Patenting Artificial Intelligence Inventions in Canada

• Author: Shahrzad Esmaili - Roch Ripley
• Position: Shahrzad Esmaili is a partner in the IP practice of Gowling WLG's Toronto office. Her practice focuses on patent procurement relating to business methods, electronics and computer inventions, mechanical devices, and consumer products. She can be reached at shahrzad.esmaili@gowlingwlg.com. Roch Ripley is a partner in the Vancouver office of ...
• Pages: 35-63

Published in Landslide® magazine, Volume 12, Number 1, a publication of the ABA Section of Intellectual Property Law (ABA-IPL), ©2019 by the American Bar Association. Reproduced with permission. All rights reserved.

This information or any portion thereof may not be copied or disseminated in any form or by any means or stored in an electronic database or retrieval system without the express written consent of the American Bar Association.

September/October 2019 n LANDSLIDE 33

Patenting

Artificial

Intelligence

Inventions

in Canada

By Shahrzad Esmaili and Roch Ripley

Articial intelligence (AI)—the use of various meth-

ods and algorithms to simulate cognitive functions in

machines—has its origins dating back to the 1950s. In

recent years, the conuence of advancements in com-

putational power and the abundance of rich data sets

has led to a surge in the number of practical applications for

AI. The time and money required to properly train, test, and

deploy AI in these applications makes protecting them using

patents a crucial consideration in any business plan.

Unsurprisingly, then, according to the World Intellectual

Property Organization, there has been an “AI patent boom”—

of the 340,000 AI-related patent applications that have been

led globally since the 1950s, over half have been published in

the last seven years.1 The number of Canadian lings for AI-

based inventions has increased during this time as well. While

some of this increase in Canadian lings may simply be a

result of Canada having a technologically advanced, G7 econ-

omy, some may also be a result of Canada’s playing a leading

role in the development of AI-based technologies generally.2

This article discusses issues relating to patentability of

such inventions in Canada and contrasts the position of the

Canadian Intellectual Property Ofce (CIPO) with that of the

United States Patent and Trademark Ofce (USPTO) and the

European Patent Ofce (EPO).

Canadian Legal Principles and Guidelines for

Software Patents

In Canada, subject matter eligibility is governed by section 2 of

the Patent Act, which denes an “invention” as “any new and

useful art, process, machine, manufacture or composition of

matter, or any new and useful improvement in any art, process,

machine, manufacture or composition of matter.”3 Correspond-

ingly, section 27(8) of the Patent Act excludes from patentability

“any mere scientic principle or abstract theorem.”4

The earliest Canadian court decision relating specically

to eligibility of computer-implemented inventions is the Fed-

eral Court of Appeal (FCA) decision in Schlumberger, which

held that merely implementing an algorithm that itself was not

patentable using a computer did not render that algorithm pat-

entable.5 More particularly, the FCA held that the mathematical

formula to be performed, as claimed, was the only inventive

Shahrzad Esmaili is a partner in the IP practice of Gowling WLG’s Toronto ofce. Her practice focuses on

patent procurement relating to business methods, electronics and computer inventions, mechanical devices,

and consumer products. She can be reached at shahrzad.esmaili@gowlingwlg.com. Roch Ripley is a partner in

the Vancouver ofce of Gowling WLG and the head of the ofce’s IP department. He devotes his practice to

IP and technology law, with a focus on domestic and foreign patent and design prosecution in the software and

electronics arts. He can be reached at roch.ripley@gowlingwlg.com.

Published in Landslide® magazine, Volume 12, Number 1, a publication of the ABA Section of Intellectual Property Law (ABA-IPL), ©2019 by the American Bar Association. Reproduced with permission. All rights reserved.

This information or any portion thereof may not be copied or disseminated in any form or by any means or stored in an electronic database or retrieval system without the express written consent of the American Bar Association.

aspect of the invention and was a mental operation that quali-

ed as a “mere scientic principle or abstract theorem.”

In a later case, Canada (Attorney General) v. Amazon.com,

Inc., relating to Amazon’s one-click online ordering system,

the FCA mandated that the statutory subject matter analy-

sis performed by the Commissioner must involve a “purposive

construction of the patent claims.”6 The court further noted that

patentable subject matter must be “something with physical exis-

tence, or something that manifests a discernible effect or change.”7

Subsequent to Amazon, the CIPO issued two practice

notices on March 8, 2013, which provided guidance relating to

examination of computer-implemented inventions.8 As part of

the eligibility analysis, the practice notices require purposive

construction to be performed to determine the essential ele-

ments of a claim using a problem-solution approach. Where

a claimed computer element is considered to be essential to

solving a problem addressed by the invention in the patent

application, then the claim is directed to statutory subject mat-

ter.9 The practice notices note that one factor to consider in

determining whether a claim reciting computer elements is

statutory is to determine whether the claimed elements address

a “computer problem”—that is, a problem with the operation

of a computer, as opposed to a problem in which the computer

merely comprises part of an operating environment.

