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Discovery of factors influencing patent value based on machine learning in patents in the field of nanotechnology

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  • Scott D. Bass

    (University of Alberta)

  • Lukasz A. Kurgan

    (University of Alberta)

Abstract

Patents represent the technological or inventive activity and output across different fields, regions, and time. The analysis of information from patents could be used to help focus efforts in research and the economy; however, the roles of the factors that can be extracted from patent records are still not entirely understood. To better understand the impact of these factors on patent value, machine learning techniques such as feature selection and classification are used to analyze patents in a sample industry, nanotechnology. Each nanotechnology patent was represented by a comprehensive set of numerical features that describe inventors, assignees, patent classification, and outgoing references. After careful design that included selection of the most relevant features, selection and optimization of the accuracy of classification models that aimed at finding most valuable (top-performing) patents, we used the generated models to analyze which factors allow to differentiate between the top-performing and the remaining nanotechnology patents. A few interesting findings surface as important such as the past performance of inventors and assignees, and the count of referenced patents.

Suggested Citation

  • Scott D. Bass & Lukasz A. Kurgan, 2010. "Discovery of factors influencing patent value based on machine learning in patents in the field of nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 217-241, February.
    • Handle: RePEc:spr:scient:v:82:y:2010:i:2:d:10.1007_s11192-009-0008-z
    DOI: 10.1007/s11192-009-0008-z
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    Cited by:

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    3. Chang, Shu-Hao & Fan, Chin-Yuan, 2016. "Identification of the technology life cycle of telematics: A patent-based analytical perspective," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 1-10.
    4. Elena M. Tur & Evangelos Bourelos & Maureen McKelvey, 2022. "The case of sleeping beauties in nanotechnology: a study of potential breakthrough inventions in emerging technologies," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 69(3), pages 683-708, December.
    5. Dušan Marković, 2018. "RETRACTED ARTICLE: Appraisal of Science and Economic Factors on Total Number of Granted Patents," Networks and Spatial Economics, Springer, vol. 18(4), pages 1019-1026, December.
    6. Chang, Shu-Hao, 2017. "The technology networks and development trends of university-industry collaborative patents," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 107-113.
    7. Yalcin, Haydar & Daim, Tugrul & Moughari, Mahdieh Mokhtari & Mermoud, Alain, 2024. "Supercomputers and quantum computing on the axis of cyber security," Technology in Society, Elsevier, vol. 77(C).
    8. Shu-Hao Chang & Chin-Yuan Fan, 2020. "Using Patent Technology Networks to Observe Neurocomputing Technology Hotspots and Development Trends," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    9. Shu-Hao Chang, 2017. "The evolutionary growth estimation model of international cooperative patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(2), pages 711-729, August.
    10. Sajad Ashouri & Anne-Laure Mention & Kosmas X. Smyrnios, 2021. "Anticipation and analysis of industry convergence using patent-level indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5727-5758, July.
    11. Torben Schubert, 2011. "Assessing the value of patent portfolios: an international country comparison," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(3), pages 787-804, September.
    12. Chung, Park & Sohn, So Young, 2020. "Early detection of valuable patents using a deep learning model: Case of semiconductor industry," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
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