by Lee Gesmer | Jul 26, 2024 | General
This is the second in a 3-part series of blog posts on Large Language Models (LLMs) and copyright. Part 1 is here: Copyright And The Challenge of Large Language Models.
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As an intellectual property lawyer who’s been involved with copyright law for almost 40 years, I’ve seen my share of copyright controversies. But the challenges posed by artificial intelligence and machine learning may be in a league of their own.
The central issue is this: AI systems are being trained on vast amounts of copyrighted material. Is this fair use or widespread copyright infringement? This is not just an academic question. Multiple lawsuits against AI companies have been filed, bringing this issue into sharp focus.
To understand how the courts are likely to evaluate these cases we need to take a look at three key legal developments that have shaped the recent fair use landscape: the two Google Books cases, Google v. Oracle, and Andy Warhol Foundation v. Goldsmith. These cases provide the current framework for how courts are likely to approach the pending AI copyright cases.
The Google Books Cases
Let’s start with the Google Books cases, which in many ways set the stage for the current AI copyright dilemma.
Authors Guild v. Google. In 2015, the Second Circuit Court of Appeals decided Authors Guild v. Google, a copyright case that had been winding through the courts for a decade. Google had scanned millions of books without permission from rights holders, creating a searchable database.
The court’s decision in favor of Google based on fair use hinged on several key points:
Transformative Use: The court found Google’s use highly transformative. Google wasn’t reproducing books for people to read; it was creating a new tool for information location and analysis.
Snippet View: While Google allowed users to see “snippets” of text containing their search terms, the court found this didn’t substitute for the actual books.
Effect on the Market: Crucially, the court found that Google Books was more likely to enhance the market for books than harm it.
Public Benefit: The court emphasized the immense public benefit of Google Books as a research tool.
This decision was a landmark fair use decision, especially for its treatment of mass digitization and non-expressive use of copyrighted works.
Authors Guild v. HathiTrust. A sister case in this saga was Authors Guild v. HathiTrust, decided by the Second Circuit in 2014. HathiTrust, a partnership of academic institutions, had created a digital library from scans provided by Google.
The court found that the creation of a full-text searchable database was a fair use, even though it involved copying entire works. Importantly, the court also held that the use of the database for text mining and computational analysis was transformative and non-expressive.
These two cases laid important groundwork for considering computational uses of copyrighted works as potentially fair use.
Google v. Oracle: Functional Code and Fair Use
Fast forward to 2021 for another landmark fair use case, this time involving software code. In Google v. Oracle, the Supreme Court held that Google’s copying of 11,500 lines of code from Oracle’s Java API was fair use.
Here’s why this matters for AI:
- Nature of the Copyrighted Work: The Court emphasized that the copied code was largely functional, not expressive. It was “further than are most computer programs… from the core of copyright.” This suggests a sliding scale of copyright protection, with functional works receiving less protection than expressive ones.
- Transformative Use: The Court found Google’s use transformative because it “sought to create new products” and was “consistent with that creative ‘progress’ that is the basic constitutional objective of copyright itself.”
- Market Effect: The Court downplayed the market harm to Oracle, noting that Oracle was “poorly positioned to succeed in the mobile phone market.”
This decision seemed to open the door for tech companies to make liberal use of others’ code in the name of innovation. But before we could fully digest its implications, the Court threw another curveball.
Andy Warhol Foundation v. Goldsmith: Appropriation Art and Fair Use
In 2023, the Court decided Andy Warhol Foundation v. Goldsmith (AWF), a case dealing with Warhol’s use of a photograph of the musician Prince. This decision put some significant guardrails on fair use:
- Transformative Use: The Court clarified that creating a new work with “new expression, meaning, or message” isn’t enough for fair use. The use needs to have a “further purpose or different character” from the original.
- Commercial Nature: The Court emphasized the importance of considering the commercial nature of the use, stating that “the degree of transformation must be weighed against the commercial nature of the use.”
- Compelling Justification: The Court introduced the concept of a “compelling justification” for copying, particularly when the secondary use serves a similar purpose to the original work.
- Market Harm: The Court stressed the need to consider not just the primary market for the original work, but also potential licensing markets.
This decision was a win for content creators, but it complicated the fair use analysis for technologies like AI.
Applying These Cases to AI
So how do these decisions apply to AI? It’s complicated, to say the least.
