytheR&Dengineersweregatheredasthreenewmaps.‘Repeatability’wasthencheckedbyanalyzingthesimilarityinresultsofusingthemapsintermsof Novelty, Quantity, and Variety of the ideas. Therefore, the second experimentconsistsof2mainparts;extractingtheAnalogsbyR&Dengineersasthefirstpart,andapplyingthebuiltmapsbasedontheresultsofpartoneasthesecondpart.Eachnewmapwasgivento7newteamsof2R&Dengineerstoallowforcomparisonwiththe results from the seven first teams, which applied the built sample TechnicalContradictionMapbytheresearcherinthefirstexperiment.Inthisexperiment,theteamswerealsoaskedtogeneratetheirideasintwo30‐minutesessions,likethefirstexperiment,toallowforcomparison.
Toelaborateonthisfurther,thenextsectionbrieflyreviewstheresults,andtheresultsrespecttotheliterature.Finally,thelimitationsofthecurrentresearchandfuturelinesofpossibleresearcharelaidout.
5.2 Research results and Discussion
As mentioned in the previous section, the ultimate aim of the current
researchwaspursuedthroughtwomainresearchquestions.Therespondsforeachmainresearchquestionisfollowedthroughtheassumptionsbehindtheproposedoriginalcontributionoftheresearch.Moreover,therearesomeexpectationsbeyondresearchquestions,whichletreflectionsonexistingtheoriesbasedontheobservedresultsofthedesignedandperformedexperiments.
Twoexpectationscanbediscussedbeyondtheresearchquestionswhereaseachonecanbefollowedindifferentlevels.Possibilitytoextractthepreviousandexistingcontradictionfromthepreviouspatentsofatechnicalsystem,possibilitytopresenttheextractedcontradictionofthepatentsofasysteminausableconfiguration,andlevel of effectiveness of success of awareness of R&D engineers of previous andexistingcontradictioninevolutionpathofatechnicalsysteminpatentabilityoftheirideasforimprovementofthesystemarethethreemainexpectations.
Eachexpectationcanbeapproachedindifferentlevels.Followingthepossiblelevelsforeachexpectationarementioned:
Expectation1:possibilitytoextractthepreviousandexistingcontradictionfromthepreviouspatentsofatechnicalsystem:
Level1:Possibilitytoextractsystematicallyandmanuallythemainresolvedcontradictionsofasimplemechanicalsystem;Level 2: Possibility to extract systematically all resolved contradictions ofanytargettechnicalsystem;
Expectation2:possibilitytopresenttheextractedcontradictionofthepatentsofasysteminausableconfigurationforR&DengineersnotfamiliarwiththeTRIZandTRIZcontradictionmodel:
Chapter5:Discussionsandconclusions
127
Level 1: Usability of the provided information for R&D engineers familiarwithTRIZ;Level2:UsabilityoftheprovidedinformationforR&DengineersnotfamiliarwithTRIZwithsomeguidelines;Level3:UsabilityoftheprovidedinformationforR&DengineersnotfamiliarwithTRIZwithoutanyguidelines;
Expectation3:ThelevelofeffectivenessofawarenessofR&Dengineersofpreviousandexistingcontradictioninevolutionpathofatechnicalsysteminthepatentabilityoftheirideasforimprovementofthesystem:
Level1:Levelofsuccessonthepatentabilityofgeneratedideasrespecttothecharacteristicsofgeneratedideas;Level2:LevelofsuccessonthepatentabilityofgeneratedideasrespecttotheconditionsofrealR&Dprojects;Level3:LevelofsuccessonthepatentabilityofgeneratedideasrespecttothesuccessindexesofR&Ddepartments;
Thecurrentresearchisgoingtoreflectinjustsomeoftheabove‐mentionedlevels based on the type and domain of possible empirical studies. Table 46,highlightsthelevelsofeachexpectationwhichareaddressedinthisresearch,therelationofthemwiththeperformedexperiments,themainobservedresults,andsomereflectionsonexistingtheories.
Table46–Summaryofresultsandreflectingonexistingtheories.
Expectations beyond the research question
The relevant part of experiments to the
expectation Observed results Reflecting on theories
Expectation 1: possibility to extract the previous and existing contradiction from the previous patents of a technical system
Level 1
Possibility to extractsystematically andmanually the mainresolvedcontradictions of asimple mechanicalsystem:
Systematic stepsto retrieve theleastandenoughrelevant patentsof the patents ofthe targetmechanicalsystem.
Systematicmanually stepsto extract themain resolvedcontradictions
Preparing Experiment:
Following systematicsteps for retrieving theleast and enoughrelevant patents forWalker by theresearcher.
Following systematicsteps for extracting themain resolvedcontradictions of 30patentsofWalkerbytheresearcher.
Possibilitytofollowthe systematicsteps for bothpurposes.
Followingsystematicstepsforreaching least andenough relevantpatent takesaround10hoursforWalker.
The problem,solution, andimprovingparameter arefounddirectly,theworseningparameters andcontrolparameter
‐ Expertise is needed forfollowing the bothsystematicsteps:
Classifying theproblem andsolutions anddedicatingthepatentstotheclasses.
Interpreting theworsening andcontrol parameterwhen they are notfound.
‐Thededicatedtimeforreaching the relevantpatentscanbecomparedwith the time forchecking thepatentability of ideas in
Chapter5:Discussionsandconclusions
128
(consists of adefinition,keywords,supportivequestions, mostprobable place,example,patterns,…).
are found moiety,andtheundesiredresult is rarelyfound.
currentactivitiesinR&Ddepartments.
Experiment II:
Following systematicsteps for extracting themain resolvedcontradictions of 6patents of Walker by 6R&D engineers familiarwithTRIZ.
Following systematicsteps for extracting themain resolvedcontradictions of 30patentsofWalkerby90R&D engineers notfamiliarwithTRIZ.
Possibility toextract the maincontradictionmanually.
115 R&Dengineers areinvolved in thetest, but 90engineersfulfilledcompletely theprocedure.
About 78% of R&Dengineers are able tofollow the procedureand it must beimproved.
Level 2
Possibility to extractsystematically allresolvedcontradictions of anytarget technicalsystem.
‐ ‐ ‐
Expectation 2: possibility to present the extracted contradiction of the patents of a system in a usable configuration for R&D engineers not familiar with the TRIZ and TRIZ contradiction model
Level 1
Usability of theprovided informationfor R&D engineersfamiliarwithTRIZ.
‐ ‐ ‐
Level 2
Usability of theprovided informationfor R&D engineersnotfamiliarwithTRIZwith some guidelinesintheformTechnicalContradiction MapT.CMap:
Structuralpresentation ofrelevantpatents
As a three‐dimensionalpatent mapbased onProblem‐Solution PatentMap
Applying thegraphical OTSM‐TRIZ model ofcontradiction
Experiment I:
ComparingT.CMaprespecttoBrainstorming,M.SMap,Far‐fieldPatentFullTextbychecking:‐UsabilityofT.CMap
Quantity
Novelty
Variety‐EffectivenessofT.CMap
Quantity
Novelty
Variety
‐ The effects ofdifferent interventionsonQuantityinorders:
1.T.CMap2.P.SMap3.Far‐field PatentFullText4.Brainstorming‐ The effects ofdifferent interventionsonNoveltyinorders:
1.T.CMap2.P.SMap3.Far‐field PatentFullText4.Brainstorming‐ The effects ofdifferent interventionsonVarietyinorders:
1.P.SMap2.T.CMap3.Brainstorming
T.CMapismoreeffectivethan others in QuantityandNoveltyandit:
Proves again thatstructuralrepresentation ofprecedents iseffective onincreasing Novelty
(Doboli andUmbarkar,2014).
Proves graphicalOTSM‐TRIZ model ofcontradiction canhelp to understandthe contradictions
clearer (Cavallucciand Khomenko,2007).
Proves that PatentMaps can be usablefortechnicalpurposes
(Tsengetal.,2007).
Chapter5:Discussionsandconclusions
129
4.Far‐field PatentFullText.
Rejects Far‐field ismore effective thanclose‐field becausethe structural andgraphicalpresentation is alsoeffectiveon theeffectofprecedents.
Experiment II:
StudyingT.CMap:‐UsabilityofT.CMap
Quantity
Novelty
Variety
‐ApplyingtheT.Cmapin a 1‐hour designsession (30 minbrainstorming, 30 minapplyingT.Cmap),theresultscanbereportedas:
Quantity:
Mean:20.3
STD:7.3‐Variety:
Mean:97.1
STD:25.9‐Novelty: Mean:123.4
T.C Map, discarding thelevel of expertise in itsproduction,isusableandeffective on increasingthe ideas characteristicsrespect to thebrainstorming.
Level 3
Usability of theprovided informationfor R&D engineersnotfamiliarwithTRIZwithout anyguidelines.
‐ ‐ ‐
Expectation 3: The level of effectiveness of awareness of R&D engineers of previous and existing contradiction in evolution path of a technical system in patentability of their ideas for improvement of the
system
Level 1
The level of successonthepatentabilityofgenerated ideasrespect to thecharacteristics ofgenerated ideas, byusingaT.CMap:
Applying OTSM‐TRIZ model ofcontradiction
Applying forclose‐fieldpatents
Experiment I:
ComparingT.CMaprespecttoBrainstorming,M.SMap,Far‐fieldPatentFullTextbychecking:‐UsabilityofT.CMap
Quantity
Novelty
Variety‐EffectivenessofT.CMap
Quantity
Novelty
Variety
Like the level 2 ofexpectation2.
T.CMapismoreeffectivethan others in QuantityandNoveltyandit:
Rejects Far‐field ismore effective thanclose‐field becausethe structural andgraphicalpresentation are alsoeffectiveon theeffectofprecedents
Far‐field is moreeffective thanBrainstorming (Chanet al., 2011) and itmore effective thanP.SMap.
Experiment I:
StudyingT.CMap:‐UsabilityofT.CMap
Quantity
Novelty
Variety
Like the level 2 ofexpectation2.
To study more stronglythe effectiveness of T.CMap.
Chapter5:Discussionsandconclusions
130
Level 2
Level of success onthe patentability ofgenerated ideasrespect to theconditions of realR&Dprojects.
‐ ‐ ‐
Level 3
Level of success onthe patentability ofgenerated ideasrespecttothesuccessindexes of R&Ddepartments.
‐ ‐ ‐
Astableshows,theempiricalstudies inthescopeof thecurrentresearch,
approachtheonelevelofeachofthreeexpectationsbeyondtheresearchquestions.Throughobservedresults,thereflectiononexistingtheoriesismentionedinthelastcolumn. The levels of approaching each expectation were defined as generalandwideaspossible.
Although the levels are defined general, it was not to address themcompletelybecauseofsomelimitations.TheaccessibilitytotheR&Dengineerswasoneofthemainlimitationsbehindperformingtheexperiments.BysupportsofanIranianresearchcenter,theaccessibilitywasprovidedforsomeIranianSMEs.Itisworthconsidering,theaccessibilitytoIranianR&Dengineerswasalsocriticalfortheultimateaimoftheresearchasthemainproblemoftheresearch,wassearchedandstudied in thescopeof IranianSMEs;Theexistenceof the sameproblemonSMEssectordespitethegeographicregioncanbestudied.
