10 Things You Don't Know About KTM

10 Things you didn’t know about KTM

[email protected] Solution Enablement Specialist

What is KTM?

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Presenter

Presentation Notes

Our KTM is not a motorbike from Austria

What is KTM?

Kofax’ Answer to

Document Drudgery

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What is KTM?

Kofax’ Intelligent Document

Recognition Solution/Toolkit/Platform

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The Golden Rule of KTM

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User productivity?

Automation?

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Automation should always be subjugated to the primary needs of customers of user productivity. Help a customer turn the discussion, requirements, criteria and metrics from accuracy metrics to productivity metrics of documents/person/day or transactions/hour.

Benefits of „User Productivity“

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Wholesaler opens its 17th store

Pan European Wholesaler Improvement invoices/person

/day Productivity Improvement

Manual processing without Kofax 800 After 3 months of “Accuracy“ effort by PS 1200 +50%

After 2 weeks of “User Productivity” effort by PS 2500 > 3 x

The Fallacy of OCR Accuracy

What OCR accuracy do you have?

What is the straight-through processing rate?

How much can we automate?

85% straight-through processing 23 fields → 99.29% field accuracy

6 chars/field → 99.89% character accuracy

What is the cost of the other 15%?

You will lose this deal against an OCR Provider because this deal is being fought over features and tech, and not business value

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Productivity vs Automation

Productivity Documents/person/day User focused Business value Optimizing core-

business processes Usability/comfort

8hrs/day Saving $€ Limit = ∞

Automation Accuracy Numbers technology focused Impossible to convert

to ROI Technology Diminishing returns Limit = 100%

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Presenter

Presentation Notes

Use these two lists to see what topics to speak about (on the left) and what topics to avoid (on the right)

Anyone can do KTM

Classify Separate Extract Folder

Validate Learning

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All you need is paper and highlighters

Classify Separate Extract Folder

Validate 11

“Doing” KTM by hand. Paper to Excel.

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Classic vs Quantum

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Newton Einstein

Schrödinger

Bohr

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Einstein was the “Last Classical Physicist” (quote from Bohr), who preferred to sit on the fence, acknowledging the new Quantum Physic, but being unwilling himself to embrace it like the next generation of Niels Bohr and Erwin Schrödinger. Bohr is considered to be one of the most important philosophical minds of the 20th century – because he was the tireless herald of Quantum Physics.

God doesn‘t play dice. Spooky Action at a Distance

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Einstein didn’t like Quantum Entanglement, which he called “Spooky Action at a Distance” Spukhafte Fernwirkung. Today this is an accepted fact and the basis of existing quantum cryptography, quantum teleportation (current record - 143 km with light and 21 meters with matter, http://en.wikipedia.org/wiki/Quantum_teleportation) and the qubits of quantum computing. Einstein didn’t like the randomness of Quantum Physics, prefering the order of the Newtonian world. Einstein didn’t like the breakdown of Causality (reversibility of cause and effect) that Quantum Physics took for granted.

Programmable vs Learning Software

Deterministic, logic, rules,

Laws, order

Probabilistic, data-driven,

Machine learning

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Analytics

Presenter

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Most software the Kofax and Partners are used to selling and developing is deterministic programmable software. Altavista and Yahoo had their day back in the 1990s with rules-based internet search. Google can along in 1998 with a data-driven approach and a simple idea (rank web pages based on references from other pages). This only required them to index the entire internet. History has shown that this was the right approach. Apple Siri is based on many thousands of hours of spoken commands, and it getting better is it hears more. Microsoft Xbox Kinect sends color-code images of the game player 60 times a second. This simple solution learns from examples of people doing actions. Most Kofax products are deterministic. It is really on KTM and VRS which are different (also OCR engines). They do not give guaranteed predicted results – all results need to be handled with probabilities.

Transition from Determinism to Data-driven/Fuzzy/Quantum

Physics 1890 – 1920 (Classical to Quantum)

Mathematics 1931 (a system cannot demonstrate its own consistency, Kurt Gödel‘s incompleteness axiom)

Computer Science 1970 – 1990 (machine learning, neural networks, speech recognition, machine translation)

Business 2000 – 2020 (Big data, analytics, learning systems)

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This transition we are seeing today in IT happened 100 years ago in Physics. We are slow in catching up, but the next decade will see a massive growth in machine learning.

