SUI GENERIS PATENT REGIME FOR AI RELATED INVENTIONS...Technology of intelligent and autonomous machines, popularly captured in term Artificial Intelligence (AI), is a new buzzword

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International Journal of Intellectual Property Rights (IJIPR)

Volume 11, Issue 1, January – June 2020, pp. 12–25, Article ID: IJIPR_11_01_002

Available online at http://iaeme.com/IJIPR/issues.asp?JType=IJIPR&VType=11&IType=1

ISSN Print: 0976-6529 and ISSN Online: 0976-6537

© IAEME Publication

SUI GENERIS PATENT REGIME FOR AI

RELATED INVENTIONS

Purwa Rathi

Senior Legal Counsel at Cognizant Technology Solutions, India

ABSTRACT

AI technology, one of the primary contributors of 4th

Industrial Revolution, has

witnessed an unprecedented growth and extraordinary progress in last decade. The

frenetic pace with which the technology has evolved left different sections of society

with mixed emotions. As the scientific community is rejoicing at marvels they have

gifted society with, the ethics community is implicating traditional qualms over moral

intuition & absolutism, while the law makers are both perplexed and exhilarated

understanding legal complications accompanying it.

Amongst array of legal challenges, rubric of patent law protecting AI solutions is

hugely confounding patent law makers. Some of intriguing questions pertain to

inventorship/ownership rights, patenting process, disclosure requirements of

inventions enabled by or borne out of AI machines. Present paper explores above

legal confines for both human and/or non-human contributor, and categorically

attempts to address the hotly contested issue of patent law adjustments via adaptation

through a sui generis framework.

Key words: Patents, Patentability, Indian Patent System, Innovators statistics

Cite this Article: Purwa Rathi, SUI Generis Patent Regime for AI Related Inventions.

International Journal of Intellectual Property Rights, 11(1), 2020, pp. 12–25.

http://iaeme.com/IJIPR/issues.asp?JType=IJIPR&VType=11&IType=1

1. INTRODUCTION

Technology of intelligent and autonomous machines, popularly captured in term Artificial

Intelligence (AI), is a new buzzword that has power of changing world reality of today.

Coined by Professor John McCarthy at a Dartmouth conference in 1956, AI is emerging as a

key driver of the „fourth industrial revolution‟[1]. Understandably, the term describes the

capacity of a computer to perform tasks commonly associated with human beings[2]. As the

machines get empowered in their ability to autonomously retrieve relevant information,

identify subtle patterns and relationships between various data segments and make intelligent

predictions or recommendations, relevant stakeholders are becoming increasingly skeptical

regarding motley of legal challenges concomitant with advancing technology.

Amidst anticipated disruption of numerous legal frameworks, impact on patent law

appears immensely pervasive and significant than any of previous technological changes[3].

Clearly, the swelling wave of innovations enabled by or borne out of AI machines is on a

collision course with basic tenets of patent laws, primary debate being centered on a

SUI Generis Patent Regime for AI Related Inventions


proposition whether or not to accord legal personhood status upon electronic entities. From

here, ensues a series of contentious topics such as patentability standards, inventorship or

ownership issues, accountability or liability issues, risk, infringement etc. as AI systems can

neither own any property nor can partake in any employment relationship[4]. To study the

impact of these issues, WIPO recently invited comments from interested communities to

various pertinent questions[5] related to above listed topics.

Detailed analysis of the suggestions posted in response to discussion points by individuals

and organizations from different member countries[6] posits a blatant truth that the existing

patent regime is not well suited to competently manage AI related inventions. While many

have advocated absolute negation of the concept of machine inventorship for such patent

applications are likely to result in unnecessary deviation from the basic rationale underpinning

the patent regime; many contended that these works shall fall in public domain[7], or

conveniently clubbed with existing software patent regime[8]. Other experts have contested

continuation of inapplicable laws and persuasively established dire need for a new statutory

framework - a sui generis system specifically tailored for addressing doctrinal challenges

related to AI technology.

The objective of present paper is to critically analyze different forms of inventions

enabled by or borne out of a human or non-human agent or a combination thereof, and

develop a framework as a probable legal solution to secure their patent interests. Part I

identifies and classifies human/non-human related AI innovations into two broad categories:

a) AI Enabled Inventions (AEIs) that either embody an advance in field of AI or apply AI to

other field, and b) AI Borne Inventions (ABIs) that are produced by AI. Part II proposes a sui

generis framework predicated upon fundamental justifications for patent rights to administer

and protect AEIs and ABIs with minimal legislative overhaul.

2. INVENTORSHIP AND OWNERSHIP PROSPECTS FOR AI

RELATED INVENTIONS

2.1. Nature & Dynamics of AI Machines

Intelligent machines of today do not exclusively rely on linear set of programming

instructions or number-crunching but also “thinking” and capacity to reason for itself[9].

Recent technologies of neural networks, genetic programming or evolutionary engineering are

some example of creative and self-replicating techniques for independent problem-solving. In

absence of any uniform definition, AI can be understood as completely autonomous machines

with cognitive features capable of learning from input data, experience and interaction,

surpassing degree of intelligence once held to be characteristic exclusive of human mind[10].

