A Tale Of Two Jurisdictions: Sufficiency Of Disclosure For Artificial Intelligence (AI) Patents In The US And The EPO – Intellectual Property – United States – Mondaq News Alerts

PatentNext Summary: In order to prepare
applications for filing in multiple jurisdictions, practitioners
should be cognizant of claiming styles in the various jurisdictions
that they expect to file AI-related patent applications in, and
draft claims accordingly. For example, different jurisdictions,
such as the U.S. and EPO, have different legal tests that can
result in different styles for claiming artificial
intelligence(AI)-related inventions.

In this article, we will compare two applications, one in the
U.S. and the other in the EPO, that have the same or similar
claims. Both applications claim priority to the same PCT
Application (PCT/AT2006/000457) (the “‘427 PCT
Application”), which is published as PCT Pub. No.
WO/2007/053868.

As we shall see, despite the application having the same or
similar claims, prosecution of the applications in the two
jurisdictions nonetheless resulted in different outcomes, with the
U.S. application prosecuted to allowance and the EPO application
ending in rejection.

****

Artificial Intelligence (AI) Overview

Pertinent to our discussion is an overview of AI. A brief
description of AI follows before analysis of the AI-related claims
at issue.

Artificial Intelligence (AI) is fundamentally a data-driven
technology that takes unique datasets as input to train AI computer
models. Once trained, an AI computer model may take new data as
input to predict, classify, or otherwise output results for use in
a variety of applications.

Machine learning, arguably the most widely used AI technique,
may be described as a process that uses data and algorithms to
train (or teach) computer models, which usually involves the
training of weights of the model. Training typically involves
calculating and updating mathematical weights (i.e., numeral
values) of a model based on input that can comprise hundreds,
thousands, millions, etc. sets of data. The trained model allows
the computer to make decisions without the need for explicit or
rule-based programming.

In particular, machine learning algorithms build a model on
training data to identify and extract patterns from the data and
therefore acquire (or learn) unique knowledge that can be applied
to new data sets.

For more information, see Artificial Intelligence & the Intellectual
Property Landscape

Sufficiency of Disclosure in the U.S.

AI inventions are fundamentally software-related inventions. In
the U.S., as a practical rule, software-related patents should
disclose an algorithm by which the software-related invention is
achieved. An algorithm provides support for a software-related
patent pursuant to 35 U.S.C. § 112(a) including (1) by
providing sufficiency of disclosure for the patent’s “written description” and (2) by “enabling” one
of ordinary skill in the art (e.g., a computer engineer or computer
programmer) to make or use the related software-related invention
without “undue experimentation.” Without such support, a
patent claim can be held invalid. For more information regarding
general aspects of the sufficiency of disclosure in the U.S. for
software-related inventions, see Why including an “Algorithm” is
Important for Software Patents (Part 2)

1. The ‘457 PCT
Application in the U.S.

U.S. Patent 8,920,327 (the “‘327 Patent”) issued
from the ‘457 PCT Application. The ”327 Patent is an
example of an AI patent that did not experience
sufficiency issues in the U.S. The below provides an overview of
the ‘327 Patent.

The ‘327 Patent is titled “Method for Determining
Cardiac Output” and includes a single independent claim
regarding a method for cardiac output from an arterial blood
pressure curve. The method is implemented via a cardiac device, as
illustrated in Figure 1 (copied below):

1127648a.jpg

Fig. 1 illustrates device 1 for implementing the invention of
the ‘327 patent, where measuring device 2 measures the
peripheral blood pressure curve, and where related measurement data
is fed into device 1 via line 3, and stored and evaluated there.
The device further comprises optical display device 4, input panel
5, and keys 6 for inputting and displaying information.

The claimed method includes an AI aspect, i.e., namely the use
of “an artificial neural network having weighting
values that are determined by learning
.”