EU’s Human Brain Project & USA’s BRAIN Initiative in 2013

10 billion€ from the EU over 10 years to build a human brain simulator to push forward brain research and test brain diseases.

100M$ from US government to revolutionize our understanding of the human mind and uncover new ways to treat, prevent, and cure brain disorders

“You don’t program it, it learns”

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Machine Learning is getting a massive funding booth from the EU, US government and Darpa in 2013. http://www.humanbrainproject.eu/�http://www.whitehouse.gov/blog/2013/04/02/brain-initiative-challenges-researchers-unlock-mysteries-human-mind http://www.darpa.mil/Our_Work/I2O/Programs/Probabilistic_Programming_for_Advanced_Machine_Learning_(PPAML).aspx

Don’t program KTM, teach it

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Robots are programmed. Robots need laws, rules, logic and programs. Dogs are trained. They need to see, mimic and learn. KTM is your puppy that mimics your customer.

Robodog will bite you

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This is what happens when you try to program a dog!

“Doing” KTM by hand. Paper to Excel.

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Here is the food that you feed to your KTM puppy. The rows in the Excel sheet are “dog food”, where it learns to mimic the human behaviour. That is why this Excel sheet needs to be filled out by your customer’s document experts

Field Analysis

File Class Capa Nro_DOC NOTA CPFC EMIS VENC EMIT

1.tif CapaLote 123987-2012

2.tif Duplicata 123987-2012 852147-A 60.000,00 07.248.659/0001-03 15/02/2013 15/02/2013 Y

3.tif Duplicata 123987-2012 1489/1 15.963,57 17.155.342/0003-45 01/12/2011 21/01/2012 Y

4.tif Duplicata 123987-2012 3112230U 2.195,30 86.438.280/0001-30 22/12/2011 19/01/2012 Y

5.tif Duplicata 123987-2012 4012391 81.045,00 10.932.276/0001-61 14/12/2011 23/01/2012 Y

6.tif Duplicata 123987-2012 3065357 F 1009,11 80.089.964/0001-97 27/10/2011 21/09/2012 Y

7.tif Nota Fiscal 123987-2012 65357 7.981,39 80.089.964/0001-97

8.tif Nota Fiscal 123987-2012 194.580 48.741,92 76.777.556/0001-50

9.tif Nota Fiscal 123987-2012 000.022.875 32.650,74 56.990.526/0001-10

10.tif Nota Fiscal 123987-2012 112230 2.195,30 86.438.280/0001-30

11.tif Nota Fiscal 123987-2012 194.562 7.454,92 03.364.370/0001-46

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Presenter

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Here is an example of “dog food”, “training data” which can be learned/mimicked

Overview of fields to extract – What a customer typically gives field format number of

characters

Document type Validation with Loss Payment Rates Budget Invoice multi-

invoice

Reference number numeric 6-7

x New ones have no ref-nr!

x x

Possilby more than one per

doc

x

x Multiple

Ref-Nr per Document

CIP database

Debtor Last Name Text unlimited x x x x

only for validation, if existing


CIP database in combination with Ref-Nr

Debtor First Name Text unlimited x x x x




Debtor Street Text unlimited x x x x CIP database in combination with Ref-Nr

Debtor House number numeric unlimited x x x x


Debtor Address2 Text unlimited x x x x


Debtor PostCode numeric 4 Swiss only Swiss only x Swiss only




Debtor City Text unlimited x x x x only for

validation, if existing



Debtor Telephone numeric x

CIP database in combination with Ref-Nr. If there is no other number in database, then manual validation.

+42 more rows

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Presenter

Presentation Notes

Here is a typical database column analysis. This is useful for robotic software like SQL, webservices, .Net, etc This is not really useful to learning software. It doesn’t want rules, it wants examples.

The most successful KTM projects focus on the user.

Make your users happy and content.

KTM is their workplace all day every day.

It is the place of encounter and collaboration between human and robot.

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Presenter

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http://terminator.wikia.com/wiki/File:Terminator.jpg http://carlibux.blogspot.co.uk/2010/12/human-vs-robot.html

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Human – Computer Interaction

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http://carlibux.blogspot.co.uk/2010/12/human-vs-robot.html The human always remains in control. When a customer says they want “80% automation” they are saying, “She should leave the computer, the robot should sit at the keyboard – she can come back for the remaining 20%”. This is not a good approach. A better, user-focused approach would say, “She remains at the desk all the time with her hands on the keyboard. He (KTM) is advising her and making her 5 times more productive”. Perfect collaboration

Validation Experience

Result Type Correct Valid User Experience

True Positive Perfect! Touchless processing.

