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Technology structural implications from the extension of a patent search method

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  • Christopher L. Benson

    (Massachusetts Institute of Technology)

  • Christopher L. Magee

    (Massachusetts Institute of Technology)

Abstract

Many areas of academic and industrial work make use of the notion of a ‘technology’. This paper attempts to reduce the ambiguity around the definition of what constitutes a ‘technology’ by extension of a method described previously that finds highly relevant patent sets for specified technological fields. The method relies on a less ambiguous definition that includes both a functional component and a component consisting of the underlying knowledge in a technological field to form a two-component definition. These two components form a useful definition of a technology that allows for objective, repeatable and thus comparable analysis of specific technologies. 28 technological domains are investigated: the extension of an earlier technique is shown to be capable of finding highly relevant and complete patent sets for each of the technologies. Overall, about 500,000 patents from 1976 to 2012 are classified into these 28 domains. The patents in each of these sets are not only highly relevant to the domain of interest but there are relatively low numbers of patents classified into any two of these domains (total patents classified in two domains are 2.9 % of the total patents and the great majority of patent class pairs have zero overlap with a few of the 378 patent class pairs containing the bulk of the doubly listed patents). On the other hand, the patents within a given domain cite patents in other domains about 90 % of the time. These results suggest that technology can be usefully decomposed to distinct units but that the inventions in these relatively tightly contained units depend upon widely spread additional knowledge.

Suggested Citation

  • Christopher L. Benson & Christopher L. Magee, 2015. "Technology structural implications from the extension of a patent search method," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 1965-1985, March.
    • Handle: RePEc:spr:scient:v:102:y:2015:i:3:d:10.1007_s11192-014-1493-2
    DOI: 10.1007/s11192-014-1493-2
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    References listed on IDEAS

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    13. Benson, Christopher L. & Magee, Christopher L., 2014. "On improvement rates for renewable energy technologies: Solar PV, wind turbines, capacitors, and batteries," Renewable Energy, Elsevier, vol. 68(C), pages 745-751.
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    Citations

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    Cited by:

    1. Park, Inchae & Triulzi, Giorgio & Magee, Christopher L., 2022. "Tracing the emergence of new technology: A comparative analysis of five technological domains," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    2. Subarna Basnet & Christopher L Magee, 2017. "Artifact interactions retard technological improvement: An empirical study," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-17, August.
    3. Tao Li & Jianqiang Luo & Kaitong Liang & Chaonan Yi & Lei Ma, 2023. "Synergy of Patent and Open-Source-Driven Sustainable Climate Governance under Green AI: A Case Study of TinyML," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    4. Singh, Anuraag & Triulzi, Giorgio & Magee, Christopher L., 2021. "Technological improvement rate predictions for all technologies: Use of patent data and an extended domain description," Research Policy, Elsevier, vol. 50(9).
    5. Choi, Seokkyu & Lee, Hyeonju & Park, Eunjeong & Choi, Sungchul, 2022. "Deep learning for patent landscaping using transformer and graph embedding," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    6. Triulzi, Giorgio & Alstott, Jeff & Magee, Christopher L., 2020. "Estimating technology performance improvement rates by mining patent data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    7. Feng, Sida & Magee, Christopher L., 2020. "Technological development of key domains in electric vehicles: Improvement rates, technology trajectories and key assignees," Applied Energy, Elsevier, vol. 260(C).
    8. Martin Ho & Henry CW Price & Tim S Evans & Eoin O'Sullivan, 2023. "Order in Innovation," Papers 2302.13076, arXiv.org.
    9. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    10. Changbae Mun & Sejun Yoon & Hyunseok Park, 2019. "Structural decomposition of technological domain using patent co-classification and classification hierarchy," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 633-652, November.
    11. Donghyun You & Hyunseok Park, 2018. "Developmental Trajectories in Electrical Steel Technology Using Patent Information," Sustainability, MDPI, vol. 10(8), pages 1-15, August.
    12. Fang Han & Christopher L. Magee, 2018. "Testing the science/technology relationship by analysis of patent citations of scientific papers after decomposition of both science and technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 767-796, August.

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    More about this item

    Keywords

    Patent searching; Technology decomposition; Technological relatedness;
    All these keywords.

    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital

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