5.3 Limitations and future developments
Therearesometechnicalandmethodological limitations thatwouldgive
rise toseveralpotentialtypesof futurestudies.Following limitationsareadirectcontinuationoftheworkperformedinthisthesisandcouldberesearchedproposalsfor future studies. It is important to note the methodological limitations of thestudies involved in this thesis. The boundaries of this study were thosecharacteristics of design or methodology that could impact or influence theinterpretationoftheresearchfindings.Inparticular,twomainissuesregardingdatacollection (environmental issue) and also analyzing the collected data(methodological issue) were identified. As previously discussed, a significantlimitationinthislineoftheresearchprogramistherelianceonthemeasuresandidentifications used to collect and formulate the data, as well as the sample ofengineers.Keyconcernsregardingthefirstissueofdatacollectionareasfollows:thesizeofthesample;theuseofself‐reporteddata;thecategoriesandknowledgeoftheparticipants;andculturalandothersimilartypesofbias.
Sample size:Inthisresearch,56peopleparticipatedinthefirstexperimentandwererandomlydividedintofourgroups.Thisisafairsamplesizeforthiskindofexperimentalresearch,assupportedbypreviousliterature(Massetti,
Chapter5:Discussionsandconclusions
131
1996;Nijstadetal.,2002; Perttulaetal.,2007).Althoughhavingusedseveralhypothesestest,itwouldbedifficulttofindsignificantrelationshipsbetweenthedata.However,thesamplesizeislessrelevantinqualitativeresearch.
Self-reported data: The participants were separated randomly into fourgroups;eachgrouphad14peoplein7randomteams(2peopleperteam).However,as there isarelianceonconducting aqualitativeresearchstudyandgathering the data, self‐reporteddata is limitedby the fact that it canrarelybeindependentlyverified.Inotherwords,whatparticipantssaidintheexperimentswastakenintoaccount.
Categories and knowledge of the participants: A broad range ofparticipantswereused;nosystematicselectionregardingtheirgender,age,education,degreeandalsotheirknowledgeinthepatentanalysiswasmade.However, as some of the participants did not have enough knowledge ofpatent ideation, bias and diversity in them analyze and point of view wasexpected.
Cultural and another type of bias:Allocatingprocessesforeachgroupandteams have been done completely at random, participantsall have biases,whethertheyareconsciousofthemornot.Itismainlyduetolocalculturalbiasandevenplaceandtimeofrunningtheexperiments.
Another significant limitation of this study is the reliance on the existingmeasuresandidentificationsusedintheliteraturetocollectandformulatethedata.TheworkbyShahetal. (2003) in ideationmetricswasessential,but flawswerefound in the Variety metric in some researches like Nelson et al., 2009. It wasmentionedintheirstudythat"fornormalizingagroupscoretheVarietycanonlybecalculatedforasetofmultipledesignideasandtheaverageVarietyscoreisnotastheVarietyscoreonlyappliestothesetitself.”SoitisrecommendedtoredefinetheVarietymetrictoperformitmorerobust.
Also,theproposedPatentMapsrelyonmanualwork,thereforedecreaseitsoperational efficiency. To solve this problem and for future studies, it isrecommended to develop software to perform the proposed approach in thisresearchmoredirectly.Thisreducesthemanualworkandallowswhoarefamiliarwithpatentanalysisandtextmining,toprofitfromtheresearchresults.
132
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[7] Appendix
146
7.1 Appendix A - Patent analysis survey in Iranian SMEs
7.1.1 Interview and results
I. General information
Nameofinterviewer:
Nameofinterviewee:
Placeofinterview:
Dateofinterview:
II. Questions
1.TheleveloffinancialsupportsofgovernmentforgrantingapatentwithinIranian
Industry?
□Low□Medium□High
2.Thelevelofusageofpatentinformation(National/International)inIranianIndustry?
□Low□ThelevelofthenecessityofpatentanalysisinIranianIndustry□High
3.ThemostpriorandpreferredsectorforusingpatentinformationamongIranian
Industry?
□Microenterprises(0‐9employees)
□Smallenterprises(10‐49employees)
□Medium‐sizedEnterprises(50‐249employees)
4.ThelevelofthenecessityofpatentanalysisinIranianIndustry?
□Low□Medium□High
5.ThelevelofusageofpatentanalysisinIranianIndustry?
□Low□Medium□High
6.ThelevelofanacquaintanceonthepatentanalysisinIranianIndustry?
□Low□Medium□High
147
1. The level of financial supports of government for granting a patent within
Iranian Industry?
Answer Choices Responses Numbers
Low 20% 4
Medium 80% 16
High 0% 0
Total 100% 20
2. The level of usage of patent information (National/International) in Iranian
Industry?
Answer Choices Responses Numbers
Low 75% 15
Medium 25% 5
High 0% 0
Total 100% 20
0%
20%
40%
60%
80%
100%
Low Medium High
1.ThegovernmentfinancialsupportforgrantingapatentinIranianIndustry?
0%
10%
20%
30%
40%
50%
60%
70%
80%
Low Medium High
2.Theusageofapatentinformation(National/International)inIranianIndustry?
148
3. The most prior and preferred sector for using patent information among
Iranian Industry?
Answer Choices Responses Numbers
Microenterprises(0‐9employees) 0% 0
Smallenterprises(10‐49employees) 45% 9
Medium‐sizedEnterprises(50‐249employees) 55% 11
Total 100% 20
4. The level of the necessity of patent analysis in Iranian Industry?
Answer Choices Responses Numbers
Low 0% 0
Medium 30% 6
High 70% 14
Total 100% 20
0%
10%
20%
30%
40%
50%
60%
Micro enterprises(0-9 employees)
Small enterprises(10-49 employees)
Medium-sizedEnterprises
(50-249 employees)
3.Whichsectorismoreinterestedinpatentinformation?
0%
10%
20%
30%
40%
50%
60%
70%
80%
Low Medium High
4.TheneedofpatentanalysisinIranianIndustry?
149
5. The level of usage of patent analysis in Iranian Industry?
Answer Choices Responses Numbers
Low 80% 16
Medium 20% 4
High 0% 0
Total 100% 20
6. The level of an acquaintance on the patent analysis in Iranian Industry?
Answer Choices Responses Numbers
Low 75% 15
Medium 25% 5
High 0% 0
Total 100% 20
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
Low Medium High
5.TheusageofpatentanalysisinIranianIndustry?
0%
20%
40%
60%
80%
Low Medium High
6.ThelevelofanacquaintanceonthepatentanalysisinIranianIndustry?
150
7.1.2 Questionnaire and results
World’sPioneeringindustriesafterpassing“efficiency”,“Quality”,and“flexibility”paradigmsarenowadaysininnovationera.Ininnovationera,competitionandresistanceinmarketsdependoncompanies’managementsbalanceinthreefieldsof“inventionandtechnologydecoding”,“creativeandinnovativedesign”and“intellectualpropertymanagement”.Therefore,thisquestionnaireispreparedtostudyandevaluategeneralandspecialconditionsofpatentownercompaniesinIran,andwewillappreciateifyoucanhelpusinimprovinganddevelopingofscientificeffortsinIranbyallottingsometimetocompletethequestionnaire. Itshouldbementionedthatcompanies’informationwillbekeptsecretandwillonlybeusedforresearchreasons.Theresultsandconclusionsoftheresearchwillbesenttoyousubsequentlyincaseyourcompanycompletesthequestionnaire.
General information
Fieldofstudy:Name:
Degree:Age(year):
Experiencesinthefield(year):Sex:
I. Company specifications
1.ThenumberofemployeeinSMEcompany?
□10to49employees□50to99employees□100‐249employees
2.ThenumberofpatentsinIranianSMEcompany?
□1‐5□5‐10□10‐15
3.Theresponsibledepartmentfortheinventions,newproductdevelopmentand
patentsinSMEcompany?
□ IntellectualPropertyDepartment
□ InnovationManagementDepartment
□ ResearchandDevelopmentDepartment
□ EngineeringDepartment
II. Patent analysis level
4.TheLevelofanacquaintanceonthepatentanalysisinSMEcompany?
□Low□Medium□High
5.RequestedandinterestedlevelforexploitingpatentinformationforSMEcompany?
151
□Strategiclevel
□Competitivelevel
□Technicallevel
□Juridicallevel
6.AnystandardoraspecificprocessforpatentanalysisprojectsinSMEcompany?
□Yes□No
III. Patent analysis purpose
7.ThemainbenefitandexpectationsofpatentanalysisprojectinSMEcompany?
□Preventingreworkanddecreasingresearchcosts
□Conductingstudiestoanupperlevelofknowledge
□Proposinganovelsolution
□Consideringnewaspectsofinvention
□Awarenessoftechnicaltrendoftechnologyinothercountries
□Securefieldsofinvestment
□Studyingpastresearchandfindingsolutionstoproblems
□Identifyingtestifiedinventionsasnewevents
8.Forwhichstepoftheinventionprocess,thepatentanalysisisexpectedtobeused?
□Identificationofproblem
□Identificationoflimitationsandcriterions
□Findingpossiblesolutions
□Creatingideas
□Studyingfacilities
□Selectinganapproach
□Creatinganinitialsample
□Refinement
9.ThemostimportantpurposeforusingpatentanalysisinSMEcompany?
□Identificationofinnovationinpatentedinventions
□Identificationoftechnologyvacuityandimportantpoints
□Analysingpatentregistrationtrends
152
□AnalysingQualityofregisteredinventionstospecificationofR&Dtasks
□Forecastingtechnicalimprovementsinaspecificfield
□Strategicplanningfortechnology
□Extractinginformationfromregisteredinventiontoidentifyinfringements
□Identificationofpromisingregisteredinventions
□TechnologyRoadmap
□Identificationoftechnologycompetitors
IV. Database
10. Themostuseddatabaseinthecompany?
□ USPTO□EPO□Other
1. The number of employee in SME company?
Answer Choices Responses Numbers
10to49employees 36% 0
50to99employees 64% 16
100‐249employees 0% 9
Total 100% 25
0%
10%
20%
30%
40%
50%
60%
70%
10 to 49 employees 50 to 99 employees 100-249 employees
1.ThenumberofemployeeinSMEcompany?
153
2. The number of patents in Iranian SME company?
Answer Choices Responses Numbers
1‐5 60% 15
5‐10 28% 7
10‐15 12% 3
Total 100% 25
3. The responsible department for the inventions, new product development and
patents in SME company?
Answer Choices Responses Numbers
IntellectualPropertyDepartment20% 5
InnovationManagementDepartment16% 4
ResearchandDevelopmentDepartment52% 13
EngineeringDepartment 12% 3
Total 100% 25
0%
10%
20%
30%
40%
50%
60%
70%
1-5 5-10 10-15
2.ThenumberofpatentsinIranianSMEcompany?
0%10%20%30%40%50%60%
IntellectualProperty
Department
InnovationManagementDepartment
Research andDevelopmentDepartment
EngineeringDepartment
3.Theresponsibledepartmentfortheinventions,newproductdevelopmentandpatentsinSME
company?
