Federal Circuit’s First Alice Analysis for Machine Learning Patents – Sterne, Kessler Goldstein & Fox P.L.L.C.
On April 18, 2025, in Recentive Analytics, Inc., et. al., 2023-2437, the Federal Circuit addressed an issue of first impression pertaining to the validity of machine-learning patents. The court’s opinion was based on “whether claims which do not go beyond applying established methods of machine-learning to a new environment are patentable” under Section 101.
. The challenged patents were divided into two groups. The first group used machine learning to optimize schedules for live events based on the user’s desired features (e.g. maximizing attendance, revenue or ticket sales), and iteratively updated this schedule with real-time data changes. The second used machine learning to optimize network maps, which determine the programs or content displayed by a broadcaster’s channels within certain geographic markets at particular times.
Applying the two-step Alice framework familiar in software patent cases, the Federal Circuit affirmed the lower court’s holding of subject matter ineligibility “because the patents are directed to the abstract idea of using a generic machine learning technique in a particular environment, with no inventive concept.” But, recognizing that “[m]achine learning is a burgeoning and increasingly important field and may lead to patent-eligible improvements in technology,” the court emphasized the limited nature of its holding: “[t]oday, 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.”
For Alice Step 1, the court held that it was “clear” that the disputed claims were directed to ineligible, abstract subject matter. In the first instance, Recentive admitted its patents didn’t claim machine learning but rather its application to network maps and event schedules. The specifications for both sets of patents were notable in this regard with both teaching that the patent claims employ “any suitable machine learning technique.” The court observed that “[b]oth sets of patents rely on the use of generic machine learning technology in carrying out the claimed methods for generating event schedules and network maps,” and the claimed machine learning technology was “conventional.”
The Federal Circuit rejected Recentive’s argument that the claimed methods’ application of machine learning to a new field of use conferred eligibility. The court recited its black-letter rule that “[a]n abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment.”
Searching then for a technological improvement, the court uncovered none. The claimed iterative training and dynamic adjusting in the machine learning model could not constitute a technological improvement as such functions are “incident to the very nature of machine learning.” Further, the greater speed and efficiency gained through the application of machine-learning to the tasks of event scheduling and network mapping could not confer patent eligibility.
Recentive argued that its claimed application of machine-learning was not generic because Recentive had found a way to make its algorithms function dynamically and uncover previously unrecognized patterns in the data. The court also cited Recentive’s second concession: that the patents did NOT claim a method for “improving a mathematical algorithm or making the machine learning better.” However, the court’s ruling did not rest solely on Recentive’s admissions. It observed that “the claims do not delineate steps through which the machine learning technology achieves an improvement.”
For Alice Step 2, Recentive argued that the inventive concept was the use of machine learning to dynamically generate and update optimized maps and schedules based on real-time data. But the Federal Circuit observed that “this is no more than claiming the abstract idea itself.” The court found nothing in the claims, 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.”
Lastly, the court rejected Recentive’s argument that it should have been granted leave to amend because “Recentive failed to propose any amendments or identify any factual issues that would alter the SS 101 analysis.”
Takeaways:
- 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, claim patent ineligible subject matter under Section 101.
- The Federal Circuit’s Alice analysis for machine language models differs little from its applications in the more traditional software contexts. The Federal Circuit’s Alice analysis for machine language models is similar to its applications in more traditional software contexts. Cir. Cir. Cir. Cir. Practitioners may wish to consider these cases when composing their Section101 arguments.
