In a question of first impression, the Federal Circuit in Recentive Analytics, Inc. v. Fox Corp. (“Recentive”) expanded the abstract idea doctrine established in Alice Corp. to systems and methods implementing machine learning models for conventional applications. While perhaps unsurprising to those familiar with § 101 jurisprudence, this decision marks a significant development in the evolving landscape of subject matter eligibility for AI and machine learning technologies that are ever growing in importance.
I. Brief Summary
Appellant, Recentive Analytics, Inc. (“Appellant”) asserted infringement of four patents—two directed to optimizing live event scheduling and two directed to dynamically generating network maps for broadcasters. Each patent relied on conventional machine learning models applied to operational challenges in television broadcasting.
The Federal Circuit held that the claims were directed to patent-ineligible subject matter, analogizing machine learning models to traditional computer-implemented systems previously deemed “abstract ideas” under Alice Corp. The court concluded that the patents lacked an inventive concept beyond automating tasks historically performed by humans.
“This case presents 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. We hold that they are not.”
– United States Court of Appeals for the Federal Circuit
II. Analysis under Alice Corp.
The Federal Circuit applied the framework set forth in Alice Corp. to assess subject matter eligibility under § 101—often referred to as the “Alice/Mayo two-part test.” At Step 1 of the Alice Corp. framework, courts first determine whether the claim is directed to one of the four statutory categories: process, machine, manufacture, or composition of matter.
If so, analysis proceeds to Step 2A, which asks whether the claim is directed to a judicial exception—such as an abstract idea, a law of nature, or a natural phenomenon. If directed to a judicial exception, analysis proceeds by assessing whether the claim integrates the judicial exception into a practical application on which patent eligibility can be conferred. For example, a claim that applies an abstract idea in a meaningful way, such as by improving an underlying technology, can be deemed patent eligible at Step 2A despite reciting an abstract idea.
If the claim does not integrate the judicial exception into a practical application, analysis proceeds to Step 2B, which evaluates whether the claim recites an “inventive concept” sufficient to transform the abstract idea into a patent-eligible invention. For example, although a claim may not integrate an abstract idea into a practical application under Step 2A, the claim may yet be subject matter eligible under Step 2B if the claim otherwise includes an inventive concept that goes beyond what is well-understood, routine, or conventional.
In Recentive, because the claims were directed to process claims, the Federal Circuit proceeded to Step 2A to determine whether the claims recited an abstract idea and, if so, whether the claim integrated the abstract idea into a practical application.
Step 2A: Identifying the Abstract Idea
At Step 2A, the Federal Circuit evaluated whether the claims were directed to an abstract idea. Importantly, the court equated machine learning technology to conventional computer systems in concluding that Appellant’s claims were directed to an abstract idea, reaffirming that invoking computers—or machine learning models—as mere tools does not confer eligibility.
More specifically, the court found that Appellant’s claims employed well-known machine learning techniques—such as neural networks, decision trees, and support vector machines—applied to a conventional technological environment. The novelty of applying these techniques to television broadcasting to “unearth” patterns in the data “unrecognizable to humans” did not suffice to establish eligibility, as the claims neither improved machine learning technology itself nor disclosed specific technical solutions.
Importantly, the Federal Circuit criticized the patents for broadly reciting functional outcomes—such as “optimizing schedules”—without explaining how these outcomes were technically achieved. This lack of specificity not only undermined subject matter eligibility under § 101, but also highlights potential vulnerabilities under 35 U.S.C. § 112, which requires that claims be supported by a sufficiently enabling disclosure.
The Recentive decision reinforces the importance for AI-related applications to describe the underlying mechanisms—whether algorithmic, architectural, or procedural—by which outcomes are achieved. Without such technical grounding, applications may be susceptible to eligibility challenges under § 101 as well as enablement challenges under § 112.
Step 2B: Searching for the Inventive Concept
At Step 2B, the Federal Circuit swiftly dismissed Appellant’s argument that real-time data optimization constituted an inventive concept that transformed the abstract idea into a patent eligible invention, which the court found were inherent to the routine application of machine learning models. Thus, the court concluded that Appellant’s claims merely reflected the abstract idea itself and did not include enough other subject matter to transform the abstract idea into a patent eligible invention.
Notably, while this is a precedential decision from the Federal Circuit that reinforces current § 101 jurisprudence, the boundaries of patent eligibility in the context of AI remains unsettled. Related cases or future appeals could draw renewed scrutiny from the U.S. Supreme Court, which has previously signaled interest in clarifying the limits of the abstract idea doctrine. Practitioners at Young Basile continue to closely monitor this evolving area, as doctrinal shifts could significantly affect how AI innovations are claimed and protected.
III. Practice Tips
At Young Basile, we work closely with clients innovating in the AI and machine learning sectors to craft sophisticated patent strategies that emphasize concrete technological advancements that are robust in light of this dynamic legal landscape.
While Recentive reinforces that embedding conventional machine learning models into a specialized business context may face eligibility challenges under § 101, this area of law remains dynamic and subject to further refinement. Accordingly, practitioners at Young Basile take great pride in helping clients navigate this relative uncertainty by focusing on claim language and disclosure that emphasizes technical improvements, inventive implementations, and real-world system interactions.
Improving AI Itself? Strategies for Potentially Winning at Step 2A:
Where the innovation is an improvement to system architecture, data processing efficiency, or training methodologies, practitioners at Young Basile recommend focusing on drafting claims to:
- Define Specific Technical Improvements: Clearly articulate how your invention enhances machine learning or the underlying technology itself—whether through novel training methodologies, unique data processing techniques, or improved model efficiency.
- Describe How, Not Just What: Go beyond stating that your machine learning model “optimizes” or “predicts”—detail the technical mechanisms or algorithmic steps that achieve these results.
- Avoid Catch-All Language: Refrain from using open-ended phrases like “any suitable machine learning technique.” Instead, specify the type of model or customization that addresses a technical challenge or provide a detailed list and technical explanation of how varying machine learning options may be utilized in isolation or in multi-modal implementations.
- Highlight Integration with System Architecture: If applicable, explain how your machine learning solution interacts with hardware or software in a non-conventional way to improve system performance.
Applying AI to a Novel System? Strategies for Potentially Winning at Step 2B:
Where the innovation lies with applying AI to a novel system, practitioners at Young Basile recommend focusing on drafting claims to:
- Emphasize non-conventional techniques: Incorporate claim limitations that reflect how machine learning interacts with the novel aspects of the underlying technical system.
- Demonstrate synergy: Demonstrate how the combination of elements produces a technological benefit, not just a business outcome.
IV. Conclusion
Machine learning technologies are now explicitly subject to the same Alice Corp. scrutiny applied to computer-implemented inventions. While Recentive offers important guidance, the boundaries of subject matter eligibility for AI remain fluid and under active judicial development. Patent eligibility remains attainable—especially where claims reflect genuine technological innovation, such as advancements in machine learning model designs or through non-conventional system integration—though the standards continue to evolve. In this fast-moving and relatively uncertain legal landscape, high-quality, strategic legal support is critical to securing and maintaining valuable patent protection in AI innovation.
Do you have questions about this decision and the potential impact on your AI patenting strategies? Please reach out to Kayvon Pourmirzaie at [email protected] who leads our AI Practice Group.