The US Court of Appeals for the Federal Circuit (CFAC) has issued a precedential decision, ruling that patents covering the use of machine learning for the scheduling of television broadcasts and live events are ineligible for patent protection.

The decision, which was delivered on Friday (18 April), upheld a district court’s granting of a motion to dismiss by broadcasting giant Fox Corp – represented by Pillsbury Winthrop – on the ground that the patents it had been accused of infringing were directed at ineligible subject matter. 

The appeals court applied the two-step Alice test to reach its decision that four patents owned by AI outfit Recentive Analytics, which was represented by Goodwin Procter, are ineligible under section 101 of the US Patent Act because they “are directed to the abstract idea of using a generic machine learning technique in a particular environment, with no inventive concept”.

However, the court pointed out that machine learning was a “burgeoning and increasingly important field” that may in the future lead to patent-eligible improvements in technology.

The dispute had kicked off in November 2022 when Recentive sued Fox Corp for infringement. It argued in its briefs that its application of machine learning was not generic because it had “worked out how to make the algorithms function dynamically, so the maps and schedules are automatically customisable and updated with real-time data”.

But the court noted that Recentive had admitted that the patents do not claim a specific method for “improving the mathematical algorithm or making machine learning better”.

It perceived nothing in the claims, whether considered individually or in their ordered combination, that would transform the patents into something “significantly more” than “the abstract idea of generating event schedules and network maps through the application of machine learning”.

It held that disputed 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 section 101 of the Patent Act.

in an article discussing the case, James J DeCarlo and Samuel S Stone, respectively a shareholder and an associate in Greenberg Traurig’s IP and technology practice, commented: “The Recentive decision highlights the need to carefully draft claims that go beyond the mere application of existing machine learning techniques.

“Patent applicants should focus on demonstrating how their inventions improve machine learning models or achieve technological advancements. Without such disclosures, machine learning-based patents may face significant hurdles under section 101.”

They added that as machine learning continues to play an increasingly important role in technological innovation, “this decision serves as a reminder of the challenges of securing patent protection in this evolving field”.

Kevin Schubert, principal at McKool Smith, commented: "The court was quick to point out that machine learning is a 'burgeoning and increasingly important field and my lead to patent-eligible improvements in technology,' strongly suggesting that the court is not ready to broadly exclude all AI patents from patent eligibility."