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

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

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Artificial Intelligence (AI)
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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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1968
Norman Oblon with Stanley Fisher and Marvin Spivak launched what was to become Oblon, McClelland, Maier & Neustadt, LLP, one of the nation's leading full-service intellectual property law firms.

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Telephone: 703-413-3000
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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.

Tracking AI Prosecution Trends at the U.S. Patent Office

  • March 5, 2021
  • Article

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Following last week’s post on this blog (AI Patent Trends in the U.S. Patent Office: Is the U.S. Losing Its Lead?), we look deeper into the filing data of AI applications to better understand how the USPTO’s treatment of inventions in this field have evolved over time. AI applications are increasing rapidly, but what happens when these applications get into substantive prosecution? Patent practitioners who understand this information can better help their clients avoid some of the pitfalls present, potentially resulting in higher allowance rates and less office actions to disposition. The data presented throughout this post was compiled using Juristat and focuses on the USPTO’s Art Units which handle the greatest number of AI filings (2122, 2129, 2121, 2124, 2123, 2128, 2127), further filtered by USPC 706, which relates to “Data Processing – Artificial Intelligence.”

Data shows that since at least 2001 (as far back as Juristat’s data goes) the average allowance rate for AI filings sits at 80%, which is slightly higher than USPTO’s overall allowance rate of 75%. However, as shown in Table I and Figure I, reproduced below, the allowance rate of AI applications has experienced a few notable peaks and dips. Most notably, applicants saw their lowest allowance rate in 2018, at a mere 62%. This low point in 2018 was the culmination of declining allowance rates, following the Supreme Court’s 2014 decision in Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 573 U.S. 208 (2014). The low point in 2018 has since recovered significantly, no doubt in large part due to the USPTO’s January 2019 issuance of Patent Subject Matter Eligibility Guidance (“2019 PEG”),   which marked a shift in the USPTO’s review process, generally leading to increased allowance of patent applications facing scrutiny over subject matter eligibility.

Table 1
 

Figure 2

Rejections based on § 101 and Alice reach a peak in 2018, the same year that the USPTO had its lowest AI allowance rate of 62%. As anticipated, these § 101 rejections rapidly increased after the Supreme Court’s Alice decision in 2014, but that should come as no surprise, as AI drafted claims can often fit the bill for the “abstract idea” classification that the Supreme Court was concerned about in its Alice decision. Importantly though, the graphics also show how major of an impact the USPTO’s 2019 Patent Subject Matter Eligibility Guidance had at curtailing § 101 rejections, with the overall number of § 101 rejections sharply declining in 2019.

 

Also noteworthy is the number of obviousness (§ 103) rejections over time.   The Supreme Court’s decision in KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398 (2007) rejected the Federal Circuit’s rigid application of the Teaching-Suggestion-Motivation (TSM) test, in light of the much more flexible Graham factors.   Potentially, as a result of KSR, obviousness rejections increased over time, and have accounted for the greatest number of rejections since 2010, with the exception of 2017 and 2018 when § 101 rejections lead the way. However, since the sharp decline in the number of § 101 rejections in 2019, obviousness rejections now far outpace the next most common basis of rejection (anticipation under § 102). With the § 101 inquiry now in a much more ascertainable place,  obviousness rejections should be expected to continue as the most common basis of rejection for AI related applications. This is consistent with rejection bases across the USPTO as a whole. Since KSR, § 103 rejections have far exceeded all other rejection bases at the USPTO.

Table 2 


Figure 2

As AI filings continue to increase, USPTO policy and procedure for handling these applications will continue to evolve. Understanding the issues and trends present when prosecuting AItechnologies at the USPTO can provide patent practitioners  with a tremendous advantage. Getting applications to allowance in shorter timeframes, will reduce a company’s budget and help them secure their stake in a rapidly expanding field.   If AI inventors can get out of the woods of the uncertainly caused by Alice, patent prosecution in the next decade of data can be less focused on patent eligibility, and more focused on the traditional bars to patentability, such as §§ 102, 103 and 112.