  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.
