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Analyzing patent topical information to identify technology pathways and potential opportunities

Author

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  • Jing Ma

    (Beijing Institute of Technology)

  • Alan L. Porter

    (Georgia Institute of Technology
    Search Technology, Inc.)

Abstract

As a basic knowledge resource, patents play an important role in identifying technology development trends and opportunities, especially for emerging technologies. However patent mining is restricted and even incomplete, because of the obscure descriptions provided in patent text. In this paper, we conduct an empirical study to try out alternative methods with Derwent Innovation Index data. Our case study focuses on nano-enabled drug delivery (NEDD) which is a very active emerging biomedical technology, encompassing several distinct technology spaces. We explore different ways to enhance topical intelligence from patent compilations. We further analyze extracted topical terms to identify potential innovation pathways and technology opportunities in NEDD.

Suggested Citation

  • Jing Ma & Alan L. Porter, 2015. "Analyzing patent topical information to identify technology pathways and potential opportunities," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 811-827, January.
    • Handle: RePEc:spr:scient:v:102:y:2015:i:1:d:10.1007_s11192-014-1392-6
    DOI: 10.1007/s11192-014-1392-6
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    References listed on IDEAS

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    1. Robinson, Douglas K.R. & Huang, Lu & Guo, Ying & Porter, Alan L., 2013. "Forecasting Innovation Pathways (FIP) for new and emerging science and technologies," Technological Forecasting and Social Change, Elsevier, vol. 80(2), pages 267-285.
    2. Janghyeok Yoon & Kwangsoo Kim, 2012. "Detecting signals of new technological opportunities using semantic patent analysis and outlier detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 445-461, February.
    3. Douglas K. R. Robinson & Lu Huang & Yan Guo & Alan L. Porter, 2013. "Forecasting Innovation Pathways (FIP) for new and emerging science and technologies," Post-Print hal-01070417, HAL.
    4. D.K. Robinson & Lu Huang & Ying Guo & Alan L. Porter, 2013. "Forecasting Innovation Pathways (FIP) for new and emerging science and technologies," Post-Print hal-01071140, HAL.
    5. Yoon, Byungun & Park, Inchae & Coh, Byoung-youl, 2014. "Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 287-303.
    6. Sungchul Choi & Janghyeok Yoon & Kwangsoo Kim & Jae Yeol Lee & Cheol-Han Kim, 2011. "SAO network analysis of patents for technology trends identification: a case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(3), pages 863-883, September.
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    Cited by:

    1. Comins, Jordan A. & Carmack, Stephanie A. & Leydesdorff, Loet, 2018. "Patent citation spectroscopy (PCS): Online retrieval of landmark patents based on an algorithmic approach," Journal of Informetrics, Elsevier, vol. 12(4), pages 1223-1231.
    2. Ma, Tingting & Zhou, Xiao & Liu, Jia & Lou, Zhenkai & Hua, Zhaoting & Wang, Ruitao, 2021. "Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    3. Zhao Qu & Shanshan Zhang & Chunbo Zhang, 2017. "Patent research in the field of library and information science: Less useful or difficult to explore?," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 205-217, April.
    4. Ma, Jing & Abrams, Natalie F. & Porter, Alan L. & Zhu, Donghua & Farrell, Dorothy, 2019. "Identifying translational indicators and technology opportunities for nanomedical research using tech mining: The case of gold nanostructures," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 767-775.
    5. Chung, Jaemin & Ko, Namuk & Yoon, Janghyeok, 2021. "Inventor group identification approach for selecting university-industry collaboration partners," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    6. Zhou, Xiao & Huang, Lu & Porter, Alan & Vicente-Gomila, Jose M., 2019. "Tracing the system transformations and innovation pathways of an emerging technology: Solid lipid nanoparticles," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 785-794.
    7. Block, Carolin & Wustmans, Michael & Laibach, Natalie & Bröring, Stefanie, 2021. "Semantic bridging of patents and scientific publications – The case of an emerging sustainability-oriented technology," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    8. Byunghoon Kim & Gianluca Gazzola & Jaekyung Yang & Jae-Min Lee & Byoung-Youl Coh & Myong K. Jeong & Young-Seon Jeong, 2017. "Two-phase edge outlier detection method for technology opportunity discovery," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 1-16, October.
    9. Won Sang Lee & So Young Sohn, 2017. "Identifying Emerging Trends of Financial Business Method Patents," Sustainability, MDPI, vol. 9(9), pages 1-21, September.
    10. Seunghyun Oh & Jaewoong Choi & Namuk Ko & Janghyeok Yoon, 2020. "Predicting product development directions for new product planning using patent classification-based link prediction," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1833-1876, December.
    11. Christian Mühlroth & Michael Grottke, 2018. "A systematic literature review of mining weak signals and trends for corporate foresight," Journal of Business Economics, Springer, vol. 88(5), pages 643-687, July.
    12. Choi, Jaewoong & Jeong, Byeongki & Yoon, Janghyeok, 2019. "Technology opportunity discovery under the dynamic change of focus technology fields: Application of sequential pattern mining to patent classifications," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    13. 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).
    14. 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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