The Input Problem
When AI systems ingest copyrighted works as training data, they’re copying entire works. Traditionally, this weighs against fair use. But AI developers have argued, based on the Google Books cases, that this is non-expressive use – they’re not using the works for their artistic value, but as data to learn patterns.
However, there are key differences with AI:
- Reconstruction of Inputs: Some AI systems can reconstruct substantial portions of their input works, blurring the line between non-expressive and expressive use. This is particularly true in the case of sounds and images.
- Expressive Outputs: Many AI systems are designed to produce expressive outputs in the same medium as their inputs. This may undermine the argument that the use of input works is purely non-expressive.
- Commercial Purpose: Many AI applications are commercial, which may weaken the non-expressive use argument, especially in light of AWF‘s emphasis on commercial purpose.
The Output Problem
The outputs of AI systems trained on copyrighted works raise another set of issues. Are these outputs transformative fair uses, or are they derivative works that infringe copyright?
The AWF decision suggests that creating something new isn’t enough – there needs to be a different purpose or character. An AI that creates images in the style of a particular artist might struggle to meet this standard.
On the other hand, the Google v. Oracle decision’s emphasis on “creative progress” might provide some support for AI developers, especially if they can show their systems are creating genuinely new and innovative works.
I’ll consider these issues in Part 3 of this series. However, they are intertwined with the “input” issues that are the primary focus of this post.
The Market Factor
One of the key factors in fair use analysis is the effect on the market for the original work. AI complicates this in ways that go beyond even the Google Books cases:
- Emerging Market for Training Data: Companies like the Associated Press have started licensing their content for AI training. If this becomes an established market, it could weigh against fair use claims for unlicensed use of training data.
- Competition with Human-Created Works: AI outputs could potentially compete with human-created works in a way that impacts the market for the originals. This is different from Google Books, which the court found might enhance the market for books.
The AWF decision’s emphasis on considering potential licensing markets further complicates this issue. If courts consider the market for training data as a legitimate potential market for copyrighted works, it could significantly narrow the scope of fair use for AI training.
Potential Solutions and Future Directions
As challenging as these issues are, we need to start thinking about solutions. Here are some possibilities:
- Licensing Schemes: Develop comprehensive licensing systems for AI training data, similar to those that exist for certain music uses. This could provide a mechanism for compensating creators while ensuring AI developers have access to necessary training data. This solution has been raised by a number of observers, but it has not caught on – its complexity makes it seem unworkable.
- New Copyright Exceptions: Create specific exemptions for AI training, perhaps limited to non-commercial or research purposes.
- International Harmonization: Given the global nature of AI development, the industry may need to work towards a more unified international approach to copyright exceptions for AI.
The Road Ahead
The intersection of AI and copyright law presents complex challenges that resist simple solutions. The Google Books cases provide some support for mass digitization and computational use of copyrighted works. Google v. Oracle suggests courts might look favorably on uses that promote technological progress. But AWF reminds us that transformative use has limits, especially in commercial contexts.
For AI companies, the path forward involves careful consideration of training data sources and potential licensing arrangements. It may also mean being prepared for legal challenges and working proactively with policymakers to develop workable solutions.
For content creators, it’s crucial to stay informed about how your work might be used in AI training. There may be new opportunities for licensing, but also new risks to consider.
For policymakers and courts, the challenge is to strike a balance that fosters innovation while protecting the rights and incentives of creators. This may require rethinking some fundamental aspects of copyright law.
I would be remiss if I didn’t point out that the copyright fair use issues discussed here are only a small slice of a much larger pie. Additional issues include the ethical aspects of copying for training purposes (ethics inevitably influence the law, and they may drive legislation here), proposed U.S. legislation, international laws (which U.S. AI companies must comply with if they want to do business outside the U.S.), the removal of copyright management information protected by the DMCA, the legality of data scraping, contract breach issues, violation of opt-out/exclusionary mechanisms (these are proliferating, including the old standby “robots.txt”) and licensing models, to list just a few.
The relationship between AI and copyright will be a defining issue in intellectual property law for years to come. Stay tuned, stay informed, and be prepared for a wild ride.
by Lee Gesmer | Jul 1, 2024 | Copyright
“AI models are what’s known in computer science as black boxes: You can see what goes in and what comes out; what happens in between is a mystery.”