Automation.

False Negative User must press ENTER.

True Negative User Corrects/Types data.

False Positive Loss of trust. Drop of productivity.

Bad data leaves Kofax.

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KTM Customer Query

This [deal] was sold on the strength of KTM being able to classify and extract data from items received…. This was then used to calculate the ‘RETURN on INVESTMENT’(ROI) which enabled them to purchase the solution. The ROI was calculated with the reasonable estimate of 65% automated processing. I would expect that we should realistically see 80% to 90% automated processing of inbound items. That said, someone communicated to the client that the best they were going to see was 15% to 20% automated processing. This obviously sent the client reeling that they weren’t going to see anything close to their expected ROI and would potentially damage their business and not see the benefits from the system as expected.

Presenter

Presentation Notes

A real customer email to Kofax in 2013. The wrong thinking is encapsulated in the following sentence The ROI was calculated with the reasonable estimate of 65% automated processing. Better would be The ROI was calculated with the reasonable estimate of improving user productivity by 150%”

So what is a reasonable

expectation of KTM?

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KTM should be able to significantly improve user productivity (perhaps 1.5-10x)

KTM will be able to extract perfectly information from readable and known documents.

KTM should be able to learn how to understand readable & unknown documents.

KTM’s value is in improving documents/person/day

Transactions/second (TPS) You will have access to near realtime performance graphs that

can optimize user experience and data throughput.

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Reasonable Customer Expectations

Benchmark Before

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Benchmark During

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Benchmark After

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US invoices – known vendors

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Goals of every KTM Project

1. Human Productivity 2. Eliminate False Positives

bad data leaving Kofax 3. Reduce False Negatives

user pressing ENTER 4. Few True Negatives

OCR Accuracy, Database problems & learning

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Fuzziness is your friend

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Kofax brings messy data from the real world into the clear digital world

Fuzziness

Fuzziness is not Random

Unpredictable

Unreliable

Complex

Fuzziness is Simple

Learning

Flexible

Tolerant

Fuzzy Software you love Google Autocomplete

Spell checkers

Grammar checkers

Spam filter

“Users who read this book…”

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Top Names US Census 2005

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Top US Names 2005

1 2 3 4 5 6 7 8 9 10

Male Female Surname 1 JAMES MARY SMITH 2 JOHN PATRICIA JOHNSON 3 ROBERT LINDA WILLIAMS 4 MICHAEL BARBARA JONES 5 WILLIAM ELIZABETH BROWN 6 DAVID JENNIFER DAVIS 7 RICHARD MARIA MILLER 8 CHARLES SUSAN WILSON 9 JOSEPH MARGARET MOORE 10 THOMAS DOROTHY TAYLOR

Vorname 1 Peter 0.80% 2 Daniel 0.80% 3 Hans 0.67% 4 Christian 0.60% 5 Thomas 0.53% 6 Walter 0.52% 7 Michel 0.49% 8 Martin 0.46% 9 René 0.45%

10 Markus 0.45% 11 Josef 0.44% 12 Patrick 0.43%

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Swiss Forenames

13 André 0.42% 14 Bruno 0.41% 15 Philippe 0.40% 16 Maria 0.40% 17 Andreas 0.40% 18 Roland 0.39% 19 Paul 0.39% 20 Marcel 0.39% 21 Werner 0.37% 22 Antonio 0.36% 23 Pierre 0.35% 24 Urs 0.34% 25 Elisabeth 0.34%

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Uses

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37,691,912 citizens

KTM is the heart of Kofax.

Touchless Processing

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KTM Search&Match

Server

Search & Match Server

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SQL Database Database

Center Firewall

CSV File

Database Fuzzy Index

PDF

PDF vs TIFF PDF/A is a Standard ISO 19005-1:2005, ISO 19005-2:2011 and future

safe

Thousands of Incompatible File Formats

Baseline TIFF Readers don‘t have to be able to read Group 4.

Any computer can read a pdf, and Chrome can „natively“.

Tiff viewers need to be installed.

PDF has layers, TIFF does not.

Searchable

PDF compresses better

TIFFs can be manipulate

PDFs have certificates, encryption, DRM, etc..

PDF High Compression – Should be in every project

602 kb

76 kb (87%)

553 kb

B&W PDF

117 kb

47 kb

114 kb

Atalasoft for PDF generation