On April 18, 2025, the United States Court of Appeals for the Federal Circuit ("Federal Circuit") issued a significant decision in Recentive Analytics, Inc. v. Fox Corp., Case No. 2023-2437 (Apr. 18, 2025), affirming dismissal by the District Court of Delaware of patent infringement claims brought by Recentive Analytics, Inc. (“Recentive”) against Fox Corp. and affiliates (collectively, "Fox").  The case centered on four patents owned by Recentive—U.S. Patent Nos. 10,911,811, 10,958,957, 11,386,367, and 11,537,960—relating to using machine learning for generating event schedules and network maps, particularly in the context of television broadcasts and live events. 

The patents generally fell into two categories:

• Machine Learning Training Patents (’367 and ’960): These patents described methods for dynamically generating event schedules by training machine learning models on historical data and updating schedules in real time based on changing parameters. 
• Network Map Patents (’811 and ’957): These patents described methods for generating and updating network maps for broadcasters, using machine learning to optimize outcomes such as television ratings. 

According to the Court, each patent ultimately relied on “generic” machine learning techniques like neural networks, support vector machines, and decision trees with standard computing hardware.  

Fox moved to dismiss the complaint in the District of Delaware, arguing that the patents were ineligible under 35 U.S.C. § 101 as directed to abstract ideas without an inventive concept.  The District Court agreed, dismissed the case, and denied Recentive leave to amend its complaint.  Recentive appealed to the Federal Circuit.  

Federal Circuit Holding
The Federal Circuit affirmed, applying the two-step framework for patent eligibility established in Alice Corp. v. CLS Bank International, 573 U.S. 208 (2014), as set forth below. The Federal Circuit noted that this case presented a question of first impression: “whether claims that do no more than apply established methods of machine learning to a new data environment are patent eligible.” 

Step One: Directed to an Abstract Idea?

The Federal Circuit agreed with the District Court that the claims were “directed to” the abstract ideas of producing event schedules and network maps using known mathematical and computational techniques. The patents did not claim any improvement to machine learning technology or the underlying computer technologies, but applied generic machine learning methods to the fields of event scheduling and network mapping. 

The lower court emphasized that the patents did not disclose any specific implementation or technological improvement in machine learning.  Instead, they simply used machine learning as a tool in a new environment, which is insufficient for patent eligibility. The court rejected Recentive's argument that applying machine learning to a new field (such as event scheduling or network mapping) rendered the claims eligible. 

To support this conclusion, the Federal Circuit also looked in detail at the specifications of the patents in dispute, noting that the “machine learning technology” disclosed is “conventional, as the patents’ specifications demonstrate.” Additional disclosures related to the computers used to implement the purported invention of the claims employed “only generic computing machines and processors” and thus did not “change the character” of the claims away from an abstract idea. The Court found that, during oral argument, Recentive admitted that the patents did not claim a specific method for “improving the mathematical algorithm or making machine learning better.” 

Step Two: Inventive Concept?

After finding that the claims were directed to a “patent-ineligible concept,” the Court moved to the next step of the Alice analysis: determining whether the claims possess an “inventive concept” that is “sufficient to ensure that the patent in practice amounts to significantly more than a patent upon the ineligible concept itself.”  The Court found that the claims here did not. 

According to the Federal Circuit, the use of machine learning to dynamically generate and update schedules or maps was, in the court’s view, simply the abstract idea itself. The claims did not describe any new or improved machine learning techniques, nor did they specify how the machine learning models achieved their results beyond generic, functional language. In particular, according to the Court, the claims did not disclose “a specific implementation of a solution to a problem in the software arts” (quoting Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1339 (Fed. Cir. 2016)) or “a specific means or method that solves a problem in an existing technological process” (quoting Koninklijke KPN N.V. v. Gemalto M2m GmbH, 942 F.3d 1143, 1150 (Fed. Cir. 2019)). 

The Court also rejected the argument that increased speed and efficiency from computer implementation could supply an inventive concept, as this is inherent in using computers for any task. 

Key Takeaway for Patenting Machine Learning Inventions
This decision provides a clear and important message for companies and inventors seeking to patent machine learning innovations:

Patents that merely apply generic machine learning techniques to new data environments or fields of use—without disclosing specific improvements to the machine learning models or methods themselves—are not patent-eligible under § 101. 

To be eligible, a machine learning patent must claim a specific technological improvement, such as a novel machine learning algorithm, architecture, or training method—not just the use of existing machine learning tools to automate or optimize tasks previously performed by humans. The court’s holding underscores the need for patent applicants in the machine learning space to focus on concrete technical innovations in their claims and specifications, rather than broad functional applications of known techniques. 

Thus, it will remain possible to obtain patent protection for AI-related inventions, but practitioners must take care to include and press inventors for details regarding the various components of the AI-model for which protection is sought. This can include details of training data features, parameters, model structure, and predictive performance. These details should be outlined within the specification, and to support eligibility covered by one or more claims. As a result, companies developing machine learning solutions should carefully consider the technical substances of their inventions to ensure that the patent application clearly articulates specific (and non-generic) improvements to machine learning technology. Comprehensive inventor interviews, a rich specification, and focused claim sets will help differentiate new applications from the Recentive patents.

On the other hand, by adding foundational machine learning (and in turn, artificial intelligence) elements to the growing list of general-purpose technologies that will not confer patent eligibility on a claim without “significantly more,” the decision aligns with prior Federal Circuit precedent that abstract ideas do not become patent-eligible simply by being implemented on a known form of computer or applied to a new field. 

Relatedly, the decision opens up a new potential line of challenge for overturning AI-related patents. Parties seeking to overturn such patents in the future should investigate whether any claim elements can be characterized as general-purpose or generic based upon the general knowledge available on the filing date, and whether the specification itself admits that elements are generic or described the limitations only in broad, functional terms.

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