Machine Learning AI Invention for Optimizing TV Scheduling, Network Mapping Held Patent Ineligible

In Recentive Analytics, Inc. v. Fox Corp., the Federal Circuit held that an invention using machine learning and artificial intelligence (AI) to optimize television scheduling and network mapping was not patent-eligible under 35 U.S.C. Section 101.

Recentive Analytics, Inc. (“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), all directed to the use of machine learning in optimizing television broadcast scheduling and network mapping. The '367 and '960 patents related to methods for dynamically generating event schedules by training machine learning models on historical data. The '811 and '957 patents were directed to systems for determining broadcast content based on geographic location and time.​

Claim 1 of the ’811 patent recited the following:

1. A computer-implemented method for dynamically generating a network map, the method comprising:

• 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.

Recentive sued Fox Corp., Fox Broadcasting Company, LLC, and Fox Sports Productions, LLC (collectively, “Fox”) for patent infringement. The district court dismissed the case, ruling that the patents were directed to patent-ineligible subject matter under 35 U.S.C. § 101. On appeal, the Federal Circuit affirmed. The court concluded that the patents were directed to abstract ideas and lacked an inventive concept sufficient to render them patent-eligible under 35 U.S.C. § 101.​

The court applied the two-step framework established in Alice Corp. v. CLS Bank International:

Step One: Determine whether the claims are directed to a patent-ineligible concept, such as an abstract idea.​

Step Two: If so, consider the elements of each claim both individually and as an ordered combination to determine whether the additional elements transform the nature of the claim into patent-eligible subject matter.​

At Step One, the court found that the claims were directed to the abstract idea of using machine learning to optimize television scheduling and network mapping. The court noted that the claims merely applied generic machine learning techniques to a particular field without adding any inventive concept.​ The court explained, “The requirements that the machine learning model be “iteratively trained” or dynamically adjusted in the Machine Learning Training patents do not represent a technological improvement.”

At Step Two, the court determined that the claims did not include any additional elements that transformed the abstract idea into a patent-eligible invention. The use of conventional computer components and generic machine learning techniques was insufficient to provide an inventive concept.​ According to the court:

In short, we perceive nothing in the claims, whether considered individually or in their ordered combination, that would transform the Machine Learning Training and Network Map patents into something “significantly more” than the abstract idea of generating event schedules and network maps through the application of machine learning.

The court also made to limit the holding as follows:

Today, 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 under § 101.

Practical Implications

This decision highlights the Federal Circuit's continued scrutiny of patent-eligibility under 35 U.S.C. § 101, particularly software-related inventions implemented using conventional computer technology. Patent practitioners should be alert to the need to show an inventive concept beyond the use of conventional technology when drafting the specification and claims to ensure patent eligibility.​

This case further narrows the pathway for patenting machine learning-based applications, such as scheduling and data analytics solutions. Companies innovating in, for example, optimization, data analytics or content delivery should preferably describe specific technical improvements to maximize their chances that their claimed invention will be considered patent-eligible under §101.

Key Takeaways:

• Merely applying machine learning models to scheduling or other AI applications will not likely meet §101 requirements.
• Claims must recite technological innovation to an AI application, not just, for example, data focused automation.
• Practitioners should focus on changes or improvements to the AI process or changes or improvements to the computing processes, not just outcomes even if such outcomes are improved.

What This Means for Inventors, Patent Applicants and/or Litigants:

• AI-based inventions and patents remain vulnerable without clear technical advancements described in the specification.
• Applying known models to known problems (like scheduling or geographic targeting) will not likely be considered patent-eligible subject matter.
• Innovators/patent applications should frame the specification and claims around how the inventive technology/processes alter system operation, not just how the invention automates decisions.

Practical Tips:

When seeking/contesting patent protection for AI or machine learning inventions, prioritize claims that:

• Emphasize novel architectures or data processing pipelines.
• Demonstrate system-level and/or process improvements.
• Avoid overly functional language tied to business objectives and/or automation.

is a partner in Manatt, Phelps and Phillip’s Intellectual Property Protection and Enforcement business unit and is the author of Patent Prosecution: Law, Practice, and Procedure, 2024 Edition, and Constructing and Deconstructing Patents (2d Edition 2016).

Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 2025 USPQ2d 628, 2025 WL 1142021 (Fed. Cir. 2025).

Id., 134 F.4 at 1216 n. 3.

Id., 134 F.4 at 1212.

Id., 134 F.4 at 1215.

Id., 134 F.4 at 1216.