These are highly distinguished from traditional human guided computer hardware

programmed to perform a particular task[11].

In present context, no General human like intelligent AI machines[12] fully capable of

independent judgment, reasoning, agency, creativity or decision making without any human

intervention, has been objectively or evidently known. In turn, most popularly known AI

machines belong to genre of „Narrow AI‟ that provides solutions to a limited set of narrowly

defined problems arrived at by varying degree of human input/interaction[13] Examples

include advancements made in fields like autonomous vehicles, predictive analytics, speech

recognition, computer vision or image recognition, customer service bots, spam filters,

recommendation systems and so on. Plainly, AI machines exhibiting such discernment as that

of a human agent is yet an unfulfilled and unrealized technology[14]. Although computers are

not yet capable of completely autonomous invention, it could still be on the horizon as AI

undergoes fast-paced innovation enabled by increased availability of improved computational

Purwa Rathi


resources, high capacity storage, advances in Big Data and advent of special hardware with

specialized chips capable of supplying enormous computational power[15].

In face of rapid technological changes and accelerated innovation activity, focus on

patenting trends and their societal effect becomes paramount. A convoluted gap between the

racing AI technology and slow-chasing legal stature already exists and has grown big enough

to necessitate radical changes in the patent system. Optimistically speaking, knowing the

challenges of tomorrow very clearly today, is rather a generous relaxation for law makers to

raise legal guards and adopt a well-defined and strictly enforceable framework to safeguard

interests of next in order AI machines.

2.2. What Makes Patenting for AEIs Different from CIIs

Necessity of a new patenting regime for AI related inventions appears no more an academic

exercise, but an immediate, fundamental problem loudly banging on patent doors. Today,

account needs to be taken of existing patent regime‟s capacity in reasonably handling changed

circumstances of boundless advancement in machine intelligence akin to existing computer

implemented inventions (CIIs). Well, amongst all advances seen in realm of computer

sciences, none has been so far capable of demonstrating intelligence that can challenge, limit

or question extent of human involvement. AI machines, in wide contrast to computer

programs have remarkable quality of extracting patterns, correlations within dataset to

conclude a meaningful output, with or without any human supervision[16]. One famous

example is Stephen Thaler‟s „Creativity Machine‟, which like a human brain, is capable of

generating novel patterns of information rather than simply associating patterns, and it is

capable of adapting to new scenarios without additional human input[17]

For computer-implemented inventions (CIIs), even the specialized computer hardware

„configured for‟ yielding novel and inventive claims simply implements programmer‟s

algorithmic instructions. A human agent has always been a moderator, and machine never

assumed to approximate mental capabilities of human as it is guided at each step to obtain a

static and specific output defined by its human operator. However, groundbreaking

innovations achieved using AI techniques have clearly established that machine can be no

more seen as a tool subservient to human commands and following digital orders. If fed with

suitable inputs, they can learn how to perform tasks, prove mathematical algorithms and find

solutions to a task independent of direct human supervision. Further, machines have even

surpass human blind spot in achieving increased productivity and efficiency at decreased cost

of innovation[18], leading to increased complexity in dealing with patentability issues of

inventions enabled by borne out of AI machines. Another critical aspect that marks a striking

contrast between AEIs and CIIs is the nature of claims drawing the boundaries of these

inventions-while static for CIIs, claim scope is dynamically varying for AEIs. Hence, it is

convenient to decide terms of grant well in advance for CIIs; but for AEIs conclusively

finalizing boundaries of invention is unsettling as there may be outputs which are foreseeable,

but cannot be promised for reasons of uncertainty.

This discussion is necessary as the inventions enabled by or borne out of AI machines

cannot be contained within legal brackets of conventional CIIs where humans are solely

awarded as the true and first inventor, and applications filed with machine as inventors are

outrightly rejected (e.g. Dabus)[19]. Speedy adoption of these technologies have the potential

to impact patent system on a scale that it is not currently equipped to accommodate. A

rethinking of traditional patent tools is definitely required[20]. Unless cured of its current

impotence, patent law may slide towards a detrimental conflation of otherwise distinctive

“human-dominant-machine” and “machine-dominant-human” continuum, thus failing

advancement of purpose underlying innovation incentivization.



2.3. Determining and Defining AEIs and ABIs

This section draws a clear distinction between AI enabled inventions (AEIs) and AI borne

inventions (ABIs), aiming to provide perspective on why a completely independent patenting

solution is necessitated for different types of learning for AI machines. Notably, machines

have learned to recursively self-replicate beyond human comprehension, by way of fetching

expansive volumes of datasets, performing algorithmic processes on its own, and even

outputting smarter and more utilitarian results than previously known models[21]. Machines

can demonstrate intelligence to the extent of improvising, autonomously, final output up to

varying degree of sophistication.

