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Supreme Court Declines Tackling Important Machine Learning Patent Case
On December 8th, the U.S. Supreme Court declined a petition to review, and therefore, let stand the April 2025 decision of the U.S. Court of Appeals for the Federal Circuit (CAFC) in Recentive Analytics, Inc. v. Fox Corp. The CAFC had held that patent claims that merely apply machine learning (a form of Artificial Intelligence or “AI”) to automate tasks or analyze data—even in novel settings—risk being classified as unpatentable abstract ideas unless they disclose specific improvements to the underlying technology or the machine learning algorithm itself.
Previously, we had reported on the CAFC’s Recentive decision. Recentive had sued Fox in Delaware district court alleging that Fox’s use of software for TV scheduling and content mapping infringed four of its patents. Recentive’s patents claim methods and systems of using machine learning to dynamically generate event schedules and update network maps in real-time. The Delaware district court granted Fox’s motion to dismiss, holding that the asserted patents were directed to ineligible subject matter under 35 U.S.C. § 101. Upon appeal the CAFC affirmed the Delaware decision.
In October, Recentive petitioned the Supreme Court to hear the case to address two specific questions:
- Whether the Federal Circuit’s approach to patent eligibility under 35 U.S.C. § 101 flouts the Supreme Court’s instruction to consider preemption, as discussed in Alice Corp. v. CLS Bank International and Mayo Collaborative Services v. Prometheus Laboratories, Inc.[1]
- 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.[2]
The Supreme Court denied the petition. As is the case with most of its denials, the Court provided no reasons or opinion. One can only therefore speculate: Is the Court saying that it fully agrees with the current framework set forth by the CAFC in Recentive, at least for now? Or, is it that the facts of the case are not compelling enough to address the questions presented by the petitioners, particularly in view of the Supreme Court’s prior decisions such as Alice? Or, perhaps, the Supreme Court is not yet ready to address the difficult questions raised by the use of AI and machine learning in inventions?
Nevertheless, as set forth in our prior article, the Recentive case underscores the need for patent applicants to go beyond merely describing functional uses of AI and machine learning in their patent claims and instead claim specific technological innovations to improve their chances for obtaining valid and defensible patents.
[1] Recentive argued that its patents would not have an overreaching effect of preempting the field of event scheduling and network mapping, but rather are entitled to protection for the specific advantages their technology provides.
[2] The term “categorically” may be an exaggeration and a subtle attempt by petitioner to inflate the importance of the question. Rather, the Federal Circuit in its holding was more measured in its statement that “[t]oday, we hold 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.”
