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

TL;DR

This paper introduces a quantitative, computer science-inspired framework to assess substantial similarity in copyright law, aiming to provide courts with a more consistent and objective tool for legal decisions.

Contribution

It proposes a novel computational model using description length to measure novelty, bridging legal concepts with formal complexity measures, and evaluates its alignment with legal rulings.

Findings

Framework aligns with legal rulings on key cases

Uses Kolmogorov complexity to quantify novelty

Supports the abstraction-filtration-comparison test

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.