Unveiling predictors influencing patent licensing: Analyzing patent scope in robotics and automation

Abstract

Ensuring the sustainability of technology transfer offices depends on effective patent licensing strategies. This study investigates novel predictors for patent licensing prediction. It emphasizes the importance of judiciously selecting suitable patent-scope metrics to enhance the likelihood of successful patent licensing agreements. This work focuses on a critical aspect of patent scope, specifically examining the number of independent claims, the length of the first claim, the depth of the claim, the Cooperative Patent classification count, the non-US family count, and the family independent count. Additionally, we consider conventional metrics previously investigated in prior research, such as claim count, the count within the International Patent Classification, and Simple Family Application. Our empirical analysis harnesses a dataset comprising patents from university technology transfers within the robotics and automation domain. We analyze the relationship between patent scope measures and licensing outcomes using data visualization and statistical techniques, including the point-biserial correlation coefficient and the t-test. Comparative analysis of the statistical results is performed to identify the most impactful predictor. Our study reveals a correlation between the number of independent claims and the success of patent licensing. In contrast, the rest of the investigated measures do not impact the success of patent licensing.