Using computer vision to measure design similarity: An application to design rights

Competition among firms has increasingly been through design. We show how computer vision algorithms can be leveraged to measure the visual similarity of design rights across large data sets of product design images. In particular: we extract and standardize 716,168 unique design images included in US design patents (1976–2023); adapt the structural similarity index measure to quantify design similarities between images; and rigorously validate the resulting measure of design rights similarity. We then use that measure to produce novel empirical evidence that a design space's similarity density exhibits an inverted U-shape with respect to the likelihood of that space's design rights being litigated—a relationship proposed previously but never tested. Our design rights similarity measure should facilitate the exploration of new research questions in the fields of design rights, innovation, and strategy. We grant open access to our code and data resources to encourage research in such fields.