154
4. The Level of an acquaintance on the patent analysis in SME company?
Answer Choices Responses Numbers
Low 76% 19
Medium 24% 6
High 0% 0
Total 100% 25
5. Requested and interested level for exploiting patent information for SME
company?
Answer Choices Responses Numbers
Strategiclevel 20% 5
Competitivelevel 8% 2
Technicallevel 60% 15
Juridicallevel 12% 3
Total 100% 25
0%
10%
20%
30%
40%
50%
60%
70%
80%
Low Medium High
4.TheLevelofanacquaintanceonthepatentanalysisinSMEcompany?
0%
20%
40%
60%
80%
Strategic level Competitive level Technical level Juridical level
5.RequestedandinterestedlevelforexploitingpatentinformationforSMEcompany?
155
6. Any standard or a specific process for patent analysis projects in SME company?
Answer Choices Responses Numbers
Yes 0% 0
No 100% 25
Total 100% 25
7. The main benefit and expectations of patent analysis project in SME company?
Answer Choices Responses Numbers
Preventingreworkanddecreasingresearchcosts 0% 0
Conductingstudiestoanupperlevelofknowledge 0% 0
ProposingaNovelsolution 56% 14
Consideringnewaspectsofinvention 12% 3
Awarenessoftechnicaltrendoftechnologyinothercountries 0% 0
Securefieldsofinvestment 0% 0
Studyingpastresearchandfindingsolutionstoproblems 32% 8
Identifyingtestifiedinventionsasnewevents 0% 0
Total 100% 25
0%
20%
40%
60%
80%
100%
120%
Yes No
6.AnystandardoraspecificprocessforpatentanalysisprojectsinSMEcompany?
0%
10%
20%
30%
40%
50%
60%
Preventingrework anddecreasingresearch
costs
Conductingstudies to anupper level
ofknowledge
Proposing anovel
solution
Consideringnew aspectsof invention
Awarenessof technical
trend oftechnology
in othercountries
Secure fieldsof
investment
Studyingpast
research andfinding
solutions toproblems
Identifyingtestified
inventionsas newevents
7.ThemainbenefitandexpectationsofpatentanalysisprojectinSMEcompany?
156
8. For which step of the invention process, the patent analysis is expected to be
used?
Answer Choices Responses Numbers
Identificationofproblem 12% 3
Identificationoflimitationsandcriterions 0% 0
Findingpossiblesolutions 28% 7
Creatingideas 60% 15
Studyingfacilities 0% 0
Selectinganapproach 0% 0
Creatinganinitialsample 0% 0
Refinement 0% 0
Total 100% 25
9. The most important purpose for using patent analysis in SME company?
Answer Choices Responses Numbers
Identificationofinnovationinpatentedinventions 60% 15
Identificationoftechnologyvacuityandimportantpoints 0% 0
Analysingpatentregistrationtrends 8% 2
AnalysingQualityofregisteredinventionstospecificationofR&Dtasks 0% 0
Forecastingtechnicalimprovementsinaspecificfield 12% 3
Strategicplanningfortechnology 0% 0
Extractinginformationfromregisteredinventiontoidentifyinfringements 12% 3
Identificationofpromisingregisteredinventions 0% 0
TechnologyRoadmap 8% 2
Identificationoftechnologycompetitors 0% 0
Total 100% 25
0%
20%
40%
60%
80%
8.Forwhichstepoftheinventionprocess,thepatentanalysisisexpectedtobeused?
157
10. The most used database in the company?
Answer Choices Responses Numbers
USPTO 48% 12
EPO 40% 10
Other 12% 3
Total 100% 25
0%10%20%30%40%50%60%70%
9.ThemostimportantpurposeforusingpatentanalysisinSMEcompany?
0%
10%
20%
30%
40%
50%
60%
USPTO EPO Other
10.Themostuseddatabaseinthecompany?
158
7.1.3 Workshop, questionnaire and results
General information
Fieldofstudy:Name:
Degree:Age(year):
Experiencesinthefield(year):Sex:
Patent analysis
1.Thenumberofpatentshasyoueverstudieduntilnow?
□1‐5□5‐10□10‐15
2.Thenumberofpatentanalysisprojectshaveyoueverparticipated?
□1‐5□5‐10□10‐15
3.Thenumberofpatentdatabaseshaveyoueverused?
□1‐5□5‐10□10‐15
4.Thenumberofpatentanalysissoftwareortoolshaveyoueverused?
□0□1□2
7.1.3.1 Results
1. The number of patents has you ever studied until now?
Answer Choices Responses Numbers
1‐5 53% 8
5‐10 40% 6
10‐15 7% 1
Total 100% 15
0%
10%
20%
30%
40%
50%
60%
1-5 5-10 10-15
Thenumberofpatentshasyoueverstudieduntilnow?
159
2. The number of patent analysis projects have you ever participated?
Answer Choices Responses Numbers
1‐2 100% 15
3‐4 0% 0
5‐6 0% 0
Total 100% 15
3. The number of patent databases have you ever used?
Answer Choices Responses Numbers
1‐2 100% 15
3‐4 0% 0
5‐6 0% 0
Total 100% 15
0%
20%
40%
60%
80%
100%
120%
1-2 3-4 5-6
2.Thenumberofpatentanalysisprojectshaveyoueverparticipated?
0%
20%
40%
60%
80%
100%
120%
1-2 3-4 5-6
3.Thenumberofpatentdatabaseshaveyoueverused?
160
4. The number of patent analysis software or tools have you ever used?
Answer Choices Responses Numbers
0 100% 15
1 0% 0
2 0% 0
Total 100% 15
0%
20%
40%
60%
80%
100%
120%
0 1 2
4.Thenumberofpatentanalysissoftwareortoolshaveyoueverused?
161
7.2 Appendix B - Characteristics of Patent Map methods
7.2.1 Ranking of Patent Map methods
Patent Map Requirements
Total
score of
Patent
Maps Patent Map
Major
analytical
method
Commonly
used form of
presentation
Scalable
up to
100
patents
Doable by
consultants
in 8 hours
(30
patents)
Readable
by R&D
engineers
in 1 hour
(30
patents)
Represent
the
Novelty
Represent
the
inventive
steps
Time
perspective
Element‐
basedMap
Qualitative
analysisIllustration 2 3 2 2 2 0 11
Mapof
Technological
Development
Qualitative
analysis
Tree‐
structured
form
1 2 2 2 2 3 12
Inter‐patent
Relations
Map
Qualitative
analysis
Tree‐
structured
form
1 2 2 1 1 0 7
Matrix Map
Qualitative
analysis
Quantitative
analysis
Matrix/
Graph 3 3 3 3 3 1 16
Systematized
ArtDiagram
Quantitative
analysisIllustration 2 2 2 2 2 0 10
Time‐Series
Map
Quantitative
analysisGraph 2 1 1 0 0 3 7
TwinPeaks
AnalysisMap
Quantitative
analysisGraph 1 1 1 0 0 3 6
Maturation
Map
Quantitative
analysisGraph 0 1 1 0 0 3 5
RankingMapQuantitative
analysisList/graph 1 1 1 0 0 3 6
ShareMapQuantitative
analysisList/Graph 1 1 1 0 0 3 6
SkeletonMap
Quantitative
analysis
Qualitative
analysis
Tree‐
structured
form
2 2 3 1 1 3 12
RadarMapQuantitative
analysisGraph 1 2 2 1 1 1 8
0=Irrelevant 1= Low relation 2=Medium relation 3=High relation
162
7.2.2 Representative examples of Patent Map
Representative Examples of Patent Map
No. Name Presentation
form
Main analytical
method Main benefit
Useful for generating
Novel ideas
according to the
problem and
solution presented in
patents?
(Yes/No/Partly)
1Element‐
BasedMapIllustration
Qualitative
analysis
‐Thesummaryofrelatedtechnology
‐Thesituationofone’sownpatentandits
differencewithotherpatents
No
2
Diagramof
Technological
Development
Tree‐
structured
form
Qualitative
analysis
‐Thepioneerpatentsforcachingthepotential
‐ThespillovereffectsofdevelopmentresultsPartly
3
Interpatent
RelationMap
(Citation
Map)
Tree‐
structured
form
Qualitative
analysis
‐Theinformationofcitedperson
‐Thepriorartcitedsection
‐Theusageofpriorartinformation
NO
4 Matrix Map Matrix/graph
Qualitative
analysis
Quantitative
analysis
Therelatedkeypatents(combinationof
problemswithsolutions)Yes
5Systematized
ArtDiagramIllustration
Quantitative
analysis
Theentireamountofpatentsassociatedtoa
preciseareaofartandreviewtherelated
intellectualpropertyactivitiesatuniversities
andgovernmentalorganizations
NO
6TimeSeries
MapGraph
Quantitative
analysis
Theapplicantstrendanalysis,patentnumber
ofapplicationsfiledandpatentsissued.NO
7TwinPeaks
AnalysisMapGraph
Quantitative
analysis
‐Theprecedenttechnologicalprogressof
companiesunderbusinessstrategy
‐Thedelayofcountriesforreachingan
aninternationalcompetitiveadvantageina
preciseart
NO
8Maturation
MapGraph
Quantitative
analysis
Identifythenumberofapplicationsfiledand
thesignsofchangeNO
9 RankingMap List/graphQuantitative
analysis
Thetechnologicalprogresstrendsinleading
organizationNO
10 ShareMap List/GraphQuantitative
analysis
Theapplicationholderinformationand
relatedpatentforaspecifictechnologyNO
11 SkeletonMap
Tree‐
structured
form
Quantitative
analysis
Qualitative
analysis
Toobtainacompleteperceptionoftherange
oftechnologicalimprovement.NO
12 RadarMap GraphQuantitative
analysis
‐Thefocusedfundingontechnicalfieldand
laboronaspecialtechnologyindifferent
organization
‐Thecomparisonofinternational
competitivenessbetweenthecompanies
NO
163
7.3 Appendix C - The instruction of providing the Technical
Contradiction Map
The map consists of a main matrix and some supportive graphs. It is suggestive to
exploiting the map in following steps:
Look at the main Matrix:
o The classes of problems which are addressed in the patent;
o The classes of means and solutions which are used in the patents for solving
the problems;
1. Think about any new problems which must be addressed by the system;
2. Think about any general idea for structure of the system for same classes of
the problems or new problems;
o The size of the bubbles is representative for the quantity of patents in each
cross of problem-solution (The bubbles with very few patent applications,
have less chance for new art for more improvement, whereas, the largest
number of patent applications were filed in some crosses represents a large
volume of information disclosed);
o For more technical information for each cross, go to the corresponding
supportive graph;
Look at the supportive Graphs:
o Each patent resolved a main contradiction. A contradiction can be clarified
by three parameters; improving, worsening, and control.
o Improving parameter; is a property of a system component that is expected
to be improved by the solution, consists of 2 parts in the top of the graph:
A component of the system in a box;
A property of that component in a box;
o Worsening parameter; is a property of a system component that prevents
the improving parameter from achieving the desired value, consists of 2
parts in the bottom of the graph:
A component of the system in a box;
A property of that component in a box;
o The worsening and improving parameter values have an inverse
relationship.
o Control parameter; is a system component property that allows trade-offs
between improving and worsening parameters and it is possible to control
values of improving and worsening parameters through it; it is written on
the line that connects the improving parameter and worsening parameter
of contradiction of a patent.
o A picture of each patent is presented next to the line of control parameter.