Trust but Verify: Peeking Inside the “Black Box” of Machine Learning
In December 2023, The New York Times filed a landmark lawsuit against OpenAI and Microsoft, alleging copyright infringement. This case, along with a number of similar cases filed against AI companies, brings to the forefront a fundamental challenge in applying traditional copyright law to a revolutionary technology: Large Language Models (LLMs). More than any copyright case that precedes them, these cases grapple with a form of alleged infringement that defies conventional legal analysis.
This article, the first in a three-part series, aims to explain how AI companies acquire vast amounts of training data and what happens to this data as it is processed into “artificial intelligence.” By understanding these technical aspects of LLMs, we can better appreciate the nuanced legal questions that arise when this technology intersects with copyright law, and why traditional methods of proving copyright infringement may be inadequate in this new technological landscape.
Disclaimer: I’m not a computer or AI scientist. However, neither are the judges and juries that will be asked to apply copyright law to this technology. It’s unlikely that they will go much beyond the level of detail I’ve used here.
What are Large Language Models (LLMs) and How Do They Work?
Large Language Models, or LLMs, are gargantuan AI systems that use a vast corpus of training data and billions of parameters. They are designed to understand, generate, and manipulate human language. They learn patterns from the data, allowing them to perform a wide range of language tasks with remarkable fluency. Their inner workings are fundamentally different from any previous technology that has been the subject of copyright litigation, including traditional computer software.
At their core, LLMs operate on a principle of prediction. Given a sequence of words an LLM attempts to predict the most likely next word. This process is repeated, with each prediction informing the next, allowing the model to generate coherent text, answer questions, or perform other language-related tasks.
The “large” in Large Language Models refers to the enormous number of parameters these models can contain – sometimes in the trillions. These parameters are essentially the model’s learned patterns and relationships, fine-tuned through exposure to massive amounts of text data. The larger the dataset, the better the AI model.
AI companies employ various methods to acquire this data –
– Web scraping and crawling. One of the primary methods of data acquisition is web scraping – the automated process of extracting data from websites. AI companies deploy sophisticated crawlers that systematically browse the internet, copying text from millions of web pages. This method allows for the collection of diverse, up-to-date information but raises questions about the use of copyrighted material without explicit permission.
– Partnerships and licensing agreements. Some companies enter into partnerships or licensing agreements to access high-quality, curated datasets. For instance, OpenAI has partnered with organizations like the Associated Press to use its news archives for training purposes.
– Public datasets and academic corpuses. Many LLMs are trained, at least in part, on publicly available datasets and academic text collections. These might include Project Gutenberg’s collection of public domain books, scientific paper repositories, or curated datasets like the Common Crawl corpus.
– User-generated content. Platforms that interact directly with users, such as ChatGPT, can potentially use the conversations and inputs from users to further train and refine their models. This practice raises privacy concerns and questions about the ownership of user-contributed data.
In the context of the New York Times lawsuit, it’s worth noting that OpenAI, like many AI companies, has not publicly disclosed the full extent of its training data sources. However, it’s widely believed that the company uses a combination of publicly available web content, licensed datasets, and partnerships to build its training corpus. The lawsuit alleges that this corpus includes copyrighted New York Times articles, obtained without permission or compensation.
Once acquired, the raw data undergoes several processing steps before it can be used to train an LLM –
– Data preprocessing and cleaning. The first step involves cleaning the raw data. This includes removing irrelevant information, correcting errors, and standardizing the format. This may involve stripping away HTML tags, removing advertisements, or filtering out low-quality content.
– Tokenization and encoding. Next, the text is broken down into smaller units called tokens. These might be words, parts of words, or even individual characters. Each token is then converted into a numerical representation that the AI can process. This step is crucial as it determines how the model will interpret and generate language.
The Training Process: How Machines “Learn” From Data
During training, the LLM is exposed to this preprocessed data, learning to predict patterns and relationships between tokens. This is an iterative process where the model makes predictions, compares them to the actual data, and adjusts its internal parameters to improve accuracy. This process, known as “backpropagation,” is repeated billions of times across the entire dataset. In a large LLM this can take months, operating 24/7 on a massive system of graphics processing chips.
The Invisible Transformation: From Text to Numbers
For purposes of copyright law, here’s the crux of the matter – after this process, the original text no longer exists in any recognizable form within the LLM. The model becomes a vast sea of numbers, with no direct correspondence to the original text. This transformation creates a fundamental challenge for copyright law –
– No Side-by-Side Comparison: In traditional copyright cases, courts rely heavily on comparing the original work side-by-side with the allegedly infringing material. With LLMs, this is impossible. You can’t “read” an LLM or print it out for comparison.
