In the precedential decision Recentive Analytics, Inc. v. Fox Corp. (Apr. 2025), the Federal Circuit held that applications of generic machine learning techniques in a particular environment are patent-ineligible under 35 U.S.C. § 101.
Recentive owned U.S. Patent Nos. 10,911,811 (“’811 patent”), 10,958,957 (“’957 patent”), 11,386,367 (“’367 patent”), and 11,537,960 (“’960 patent”). The ’367 and ’960 patents claimed methods involving the application of generic machine learning algorithms to optimize the scheduling of live events. The ’811 and ’957 patents claimed methods that use training data and machine learning models to generate optimized network maps.
The Federal Circuit concluded that claims merely applying established machine learning techniques to a new data environment are not patent eligible. The court explained that requirements such as “iteratively training” or “dynamically adjusting” a model do not represent a technological improvement because they are “incident to the very nature of machine learning.” It further emphasized that “the claimed methods are not rendered patent eligible simply because they perform a task previously undertaken by humans with greater speed or efficiency.”
This decision can be contrasted with Example 47 of the USPTO’s July 2024 Subject Matter Eligibility Examples. In that example, Claim 3—directed to using a neural network to detect malicious network packets—was deemed eligible because it integrated the judicial exception into a practical application by improving network security. Claim 1—directed to an application-specific integrated circuit (ASIC) comprising neurons and synaptic circuits—was also found patent eligible because it did not recite a judicial exception in the first place.
Recentive reinforces that merely implementing generic algorithms in a particular data environment will not suffice for the subject matter eligibility requirement. As we can see from Example 47, one way to demonstrate subject matter eligibility is to emphasize how a claimed model meaningfully enhances system functionality or achieves a technological improvement; another possible – but rarely used in practice – way could be to claim the hardware components to avoid framing the claim as directed to an abstract idea.