{"id":7366,"date":"2025-10-01T00:00:35","date_gmt":"2025-10-01T04:00:35","guid":{"rendered":"https:\/\/kilpatricktownsend.jp\/?p=7366"},"modified":"2025-10-13T19:38:42","modified_gmt":"2025-10-13T23:38:42","slug":"patent-office-appeals-review-decision-suggests-policy-shift-toward-broader-eligibility-for-ai-related-patent-applications","status":"publish","type":"post","link":"https:\/\/kilpatricktownsend.jp\/en\/patent-office-appeals-review-decision-suggests-policy-shift-toward-broader-eligibility-for-ai-related-patent-applications\/","title":{"rendered":"Patent Office Appeals Review Decision Suggests Policy Shift Toward Broader Eligibility for AI-Related Patent Applications"},"content":{"rendered":"

Written by\u00a0Cathy E. Cretsinger, Ph.D.<\/a>,\u00a0Neslihan I. Doran-Civan<\/a>\u00a0and\u00a0David B. Raczkowski, Ph.D.<\/a><\/p>\n

Last week, the USPTO published a rare Appeals Review Panel (ARP) decision in\u00a0Ex parte Desjardins<\/em>, Appeal 2024-000567, September 26, 2025 (\u201cARP Decision\u201d), that reversed a finding of ineligible subject matter in a patent application directed to training of machine learning models. This decision, authored by new USPTO Director John Squires in the first week of his tenure, appears to signal new USPTO leadership\u2019s intent to limit examiners\u2019 reliance on subject-matter eligibility to reject claims, particularly when read in combination with the memo of August 4, 2025, from Deputy Commissioner for Patents, Charles Kim.<\/p>\n

The patent application at issue (U.S. Patent Application No. 16\/319,040) was directed to training of a machine learning model to perform multiple different tasks. A representative claim read:<\/p>\n

1. A computer-implemented method of training a machine learning model,
\nwherein the machine learning model has at least a plurality of parameters and has
\nbeen trained on a first machine learning task using first training data to determine first values of the plurality of parameters of the machine learning model, and
\nwherein the method comprises:
\ndetermining, for each of the plurality of parameters, a respective measure of an
\nimportance of the parameter to the first machine learning task, comprising:
\ncomputing, based on the first values of the plurality of parameters
\ndetermined by training the machine learning model on the first machine learning
\ntask, an approximation of a posterior distribution over possible values of the
\nplurality of parameters,
\nassigning, using the approximation, a value to each of the plurality of parameters, the value being the respective measure of the importance of the parameter to the first machine learning task and approximating a probability that the first value of the parameter after the training on the first machine learning task is a correct value of the parameter given the first training data used to train the machine learning model on the first machine learning task;
\nobtaining second training data for training the machine learning model on a second,
\ndifferent machine learning task; and
\ntraining the machine learning model on the second machine learning task by training the machine learning model on the second training data to adjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task,
\nwherein adjusting the first values of the plurality of parameters comprises adjusting the first values of the plurality of parameters to optimize an objective function that depends in part on a penalty term that is based on the determined measures of importance of the plurality of parameters to the first machine learning task.<\/p>\n

The patent examiner rejected the claims as being obvious under 35 U.S.C. \u00a7103 but did not reject the claim as being directed to patent-ineligible subject matter under 35 U.S.C. \u00a7101. On appeal, the Patent Trial and Appeal Board (PTAB) affirmed the rejection under \u00a7103 and introduced a new ground of rejection under \u00a7101. Director Squires exercised his authority to convene an ARP to reconsider the PTAB Decision.<\/p>\n

The ARP left intact the \u00a7103 rejection but vacated the \u00a7101 rejection introduced by the PTAB. Applying the two-step patent eligibility inquiry as set forth in\u00a0Alice Corporation v. CLS Bank International<\/em>, 573 U.S. 208 (2014), and in the Manual for Patent Examining Procedure (MPEP) at \u00a72106, the ARP agreed with the PTAB that each claim recited a judicial exception, specifically, \u201cat least one abstract idea.\u201d (ARP Decision at 6-7).<\/p>\n

Moving to the next prong of the inquiry, the ARP found that the PTAB had erred. The claims did \u201cintegrate the judicial exception into a practical application\u201d as provided by MPEP \u00a72106.04(II)(A)(2). The ARP relied on\u00a0Enfish, LLC v. Microsoft Corp.<\/em>, 822 F.3d 1327, 1339 (Fed. Cir. 2016), in which \u201cthe Federal Circuit held that the eligibility determination should turn on whether \u2018the claims are directed to an improvement to computer functionality versus being directed to an abstract idea.\u2019\u201d ARP Decision at 8, citing\u00a0Enfish<\/em>, 822 F.3d at 1336. The claim language reciting \u201cadjust the first values of the plurality of parameters to optimize performance of the machine learning model on the second machine learning task while protecting performance of the machine learning model on the first machine learning task\u201d was found to \u201cconstitute[] an improvement to how the machine learning model itself operates, and not, for example, the identified mathematical calculation.\u201d ARP Decision at 9.<\/p>\n

