Angela Dunning

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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.

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.

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. 

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. 

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.

In Punchbowl, Inc. v. AJ Press, Inc., the Ninth Circuit revived a trademark infringement case previously dismissed on grounds that the First Amendment shields “expressive” trademarks from Lanham Act liability unless plaintiff can show the mark (1) has no artistic relevance to the underlying work, or (2) explicitly misleads as to its source.[1]  This is known as the Rogers test, and effectively operates as a shield to trademark liability where it applies.  Last year, the Supreme Court limited application of the Rogers test in Jack Daniel’s Properties, Inc. v. VIP Products LLC, [2] holding that it does not apply where the challenged use of a trademark is to identify the source of the defendant’s goods or services.  In those instances, a traditional likelihood of confusion or dilution analysis is required. 

This third part of our four-part series on using synthetic data to train AI models explores the interplay between synthetic data training sets, the EU Copyright Directive and the forthcoming EU AI Act.

This second part of our four-part series on using synthetic data to train AI models explores how the use of synthetic data training sets may mitigate copyright infringement risks under EU law.

This is the first part of series on using synthetic data to train AI models. See here for Parts 2, 3, and 4.

The recent rapid advancements of Artificial Intelligence (“AI”) have revolutionized creation and learning patterns. Generative AI (“GenAI”) systems have unveiled unprecedented capabilities, pushing the boundaries of what we thought possible. Yet, beneath the surface of the transformative potential of AI lies a complex legal web of intellectual property (“IP”) risks, particularly concerning the use of “real-world” training data, which may lead to alleged infringement of third-party IP rights if AI training data is not appropriately sourced.