Predicting the 'Unpredictable': An Empirical Analysis of U.S. Patent Infringement Awards

Abstract

Patent infringement awards are commonly thought to be unpredictable, which raises concerns that patents can lead to unjust enrichment and impede the progress of innovation. We investigate the predictability of patent damages by conducting a large-scale econometric analysis of award values. We begin by analyzing the outcomes of 340 cases decided in US federal courts between 1995 and 2008 in which infringement was found and damages were awarded. Our data include the amount awarded, along with information about the litigants, case specifics and economic value of the patents-at-issue. Using these data, we construct an econometric model that explains over 75% of the variation in awards. We further conduct in-depth analysis of the key factors affecting award value, via targeted regressions involving selected variables. We find a high degree of significance between award value and ex ante-identifiable factors collectively, and we also identify significant relationships with accepted indicators of patent value. Our findings demonstrate that infringement awards are systematically predictable and, moreover, highlight the critical elements that can be expected to result in larger or smaller awards.