SCOTUS Scraps Recentive’s Petition Seeking Clarity on Eligibility for Machine Learning Claims

The U.S. Supreme Court today declined to grant a petition filed by Recentive Analytics, Inc. asking the Court to weigh in on whether the U.S. Court of Appeals for the Federal Circuit’s (CAFC’s) approach to patent eligibility for machine learning claims is improper.

The petition was filed in October following an April 2025 decision by the CAFC that addressed an issue of first impression in the patent eligibility context; the opinion held that “claims that do no more than apply established methods of machine learning to a new data environment” are not patent eligible.

Recentive originally sued Fox Corp., Fox Broadcasting Company, LLC, and Fox Sports Productions, LLC for infringement of four U.S. Patent, Nos. 10,911,811; 10,958,957; 11,386,367; and 11,537,960. The patents are directed to solving problems in the entertainment industry and television broadcasting with respect to optimizing the scheduling of live events and “network maps,” which “determine the programs or content displayed by a broadcaster’s channels within certain geographic markets at particular times.”

The district court ultimately granted Fox’s motion to dismiss the suit for failure to state a claim on the ground the patents were ineligible under Section 101. The court said the claims of the patents failed at Alice step one as they were “directed to the abstract ideas of producing network maps and event schedules, respectively, using known generic mathematical techniques,” and at step two the claims failed to show an “inventive concept” as “the machine learning limitations were no more than ‘broad, functionally described, well-known techniques” and claimed “only generic and conventional computing devices.’”

On appeal, the CAFC agreed that “the patents are directed to the abstract idea of using a generic machine learning technique in a particular environment, with no inventive concept.” While Recentive argued that its application of machine learning in the patents is not generic because it improved the technology by manipulating the algorithms to function so that “the maps and schedules are automatically customizable and updated with real-time data,” the CAFC said Recentive conceded that the patents do not claim a specific method of improving the algorithm. Furthermore, neither the claims nor the specifications describe how any improvement was accomplished via steps or otherwise. “[T]he only thing the claims disclose about the use of machine learning is that machine learning is used in a new environment,” wrote the CAFC.

The claims are also not rendered eligible just because they perform a task previously carried out by humans with greater speed and efficiency. “We have consistently held, in the context of computer-assisted methods, that such claims are not made patent eligible under § 101 simply because they speed up human activity,” the opinion said. That applies whether the issue is raised at step one or step two, it added.

Turning to Alice step two, the CAFC rejected Recentive’s argument that the inventive concept of the claims is “using machine learning to dynamically generate optimized maps and schedules based on real-time data and update them based on changing conditions.” It instead agreed with the district court that this amounted to nothing more than claiming the abstract idea itself.

The opinion ended with a note that “[m]achine learning is a burgeoning and increasingly important field and may lead to patent-eligible improvements in technology.” The court explained that its instant opinion held “only that 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.”

In June, Recentive filed a petition for rehearing or rehearing en banc arguing that the Federal Circuit’s decision conflates Section 101 patent-eligibility and other patentability inquiries for novelty (35 U.S.C. § 102) and obviousness (35 U.S.C. § 103). That petition was denied in July and Recentive subsequently petitioned the Supreme Court. The questions it presented were:

“1. Whether the Federal Circuit’s approach to patent eligibility under 35 U.S.C. § 101 flouts this Court’s instruction to consider preemption, as discussed in Alice Corp. v. CLS Bank International and Mayo Collaborative Services v. Prometheus Laboratories, Inc.

2. Whether the Federal Circuit erred in holding that claims directed to the application of machine-learning techniques to new data environments are categorically ineligible for patent protection under Section 101, absent a showing of improvement to the underlying machine-learning model itself.”

Fox waived its right to respond.

Following the CAFC decision in Recentive, Dina Blikshteyn of Haynes Boone said those seeking to patent machine learning claims and inventions in the AI space should take heed, adding that their focus “must shift from the trained machine learning model simply generating a result (i.e., generating schedules or analyzing networks as was the case in Recentive) to how the machine learning model performs the task in a technically novel manner.”