On August 4, 2025, the USPTO issued a memo to patent examiners with the subject “Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101.” [1]
Much has been made of these reminders and what they might signal in terms of a possible shift in how the Office treats rejections under § 101
When we first covered Ex parte Desjardins, we noted that the decision, which was issued just days after Director John Squires took office, could mark the beginning of a new era for AI and software patent eligibility at the USPTO. That prediction now appears well-founded. On November 4, 2025, Director Squires designated the Desjardins
On October 8, 2025, the Office announced the “Automated Search Pilot Program,” a new initiative that will use an internal AI tool to conduct a prior art search before an application undergoes substantive examination. The pilot program slated to begin on October 20, 2025 offers applicants a unique, early look at an AI-generated
PatentNext Summary: The Desjardins decision, co-authored by new USPTO Director John Squires, signals a potential shift toward greater patent eligibility for AI and software innovations. By vacating a § 101 rejection and warning that “categorically excluding AI innovations from patent protection in the United States jeopardizes America’s leadership in this critical emerging technology,” the
PatentNext Summary: The USPTO issued “Reminders” for examiners in Tech Centers 2100/2600/3600 addressing §101 eligibility for software and Artificial Intelligence(AI) / Machine Learning (ML)-related inventions; while not changing the MPEP, the guidance is meant to sharpen examination practice. It clarifies Step 2A, Prong One by limiting “mental process” to what can be practically performed in
Agentic AI is transforming artificial intelligence by enabling systems to act independently, making decisions and solving problems autonomously across various industries. Its potential rapid development poses unique challenges for intellectual property protection, requiring innovative strategies to ensure these advancements are effectively safeguarded within the evolving IP landscape.
Introduction
Last year, we explored how Multimodal AI
PatentNext Summary: Generative Artificial Intelligence (GenAI) patent application filings continue to rise at the U.S. Patent and Trademark Office (USPTO), with a significant concentration in Tech Center 2100, which focuses on computer architecture and software, particularly AI and simulation technologies. GenAI inventions commonly face Section 103 (obviousness) and Section 101 (subject matter eligibility) rejections, with
PatentNext Takeaway: When deciding whether to patent AI-based inventions or maintain them as trade secrets, key considerations include the extent of public disclosure and the detectability of the AI model. Deploying an AI model in consumer-facing devices or making its output public often supports patenting to secure exclusivity. On the other hand, low detectability and sensitive training data, such as personal or medical information, may favor trade secret protection. Balancing these factors alongside the need for sufficient disclosure to meet patent requirements allows businesses to safeguard their innovations while mitigating risks.Continue Reading AI-based Inventions: Patenting vs. Trade Secret Considerations
PatentNext Takeaway: This post highlights the FDA’s increasing regulatory efforts for artificial intelligence (AI) and machine learning (ML)-enabled medical devices (MLMDs), with a focus on managing device AI/ML updates through Predetermined Change Control Plans (PCCPs). The FDA emphasizes five guiding principles for PCCPs to ensure safety, risk management, and transparency for MLMDs throughout their lifecycle.
PatentNext Takeaway: WIPO published a Patent Landscape Report on GenAI. The Patent Landscape Report discusses trends in GenAI, including trends in: GenAI scientific publications, GenAI patents, GenAI models, types of data used in GenAI, and GenAI application areas.Continue Reading WIPO Issues a Patent Landscape Report on Generative AI