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.
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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. ยง 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 …….