Quarterly Journal 51-1 Volume 51, Issue 1 June 2023

Machine Learning is a powerful software tool that relies on digital copying. Whether such copying constitutes copyright infringement is not clear. So far, scholarship on whether machine learning constitutes copyright infringement has focused heavily on whether the “fair use” exception to copyright infringement applies to machine learning. Neither scholarship nor case law has provided a clear answer to whether machine learning is fair use.

However, case law can help determine whether machine learning is infringing in the first place, providing clarity on whether machine learning infringes copyright in some cases where fair use is of little help. Some circuits have held that digital copying, like the copying that takes place in machine learning, necessarily constitutes copyright infringement, while others have interpreted the Copyright Act such that digital copying can sometimes take place without infringing. This article argues that the former interpretation neglects the requirement that a copy be fixed for more than a transitory duration, and that the latter interpretation is more consistent with the text and purpose of the Copyright Act. It argues that courts should use the latter interpretation in future copyright infringement cases involving machine learning. In particular, courts should apply the Second Circuit’s decision in Cartoon Network v. CSC Holdings. Using such an approach, courts may find in many cases that machine learning does not infringe copyright.

Powerful case law sits within easy reach of practitioners, but most do not use and many do not even know about it. The Court of Customs and Patent Appeals (CCPA), which always sat en banc and was the predecessor to today’s Court of Appeals for the Federal Circuit (CAFC), developed a rich body of jurisprudence relating to U.S. patent law. In this article we will analyze CCPA precedent by points of law and hope that our efforts will inspire the patent bar to apply these CCPA cases.

Artificial Intelligence has started to produce creative musical works with astonishing speed and complexity. This note discusses whether these AI compositions meet the Copyright Act’s authorship requirements. Focusing on the “quantum of creativity” requirement the Supreme Court laid out in Feist Publications v. Rural Telephone Service Company, the note discusses ways creativity might be identified in musical and legal frameworks. This note suggests that copyright might be vested by identifying this creativity through a principle of “Relatable Expression” in musical AIs. Relatable Expression would allow a limited copyright held by a user capable of authorship as long as that user makes a copyrightable expression input into the AI algorithm, which is present in the output score, and then only to the extent that the user’s expression is essential or intertwined to the final work.

The fashion industry is a unique creative industry in that it thrives despite rampant copying. Many scholars have argued against enacting legislation to protect fashion designs, believing that the lack of copyright protection fuels innovation and creativity. This may be true when considering the copying of fashion designs in its typical context: large, established corporations copying designs from luxury brands to sell at much lower prices. However, the same is not true when fast fashion companies copy designs from small, independent designers who are often the individuals developing the most creative designs. The fast fashion industry has become increasingly harmful for small, independent designers in recent years as social media has increased visibility of their designs. In addition to disproportionately harming smaller independent designers, the fast fashion industry continues to exploit cheap labor and harm the environment—making the need for a legal solution especially ripe today. This note recommends reconsidering legislation to provide copyright protection to certain fashion designs to address the harms facing independent designers, the individuals making clothes, and the environment.

“You are what you eat” still reigns true, but in the twenty-first century, you are the media you consume. This note highlights how consumers unwittingly trade highly personal information, predictive of their inner most thoughts and desires in exchange for modern convenience— a practice that is largely unregulated on the federal level. Currently, intellectual property laws are ill-equipped to enforce ownership rights for consumers in their personal data. But blockchain technology has introduced unique property-like interests in digital assets such as traceability, chain of title, and single-party ownership. Blockchain technology can be leveraged to administer a new form of intellectual property ownership for consumer biometric data. This note proposes the Consumer Blockchain Privacy Act, a blockchain-centered solution to biometric data ownership.