Most AI machines are trained by labeling and categorization of underlying data,

commonly known as „supervised learning‟[22]. Such works are „enabled‟ by AI techniques,

which are employed in setting a desired output to a particular problem and then fitting a

supervised learning component into a bigger system. Primarily, the steps of selecting features

to represent data, transforming data, choosing an appropriate algorithm , tuning of parameters,

and finally assessing quality of resulting model via a feedback mechanism is not completely

deprived of human dominion as virtually all steps contain some modicum of human activity

or creativity[23].

Human intervention is manifested in various forms as they invest higher-order cognitive

skills such as reasoning, comprehension, meta-cognition, or contextual perception of abstract

concepts in selection and curation of input data, configuration of training model, defining

(technical) problem statement or improving target performance metrics[24]. Results are then

examined by domain experts or practitioners to obtain desired behaviors qualifiable as

commercially valuable technical output. Having fruitfully contributed to the inventive

concept, not insignificant in quality when measured against the dimension of full invention,

the human agent meaningfully proves playing of a measurable role as a „co-contributor‟ or

„joint inventor‟[25]of derivable AI enabled invention (AEI).

On the contrary, in an unsupervised learning mode, the machine learns patterns within

input and does not require any human feedback or labeling for discovering structure of data or

detecting outliers. Theoretically, an unsupervised system can achieve “artificial general

intelligence”[26]. Here, the machine learns the way human learns-„on its own‟. In the process

of uncovering patterns, the machine may exhibit inventive skills in performing exploratory

analysis or dimensionality reduction in given data. Evidently, human has a very limited role in

the inventive play of generating these better trained models. So, this output remains entirely

„machine borne‟, and final product discretely an AI borne invention (ABI).

Certainly, such intelligent machines deserves due recognition as they significantly

expanded the range of things that a human can discover. It will be against the moral fabric of

patent system to acknowledge non-contributing human agent as a joint inventor, whose role

has merely been managerial, administrative or financial. Consequentially, for ABIs, human

contribution will always remain lowly visible as most of computing effort along with

intellectual contribution is passed onto AI machine.

Concluding from above, it will be unfair to over-reward machines (for AEIs) or humans

(for ABIs), for conceptions they never contributed to substantially, when examined in

isolation[27]. Also, it will be in contravention to fundamental principle of attribution of

inventorship to true inventor, which at least in case of AEIs and ABIs is rarely a product of

human or non-human agent alone[28]. Thus, an optimal balance over impersonal realities of

inventorship may only be struck by acknowledging de-facto contribution of a sensible

combination of human intellectual effort and unsupervised machine‟s effectiveness in

improving or optimizing system performance relative to some objective function[29].

Undeniably, this rightful acknowledgment of true inventors creates an absurd situation with

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lurking issues of ownership, accountability, infringement risks, or liability, which are

presently taken charge of by human agents all alone.

2.4. Inventorship and Ownership Issues of AEIs & ABIs

In light of ratio of human-to-machine contribution to inventive processes progressively

shifting in favor of machine, a more rationale and justified conceptual model of patenting

AEIs & ABIs is suggested[30]. However, for convenience sake, many have advocated

complete eradication of concept of crediting machines as one of the inventors, or alternately

clubbing it with CII patenting regime. In addition, other mystifying scenarios are also being

considered when such AEIs and ABIs are chosen to be protected under trade secrets or

through extensive nondisclosure agreements as a safer and independent course of action.

Clearly, this is not a mandate for a well-functioning, robust patent framework, which has

earnestly evolved over many years to uphold legitimate interests of inventors within their

proper bounds.

As discussed previously, by virtue of their inherent abilities, AI machines may

autonomously replicate. During such replication, some forms of “not-so intelligent” machine-

dominant-human systems may even replicate the bias, unfairness and discrimination in data

on which they feed. Other limitations include overgeneralizations in pattern detection,

reduced accuracy resulting from incomplete data sets, and inherent limitations surrounding

the use of existing data to anticipate or predict future novel legal and ethical issues[31]. In

these circumstances, intellectual and meaningful domination of human agent over such not-

so-intelligent non-human agents becomes inevitable. Tying AI‟s action to a human agent,

remains as only viable solution to fill this accountability gap because- first, our legal system is

built on a fundamental assumption that penalties and remedies can only be levelled against

humans; and second, we cannot punish, imprison, or impose fines on AI machines whether it

has legal personhood or not[32].

So, how do we intend to address the most controversial inventorship/ownership issue for

AEIs & ABIs[33]? Who‟s accountable – developer, manufacturer, operator, owner, user or

machine itself. Can the co-inventors be co-owners as well? European Parliament resolution

of 16 February 2017 with recommendations to the Commission on Civil Law Rules on

Robotics declared that accountability and liability of AI machine per se for damage done to

third party certainly makes no sense[34]. So far, it is deliberated and discussed over various

forums that determining liability of a non-human agent seems to be an impracticable solution

today[35]. Logically, a human agent who conceptualized the machine and had been a co-

inventor in its predictable outcomes should be the one bearing responsibility of infringing or

damaging acts alone, simply because machines cannot.

