Abstract

We develop natural language processing techniques to identify the creation and impact of new technologies in the population of U.S. patents. We validate the new techniques and their improvement over traditional metrics based on patent classification and citations in two case-control studies. First, we collect patents linked to awards such as the Nobel prize and the National Inventor Hall of Fame. These patents likely cover radically new technologies with a major impact on technological progress and patenting. Second, we identify patents granted by the United States Patent and Trademark Office but simultaneously rejected by both the European and Japanese patent office. Such patents arguably lack novelty or cover small incremental advances over prior art and should have little impact on technological progress. We provide open access to code, data, and new measures for all utility patents granted by the USPTO up to May 2018 (see https://zenodo.org/record/3515985, DOI: 10.5281/zenodo.3515985).

Highlights

  • The creation of new technologies is vital for firm productivity and economic growth (Romer, 1990)

  • To measure the technical novelty and impact of patents, prior and current work has traditionally relied on patent classification and citations1

  • All text-based novelty measures, measuring technical novelty at the time of filing, outperform the traditional novelty measures based on patent classification and citations in terms of both t-statistic and Cohen’s d

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Summary

Introduction

The creation of new technologies is vital for firm productivity and economic growth (Romer, 1990). To measure the technical novelty and impact of patents, prior and current work has traditionally relied on patent classification and citations. To measure the technical novelty and impact of patents, prior and current work has traditionally relied on patent classification and citations1 Patent citations capture prior art but do not reflect the technical content of the patent itself. Patent citations arguably cannot accurately measure the novelty of the technical content. Patent classification does reflect the subject matter of the patent, but patent (sub)classes are usually too broad to capture the detailed technical content of the invention and measure technical nov­ elty (Thompson and Fox-Kean, 2005; Arts et al, 2018; Righi and Simcoe, 2019). According to U.S law, a patent must “contain a written description of the invention ... in such full, clear, concise, and exact terms as to enable any person skilled in the art ... to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of

See for instance
See for example
Text-based measures
Traditional measures
Validation award patents
Results
Validation rejected patents
Discussion and conclusion
Full Text
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