New research on patent quality

The article closely examines various patent quality indicators and how they relate to each other. Patent data are to innovation scholars what electric signals are to neuroscience research and what atmospheric data are to climate change researchers. They offer a unique window into the functioning of the innovation process and have led to major breakthroughs in the last decades.

Yet, the community still knows very little about patent quality indicators. The article provides a careful analysis and derives some practical takeaways for researchers to use. It illustrates that some common practices, such as controlling for technological field effects in statistical analyses, may not adequately address the complexity of the data generating mechanisms across technological fields. It also shows that other common practices, such as aggregating various patent quality indicators, may be counterproductive. The paper is a must-read for scholars using patent data.

Abstract:

The quality of novel technological innovations is extremely variable, and the ability to measure innovation quality is essential to sensible, evidence-based policy. Patents, an often vital precursor to a commercialised innovation, share this heterogeneous quality distribution. A pertinent question then arises: How should we define and measure patent quality? Accepting that different parties have different views of, and different sets of terminologies for discussing this concept, we take a multi-dimensional view of patent quality in this work. We first test the consistency of popular post-grant outcomes that are often used as patent quality measures. Finding these measures to be generally inconsistent, we then use a raft of patent indicators available at the time of grant to dissect the characteristics of different post-grant outcomes. We find broad disagreement in the relative importance of individual characteristics between outcomes and, further, significant variation of the same across technologies within outcomes. We conclude that measurement of patent quality is highly sensitive to both the observable outcome selected and the technology type. Our findings bear concrete implications for scholarly research using patent data and policy discussions about patent quality.