Patenting Computer Implemented Inventions at the EPO: Part Two

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Patenting Computer Implemented Inventions at the
EPO Part Two
30th November 2021 By Michael Ford, Senior
Associate and Frances Wilding, Partner and Stuart
Clarkson, Partner In this second article we
examine how the requirement of sufficiency is
applied to AI inventions, including the various
types of data that may need to be included in the
application.
Sufficiency

• Article 83 of the EPC requires that the
  European Patent Application shall disclose the
  invention in a manner sufficiently clear and
  complete for it to be carried out by a person
  skilled in the art. EPO Examiners must therefore
  consider whether there is enough information in
  a patent application to allow the skilled person
  to reproduce the invention, and the application
  must provide sufficient information that the
  burden on the skilled person is not undue and
  that no inventive skill is required to work the
  invention.
• For AI inventions, the EPO Examiner may ask
• Is the technical effect plausibly achieved over
  the whole of the claimed scope?
• Is there a clear causal link between the input
  and output of a machine learning process?
• Will the trained model produce reliable
  predictions?
• A lack of reproducibility can lead to sufficiency
  or inventive step objections. If the desired
  technical effect is expressed in the claim but
  the application lacks reproducibility, an
  objection of lack of sufficient disclosure under
  Article 83 EPC may be raised. If the technical
  effect is not reproducible and is not expressed
  in the claim but is instead part of the
  objective technical problem to be solved, an
  objection of lack of inventive step under
  Article 56 EPC may be raised.