– Black Box Nature: The internal workings of LLMs are often referred to as a “black box.” Even the developers may not fully understand how the model arrives at its outputs.
– Dynamic Generation: LLMs don’t store and retrieve text; they generate it dynamically based on learned patterns. This means that any similarity to copyrighted material in the output is a result of statistical prediction, not direct copying.
– Distributed Information: Information from any single source is distributed across countless parameters in the model, making it impossible to isolate the influence of any particular work.
Legal Implications
This technological reality creates several unprecedented challenges for copyright law –
– Proving Infringement: How can a plaintiff prove infringement when the allegedly infringing material can’t be directly observed or compared?
– Fair Use Analysis: Traditional fair use factors, such as the amount and substantiality of the portion used, become difficult to apply when the “portion used” is transformed beyond recognition.
– Substantial Similarity: The legal test of “substantial similarity” between works becomes almost meaningless in the context of LLMs.
– Expert Testimony: Courts will likely have to rely heavily on expert testimony to understand the technology, but even experts may struggle to definitively prove or disprove infringement.
For all of these reasons, to prove copyright infringement plaintiffs such as the New York Times may have to rely on the “intermediate” copies that are used to start the training process, rather than the “end-product” LLMs that are accessed by users. And this, as we’ll see in my next post in this series, may favor OpenAI and other AI companies.
Conclusion
The NYT v. OpenAI case highlights a fundamental mismatch between traditional copyright law and the reality of LLM technology. As I move forward in this series, I’ll explore the legal theories and potential new frameworks that might be necessary to address these challenges. The outcome of this case could reshape our understanding of copyright in the digital age, potentially requiring new legal tests and standards that can account for the invisible, transformed nature of information within AI systems.
Part 2 in this series will focus on the legal issues around the “input problem” of using copyrighted material for training. Part 3 will look at the “output problem” of AI-generated content that may copy or resemble copyrighted works, including what the AI industry calls “memorization.” As we’ll see, each of these issues presents its own unique challenges in the context of a technology that defies traditional legal analysis.
by Lee Gesmer | Jun 18, 2024 | Litigation
Being a litigation attorney can be a scary business. You’re constantly thinking about how to organize the facts to fit your theory of the case, what legal precedent you may have overlooked, discovery, trial preparation and much, much more.
With all that pressure it’s not surprising that lawyers make mistakes, and one of the scariest things in litigation practice is the risk of missing a deadline. Depending on a lawyer’s caseload it may be difficult to keep track of deadlines. There are pleading deadlines, discovery deadlines, motion-briefing deadlines and appeal deadlines, to name just a few. And with some deadlines there is absolutely no court discretion available to save you, appeal deadlines being the best example of this.
So, despite computerized docketing systems and best efforts, lawyers sometimes miss deadlines. That’s just a painful fact of life. Sometimes the courts will exercise their discretion and allow lawyers to make up a missed deadline. Many lawyers have spent many sleepless nights waiting to see if a court will overlook a missed deadline and give the lawyer a second chance.
But sometimes they won’t. A recent painful example of this is the 6th Circuit decision in RJ Control Consultants v. Multiject. That case involved a complex topic, the alleged illegal copying of computer source code. The case had been in litigation since 2016 and had already been the subject of two appeals. In other words, a lot of time and money had been invested. A glance at the docket sheet confirms this, with over 200 docket entries.
The mistake in that case was pedestrian: the court set a specific expert-disclosure deadline of February 26, 2021. By that date each party was obligated to provide expert reports. In federal court expert reports require a proposed expert to provide a detailed summary of the expert’s qualifications, opinions, and the information the expert relied on for his or her opinion. Fed. R. Civ. P. 26(a)(2)(B). The rule is specific and onerous. It often requires a great deal of time and effort to prepare expert disclosures.
In the Multiject case neither party submitted expert reports by February 26, 2021. However, the real burden to do so was on the plaintiff, which has a challenging and complex burden of proof in software copyright cases. In a software copyright case it’s up to the plaintiff’s expert to analyze the code and separate elements that may not be protected (by reason of scenes a faire and merger, for example) from those that are protected expression. As the 6th Circuit stated in an interim appeal in this case –
The technology here is complex, as are the questions necessary to establish whether that technology is properly protected under the Copyright Act. Which aspects or lines of the software code are functional? Which are expressive? Which are commonplace or standard in the industry? Which elements, if any, are inextricably intertwined?