3. Propose new ideas for addressing and resolving the contradictions in a
cross.
164
7.4 Appendix D - Walker Patents Profile
165
166
167
168
169
7.5 Appendix E - Technical Contradiction Map
170
171
172
173
174
7.6 Appendix F - Generated ideas (Experiment I&II)
7.4.1 Experiment I: Group A
Team Treatment Idea descriptions
Time of
generating
ideas
(Out of 30 min)
no. of
Ideas
1 B.S
FoldableandportableWalker 2
16
AdjustableWalkerlegsfordifferentpersons 4
AdjustableWalkerlegsforuseonthestairways 5
Usingdampersystemtoimproveusage 6
TelescopiccapabilityforchangingWalkertowalkingstick 7
Walkerwithhandleheatingorcooling 9
Joinwheeltotransportandinclinedsurfaces 10
Usematerialsandlightweightalloystoreduceweight 11
Addafoldingseatfortiredness 12
Addasensortofeelthetypeofpath 14
Addshoppingbasket 16
Addlightstomoveatnight 18
Announcinghelpatfallingtime 19
Useasanelectricmotor 22
Addtaximeterandaccessories 26
Addtheumbrellaorroofduringrain 27
1 P.S
Addchildren'slock 9
7
Automaticadjustableheightwiththeuser'sbody 11
Addmusclestimulationsystem 13
AddMP3playertoexcitemovement 14
Nanocoverageforeasiercleaning 18
Addfoldingtoilet 23
AddGPStoidentifythepath 28
2 B.S
Walkerswithwheelsandsteeringandbraking 2
8
RetractabletelescopicWalker 5
Equippedwithnavigationandmonitorsystemandlocationand
guidetrack8
Equippedwithmedicationreminders 10
Consideringthesystemsendalertsandmessagestoaphone
numberatthetimefalling12
Walkerwithumbrellaandawnings 18
Usingasascooter 20
Theabilityoftransformingtoseat 25
2 P.S
Useofnewlightermaterials(composites) 10
4Automaticadjustableheightwiththeuser'sbody 20
Sensingthedistancetoobstacles 25
Locktoconnecttotheescalatorfortheclimbingandcomedown 30
3 B.S
ModularfoldingWalker 2
12Usingnewmaterials(nanotechnologyandcomposites)toreduce
weightandreduceenvironmentalissues4
Walkerupperbodyconnectiontoreducepressureonthehands 6
175
Walkerconnectionlegs 9
WalkerWheelforeasiermobility 11
AddGPStoidentifythepath 15
Walkie‐talkiesaddshort‐rangecommunicationsystem 17
Addaudiosystemandhands‐freecommunicationonWalker 19
Integratedpedometerandatimertohelptreatmentprogram 23
Addshoppingbasket 25
Addtheumbrella 27
Addseatstorest 30
3 P.S
Addwarningsystemforfallprotection 10
5
UserMonitoringvitalsigns 15
Installlightingsystemsforuseindarkenvironments 22
Automaticsizeadjustmentbasedonuserrecognition 25
PortableWalker 30
4 B.S
AddMP3playerforfun 2
17
SmartWalkertodetectphysicalsituationofusertowarnincase
ofemergency3
Withadjustableheightandsizeforeachuser 5
Addsmallbasket 8
Walkerwithawnings 9
Addwirelesstelephoneforhomeuse 10
Addglovesinsteadofhandlefordisabledpersons 12
SmartWalkertodetectionobstacles 17
AbilitytoassemblyanddisassemblyofWalkerbyuser 19
UsingWalkerlegsasacane(Multi‐purpose) 20
Abilityofassemblyintoaseat 21
Theabilitytorecognizedisabilitiesandnotifythefamily 22
SmartWalkerforblindpeople 24
Havingaremotetrackingsystemtodetectthepositionofuserby
others26
Self‐cleaningability(usingNanomaterials) 27
Withalarmorwarningsystemtouseonshoppingcentreor… 29
Energyconversionkinematictopowertheheatingorcooling
(HVAC)30
4 P.S
Addcontrolmechanisminhandlepartforsitting(hydraulic) 5
5
FoldableWalkerusingthetelescopemechanism 10
Telescopiclegsdesign 15
Walkerconvertstothetoiletforemergencies 20
Levelsdetectionandtheabilitytoswitchtothedifferentlevels 25
5 B.S
Walkerwithaseat 3
7
Walkertoclimbthestairs 8
Walkerwithalarms 13
FoldingWalker 18
Addahandletobalancewhiledoinghouseholdchores 20
Walkerwiththeabilitytocontrolvitalsigns 25
SpringWalkerforUnevensurfaces 30
5 P.S
Walkerwithwheelsandspeedcontrol 8
4Addasafetybelt 10
Addshoppingbasket 15
Addtrackingsystem 20
6 B.S WheelWalker 1 22
176
Gridandlightweightbody 2
Compositebodystyle 3
Addholderofmobile 5
Withspecialseatrest 7
Walkerwithwheelsandseat 9
FoldingWalker 11
UserMonitoringvitalsigns 13
Reflectiveandphosphorusstyle 15
Addsmallhandleforputtingshoppingbags 17
Anti‐sweathandle 19
AutomaticWalker 20
AddGPS 21
Withalarmsystem 21
MotorizedWalker 22
Formovingstairs 23
Exoskeletons 24
Withmassagers 25
WithAir‐Bag 26
Walkersnowplow 27
Walkerwithawnings 29
Addcoolingsystem 30
6 P.S
FoldableWalker 3
11
Useoflightermaterialstoreduceweight 4
Walkerwithcontrolbrakesonhandle 6
AddGPS 9
Adjustableheight 12
Usetheseatforsitting 15
Usingthreewheelsforclimbingstairs 16
Possibilityofsensingheartrateandbloodpressure 19
Addmultimediafeatures 22
IncreasetheheightofWalkerhandleintounderarmsforeaseof
movement24
Addmobileholder 29
7 B.S
Addtheumbrellaorroofduringrain 2
6
Walkerwithfourwheels 3
Addtwowheelstofrontlegs 6
Usingcompositematerials 9
Usingreflectorontheframe 16
Contactledlightstoilluminatethepathatnight 23
7 P.S
Usinggreenandeco‐friendlymaterials(BioComposite) 10
3TwoWalkerlegsconnectedtobodyofuser 20
Changeablefrontlegsforclimbingstairs 30
177
7.4.2 Experiment I: Group B
Team Treatment Idea descriptions
Time of
generating ideas
(Out of 30 min)
no. of
Ideas
1 B.S
Madebycarbonfibreandfolding 4
10
Withheatedhandlesandwristhook 6
Capableofclimbingstairs 7
Withvitalsignsmonitoring 9
Addsensorsforblindperson 14
Addrechargeablelithiumbatteryorsolar 18
Seattorelaxduringwalking 20
RadioandMP3player 22
Watertankfordrinkingandtakingdrugs 26
ConvertibilitytotheLuge 30
1 T.C
Usingslidersystemforwalkingonallsurfaces 5
8
Usesmallelectromotorandsensorandtosetfourlegs
independently7
Helpingthepatienttositbyusingfoldingsystem 8
Helpingusertorestthroughtelescopicfoldingsystemofseat(3
to4pieces)10
Helptomoveatdifferentsurfacesbyusingnewmaterialfor
Walkerlegs14
UsingAir‐bagineachWalkerlegs 17
Adjusttheheightofuser'shandusingtheanglesensor 20
Walkersizesettingusingmotionsensors 25
2 B.S
AddMP3andLCD 2
17
Installtheelectricalgeneratorforpowersupply 3
Wheelsforeasymovement 4
FoldableWalkerforsittingandstand 5
Installthefanforcooling 7
SpringWalkertoreduceimpact 8
Lightsforuseatnight 10
Addaswivelchairfortherest 12
Addanalarmconnectedtoamobilephoneincaseoffalling 13
Gridandlightweightbody 16
AddasmallboxinfrontofWalkertoputthenecessary
equipment19
Addanelectronicdevicetoinformeatthedrug 20
Newdesignoffrontlegstouseinthestairs 22
Walkerwithsensorstodetectobstaclesfortheelderlypeople 25
Addabaseandwheelforusing 26
Walkerlegsattachedtotheknees 27
Withmassagers 29
178
2 T.C
Possibilitytoadjustthelegswithsurfaces 8
11
Walkerhandlesstayparallelwithfloorandbodystaybalanced 9
Usingporousmaterialsforup‐rightWalker 11
Forincreasemanoeuvrabilityandbalanceofuser,changethe
steeringwheelfromfronttoback(Trolley)13
Whenthearmcollectedusingahandleveronthehandle 15
Nestinglegsforrestingtime 16
Foldingplatformforsitting 17
Improvednavigationinthedarkenvironmentbyusing
materialsofphotocell20
Addleverlocksinthefrontarmandcontrolledbyahandlefor
stairs23
Usingflexiblepadsonthebottomoflegsdependsonground 25
Usingcompositematerials 28
3 B.S
Addsupportsfordisabledpeople 1
11
Addpedometer 2
FoldableWalker 4
EquippedWalkerwithanairbagtopreventfalls 6
Theabilitytotransformfromfourtotwolegs 8
Useoflightermaterials 13
Useheatandcoldinsulation 17
Insteadoffourlegsusingaflatplateforunevensurfaces 21
Usingreflectivematerial 25
Addfoldableseat 27
Walkerwithwheelinfrontlegsandfrictionbreaksforrearlegs 29
3 T.C
AddGPSinordertoidentifyrightdirection 10
4
Usingphosphorusmaterial(luminous)inWalkerframeto
avoidcollisionswithvehiclesatnight16
Themodularcomponentsinsteadofintegratedcomponentsfor
easyrepairthedamagedsection22
Foldingseatforsittinginanemergencycase 26
4 B.S
Addahandlebikeforeasyuse 3
17
EmbedaseatforWalker 6
Embedwheelsformorespeedandbrakehandle 7
Addlights,hornandbasket 8
Dualwheelsforclimbingstairs 9
FoldableWalker 12
Addsafetybelt 13
Changeabletoascooter 14
Changeabletoacane 15
RadioandLCD 16
Addsidemirrorstoavoidanaccidentwithamotorbikefrom
behind17
Usingslideheightadjustmentfordifferentpeople 19
Addfoldablesmalltable 20
DesignlikeTwinstrollers 23
179
Addtheumbrella 24
AddGPSforAlzheimer's 25
ElectricWalkerwithfootrestforstairs 30
4 T.C
Usingthermochromics’materials 6
10
Usingphotochromicmaterials 7
TheuseofshapememoryalloySMAtoimprovetheassembly
anddisassemblyWalker8
SmartWalkertodetectobstacles 10
Addacamerawithazoomin‐zoomoutcapabilityforelderly
peoplewithlowvision12
AddaWalkerwheelwithadjustablespeed 15
Walkertoclimbstairs(chainchangebike) 17
Forcrossingtheslipperysurfaces,usingspecialWalkerlegs 21
Apowersupplyandcontrolequipmentfordisabledpeople 25
Addamemoryforstoringinformationofpeoplewith
Alzheimer'sdisease30
5 B.S
InstallGPSandconnecttopoliceandhospital 3
5
Asapantsformechanicalreinforcementofmuscleswhile
