The US Court of Appeals for the Federal Circuit affirmed a district court’s ruling that patents applying established machine learning methods to new data are not patent eligible under 35 U.S.C. §101. Recentive Analytics, Inc. v. Fox Corp. et al., Case No. 23-2437 (Fed. Cir. Apr. 18, 2025) (Dyk, Prost, Goldberg, JJ.)

Recentive sued Fox, alleging infringement of four patents designed to tackle long-standing challenges in the entertainment industry – namely, optimizing the scheduling of live events and refining “network maps,” which determine the content aired on specific channels across various geographic markets at set times. These patents aim to streamline broadcast operations and enhance programming efficiency.

The patents at issue can be divided into two categories: network maps and machine learning training. The machine learning training patents focus on generating optimized event schedules by training machine learning models with parameters such as venue availability, ticket prices, performer fees, and other relevant factors. The network map patents describe methods for dynamically generating network maps that assign live events to television stations across different geographic regions. These methods utilize machine learning to optimize television ratings by mapping events to stations and updating the network map in real time based on changes to the schedule or underlying criteria. The patents’ specifications explain that the methods employ “any suitable machine learning technique” using generic computing machines.

Fox moved to dismiss on the grounds that the patents were subject matter ineligible under § 101. Recentive acknowledged that the concept of preparing network maps had existed for a long time. Recentive also recognized that the patents did not claim the machine learning technique. Nonetheless, Recentive argued that its patents claimed eligible subject matter because they involve using machine learning to generate custom algorithms based on training the machine learning model. Recentive characterized its patents as introducing “the application of machine learning models to the unsophisticated, and equally niche, prior art field of generating network maps for broadcasting live events and live event schedules.”

The district court disagreed and granted Fox’s motion. Applying the Alice framework, at step one, the court determined that the asserted claims were “directed to the abstract ideas of producing network maps and event schedules, respectively, using known generic mathematical techniques.” At step two, the court determined that the machine learning limitations were no more than “broad, functionally described, well-known techniques” that claimed “only generic and conventional computing devices.” The court denied Recentive’s request for leave to amend because it determined that any amendment would be futile. Recentive appealed.

For the Federal Circuit, this 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.

Step One

While Recentive claimed that its machine learning approach was uniquely dynamic and capable of uncovering hidden patterns in real time, the Federal Circuit found these features to be merely standard aspects of how machine learning operates. The Court explained that iterative training and model updates are not breakthroughs but rather are fundamental to the technology itself. Recentive conceded that its patents did not disclose any novel method for enhancing machine learning algorithms – just their routine application. Recentive also conceded that before the advent of machine learning, event planners relied on “event parameters” such as ticket sales, weather forecasts, and other data to guide scheduling decisions, a process the patents themselves acknowledge was traditionally manual and inflexible. The same is true for network maps, which were historically crafted by humans to determine content placement across channels. The Court found that Recentive’s assertion that applying machine learning to this context was more than an abstract concept and therefore rendered the claims patent eligible lacked merit. Courts have consistently held that claims that simply place an abstract idea into a new field of use do not transform it into a patent-eligible invention.

The Federal Circuit made clear that applying existing technology to a new dataset or context does not, on its own, confer eligibility. Federal Circuit precedent teaches that true innovation demands more than repackaging conventional methods within a different domain, regardless of how novel the application may seem. The Court noted that Recentive’s claim that its patents qualified merely because they incorporate machine learning into event planning and network mapping stood in direct contradiction to settled § 101 jurisprudence.

Step Two

Recentive claimed that its patents involved an inventive concept by using machine learning to dynamically create optimized maps and schedules based on real-time data, updating them as conditions changed. The Federal Circuit disagreed, affirming the district court’s decision that this merely described the abstract idea itself. The Court found nothing in the patent or the claims that added anything more to transform the abstract concept of generating event schedules and network maps using machine learning into a patent-eligible invention.

No Point in Amending

The Federal Circuit rejected Recentive’s argument that the district court should have granted leave to amend its complaint, noting that Recentive neither proposed specific amendments nor identified factual issues that would impact the § 101 analysis.

Practice Note: Recognizing that machine learning is a burgeoning and increasingly important field that may lead to patent-eligible improvements in technology, the Federal Circuit was careful to circumscribe its ruling: “Today, 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.”