Along with benefits of inventorship, risks associated with its ownership unconditionally

ensue. How to make human inventors fully accountable for collaborative endeavors without

inadvertently impacting them of wrongs they never intended machine to perform? What about

acknowledging machines and humans as co-inventors while vesting ownership entirely upon

the human agents, simply endorsing high level principles of patenting regime[36].

Apparently, concepts of inventorship and ownership may not be completely entwined; for it

seems explicable to adopt a unique approach that is theoretically sound and practically

workable in addressing inventorship/ownership issues of AEIs and ABIs. Next section of this

article presents a sui-generis framework as this unique approach.



3. SUI GENERIS PATENTING FRAMEWORK

First, this part will briefly review the conceptual background of patent laws as applicable to

AI related disclosures, and then examine the proposed framework particularly in context of

disclosures required for AEIs and ABIs, which have been a cornerstone of patent policy[37].

Some reforms in patenting process and other administrative procedures are suggested for

quick adoption and conformance. Once established, the proposed framework after being run

through a number of simulations may be further examined for its faults and revised on its

workability.

3.1. Legal Requirements & Disclosure Justification

Patents are awarded as a quid pro quo for disclosing the invention all across the globe[38].

Disclosure theory centrally focuses on inventor receiving exclusive patent rights in exchange

for fully disclosing the invention to society, rather than keeping the invention secretive.

Recent America Invents Act reads:

“The specification shall contain a written description of the invention, and of the manner

and process of making and using it, in such full, clear, concise, and exact terms as to enable

any person skilled in the art to which it pertains, or with which it is most nearly connected, to

make and use the same, and shall set forth the best mode contemplated by the inventor or joint

inventor of carrying out the invention[39]”.

As explained, detailed submission of „useful technical information‟ in complete patent

specification is quintessential for receiving substantive patent rights as “the test for

sufficiency is whether the disclosure of the application relied upon reasonably conveys to

those skilled in the art that the inventor had possession of the claimed subject matter as of the

filing date.”[40] For AEIs, it is important to verify how humans are involved in different

aspects of its conceptualization and constructive reduction to practice[41].

Lately, some aberrations are observed in making true admissions for AI related patent

specifications. Though, 35 U.S.C. Section 103 states: “Patentability shall not be negated by

the manner in which the invention was made”, AI machines may be sometimes deployed to

invent en masse thousands of alternative patent applications or defensive publications merely

by linguistic manipulation[42]. This form of non-inventive claiming can rattle the current

patent landscape especially when it comes to identifying true and onerous machine

inventing[43].

Only relief comes from the fact that such claim language to serve as a new, inventive and

useful disclosure or to play as an analogous prior art may have to be in a form of printed

publication, be publicly accessible and most importantly satisfactorily enabling to render a

disclosure patent eligible or other following invention invalid[44]. Evidently, these

mechanically generated claims will not be adequately supported by an appropriate written

detailed description or any other background information, and hence the burden will always

remain on the patent office to determine ex post facto whether the disclosure qualifies for an

eligible patent grant or if such claims floated as a prior art disclosure is disclosed in sufficient

detail to be invalidating. Importantly, seldom are the chances that these machine generated

random claims obtained by manipulating phrases overcome obviousness rejections. These

factors should remind us that while admitting AI applications, the patent offices must examine

them through disclosure and explainability lens to assure that unwieldy thicket of technical

information is transformable to a full inventive repository[45].

Purwa Rathi


3.2. What Constitutes Sufficient Disclosure for AEIs

One of the major hassles towards accepting AEIs as patent worthy is based on a presumption

that these patent applications are incapable of properly and fully disclosing technical

constructs, for larger part of invention building happens within deep layers of intelligent

machine, not exactly known to any human. Therefore, whatever is submitted in the name of

complete disclosure will always be deficit of pertinent information that makes the invention

reproducible.

However, there still exists an invaluable portion of disclosure that has merits owing to

human contribution of intellectual nature that goes beyond the provision of a mere abstract

idea, as discussed earlier. Important highlight of human contribution begins right from

providing insights on training data used for building training models (AKA “pre-trained

model,” “learned model,” etc.) to defining weighted parameters and determining

implementation detailed of algorithm to obtain a trained model.

So, let‟s explore in which all ways sufficiently detailed disclosure allows a reproduction

of the intended technical solution[46]. It will also help in gaining an ancillary understanding-

if the disclosure around machine contribution can stay a bit compromised, and yet fulfil

foundational requirements of patent obtainment process. So, contributions and disclosures by

relevant stakeholders, especially data scientist and programmers, in invention building

process requires consideration at granular level before awarding inventorship or other moral

rights in a patent application[47].