The defendant, on the other hand, had a choice: it could submit its own expert report or just wait until it saw the plaintiff’s report. It could challenge the plaintiff’s report before trial or the plaintiff’s expert’s testimony at trial. So the defendant’s failure to file an expert report was not fatal to its case – it could wait.
The plaintiff’s expert was David Lockhart, and when the plaintiff failed to submit his report on the due date, the defendant filed a motion to exclude the report, and for summary judgment. The plaintiff asked for a chance to file Lockhard’s report late, but the court showed no mercy – it denied the motion and, since the plaintiff would need an expert to establish illegal copying, granted the defendant’s motion for summary judgment.
In other words, end-of-case.
Why was the court unwilling to cut the plaintiff a break in this case? While the 6th Circuit discussed several issues justifying the denial, the one that strikes home for me is the plaintiff’s argument that they “reasonably misinterpreted” the court’s discovery order and made an “honest mistake” as to when the report was due. However, in the view of the trial judge this was not “harmless” error since it disrupted the court’s docket. The legal standard was “abuse of discretion,” and the Sixth Circuit held that the trial judge did not abuse his discretion in excluding Lockhart’s expert report after the missed deadline.
This is a sad way for a case to end, and the price is paid by the client, who likely had nothing to do with the missed deadline, but whose case was dismissed as a consequence. As I mentioned, the case began in 2016, and it was heavily litigated. There are seven reported decisions on Google Scholar, which is an unusually large number, and suggests that a lot of time and money was invested by both sides. To make matters worse, not only did the plaintiff lose this case, but the court awarded the defendants more than $318,000 in attorneys fees.
Be careful out there.
RJ Control Consultants v. Multiject (6th Cir. April 3, 2024)
(Header image credit: Designed by Wannapik)
by Lee Gesmer | May 26, 2024 | Copyright
Copyright secondary liability can be difficult to wrap your head around. This judge-made copyright doctrine allows copyright owners to seek damages from organizations that do not themselves engage in copyright infringement, but rather facilitate the infringing behavior of others. Often the target of these cases are internet service providers, or “ISPs.”
Secondary liability has three separate prongs, “contributory” and “vicarious” infringement, and “inducement.” The third prong – inducement – is important but seen infrequently. For the elements of this doctrine see my article here.
Here’s how I outlined the elements of contributory and vicarious liability when I was teaching CopyrightX:
These copyright rules were the key issue in the Fourth Circuit’s recent blockbuster decision in Sony v. Cox Communications (4th Cir. Feb. 20, 2024).
In a highly anticipated ruling the court reversed a $1 billion jury verdict against Cox for vicarious liability but affirmed the finding of contributory infringement. The decision is a significant development in the evolving landscape of ISP liability for copyright infringement.
Case Background
Cox Communications is a large telecommunications conglomerate based in Atlanta. In addition to providing cable television and phone services it acts as an internet service provider – an “ISP” – to millions of subscribers.
The case began when Sony and a coalition of record labels and music publishers sued Cox, arguing that the ISP should be held secondarily liable for the infringing activities of its subscribers. The plaintiffs alleged that Cox users employed peer-to-peer file-sharing platforms to illegally download and share a vast trove of copyrighted music, and that Cox fell short in its efforts to control this rampant infringement.
A jury found Cox liable under both contributory and vicarious infringement theories, levying a jaw-dropping $1 billion in statutory damages – $99,830.29 for each of the 10,017 infringed works. Cox challenged the verdict on multiple fronts, contesting the sufficiency of the evidence and the reasonableness of the damages award.
The Fourth Circuit Opinion
On appeal, the Fourth Circuit dissected the two theories of secondary liability, arriving at divergent conclusions. The court sided with Cox on the issue of vicarious liability, finding that the plaintiffs failed to establish that Cox reaped a direct financial benefit from its subscribers’ infringing conduct. Central to this determination was Cox’s flat-fee pricing model, which remained constant irrespective of whether subscribers engaged in infringing or non-infringing activities. The mere fact that Cox opted not to terminate certain repeat infringers, ostensibly to maintain subscription revenue, was deemed insufficient to prove Cox directly profited from the infringement itself.
