Appeals Court Finds Machine Learning Applied in a New Data Environment Ineligible for Patenting

On Friday, April 18th, a panel of the Federal Circuit Court of Appeals (CAFC) issued a decision affirming denial of a set of patent claims directed to the use of machine learning as being ineligible for patenting.  See, Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. Apr. 18, 2025). 

The claims in dispute used machine learning to generate network maps and schedules for television broadcast and live events.  The claims also referenced training and updating ML models and other specific machine learning aspects. See below.

The court applied the two-step analysis set out by the Supreme Court under Alice.  Noting that this is a case of first impression, the court held that claims which “do no more than apply established methods of machine learning to a new data environment” are not patent eligible (Recentive Analytics at *10).   The court found iteratively training or dynamically adjusting a model to not be a technological improvement. Also merely applying machine learning to a new environment of event scheduling and network maps was not “a specific implementation of a solution to a problem in the software arts” like Enfish.

This decision has the potential to be quite significant.  It arguably applies a stricter standard than the current standards applied by the U.S. Patent and Trademark Organization (USPTO) in assessing machine learning patent eligibility involving training.  We expect the USPTO may revise its guidelines accordingly and for Examiners to cite to this decision in formulating rejections for lack of eligible subject matter for inventions that relate to the use of machine learning and AI in new applications.  

The door is not completely closed for AI-related inventions that apply known ML techniques.  Patent applicants with AI-related inventions may still be able to show technological improvements to support eligibility but will have to go beyond the mere use of known AI in a new data environment as in the Recentive Analytics decision.  You can read more here.

Authored by: Michael Messinger

Example Claims Struck Down

Machine Learning Patent (11,386,367)

• receiving one or more event parameters for series of live events, wherein the one or more event parameters comprise at least one of venue availability, venue locations, proposed ticket prices, performer fees, venue fees, scheduled performances by one or more performers, or any combination thereof;
• receiving one or more event target features associated with the series of live events, wherein the one or more event target features comprise at least one of event attendance, event profit, event revenue, event expenses, or any combination thereof;
• providing the one or more event parameters and the one or more event target features to a machine learning (ML) model, wherein the ML model is at least one of a neural network ML model and a support vector ML model;
• iteratively training the ML model to identify relationships between different event parameters and the one or more event target features using historical data corresponding to one or more previous series of live events, wherein such iterative training improves the accuracy of the ML model;
• receiving, from a user, one or more user-specific event parameters for a future series of live events to be held in a plurality of geographic regions;
• receiving, from the user, one or more user-specific event weights representing one or more prioritized event target features associated with the future series of live events;
• providing the one or more user-specific event parameters and the one or more user-specific event weights to the trained ML model;
• generating, via the trained ML model, a schedule for the future series of live events that is optimized relative to the one or more prioritized event target features;
• detecting a real-time change to the one or more user-specific event parameters;
• providing the real-time change to the trained ML model to improve the accuracy of the trained ML model; and
• updating, via the trained ML model, the schedule for the future series of live events such that the schedule remains optimized relative to the one or more prioritized event target features in view of the real-time change to the one or more user-specific event parameters.

Network Map Patent (10,911,811)

• receiving a schedule for a first plurality of live events scheduled to start at a first time and a second plurality of live events scheduled to start at a second time;
• generating, based on the schedule, a network map mapping the first plurality of live events and the second plurality of live events to a plurality of television stations for a plurality of cities,
• wherein each station from the plurality of stations corresponds to a respective city from the plurality of cities,
• wherein the network map identifies for each station (i) a first live event from the first plurality of live events that will be displayed at the first time and (ii) a second live event from the second plurality of live events that will be displayed at the second time, and
• wherein generating the network map comprises using a machine learning technique to optimize an overall television rating across the first plurality of live events and the second plurality of live events;
• automatically updating the network map on demand and in real time based on a change to at least one of (i) the schedule and (ii) underlying criteria,
• wherein updating the network map comprises updating the mapping of the first plurality of live events and the second plurality of live events to the plurality of television stations; and
• using the network map to determine for each station (i) the first live event from the first plurality of live events that will be displayed at the first time and (ii) the second live event from the second plurality of live events that will be displayed at the second time.