Data Scientist: Since data is a primary feed or raw material for an AI algorithm to function

and produce an actionable output, role of data scientist becomes eminent. Their valuable

contribution, therefore, needs a critical evaluation. In case of intelligent data mining, a data

scientist is primarily tasked with formalizing of technical problem, curation of

structured/unstructured data that eventually assists AI scientist in selecting the fundamental

blocks- methods, algorithms, architecture, NN topology, etc. to be used[48]. The real-world

data is messy and often needs to be normalized, transformed, have outliers removed, or

otherwise processed so that the AI model can produce useful, concrete, and tangible results. In

order to do so, the data scientist can either use known techniques from a library or software

tool or develop proprietary algorithms that may be adapted to the context of technical

problem, such as designing specific classification algorithms. Right from input data

preparation and its quality ranking[49]– how is data gathered, pre-processed, handled, or

parsed upon use by the AI model constitutes measurable parameters for generating a useful

invention.

Under such situations, where the data scientist employs inventive techniques to prepare

quality data of particular relevance, provides guidance to AI machine to uniquely contribute

towards finally commercially valuable output in a non-obvious way, then he shall share

titlehood of such invention. On the contrary, if a data scientist merely collaborates with an

expert and performs an obvious step of creating training data set under directions of such

expert, then it is a mere administrative activity or workshop variation of what was already

known. Hence, as is prescribed in patent law, contribution by way of non-technical factors

will not confer any inventorship/ownership in patent rights.

Programmer/Developer: Next, the developer or programmer selects a set of mathematical

models or writes an initial algorithm to process curated data and build the training model. A

trained model is an algorithm based upon a mathematical function that generates optimal

output based on the learned patterns in the training data. Determination of optimal

architecture before the training process relies much on heuristic* methods and human know-

how[50]. During the training process, training data is fed into the model, based on which the

training algorithm optimizes trainable parameters to minimize loss function. Here too, choice



of particular training methods requires technical know-how for which it relies on certain

heuristic methods*- an approach to problem-solving relying on experience and intuition rather

than a pure scientific methodology. Heuristic methods are often used due to the lack of

sufficient computing power or the absence of exact methods for the solving of certain

problems. Role and contribution of programmer therefore inarguably remains noteworthy, and

qualifiable for patent inventorship.

Post creation of trained model, the machine can make predictions and recommendations,

and also continue improving its end results with self-training and learning. These details-

everything from mapping of input data to the model, set of mathematical constructs, training

process to obtain the training model and validation methodologies are important inclusions for

disclosure of an AEI[51]. How the training data is collected, data mapped into “features” (the

actual inputs of the model), input data pre-processed for feature extraction (if any), or model

being trained, type of data or features provided to the trained model, or model output post-

processed or interpreted are a set of important questions, the answers to which the examiner

and those interested in field will be tempted to look for in such patent applications.[52]

A marked distinction should be established between the direct output of a model and the

potential practical application(s) achievable post processing of intermediary output, if any

claim lists so. For example, in some cases the raw output of a model has to be transformed,

normalized, or run through another algorithm to provide useful output data. In others, the

output of one model may be used (with or without intermediate processing) as input to

another model, say for example, a particular layer of a neural network encoding a semantic

meaning of the input. Such modifications and possible end results obtainable from these

modifications need to be vividly and sufficiently disclosed.

Similarly, if a deep neural network is generated as trained model structure that has

artificial neurons organized in multiple layers to process input data with multiple levels of

abstraction[53], then the model structure along with specific non-generic features (e.g., a

neural network with non-conventional number of nodes at given layers, multiple hidden

layers, etc.) and mapping of input data to categorical, commercially valuable output to

generate labels on future inputs are crucial details that will be expected from such disclosures.

Further, other significant details such as mode of implementing training parameters, training

algorithm (e.g. regularizers, tree size, learning rates), hyper parameters, input variables,

optimization variables, training data sets, validation data sets or number of layers utilized to

derive potentially meaningful and useful output, and other such details requires a detailed

discussion in patent draft[54]. Network‟s detection of fine features in input data, working of

multiple neural networks in parallel or in tandem, application of weighting function –all of

these are fundamental aspects for a neural network tool, and hence all details related to even

setting of weighting parameters, teasing out subtle proxies or patterns within data or finding

differences in input data are other examples indicative of extent of disclosure warranted[55].

Other seemingly important disclosure includes type of algorithm, type of training methods

used to develop algorithms, type of training data, period of training, optimization of outputs,

and other such extensive implementation details etc. Even if models appear „intelligent‟, they

generate output by merely relying on probability calculations. They are not autonomous (i.e.,

they do not „reason‟ on their own) and need to be fine-tuned by machine learning experts.

While it can be challenging to explain why an AI algorithm made a particular decision or took

a specific action (due to the black box nature of such algorithms once they are fully trained), it

is generally not difficult to describe the structure of algorithm or how a system embodying it

works.

Purwa Rathi


3.3. Disclosing Foresseable Aspects of AEIs

For AEIs, one characteristic feature is the ability of machines to graduate itself after being

trained once. Under such circumstances who should be held accountable for unpredicted

results, which may manifest tomorrow? Should we be interested in capturing these hidden

details; or is it absolutely fine to continue with the traditional paradigm of disclosure? One

compelling reason for considering such foreseeable output as part of present disclosure stems

from fact that these futuristic, anticipated results are borne out of extreme human endeavor

and diligence, and are peculiarly not entirely machine generated[56]. Selection of data and

training of the algorithm to produce foreseeable results are outcome of individual‟s

intellectual labor as final predictive outcome is originally ideated, implemented and intimated

by human mind. Therefore, his rights over such foreseeable and predictable variations of

invention cannot be out rightly denied, as exercised in KSR Int‟l Co. v. Teleflex.