walking6
SuchasSolomoncarpetbutflexibleandelectronic 16
Usesuchasextraequipmentlikebeds,wheelchairs,crutches,
fishingholder20
Suchasabackpacksthatreducetheweight 27
5 T.C
UsingelasticmaterialsforWalkerframe 9
5
Usinghigh‐strengthandlightweightmaterialsfor
Compensationweight15
UsingspringforWalkerlegsforincreasingbettermoving
forward18
Smartmaterialsforcoolingandheatinguser 20
Telescopicfrontandrearlegstoenablemanoeuvreupand
downonslopes25
6 B. S
FoldableWalker 3
9
Useoflightandunbreakablematerials 5
UsingantiactivateX‐RAYdevicematerials 7
UsingNon‐metallicmaterialforlegstoreduceweight 10
Addelectroniccircuitandthermalsensors 14
Adddataprocessorofheartbeat 17
AddGPSandnavigationfacilities 20
Improvethesystemofwheelsandengineinstallation 24
Walkerwithflexibilityfordifferentsurfaces 28
6 T.C
TheuseoflightweightmaterialstoreduceweightWalker 5
7
Walkerwithtwoseparatelegstohelpknee 8
Walkerwithgelatinousmaterialthatdoesnotinjurewhenuser
fall10
SmartWalkerwiththedrugprogram 14
Walkerwithresiliencematerialsforbetterfeelingofuser 20
180
Walkerlegswithspecialmaterialsforhousecleaning 25
Walkerwithfoldableandintegratedframe 28
7 B.S
Walker'shandlewithsensorstosenseenergyofusertoprevent
theuserfalling1
19
Walkerheightadjustablelegsfordifferentuser 2
Walkerwithasystem(anelectriclift)tomakelighterweight
feelduringliftingWalkertouser3
SmartWalkerwithautomaticsurfacedetectionforadjusting
thelevelofconnectionoflegstoeachsurfaces5
Walkerrobotwithflexiblelegsforclimbingstairs 8
Walkerwiththevitalsigns(bloodpressure,pulse,...) 10
Walkerwithawarningsystemtothosearoundtheuseratthe
timeoffallingorimbalance12
Foldingandassemblies’Walkerforeasytransport 13
WalkerwiththewarningandalerttheuserifWalkerstandson
thesurfaceisunsuitable15
Walkerwithlightingsystemfornightuseanddarkplaces 17
Walkerwiththeabilitytoadjustallaspectsoflegsandrodsto
adapttodifferentpeople(suchaschildrenandadults)18
Walkerwithaprotectiveumbrella(sun,rainandsnow) 20
Walkerwithabilitytoestimatethedistancetothetarget,for
example,acameraisinstalledonWalker22
WalkerequippedwithaGPStolocate 23
Walkerwithincreasingabilityofhandsforpeoplewithweak
hands25
Walkerwithheatingandcoolingsystems 26
Walkerequippedwithafoldingseatforthenecessaryuser
tiredness27
Walkerwithasystemthatwillmaintainbalanceduringwalking 28
Walkerwithacollisionwarningsystemtobarrier 30
7 T.C
Useofnewmaterialsformorecomfortgriphandleandprevent
slippingonthehandle5
9
Withahandleintheseat(Walkerwithseat)thatpushupthe
seattomoveandtohelpuserstand6
Addaspecialhandletohelpgetupfromthegroundandusea
Walker8
Ribbedwheeldesignforwinterandsummertoprevent
slippingondifferentsurfacessuchasmountainbike10
Walkerframewithwirematerialstohelpbendingandfolding
duringwalkingandkeepstrength15
AddAirbagsforthecollision 20
AddaT‐shapedbasetothefrontoftheWalker(tripodismore
stableandeasytouse)22
Usingpolymericmaterialsforlightnessandpreventrusting 25
Walkerwithplasticarmsandlegsandfrontframeonaccordion
shape28
181
7.4.3 Experiment I: Group C
Team Treatment Idea descriptions
Time of generating
ideas
(Out of 30 min)
no. of
Ideas
1 B.S
Walkerwithaseatandaplacetoputfood(foodtable
Slider)2
10
WithsensorsonthehandleWalkertomeasureblood
pressure,heartrate,temperatureandbloodsugarclose
andsaveandsendviamobile
5
Foldingcapabilityandbecomeapipetocarryina
backpack6
Thepatient'sweightandapplyingpressureonthehandle
andbaseWalker,forenergystorageandconversionto
electricalenergythatisrequiredWalker,forexample,for
useinsensorsorelsewhere
8
Walkerconvertstocanewhenthepatientisfeelingbetter 10
Addatransparencyguardtopreventsplashingwater
duringraintime13
Changetheframetothetriangular‐shaped(3wheels) 20
Addacanopy 22
Thepossibilityofdoublewidthwithatelescopicchange 26
Adjustablelegsfordifferentsurfaces(handlestayparallel
tofloor)forkeepingbalance28
1 P.T
Walkerwithownervoicerecognitionsystemforcoming
totheowner15
3Controlsystem,automaticspeedregulationandthe
rechargeablebatteryformove20
Walkerwithanimbalancesensortoactandkeeppatient 25
2 B.S
Addacomfortableandsofttohandleforuser 1
10
Handleswithanti‐sweat 2
Addanumbrella 4
Changeablelegsfordifferentsurfaces 7
Thepossibilityofchangetoacane 11
convertibletoscooters 15
Audioandvideoentertainmentsystemsforuser 17
AddGPS 19
Addsuitablelegsforuseonstairs 25
Addcargo 27
2 P.T
Addlight 10
8
AddAirbag 12
Foldableseatforrest 15
Adjustableheightfordifferentuser 16
Walkerwithholedetection 19
Walkerspringlegs 22
FoldableWalker 26
Addaportabletoilet 28
3 B.S
WalkersmartfordifferentagesbyaddingLCD(forkids
cartoonplay,foradultsshowvitalsigns)2
10
SmartWalkerformovingatdifferentlevels(stairsand
slopes)5
Theabilitytocoverdistances(sidewalktobusstairsand
...)7
Addfan,umbrella,sunroof,... 8
IncreaseflexibilityWalkertocarryontheplaneandcar 10
Walkermodularcomponentsforeasytransport 13
182
Addequipmentforphysiotherapy 15
Addairbag 18
UsenewmaterialforstrengthWalkerframe 23
AddMobileholder 26
3 P.T
Walkerwithshockabsorberstoreducetheimpactsof
movementondifferentsurfaces15
2Addwheelswithpowersupplyfromhandle(manual
transmission)30
4 B.S
Equippedtomonitorforentertainment 2
18
AddaGPStoWalker 3
Addwheeltofrontlegs 4
Equippedsensorofvitalsignsforsendinginformationto
thehealthcentres6
Withtheopening,closingandfoldingcapability 7
Poweredbyloudspeakertoamplifythevoiceofthe
elderly8
Puttingautomaticawningstoprotectfromlightandrain 10
Policecallincaseofgettinglost 12
Equippedwithcoolingandheatingsystem 14
Usinglightcompositematerials 15
Setthealarmsforusingmedicines 17
Withthelightingsystematnight 18
Usereflectivebody 20
Foldableseatforrest 22
Addfirst‐aidkit 25
Walkerwithengineforeasymoving 27
Self‐cleaningsystem 28
Anti‐theftsystem 30
4 P.T
Walkerwithfoldingchairsforrelaxing 12
6
Electricwheelchairbyputtingalightengine 15
Addadigitaldevice(smartphoneorGPS) 18
Addwheeltorearlegsinordertobalancethepatient
duringmove20
Usingwheelsofatankforroughpaths 25
Addalarmsystemandconnecttotheambulanceandfire
fighting30
5 B.S
Usinglightweightcarbonmaterials 2
5
Addingwheelandbrakesystem 4
Newdesignhandleframetorelyontheforearminstead
ofthewristforwalking13
NewdesigntoavoidbendinguseronWalkers(longer
handles)19
Usingarobotconnectedtotheuserbrainandlegsfor
maintainbalance26
5 P.T
Walkerwithspecialwheelssnowyandicyconditions 8
4
Walkerwithonewheelinfrontandtwowheelsatthe
rearsothatahorizontalshaftattachedtothefrontwheel
anditsconductivityWalker
14
Walkerpoweredbybatterywiththree‐wheels 20
Retractableandportablemotorized 24
6 B.S
Portablefoldingbag 2
15
Walkerlegsspringforlowerpowerconsumptiontomove 3
Ergonomicdesignforthespine 5
Equippedwithairbagsafetysystemtopreventphysical
injuries7
Softcoverwithelastic 9
Equippedwithsafetybelt 11
Equippedwithwheelsandbrakesandhorn 15
Equippedwithvideoandsound 17
Withspecialpartforpersonalbelongings 19
183
SmartWalkerforblindpeople 22
WithGPS 24
Equippedwiththemedicationusagealarm 25
Smartalertsystemvitalsigns 27
Adjustablelegsfortheescalatorsandramps 29
Equippedwithportablechairforrefreshment 30
6 P.T
Walkertobeabletoski 10
6
Walkeralsobeusedforcoastalhotspots 17
WalkerwithJackdeviceforuseatvariouslevelsand
helpingtostandandsit19
Walkerstandsinterchangeablefordifferentlevels 20
AdjustableWalker(widthandlength)forthelargeand
smallperson25
Useautomaticsystemtostandandsit(likeCitroencar) 27
7 B.S
Walkerwithwheelandbrakes 1
11
Jointheseatforemergencies 4
LiftingcategoriesWalkertothepatient'sarmpittohelp
thedisabled7
Addamotorformovingautomatically 9
Walkerwithcontrolandmoveinadifferentdirectionfor
peoplewithdisabilities12
AdjustableHeight 15
Walkerwiththreewheelsforclimbingstairs 19
Addmp3andradiotransmitter 23
AddGPSformissingpatient 25
AddabasketinfrontofWalkertoputmobileand
personalbelongings27
Foldingcapabilitiesandportability 30
7 P.T
CombinedWalkerandwheelchair 12
5
Addasmallwheelattheendofeachlegsforeaseof
movement17
Walkerwithcontrolsystemandsensorstomonitoruser
performance20
Addaremotecontrolsystemforchildrenorpeoplewith
disabilities25
Addingafallprotectionsystemtoact(airbags) 30
184
7.4.4 Experiment I: Group D
Team Treatment Idea descriptions
Time of
generating ideas
(Out of 30 min)
no. of
Ideas
1 B.S 01
Adjustablebaseandlegs 5
5Withanalarmsystematthetimeofimbalance 10
AddingradiotoWalker 15
Allowsthemeasurementofvitalsigns 20
InstallGPStoavoidmissing 25
1 B.S 02
TheuseoftelescopicbaseforWalkers 2
4Usingnewmaterialforlighterweight 5
Helptomoveatdifferentlevelsbasedsensor 9
Increasesafetyandreflectorandlampbase 18
2 B.S 01
PortableFoldingWalker 1
17
LightweightWalker 3
Walkerforusingatthebeach 4
