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Patent trends as a technological forecasting tool

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  • Campbell, Richard S.

Abstract

Patent indicators provide a very useful forecasting tool for decision makers in the public and private sectors. These tools are useable for Research and Development planning, for competition analyses, and for analytical studies of how technologies emerge, mature and pass away. Currently in the public and private sectors there are numerous indicators of performance for sales, finance and similar issues but no corresponding information on technological performance. Battelle, Pacific Northwest Laboratory, has identified several families of indicators which can be extracted from patent data to measure corporate or national technological performance. These include measures such as: - Patent activity level of firms' intellectual property - Age of firms' technology bases - Relative legal/technological strength of firms and similar measures.

Suggested Citation

  • Campbell, Richard S., 1983. "Patent trends as a technological forecasting tool," World Patent Information, Elsevier, vol. 5(3), pages 137-143.
    • Handle: RePEc:eee:worpat:v:5:y:1983:i:3:p:137-143
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    1. Alptekin Durmuşoğlu, 2017. "Effects of Clean Air Act on Patenting Activities in Chemical Industry: Learning from Past Experiences," Sustainability, MDPI, vol. 9(5), pages 1-10, May.
    2. Oleg Ena & Nadezhda Mikova & Ozcan Saritas & Anna Sokolova, 2016. "A methodology for technology trend monitoring: the case of semantic technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1013-1041, September.
    3. Munan Li, 2015. "A novel three-dimension perspective to explore technology evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1679-1697, December.
    4. Najmeh Masoumi & Reza Khajavi, 2023. "A fuzzy classifier for evaluation of research topics by using keyword co-occurrence network and sponsors information," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1485-1512, March.
    5. Tobias Schultheiss & Uschi Backes-Gellner, 2024. "Does updating education curricula accelerate technology adoption in the workplace? Evidence from dual vocational education and training curricula in Switzerland," The Journal of Technology Transfer, Springer, vol. 49(1), pages 191-235, February.
    6. Chi-Yo Huang & Liang-Chieh Wang & Ying-Ting Kuo & Wei-Ti Huang, 2021. "A Novel Analytic Framework of Technology Mining Using the Main Path Analysis and the Decision-Making Trial and Evaluation Laboratory-Based Analytic Network Process," Mathematics, MDPI, vol. 9(19), pages 1-24, October.
    7. Liao, Pin-Chao & Zhang, Kenan & Wang, Tao & Wang, Yanqing, 2016. "Integrating bibliometrics and roadmapping: A case of strategic promotion for the ground source heat pump in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 292-301.
    8. Haupt, Reinhard & Kloyer, Martin & Lange, Marcus, 2007. "Patent indicators for the technology life cycle development," Research Policy, Elsevier, vol. 36(3), pages 387-398, April.
    9. Hong Joo Lee & Hoyeon Oh, 2020. "A Study on the Deduction and Diffusion of Promising Artificial Intelligence Technology for Sustainable Industrial Development," Sustainability, MDPI, vol. 12(14), pages 1-15, July.
    10. Carsten C. Guderian, 2019. "Identifying Emerging Technologies with Smart Patent Indicators: The Example of Smart Houses," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(02), pages 1-24, April.
    11. Arash Hajikhani & Arho Suominen, 2022. "Mapping the sustainable development goals (SDGs) in science, technology and innovation: application of machine learning in SDG-oriented artefact detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6661-6693, November.
    12. Erzurumlu, S. Sinan & Pachamanova, Dessislava, 2020. "Topic modeling and technology forecasting for assessing the commercial viability of healthcare innovations," Technological Forecasting and Social Change, Elsevier, vol. 156(C).
    13. Ju, Yonghan & Sohn, So Young, 2015. "Patent-based QFD framework development for identification of emerging technologies and related business models: A case of robot technology in Korea," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 44-64.
    14. Taeyeoun Roh & Yujin Jeong & Byungun Yoon, 2017. "Developing a Methodology of Structuring and Layering Technological Information in Patent Documents through Natural Language Processing," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    15. Brockhoff, Klaus K., 1991. "Instruments for patent data analyses in business firms," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 264, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    16. Fareri, Silvia & Apreda, Riccardo & Mulas, Valentina & Alonso, Ruben, 2023. "The worker profiler: Assessing the digital skill gaps for enhancing energy efficiency in manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    17. Fu, Ben-Ran & Hsu, Sung-Wei & Liu, Chih-Hsi & Liu, Yu-Ching, 2014. "Statistical analysis of patent data relating to the organic Rankine cycle," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 986-994.
    18. Nadezhda Mikova & Anna Sokolova, 2014. "Selection of information sources for identifying technology trends: A comparative analysis," HSE Working papers WP BRP 25/STI/2014, National Research University Higher School of Economics.
    19. Jungpyo Lee & So Young Sohn, 2017. "What makes the first forward citation of a patent occur earlier?," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 279-298, October.
    20. Islam, M.R. & Mekhilef, S. & Saidur, R., 2013. "Progress and recent trends of wind energy technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 456-468.
    21. Chung-Chu Chuang & Chung-Min Tsai & Hsiao-Chen Chang & Yi-Hsien Wang, 2021. "Applying Quantile Regression to Assess the Relationship between R&D, Technology Import and Patent Performance in Taiwan," JRFM, MDPI, vol. 14(8), pages 1-14, August.
    22. Choi, Jinho & Hwang, Yong-Sik, 2014. "Patent keyword network analysis for improving technology development efficiency," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 170-182.
    23. Christopher L. Benson & Christopher L. Magee, 2013. "A hybrid keyword and patent class methodology for selecting relevant sets of patents for a technological field," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 69-82, July.

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