Along with submitting technical details of present technical output, the applicant shall

also disclose in sufficient detail his insights on foreseeable results[57] that may be exhibited if

machine continues to operate on a similar data set, execute algorithmic instructions in a linear

fashion or improvise to an extent previously established by human co-inventor. In order to

demonstrate that certain end results are foreseeable, predictable and succinctly replicable, a

very detailed account of obtaining them shall be submitted as a conclusive proof. Preferably,

detailed algorithmic instructions used for obtaining technical result must be characterized in

written documentation or included as software codes meaningfully explained in English

language in patent specification[58].

Disclosures related to training phase including how a model is trained, what weights are

used with respect to what variables and substantive features contributing to corresponding

advantages resulting from execution of training model will be crucial for determining

spectrum of human intervention requisite in claiming foreseeable results. Whether or not the

disclosure of sensitive training data sets is required and to what extent may be dependent

entirely on the criticality of such training data sets for carrying out the invention. Primary

reason being data is usually a subject matter of other forms or types of IPRs, and when

exclusively claimed, it is essentially disclosed as a part of submission. However, if the

training data set is not critical for reproducing/explanatory purposes, disclosure may not be

necessary. Initial algorithm will be sufficient as they are relatively constant, and merely

adapts to varying data over time.

Agreeably, there may not be exact mode of implementation for achieving foreseeable

results as machine is continually upgrading its internal state in response to training data,

improving its performance, and adapting to changes in database contents[59]. Nonetheless,

the inventor shall submit with enough specificity the seed information, specific input

configuration including newly invented methodologies or approaches that can unambiguously

explain the predictable real-valued output[60]. Purpose is to explain the machine learning

output, i.e. to understand the factors driving the given model to a concrete output.

At the same time, one has to be mindful of not letting this submission stand in

contravention to long-established axiom of “acknowledging inventorship only when there is

an actual participation in creation of invention beyond identifying of a goal or foreseeable

result, rule embodied in Oasis Research, LLC v. Carbonite, Inc”. Real participation will be

established only when the specification states the possibly predictable variations or

improvisations of invention explainable from submitted content.

3.4. Procedural Flow for ABIs Comprising Unforeseeable Aspects

As detailed above, foreseeable aspects of AEIs may be captured in a detailed disclosure to

claim ownership over end results that a human agent presumes machines may output in due



course of time. On the contrary, if it is discovered that machine has intelligently ingested new

data and evolved itself to a magnitude inexplicable by previously patented technology, then

logically a new patent eligible subject matter is borne, now referred as ABIs. Say for example,

distinctiveness of previously designed machine and its particular results are now

irreproducible or uninterpretable with regard to expansive functionality of machine, topology

of machine or type of data manipulated in course of achieving new and inexplicable results.

Simply put, AI independently creates a patentable invention and role of human agent is no

more that of a non-inventing onlooker. No exclusivity can be declared over product that is

efficiently generated, simulated, reflected[61] upon and evaluated amidst large number of

potential solutions by sophisticated machine without usual limitations imposed by human

biases or time constraints[62]. How to accredit machines with sole inventorship and make its

human counterpart responsible? One probable solution to overcome this overhanging problem

has been proposed here with some procedural adjustments suggested for present patenting

system.

Filing of a Technical Note aka Provisional Specification : To begin with, when a machine

that was previously acknowledged as a co-inventor with human agent for a patentable subject

matter along with its probable foreseeable results, develops an invention absolutely

autonomously, human co-inventor may notify the patent office upon encountering AI Borne

invention (ABI). Human co-inventor may have to establish in a technical note how ABI is not

similar to parent patent application previously submitted for a similar subject matter. Once

such an intimation along with a preliminary technical note is received by patent office, it may

permit the applicant to treat this technical note similar to a provisional application for

purposes of obtaining a priority date before competitors could appropriate the invention.

Following the usual course, human-co-inventor may now begin building upon the

disclosed technical note aka provisional application to deduce necessary information that lead

to the newly innovated product of machine. In order to explain it fully, he may have to

reverse-engineer the final product to figure out technical approach that lead machine to build

a new product. Similar to fair-use doctrine in copyright law that permits reverse-engineering

of copyrighted software for at least some purposes[63], reverse-engineerability of a new

found product can make successful integration of technical output with a practical application.

This will also serve primary utilitarian purpose of patent law aimed at incentivizing and

rewarding innovative activities, diffusing knowledge for proper use of benefit of society or

progress of science and useful arts[64]. As Professor Jane Ginsburg has observed, “[e]ven the

most sophisticated generative machines – those that employ adversarial neural networks to

generate outputs – are no more than complex sets of algorithmic instructions whose abilities

are entirely attributable to how programmers train them with input data, and how

programmers instruct them to analyze that input data.

