Summary: In Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. Apr. 18, 2025), the Federal Circuit delivered a clear warning: simply applying generic AI-based models to new environments is not enough to secure patent protection under 35 U.S.C. § 101. The court reaffirmed that without concrete technical improvements to the AI technology itself, claims will be dismissed as abstract and patent-ineligible. "Do it with AI" is no longer a viable strategy. Applicants are advised to focus on describing and claiming specific technical improvements in order to survive eligibility challenges.
Background
In Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. Apr. 18, 2025), Recentive Analytics, Inc. appealed to the United States Court of Appeals for the Federal Circuit (CAFC) a decision by the United States District Court for the District of Delaware (No. 1:22-cv-01545-GBW), which held that four patents owned by Recentive Analytics are ineligible for patent protection under 35 U.S.C. § 101.
The patents at issue generally relate to the use of artificial intelligence (AI)-based models to generate network maps and schedules for television broadcasts and live events. They describe how AI-based models can be used to optimize live event scheduling and the creation of network maps, which determine the programs or content displayed by a broadcaster’s channels within particular geographic markets at specific times.
Specifically, the patents at issue are U.S. Patent Nos. 10,911,811 (’811 patent), 10,958,957 (’957 patent), 11,386,367 (’367 patent), and 11,537,960 (’960 patent).
The CAFC categorized the ’367 and ’960 patents, both titled Systems and Methods for Determining Event Schedules, as the “Machine Learning Training” patents. These patents share specifications and are concerned with the scheduling of live events.
The CAFC categorized the ’811 and ’957 patents, both titled Systems and Methods for Automatically and Dynamically Generating a Network Map, as the “Network Map” patents. These patents share specifications and are directed to the creation of network maps for broadcasters.
Issue, Holding, and Reasoning
The CAFC framed the issue as whether "claims that do no more than apply established methods of machine learning to a new data environment are patent eligible."
The CAFC affirmed the district court’s decision, holding that although AI-based technology is important, “patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under § 101.”
While the CAFC used the term “machine learning” throughout its opinion, this article uses the broader terms “AI-based models” and “AI-based methods” to reflect a wider range of technologies.
In reaching its decision, the CAFC reasoned that the claims are directed to abstract ideas because they merely apply generic AI-based models to a new data environment without reciting any technological improvement to the AI-based methods themselves. The CAFC further concluded that the claims do not contain any inventive concept sufficient to transform the abstract idea into patent-eligible subject matter. Merely applying generic AI-based methods to a new data environment, standing alone, does not constitute an inventive concept capable of conferring patent eligibility.
Recommendations to Patent Applicants and Practitioners
For those seeking to protect innovations that apply AI-based methods to a particular field of use, this decision serves as a cautionary reminder: it may not be sufficient to simply claim the application of AI under 35 U.S.C. § 101, even if the application is novel and non-obvious. The CAFC has clearly signaled that "do it with AI" inventions that merely use generic, off-the-shelf AI-based models, without technical improvement or a transformative inventive concept, are no longer patent eligible.
We advise patent applicants and practitioners to consider the following:
- Disclose and claim, in detail, any specific technical improvements achieved by the invention.
Examples of specific technical improvements include solutions to specific technical problems, enhancements to the functioning of a computer system, improvements to AI models, and improvements to AI model training and/or inference infrastructure.
At a minimum, dependent claims should include limitations describing how the specific technical improvements are achieved. - Describe how the AI-based models or methods used by the invention are not generic.
Example AI-related aspects on which to focus include: - Novel model architectures;
- Novel arrangements of multiple models;
- Novel techniques for collecting and/or preparing training data;
- Novel techniques for generating synthetic training data;
- Novel model training techniques; and
- Novel techniques for using AI model outputs.
- When describing novel aspects of the AI-based models or methods, ensure that sufficient details are provided to satisfy the written description and enablement requirements under 35 U.S.C. § 112.
Our team is available to assist in developing effective strategies for drafting and prosecuting patent applications involving AI-based technologies. Please do not hesitate to contact us if you have any questions.
