the firm's post-grant practitioners are some of the most experienced in the country.

John S. Kern
Kevin M. McKinley
Arthur I. Neustadt
Kasumi  Kanetaka
Yuki  Onoe
Jay E. Rowe, Jr., Ph.D.
Alec M. Royka
Erik M. Stang, Ph.D.
Yorikatsu  Hohokabe, Ph.D.
Robert T. Pous
Carl E. Schlier
Kurt M. Berger, Ph.D.
Long  Phan, Ph.D.
Craig R. Feinberg
Ryan W. Smith
Stephen G. Baxter, Ph.D.
David M. Longo, Ph.D.
Yin Y. Nelson, Ph.D.
Christopher  Ricciuti
James R. Love
Anna Z. Lloyd
Ching-Cheng (Tony)  Chang
Surinder  Sachar
Soumya  Panda
Andrew M. Ollis
Teddy S. Gron
Christopher I. Donahue
Tao  Feng, Ph.D.
Richard D. Kelly
Johnny  Ma
Nanlin  Wang, Ph.D.
Derek  Lightner, Ph.D.
Eric W. Schweibenz
Robert  Tarcu
Eckhard H. Kuesters
Jenchieh (Joseph) Yuan
Yuanyi (Alex) Zhang, Ph.D.
Marina I. Miller, Ph.D.
Philippe J.C. Signore, Ph.D.
Steven B. Chang
Michael R. Casey, Ph.D.
Daniel J. Pereira, Ph.D.
John F. Presper
Yanwen  Fei
Charles L. Gholz
Colin B. Harris
Diane  Jones
Grace E. Kim
Akihiro  Yamazaki
Frank J. West
Kevin L. Hartman, Ph.D.
Tia D. Fenton
John  Sipos
Peifang  Tian, Ph.D.
Bogdan A. Zinchenko
Maki  Saitoh
Kevin Ross  Davis
Edwin D. Garlepp
Aristotelis M. Psitos
Brian B. Darville
Aldo  Martinez
Stefan Uwe  Koschmieder, Ph.D.
Sameer  Gokhale
Jianping (James)  Wu
Chika (Teranishi) Iitoyo
Elissa L. Sanford
Thomas M. Cunningham, Ph.D.
Jeffrey B. McIntyre
Nicholas  Rosa, Ph.D.
Robert W. Downs
Dale M. Shaw
J. Derek  Mason, Ph.D., CLP
Alexander B. Englehart
Vincent K. Shier, Ph.D.
Norman F. Oblon

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Headquartered within steps of the USPTO with an affiliate office in Tokyo, Oblon is one of the largest law firms in the United States focused exclusively on intellectual property law.

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The United States Patent and Trademark Office (USPTO) issued final rules implementing the inventor's oath or declaration provisions of the America Invents Act (AIA) on August 14, 2012.

Drafting AI Claims in a Way That Infringement is Detectable

  • November 1, 2021
  • Article

Associated People


Drafting claims with infringement in mind has always been a challenge. For instance, claims should be drafted to ensure that they can be infringed by a single party in order to address divided infringement issues. Similarly, it may be useful to draft claims in a way that avoids requiring end-user infringement as end-users may not be the best target when considering litigation. In the same vein, it is important to draft claims in a way that infringement is detectable, as a patent owner must have facts that provide a plausible entitlement to relief. This means that the patent owner must have some basis to allege that the patent claims are being infringed. If the claims include features that are not readably detectable, the claims may, in effect, be useless when considering infringement. This is especially true in Artificial Intelligence where many features are difficult to easily detect without intimate knowledge of the AI system. In fact, AI systems are often considered black box systems in which the inner workings are not evident to the outside and sometimes not even to the operator of the AI system.

This characteristic of AI systems should be carefully considered when drafting and prosecuting AI patent applications. For example, it is often more useful to draft claims that are directed to the inputs and outputs of a system then to explicitly recite the specific type of machine learning algorithm used in the system. This of course sounds easier in principle than it is in practice as broad claims directed to AI systems often run afoul of §101 or are simply rejected based on prior art. Nevertheless, considering how AI systems will be discussed in promotional material or in publicly available materials is a very important part of generating claims that are powerful when considering infringement.

When considering these issues, it may be helpful to look at an example of an AI invention directed to a system for detecting images of animals, which includes a convolutional neural network and a large, tagged dataset of different animals. Even if this exemplary system includes novel aspects directed to the operation of the convolutional neural network, a claim directed to this invention should be careful to avoid too narrowly reciting these features in the independent claims because it may be difficult to detect such features. Instead, the claim should attempt to focus on features which would more likely be advertised or discussed publicly by a potential infringer. For instance, the infringer may describe that the infringing system is able to detect different images of animals even when the animals are captured from different angles. These functional features of the convolutional neural network could be claimed in place of explicitly claiming the underlying algorithm. For instance, the claim could recite a classifier configured to apply a neural network that compensates for the angle at which the image is captured. By focusing on features that are easily detectable the claims can ensure that the claims remain a powerful tool against potential infringers.

Although there are many issues that make drafting AI claims challenging, it is important not to overlook detection of infringement as this issue could become very important if the claims are ever to be asserted.