Canada’s Manual of Patent Ofce Practice (MOPOP)

provides additional guidance for drafting and prosecuting soft-

ware-related inventions. For example, a method of “controlling

a computer’s operations to achieve a technological result” would

be considered statutory. Further, a claim element that consid-

ered in isolation is common general knowledge is not necessarily

precluded from being an essential element of the claimed inven-

tion.10 The MOPOP notes that where it is explicit in the patent

application that the nature of the problem is a computer-related

one, then examination should generally proceed on that basis,

and a “signicant focus in the description” regarding details of

the solution can assist in identifying the problem.11

From a Canadian perspective, in order to improve the odds

of avoiding or overcoming a subject matter eligibility rejec-

tion directed at an AI-based invention, the description should

ideally describe the technical problem solved by the inventors.

Preferably, the technical problem can be characterized as a

“computer problem” (e.g., the use of a neural network to more

quickly classify images compared to conventional, non-AI

implementations). Moreover, providing a signicant amount of

technical detail in the description regarding the hardware and

software used to solve the technical problem may be helpful in

establishing that such hardware and software is essential, and

that a claim that recites them is accordingly patent eligible.

Application of the Case Law

In Canada, once a patent examiner concludes that examina-

tion has reached an impasse, the examiner may issue a nal

rejection, which automatically refers an application to the

Patent Appeal Board (PAB) for further consideration. The

PAB reviews the application and issues a “Commissioner’s

Decision” in respect of patentability.

Commissioner’s Decision No. 133912 relates to a patent

application for applying and adapting a predictive model such

as a neural network for automating detection of fraudulent

activity in nancial transaction processing such as credit card

transactions rather than prior art mathematical models based on

parameter analysis. Claim 1 of the subject application included

features such as training a predictive modeling means with data

(e.g., consumer proles and past fraud-related variables).

In this decision, the PAB considered whether the com-

puter limitations (e.g., a computer-implemented process and

a computer-controlled transaction processing system) were

essential features of the claimed solution. The PAB rst

dened the practical problem as how to adapt a predictive

model (or neural network) to improve credit card fraud detec-

tion and the solution as providing a specic derived training

data set that can be applied to a neural network predictive

model algorithm. The PAB noted that the computerized fea-

tures were “material to the operating environment of the

conventional fraud transaction system, but not essential to the

solution of providing a training data set that can be applied

to a neural network.”13 The PAB commented that although

neural networks were typically executed using computers,

“needing a computer for practical convenience . . . does not

make the computer essential for the working of an inven-

tion.”14 Moreover, where the claim does not dene a solution

to a computer problem “or overcome any technical problem

in the operation of the computer system,” then the computer

is likely being used as a matter of convenience to perform

calculations.15 The PAB found that the computer limitations

of the claim were “primarily to perform the neural network

calculations in an expeditious and efcient manner” and

consequently provided only “a convenient working environ-

ment.”16 Accordingly, the PAB concluded that the computer

limitations were inessential, that the essential elements of the

claims dened only data processing and mathematical calcu-

lations, and that the claims were ineligible.

As indicated by the above Commissioner’s decision,

avoiding and overcoming a subject matter eligibility rejec-

tion can be difcult for an AI-based invention, in part due to

black box implementation of AI-related tools such as neu-

ral networks, which can make drafting a detailed description

challenging. Notwithstanding this, the CIPO has recently

issued many patents directed to inventions that practically

apply AI, and techniques exist for drafting and prosecut-

ing applications to increase the likelihood that they will be

found allowable. For example, in the context of a neural

network, the description may include various types and con-

gurations of the layers of the neural network; the effect of

different types of training data on network performance and

training times; and the advantageous performance of a neural

network-based implementation as opposed to conventional,

non-AI implementations. Including information such as this

in an application and emphasizing these points during prose-

cution can help establish eligibility.

In view of the number of Canadian patent applications

that are Patent Cooperation Treaty (PCT) national entries,

the drafting and prosecution of an application is not done

in isolation. Rather, Canadian prosecution often occurs in a

broader context of a number of international lings. There-

fore, how Canadian practice compares to practice in other