R&D trend analysis based on patent mining: An integrated use of patent applications and invalidation data

Xiaotong Han, Donghua Zhu, Ming Lei and Tugrul Daim

Technological Forecasting and Social Change, 2021, vol. 167, issue C

Abstract: Formulating good R&D strategy requires sound knowledge of the past and present R&D trends in various industry sectors. Therefore, this paper outlines a framework for mining industry level R&D trends from patents that were designed for enterprises. Unlike the current alternatives, the approach presented here considers both patent applications and invalidated patents, i.e., those patents that have expired, lapsed, or been revoked. The result is a richer and more comprehensive analysis that covers the full lifespan of a targeted technology from emergence to decline. The framework comprises of a LDA topic model that identifies the technologies and sub-technologies, and of each individual patent and invalidated patent. Then, two specifically designed measures chart the stages of the technologies’ life. An application metric reflects annual levels of interest in an area, while an invalidation metric traces waning interest. The output is a series of trend maps that show the levels of interest and disinterest in different avenues of inquiry over time. Charted on different axes, these two metrics create two distinct trend lines that reflect the different changes over a technology's lifecycle. A case study that focused on China's 3-D printing technology illustrates the approach. The analysis results are highly consistent with the present technology trends across industries, which indicates that the method could serve as a useful reference tool for analyzing R&D trends and creating new R&D strategies.

Keywords: Technology opportunity analysis; Patent invalidation; Patent mining; Topic model; R&D trend (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:167:y:2021:i:c:s0040162521001232

DOI: 10.1016/j.techfore.2021.120691

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