Formalizing Human Ingenuity: A Quantitative Framework for Copyright Law's Substantial Similarity (2206.01230v2)

Abstract: A central notion in U.S. copyright law is judging the substantial similarity between an original and an (allegedly) derived work. Capturing this notion has proven elusive, and the many approaches offered by case law and legal scholarship are often ill-defined, contradictory, or internally-inconsistent. This work suggests that key parts of the substantial-similarity puzzle are amendable to modeling inspired by theoretical computer science. Our proposed framework quantitatively evaluates how much "novelty" is needed to produce the derived work with access to the original work, versus reproducing it without access to the copyrighted elements of the original work. "Novelty" is captured by a computational notion of description length, in the spirit of Kolmogorov-Levin complexity, which is robust to mechanical transformations and availability of contextual information. This results in an actionable framework that could be used by courts as an aid for deciding substantial similarity. We evaluate it on several pivotal cases in copyright law and observe that the results are consistent with the rulings, and are philosophically aligned with the abstraction-filtration-comparison test of Altai.

• The paper presents a novel framework that quantifies similarity between original and derivative works using description-length metrics.
• It employs abstraction-filtration methods inspired by landmark cases to separate non-copyrightable elements from creative input.
• The approach integrates zero-knowledge proofs to secure proprietary algorithms while offering objective metrics for legal adjudication.

Formalizing Human Ingenuity: A Quantitative Framework for Copyright Law's Substantial Similarity

The paper "Formalizing Human Ingenuity: A Quantitative Framework for Copyright Law's Substantial Similarity" addresses the elusive concept of substantial similarity within U.S. copyright law by introducing a computational framework grounded in theoretical computer science. This framework aims to offer a quantitative method to determine the degree of similarity between an original work and an allegedly infringing derivative work, which could aid in consistently adjudicating copyright cases.

Framework Overview

The authors propose a model inspired by Levin-Kolmogorov complexity to estimate the "novelty" involved in creating the allegedly derived work with and without access to the original work. The key metric is derived from the notion of description length, which quantifies how succinctly one can encode the process of generating the work. This approach leverages description length as a proxy for measuring the expressiveness borrowed from the original work, therefore gauging substantial similarity.

Methodology

The framework involves the plaintiff and defendant offering algorithms that either utilize the original work or develop the derived work independently. Several key steps are outlined:

1. Abstraction and Filtration: This step requires identifying the non-copyrightable elements of both the original and derived works, resonating with the abstraction-filtration-comparison test often applied in copyright cases like Computer Associates v. Altai.
2. Description-Length Metrics: By employing conditional Levin-Kolmogorov complexity, the framework assesses the computational cost of producing the derived work from both perspectives—using all available resources (plaintiff's view) and without direct access to the original work’s copyrighted elements (defendant's view).
3. Empirical and Theoretical Derivation Similarity: These are formalized into metrics contrasting the effective description lengths calculated from both parties. The empirical metric forms an adversarial test applied by courts, while the theoretical metric is an abstract evaluation.

Legal Precedents and Validation

The authors validate their framework by examining key copyright cases in U.S. history. For example, in Feist Publications, Inc. v. Rural Telephone Service Co., the framework aligns with the court's finding that mere compilation of facts, without creative input, lacks originality. Similarly, in Baker v. Selden, the framework appropriately distinguishes between the copyrightable expression and the underlying method or procedure, which is not protected under copyright law.

Practical and Theoretical Implications

Practically, this framework could streamline and objectify judicial processes by providing a robust, quantifiable standard for adjudicating substantial similarity. Theoretically, it reinforces the idea-expression dichotomy and supports doctrines such as the merger doctrine and filtration tests in copyright law.

Cryptographic Considerations

Recognizing confidentiality and the potential proprietary nature of the algorithms proposed in court, the authors suggest an approach using zero-knowledge proofs. This would secure the details of the algorithms while still allowing courts to verify the derivation similarity, preserving trade secrets or sensitive information during litigation.

Limitations and Future Directions

The framework is calibrated specifically for U.S. copyright law, albeit with potential extensions to other jurisdictions. The challenge remains in fully addressing elements such as fair use and contexts involving partially hidden information or trade secrets. Future work may extend this framework to include these broader considerations, as well as potentially integrating randomized algorithms into this deterministic model.

In conclusion, the paper offers a novel quantitative lens to an age-old qualitative legal challenge, proposing a framework that bridges legal theory and computational measures. By grounding substantial similarity in formal computational metrics, it pushes the boundaries of how copyright law could potentially integrate more definitive, algorithmic insights into legal interpretations, thus supporting more uniform rulings in complex copyright litigation.