Walkerhasashower 6
Walkerwitfan 8
Walkerwithfoldablebed 10
ModularWalkertouseinnewdemand 11
Walkerturnedintocane 13
Walkerwithaheater 15
Walkerwithseat 17
Adjustablesystemsfordifferentheight 19
Walkerturnedtothetent 20
Addabasketforadditionalequipment 22
Walkerawnings 24
Walkerlegstoride(skating) 26
Walkerwithahornandsiren 28
Walkermotorized 30
2 B.S 02
Warningsystemsinhospitals 7
3MeasuringvitalsignsbyconnectingamonitortoWalker 11
SettingthesizeoftheWalkerwithfingerprint 15
3 B.S 01
FoldingWalkertouseinthesubway 3
8
Walkerwithtelescopicheightadjustment 7
Walkerwithadifferentlegforheightadjustmentonslopesand
stairs10
WheeledWalkerwithbrakes 13
TheradioonWalkerforfun 17
Interchangeablelegsforhomeuse 20
Lightweightconstructionwithnewmaterial 25
Joinumbrellaforspecificweather 30
3 B.S 02
Helpthepatientforthesittingpositioninthenecessary
conditionbyaddingaseat5
3Equippedwithwheellocks 10
Useofcompositebodyformorestrength 15
4 B.S 01
Usableasacarriage 3
12
VitalsignssensorsinstalledonWalkerframeforinformingthe
relativesandmedicalcentres5
AppropriateWalkerforclimbingstairs 7
Addablinkerforwarning 10
Walkerwithbaskettoputthepurchase 14
Walkerwithhappyandattractivecolourschemetomake
happiness17
185
Walkermadeoflightweightandsturdy 19
WithGPS 22
Withanti‐sweathandle 25
Apop‐upseatfortiredness 26
Anumbrellatoavoidtherain 28
Changeabletotheelectricwheelchair 30
4 B.S 02 Withtheconnectiontotheuser'swaist 15
3Walkerwearablewithairbag 20
Walkerforusingonsnow 30
5 B.S 01
ExciteusersbyplaymusicwithputtingatablettoWalker 2
10
Addaawningsforprotectionfromrainandsun 4
AddspringtoWalkerlegstoavoidimpacts 8
Walkerwithmedicineremindersystem 12
Addusersupportfordisabilities 16
Addlightstomoveatnight 18
Withfoldableseat 20
Withfingersensordimensions 22
Walkertopreventslipping 24
Walkerwithaspecialmaterialtoavoidfreezeframe 26
5 B.S 02
Three‐dimensionaltriangularlegstopassthestairs 5
4Addafoldablebed 8
Joinconsoletokeepmobile 12
Addanalternatortostoreelectricityanduseflashlightsfor
Mobile15
6 B.S 01
Addvitalsignssuchasheartratesensor 2
16
AddboardsonWalker 4
Walkerfoldingandassemblyforeasytransport 6
Addalightingsystemfornightuse 9
Abilitytoalerttheusertofindtheappropriatesurfaces 13
Withseatforrest 15
Addabaskettoputthepurchase 18
Walkerwarningatthetimeoffalling 20
Theabilitytotransformthecane 20
Foruseinescalator 21
Walkergridbodyandlightweight 22
Walkerwithachangeablelegforvariouslevels 25
Adjustablesizesforchildrenandadult 26
ModularBody 27
Joinwheelsformorespeed 28
Walkerwithfirstaidequipment 30
6 B.S 02 Addbrakestocontrolspeed 2
3Addhornandluminoussystem 7
SmartWalkerwithtabletandGPS 10
7 B.S 01
Bodylightweightforpeoplewithdisabilities 5
12
HeatingandcoolingWalkerframe 8
Suitableforslipperysurfaces 10
Suitableformovingstairs 13
ThefoldingandpotableWalker 15
Withsafetybelt 17
WithGPS 20
WithLCD&MP3 22
Addaprotectiveumbrella 25
Medicinetimealertstopatient 26
Addlightsfornight 28
Usereflectivematerialindarkplaces 30
7 B.S 02 Walkerwithdetachablebaseandconvertiblebedforinjection 20
2Walkerwithadjustableseatheighttositonthefloorandstand
up24
186
7.4.5 Experiment II: Group A
Team Treatment Idea descriptions
Time of
generating
ideas
(Out of 30 min)
no. of
Ideas
1 B.S
DiversityinWalkerbodycolour 2
18
Changetheangleofhandlestohelpbalancetheuser 4
Adjustableheight,foreaseofclimbingstairs 5
Foldinglegsandbody 7
Abilitytoinstalllightsfornightvision 10
Installingalarmorbeeptonotifythefamily 11
InstallpagertocontacttheEmergencyCentretohelpor
ambulance11
Theabilitytobecomechairtorelax 12
Fittedwithabaskettoholdmarketbasket 13
Installnavigationwithaddressingcapability 15
Lightbodyweightwithcarboncompounds 17
Walkerwithcoversforhands(gloves)onthehandle 18
Walkerwithmirrorsonthebody 19
ResistanttireswithwearresistantonendofWalkerlegs 20
Ergonomichandlesthatpreventsweatingandslipping
hands22
ThepossibilityofinstallingawningsonWalker 24
Walkerbodywithcellphoneplace 27
Walkerwithmusicplayerandplaysetonhandles 29
1 T.C 01
Adjustabledistancebetweenthelegsandbodywithspring
mechanism14
7
RemoveoneofthelegsandcreateWalkertripodtoreduce
weight17
Addasteeringwheelatthetopofthecentralaxisanda
tripodonthebottom19
Theuseofpolymeralloysinsteadofsteelalloysinthebody
inordertobelightweight22
AddGPS 23
Addadditionalhandlestopracticeexercisemovements 24
AddaWalkerbelttokeepbalancetheupperbodyof
patient28
2 B.S
TheuseoflightandstrongmaterialinWalkerdesignfor
obesepeople1
11
Walkerhandleswiththermalglovesforwinter 3
Walkerwithawning 4
Addmobileholder 5
Walkerhandletobeloweredandraisedwiththesizeofthe
patient8
187
Atthesametime,canbeusedwithandwithoutwheelsand
controlledbyaswitch10
Walkerelectricwithspeedadjustmentwheelbyaswitch 14
Addingasmallcomputerscreentomonitorroadconditions 17
Addsensorsmonitoringheartrate 21
Addafallingwarningandbalancesensors 24
Addsensorstothelegsforunderstandingthesituationof
theroad27
2 T.C 01
WalkeralwayscleanusingNanocoating 7
9
Walker'shandlecoolinginthesummer 9
WithoxygenholderontheWalkerlegs 13
Addingalarmsforusingdrugs 19
Convertibletoachair 21
Designthetipofthelegsforeasymoveontheasphalt 24
Flexibilityandformabilityforrehabilitation 26
Walkerergonomicdesigntofitdifferentbody 28
Specialinsoledesignforslipperysurfaces 30
3 B.S
Intelligentsystemtoinformhealthcarecentreorfamily 2
9
Walkerwiththeslidingjointsformorespace 5
DesignedfoldableWalker 6
Walkerwithtwofrontlegsonwheels 9
Walkerwithasmallseatforachild 12
UsingattractivecolouringWalkerframeforpatientmorale
boost15
DesignWalkerwithlightweightmaterial 18
Addingseatbelts 23
Addabagandshoppingcart 25
3 T.C 01
Walker'sopeningandclosinghorizontallytorelax 9
5
Patientprotectionsystemtoavoidfallingonthemove 14
Walkerwheels’legswithself‐cleaningsystem 17
Addingalightforusingindarkplaces 23
Walkerheightadjustableonspringyform 27
4 B.S
Usethesmallwheelstomoveeasier 1
17
RetractableWalkerfortransport 2
Walkerwithreflectivelegsfornightvision 5
Addamonitorvitalsigns 6
AddGPS 7
Addashoppingcart 9
Addaholdertoputthefood 11
AddaplacetositinthefrontofWalker 13
Walkerwithwheelsandbrakes 16
Foldablelikeapram 18
Lightandstrong(likethefuselage) 20
Walkerwithfoldingseats 22
Thesoftandreplaceablehandles 24
Addmovingtable 25
188
AddAirbags 27
Addradioandrecorder 28
Handleswithfallwarningsystem 30
4 T.C 01
Addawirelesssystemtotransmitpatientinformation 8
11
Likeakidscooterbutwithhigherseat 10
Walkerwiththeopenspaceinthefrontandcloseinthe
back15
Addedodometerandtestfacilitiestostrengthenthe
musclesoffeet18
Walkerwithspeedcontrolwheel 20
Userfriendlyandtheabilitytotuneupofbyuser 23
Dualwheelsforclimbingstairs 25
Walkerfortheblindandtransmitasenseoftheearth 26
Legspaceisautomaticallyretractingwhenyouwalk 27
SettheWalkerdimensionswithafingerprintsensor 29
Installalarmstopreventcollisionwithobstacles 30
5 B.S
Walkerwheelchairswithadjustablespeed 3
10
Walkerwithhandbrake 5
Addareflector 7
Walkerwithbottlesplaceorsmallbasket 9
Addanumbrellaforprotectrainandsun 11
AddLCDforfun 14
Walkerwithresilientlegs 18
Walkerwithlightfordarkenvironment 21
Self‐controlWalker 24
Walkerwithdifferentlegsfordifferentsurfaces 26
5 T.C 01
Walkerforrampsandroll 5
6
SuitableWalkersclimbingstairs 8
Walkerwiththeabilitytoadjusttotheambient
temperature(frostandheat)12
WalkerwithapathmemoryforAlzheimer's 15
Walkerwithadjustablephysicalcharacteristicsofthe
patient19
Walkerjointtothebodylikeanarmour 25
6 B.S
LockingWalkertothebodyofthepatient 2
13
DesignforfoldingandmultipleuseofWalker 4
Addabrakingsystems 6
Addingaccessorieslikebeeps,lights… 8
Walkerwithshoppingbags 10
Walkerwithfoldableumbrella 12
Walkerwiththegridframe 14
FoldingWalkerfortravel 16
Walkerwithwatertankanddrugbox 18
WalkerframewithheatandcoldInsulator 20
Walkerframewithaerodynamicdesign 22
Walkerframewithsmallchair 24
189
PortableWalkerforthejourney 26
6 T.C 01
GuidedWalkerwithtraineddogs 9
10
Walkerwithatelescopicsystemtoadjust 12
Walkerwiththeabilitytotransformintoawheelchairand
viceversa16
Walkerattachedtothelegswithnohandles 19
VersatilerehabilitationWalker 21
SkatingWalker 23
Walkerfornormalwalkingontheice 24
Walkerforbath 27
InterchangeableWalkerframe 29
ElectricWalker 30
7 B.S
Addlightingequipment 2
14
FoldableWalker 3
Walkertripodwithwheels(twofrontwheelsclosetoeach
other)5
Walkerwithawnings 7
WalkerwithGPS 8
Withleveldetectionsensorandobstacles 11
Usingthecompositematerialstolightenthebodyweight 14
Walkerwithtelescopichandle 16
UsingtheLEDsforbetterillumination 18
Addthemp3playerforentertainment 19
Addasmalltableforstudy 22
