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Ross Intelligence petitions for full Third Circuit review in Westlaw copyright dispute

Ross Intelligence has requested an en banc rehearing from the Third Circuit, challenging a panel ruling that denied fair use protections for training its AI on Westlaw headnotes.

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law360.com
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Key terms

Machine Learning (ML)
Methods that allow systems to learn patterns from data and improve over time.
Generative AI
AI systems that produce new content such as text, images, audio, video, or code.

What happened

Ross Intelligence has formally petitioned the U.S. Court of Appeals for the Third Circuit for an en banc rehearing regarding a copyright infringement case brought by Thomson Reuters. The dispute centers on Ross Intelligence's use of Westlaw headnotes to train its AI-powered legal research platform. A previous appellate panel ruled against Ross Intelligence, concluding that the company's ingestion of these materials did not qualify as fair use under copyright law. In its petition, Ross Intelligence argues that the panel fundamentally misapplied the fair use doctrine, asserting that the court 'asked the wrong question' at every stage of its analysis.

Ross Intelligence is seeking a rehearing by the full Third Circuit following a panel decision that rejected its fair use defense. The underlying litigation involves allegations that Ross Intelligence copied thousands of Westlaw headnotes to train its AI legal research tool.

The company contends that the appellate panel's analysis was flawed, specifically regarding how the fair use doctrine applies to the transformative nature of AI training. Ross Intelligence argues that the court failed to properly weigh the public interest and the nature of the technology involved.

Thomson Reuters, the owner of Westlaw, has maintained that the unauthorized use of its proprietary headnotes constitutes clear copyright infringement, a position that the initial panel supported in its ruling.

Source details: law360.com

Why it matters

This case represents a critical intersection between copyright law and the development of AI models trained on proprietary datasets. The outcome of this appeal will likely establish a significant precedent for how AI companies can utilize copyrighted content for training purposes without infringing on the rights of original publishers. If the Third Circuit grants the rehearing, it could lead to a more nuanced interpretation of fair use in the context of machine learning. Conversely, if the current ruling stands, it may impose substantial legal hurdles for AI developers who rely on large-scale ingestion of protected legal or academic texts, potentially altering the competitive landscape for AI-driven research tools. The legal community is closely watching this development as it directly impacts the viability of AI platforms that depend on high-quality, proprietary data to function effectively. The case highlights the ongoing tension between the transformative potential of AI and the protection of intellectual property rights in the digital age.

The case is a landmark for AI developers who utilize large datasets for training. A definitive ruling on whether such training constitutes fair use will determine the legal risk profile for companies building specialized AI models.

Legal research platforms are increasingly reliant on AI to parse vast amounts of case law. A restrictive ruling could limit the ability of these platforms to innovate or force them to negotiate costly licensing agreements with established legal publishers.

The petition underscores the broader debate over whether AI training is a 'transformative' use of data or merely a mechanism for reproducing protected content in a new format.

Interactive Mechanism

Interactive Mechanism: How It Actually Works

Explore the underlying technology behind this development interactively.

Thinking Budget (Test-Time Tokens):1,024 tokens
Complex Accuracy79%Math & Code Logic
Latency3.2sTime to first full output
Inference Cost$0.0092Per query estimated
Reasoning StyleStep VerificationInternal chain depth
Active Thinking Trace:
1Deconstruct user problem into formal constraints
2Propose candidate hypotheses & step-by-step calculation
3Self-correction: Backtrack and refute subtle edge cases
4Exhaustive consistency check & final output synthesis
Core takeaway: Test-time compute fundamentally changes AI economics. Instead of only scaling during pre-training, giving reasoning models more tokens at inference time allows them to systematically solve PhD-level STEM problems.
Interactive Concept Check+10 Points
AI Models Explained Quiz

Which component of an AI application is the machine-learning model itself?

What to watch next

The primary focus is whether the Third Circuit will grant the petition for an en banc review, which would involve the full bench of judges rather than the original three-judge panel. If the court declines, the current ruling against Ross Intelligence will remain in effect, potentially forcing the company to seek further recourse at the Supreme Court. Observers should monitor the court's docket for a response from Thomson Reuters and any subsequent orders from the Third Circuit. Additionally, the legal arguments presented in the petition regarding the specific application of fair use to AI training processes will serve as a bellwether for future litigation involving and copyright infringement.

The Third Circuit's decision on whether to grant the en banc petition is the immediate next step. En banc reviews are relatively rare, making the court's decision a high-stakes moment for the parties involved.

If the court denies the petition, the case may move toward a final judgment or a potential appeal to the U.S. Supreme Court, which would further delay a final resolution.

The specific legal reasoning provided by the court in its decision to grant or deny the rehearing will be scrutinized for signals regarding how the judiciary views the intersection of AI and copyright law.

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