Charles Tait Graves: Can You Sue to Protect a Trade Secret You Misappropriated From Someone Else?

Who has standing to sue for trade secret misappropriation? Can a person sue to protect a trade secret they misappropriated from someone else?  Is mere "possession" of the trade secret enough for standing to sue and entitlement to a remedy? Or does a complainant have to prove they are the "owner" of the trade secret in a more formal sense?

A new case suggests the answer may be: "Yes, a person can probably sue to protect a trade secret that they misappropriated from someone else, at least under state law, but not under federal law."

A recent Tenth Circuit case, Snyder v. Beam, addresses this question. In this post, I'll discuss Snyder and its implications, and I will also highlight a 2023 article by Charles Tait Graves, revealing that this issue had been boiling up on the state law side for some time. Snyder did not come out of the blue. Rather, the passage of a federal trade secret statute with a divergent rule has simply brought the issue to the fore.

Common Questions on AI and Trade Secrecy

I have been giving some talks on my article, “Keeping ChatGPT a Trade Secret While Selling It Too,” which is now published in the Berkeley Technology Law Journal. The article addresses a legal puzzle: How can companies protect generative AI technology through trade secret law, while also selling new AI products to the public? I have gotten some really interesting questions from audience members on AI and trade secrecy. I thought I'd share them along with my answers.  If you disagree with my answers or how I am characterizing the technology, I'd really love to hear your thoughts.

Catalog of Court-Mandated AI Disclosures (cf. USPTO Guidance)

Guest post by Victoria Fang, a JD candidate at Stanford Law. Before law school, Fang worked as a patent examiner at the USPTO in the medical imaging and diagnostics space.

In the past year, two “ChatGPT lawyers,” a California eviction law firm, Michael Cohen, and a Colorado attorney have each made headlines for making the same mistake—citing fake cases in legal filings. In attempts to speed up their legal research, these lawyers used generative AI tools like ChatGPT and Google Bard that “hallucinated” nonexistent case law. 

Indeed, use of generative AI by litigants raises issues of accuracy and confidentiality. ChatGPT has been known to “hallucinate” and has other limitations, including being limited to information on the internet before certain date cutoffs and not actively searching the internet or dedicated legal databases for new information. 

Courts have responded to the increased use of generative AI by litigants through judge- or case-specific guidance, standing orders, and local rules, which I have summarized in this spreadsheet. These court mandates have been collated from various news articles, Ropes & Gray’s Artificial Intelligence Court Order Tracker launched in January 2024, and independently searching uscourts.gov. As summarized in the catalog, only a few courts or judges outright prohibit the use of AI. Among courts that do not prohibit the use of AI, some courts require disclosure and/or certification, and others make clear that verification by a human is required. A number of judges even put a special emphasis on confidentiality. More recently, judges have begun requiring litigants to keep a record of the prompts and responses they used, in case issues arise.

We Robot Comments on Ryan Abbott's Everything is Obvious

I’ve been busy with lots of interesting conferences and workshops in the past few weeks, and since I wrote out detailed notes for two of them, I thought I would post them for people who weren’t able to attend. First, my comments from the We Robot conference two weeks ago at Stanford:

Ryan Abbott’s Everything is Obvious is part of an interesting series of articles Ryan has been working on related to how developments in AI and computing affect legal areas such as patent law. In an earlier article, I Think, Therefore I Invent, he provocatively argued that creative computers should be considered inventors for patent and copyright purposes. Here, he focuses on how these creative computers should affect one of the most important legal standards in patent law: the requirement that an invention not be obvious to a person having ordinary skill in the art.

Ryan’s definition of “creative computers” is purposefully broad. The existing creative computers he discusses are all narrow or specific AI systems that are programmed to solve particular problems, like systems from the 1980s that were programmed to design new microchips based on certain rules and IBM’s Watson, which is currently identifying novel drug targets for pharmaceutical research. And Ryan thinks patent law already needs to change in response to these developments. But I think his primary concern is the coming of artificial general intelligence that surpasses human inventors.

Do Machines, and Women, Need a Different Obviousness Standard?

This blog post addresses two different articles that might at first blush seem to be very different. The first is Ryan Abbott's new article Everything Is Obvious, which explores the implications of machine-generated IP for the nonobviousness standard of patentability. Abbott argues the inventiveness standard should be adjusted to take into account the new reality that inventors are frequently assisted by machines or, in some cases, are machines. The second article is Dan Burk's Diversity Levers, published in 2015 in the Duke Journal of Gender Law & Policy. In the article, Burk argues the standard for nonobviousness should be adjusted to take into account the unique mindset and institutional situation of female inventors. (To be clear, Burk is not coming at this issue out of the blue. He has previously written about feminism in collision with copyright, arguing that copyright can be used to suppress feminist discourse).

Abbott's thesis is that, in comparison to machines, humans are all a little less skilled, so a human-based obviousness standard will necessarily lead to too many patents if machines are commonly employed. Burk's point is that, in comparison to men, women are typically more risk-adverse, so a male-based obviousness standard will necessarily lead to too few female-invented patents.