Posts in Artificial Intelligence

Artificial intelligence (AI) and quantum computing are compressing the invention cycle itself. That compression is no longer theoretical. Discovery Loop, a new public benefit corporation founded by leading former Google and Google DeepMind researchers, has announced a mission to automate experimental loops of the scientific method; the AItonomy Foundation similarly frames automated experimental loops as a path to accelerating science and engineering. The trend is also visible in Faraday, a recent AI model for automated scientific discovery that links model-driven hypothesis generation with iterative experimentation and R&D workflows, further illustrating how AI systems are moving from passive analysis toward active participation in the scientific method.

On June 30, 2026, the House Judiciary Subcommittee on Courts, Intellectual Property, Artificial Intelligence, and the Internet held a hearing titled, “A Midlife Crisis? IP and the Internet After 40,” which examined whether current U.S. intellectual property laws and enforcement mechanisms are effective against Internet-driven infringement and counterfeiting. The hearing focused on online counterfeiting, digital piracy, and AI-enabled or generated infringement and digital replicas. The hearing also considered the difficulty in enforcing rights against overseas operators, and enforcement tools to address these issues, including Schedule A litigation and website blocking.

For more than two centuries, the patent system has rested on a simple bargain: inventors publicly disclose their inventions in exchange for a limited monopoly. That bargain assumes disclosure primarily benefits competitors, researchers, and the public. Today, however, the audience has changed. Patent filings are now mined not only by competitors, but by governments, intelligence agencies, sovereign investors, and increasingly sophisticated artificial intelligence systems capable of analyzing millions of documents simultaneously. The result is that patent databases have become one of the world’s richest sources of open-source technological intelligence.

As AI assistants take over product discovery and purchase, the likelihood of confusion test is losing the consumer it was built around. And brands are therefore losing their primary line of defense. For decades, building a brand has meant the same, patient work. You develop a differentiated story, then bring it to life everywhere a customer might meet it: Advertising; digital; packaging; product design; customer care. Every piece is guided by consumer insight, a hard-won read on what a person feels at each stage of the journey, from the first moment of awareness through consideration to the purchase itself.

The United States Patent and Trademark Office (USPTO) published a Federal Register Notice Tuesday announcing administrative updates to the General Requirements Bulletin (GRB). The GRB outlines the scientific and technical qualifications required for admission to the registration examination to practice in patent matters before the Office. The update moves one Category B degree, Biomedical Science, into Category A, broadening the pool of degrees that automatically qualify an applicant to sit for the patent bar exam.

On Tuesday, IFI CLAIMS Patent Services released its annual report on artificial intelligence patenting, IFI Insights: Inventing AI. According to the report, worldwide AI patent grants reached 107,279 in 2025, marking the first time the total has surpassed 100,000 in a single year. The figure also represents an 83% increase over the past three years. Globally, AI patent applications reached 209,518 in 2025, of which 23% related to generative AI and 9% related to agentic AI, which is up from 5% in IFI’s previous study.

As government contractors rapidly integrate LLMs and generative AI into their operations, regulators are scrambling to adapt to shifting data ownership requirements.  The General Services Administration (GSA) recently proposed a new GSAR clause (552.239–7001) to standardize AI procurement, and the current draft has been met with widespread industry criticism. By asserting expansive government ownership over “data outputs” and “custom development,” the proposed rule inadvertently exposes a fault line between government data protection and commercial IP rights.

An inventor pastes an unfiled disclosure into a chatbot to clean up the wording. An associate runs a draft specification through an AI tool to pressure-test claim support. A client forwards the analysis an AI gave them about their own case. All routine now, and each has produced a warning that AI quietly destroys patent rights: the prompt becomes prior art, novelty is lost, inventorship is corrupted, the application turns suspect. Some of those warnings are real. Most misidentify what went wrong.

The full U.S. Senate Judiciary Committee held a hearing Tuesday, titled “From Genes to Machines: the Patent Eligibility Debate,” during which witnesses testified about the pros and cons of reforming U.S. patent eligibility law, with human genome patentability featuring as a key sticking point. While the hearing became heated at times—mostly over the peripheral topic of drug pricing—witnesses and Committee members ultimately seemed willing to work together on language that might move the bill forward.

As the full Senate Judiciary Committee prepares to hold a major hearing on the state of U.S. patent eligibility law tomorrow, Amy Semet, Associate Professor of Law at the University at Buffalo School of Law, through her affiliation with the IP Policy Institute, has published a research paper providing the first empirical data on subject matter eligibility issues for artificial intelligence (AI) patents asserted in U.S. district court litigation. The research paper finds that not only are AI inventions invalidated at a higher rate than non-AI inventions, but also, unexpectedly, that obviousness invalidations for AI patents are low due to an incredibly high rate of subject matter eligibility invalidations in the sector.

A faith-technology company has done something the IP world should notice. Gloo — a Boulder, Colorado, firm that serves churches, ministries, and Christian universities, and now trades on the Nasdaq — built a benchmark it calls Flourishing AI Christian, or FAI-C. The finding is blunt: Today’s leading large language models, tested on questions of meaning, character, and faith, come up short. On a 100-point flourishing scale, the frontier models averaged 61. On the faith dimension, they scored worst of all.

The U.S. Court of Appeals for the Federal Circuit (CAFC) issued a decision today in Dental Monitoring SAS v. Align Technology, Inc., affirming a district court ruling that found several patent claims covering deep learning based dental image analysis invalid as directed to ineligible subject matter under Section 101. Dental Monitoring SAS owns U.S. Patent 11,049,248 and U.S. Patent 10,755,409, both of which relate to dental arch image analysis. The ‘248 patent covers “a method for assessing the shape of an orthodontic aligner using a deep learning device,” while the ‘409 patent covers a method for acquiring and analyzing an image of a dental arch using the same type of device.

The House Judiciary Committee’s Subcommittee on Courts, Intellectual Property, Artificial Intelligence, and the Internet held a hearing on Tuesday, titled “A Midlife Crisis? IP and the Internet After 40.” The hearing examined the changes that have occurred over the last four decades of the internet and featured witnesses including celebrities, academics and computer and media experts.

The Canadian Intellectual Property Office (CIPO) has materially changed how examiners are instructed to apply the law on patentable subject matter. In March 2026, CIPO replaced its November 2020 practice notice on computer-implemented inventions, medical diagnostic methods, and medical uses. The 2026 notice represents an important development for current Canadian prosecution practice.

On June 15, 2026, a federal court in San Francisco dismissed xAI’s trade secret claims against OpenAI with prejudice. The dismissal came with prejudice for a reason that sits at the heart of this article: xAI had sought to keep the case alive long enough to obtain, in discovery, the evidence it lacked at filing. The court refused, holding that a plaintiff “was required to have completed its investigation of its claims before filing suit, not after.” Put differently: the court did not treat a job interview as a trade secret extraction ceremony.