IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v191y2023ics004016252300166x.html
   My bibliography  Save this article

Development of technology opportunity analysis based on technology landscape by extending technology elements with BERT and TRIZ

Author

Listed:
  • Wang, Jinfeng
  • Zhang, Zhixin
  • Feng, Lijie
  • Lin, Kuo-Yi
  • Liu, Peng

Abstract

Technology opportunity analysis (TOA) has been the subject of many prior studies, most of which have focused on deconstructing and restructuring the original knowledge structure in a single domain. This study suggests a method by extending technology elements with BERT and TRIZ that endeavors to address these issues. First, patents collected from the Derwent database were used as data sources. Second, BERT was employed to construct a technology landscape as a vector space model where similar technology elements are classified into the same technology topic. Meanwhile, TEMPEST was employed to cluster technology topics and elements according to different functions and other dimensions. Third, technology elements were extended by function-oriented search (FOS), which is a useful method of TRIZ. It includes extracting new technology elements from newly retrieved patents about implementing a specific function in other domains. Fourth, technology opportunities were identified by recombining original and new technology elements and then verifying their feasibility. Finally, the proposed approach was employed in empirical analysis for unmanned ships and 10 technology opportunities generated through knowledge migration. The process designed in this study combines quantitative modeling and qualitative analysis, which realizes accurate search and efficient innovation among different domains.

