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

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  • Han, Xiaotong
  • Zhu, Donghua
  • Lei, Ming
  • Daim, Tugrul

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here considers both patent applications and invalidated patents, i.e., those patents that have expired, lapsed, or been revoked.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.

Suggested Citation

  • Han, Xiaotong & Zhu, Donghua & Lei, Ming & Daim, Tugrul, 2021. "R&D trend analysis based on patent mining: An integrated use of patent applications and invalidation data," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    • Handle: RePEc:eee:tefoso:v:167:y:2021:i:c:s0040162521001232
    DOI: 10.1016/j.techfore.2021.120691
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    4. Yogesh K. Dwivedi & A. Sharma & Nripendra P. Rana & M. Giannakis & P. Goel & Vincent Dutot, 2023. "Evolution of Artificial Intelligence Research in Technological Forecasting and Social Change: Research Topics, Trends, and Future Directions," Post-Print hal-04292607, HAL.
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    7. Ghaffari, Mohsen & Aliahmadi, Alireza & Khalkhali, Abolfazl & Zakery, Amir & Daim, Tugrul U. & Yalcin, Haydar, 2023. "Topic-based technology mapping using patent data analysis: A case study of vehicle tires," Technological Forecasting and Social Change, Elsevier, vol. 193(C).

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