Walkersolarbattery 25
TheabilityofgettinghitbythebodyframeofWalker 28
Walkerwitharemovableplasticendlegs 30
7 T.C 01
Walkersemi‐automaticforliftingfromthebedandchairs 8
8
ErgonomicWalkersettingwithbody 12
Walkerwithleaningback 16
WalkerExo‐skeleton 19
Walkerself‐drivingwithengine 22
Walkerflexiblewithazigzagmotion 26
WithpedaltosettheWalkerlegsandhandles 29
190
7.4.6 Experiment II: Group B
Team Treatment Idea descriptions
Time of
generating
ideas
(Out of 30 min)
no. of
Ideas
1 B.S
Madebycarbonfiberandfolding 4
10
Withheatedhandlesandwristhook 6
Capableofclimbingstairs 7
Withvitalsignsmonitoring 9
Addsensorsforblindperson 14
Addrechargeablelithiumbatteryorsolar 18
Seattorelaxduringwalking 20
RadioandMP3player 22
Watertankfordrinkingandtakingdrugs 26
ConvertibilitytotheLuge 30
1 T.C 04
Usingslidersystemforwalkingonallsurfaces 5
8
Usesmallelectromotorandsensorandtosetfourlegs
independently7
Helpingthepatienttositbyusingfoldingsystem 8
Helpingusertorestthroughtelescopicfoldingsystemofseat
(3to4pieces)10
Helptomoveatdifferentsurfacesbyusingnewmaterialfor
Walkerlegs14
UsingAir‐bagineachWalkerlegs 17
Adjusttheheightofuser'shandusingtheanglesensor 20
Walkersizesettingusingmotionsensors 25
2 B.S
AddMP3andLCD 2
17
Installtheelectricalgeneratorforpowersupply 3
Wheelsforeasymovement 4
FoldableWalkerforsittingandstand 5
Installthefanforcooling 7
SpringWalkertoreduceimpact 8
Lightsforuseatnight 10
Addaswivelchairfortherest 12
Addanalarmconnectedtoamobilephoneincaseoffalling 13
Gridandlightweightbody 16
AddasmallboxinfrontofWalkertoputthenecessary
equipment19
Addanelectronicdevicetoinformeatthedrug 20
Newdesignoffrontlegstouseinthestairs 22
Walkerwithsensorstodetectobstaclesfortheelderlypeople 25
Addabaseandwheelforusing 26
Walkerlegsattachedtotheknees 27
Withmassagers 29
191
2 T.C 04
Possibilitytoadjustthelegswithsurfaces 8
11
Walkerhandlesstayparallelwithfloorandbodystay
balanced9
Usingporousmaterialsforup‐rightWalker 11
Forincreasemaneuverabilityandbalanceofuser,changethe
steeringwheelfromfronttoback(Trolley)13
Whenthearmcollectedusingahandleveronthehandle 15
Nestinglegsforrestingtime 16
Foldingplatformforsitting 17
Improvednavigationinthedarkenvironmentbyusing
materialsofphotocell20
Addleverlocksinthefrontarmandcontrolledbyahandlefor
stairs23
Usingflexiblepadsonthebottomoflegsdependsonground 25
Usingcompositematerials 28
3 B.S
Addsupportsfordisabledpeople 1
11
Addpedometer 2
FoldableWalker 4
EquippedWalkerwithanairbagtopreventfalls 6
Theabilitytotransformfromfourtotwolegs 8
Useoflightermaterials 13
Useheatandcoldinsulation 17
Insteadoffourlegsusingaflatplateforunevensurfaces 21
Usingreflectivematerial 25
Addfoldableseat 27
Walkerwithwheelinfrontlegsandfrictionbreaksforrear
legs29
3 T.C 04
AddGPSinordertoidentifyrightdirection 10
4
Usingphosphorusmaterial(luminous)inWalkerframeto
avoidcollisionswithvehiclesatnight16
Themodularcomponentsinsteadofintegratedcomponents
foreasyrepairthedamagedsection22
Foldingseatforsittinginanemergencycase 26
4 B.S
Addahandlebikeforeasyuse 3
17
EmbedaseatforWalker 6
Embedwheelsformorespeedandbrakehandle 7
Addlights,hornandbasket 8
Dualwheelsforclimbingstairs 9
FoldableWalker 12
Addsafetybelt 13
Changeabletoascooter 14
Changeabletoacane 15
RadioandLCD 16
Addsidemirrorstoavoidanaccidentwithamotorbikefrom
behind17
Usingslideheightadjustmentfordifferentpeople 19
192
Addfoldablesmalltable 20
DesignlikeTwinstrollers 23
Addtheumbrella 24
AddGPSforAlzheimer's 25
ElectricWalkerwithfootrestforstairs 30
4 T.C 04
Usingthermochromics’materials 6
10
Usingphotochromicmaterials 7
TheuseofshapememoryalloySMAtoimprovetheassembly
anddisassemblyWalker8
SmartWalkertodetectobstacles 10
Addacamerawithazoomin‐zoomoutcapabilityforelderly
peoplewithlowvision12
AddaWalkerwheelwithadjustablespeed 15
Walkertoclimbstairs(chainchangebike) 17
Forcrossingtheslipperysurfaces,usingspecialWalkerlegs 21
Apowersupplyandcontrolequipmentfordisabledpeople 25
Addamemoryforstoringinformationofpeoplewith
Alzheimer'sdisease30
5 B.S
InstallGPSandconnecttopoliceandhospital 3
5
Asapantsformechanicalreinforcementofmuscleswhile
walking6
SuchasSolomoncarpetbutflexibleandelectronic 16
Usesuchasextraequipmentlikebeds,wheelchairs,crutches,
fishingholder20
Suchasabackpacksthatreducetheweight 27
5 T.C 04
UsingelasticmaterialsforWalkerframe 9
5
Usinghigh‐strengthandlightweightmaterialsfor
Compensationweight15
UsingspringforWalkerlegsforincreasingbettermoving
forward18
Smartmaterialsforcoolingandheatinguser 20
Telescopicfrontandrearlegstoenablemaneuverupand
downonslopes25
6 B.S
FoldableWalker 3
9
Useoflightandunbreakablematerials 5
UsingantiactivateX‐RAYdevicematerials 7
UsingNon‐metallicmaterialforlegstoreduceweight 10
Addelectroniccircuitandthermalsensors 14
Adddataprocessorofheartbeat 17
AddGPSandnavigationfacilities 20
Improvethesystemofwheelsandengineinstallation 24
Walkerwithflexibilityfordifferentsurfaces 28
6 T.C 04
TheuseoflightweightmaterialstoreduceweightWalker 5
7Walkerwithtwoseparatelegstohelpknee 8
Walkerwithgelatinousmaterialthatdoesnotinjurewhen
userfall10
193
SmartWalkerwiththedrugprogram 14
Walkerwithresiliencematerialsforbetterfeelingofuser 20
Walkerlegswithspecialmaterialsforhousecleaning 25
Walkerwithfoldableandintegratedframe 28
7 B.S
Walker'shandlewithsensorstosenseenergyofuserto
preventtheuserfalling1
19
Walkerheightadjustablelegsfordifferentuser 2
Walkerwithasystem(anelectriclift)tomakelighterweight
feelduringliftingWalkertouser3
SmartWalkerwithautomaticsurfacedetectionforadjusting
thelevelofconnectionoflegstoeachsurfaces5
Walkerrobotwithflexiblelegsforclimbingstairs 8
Walkerwiththevitalsigns(bloodpressure,pulse,...) 10
Walkerwithawarningsystemtothosearoundtheuseratthe
timeoffallingorimbalance12
Foldingandassemblies’Walkerforeasytransport 13
WalkerwiththewarningandalerttheuserifWalkerstands
onthesurfaceisunsuitable15
Walkerwithlightingsystemfornightuseanddarkplaces 17
Walkerwiththeabilitytoadjustallaspectsoflegsandrodsto
adapttodifferentpeople(suchaschildrenandadults)18
Walkerwithaprotectiveumbrella(sun,rainandsnow) 20
Walkerwithabilitytoestimatethedistancetothetarget,for
example,acameraisinstalledonWalker22
WalkerequippedwithaGPStolocate 23
Walkerwithincreasingabilityofhandsforpeoplewithweak
hands25
Walkerwithheatingandcoolingsystems 26
Walkerequippedwithafoldingseatforthenecessaryuser
tiredness27
Walkerwithasystemthatwillmaintainbalanceduring
walking28
Walkerwithacollisionwarningsystemtobarrier 30
7 T.C 04
Useofnewmaterialsformorecomfortgriphandleandpreventslippingonthehandle
5
9
Withahandleintheseat(Walkerwithseat)thatpushuptheseattomoveandtohelpuserstand
6
AddaspecialhandletohelpgetupfromthegroundanduseaWalker
8
Ribbedwheeldesignforwinterandsummertopreventslippingondifferentsurfacessuchasmountainbike
10
Walkerframewithwirematerialstohelpbendingandfoldingduringwalkingandkeepstrength
15
AddAirbagsforthecollision 20
AddaT‐shapedbasetothefrontoftheWalker(tripodismorestableandeasytouse)
22
Usingpolymericmaterialsforlightnessandpreventrusting 25
Walkerwithplasticarmsandlegsandfrontframeonaccordionshape
28
194
7.4.7 Experiment II: Group C
Team Treatment Idea descriptions Time of generating ideas
(Out of 30 min) no. of Ideas
1 B.S
UsingsomewheelsintheformofaU‐
shapedattheendoflegs1
10
Walkerwithdetachableseat 5
Walkerforcyclingandwalkingatthesame
time7
Sensortoadjusttheheightandsize 10
FoldingsofasWalker 15
Walkerwithslidingsystemtoopenand
close18
Walkerwithroughendlegsforslippery
surfaces21
WalkerthatcanbeusedtoSportsactivities 24
Walkerwithholderintheback 27
Walkerwithsturdymaterialand
lightweight30
1 T.C 02
MultilevelWalkerfordifferentmodes 9
6
Walkerwithreplaceablelegsindifferent
surfaces14
Walkerwithtripod 17
Electrichandlesforliftingandstanding
patient22
Walkerwithdetachablehandlesforusing
asacane26
Walkerwithpuzzlesystemforchangesin
differentshapeofseating30
2 B.S
Puttingthecart 1
14
WalkerwithGPStofindpath 3
Walkerwithchangeableanglesaccording
tothebodyofpatient5
Addlightingequipment 7
Installtheelectricalgeneratoronthelegs
tosupplyelectricityduringwalking9
Automaticwashingsystem 11
Walkerwithtents 12
Walkerwithcustomizablehandlesto
armpit16
Connectiontothebodyofthepatient 19
Walkerwithhangingseattohelpthe
patientwithweakfeet21
Walkerwithscreentoseetheroute 24
195
FoldableWalkerasabackpack 26
Walkerwithfrontlegsfixedinfrontand
rearlegswithwheelsinUshape28
Walkerwithbumpertoavoidcollision 30
2 T.C 02
Walkerwithfootmassager 6
8
Walkerwithglassandwipers 7
WalkerwithHeatingandcoolingsystems 10
Walkerwiththeabilitytobecome
