Foley Attorneys Analyze Patent Takeaways in Recent Machine Learning Ruling
Patent Takeaways In Fed. Circ.’s 1st Machine Learning Ruling
This article was originally published in Law360 on May 13, 2025, and is republished here with permission.
The U.S. Court of Appeals for the Federal Circuit’s most recent recasting of patent eligibility in the machine learning space should give artificial intelligence and technology companies pause when considering how to obtain effective, assertable patent assets for their technology.
Specifically, the April 18 ruling in Recentive Analytics Inc. v. Fox Corp. affirmed the invalidity of patents that are interpreted to merely apply existing machine learning techniques to new data environments.
The court reasoned that such claims, absent a demonstrated technical improvement that goes beyond the abstract idea itself, do not satisfy the requirements of Title 35 of the U.S. Code, Section 101.
Given the widespread use of machine learning across industries, this decision could carry significant implications for patent applicants. However, companies in the AI and machine learning space can take a few practical steps when describing their technology in patent applications to avoid falling prey to a similar outcome.
The Federal Circuit’s Approach to Eligibility
The Federal Circuit’s Recentive decision offers new and important guidance for navigating the two-step Alice test, derived from the U.S. Supreme Court’s 2014 decision in Alice Corp. v. CLS Bank International, when pursuing patent protection for machine learning-based inventions.
To address the first step and avoid a finding that the invention is directed to an abstract idea, companies may want to strategically frame their claims around specific technical improvements rather than broad applications of machine learning to known tasks.
The court in Recentive found the claims ineligible because they merely applied generic machine learning methods to conventional industry functions, such as scheduling and broadcasting, which the court viewed as abstract ideas.
To reduce the risk of a similar outcome, companies could focus on highlighting how their invention improves the operation of a computer system or enhances the machine learning technology itself, rather than simply applying machine learning to a domain-specific use case.
For the second step, companies might consider emphasizing how their claimed invention introduces an inventive concept that is not inherent to standard machine learning functionality.
In Recentive, the court found that elements such as iterative training and dynamic updates were routine aspects of machine learning systems and did not represent meaningful technological innovations.
To differentiate their inventions, companies could include detailed descriptions of novel configurations, system architectures or processing techniques that go beyond the expected behavior of generic machine learning.
For example, describing how these features contribute to measurable technical performance improvements, such as reduced computational load, faster model convergence, improved accuracy on complex datasets, or enhanced adaptability to real-time inputs, may strengthen the argument for patent eligibility.
Practical Takeaways for Patent Practitioners
The Recentive decision provides important guidance for patent practitioners drafting and prosecuting machine learning-related patents. In this regard, patents related to machine learning could benefit from clearly identifying and detailing how the invention advances the underlying machine learning techniques or their implementation.
For example, instead of broadly claiming “iterative model training,” the specification could explicitly disclose unique modifications or improvements to traditional training processes. This could include describing novel preprocessing steps, unconventional optimization techniques, or a customized neural network architecture that demonstrably improves prediction accuracy.
The court’s decision also indicated that insufficient disclosure can be a significant weakness, and it is imperative to describe the “how” behind any claimed improvement. For example, if the innovation relates to dynamically adapting models based on real-time data, the specification could include algorithmic or computational descriptions that illustrate how these adaptations significantly outperform conventional methodologies.
Merely applying known or established machine learning methods to new data environments might not, on its own, be enough to establish patent eligibility. Instead, claims could be framed to specify how the innovation contributes beyond the general application of machine learning.
For example, when working with health care data, the claims and specification might describe how the solution addresses technical challenges specific to that context, such as managing sparse or irregular data inputs, improving processing efficiency through faster convergence, or incorporating safeguards that respond to patient privacy and compliance concerns.
Furthermore, while individual steps in a process may be conventional, claims could still be eligible if the combination of steps provides a unique technological benefit. For example, describing how specific sequences of data collection, preprocessing, training, and inference work together to yield efficiency gains or improved accuracy might help reinforce that the claimed invention is more than a generic application of machine learning.
