PatentNext Summary: The Federal Circuit’s decision in Recentive Analytics, Inc. v. Fox Corp. found that applying generic machine learning techniques to a new environment, without a specific technological improvement, is patent-ineligible under 35 U.S.C. § 101. The court emphasized that claims must articulate concrete technological advancements rather than merely applying established methods to different domains. The ruling offers key guidance for patent practitioners, highlighting the need for detailed descriptions of technical innovation and cautioning against relying on field-of-use limitations or functional claiming. As AI technologies continue to advance, careful patent drafting that focuses on novel implementations will be critical for surviving eligibility challenges.

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The Federal Circuit’s recent decision in Recentive Analytics, Inc. v. Fox Corp., No. 2023-2437 (Fed. Cir. Apr. 18, 2025), marks another significant moment in the evolving intersection of artificial intelligence (AI) and patent law. The ruling affirmed the district court’s dismissal of claims under 35 U.S.C. § 101, holding that applying generic machine learning to a new data environment—without claiming a specific improvement to the technology itself—constitutes an abstract idea and is therefore patent-ineligible.

This case is notable not just for its holding, but also for the clarity it offers on how courts are likely to assess the eligibility of AI-driven innovations going forward. For legal practitioners and applicants alike, the decision offers both a cautionary tale and a guidepost on how to craft applications that can survive § 101 scrutiny.

On a lighter note, the Federal Circuit did recognize the newness and importance of machine learning, and provided (in its conclusion) a statement qualifying its decision to generic machine learning patent claims:

Machine learning is a burgeoning and increasingly important field and may lead to patent-eligible improvements in technology. 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.

Background: The Patents and the Invention
Recentive Analytics (“Recentive ”), whose machine learning technology has been used by the National Football League (NFL) to set its schedule, alleged that Fox used infringing software to schedule its regional television broadcasts, including NFL games. 

Recentive owned four patents across two families:

  1. Machine Learning Training Patents (U.S. Patent Nos. 11,386,367 and 11,537,960) – focused on dynamically generating optimized schedules for live television broadcasts using machine learning models trained on historical data.
  2. Network Map Patents (U.S. Patent Nos. 10,911,811 and 10,958,957) – addressed the generation of “network maps” that determine how television programs are displayed on specific channels in designated geographic markets.

According to Recentive, the traditional manual methods used by broadcasters were crude and incapable of responding to real-time changes in viewer preferences. Their technology purportedly provided a solution through dynamic, machine-learning-based scheduling and map generation.

After being sued for infringement, Fox challenged the validity of the patents under § 101. The district court agreed and dismissed the claims, finding them directed to abstract ideas implemented with generic machine learning techniques.

The Federal Circuit’s Analysis
The Federal Circuit affirmed the lower court’s ruling, reinforcing its approach to § 101 jurisprudence with respect to AI-related claims. Judge Dyk, writing for the panel and noting that the case presented a question of first impression, approached the central issue as follows:

“Whether claims that do no more than apply established methods of machine learning to a new data environment are patent eligible.”

The panel answered this question, finding that such claims were not patent eligible. The panel emphasized that merely using AI or machine learning in a conventional way is not sufficient to convert an otherwise abstract idea into patent-eligible subject matter.

The Federal Circuit found fault with Recentive’s patents for the following reasons. 

1. Generic Use of Machine Learning
The claims did not seek to protect a new machine learning algorithm. Rather, they involved applying conventional machine learning models—described broadly as “any suitable machine learning technique”—to an existing problem in broadcast scheduling. The specifications and claims did not articulate any modification or advancement in the underlying technology. As a result, the use of machine learning was deemed “generic,” and therefore abstract.

2. Lack of Technological Improvement
Recentive argued that their inventions offered a technical solution to a technical problem by dynamically generating schedules and maps. However, the court found that features like iterative training and dynamic data updates are inherent to machine learning itself and do not reflect any technological advancement. Without details about how these outcomes were achieved through innovation, the claims fell short.

3. Insufficient Implementation Details
Critically, the Federal Circuit emphasized that the patents failed to provide implementation details that would distinguish the claims from a mere directive to apply machine learning. The absence of delineated steps or specific algorithms meant that the claims amounted to aspirational goals rather than technical instructions.

