“Do It With AI” Not Enough – Federal Circuit Strikes Down Machine Learning Patent under § 101

In Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. Apr. 18, 2025), the Federal Circuit affirmed a district court’s holding that claiming the application of machine learning to new data environments, alone, is unpatentable under § 101.

Patent-owner Recentive sued Fox Corp., asserting four patents related to machine-learning-generated network maps and schedules for TV broadcasts and live events. The district court granted Fox’s motion to dismiss, finding the patents ineligible under the two-step Alice inquiry. Under step one, the district court held the claims were “directed to the abstract ideas of producing network maps and event schedules, respectively, using known generic mathematical techniques.” Under step two, the district court found that the patents were not directed to an “inventive concept” because the machine learning techniques described were “broad, functionally-described, well-known techniques” that would not “amount [] to significantly more than a patent upon the [ineligible concept] itself.”

Upon de novo review, the Federal Circuit affirmed, finding “nothing in the claims . . . that would transform the [] patents into something ‘significantly more’ than the abstract idea.” Looking to the patent specifications, the Federal Circuit reasoned that the claimed techniques applied conventional machine learning techniques. Claims merely applying established methods of machine learning to a new data environment are ineligible.