论文标题
正式化人类创造力：版权法的实质性相似性的定量框架
Formalizing Human Ingenuity: A Quantitative Framework for Copyright Law's Substantial Similarity
论文作者
论文摘要
美国版权法中的一个中心概念正在判断原始作品和（据称）衍生作品之间的实质性相似性。捕获这一概念已被证明难以捉摸，案例法和法律奖学金提供的许多方法通常是不明智的，矛盾的或内部矛盾的。 这项工作表明，实质相似性难题的关键部分是可以通过理论计算机科学启发的建模来修正的。我们提出的框架可以定量评估需要多少“新颖性”来生产访问原始作品的衍生作品，而不是重现它，而无需访问原始作品的受版权保护元素。 “新颖性”是通过kolmogorov-levin复杂性的精神来捕获的描述长度的计算概念，这对于机械转换和上下文信息的可用性是可靠的。 这导致了一个可操作的框架，法院可以将其用作决定实质性相似性的帮助。我们对版权法中的几个关键案例进行了评估，并观察到结果与裁定一致，并且在哲学上与Altai的抽象滤过及其比较测试一致。
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.
