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.
Angela Dunning
Training AI models on Synthetic Data: No silver bullet for IP infringement risk in the context of training AI systems (Part 3 of 4)
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.
Training AI models on Synthetic Data: No silver bullet for IP infringement risk in the context of training AI systems (Part 2 of 4)
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.
Training AI models on Synthetic Data: No silver bullet for IP infringement risk in the context of training AI systems (Part 1 of 4)
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.
White House Unveils Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence
On October 30, 2023, the Biden Administration issued a landmark Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (the “Order”), directing the establishment of new standards for artificial intelligence (“AI”) safety and security and laying the foundation to ensure the protection of Americans’ privacy and civil rights, support for American workers, promotion of responsible innovation, competition and collaboration, while advancing America’s role as a world leader with respect to AI.
Significant Roadblocks for Plaintiffs in Generative Artificial Intelligence Lawsuit: California Judge Dismisses Most Claims Against AI Developers in Andersen v. Stability AI
By Angela Dunning and Lindsay Harris.[1] Note, Cleary Gottlieb represents Midjourney in this matter.
On October 30, 2023, U.S. District Judge William Orrick of the Northern District of California issued an Order[2] largely dismissing without prejudice the claims brought by artists Sarah Andersen, Kelly McKernan and Karla Ortiz in a proposed class action lawsuit against artificial intelligence (“AI”) companies Stability AI, Inc., Stability AI Ltd. (together, “Stability AI”), DeviantArt, Inc. (“DeviantArt”) and Midjourney, Inc. (“Midjourney”). Andersen is the first of many cases brought by high-profile artists, programmers and authors (including John Grisham, Sarah Silverman and Michael Chabon) seeking to challenge the legality of using copyrighted material for training AI models.