Practical Takeaways for Patent Applicants
The Recentive decision also carries important implications for patent applicants who develop or rely on machine learning-based technologies. In light of this decision, it could be helpful for applicants to reevaluate their patent portfolios to assess how clearly their machine learning-related claims articulate specific technical improvements relative to the state of the art or some other technique.
Internal audits can play a key role in this process, helping to identify patents that may lack detailed disclosures, and prompting consideration of whether continuation filings with enhanced specifications would be beneficial. Such reviews might also uncover opportunities to clarify claim scope, introduce more concrete examples or address emerging legal standards around abstract ideas.
In parallel, collaboration between technical and legal teams can support the development of stronger and more resilient applications. Encouraging early and ongoing engagement among inventors, engineers, and patent professionals might help surface patentable aspects of an innovation that could otherwise be missed.
In some cases where patent eligibility appears uncertain, applicants might consider whether trade secret protection provides a practical alternative, particularly for proprietary algorithms, training data sets, or model architectures that are difficult to detect or reverse-engineer. Such strategies may be relevant in fast-moving markets, where long patent timelines do not align with product cycles.
In this regard, effective trade secret management could include adopting robust confidentiality agreements, role-based access controls, internal awareness initiatives, and data safeguards.
Further, establishing internal review processes to monitor legal developments, assessing their relevance to pending or future applications, and discussing potential adjustments in claim strategy could provide valuable insights as eligibility standards continue to evolve.
Industry Impact
The Recentive decision highlights an ongoing challenge within U.S. patent law: striking a balance between protecting innovation and avoiding overly broad claims that impermissibly preempt entire industry sectors.
This ruling suggests a more restrictive path to eligibility for machine learning inventions, which may influence how patent applications in AI and machine learning are drafted going forward. As a practical consequence, inventors and patent owners may encounter heightened standards for disclosure and claim specificity.
As such, it could be helpful to approach portfolio development with greater attention to how each filing communicates a genuine technical contribution or advancement, rather than relying solely on the application of machine learning techniques to known problems.
What the Court Did Not Hold in Recentive
While the ruling narrows the scope of patent eligibility for machine learning inventions, it also leaves open several possibilities. For example, not all machine learning-related claims appear to be ineligible, and claims clearly aimed at technological improvements in how machine learning techniques operate can still be patented.
Additionally, this decision indicates that simply applying machine learning to a different field, without identifying a technical advancement, may not be sufficient.
However, claims that recite high-level machine learning processes might still be considered eligible if they are paired with inventive technical elements that contribute to a meaningful innovation. For example, a claim directed to a system for autonomous vehicle control might generally recite training a neural network to predict optimal steering angles based on sensor data.
While the training itself might be considered a general machine learning technique, the claim could be deemed eligible if it further specifies an inventive technical element, such as integrating a novel sensor fusion architecture that provides a significantly more accurate representation of the vehicle’s surroundings, leading to demonstrably improved safety and responsiveness compared to existing autonomous systems.
In this case, the inventive sensor fusion architecture, combined with the general machine learning training, could provide the additional technical contribution needed for eligibility.
Such observations may provide applicants with useful context when evaluating how best to frame and support machine learning-related claims going forward.
Conclusion
The Recentive decision serves as a cautionary guide for patent practitioners navigating the complexities of machine learning inventions. It highlights how thoughtful drafting, clear articulation of technical contributions and detailed disclosures can help address scrutiny under the current patent eligibility landscape.
In this regard, practitioners and patent applicants may benefit from adapting their strategies in response to these evolving standards.
[1] Recentive Analytics, Inc. v. Fox Corp. et al. , Appeal No. 2023-2437 (Fed. Cir. Apr. 18, 2025).
[2] Alice Corp. Pty. Ltd. v. CLS Bank Int’l , 573 U.S. 208 (2014).