Claim 1 is copied below (with the AI aspect
bolded):

1. A method for determining
cardiac output from an arterial blood pressure curve measured at a
peripheral region, comprising the steps of:

measuring the arterial blood
pressure curve at the peripheral region; arithmetically
transforming the measured arterial blood pressure curve to an
equivalent aortic pressure; and

calculating the cardiac output
from the equivalent aortic pressure,

wherein the arithmetic
transformation of the arterial blood pressure curve measured at the
peripheral region into the equivalent aortic pressure is performed
by the aid of an artificial neural network
having weighting values that are determined by
learning
.

Figure 3 of the ‘327 patent (copied below) is a schematic
illustration of the artificial neural network, as recited in claim
1.

1127648b.jpg

The specification of the ‘327 patent describes that “FIG. 3 illustrates the structure of the neural network…,
and it is apparent that the neural network … is comprised of
three layers 14, 15, 16.” The specification discloses that a
supervised learning algorithm is used to train the weights of the
model, e.g., “[t]he weights and the bias for the latter two
layers 15 and 16 are determined by supervised learning.”

The input data fed to the supervised learning algorithm to train
the AI model includes “associated blood pressure curve pairs
actually determined by measurements in the periphery or in the
aorta, respectively, are used.” The measurements used for the
input data may come “from patients of different ages, sexes,
constitutional types, health conditions and the like.”

No issues with respect to sufficiency were raised during the
prosecution of the application in the U.S. that was issued as the ‘327 patent.

More generally, issues of sufficiency in the U.S. typically
arise in litigation, and result in expert testimony, i.e., “a
battle of the experts,” where expert witnesses (e.g.,
typically university professors or industry consultants) from
opposing sides opine on the knowledge of a person of ordinary skill
in the art and sufficiency of disclosure in view of that
person.

Sufficiency of Disclosure in the European Patent Office
(EPO)

The EPO has developed its own, yet similar, stance on AI-related
invention when compared with the U.S. Nonetheless, outcomes of
prosecution can be different. The below provides a cursory overview
of developments in the EPO with respect to AI-related inventions
and analyzes the treatment of an EPO application as filed based on
the PCT Application ‘457 (which is the same PCT Application as
for the ‘327 patent discussed above).

1. Artificial Intelligence
Inventions can be patented pursuant to EPO law

Generally, artificial intelligence inventions may be patented in
the European Patent Office (EPO). For example, in its Guidelines
for Examination, the EPO defines AI and machine learning as “based on computational models and algorithms for
classification, clustering, regression and dimensionality
reduction, such as neural networks, genetic algorithms, support
vector machines, k-means, kernel regression and discriminant
analysis.” Section 3.3.1 (Artificial intelligence and machine
learning)
.

As such, the EPO dubs AI and machine learning as “per se of
an abstract mathematical nature,” irrespective of whether such
models may be trained with training data. Id. Thus, simply
claiming a machine learning model (e.g., such as a “neural
network”) does not, alone, necessarily imply the use of a “technical means” in accordance with EPO law.

Nonetheless, the Guidelines for Examination at the EPO recognize
that the use of an AI model, when claimed as a whole with the
additional subject matter, may demonstrate a sufficient technical
character. Id. As an example, the Guidelines for
Examination at the EPO states that “the use of a neural
network in a heart-monitoring apparatus for the purpose of
identifying irregular heartbeats makes a technical
contribution.” Id. As a further example, the EPO
Guidelines for Examination further states that “[t]he
classification of digital images, videos, audio or speech signals
based on low-level features (e.g. edges or pixel attributes for
images) are further typical technical applications of
classification algorithms.” Id.

2. Sufficiency of Disclosure
for an Artificial Invention at the EPO

In a decision in 2020, the EPO Board of Appeals rejected a
machine learning-based patent application that claimed an “artificial neural network” because the patent
specification failed to sufficiently disclose how the artificial
neural network was trained. See T0161/18 (Equivalent aortic pressure / ARC
SEIBERSDORF). The application in question claimed priority to the
PCT Application ‘457, which is the same parent application as
the ‘327 patent, as discussed above.

The claims were the same or similar as to those in the U.S.,
where the claims-at-issue directed to determining cardiac output
from an arterial blood pressure curve measured at a periphery, and
recited, in part (with respect to AI), that the “blood
pressure curve measured on the periphery is converted into the
equivalent aortic pressure with the help of an
artificial neural network
, the weighting values ??of
which are determined by
learning
.”

