Posts in Artificial Intelligence

U.S. Patent and Trademark Office (USPTO) Director John Squires delivered his first public remarks on Friday, addressing a number of key issues he has been focused on during his first five weeks in office. With respect to the Office’s backlog, he told attendees of the American Intellectual Property Law Association (AIPLA) Annual Meeting that his administration “inherited an unexamined patent application backlog that was an absolute dumpster fire.”

For decades, inventors, practitioners, and researchers alike have faced the same tradeoff. Free tools surface only the most obvious references, missing decisive prior art. Professional platforms offer depth, but require significant training and demand five-figure subscriptions. The patent system promises to promote innovation by making knowledge accessible. But to fully realize this vision, patent knowledge must be available on tap to everyone who needs it, in whichever form best serves each individual user. Today’s AI technology has unlocked the possibility of universal access to professional-grade patent intelligence.

A New York judge ruled on Monday that OpenAI cannot stop a consolidated, multi-district class action brought against by dozens of authors for direct copyright infringement by the outputs of its large language model (LLM), ChatGPT. OpenAI argued that the plaintiffs had failed to allege substantial similarity between the works and ChatGPT’s outputs, but Judge Sidney Stein of the U.S. District Court for the Southern District of New York said  that “[a] more discerning observer could reasonably conclude that the allegedly infringing outputs are substantially similar to plaintiffs’ copyrighted works.”

Reddit filed a lawsuit yesterday against artificial intelligence (AI) company Perplexity AI and three other defendants for their alleged illegal circumvention of Reddit security measures meant to protect misuse of its content and data. Reddit, which describes itself in the complaint as “one of the largest repositories of human conversation in existence,” likened the actions of Oxylabs UAB, AWMProxy, and SerpApi to those of “would-be bank robbers.” Through their development of tools that bypass both Google’s and Reddit’s anti-scraping measures, and their scraping of Reddit content from Google search results, these defendants, “knowing they cannot get into the bank vault, break into the armored truck carrying the cash instead,” said the complaint.

Growing consensus is emerging that artificial intelligence (AI) may assist biotechnology and life sciences companies to draft patents that satisfy written description and enablement requirements. The U. S. Supreme Court’s decision in Amgen Inc. v. Sanofi reiterated that patentees cannot claim a wide functional class of inventions without providing sufficient guidance or examples to enable others to use the full range of what is claimed (Amgen Inc. v. Sanofi, 598 U.S. 594 (2023)). The Supreme Court did recognize in some cases that disclosing a general property common to a class or genus of embodiments may be enough to satisfy the enablement requirement.

In Ex parte Desjardins, the U.S. Patent and Trademark Office’s (USPTO’s) Appeals Review Panel (ARP) – which in this instance included Director John A. Squires, Acting Commissioner Valencia Martin Wallace, and Vice Chief Judge Michael W. Kim – considered a claim directed to training machine learning models. This decision has already been celebrated by many in the IP community for the positive impact it could have for patent applicants and patentees, and indeed this was a great way for Director Squires to make his presence known straight out of the gate.

As the administration continues its efforts to restore our patent system, lost amid all the talk about discretionary denials, injunctive relief, patent fees, etc. is patent quality. Today’s conversations about restoring the remedy of injunctive relief to strengthen our patent system are incomplete unless the other half of the patent coin is mentioned too – patent quality. Otherwise, the administration’s actions will bring us back in time to the era before the America Invents Act (AIA), in which poor quality patents were swords to extort money from operating companies.

The $1.5 billion settlement in Bartz v. Anthropic, recently granted preliminary approval, is the largest copyright settlement in American legal history. That’s impressive, but more important, it shows tech companies must play by the same rules as everyone else. Tech companies regularly ask for special treatment, arguing their innovations are too important to be slowed down by existing laws. But when these companies grow big enough to affect billions of people’s lives, those early shortcuts become serious problems.

Given the recent proliferation of artificial intelligence (AI) patent drafting technology, some in the legal services industry are asking whether AI is the patent profession’s “ultimate bad day,”on par with the dinosaurs’ ultimate bad day posited by Nobel Prize-winning physicist Luis Alvarez in 1980. Like the asteroid thought to cause a mass extinction of the dinosaurs, will AI be a formidable impactor that renders patent prosecution an unprofitable practice area in law firms and alternative legal service providers (ALSPs)? Will AI decimate patent prosecution as a viable career?

Taking their cue from the recent Bartz v. Anthropic saga, the authors of a neuroscience book and professors at the State University of New York filed a class action complaint on October 9 with the U.S. District Court for the Northern District of California, alleging that Apple Inc. committed mass copyright infringement by using pirated books to train its artificial intelligence systems. Plaintiffs Susana Martinez-Conde and Stephen Macknik claimed that Apple built its Apple Intelligence platform, including its OpenELM and Foundation Models, by making unauthorized copies of copyrighted works without permission or compensation.

Dr. Stephen Thaler has taken his fight to get works created by artificial intelligence (AI) machines recognized as copyrightable to the U.S. Supreme Court. In his petition for certiorari, filed October 9 by Ryan Abbott of Brown, Neri, Smith & Khan, Thaler is asking the court to take up the question: “Whether works outputted by an AI system without a direct, traditional authorial contribution by a natural person can be copyrighted.”

In 2025, three federal courts finally confronted a question that had hovered over artificial intelligence for years: can machines legally learn from copyrighted works? Each opinion—Thomson Reuters v. Ross Intelligence, Bartz v. Anthropic, and Kadrey v. Meta Platforms—applied the four-factor fair-use test under 17 U.S.C. §107 to large-scale model training. Together, they form the first real framework for evaluating how copyright interacts with machine learning.

The U.S. Patent and Trademark Office (USPTO) will launch an AI search pilot program for utility patents and will begin accepting petitions to participate in the program as of October 20, according to a draft Federal Register Notice (FRN) published today. The official notice will be published tomorrow, October 8. Petitions will be accepted through April 20, 2026, or the date that each tech center (TC) is docketed at least 200 applications accepted, whichever comes first.

Breakthroughs in artificial intelligence (AI) and quantum computing are being announced at a rapid pace. At the same time, we’re seeing more and more legal disputes related to these emerging and highly competitive markets. Just this month, xAI sued a former engineer alleging theft of trade secrets tied to its Grok AI platform. Meanwhile, in quantum computing, Japanese scientists cracked the longstanding ‘W state’ entanglement problem, raising new possibilities for teleportation, and making hardware IP more valuable than ever.

The recent $1.5 billion settlement between a major AI company and authors over copyright infringement represents far more than legal resolution—it marks the dawn of legitimate AI training data markets. This watershed moment signals the beginning of a necessary evolution toward market-based licensing schemes, much like how the music industry adapted to digital distribution by developing fair compensation frameworks for artists.