On November 4, 2025, the Director of the United States Patent and Trademark Office (USPTO) designated as precedential an appeals review panel (ARP) decision vacating the Patent Trial & Appeal Board’s § 101 rejection of claims directed to training machine learning models. Ex parte Desjardins, Appeal No. 24-000567 (ARP Sept. 26, 2025) (precedential).
The Board had previously concluded that claims covering continual learning techniques (such as adjusting model parameters to maintain performance across sequential tasks) were directed to an unpatentable abstract idea. The ARP, which included the USPTO Director, reversed that determination, holding that the claims integrated the abstract concept into a practical application by improving the functioning of machine learning models themselves. However, the ARP still rejected the claims under § 103 for obviousness.
Key takeaways
- Technical improvements matter. Artificial intelligence (AI)-related inventions can satisfy Alice Step 2A when they demonstrate technical improvements, such as mitigating catastrophic forgetting and reducing storage complexity.
- No blanket exclusion. The opinion cautions against categorically excluding AI innovations under § 101 and emphasizes that §§ 102, 103, and 112 remain the proper tools for assessing patent scope.
- Precedential impact. The decision signals the USPTO’s commitment to aligning examination practices with US Court of Appeals for the Federal Circuit precedent while fostering innovation in AI and machine learning.
Practice note: For applicants, this precedential designation underscores the importance of framing AI-related claims around specific technical improvements rather than abstract concepts, which can be pivotal in overcoming § 101 challenges.