4. Field-of-Use Limitations
The court rejected the idea that applying AI to a novel domain—such as television scheduling— could rescue the claims. A field-of-use limitation is insufficient to render an abstract idea patent eligible. Merely moving generic AI into a different industry does not convert it into an inventive concept under § 101.

5. Speed and Efficiency Are Not Enough
Finally, the court dismissed arguments based on performance improvements. Speed and efficiency gains, without a corresponding technological breakthrough, do not transform an abstract idea into patent-eligible subject matter.

Comparison to Past Precedents
Recentive sought to analogize its claims to precedents where software patents were upheld:

  • In Enfish, LLC v. Microsoft Corp., claims were found eligible because they recited a specific improvement to computer database functionality.
  • In McRO, Inc. v. Bandai Namco Games America Inc., the use of rule-based automation for lip-syncing yielded a technological improvement.
  • In Koninklijke KPN N.V. v. Gemalto M2M GmbH, the claims addressed error detection in data transmission—a concrete technical advance.

The Federal Circuit rejected these comparisons, stating that Recentive’s patents lacked the detailed implementations and clear technological benefits present in those cases.

Instead, the court likened the patents to those in Electric Power Group, LLC v. Alstom S.A. and SAP Am., Inc. v. InvestPic, LLC, where the claims involved collecting and analyzing data without describing how the methods improved technology.

Alice Step Two: The Inventive Concept
Under Alice Corp. v. CLS Bank International, step two of the eligibility test asks whether the claims contain an “inventive concept” sufficient to transform the abstract idea into a patent-eligible application.

Recentive pointed to the use of real-time data, dynamic outputs, and machine learning as their inventive concept. The court was not persuaded. These features were considered part and parcel of what machine learning already does. Since there was nothing unconventional about their use, the claims failed Alice step two.

Implications for AI and Software Patents
This decision illustrates a broader trend in AI patent jurisprudence: courts remain skeptical of claims that rely on generic use of machine learning without articulating technological innovation. Importantly, the court left the door open for AI patents that improve the underlying algorithms or computer functionality—but it signaled that “do it using AI” will not suffice. This is not surprising given that the Supreme Court’s Alice decision held that generic claims reciting, in effect, “do it on a computer” are also not patent-eligible.

Attorneys drafting AI-related patent applications must therefore be vigilant in distinguishing true technological advancements from applications of known techniques.

Best Practices: Drafting Patent Applications to Survive § 101 Challenges
The Recentive decision underscores the importance of meticulous drafting when seeking patent protection for AI-driven innovations. Below are some best practices to improve the chances of success:

1. Claim a Specific Technological Improvement
Avoid merely reciting the use of machine learning or AI. Instead, clearly identify a novel technical feature or architecture. Demonstrate how the invention changes the way a computer operates or how the algorithm improves performance.

2. Describe the Innovation in Detail
Include specific implementation steps, data flows, and algorithmic mechanisms. Vague language such as “any suitable machine learning model” invites eligibility challenges. Provide concrete examples and explain how the result is achieved.

3. Differentiate from Conventional Methods
Show how the invention departs from prior art or conventional techniques. Highlight not only what the invention does but how it accomplishes it in a novel and non-obvious way.

4. Avoid Field-of-Use Limitations
Ensure the inventive concept is not limited to the application of generic technology in a new context. Field-specific applications are insufficient unless coupled with a unique technical implementation.

5. Include Technical Benefits in the Specification
Tie the benefits of the invention—such as reduced computational load, increased accuracy, or novel data processing—to concrete technical improvements. Avoid framing benefits solely in terms of business advantages or efficiency gains.

6. Claim Structurally—Not Functionally
Whenever possible, claim system components, data structures, and processes in structural or algorithmic terms rather than abstract functional language. Courts are more likely to uphold claims that describe specific arrangements and processes.

7. Use Dependent Claims Strategically
Include dependent claims that recite specific machine learning models, feature extraction methods, or training protocols. This helps in narrowing the scope of the claims while preserving eligibility under § 101.

Conclusion
The Recentive decision serves as a timely reminder that AI-driven innovations must be carefully framed to withstand eligibility scrutiny. Generic applications of machine learning are unlikely to survive § 101 challenges unless tied to specific, concrete technological improvements. As AI continues to evolve, so too must the strategies employed to protect it through intellectual property.

Patent practitioners must adapt by focusing not only on the novelty and utility of an invention, but on articulating the technical “how” in a way that the courts will find both meaningful and eligible.

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