In absence of human supervision, these smart machines may continue endlessly upgrading

themselves, their valuable technological finding meeting a dead end without any tangible

application or profitable end use. If no recognition is meant for these inventions, then why

would there still be any such invention. Thus, human who tooled ABI in a particular way to

generate the inventive output, irrespective of the fact that the "heavy lifting" has been done by

the AI system itself, must be entitled with patent rights[65].

Filing of a Complete Specification: Once the technical note is admitted, a complete patent

specification demonstrating in fullest detail technical solution to a technical problem, and

having utilitarian impact shall be submitted by human co-inventor within a time period of one

year from filing a provisional application, in a manner very similar to conventional patent

process. Within this period of 1 year, the human-co-inventor may draw up a way of

manipulating black box operations towards a tangible application and create a patentable

Purwa Rathi


solution. One may argue that conceptualizing an altogether a new, useful and inventive

product takes a considerable time, and period of one year may not justify development of a

quick patent worthy solution. True it is, but the rationale is- here the product has already been

invented by machine, and a human agent has to simply reverse engineer it and decode the

technical means followed (discussed in next section).

For this enhancement, if the human agent is acknowledged as co-inventor in partnership

with machine, the desire for driving ABIs to patented products is uplifted for human co-

inventor. But now a next logical question follows- why would a human-agent even declare

that he has reverse engineered the product? Why can‟t he simply claim to have devised the

machine and let the application proceed similar to AEIs? Next section attempts to find a way

out.

3.5. Disclosure Requirements for ABIs

Answering to a thorny question raised in previous paragraph, we need to first understand

reverse engineering from machine learning context[66] In principal, it is the possibility to

extract or deduce certain elements of the machine learning process through access to other

elements, which usually is controversial. Straightforwardly, it is unrealistic even for experts to

predict what the algorithmic engine is capable of doing after it has rewritten itself several

times over using machine learning without human intervention[67].

In nutshell, these black engines mostly remains inscrutable, and it is extremely

challenging to reconstruct its internal workings or even recreate private data on which the

machine has trained itself. Extracting exact parameters, decoding opaque algorithms or

reverse engineering well trained and complex model characteristics[68] are discoveries

virtually impossible. Once trained, ML algorithms are not aptly indicative why it gives a

particular response to a set of data inputs[69]. Amidst these apprehensions, it is largely

understood that written description expected of a wholly disclosed patent application may be

bereft of significant technical implementation or executional “how‟s” of disclosure, as evident

in Ex Parte Lyren[70]. Besides, the claims may be only directed towards systems architecture

achieving the final output, and not exactly detail the steps or process flow of claimed output.

Analyzing few patents (US Patents 5659666, 7454388, 10423875) of Stephen Thaler‟s

Creativity Machine, it was observed that all of these applications embodied only system or

device claims, ignoring the process/method claims. Though such patent applications have to

be looked into greater detail, and are part of my future work, but it became quite evident that

much legal uncertainty exists in drafting ABI related process claims, where methodologies

and associated details may be subject to different interpretations by various courts.

Under these constraints, an equitable adjustment restricting the requirements of

submission only to final utilitarian output achievable by machine along with enough

explanation fastening the final output of machine with target use, seems a workable

proposition. It implies that the applicant may need not have to disclose exact details of

process flow by which the system arrived at final outcome. Candidly speaking, true disclosure

relating to process/method patent claims for ABIs apparently subjects them to patentable

subject matter exceptions (for being abstract) or denied for not disclosing enough. Preferable

would be omitting such method claims, and reinstituting faith in product patent regime for

ABIs[71].

In consideration of such relaxation, product patent applications may be arranged for a

quicker prosecution and shorter patent term as for petty patents[72]. Fast-paced grants may be

key motivational factor for human (co-inventor) for disclosing submission under category of

ABIs. Besides, it will also offer a psychological advantage of social recognition for his useful

discovery.



Simultaneously, for the patent office, it will be less burdening to assess product patents as

they may not have to delve deeper in complex performance details of ABIs. Further, product

patent regime will promote scientific advancements demonstrated by ABIs instead of leaving

them as plethora of meaningless references having limited practical application without

human supervision[73]. Most importantly, above explained sui generis patenting system can

be seamlessly integrated in current patenting doctrine without requiring major overhauls.

Radical though they may be, the changes that this framework will bring shall, if properly

managed, reinforce the societal and economic benefits that the patent system was always

meant to deliver. The solution-sui generis legal framework for AI enabled inventions- does

not solve the one-size-fits-all problem inherent to the patent system, but caters to the

challenges of building a coherent AI subject matter doctrine and correcting deficiencies of

patent law currently dealing with it.