However, the court took a different stance on contributory infringement, upholding the jury’s finding that Cox materially contributed to known infringement on its network. The court was unconvinced by Cox’s assertions that general awareness of infringement was inadequate, or that a level of intent tantamount to aiding and abetting was necessary for liability to attach. Instead, the court articulated that supplying a service with the knowledge that the recipient is highly likely to exploit it for infringing purposes meets the threshold for contributory liability.
Given the lack of differentiation between the two liability theories in the jury’s damages award, coupled with the potential influence of the now-overturned vicarious liability finding on the damages calculation, the court vacated the entire award. The case now returns to the lower court for a new trial, solely to determine the appropriate measure of statutory damages for contributory infringement.
Relationship to the DMCA
This article’s header graphic illustrates the relationship between the secondary liability doctrines and the protection of the Digital Millennium Copyright Act (DMCA), Section 512(c) of the Copyright Act. As the graphic reflects, all three theories of secondary liability lie outside the DMCA’s safe harbor protection for third-party copyright infringement. The DMCA requires that a defendant satisfy multiple safe harbor conditions (See my 2017 article – Mavrix v. LiveJournal: The Incredible Shrinking DMC for more on this). If a plaintiff can establish the elements of any one of the three theories of secondary liability the defendant will violate one or more safe harbor conditions and lose DMCA protection.
Implications
The court’s decision signals a notable shift in the contours of vicarious liability for ISPs in the context of copyright infringement. By requiring a causal nexus between the defendant’s financial gain and the infringing acts themselves, the court has raised the bar for plaintiffs seeking to prevail on this theory.
The ruling underscores that simply profiting from a service that may be used for both infringing and non-infringing ends is insufficient; instead, plaintiffs must demonstrate a more direct and meaningful link between the ISP’s revenue and the specific acts of infringement. This might entail evidence of premium fees for access to infringing content or a discernible correlation between the volume of infringement and subscriber growth or retention.
While Cox may take solace in the reversal of the $1 billion vicarious liability verdict, the specter of substantial contributory infringement damages looms large as the case heads back for a retrial.
For ISPs, the ruling serves as a warning to reevaluate and fortify their repeat infringer policies, ensuring they go beyond cosmetic compliance with the DMCA’s safe harbor provisions. Proactive monitoring, prompt responsiveness to specific infringement notices, and decisive action against recalcitrant offenders will be key to mitigating liability risks.
On the other side of the equation, copyright holders may need to recalibrate their enforcement strategies, recognizing the heightened evidentiary burden for establishing vicarious liability. While the contributory infringement pathway remains viable, particularly against ISPs that display willful blindness or tacit encouragement of infringement, the Sony v. Cox decision underscores the importance of marshaling compelling evidence of direct financial benefit to support vicarious liability claims.
As this case enters its next phase, the copyright and technology communities will be focused on the outcome of the damages retrial. Regardless of the ultimate figure, the Fourth Circuit’s decision has already left a mark on the evolving landscape of online copyright enforcement.
Header image is published under the Creative Commons Attribution 4.0 License.
by Lee Gesmer | May 17, 2024 | Copyright
Many aspects of copyright law are obscure and surprising, even to lawyers familiar with copyright’s peculiarities. An example of this is copyright law’s three-year statute of limitations.
The Copyright Act states that “no civil action shall be maintained under the provisions of this title unless it is commenced within three years after the claim accrued.” 17 U. S. C. §507(b). In the world of copyright practitioners this is understood to mean that so long as a copyright remains in effect and infringements continue, an owner’s rights are not barred by the statute of limitations. However, they may be limited to damages that accrued in the three years before the owner files suit. This is described variously as a “three-year look-back,” a “rolling limitations period” or the “separate-accrual rule.”
This is what allowed Randy Wolfe’s estate to sue Led Zeppelin in 2014 for an alleged infringement that began in 1971.
However, there is a nuance to this doctrine – what if the copyright owner isn’t aware of the infringement? Is the owner still limited to damages accrued in the three years before he files suit?
That is the scenario the Supreme Court addressed in Warner Chappell Music, Inc. v. Nealy (May 9, 2024).
Background Facts
Songwriter Sherman Nealy sued Warner Chappell in 2018 for infringing his music copyrights going back to 2008. Warner responded that under the “three year look-back” rule Nealy’s damages were limited to three years before he filed suit. Nealy argued that his damages period should extend back to 2008, since his claims were timely under the “discovery rule” – he was in prison during much of this period and only learned of the infringements in 2016.
