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Published in: AI Law

Generic AI Patents – Patent Ineligibility

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The world of AI patents is evolving rapidly, and a recent ruling by the United States Court of Appeals for the Federal Circuit-Recentive Analytics, Inc. v. Fox Corp. (April 18, 2025)-has significant implications for innovators, companies, and patent attorneys. The Court examined the patent eligibility of four machine learning patent applications, and found them ineligible for patent protection.  The case provides insights for patent attorneys into how the courts and the USPTO are interpreting subject-matter eligibility under 35 U.S.C. § 101. Today, we’ll break down the court’s decision, explain the USPTO’s updated guidance, and offer practical advice for those seeking to protect AI inventions.

Summary of the Recent Federal Circuit Ruling

The dispute in Recentive Analytics, Inc. v. Fox Corp. centered on four patents owned by Recentive Analytics which was directed towards methods for using machine learning to generate television network maps and optimize schedules for television broadcasts and live events.

Key Case Facts:

  • Patents at Issue: U.S. Patent Nos. 10,911,811, 10,958,957, 11,386,367, and 11,537,960.

  • Subject Matter Issue: Use of machine learning to optimize event scheduling and network mapping for broadcasters.

  • District Court Decision: The lower court dismissed the case, finding the patents ineligible under § 101.

  • Federal Circuit Decision: Affirmed. The court held that the patents were directed to abstract ideas (using generic machine learning in a particular context) and lacked an inventive concept.

Court’s Reasoning:

Using the Federal Circuit two-step Alice framework the Court found the patents to be invalid:

  1. Step 1: Are the claims directed to a patent-ineligible concept (e.g., abstract idea) versus a patent elligible subject matter (e.g. process, machine, manufacture or composition of matter)?

    • Answer: Yes. The claims merely applied generic machine learning techniques to the well-known problems of event scheduling and network mapping.

  2. Step 2: Do the claims include an “inventive concept” that transforms the abstract idea into a patent-eligible application?

    • Answer: No. The use of machine learning was described at a high level and did not specify any unconventional technological improvement or novel application.

The court concluded that simply applying a well-known machine learning technique to a particular field (e.g., broadcasting) does not make the invention patent-eligible.

The USPTO’s Subject-Matter Eligibility Flowchart

To assess patent eligibility, the USPTO recommends following a structured flowchart:

USPTO SME Patent Test for AI Patents
In determining the subject matter elligibility of an AI Patent, the courts consider the two-part Alice test.

Step 1: Determine Whether the Claim Is Directed to a Process, Machine, Manufacture, or Composition of Matter

  • If not, the claim is not patent-eligible.

  • If yes, proceed to Step 2A.

Step 2A: Prong One – Judicial Exceptions

  • Identify if the claim is directed to a judicial exception (law of nature, natural phenomenon, or abstract idea).

  • For AI patents, this often involves determining whether the claim is merely an abstract idea (e.g., a mathematical algorithm or mental process).

Step 2A: Prong Two – Integration into a Practical Application

  • Even if the claim is directed to a judicial exception, determine if it is “integrated into a practical application.”

  • Example: Does the AI invention improve computer functionality, solve a technical problem, or provide a concrete, real-world benefit?

Step 2B: Inventive Concept

  • If the claim is directed to a judicial exception and not integrated into a practical application, determine if it includes an “inventive concept.”

  • This requires something significantly more than the exception itself-such as a novel, non-generic technological improvement.

Applying the ALICE Test Flowchart to AI Patents

Applying the Alice test flowchart to the Recentive case:

  1. Step 1: The claims are directed to processes (computer-implemented methods).

  2. Step 2A, Prong One: The claims are directed to abstract ideas (using machine learning to optimize schedules and network maps).

  3. Step 2A, Prong Two: The claims do not integrate the abstract idea into a practical application in a way that is significantly different from how it was done before (i.e., using generic machine learning techniques).

  4. Step 2B: The claims lack an inventive concept. They do not specify any novel or unconventional technological improvement.

Result: The patents are not eligible under § 101.

Unique Insights: What Makes an AI Patent Eligible?

The Recentive case and the USPTO’s guidance highlight several key points for AI patent attorneys and applicants:

  1. Specificity Matters: Claims that recite generic machine learning techniques (e.g., “using a neural network”) without specifying how the AI solves a technical problem or improves computer functionality are unlikely to survive a § 101 challenge.

  2. Concrete Technical Improvements: Patents that describe how the AI improves the functioning of a computer, solves a technical problem in a novel way, or provides a concrete, real-world benefit are more likely to be eligible.

  3. Avoid Overgeneralization: Avoid claiming broad, high-level concepts. Instead, focus on the specific technical implementation and the unique advantages provided by the AI.

  4. Document Technical Details: The patent specification should clearly describe the technical details of the AI system, including how it is trained, how it operates, and how it achieves its results.

Practical Tips for AI Patent Attorneys

If you are seeking an AI patent attorney or a company seeking to protect AI inventions, contact a patent attorney who can assist with the following strategies:

  • Draft Narrow, Technical Claims: Focus on the specific technical implementation and improvements provided by the AI.

  • Highlight Technical Improvements: Clearly explain how the AI solves a technical problem or improves computer functionality.

  • Use the USPTO Flowchart: Use the USPTO’s subject-matter eligibility flowchart as a checklist during drafting and prosecution.

  • Stay Updated: Monitor changes in USPTO guidance and Federal Circuit decisions to ensure your strategies remain effective.

Conclusion

The Recentive Analytics, Inc. v. Fox Corp. ruling is a reminder that simply applying AI or machine learning to a well-known problem is not enough to secure a patent. To succeed, AI patent attorneys and applicants must demonstrate that their inventions provide a concrete, technical improvement and are not merely abstract ideas. By following the USPTO’s updated guidance and focusing on the specifics of the AI implementation, you can maximize your chances of obtaining strong, enforceable AI patents.

If you’re searching for an experienced AI patent attorney to help navigate these complex issues, contact us today to schedule a consultation.