Notably, the ARP Decision also included language directing examiners and PTAB panels on how to evaluate patent eligibility under \u00a7101:<\/p>\n

Under a charitable view, the overbroad reasoning of the original panel below is perhaps understandable given the confusing nature of existing \u00a7 101 jurisprudence, but troubling, because this case highlights what is at stake. Categorically excluding AI innovations from patent protection in the United States jeopardizes America’s leadership in this critical emerging technology. Yet, under the panel’s reasoning, many AI innovations are potentially unpatentable-even if they are adequately described and nonobvious-because the panel essentially equated any machine learning with an unpatentable “algorithm” and the remaining additional elements as “generic computer components,” without adequate explanation. Dec. 24. Examiners and panels should not evaluate claims at such a high level of generality.<\/p>\n

\u2026<\/p>\n

At the same time, the claims at issue stand rejected under \u00a7 103. This case demonstrates that \u00a7\u00a7 102, 103 and 112 are the traditional and appropriate tools to limit patent protection to its proper scope. These statutory provisions should be the focus of examination.<\/p>\n

ARP Decision at 9-10.<\/p>\n

With the publication of this Decision, the new USPTO Director appears to be signaling a policy shift. In particular, improvements in the operation of machine learning models (and potentially other complex computer algorithms) should be considered as patent-eligible improvements to the functionality of a computer system. The more general statement that \u00a7\u00a7 102, 103, and 112 (novelty, non-obviousness, and sufficiency and clarity of the disclosure and claims) should be the focus of examination may carry over into other technologies.<\/p>\n

While this ARP Decision may result in the USPTO finding more claims eligible under \u00a7101, applicants should keep in mind that the USPTO\u2019s decisions are not binding on the courts. The Federal Circuit most recently reiterated this point in\u00a0Rideshare Displays, Inc. v. Lyft, Inc.<\/em>, No. 2023-2033 (Fed. Cir. Sept. 29, 2025) (nonprecedential).<\/p>\n

Takeaways for evaluating inventions for patenting:<\/p>\n

1.Claims that recite an improvement in the operation of a machine-learning model are now more likely to be considered patent-eligible by the USPTO.<\/em><\/p>\n

2.Patent examiners may begin to focus more on questions of novelty and non-obviousness and less on subject-matter eligibility. Applicants may receive fewer rejections under 35 U.S.C. \u00a7101 or find them easier to overcome. This change may extend beyond machine-learning or computer-implemented inventions.<\/em><\/p>\n

3.Courts are not bound by USPTO interpretations of statutes. While it may become easier to obtain a patent in some technology areas, enforcement will likely remain a challenge.<\/em><\/p>\n

Click here<\/a> for the Japanese version.<\/p>\n","protected":false},"excerpt":{"rendered":"

Written by\u00a0Cathy E. Cretsinger, Ph.D.,\u00a0Neslihan I. Doran-Civan\u00a0and\u00a0David B. Raczkowski, Ph.D. Last week, the USPTO published a rare Appeals Review Panel (ARP) decision in\u00a0Ex parte Desjardins, Appeal 2024-000567, September 26, 2025 (\u201cARP Decision\u201d), that reversed a finding of ineligible subject matter in a patent application directed to training of machine learning models. This decision, authored by […]<\/p>\n","protected":false},"author":26,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"footnotes":""},"categories":[131],"tags":[334,335,176,184],"class_list":["post-7366","post","type-post","status-publish","format-standard","hentry","category-alerts","tag-arp-decision","tag-ex-parte-desjardins","tag-kilpatrick-en","tag-uspto"],"acf":[],"yoast_head":"\nPatent Office Appeals Review Decision Suggests Policy Shift Toward Broader Eligibility for AI-Related Patent Applications - Kilpatrick<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/kilpatricktownsend.jp\/en\/patent-office-appeals-review-decision-suggests-policy-shift-toward-broader-eligibility-for-ai-related-patent-applications\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Patent Office Appeals Review Decision Suggests Policy Shift Toward Broader Eligibility for AI-Related Patent Applications - Kilpatrick\" \/>\n<meta property=\"og:description\" content=\"Written by\u00a0Cathy E. 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