Nealy lost on this issue in the district court, which limited his damages to the infringer’s profits during the 3 years before he filed suit. The 11th Circuit reversed, holding that Nealy could recover damages beyond 3 years if his claims were timely – meaning that the case was filed within three years of when Nealy discovered the infringement.
The Supreme Court Decision
The Supreme Court affirmed the 11th Circuit and resolved a circuit split, holding:
1 – The Copyright Act’s 3-year statute of limitations governs when a claim must be filed, not how far back damages can go.
2 – If a claim is timely, the plaintiff can recover damages for all infringements, even those occurring more than 3 years before suit. The Copyright Act places no separate time limit on damages.
However, lurking within this ruling is another copyright law doctrine that the Court did not address that could render its ruling in Nealy moot – that is the proper application of the “discovery rule” under the Copyright Act. Under the discovery rule a claim accrues when “the plaintiff discovers, or with due diligence should have discovered” the infringement. (Nealy, Slip Op. p. 2). Competing with this is the less liberal “occurrence” rule, which holds that, in the absence of fraud or concealment, the clock starts running when the infringement occurs. Under the discovery rule Nealy would be able to recover damages back to 2008. Under the occurrence rule his damages would be limited to the three years before he filed suit, since he does not allege fraud or concealment.
However, the question of which rule applies under the Copyright Act has never been addressed by the Supreme Court, and is itself the subject of a circuit split. The Court assumed, without deciding and solely for purposes of deciding the issue before it, that the discovery rule does apply to copyright claims. If the discovery rule applies Nealy has a claim to retroactive damages beyond three years. If it does not, Nealy’s damages would be limited to the three years before he filed suit.
Justice Gorsuch, joined by Justices Thomas and Alito, focused on this in his dissent, arguing the Court should not have decided the issue when the “discovery vs. occurrence” issue has not been addressed:
The Court discusses how a discovery rule of accrual should operate under the Copyright Act. But in doing so it sidesteps the logically antecedent question whether the Act has room for such a rule. Rather than address that question, the Court takes care to emphasize that its resolution must await a future case. The trouble is, the Act almost certainly does not tolerate a discovery rule. And that fact promises soon enough to make anything we might say today about the rule’s operational details a dead letter.
Clearly, in the view of at least three justices, if and when the discovery vs. occurrence rule issue comes before the Court it could decide against the discovery rule in copyright cases, rendering its decision on damages in the Nealy case, and cases like it, moot.
State of the Law Today
What does this all boil down to? Here are the rules as they exist today –
– A copyright owner has been aware of an infringing musical work for 20 years. She finally sues the infringer. Her damages are limited by the three year damages bar. They may be limited even further based on the laches doctrine.
– A copyright owner has been meditating alone in a cave in Tibet for 20 years. She’s had no access to information from the outside world. Upon her return she discovers that someone has been infringing her literary work for the last 20 years. Depending on whether the federal circuit applies the discovery or the occurrence rule, she may recover damages for the entire 20 period, or just the preceding three years. Her lawyers should do some careful forum shopping.
– A copyright owner discovers someone has secretly been infringing her copyright in computer source code for 20 years. The source code was non-public, and therefore the infringement was concealed. She may recover damages for the full 20 year period.
Implications
The decision is a win for copyright plaintiffs, allowing them to reach back and get damages beyond 3 years – assuming their claims are timely and they are in a circuit that apples the discovery rule. But the Court left the door open to decide the more important question of whether the discovery rule applies to the Copyright Act’s statute of limitations at all. If not, the window for both filing claims and recovering damages will shrink. When this issue will reach the Supreme Court is uncertain. However, the Court has the opportunity to take it up as soon as next term. See Hearst Newspapers, LLC v. Martinelli, No. 23-474 (U.S. petition for cert. filed Nov. 2, 2023). In the meantime, the outer boundary of damages is limited only by the discovery rule (if it apples), not any separate damages bar. Plaintiffs with older claims should take note, as should potential defendants doing due diligence on liability exposure.
Update: On May 20, 2024, the Supreme Court of the United States denied the petition for certiorari in Hearst Newspapers, L.L.C. v. Martinelli, thereby declining to decide whether the discovery rule applies to copyright infringement claims and leaving the rule intact.
Header image attribution: Resource by Nick Youngson CC BY-SA 3.0 Pix4free