4. CONCLUSION

In light of legal uncertainty in the context of rapidly advancing AI technology, it is important

for policy makers to give serious consideration to the issue of inventorship/ownership to AEIs

and ABIs. For purposes of issuing guidance in this area, it is imperative to reconsider the

boundaries of patentability, patenting process, decide how solution of today can help us

prepare for super intelligent machines of tomorrow, and what adaptations may be necessary to

ensure that the patent system‟s fundamental objectives are high held. While a probable sui-

generis model of patenting AEIs and ABIs is suggested, it falls to policy makers and eminent

thinkers to examine the fundamental rationale and justifications proposed framework may

fulfill. Whatever may be the outcome, the fact remains that it is extremely urgent to address

the patenting issues associated with AI machines in a proactive manner, before the courts

begin setting unsettling precedents for this technology domain.

REFERENCES

[1] (Kemp, 2018)

[2] (Burke, Todd J.; Trazo, Scarlett, 2019)

[3] (Artificial Intelligence Collides with Patent Law, 2018)

[4] (Shemtov, 2019)

[5] (WIPO, 2019)

[6] (Public Consultation on AI and IP Policy – Submissions, 2020)

[7] (Vertinsky and Rice, 2002)

[8] (Shemtov, 2019)

[9] (Kohlhepp, 2008)

[10] (McLaughlin, COMPUTER-GENERATED INVENTIONS, 2018)

[11] (SUMMERFIELD, The Impact of Machine Learning on Patent Law, Part 1: Can a

Computer „Invent‟?, 2018)

[12] (Müller & Bostrom)

[13] (Davidson, 2019)

[14] (Joshi, 2019)

[15] (Josef Drexl, 2019); See also (Fraser, 2016)

[16] McLaughlin, 2018)

Purwa Rathi


[17] See Artificial Neural Networks, IMAGINATION ENGINES INC., http://imagination-

engines. com/iei_ann.php [https://perma.cc/BB8K-G3FH] (last visited Jan. 25, 2016)

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[19] (Morgan, 2020)

[20] (RAVID & LIU, 2017)

[21] (Shalev-Shwartz & Ben-David, 2014)

[22] (Chui, Manyika, & Miremadi, 2018)

[23] (Amershi, Cakmak, Knox, & Kulesza1, 2014)

[24] (Surden, 2014)

[25] (McLaughlin, Computer-Generated Inventions, 2018)

[26] (Heller, 2019)

[27] (Samuelson, 1985)

[28] (Abott, 2015)

[29] (SUMMERFIELD, The Impact of Machine Learning on Patent Law, Part 2: „Machine-

Assisted Inventing‟, 2018)

[30] (Fraser, 2016)

[31] (Surden, 2014)

[32] (Bartlett, Solving the AI Accountability Gap, 2019)

[33] (Tull & Miller, 2018)

[34] (Firth-Butterfield & Chae, 2018)

[35] (Samuelson, 1985)

[36] (Schuster, 2019)

[37] (Nicholas, 2013)

[38] (Fruh, 2019)

[39] 35 U.S.C. § 112 (emphasis added); America Invents Act sec. 4(c) (striking “of carrying

out his invention” and inserting “or joint inventor of carrying out the invention”

[40] Ariad Pharm., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1351 (Fed. Cir. 2010) (en banc).

[41] (Ouellette, 2012)

[42] (Surden, 2014)

[43] (Cloem.com, n.d.)

[44] (Bjorkman, Voortmans, & Block, 2013)

[45] (Fruh, 2019)

[46] 35 U.S.C. §§ 101–103, 112 (2006), amended by Leahy-Smith America Invents Act, Pub.

L. No. 112-29, §§ 3(b)–(c), 4(c), 125 Stat. 284, 285, 296 (2011).

[47] (Tremaine, 2019)

[48] (DeCosta & Carrano, 2017)

[49] (Clifford, 1997)

[50] (Josef Drexl, 2019)

[51] (Borella, 2018)

http://imagination-engines/

http://imagination-engines/

https://perma.cc/BB8K-G3FH



[52] (Hulbert & Berghoff, 2019)

[53] (Thaler, 2013)

[54] (Borella, Gin, & Herndon, 2019)

[55] (Russell & Norvig, 2009)

[56] (Abbott, 2016)

[57] (Balganesh, 2009)

[58] (Bhandari, 2009)

[59] (SUMMERFIELD, The Impact of Machine Learning on Patent Law, Part 1: Can a

Computer „Invent‟?, 2018)

[60] (Schuster, 2019)

[61] (Malenfant, Jacques, & Demers, 1996)

[62] (Plotkin, 2009)

[63] (Cohen & Lemley, 2001)

[64] (Motion Picture Patents Co. v. Universal Film Mfg. Co., 1917)

[65] (Block, 2017)

[66] (Josef Drexl, 2019)

[67] (Pai, 2017)

[68] (Tramer, Zhang, Juels, Reiter, & Ristenpart, 2016)

[69] (Lewis & Monett, 2017)

[70] (Higgins, 2019)

[71] (Rathi, 2019)

[72] (Richards, 1998)

[73] (Hattenbach & Glucoft, 2015)

AUTHOR DETAILS

Purwa Rathi is Senior Legal Counsel at Cognizant technology Solutions where she

specializes in patent analytics, drafting and prosecution. This article does not necessarily

represent the views of the firm or its clients.

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