Lessons From the Field of Chemistry
Objections under Article 83 EPC may be raised
against applications concerning AI because it is
not always immediately clear how or why an AI
invention works.
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For example, a well thought-out training dataset
and reward function may lead you to a machine of
remarkable utility without the inventor ever
truly understanding why the numbers underlying
that particular machine are so successful. Attorn
eys working in chemistry have long dealt with the
problem of an inventor not knowing why
composition A has better properties than
composition B, or why yield of a particular
reaction product X increases when you change
reaction variable Y, etc. nevertheless lots of
experiments may show that such an invention
works. Although it may be relatively easy for a
chemist to make a new composition as such, it is
much more difficult to make new compositions
that have a desired new or improved
result. Essentially, the outcome of a new
chemical reaction is unpredictable. These
experiences in chemistry have parallels in the
field of AI. For example, consider an
image-classification AI machine learning method.
The machines focus is getting to the right
answer as opposed to understanding the problem
itself. This means that it may not be apparent
why there is such a strong weighting associated
with a particular combination of pixels in a
particular stage of a neural network. But by
testing the machine, and checking the results
independently, the inventor can be sure that
their AI machine is a good one, in just the same
way that a drug which results in increased
survival is a good drug, even if the precise
mechanism underlying its efficacy is not
known. Therefore, we now recommend using
experimental data and comparative tests in
applications relating to AI, as is already common
in chemistry applications. According to EPO
examination guidelines and case law, it is better
to include both experimental data and
comparative tests in the patent application
itself. Later-filed evidence can be taken into
consideration, even if it was not included in the
original application, but only under certain
conditions. Later-filed evidence may not serve as
the sole basis to establish that the application
solves the problem it sets out to solve it can
only be used to back up findings in the patent
application, if it is already credible from the
application that the problem is indeed solved.
Data
In the field of chemistry, these requirements
have led to the practice of ensuring
experimental data is included in patent
applications. This may include experimental data
to demonstrate how the invention is carried out
and to demonstrate the result, which is
achieved, and it may also include comparative
data to demonstrate that the result in an
improvement in the art. This standard practice in
the world of chemical drafting is likely to be
useful to the AI field. Consider, for example,
that in order for a particular patent application
directed to a new chemical composition to meet
the requirement of sufficiency, the patent
application
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must provide enough information for the skilled
person to be able to produce the composition. It
is often necessary to include full experimental
details of at least one way of producing a
particular product, for example, including
starting materials, apparatus and reaction
conditions. For AI inventions, this is comparable
to describing the principles underlying the data
included in the dataset, and any assumptions or
parameters built into the AI model. Consider
another scenario which concerns a chemical
application where a particular result is
limiting on the scope of the claims. For example,
the claims may be directed to a method that
provides a particular result, or to use of a
composition for a particular purpose. In such
cases, in addition to explaining how the method
or use is carried out, it is generally necessary
to provide data to prove that the claimed result
is actually obtained by the invention in order
to satisfy sufficiency requirements. For AI
inventions, a comparable example would be a
machine designed to better distinguish between
different types of animal in an image. If this
result is indeed achieved, then the application
should provide evidence of this. A requirement
of sufficiency is that the invention can be
performed across substantially the whole of the
area claimed and, in chemistry, without clearly
knowing why a new chemical composition works
better than already known compositions, it can be
difficult to justify a broad definition of the
new composition in the claims. Nevertheless, the
applicant may want to obtain patent protection
covering similar compositions that are expected
to show similar improvements. In practice, when
chemical claims are drafted broadly, Examiners
can object that it is not plausible that a
technical effect that has been demonstrated for
one particular composition would be obtained for
all embodiments falling within the scope of the
claims. In some circumstances, plausibility of
different embodiments can be argued based on
known scientific principles. However, additional
experimental data relating to a number of
embodiments across the scope of the claims can be
helpful to overcome any concerns the Examiner
may have. We can also apply these considerations
to AI inventions. For example, a classifier which
distinguishes between different types of animal
in an image may use a training dataset including
pictures of cats and dogs, and a particular model
embodying a set of assumptions. In this example,
the model is not just effective, but also fast
and lightweight in terms of processing
resources. The applicant would like to claim the
model as a classifier per se, for categorising
any two objects into two groups. However, an
Examiner may not consider such a broad invention
to be plausible based on data relating only to
cats and dogs. For example, it is possible that
there is something special about cat and dog
images which isnt true of other sets of images,
such as of bolts and screws or oil slicks and
rainbows. Therefore, including diverse examples
of datasets in the application may help to show
plausibility in such an example.
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As an example, an application undergoing
prosecution at the EPO relates to processing
images of physical samples on a production line
to identify defects on the imaged physical
samples, and the claims explicitly define
executing unsupervised training of a generator
neural network
Comparative test data was included in the
specification as filed, demonstrating the
ability of the invention to decrease losses
faster than prior art AI methods. Reduced losses
in this context refers to a method that is
particularly effective at training the generator
neural network to remove defects from input
images. This is shown in the graph below
The application looks set to grant shortly.
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Applications Combining AI and Chemistry
AI is now also finding more and more applications
in Chemistry itself. For example, the below
figure shows the growth in the number of patent
applications filed worldwide in the CPC
classifications C01-C14 (which relate to
traditional Chemistry) and C21-C30 (which relate
to Metallurgy) and which also include the words
artificial intelligence or machine learning
in the description. In many of these
applications, AI methods are used to identify
existing or new chemicals, compositions or
materials which have desirable properties. For
example, an AI machine may identify existing
drugs, known for treating one particular disease,
as being suitable for use in the treatment of an
unrelated disease. Alternatively, AI may be used
to design new materials which have improved
mechanical or electrical properties. In such
cases, care must be taken to ensure that the
patent application sufficiently describes both
the AI and the chemical aspects of the
invention. As an example that also illustrates
the potential issues discussed above, one
application we have identified concerns an AI
method for designing a material for an aircraft
component. A neural network is trained to
correlate structural features of alloys with
material properties, such as yield strength,
using a training data set of images of alloy
structures having varied compositions and
properties. The claimed method uses the neural
network to identify a set of structural features
capable of achieving a desired combination of
properties for a particular application. The
application also attempts to claim a step of
manufacturing an alloy having the identified
structure and, thus, the desired properties. The
patent application has received sufficiency
objections in Europe under Article 83 EPC.
First, the Examiner has objected that generic
theoretical models linking alloy properties to
structural features are not known for alloy
systems in general, nor are they known for the
specific type of titanium-based alloy provided in
the Examples of the application. The Examiner
has also doubted that the AI model described can
predict optimized microstructures for an alloy
having desired properties. Instead, the Examiner
thinks that the AI could be used to analyse an
image of a previously unknown alloy structure
and predict its properties, which is a
fundamentally different task. The Examiner has
therefore objected that excessive experimentation
would still be required for the skilled person
to determine suitable structural features for a
given set of properties. Second, the EPO
Examiner has objected that the application does
not explain how to manufacture an alloy having a
particular structure identified by the AI. The
Examiner notes that developing a manufacturing
method for a particular alloy requires extensive
experimentation and that the skilled person would
also need to first consider whether any
structure output by the AI was even chemically or
physically feasible. This case also highlights
the difficulties which can be encountered when
prosecuting applications at the overlap between
AI and Chemistry. We recommend that such
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applications are drafted with input from both AI
and Chemistry attorneys, and that experimental
data is used to support both AI and chemical
aspects of the application.
Summary
To decrease the chances of a sufficiency
objection, we recommend that all relevant data
(including experimental and comparative data) is
included in the application where this is
available to the applicant, particularly data
that distinguishes the invention from the prior
art known to the inventor.
This is for general information only and does not
constitute legal advice. Should you require
advice on this or any other topic then please
contact hlk_at_hlk-ip.com or your usual Haseltine
Lake Kempner advisor.