Motorcycles15
ThecombinationoftreadmillandWalker 17
Walkerwiththelegsself‐settingforthebus
ride20
UseofspringinthebodyofWalker 24
Walkerlightweightpolymer 27
3 B.S
Walkerstoclimbtheramp 3
11
Walkerwithhandlestabilitycontrol 5
FoldableWalkerforgettingonthecar(like
theblindpeople)8
Connecttotheupperbodyinsteadof
connectingtohandsandarmstoreducethe
strainonhandsandfatigue
10
Walkermadebycarbonfiber 13
GridandstyleWalker 17
Walkerwithbrakeandwheels 19
Walkerforwalkingonthesnow 23
Walkerforskiing 24
Equippedwithsafetybelts 28
Walkerwithmusicplayer 30
3 T.C 02
Walkerwithreplaceableveneer 9
7
Walkerwithassemblyanddisassembly
capability11
Walkerforthebeach 16
Walkerhandleswithcapabilitytousefor
fishingrod19
Walkerwalkingstick 22
Walkerwithhangingseatandfolding 25
Healthyreplaceablehandles 29
4 B.S
WheeledWalkertomoveeasier 1
8
Walkerpartiallyretractable 3
Walkerforschoolwithbackrestandopen
front8
Walkerself‐sizeforkids 10
Addseatforemergencies 13
Walkerwithinterchangeablebody
especiallyatthebaseandhandles16
Withlightsandhorn 21
196
Walkerwithabalancesystemduring
walking25
4 T.C 02
Walkerfortherapy 7
4Walkerforwalkingonthegrass 12
Patient'sbodyholderWalker 18
WearableWalker 24
5 B.S
Walkerwithaseat 2
18
Guidingthepatientswiththehandlesfrom
sides3
Drugwarningsystem 6
Withreflectiveandphosphoricframe 10
Theuseofnewmaterialssuchas
nanotechnologyandcompositestoreduce
weightandreduceenvironmentalissues
12
WithAirbag 15
Interchangeablebase 17
Maintainbalancewhiledoinghousehold
choreswithextrahandle19
ProtectiveWalkerfromwind,rainandsun 20
Withtelescopicbase 22
MotorizedWalkertoeasymove 24
Slidingsystemsforclosing 25
OpenfrontbaseWalker 26
Walkerfordisabledsports 27
Walkerstoclimbthestairs 27
Walkercombinationswheelchairfor
disabledpeople28
Addlightsformovingatnight 29
Attachingthesmallbasket 30
5 T.C 02
DesignaWalkertomoveinbothsittingand
standingposition10
8
WalkerBike 12
Walkerwithtallhandlestothearmpitslike
crutches15
Walkerwithsemi‐uprightpositionfor
moving19
WalkerDouble(withnurse) 21
WalkerchairsandFoldingsofas 23
WalkerwithHybridEngine 26
Walkerwithsensingcamera 29
6 B.S
Designforuseonstairs 1
14
Addwalkie‐talkiesshort‐range
communicationssystem2
Addfirstaidboxes 5
Walkerwiththreewheels 7
Walkerwithalarms(Voice,vibration) 10
197
Lightweightcompositebody 13
Walkerwithaspringybaseforuneven
surfaces17
Hands‐freeaudioandvideocommunication
onWalker19
Walkerwithpedometerandtimertohelp
treatmentprogram21
Connectingtothefeetinsteadofhands 24
Withaparttoaddseattorelax 25
Withanti‐theftsystem 26
Addaholderforshopping 28
Useheatandcoldinsulationmaterialsin
bodyframe30
6 T.C 02
Walkercalculatethenumberofstepsand
calories6
10
Walkerforcarryingheavyloads,suchas
lifts9
Walkerforusedinwatertherapy 11
Walkeropenedthefrontframetoaccess
thetable17
ConvertibleWalkertothescooter 20
Walkerwithelasticbodytogettheblowsof
theroad22
Walkerstairliftsautomatic 24
MultifunctionalWalker,crutches,
wheelchair,etc.26
Walkerwithattachedwheelsthegroundon
theimbalancedposition28
Walkerforwalkingandsimultaneously
rehabilitationcapability30
7 B.S
WheeledWalkerwithseat 2
9
FoldingWalker 3
Withcoolingsystem 5
Walkerfoldedwithfragmentationstructure 7
Addlocatingandpositioningsystemswith
GPS10
Withspecialseatrest 14
Walkerwithcontrolofvitalsystem 17
Addthecartandmobilephone 20
Addumbrellasorcanopies 24
7 T.C 02
Exoskeletons 8
5
Walkerwithmagneticcapabilityandgroundclearance
14
AppropriateWalkertouseforalllevelsandsurfaces
17
Walkerwithmemoryandrouting 21
Walkerwithanti‐theftalarmbyannouncingthepolicestation
26
198
7.4.8 Experiment II: Group D
Team Treatment Idea descriptions Time of generating ideas
(Out of 30 min)
no. of
Ideas
1 B.S
FoldableWalker 2
13
Theself‐assemblyanddisassemblyWalker 6
Withtelescopiclegs 8
Withdisposablecoatedhandles 11
Theabilitytoinstallindifferentplacesfor
differentpurposes13
Walkerforchildren 16
Walkerlikego‐kart 18
Walkerwithhelpedbarsfrombehindfor
children19
Withthecontrollerabovethechild 22
WiththetransmitteronaWalkertofindthe
child24
Walkerwithcompositematerial 25
Walkerwithfallprotection 28
Addairbagtoprotectpatient 30
1 T.C 03
AdjustableWalkermotionwiththepatient's
head5
9
Walkerforusingintheallsurfaces 8
Semi‐automaticWalkerwithasteeringwheel
insteadofaU‐shapedhandle9
Walkerwithsemi‐seatedpositionwithoneleg
andmovingonwheels13
Walkerwithfoldingfrontlegsonthestairsfor
goingupthestairs16
Walkerasaclothingwithminimumvisibility 19
Walkerwhichholdthepatientbykeepinghis
shoulder22
HandlesandleverstosteertheWalker 26
Walkerframeandwheelsmadebyhollow
materialforlightness28
2 B.S
Walkerwithlightseatpendant 3
8
CircularWalkerinsteadofsquareone 6
TriangularWalkerinsteadofsquareone 8
Addastudytable 11
Addanalarmtonotifythepatient'sneeds 14
Addasmallbaseholderforbalance 18
Walkerwithalonghandletoarmpit 23
Walkerforstairclimbing 27
199
2 T.C 03
Usingalevertowalkbyhandsforhelping
peoplewithweakfeet10
7
AdequateWalkertotheangleofgait 14
Walkerwithaflexiblebodyinmotion 17
Bodywithspringymaterial 21
ThebodymadefromNanoforcleaningand
lightness23
Intelligentbodytotheambientforadjusting
thetemperature26
Automaticadjustmentdimensionswith
physicalconditions28
3 B.S
Walkerwithaspecialframeforgettinghit 1
12
Walkerwithahandleonthesidetosupport
user3
AddReflective 4
Addlightsforusingatnight 6
Addfallwarningsystem 9
Addthealarmfortakingdrugs 13
AddFoldingchairs 16
Addabrakeandabasket 17
AddGPS 19
Addsnowchainsforwheels 23
Addawning 27
AdddifferentholeintheWalkerframeto
adjustbyuser30
3 T.C 03
WithbootsinsteadofWalkerlegsandgloves
insteadofWalkerhandles6
9
Walkervariableendlegsondifferentsurfaces 9
Addlateralhandlesforbalance 12
Addadditionalfoldablelegsforuserbalance 15
Semi‐automaticWalkertoguidetheelderly
peopletodestination18
Walkercapableoffloatingonwater 21
Walkerwithlifting 24
WalkerRobot 27
Walkerwithwheelslikearmytanks 30
4 B.S
Withthefoldabilityforhandleandlegs 2
13
Convertibletoseat 4
Convertibletobed 5
Walkerwithawnings 7
Rockingchairwithwheels 10
Walkerwithashoppingcart 11
Abilitytoaddsensorstodetectobstacles 14
Addhornandlights 16
Adddeviceformeasuringheartrate 18
AddPedometer 20
200
Addoxygen‐masksystemforemergencies 22
Walkerwithheightsensor 26
Walkerwithmedicalalarmsystem 29
4 T.C 03
Withtheabilitytobalancethebodyonuneven
surfaces9
7
Withtheabilitytobalancethebodyinthepool
andwatertreatment13
ElectricWalkerwithsolarcells 17
Walkermultiuser 19
Walkerself‐controlwithouttheneedfor
guidance21
Addaudioandvideosystems 25
Addingheatandcoldinsulationtohandles 30
5 B.S
Addreflectivelightsandhornandcart 1
9
Walkerfordifferentsizes 4
Addbackrest 7
Uselikescooters 10
Walkerwithhandleslikegloves 13
Fittothepatient'shandsandfeet 18
Self‐navigationWalker 22
Connecttothehealthcentersandhome 25
Witharemotecontrol 27
5 T.C 03
Addanantifreezesolutiontouseinicy
surfaces8
4
Automaticsystemaccordingtothesurfaces
(withorwithoutwheel)15
Withanelasticbody 20
Newbodydesignwithdifferentlegstobalance
patient26
6 B.S
Walkerformovingstairs 1
14
Addasmallboxforputtingdownthestuffof
patient2
Addthespeedcontrol 4
Balancesensortopreventfalling 6
Walkerwithwheelsandbrakeknob 9
Automaticelectricmotor 10
Dualwheelsforclimbingstairs 12
Guidingwithasteeringleverlikeanaircraft 15
WithGPS 17
Obstacledetectionsensor 19
Theabilitytobecomecrutches 23
Walkerwithhandlesanti‐sweat 25
Walkerwithspecificlightweightmaterial 27
Withreplacementpartsforchangingbyuser 30
6 T.C 03 Withergonomichandlesforbody 5
5Designdifferentforusingindifferentsurfaces 9
201
Walkerfortheelderlyandbalancethebody
fromabove16
Electricmotorsforliftingpatientsfrombeds
andchairs19
Forsettingsize,usethefingerprint 25
7 B.S
RadioandGPSforAlzheimer's 2
17
Designhandleslikebikeswithbrake 4
Withalarmsystem 6
Designfishnetframeandstrong 8
AssembledanddisassembledlikeIkeaproduct 10
Withtelescopicframe 13
Withfoldablelegs 14
Controlbodyfromthebackofpatient 16
AddaseatforWalker 19
Withheatingandcoolingappliances 20
Changeabletothescootersshape 23
MotorizedWalker 25
Limitingthespeedofthewheelswiththe
program26
Exoskeletons 27
Ergonomicdesignfortheplacementofthe
spine28
Withlonghandleforleaningonthearm 29
Walkerwithwebcamtocallhome 30
7 T.C 03
Walkerwithspringybody 5
10
Walkerwithsuspensionsystem 7
Walkerwithpedalforadjustingdimensions 9
Walkerwithsoundamplificationdevices 12
Walkerwithinsulationcoating 16
Walkerwithanti‐sliplegs 18
ThescooterWalkerusestowalkbyleaning
forwardandviceversa21
Walkerwiththealarminunbalancedsituation 24
Walkerself‐guidedautomatic 27
Walkerwithlevertosteerleftandright 30