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Machine Learning Is Obvious… – New Technology



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A recent board of appeal decision (discussed in this post) found that using a neural network,
per se, is obvious and that a lack of any discussion of the
training data for a neural network renders the disclosure of an
application insufficient to enable the invention being put into
practice. In T1191/19, hot off the press, the same Board
found that using a known machine learning technique without details
of how it is adapted for a novel application is obvious, expanding
on the previous decision. Additionally, the applicant again failed
to describe the training data in their application and,
unsurprisingly, the Board’s view on this was consistent with
its earlier decision: the disclosure was insufficient. The Board
also had an interesting way of dealing with the lack of clarity
found at first instance.

The application is related to applying meta-learning to model
and guide processes related to brain plasticity. At the start, the
Board had to grapple with claim terms found to be unclear at first
instance. The Board had an attractive solution to this. The Board
noted that a document newly cited by the applicant in support of
the clarity of the claim terms used the same terminology as in the
claim. Thus, the Board felt it could move straight to inventive
step and leave the question of clarity open (the implicit reasoning
being that whatever the terms mean, they will mean the same thing
in the claim and cited document).

In its submissions, the appellant argued that the new document
uses the same general meta-learning scheme as claimed in the
application and that applying meta-learning to model and guide
processes related to brain plasticity was a novel and inventive
strategy. On that basis, the Board considered the submitted
document the most relevant document for assessing inventive step.
The Board held that the mere application of a known machine
learning technique to a problem in a particular field is a general
trend in technology and cannot be inventive in itself, citing the
decision discussed in the post linked above. In the present case,
the problem at hand was predicting personalised interventions for a
patient in processes of which the substrate is neuronal plasticity.
The question to be asked, then, was whether the claimed method
applied the meta-learning scheme of the new document to the
specific problem at hand in a manner which would not have been
obvious to the skilled person. The Board did not find any
non-obvious detail of applying the meta-learning scheme to the
problem beyond a mere reiteration at an abstract level of the
disclosed technique. The claims were found to lack inventive step.
Beyond the specifics of this case, the conclusion is that a
straightforward application of a disclosed machine learning method
to a new problem, without any non-obvious details of how the method
is applied, was considered obvious.

The Board also considered whether the disclosure of the
application was sufficient to put the invention into practice. The
Board found that the application does not disclose how the
meta-learning scheme was applied to the problem at hand in a manner
sufficiently clear and complete for it to be carried out by the
person skilled in the art. Specifically, the application did not
disclose any example set of training data and validation data,
which the meta-learning scheme required as input. The application
did not even disclose the minimum number of patients from which
training data should be compiled to give a meaningful prediction,
nor the set of relevant parameters. No details of the heuristics
mentioned in the claims for the solution of the problem at hand
were disclosed. Nor were any details disclosed of the structure of
the artificial neural networks used as classifiers, their topology,
activation functions, end conditions or learning mechanism. At the
level of abstraction of the application, the available disclosure
was an invitation to a research programme. Given the lack of
disclosure in this specific case, it is difficult to know what
level of disclosure would be sufficient in the Board’s eyes.
One thing, however, is clear: applicants must at least include some
information about the training data used and the structure of
models used. Applicants must also include at least some information
on how the models are trained and any other rules associated with
their training or use in inference.

As in the earlier case in front of the same Board, the appellant
did not respond to the preliminary opinion the Board sent out
before the appeal hearing, nor did they attend the hearing.
Appellants are, of course, in their right to do so. However,
consequently, the Board in these cases pronounced on points
fundamental to the patenting of machine learning inventions without
the benefit of testing these points in discussion with the
appellant. What is more, the level of the decisions in these cases
and the appellant’s involvement suggest that these cases may
not have been the best ones to move the needle on our understanding
of how to assess machine learning. We will only advance the
understanding of these questions when the Boards of Appeal get to
rule on cases that test the limit of what can be patented and how -
cases with promising subject matter that are vigorously argued.
When that happens, you will be sure to read about it here.

Originally published 02 May 2022

The content of this article is intended to provide a general
guide to the subject matter. Specialist advice should be sought
about your specific circumstances.

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