Posts in Trade Secrets

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.

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.

The U.S. Court of Appeals for the Federal Circuit (CAFC) in a precedential decision today reversed a district court’s judgment upholding a jury verdict of trade secret misappropriation and damages. The CAFC majority, with Judge Prost dissenting, found that the statute of limitations to bring a claim under the Defend Trade Secrets Act (DTSA) had expired. The decision reverses a $59.4 million damages award and was authored by Judge Dyk.

On Friday, the U.S. Court of Appeals for the Federal Circuit issued a precedential decision in Versata Software, LLC v. Ford Motor Co. reversing the Eastern District of Michigan’s ruling on judgment as a matter of law (JMOL) reducing Versata’s unjust enrichment damages to $0 after holding that the district court erred in precluding such damages from being awarded by a jury. The Federal Circuit also reinstated the jury verdict’s full award on Versata’s breach of contract claim after finding that the jury properly relied on a damages basis established via the parties’ licensing history, and affirmed the district court’s denial of JMOL to Ford on the knowledge required for trade secret liability.

Artificial intelligence (AI) is moving faster than traditional intellectual property (IP) strategy was designed to handle. The issue is not simply speed, although speed is certainly part of the problem. The deeper challenge is that AI innovation does not fit neatly into the legacy IP operating model. The assets, development cycles, regulatory environment, and commercial pathways are all different. And the value drivers are increasingly distributed across a spectrum of AI-related intangible domains, which include patents, trade secrets, data rights, software architecture, licensing models, and customer contracts.

The U.S. Supreme Court has been asked to grant certiorari to resolve whether the Defend Trade Secrets Act (DTSA) permits an unjust enrichment award without any showing of actual loss resulting from the defendant’s misappropriation of trade secrets. The defendant in Tata Consultancy Services Ltd. v. Computer Sciences Corp. has petitioned for certiorari, arguing that actual loss is a prerequisite for an unjust enrichment award. The petition challenges a Fifth Circuit decision affirming a $56 million unjust enrichment award and a $112 million punitive award in favor of Computer Sciences Corp. (“CSC”), measured by the costs Tata Consultancy Services (TCS) avoided through its trade secret theft rather than by any proven actual loss to CSC.

A recent U.S. Court of Appeals for the Federal Circuit decision applying California trade secret law offers a timely reminder that published patent materials cannot easily be recast as trade secrets. In International Medical Devices, Inc. v. Cornell, the Federal Circuit reversed trade-secret liability and vacated related damages and injunctive relief after concluding that the plaintiffs had not shown protectable trade secrets under the California Uniform Trade Secrets Act.

China was not the only actor being scrutinized today during a full Senate Judiciary Committee hearing, titled “Stealth Stealing: China’s Ongoing Theft of U.S. Innovation.”  Senator Thom Tillis (R-NC) stood in for Senator Chuck Grassley (R-IA) as Chair and opened the hearing with a warning that, in addition to its blatant IP theft—which is estimated to cost the United States between $400 billion and $600 billion per year—China is more recently evolving from “imitator to innovator.” “The United States must overcome its historic and ideological views that China is unable to innovate,” Tillis said.

The U.S. Court of Appeals for the Federal Circuit (CAFC) issued a precedential decision Friday in International Medical Devices, Inc. v. Cornell, reversing the United States District Court for the Central District of California’s denial of judgment as a matter of law (JMOL) on trade secret misappropriation, breach of contract, and patent invalidity claims. The district court had found that Dr. Robert Cornell and several other defendants misappropriated four trade secrets related to cosmetic penile implants, breached a nondisclosure agreement (NDA), and that two patents were invalid for failure to name an inventor. The CAFC reversed the denial of JMOL on those claims but affirmed the district court’s denial of JMOL for the defendants with respect to counterfeiting liability.

For decades, management scholars and practitioners have grappled with what I call the “knowledge problem” in organizations—the stubborn difficulty of codifying and transferring expertise that resides in individual employees’ heads and habits. The most valuable organizational knowledge has always been tacit: the judgment calls, the contextual adaptations, the intuitive “feel” for how things get done. This knowledge walked out the door every evening and, more problematically, departed permanently when employees moved to competitors.

Two recent federal district court decisions highlight the significant risks of sharing confidential information with a generative AI platform. In Trinidad v. OpenAI, the court dismissed the plaintiff’s trade secret claims under the Defend Trade Secrets Act (DTSA) because the plaintiff had voluntarily disclosed her allegedly proprietary frameworks to OpenAI while using ChatGPT to create them.Then, Judge Rakoff in United States v. Heppner held that documents created using publicly available generative AI are not protected by the attorney-client privilege—in part because communications memorialized through an AI platform are not confidential when the platform is not contractually bound to keep them secret.

The strength of many of today’s most valuable companies is based significantly on intangible assets, like trademarks, patents, trade secrets and brand reputation. Hard-assets or “tangibles,” like real estate and equipment, are a relative blip on many large businesses value radar. What is surprising is the extent to which these companies are dominated by intangible assets and what that means for how they are understood and financed.

Whether the plaintiff has adequately identified the trade secrets that have allegedly been misappropriated is a commonly litigated and critical issue under the Defend Trade Secrets Act (DTSA). Unlike other types of intellectual property—such as patents, copyrights, and trademarks—where the property has already been identified and registered, trade secrets by definition are secret and cannot be identified publicly without destroying the subject matter of the plaintiff’s legal claim. Yet defendants still need to know what secrets they have allegedly misappropriated, and the court needs to know what the case is about.

The U.S. Court of Appeals for the Federal Circuit (CAFC) on Wednesday affirmed a district court decision finding that Applied Predictive Technologies, Inc. (APT) had failed to sufficiently identify its trade secrets under either the state or federal trade secrets statutes. Business analytics company APT sued MarketDial, Inc. and John Stoddard in the U.S. District Court for the District of Utah for patent infringement and trade-secret misappropriation and later added Morgan Davis as a defendant and added breach-of-contract and tort claims.