In an important decision, Recentive Analytics, Inc. v. Fox Corp., et al., No. 2023-2437 (Fed. Cir. Apr. 18, 2025), the Federal Circuit held 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."
Recentive sued the Fox defendants, alleging infringement of four software patents. Two of the patents claimed machine learning training for scheduling live events, and the other two claimed generating a network map using a machine learning technique for broadcasters to determine what content is displayed for channels at particular geographic markets and at particular times. The district court found all four patents invalid under 35 U.S.C. §101. On appeal, Recentive argued that the claims were eligible based on the application of machine learning resulting in improvements that generated automatically customizable and real-time updated maps. The Federal Circuit disagreed – finding neither the claims nor the specification of the patents described how such improvements were accomplished and that the claims "do not delineate steps through which the machine learning technology achieves an improvement." The Federal Circuit indicated that allowing "a claim that functionally describes a mere concept without disclosing how to implement that concept risks defeating the very purpose of the patent system."
The Federal Circuit also found that, rather than claiming a specific method for "improving the mathematical algorithm or making the machine learning better", the patents claimed the use of known machine learning techniques in a new environment, i.e., event scheduling and network mapping for broadcasting. Further, the Federal Circuit found nothing else in the claims that would transform the abstract idea into an inventive concept, concluding that using machine learning to dynamically generate and update maps and schedules in real-time is simply claiming the abstract idea itself.
This decision highlights the importance of providing technical details that encompass improvements in the underlying technology when drafting software applications, particularly those involving machine learning or artificial intelligence (AI), such as describing how the machine learning or AI models improve machine learning or computer technology, e.g., by reducing latency, memory usage, etc.
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