Federal Circuit Rules Applying Conventional Machine Learning to New Data Environments Is Not Patent-Eligible

Authored by Babak Akhlaghi on May 8, 2025. In Recentive Analytics, Inc. v. Fox Corp. et al., Case No. 23-2437 (Fed. Cir. Apr. 18, 2025) (Dyk, Prost, Goldberg, JJ.), the Federal Circuit held that applying conventional machine learning methods to a new data environment are not patent-eligible under 35 U.S.C. § 101.

Patents in Question

Recentive is the owner of 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”). These patents cover the use of machine learning to dynamically generate schedules for television broadcasts and live events. The Court, in my view, overly simplified a representative claim from the ‘367 patent. It characterized the claim as a method involving: (i) collecting event parameters and target features; (ii) iterative training of a machine learning model to identify data relationships; (iii) generating an optimized schedule; and (iv) updating the schedule by detecting input changes and iteratively optimizing further. However, the actual claim is significantly more detailed and precise in its focus.

Representative Claim from the ‘367 Patent

Representative claim 1 is reproduced below:

“1. A computer-implemented method of dynamically generating an event schedule, the method comprising:

receiving one or more event parameters for series of live events, wherein the one or more event parameters comprise at least one of venue availability, venue locations, proposed ticket prices, performer fees, venue fees, scheduled performances by one or more performers, or any combination thereof;

receiving one or more event target features associated with the series of live events, wherein the one or more event target features comprise at least one of event attendance, event profit, event revenue, event expenses, or any combination thereof;

providing the one or more event parameters and the one or more event target features to a machine learning (ML) model, wherein the ML model is at least one of a neural network ML model and a support vector ML model;

iteratively training the ML model to identify relationships between different event parameters and the one or more event target features using historical data corresponding to one or more previous series of live events, wherein such iterative training improves the accuracy of the ML model;

receiving, from a user, one or more user-specific event parameters for a future series of live events to be held in a plurality of geographic regions;

receiving, from the user, one or more user-specific event weights representing one or more prioritized event target features associated with the future series of live events;

providing the one or more user-specific event parameters and the one or more user-specific event weights to the trained ML model;

generating, via the trained ML model, a schedule for the future series of live events that is optimized relative to the one or more prioritized event target features;

detecting a real-time change to the one or more user-specific event parameters;

providing the real-time change to the trained ML model to improve the accuracy of the trained ML model; and

updating, via the trained ML model, the schedule for the future series of live events such that the schedule remains optimized relative to the one or more prioritized event target features in view of the real-time change to the one or more user-specific event parameters.”

Background of the Lawsuit

On November 29, 2022, Recentive filed a lawsuit against Fox, alleging patent infringement. Fox moved to dismiss, arguing the patents were ineligible under Section 101. Recentive admitted that the concept of preparing network maps had long existed and that, before computers, humans performed this task manually. It also acknowledged that the patents didn’t claim the machine learning technique itself but rather its application in event scheduling and network map creation.

Recentive contended that its patents involved patentable subject matter by uniquely utilizing machine learning to create customized algorithms. These algorithms, trained on specific data, automatically generate real-time television event schedules. The process incorporated iterative training on event parameters and target features to identify relationships in the data. According to Recentive, this introduced machine learning to the previously unsophisticated field of generating network maps and live event schedules for broadcasting.

District Court Decision

The district court granted Fox’s motion to dismiss, applying the two-step test from Alice Corp. v. CLS Bank International, 573 U.S. 208 (2014). At step one, the court determined that the claims focused on abstract ideas, specifically producing network maps and event schedules using generic mathematical techniques. At step two, the court concluded the claims lacked an “inventive concept” that would make them patent-eligible. The machine learning elements were deemed broadly described and conventional. Recentive appealed.

Federal Circuit Appeal and Ruling

On appeal, the Federal Circuit addressed a novel issue: whether claims applying established machine learning methods to a new data environment are patent-eligible. The Court concluded they are not.

Step One: Abstract Idea

Under step one of the Alice inquiry, the Court evaluated whether the claims were directed to an abstract idea or an improvement in computer capabilities. It held the claims focused on the former, citing Recentive’s admissions that preparing network maps had long been done manually before computers, that the patents did not claim the machine learning technique itself, and that the machine learning used was acknowledged as conventional. The iterative training and dynamic adjustment of the model, according to Recentive, reflected typical machine learning processes, not technological improvements.

Recentive argued its methods were not generic because they dynamically customized maps and schedules using real-time data and revealed previously hidden patterns. However, the Court found the claims failed to disclose a specific implementation of such improvements. Instead, they merely applied machine learning to a new field, event scheduling and network map creation, which the Court classified as an abstract idea. It reiterated that limiting an abstract idea to a specific environment does not render it patent-eligible. Citing cases like SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018), the Court emphasized that applying an existing technology to a new field does not establish eligibility.

The Court also rejected the argument that machine learning’s ability to perform tasks faster and more efficiently than humans established eligibility. It reaffirmed that increased speed and efficiency, absent advancements in computer functionality, do not satisfy § 101. Citing Content Extraction, 776 F.3d at 1347; DealerTrack, 674 F.3d at 1333.

Step Two: Lack of Inventive Concept

At step two, the Court analyzed the claim elements individually and as an ordered combination to determine if they transformed the abstract idea into a patent-eligible invention. It held that merely stating the abstract idea and adding “apply it” was insufficient to meet this standard, citing cases like Trinity Info Media, LLC v. Covalent, Inc., 72 F.4th 1355, 1363 (Fed. Cir. 2023) and Alice. Ultimately, the Court concluded the claims lacked an inventive concept and affirmed their ineligibility under § 101.

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