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Ryan N. Phelan

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PatentNext is moderated by Ryan N. Phelan, a registered U.S. Patent Attorney and Software and Computer Engineer. Ryan previously worked in the IT industry as a consultant at Accenture, where he regularly consulted Fortune 500 companies in software and computing technologies. Ryan is featured in the IAM Strategy 300 & 300 Global Leaders guides, and was selected for inclusion in The Best Lawyers in America© list in the practice area of Patent Law. Ryan is also an adjunct professor at Northwestern University’s Pritzker School of Law where he teaches coursework on Patenting Software Inventions. Learn more about Ryan.

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PatentNext Summary: The USPTO has rescinded its February 2024 inventorship guidance for AI-assisted inventions and replaced it with revised guidance issued on November 28, 2025, so prior materials relying on the earlier guidance should be treated as outdated. The revised guidance emphasizes traditional conception-based inventorship, makes clear that AI may assist but cannot be an inventor, and flags a practical priority risk where a foreign filing naming an AI system as the sole inventor can undermine U.S. priority claims. It also underscores that the guidance reflects USPTO examination policy—not binding law—so courts (or Congress) may ultimately provide the controlling rules.Continue Reading The USPTO’s Revised Guidance regarding AI-Assisted Inventions

PatentNext Summary: An Information Disclosure Statement (IDS) is the primary procedural vehicle for satisfying the duty of candor and good faith owed to the USPTO during patent prosecution. The consequences of failing to disclose material information can be severe, including a later finding of inequitable conduct and resulting unenforceability of an issued patent. This article summarizes the legal framework governing materiality, the scope of the disclosure duty, foreign and parallel-application considerations, and practical IDS best practices for attorneys seeking to reduce risk while building a strong prosecution record.Continue Reading The Pitfalls of Failure to Disclose Material Information to the USPTO and Information Disclosure Statement (IDS) Best Practices

PatentNext Summary: Announcing the IPO’s AI Patenting Handbook V3.0, which is a newly updated third edition of IPO’s practical guide for attorneys working with AI-related inventions and technologies. It offers a clear framework for understanding modern AI (including foundation models and generative AI), drafting and prosecuting stronger AI patent applications, and navigating enforcement, global practice, and emerging AI inventorship and governance issues—designed as a day-to-day reference for in-house and outside counsel.Continue Reading Artificial Intelligence (AI) Patenting Handbook: Version 3.0

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 the human mind—stating that AI claim limitations not performable mentally are not “mental processes”—and by distinguishing claims that merely involve a judicial exception (e.g., Example 39) from those that recite one (e.g., Example 47). For Step 2A, Prong Two, examiners must evaluate the claim as a whole to identify a practical application, giving weight to meaningful additional limitations and to improvements in computer capabilities or a technical field, even if the improvement is only implicit in the specification. The Reminders caution against oversimplified “apply it” rejections, require a preponderance of evidence for “close call” §101 rejections, and reinforce compact prosecution that fully addresses §§102/103/112 for every claim in the first action.Continue Reading USPTO Issues “Reminders” Supporting AI and Software Patenting and instructing Patent Examiners on the Limits of Section 101 Patent Eligibility 

PatentNext Summary: Recent rulings from the Northern District of California in Bartz v. Anthropic and Kadrey v. Meta provide the first substantive guidance on how the fair use doctrine applies to AI training, particularly for large language models (LLMs). Both courts found that using lawfully obtained copyrighted books for LLM training can qualify as “highly…

PatentNext Summary: In Brightex Bio-Photonics, LLC v. L’Oreal USA, Inc., the U.S. District Court for the Northern District of California invalidated patent claims relating to AI-driven cosmetic recommendations, finding them directed to an abstract idea under 35 U.S.C. § 101. The court held that while the specification referenced artificial intelligence, the claims themselves failed…

PatentNext Summary: In two recent decisions, the Federal Circuit reaffirmed that merely applying artificial intelligence or digital techniques to a specific “field of use” does not satisfy patent eligibility under 35 U.S.C. § 101. In Recentive Analytics v. Fox Corp., claims directed to AI-assisted television scheduling were deemed abstract for lacking inventive implementation. Similarly, in…

PatentNext Summary: The Federal Circuit’s decision in Recentive Analytics, Inc. v. Fox Corp. found that applying generic machine learning techniques to a new environment, without a specific technological improvement, is patent-ineligible under 35 U.S.C. § 101. The court emphasized that claims must articulate concrete technological advancements rather than merely applying established methods to different domains.

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