Suggested Citation

  • Wang, Jinfeng & Zhang, Zhixin & Feng, Lijie & Lin, Kuo-Yi & Liu, Peng, 2023. "Development of technology opportunity analysis based on technology landscape by extending technology elements with BERT and TRIZ," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:tefoso:v:191:y:2023:i:c:s004016252300166x
    DOI: 10.1016/j.techfore.2023.122481
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S004016252300166X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2023.122481?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Serkan Altuntas & Zulfiye Erdogan & Turkay Dereli, 2020. "A clustering-based approach for the evaluation of candidate emerging technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1157-1177, August.
    2. Teng, Fei & Sun, Yuling & Chen, Fang & Qin, Aning & Zhang, Qi, 2021. "Technology opportunity discovery of proton exchange membrane fuel cells based on generative topographic mapping," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    3. Nailong Wu & Xinyuan Chen & Shaonan Chen & Haodong Yuan & Jie Qi & Yueying Wang & Chih Chiang, 2021. "Inertial Gyro Wave Energy Conversion Nonlinear Modeling and Power-Index Predictive Control for Autonomous Ship," Complexity, Hindawi, vol. 2021, pages 1-13, November.
    4. Zhang, JingJing & Yan, Yan & Guan, JianCheng, 2019. "Recombinant distance, network governance and recombinant innovation," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 260-272.
    5. Mariotti, Francesca & Haider, Sajjad, 2020. "Managing institutional diversity and structural holes: Network configurations for recombinant innovation," Technological Forecasting and Social Change, Elsevier, vol. 160(C).
    6. Sam Arts & Lee Fleming, 2018. "Paradise of Novelty—Or Loss of Human Capital? Exploring New Fields and Inventive Output," Organization Science, INFORMS, vol. 29(6), pages 1074-1092, December.
    7. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    8. 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.
    9. Kim, Juram & Kim, Seungho & Lee, Changyong, 2019. "Anticipating technological convergence: Link prediction using Wikipedia hyperlinks," Technovation, Elsevier, vol. 79(C), pages 25-34.
    10. Ardito, Lorenzo & D'Adda, Diego & Messeni Petruzzelli, Antonio, 2018. "Mapping innovation dynamics in the Internet of Things domain: Evidence from patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 317-330.
    11. 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.
    12. Wang, Zhinan & Porter, Alan L. & Wang, Xuefeng & Carley, Stephen, 2019. "An approach to identify emergent topics of technological convergence: A case study for 3D printing," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 723-732.
    13. 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.
    14. Tiziano Fagni & Fabrizio Falchi & Margherita Gambini & Antonio Martella & Maurizio Tesconi, 2021. "TweepFake: About detecting deepfake tweets," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-16, May.
    15. Lee, Changyong & Jeon, Daeseong & Ahn, Joon Mo & Kwon, Ohjin, 2020. "Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database," Technovation, Elsevier, vol. 96.
    16. Albino, Vito & Ardito, Lorenzo & Dangelico, Rosa Maria & Messeni Petruzzelli, Antonio, 2014. "Understanding the development trends of low-carbon energy technologies: A patent analysis," Applied Energy, Elsevier, vol. 135(C), pages 836-854.
    17. Noh, Heeyong & Kim, Kyuwoong & Song, Young-Keun & Lee, Sungjoo, 2021. "Opportunity-driven technology roadmapping: The case of 5G mobile services," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    18. Lijie Feng & Yilang Li & Zhenfeng Liu & Jinfeng Wang, 2020. "Idea Generation and New Direction for Exploitation Technologies of Coal-Seam Gas through Recombinative Innovation and Patent Analysis," IJERPH, MDPI, vol. 17(8), pages 1-21, April.
    19. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    20. Guo, Junfang & Wang, Xuefeng & Li, Qianrui & Zhu, Donghua, 2016. "Subject–action–object-based morphology analysis for determining the direction of technological change," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 27-40.
    21. Sunhye Kim & Inchae Park & Byungun Yoon, 2020. "SAO2Vec: Development of an algorithm for embedding the subject–action–object (SAO) structure using Doc2Vec," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-26, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    2. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    3. Lee, Changyong & Jeon, Daeseong & Ahn, Joon Mo & Kwon, Ohjin, 2020. "Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database," Technovation, Elsevier, vol. 96.
    4. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    5. Wang, Chang & Geng, Hongjun & Sun, Rui & Song, Huiling, 2022. "Technological potential analysis and vacant technology forecasting in the graphene field based on the patent data mining," Resources Policy, Elsevier, vol. 77(C).
    6. Li, Xin & Wu, Yundi & Cheng, Haolun & Xie, Qianqian & Daim, Tugrul, 2023. "Identifying technology opportunity using SAO semantic mining and outlier detection method: A case of triboelectric nanogenerator technology," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    7. Choi, Jaewoong & Lee, Changyong & Yoon, Janghyeok, 2023. "Exploring a technology ecology for technology opportunity discovery: A link prediction approach using heterogeneous knowledge graphs," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    8. Park, Mingyu & Geum, Youngjung, 2022. "Two-stage technology opportunity discovery for firm-level decision making: GCN-based link-prediction approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    9. Richarz, Jan & Wegewitz, Stephan & Henn, Sarah & Müller, Dirk, 2023. "Graph-based research field analysis by the use of natural language processing: An overview of German energy research," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    10. Su, Yu-Shan & Huang, Hsini & Daim, Tugrul & Chien, Pan-Wei & Peng, Ru-Ling & Karaman Akgul, Arzu, 2023. "Assessing the technological trajectory of 5G-V2X autonomous driving inventions: Use of patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    11. Jing Ma & Yaohui Pan & Chih-Yi Su, 2022. "Organization-oriented technology opportunities analysis based on predicting patent networks: a case of Alzheimer’s disease," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5497-5517, September.
    12. 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.
    13. 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).
    14. Marić, Josip & Opazo-Basáez, Marco & Vlačić, Božidar & Dabić, Marina, 2023. "Innovation management of three-dimensional printing (3DP) technology: Disclosing insights from existing literature and determining future research streams," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    15. Jiang, Cuiqing & Zhou, Yiru & Chen, Bo, 2023. "Mining semantic features in patent text for financial distress prediction," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    16. Choi, Hyunhong & Woo, JongRoul, 2022. "Investigating emerging hydrogen technology topics and comparing national level technological focus: Patent analysis using a structural topic model," Applied Energy, Elsevier, vol. 313(C).
    17. Wu, Yingwen & Ji, Yangjian, 2023. "Identifying firm-specific technology opportunities from the perspective of competitors by using association rule mining," Journal of Informetrics, Elsevier, vol. 17(2).
    18. Ki Hong Kim & Young Jae Han & Sugil Lee & Sung Won Cho & Chulung Lee, 2019. "Text Mining for Patent Analysis to Forecast Emerging Technologies in Wireless Power Transfer," Sustainability, MDPI, vol. 11(22), pages 1-24, November.
    19. Juhyun Lee & Sangsung Park & Junseok Lee, 2023. "Exploring Potential R&D Collaboration Partners Using Embedding of Patent Graph," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    20. Lee, MyoungHoon & Kim, Suhyeon & Kim, Hangyeol & Lee, Junghye, 2022. "Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph," Technological Forecasting and Social Change, Elsevier, vol. 180(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:191:y:2023:i:c:s004016252300166x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.