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Technological improvement rate estimates for all technologies: Use of patent data and an extended domain description

arXiv 2004.13919 ↗First posted 29 Apr 2020econ.GN cs.CY physics.soc-phcited by 3

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Abstract (arXiv)

In this work, we attempt to provide a comprehensive granular account of the pace of technological change. More specifically, we survey estimated yearly performance improvement rates for nearly all definable technologies for the first time. We do this by creating a correspondence of all patents within the US patent system to a set of technology domains. A technology domain is a body of patented inventions achieving the same technological function using the same knowledge and scientific principles. We obtain a set of 1757 domains using an extension of the previously defined classification overlap method (COM). These domains contain 97.14% of all patents within the entire US patent system. From the identified patent sets, we calculated the average centrality of the patents in each domain to estimate their improvement rates, following a methodology tested in prior work. The estimated improvement rates vary from a low of 1.9% per year for the Mechanical Skin treatment - Hair Removal and wrinkles domain to a high of 228.8% per year for the Network management - client-server applications domain. We developed a one-line descriptor identifying the technological function achieved and the underlying knowledge base for the largest 50, fastest 20 as well as slowest 20 of these domains, which cover more than forty percent of the patent system. In general, the rates of improvement were not a strong function of the patent set size and the fastest improving domains are predominantly software-based. We make available an online system that allows for automated searching for domains and improvement rates corresponding to any technology of interest to researchers, strategists and policy formulators.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Apr 2020.

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Citing papers per year: 2021: 1; 2022: 1; 2023: 1.

3 papers.