In a recent en banc decision concerning the standard for assessing obviousness challenges to design patents, the United States Court of Appeals for the Federal Circuit discarded its long-standing standard, known as the Rosen-Durling test and regarded by many as overly-rigid, and held that the standard for design patents should be the same as for utility patents.  The decision in LKQ Corporation v. GM Global Technology Operations LLC[1] will have significant implications for design patent applicants and owners going forward.

Late last month, the Department of Commerce’s National Institute of Standards and Technology (“NIST”) released four draft publications regarding actions taken by the agency following President Biden’s executive order on AI (the “Order”; see our prior alert here)[1] and call for action within six months of the Order.  Adding to NIST’s mounting portfolio of AI-related guidance, these publications reflect months of research focused on identifying risks associated with the use of artificial intelligence (“AI”) systems and promoting the central goal of the Order: improving the safety, security and trustworthiness of AI.  The four draft documents, further described below, are titled:

Yesterday, the Supreme Court denied certiorari in Hearst Newspapers, LLC v. Martinelli, declining to determine whether the “discovery rule” applies in Copyright Act infringement cases and under what circumstances.  As a result, most circuits will continue to apply the rule to determine when an infringement claim accrues for purposes of applying the Copyright Act’s three-year statute of limitations.

There has been a push at the state and federal level to regulate AI-generated deepfakes that use the voices and likenesses of real people without their approval.  This legislative momentum stems from a series of high profile incidents involving deepfakes that garnered public attention and concern.  Last year, an AI-generated song entitled “Heart on My Sleeve” simulated the voices of recording artists Drake and The Weeknd.  The song briefly went viral before being pulled from streaming services following objections from the artists’ music label.  Another incident involved an advertisement for dental services that used an AI-generated Tom Hanks to make the sales pitch.  As AI becomes more sophisticated and accessible to the general public, it has raised concerns over the misappropriation of people’s personas.  In recent months, several states have introduced legislation targeting the use of deepfakes to spread election-related misinformation.  At the federal level, both the House and Senate are considering a federal right of publicity that would give individuals a private right of action.  At the state level, Tennessee has become the first state update its right of publicity laws targeted towards the music industry, signing the Ensuring Likeness Voice and Image Security (the “ELVIS Act”) into law on March 21, 2024, which takes effect July 1, 2024.

Last week, a divided Supreme Court held in Warner Chappell Music, Inc. et al. v. Nealy et al. that a copyright plaintiff who timely files an infringement lawsuit based on the “discovery rule” may recover damages for infringements that occurred outside the Copyright Act’s three-year statute of limitations period.[1]  A claim generally accrues when an infringing act occurs, but many circuits apply a “discovery rule,” pursuant to which a claim accrues when a plaintiff has (or with reasonable diligence should have) discovered the infringement, which could be many years later.  Courts applying this rule have recently disagreed on how far back damages are available, with the Second Circuit holding that a copyright claimant may recover only three years’ of damages, even if the suit was otherwise timely under the discovery rule.  The Supreme Court rejected that conclusion, holding that “no such limit on damages exists” in the Copyright Act, which “entitles a copyright owner to recover damages for any timely claim” no matter when the infringement occurred.  

Last week the Fourth Circuit reversed a $1 billion copyright verdict against an internet service provider and ordered a new trial on damages allegedly arising from illegal music downloads by its subscribers.  In Sony Music Entertainment et al. v. Cox Communications Inc. et al.,[1] a group of music producers belonging to the Recording Industry Association of America brought suit against Cox for contributory and vicarious copyright infringement based on allegations that Cox induced and encouraged rampant infringement on its service.  In 2019, a jury found Cox liable on both theories for infringement of 10,017 copyrighted works and awarded $99,830.29 per work, for a total of $1 billion in statutory damages.  On appeal, the Fourth Circuit issued a mixed ruling – upholding the finding of contributory infringement but reversing the vicarious liability verdict and remanding for a new trial on damages. 

The United States Patent and Trademark Office (“USPTO”) issued guidance on February 13, 2024 (the “Guidance”) regarding the patentability of inventions created or developed with the assistance of artificial intelligence (“AI”), a novel issue on which the USPTO has been seeking input from various public and private stakeholders over the past few years.  President Biden mandated the issuance of such Guidance in his executive order on AI (see our prior alert here)[1] in October 2023.  The Guidance aims to clarify how patent applications involving AI-assisted inventions will be examined by patent examiners, and reaffirms the existing jurisprudence maintaining that only natural persons, not AI tools, can be listed as inventors.  However, the Guidance clarifies that AI-assisted inventions are not automatically ineligible for patent protection so long as one or more natural persons “significantly contributed” to the invention.  Overall, the Guidance underscores the need for a balanced approach to inventorship that acknowledges both technological advancements and human innovation.  The USPTO is seeking public feedback on the Guidance, which is due by May 13, 2024.

This week saw yet another California federal court dismiss copyright and related claims arising out of the training and output of a generative AI model in Tremblay v. OpenAI, Inc.,[1]a putative class action filed on behalf of a group of authors alleging that OpenAI infringed their copyrighted literary works by using them to train ChatGPT.[2]  OpenAI moved to dismiss all claims against it, save the claim for direct copyright infringement, and the court largely sided with OpenAI. 

The rapid development of AI is introducing new opportunities and challenges to dispute resolution. AI is already impacting the document review and production process, legal research, and the drafting of court submissions. It is expected that the use of AI will expand into other areas, including predicting case outcomes and adjudicating disputes. However, the use of AI in litigation also bears risk, as highlighted by a recent First-tier Tribunal (Tax) decision, where an appellant had sought to rely on precedent authorities that, in fact, were fabricated by AI (a known risk with AI using large language models, referred to as hallucination).[1] While, in this particular case, no further consequences seemed to follow (in light of the fact that the appellant, a litigant in person, “had been unaware that the AI cases were not genuine and that she did not know how to check their validity”[2]), the Tribunal did highlight that “providing authorities which are not genuine and asking a court or tribunal to rely on them is a serious and important issue”,[3] suggesting that litigants may incur certain risks by relying on authorities suggested by AI, unless these are independently verified. On 12 December 2023, a group of senior judges, including the Master of the Rolls and the Lady Chief Justice, issued guidance on AI for judicial office holders, which, amongst other things, discourages the use of AI for legal research and analysis and highlights the risk of AI being relied on by litigants to provide legal advice and/or to produce evidence.[4]

This is the fourth and final part of our series on using synthetic data to train AI models. See here for Parts 1, 2 and 3.