Claim 1 is reproduced below (in English based on a machine
translation of the original opinion German):

1. A method for determining the
cardiac output from an arterial blood pressure curve measured at
the periphery, in which the blood pressure curve measured at the
periphery is mathematically transformed to the equivalent aortic
pressure and the cardiac output is calculated from the equivalent
aortic pressure, characterized in that the transformation of the
blood pressure curve measured on the periphery is converted into
the equivalent aortic pressure with the help of an
artificial neural network
, the weighting values ??of
which are determined by learning.

A. Sufficiency of
Disclosure

The Board analyzed the claim in view of the specification
pursuant to Article 83 EP (Sufficient disclosure). As described by
the Board, Article 83 EPC requires that the invention be disclosed
in the European patent application so clearly and completely that a
person skilled in the art can carry it out. For this, the
disclosure of the invention in the application must enable the
person skilled in the art to reproduce the technical teaching
inherent in the claimed invention on the basis of his general
specialist knowledge.

The Board then turned to the specification to determine whether
it disclosed enough support to meet these requirements in view of
the claimed “artificial neural network.” However, the
specification was found lacking because it failed to “disclose which input data are
suitable for training the artificial neural network according to
the invention, or at least one data set suitable for solving the
technical problem at hand
.

Instead, the Board found that the specification “merely
reveals that the input data should cover a broad spectrum of
patients of different ages, genders, constitution types, health
status and the like.”

Therefore, the Board found that the training of the artificial
neural network could therefore not be reworked by the person
skilled in the art, and the person skilled in the art can therefore
not carry out the invention.

Because of these deficiencies, the Board found that the
specification failed to provide sufficient disclosure pursuant to
Article 83 EPC.

B. Inventive
Step

For similar reasons, the Board further found that the claimed
subject matter lacked an “inventive step” pursuant to
Article 56 EPC. Specifically, the Board found that the claimed “artificial neural network” was not adapted for the
specific, claimed application because the specification failed to
disclose how the artificial neural network was trained, and
specifically failed to disclose weight values that resulted from
such training. For this reason, the claimed “artificial neural
network” could not be distinguished from the cited prior art,
which resulted in failure to demonstrate the requisite inventive
step.

As the Board described:

In the present case, the claimed
neural network is therefore not adapted for the specific, claimed
application. In the opinion of the Chamber, there is therefore only
an unspecified adaptation of the weight values, which is in the
nature of every artificial neural network. The board is therefore
not convinced that the claimed effect will be achieved in the
claimed method over the entire range claimed. This effect cannot,
therefore, be taken into account in the assessment of inventive
step in the sense of an improvement over the prior art.

Conclusion

Accordingly, at least with respect to patent applications filed
in the EPO, and where an AI or machine learning model is to be
distinguished from the prior art, then a patent applicant may want
to include an example training data set, example trained weights,
or at least sufficiently describe the input used to train the model
on a specific, claimed application or end-use. For example, at
least one example of data can be provided (or claimed) to show the
inputs used to train specific weights, which may allow for the
claim to have sufficient disclosure, and, at the same time allow
the claim to cover a spectrum of AI models trained with a
particular set of data.

For the time being, such disclosure for an EPO case could be
considered as additional when compared with the sufficiency of
disclosure in the U.S. However, it is to be understood that the
U.S. Patent office has also indicated the importance of including
training data or specific species of data used to train a model in
its example guidance. See How to Patent an Artificial Intelligence (AI)
Invention: Guidance from the U.S. Patent Office (USPTO)
. In any
event, while there have been few court cases on AI-related
inventions in the U.S. (see How the Courts treat Artificial Intelligence (AI)
Patent Inventions: Through the Years since Alice
), future cases
may indicate whether the U.S. will trend towards the EPO’s
decision in T0161/18 with respect to the sufficiency of
disclosure.

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.

Spread the love

Leave a Reply

Your email address will not be published.