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Stochastic technology life cycle analysis using multiple patent indicators

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  • Lee, Changyong
  • Kim, Juram
  • Kwon, Ohjin
  • Woo, Han-Gyun

Abstract

Technology life cycle analysis plays a crucial role in setting up investment-related strategies. The dominant approach to technology life cycle analysis utilizes curve fitting techniques to observe technological performance over time. However, doubts have been expressed about the accuracy and reliability of this method, due to its use of single indicators and the necessity of making assumptions about pre-determined growth curves. As a remedy, we propose a stochastic technology life cycle analysis that uses multiple patent indicators to examine a technology's progression through its life cycle. We define and extract seven time-series patent indicators from the United States Patent and Trademark Office database, and employ a hidden Markov model—which is an unsupervised machine learning technique based on a doubly stochastic process—to estimate the probability of a technology being at a certain stage of its life cycle. Based on this model, this paper also investigates patterns of technology life cycles, future prospects of a technology's progression, and characteristics of patent indicators between technology life cycle stages. The systematic process and quantitative outcomes the proposed approach offers can facilitate responsive and objective technology life cycle analysis. A case of molecular amplification diagnosis technology is presented.

Suggested Citation

  • Lee, Changyong & Kim, Juram & Kwon, Ohjin & Woo, Han-Gyun, 2016. "Stochastic technology life cycle analysis using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 106(C), pages 53-64.
    • Handle: RePEc:eee:tefoso:v:106:y:2016:i:c:p:53-64
    DOI: 10.1016/j.techfore.2016.01.024
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    Cited by:

    1. Jose M. Vicente-Gomila & Anna Palli & Begoña Calle & Miguel A. Artacho & Sara Jimenez, 2017. "Discovering shifts in competitive strategies in probiotics, accelerated with TechMining," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1907-1923, June.
    2. Myoungjae Choi & Sun-Hi Yoo & Jongtaik Lee & Jeongsub Choi & Byunghoon Kim, 2022. "A modified gamma/Gompertz/NBD model for estimating technology lifetime," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5731-5751, October.
    3. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    4. Perruchas, François & Consoli, Davide & Barbieri, Nicolò, 2020. "Specialisation, diversification and the ladder of green technology development," Research Policy, Elsevier, vol. 49(3).
    5. Nicoló Barbieri & François Perruchas & Davide Consoli, 2020. "Specialization, Diversification, and Environmental Technology Life Cycle," Economic Geography, Taylor & Francis Journals, vol. 96(2), pages 161-186, March.
    6. Huang, Ying & Li, Ruinan & Zou, Fang & Jiang, Lidan & Porter, Alan L. & Zhang, Lin, 2022. "Technology life cycle analysis: From the dynamic perspective of patent citation networks," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    7. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    8. Youngjae Choi & Sanghyun Park & Sungjoo Lee, 2021. "Identifying emerging technologies to envision a future innovation ecosystem: A machine learning approach to patent data," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5431-5476, July.
    9. Yoonjung An & Mintak Han & Yongtae Park, 2017. "Identifying dynamic knowledge flow patterns of business method patents with a hidden Markov model," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 783-802, November.
    10. Juram Kim & Changyong Lee, 2017. "Stochastic service life cycle analysis using customer reviews," The Service Industries Journal, Taylor & Francis Journals, vol. 37(5-6), pages 296-316, April.
    11. Jeon, Daeseong & Lee, Junyoup & Ahn, Joon Mo & Lee, Changyong, 2023. "Measuring the novelty of scientific publications: A fastText and local outlier factor approach," Journal of Informetrics, Elsevier, vol. 17(4).
    12. Ryosuke L. Ohniwa & Aiko Hibino, 2019. "Generating process of emerging topics in the life sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1549-1561, December.
    13. Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.
    14. Lin, Deming & Liu, Wenbin & Guo, Yinxin & Meyer, Martin, 2021. "Using technological entropy to identify technology life cycle," Journal of Informetrics, Elsevier, vol. 15(2).
    15. Kim, Juram & Lee, Gyumin & Lee, Seungbin & Lee, Changyong, 2022. "Towards expert–machine collaborations for technology valuation: An interpretable machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    16. Jang, Hyun Jin & Woo, Han-Gyun & Lee, Changyong, 2017. "Hawkes process-based technology impact analysis," Journal of Informetrics, Elsevier, vol. 11(2), pages 511-529.
    17. Fredström, Ashkan & Wincent, Joakim & Sjödin, David & Oghazi, Pejvak & Parida, Vinit, 2021. "Tracking innovation diffusion: AI analysis of large-scale patent data towards an agenda for further research," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    18. Song, Kisik & Kim, Kyuwoong & Lee, Sungjoo, 2018. "Identifying promising technologies using patents: A retrospective feature analysis and a prospective needs analysis on outlier patents," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 118-132.
    19. 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).
    20. Jeon, Daeseong & Ahn, Joon Mo & Kim, Juram & Lee, Changyong, 2022. "A doc2vec and local outlier factor approach to measuring the novelty of patents," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    21. Wang, Yu-Hui & Hsieh, Chia-Ching, 2018. "Explore technology innovation and intelligence for IoT (Internet of Things) based eyewear technology," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 281-290.
    22. Pantano, Eleonora & Priporas, Constantinos-Vasilios & Stylos, Nikolaos, 2018. "Knowledge Push Curve (KPC) in retailing: Evidence from patented innovations analysis affecting retailers' competitiveness," Journal of Retailing and Consumer Services, Elsevier, vol. 44(C), pages 150-160.

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