The Recent Expansion of Patent Eligibility for AI Inventions Before the USPTO
The new United States Patent and Trademark Office (USPTO) Director John A. Squires was sworn in on September 22, 2025, and wasted no time that week in expanding patent eligibility for AI-related inventions. In particular, the new Director presided over the September 26 Appeals Review Panel (ARP) decision in Ex parte Desjardins, Appeal 2024-000567. In its decision, the ARP began explicitly steering USPTO claim interpretation policy under 35 U.S.C. § 101 in a new direction aiming to reduce patent eligibility scrutiny and potentially minimize the now-classic hurdles associated with interpreting abstract ideas and their practical implementations under the established Alice/Mayo framework.
Key Case: Ex parte Desjardins
In Desjardins, the ARP interpreted a claimed machine learning training pipeline as a technological improvement. The ARP identified the claim term relating to training a machine learning model with multiple parameters on a second task to “adjust first values of the plurality of parameters to optimize the machine learning model on the second machine learning task while protecting performance of the model on [a] first machine learning task” as constituting a patent eligible improvement on how the machine learning model operates. The ARP noted advantages such as lower storage capacity requirements, reduced system complexity, and the ability to learn new tasks without losing knowledge on previous tasks.
Previously, such broadly claimed processing operations were often interpreted as applying generic computer parts to an abstract idea. The ARP acknowledged its departure from the norm practiced by most examiners, emphasizing that claims should not be evaluated at such a high level of generality.
Director Squires’ Vision
Director Squires, in a statement before the Subcommittee on Intellectual Property, stressed that “patent eligibility is not an abstract debate” but “a matter of national security, of resilience, and of ensuring that America’s system of innovation remains robust enough to confront the challenges of the twenty-first century.” He advocated for a less restrictive interpretation under 35 U.S.C. § 101, arguing that the statute should not be a blunt instrument to exclude entire technological fields.
Patent Trial and Appeal Board Cases
Since the ARP’s decision in Ex parte Desjardins, the Patent Trial and Appeal Board (PTAB) has largely followed Director Squires’ leadership, finding new acceptance for broadly drafted processing claims within the Alice/Mayo framework. For instance:
Ex parte Mittal
In Ex parte Mittal, Appeal 2025-002097 (November 24, 2025), the PTAB reversed patent eligibility rejections of a claimed method of retraining a deployed machine learning model to detect and correct data-drift over time. The claimed method steps were identified as improvements in the functioning of a computer rather than merely directing use of a computer and machine learning.
Ex parte Brush
The PTAB in Ex parte Brush, Appeal 2025-002376 (November 17, 2025) reversed patent eligibility rejections of a claimed machine-learning system that converts heterogeneous electronic health record data into model-ready feature catalogs. The PTAB determined that the claimed steps integrated any mental process or abstract idea into a practical application, noting advantages in addressing data transfer bottlenecks.
Ex parte Wang
In Ex parte Wang, Appeal 2025-001388 (October 29, 2025), the PTAB identified the claimed steps as not practically performed in the human mind, indicating improvements to operations of a machine learning model. The PTAB directly cited Ex parte Desjardins, reinforcing the notion that claims must reflect improvements to how the machine learning model operates.
Ex parte Kuusela
Conversely, in Ex parte Kuusela, Appeal 2025-001619 (November 24, 2025), the PTAB affirmed patent eligibility rejections for a claimed method of radiology therapy planning due to a lack of limitations directed toward modifying or developing a machine learning model, illustrating that not all machine learning claims are patent eligible.
Implications for the Future
Overall, decisions in Ex parte Mittal, Ex parte Brush, and Ex parte Wang indicate that the PTAB is following the precedent established in Ex parte Desjardins, embodying Director Squires’ call for an expanded interpretation of patent eligible subject matter. The PTAB has started to set a pattern showing expanded avenues of patent eligibility for unique machine learning models. This new policy shift at the PTAB is occurring entirely within the existing statutory and regulatory framework, without requiring Congressional amendment or rulemaking.
The Federal Courts have yet to address these emerging eligibility approaches, leaving open the question of whether they will adopt the same interpretive posture. Until then, the PTAB decisions signal a meaningful recalibration of patent eligibility analysis at the USPTO, significantly influencing the drafting and prosecution strategy for AI inventions moving forward.
