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The CAFC Found Machine Learning Patents Ineligible Subject Matter Under § 101
04/29/2025On April 18, 2025, the U.S. Court of Appeals for the Federal Circuit (“CAFC”) affirmed a decision by the U.S. District Court for the District of Delaware (“district court”) that found four Recentive Analytics, Inc. (“Recentive”) patents directed to the use of machine learning—U.S. Patent Nos. 11,386,367 (the “’367 patent”); 11,537,960 (the “’960 patent”); 10,911,811 (the “’811 patent”); and 10,958,957 (the “’957 patent”) (collectively, “asserted patents”)—ineligible under 35 U.S.C. § 101. Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437, ---F.4th---, 2025 WL 1142021 (Fed. Cir. Apr. 18, 2025). The case presented a question of first impression: whether claims that do no more than apply established methods of machine learning to a new data environment are patent eligible.
The ’367 and ’960 patents are described as the “Machine Learning Training” patents. Both patents are titled “Systems and Methods for Determining Event Schedules,” share a specification, and concern the scheduling of live events for television. The CAFC found that the representative claim for the Machine Learning Training patents recites a method containing: (i) a collecting step (receiving event parameters and target features); (ii) an iterative training step for the machine learning model (identifying relationships within the data); (iii) an output step (generating an optimized schedule); and (iv) an updating step (detecting changes to the data inputs and iteratively generating new, further optimized schedules).
The ’811 and ’957 patents are described as the “Network Map” patents. Both patents are titled “System and Methods for Automatically and Dynamically Generating a Network Map,” share a specification, and concern the creation of network maps for television broadcasters. The CAFC found that the representative claim for the Network Map patents recites a method containing: (i) a collecting step (receiving current broadcasting schedules); (ii) an analyzing step (creating a network map); (iii) an updating step (incorporating real-time changes to the data inputs); and (iv) a using step (determining program broadcasts using the optimized network map).
At the district court, all four patents were found ineligible under § 101 because “the asserted claims were directed to the abstract ideas of producing network maps and event schedules, . . . using known generic material techniques,” and the machine learning limitations were no more than “broad, functionally described, well-known techniques” claiming “only generic and conventional computing devices.” The district court dismissed Recentive’s patent infringement complaint and denied its motion to amend, finding that any amendment to the complaint would be futile. Recentive appealed.
Reviewing the district court’s dismissal de novo, the CAFC found that, under Alice step one, the asserted claims were directed to ineligible, abstract subject matter. The CAFC found that the Machine Learning Training and Network Map patents rely on generic machine learning technology to carry out the claimed methods for generating event schedules and network maps. The CAFC further found that the requirement, for the Machine Learning Training patents, that the machine learning model be “iteratively trained” or dynamically adjusted does not represent a technological improvement.
Under Alice step two, the CAFC found that nothing in the asserted patent claims would transform the Machine Learning Training and Network Map patents into something “significantly more” than the abstract idea of generating event schedules and network maps through the application of machine learning. The CAFC noted Recentive’s contention that the inventive concept is “using machine learning to dynamically generate optimized maps and schedules based on real-time data and update them based on changing conditions,” and stated that such position fails to identify anything in the claims that would “‘transform’ the claimed abstract idea into a patent-eligible application.”
Additionally, the CAFC rejected Recentive’s argument that the district court erred in denying the motion for leave to amend, finding that Recentive failed to propose any amendment or identify any factual issue that would alter the § 101 analysis.
In its conclusion, the CAFC stated that “[m]achine learning is a burgeoning and increasingly important field and may lead to patent-eligible improvements in technology.” Nevertheless, “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.”
