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WIPO Conversation on IP and AI Second Session
Purwa Rathi
Senior Legal Counsel, Cognizant Technology Solutions
Commenting in a personal capacity. These views do not necessarily represent views of firm or its clients
In response to comments invited on ‘REVISED ISSUES PAPER ON INTELLECTUAL
PROPERTY POLICY AND ARTIFICIAL INTELLIGENCE’, the following submission is made in
personal capacity. The current submission will concentrate on the subject of patents, specifically
Issue 2 (Inventorship and Ownership), Issue 3 (Patentable Subject Matter and Patentability
Guidelines), Issue 4 (Inventive Step or Non-Obviousness), Issue 5 (Disclosure) and Issue 6
(General Policy Considerations for the Patent System). I thank the WIPO for this opportunity and
provide my answers to the questions regarding Patents below.
RESPONSE:
PART I: INVENTORSHIP AND OWNERSHIP PROSPECTS FOR AI RELATED INVENTIONS
A. 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. 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. These are highly distinguished from
traditional human guided computer hardware programmed to perform a particular task.
In present context, no General human like intelligent AI machines fully capable of
independent judgment, reasoning, agency, creativity or decision making without any human
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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. 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. 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 resources, high capacity storage, advances in
Big Data and advent of special hardware with specialized chips capable of supplying enormous
computational power.
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.
For the purposes of this submission, and in background of definitions provided by WIPO
to distinguish between “AI-generated” and “AI-assisted” works, two separate definitions have
been created on similar lines:
a) AI Enabled Inventions (AEIs) that either embody an advance in field of AI or apply AI to
other field (referring to AI-assisted definition from WIPO) , and
b) b) AI Borne Inventions (ABIs) that are produced by AI (referring to AI-generated definition
from WIPO).
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B. 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. 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.
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, 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
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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). 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. 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.
C. DETERMINIING 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. 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’. 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
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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.
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. 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’ 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”. 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. Also,
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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.
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. Undeniably, this rightful acknowledgment of true inventors creates an
absurd situation with lurking issues of ownership, accountability, infringement risks, or liability,
which are presently taken charge of by human agents all alone.
D. 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. 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. In these circumstances,
intellectual and meaningful domination of human agent over such not-so-intelligent non-human
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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.
So, how do we intend to address the most controversial inventorship/ownership issue for
AEIs & ABIs? 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. 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. 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. 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.
PART II. 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
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disclosures required for AEIs and ABIs, which have been a cornerstone of patent policy. 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.
A. LEGAL REQUIREMENTS & DISCLOSURE JUSTIFICATION
Patents are awarded as a quid pro quo for disclosing the invention all across the globe.
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”.
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.” For AEIs,
it is important to verify how humans are involved in different aspects of its conceptualization and
constructive reduction to practice.
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
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manipulation. This form of non-inventive claiming can rattle the current patent landscape
especially when it comes to identifying true and onerous machine inventing.
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. 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.
B. 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.)
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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. 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.
a) 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. 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 – 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
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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.
b) 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. 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.
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.
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
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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, 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. 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.
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
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(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.
C. 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. 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 outrightly 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 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.
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
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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. 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. 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.
D. 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
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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 upon
and evaluated amidst large number of potential solutions by sophisticated machine without usual
limitations imposed by human biases or time constraints. 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.
a) 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, reverse-engineerability of a new found product can make successful
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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. 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.
b) 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 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
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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.
E. DISCLOSURE REQUIREMENTS FOR ABIs
Answering to a thorny question raised in previous paragraph, we need to first understand
reverse engineering from machine learning context. 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.
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 are discoveries virtually impossible. Once trained, ML
algorithms are not aptly indicative why it gives a particular response to a set of data inputs. 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. 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.
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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.
In consideration of such relaxation, product patent applications may be arranged for a quicker
prosecution and shorter patent term as for petty patents. 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. 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.
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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.