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Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT

Author

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  • Lijie Feng

    (School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China
    School of Economic & Management, Shanghai Maritime University, Shanghai 201306, China)

  • Yuxiang Niu

    (School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Zhenfeng Liu

    (School of Economic & Management, Shanghai Maritime University, Shanghai 201306, China)

  • Jinfeng Wang

    (School of Economic & Management, Shanghai Maritime University, Shanghai 201306, China)

  • Ke Zhang

    (School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China)

Abstract

As innovative technology is being developed at an accelerated rate, the identification of technology opportunities is especially critical for both companies and governments. Among various approaches to search for opportunities, one of the most frequently used is to discover technology opportunity from patent data. In line with it, this paper aims to propose a hybrid approach based on morphological analysis (MA) and unified structured inventive thinking (USIT) for technology opportunity discovery (TOD) through patent analysis using text mining and Word2Vec clustering analysis to explore the intrinsic links of innovation elements. A basic morphology matrix is constructed according to patent information and then is extended using the innovation algorithms that are reorganized from USIT. Technology opportunities are analyzed at two layers to generate new technical ideas. To illustrate the research process and validate its utility, this paper selects the technology of coalbed methane (CBM) extraction as a use case. This hybrid approach contributes by suggesting a semi-autonomous and systematic procedure to perform MA for TOD. By integrating the innovation algorithms, this approach improves the procedure of value extension in MA.

Suggested Citation

  • Lijie Feng & Yuxiang Niu & Zhenfeng Liu & Jinfeng Wang & Ke Zhang, 2019. "Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT," Sustainability, MDPI, vol. 12(1), pages 1-35, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2019:i:1:p:136-:d:301160
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    References listed on IDEAS

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    1. Luigi Aldieri & Maxim Kotsemir & Concetto Paolo Vinci, 2020. "The role of environmental innovation through the technological proximity in the implementation of the sustainable development," Business Strategy and the Environment, Wiley Blackwell, vol. 29(2), pages 493-502, February.
    2. Yoon, Janghyeok & Park, Hyunseok & Seo, Wonchul & Lee, Jae-Min & Coh, Byoung-youl & Kim, Jonghwa, 2015. "Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 153-167.
    3. Changyong Lee & Gyumin Lee, 2019. "Technology opportunity analysis based on recombinant search: patent landscape analysis for idea generation," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 603-632, November.
    4. Park, Inchae & Yoon, Byungun, 2018. "Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network," Journal of Informetrics, Elsevier, vol. 12(4), pages 1199-1222.
    5. Yoon, Byungun & Magee, Christopher L., 2018. "Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 105-117.
    6. Kyuwoong Kim & Kyeongmin Park & Sungjoo Lee, 2019. "Investigating technology opportunities: the use of SAOx analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 45-70, January.
    7. Lee, Jeongjin & Kim, Changseok & Shin, Juneseuk, 2017. "Technology opportunity discovery to R&D planning: Key technological performance analysis," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 53-63.
    8. Lee, Changyong & Kang, Bokyoung & Shin, Juneseuk, 2015. "Novelty-focused patent mapping for technology opportunity analysis," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 355-365.
    9. Veronika Frigyesi & Patrice Laget & Mark Boden, 2019. "Exploitation of patent information in R&D output analysis for policymaking," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1717-1736, December.
    10. Noh, Heeyong & Song, Young-Keun & Lee, Sungjoo, 2016. "Identifying emerging core technologies for the future: Case study of patents published by leading telecommunication organizations," Telecommunications Policy, Elsevier, vol. 40(10), pages 956-970.
    11. Kim, Jieun & Lee, Changyong, 2017. "Novelty-focused weak signal detection in futuristic data: Assessing the rarity and paradigm unrelatedness of signals," Technological Forecasting and Social Change, Elsevier, vol. 120(C), pages 59-76.
    12. 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.
    13. Lee, Sangjae & Kim, Wanki & Kim, Young Min & Lee, Hyoung Yong & Oh, Kyong Joo, 2014. "The prioritization and verification of IT emerging technologies using an analytic hierarchy process and cluster analysis," Technological Forecasting and Social Change, Elsevier, vol. 87(C), pages 292-304.
    14. Zhenfeng Liu & Jian Feng & Jinfeng Wang, 2019. "Effects of the Sharing Economy on Sequential Innovation Products," Complexity, Hindawi, vol. 2019, pages 1-18, January.
    15. Schellner, Irene, 2002. "Japanese File Index classification and F-terms," World Patent Information, Elsevier, vol. 24(3), pages 197-201, September.
    16. 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.
    17. T Ritchey, 2006. "Problem structuring using computer-aided morphological analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(7), pages 792-801, July.
    18. Ola Olsson, 2005. "Technological Opportunity and Growth," Journal of Economic Growth, Springer, vol. 10(1), pages 31-53, January.
    19. Kwon, Heeyeul & Park, Yongtae & Geum, Youngjung, 2018. "Toward data-driven idea generation: Application of Wikipedia to morphological analysis," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 56-80.
    20. Silin, Dmitriy & Patzek, Tad, 2006. "Pore space morphology analysis using maximal inscribed spheres," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 336-360.
    21. Kai Hu & Huayi Wu & Kunlun Qi & Jingmin Yu & Siluo Yang & Tianxing Yu & Jie Zheng & Bo Liu, 2018. "A domain keyword analysis approach extending Term Frequency-Keyword Active Index with Google Word2Vec model," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1031-1068, March.
    22. Breitzman, Anthony & Thomas, Patrick, 2015. "The Emerging Clusters Model: A tool for identifying emerging technologies across multiple patent systems," Research Policy, Elsevier, vol. 44(1), pages 195-205.
    23. Geum, Youngjung & Park, Yongtae, 2016. "How to generate creative ideas for innovation: a hybrid approach of WordNet and morphological analysis," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 176-187.
    24. Song, Kisik & Kim, Karp Soo & Lee, Sungjoo, 2017. "Discovering new technology opportunities based on patents: Text-mining and F-term analysis," Technovation, Elsevier, vol. 60, pages 1-14.
    25. 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.
    26. Byunghoon Kim & Gianluca Gazzola & Jae-Min Lee & Dohyun Kim & Kanghoe Kim & Myong K. Jeong, 2014. "Inter-cluster connectivity analysis for technology opportunity discovery," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1811-1825, March.
    27. Xiao Zhou & Lu Huang & Yi Zhang & Miaomiao Yu, 2019. "A hybrid approach to detecting technological recombination based on text mining and patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 699-737, November.
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