Patent Eligibility for AI Inventions: Lessons from the Federal Circuit

The intersection of artificial intelligence and patent law continues to evolve, raising important questions for innovators and patent practitioners alike. In a significant ruling, the U.S. Court of Appeals for the Federal Circuit recently provided new guidance on patent eligibility under 35 U.S.C. § 101 for inventions involving machine learning. The decision in Recentive Analytics, Inc. v. Fox Corp. underscores the challenges—and opportunities—of securing patent protection for AI-related technologies.

In this case, the Federal Circuit confronted, for the first time, the patent eligibility of claims applying generic machine learning methods to specific data environments. The patents at issue described systems for optimizing event schedules and television network maps using machine learning algorithms. Despite recognizing the importance of machine learning as a transformative technology, the Court affirmed the district court’s ruling that the asserted patents were invalid under § 101. This decision provides valuable insights for patent prosecution and litigation strategies in the AI domain.

The Patents at Issue: Machine Learning in Scheduling and Broadcasting
The dispute arose from two families of patents owned by Recentive Analytics. One set of patents focused on using machine learning to optimize the scheduling of live events—a valuable tool in the entertainment industry. The representative claims involved four key steps: collecting event data, training a machine learning model on that data, generating optimized schedules, and updating the schedules based on new information.

The second set of patents targeted the application of machine learning to generate optimized “network maps” for television broadcasting. These maps determine what content is aired on which channels in specific geographic regions and time slots. The claims similarly involved collecting broadcasting schedules, analyzing the data to create a network map, updating the map in real-time, and using the map to determine programming.

While these patents applied machine learning in practical contexts, they did not, as the Federal Circuit emphasized, disclose improvements to the underlying machine learning models themselves. This distinction proved critical to the Court’s analysis.

The Federal Circuit’s Ruling: Applying the Alice Framework
The Federal Circuit applied the familiar two-step analysis established by the Supreme Court in Alice Corp. v. CLS Bank Int’l to determine patent eligibility. Under step one, the Court concluded that the claims were directed to abstract ideas: namely, producing schedules and network maps using generic machine learning techniques. The Court emphasized that human actors have long performed similar tasks, and that applying conventional machine learning to automate these processes did not, in itself, constitute a patentable invention.

Significantly, the patent owner conceded during oral argument that the patents did not disclose any specific improvements to machine learning algorithms. Instead, the patents described the use of standard machine learning techniques to process data and output results faster than human efforts. The Federal Circuit has consistently held that such applications of conventional technology to accelerate human activities remain ineligible for patent protection.

At step two of the Alice analysis, the Court examined whether the claims contained an “inventive concept” sufficient to transform the abstract idea into patent-eligible subject matter. Recentive argued that using machine learning to dynamically generate and update schedules and maps based on real-time data qualified as such a concept. However, the Court disagreed, characterizing these features as inherent to the abstract idea itself. Without a specific technical improvement or novel mechanism for implementing the machine learning, the claims failed to satisfy the requirements of § 101.

The Court also rejected Recentive’s request for leave to amend its claims, noting that the company failed to propose any amendments or identify factual issues that could alter the eligibility analysis. As such, any amendment would have been futile.

Key Takeaways for Patent Practitioners and Innovators
This decision offers important lessons for those preparing and prosecuting patents in the AI space:

1. Generic Applications of Machine Learning Are Insufficient: Simply applying conventional machine learning algorithms to new data environments or industries does not meet the threshold for patent eligibility under § 101. The invention must disclose a specific improvement to the machine learning technology or demonstrate a novel implementation.
   
2. Focus on Technical Improvements: While the Court invalidated the patents in this case, it explicitly left the door open for AI inventions that enhance machine learning models themselves. Improvements to the algorithms, data processing techniques, or system architectures that enable more efficient or effective machine learning could qualify as patent-eligible subject matter.
   
3. Early Consideration of Litigation Defenses: Given the prevalence of § 101 challenges at the motion-to-dismiss stage in patent litigation, companies developing AI technologies should consider potential eligibility issues early in the patent drafting process. Identifying and articulating specific technical contributions in the claims and specifications can strengthen the likelihood of surviving judicial scrutiny.
   
4. Clarify the Inventive Concept: Claims should not merely recite the use of machine learning in a broad or functional manner. Instead, they should delineate how the machine learning method achieves a technical improvement, whether through enhanced model accuracy, novel training methods, or innovative data structures.
   

Looking Ahead: Opportunities in AI Patent Strategy
The Recentive Analytics decision reinforces the need for precision and technical depth in patent applications involving artificial intelligence. While the ruling invalidated patents that applied off-the-shelf machine learning to familiar tasks, it also highlighted a path forward for innovators seeking to protect genuine advancements in AI technology.

As machine learning continues to permeate industries ranging from healthcare to finance, the ability to secure meaningful patent protection hinges on demonstrating how these innovations advance the state of the art. For companies and inventors working at the cutting edge of AI, collaborating with experienced patent counsel can help craft robust applications that withstand legal challenges and maximize the value of their intellectual property.

At Campo Law, we specialize in navigating the complexities of patent strategy for emerging technologies. If your business is developing AI-driven solutions and you want to safeguard your innovations, contact us today to discuss how we can help you achieve strong, defensible patent protection.