Background: Patent Eligibility and Section 101

Under 35 U.S.C. §101, patentable subject matter excludes abstract ideas, natural phenomena and laws of nature. Courts routinely apply the two-step Alice framework to ensure that patent claims offer a true inventive concept rather than claiming basic building blocks of innovation. This standard has shaped the landscape for software and machine learning inventions, where the line between a genuine technical advance and a generic application often determines patentability.

Case Overview: Recentive Analytics v. Fox Corp.

In Recentive Analytics, Inc. v. Fox Corp., Recentive asserted four patents covering the use of machine learning to create event schedules—like NFL games—and generate network maps for broadcasters. These tasks traditionally relied on manual processes by humans. Recentive’s patents described training generic algorithms on real-time data to optimize schedules and content lineups in specific geographic markets, aiming for automated, dynamic updates.

The Court’s Analysis Under Alice

The Federal Circuit affirmed the district court’s dismissal on §101 grounds. At Alice step one, the court found the claims directed to abstract ideas: applying established machine learning to a new data environment. Because Recentive conceded it did not claim improvements to the algorithms themselves, the use of ML remained generic. At step two, the court saw no “inventive concept” beyond the abstract idea; iterative training and real-time data inputs were routine aspects of existing ML techniques.

Implications for AI Innovators

This decision underscores that simply transplanting off-the-shelf machine learning into a novel field won’t satisfy patent eligibility requirements. Innovators must identify or develop specific technical enhancements—such as new model architectures, training methods or data processing techniques—that improve the performance or efficiency of the ML system. Detailed disclosure of algorithmic refinements and their impact on system behavior is critical to survive Alice scrutiny.

Key Takeaways

• Applying generic ML to a new domain remains an abstract idea unless the patent describes concrete algorithmic improvements.
• Patent applicants should document specific enhancements to model architectures or training protocols.
• The Alice test demands more than integration of routine ML steps; it requires inventive contributions to the technology itself.

Key points

• Generic applications of off-the-shelf machine learning in new environments are abstract ideas and patent-ineligible under §101
• Recentive’s broadcast scheduling and network map patents lacked specific technical improvements to their ML algorithms
• Successful AI patents must show concrete algorithmic enhancements beyond standard ML use