IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v121y2019i2d10.1007_s11192-019-03224-7.html
   My bibliography  Save this article

Technology opportunity analysis based on recombinant search: patent landscape analysis for idea generation

Author

Listed:
  • Changyong Lee

    (Ulsan National Institute of Science and Technology)

  • Gyumin Lee

    (Ulsan National Institute of Science and Technology)

Abstract

This research responds to the need for the use of quantitative data and scientific methods for technology opportunity analysis by focusing on idea generation. Interpreting innovation as a process of recombinant search, we propose a patent landscape analysis to generate ideas which are likely to have more novelty and value than others. For this, first, a patent landscape is constructed from patent classification information as a vector space model, where each position represents a configuration of technological components and corresponds to an idea and, if they exist, relevant patented inventions. Second, the novelty of ideas is assessed via the modified local outlier factor based on the distribution of existing patented inventions on the landscape. Finally, the value of ideas is estimated via naïve Bayes models based on the forward citations of existing patented inventions. In addition, this study also investigates the recombinant synergies between different technological components and the relationships between novelty and value of ideas. A case study of pharmaceutical technology shows that our approach can guide organisations towards setting up effective search strategies for new technology development.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:121:y:2019:i:2:d:10.1007_s11192-019-03224-7
    DOI: 10.1007/s11192-019-03224-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-019-03224-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-019-03224-7?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. Yongho Lee & So Young Kim & Inseok Song & Yongtae Park & Juneseuk Shin, 2014. "Technology opportunity identification customized to the technological capability of SMEs through two-stage patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 227-244, July.
    2. Juan Alcácer & Michelle Gittelman, 2006. "Patent Citations as a Measure of Knowledge Flows: The Influence of Examiner Citations," The Review of Economics and Statistics, MIT Press, vol. 88(4), pages 774-779, November.
    3. Janghyeok Yoon & Hyunseok Park & Kwangsoo Kim, 2013. "Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 313-331, January.
    4. Manuel Trajtenberg, 1990. "A Penny for Your Quotes: Patent Citations and the Value of Innovations," RAND Journal of Economics, The RAND Corporation, vol. 21(1), pages 172-187, Spring.
    5. Kim, Hyunwoo & Hong, Suckwon & Kwon, Ohjin & Lee, Changyong, 2017. "Concentric diversification based on technological capabilities: Link analysis of products and technologies," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 246-257.
    6. Lee, Mingook & Lee, Sungjoo, 2017. "Identifying new business opportunities from competitor intelligence: An integrated use of patent and trademark databases," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 170-183.
    7. Bronwyn H. Hall & Adam Jaffe & Manuel Trajtenberg, 2005. "Market Value and Patent Citations," RAND Journal of Economics, The RAND Corporation, vol. 36(1), pages 16-38, Spring.
    8. 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.
    9. Kim, Jeeeun & Lee, Sungjoo, 2015. "Patent databases for innovation studies: A comparative analysis of USPTO, EPO, JPO and KIPO," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 332-345.
    10. 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.
    11. Strumsky, Deborah & Lobo, José, 2015. "Identifying the sources of technological novelty in the process of invention," Research Policy, Elsevier, vol. 44(8), pages 1445-1461.
    12. Fleming, Lee & Sorenson, Olav, 2001. "Technology as a complex adaptive system: evidence from patent data," Research Policy, Elsevier, vol. 30(7), pages 1019-1039, August.
    13. Aharonson, Barak S. & Schilling, Melissa A., 2016. "Mapping the technological landscape: Measuring technology distance, technological footprints, and technology evolution," Research Policy, Elsevier, vol. 45(1), pages 81-96.
    14. Kim, Juram & Kim, Seungho & Lee, Changyong, 2019. "Anticipating technological convergence: Link prediction using Wikipedia hyperlinks," Technovation, Elsevier, vol. 79(C), pages 25-34.
    15. Sam Arts & Bruno Cassiman & Juan Carlos Gomez, 2018. "Text matching to measure patent similarity," Strategic Management Journal, Wiley Blackwell, vol. 39(1), pages 62-84, January.
    16. 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.
    17. 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.
    18. Lee, Changyong & Cho, Yangrae & Seol, Hyeonju & Park, Yongtae, 2012. "A stochastic patent citation analysis approach to assessing future technological impacts," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 16-29.
    19. Jan M. Gerken & Martin G. Moehrle, 2012. "A new instrument for technology monitoring: novelty in patents measured by semantic patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 645-670, June.
    20. 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.
    21. Narin, Francis & Noma, Elliot & Perry, Ross, 1987. "Patents as indicators of corporate technological strength," Research Policy, Elsevier, vol. 16(2-4), pages 143-155, August.
    22. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
    23. Benner, Mary & Waldfogel, Joel, 2008. "Close to you? Bias and precision in patent-based measures of technological proximity," Research Policy, Elsevier, vol. 37(9), pages 1556-1567, October.
    24. Park, Youngjin & Yoon, Janghyeok, 2017. "Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 170-183.
    25. Changyong Lee & Hakyeon Lee, 2015. "Novelty-focussed document mapping to identify new service opportunities," The Service Industries Journal, Taylor & Francis Journals, vol. 35(6), pages 345-361, April.
    26. 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.
    27. Schilling, Melissa A. & Green, Elad, 2011. "Recombinant search and breakthrough idea generation: An analysis of high impact papers in the social sciences," Research Policy, Elsevier, vol. 40(10), pages 1321-1331.
    28. Joshua Lerner, 1994. "The Importance of Patent Scope: An Empirical Analysis," RAND Journal of Economics, The RAND Corporation, vol. 25(2), pages 319-333, Summer.
    29. Dahlin, Kristina B. & Behrens, Dean M., 2005. "When is an invention really radical?: Defining and measuring technological radicalness," Research Policy, Elsevier, vol. 34(5), pages 717-737, June.
    30. Gerard Hoberg & Gordon Phillips, 2010. "Product Market Synergies and Competition in Mergers and Acquisitions: A Text-Based Analysis," The Review of Financial Studies, Society for Financial Studies, vol. 23(10), pages 3773-3811, October.
    31. Dietmar Harhoff & Stefan Wagner, 2009. "The Duration of Patent Examination at the European Patent Office," Management Science, INFORMS, vol. 55(12), pages 1969-1984, December.
    32. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    33. Sam Arts & Reinhilde Veugelers, 2015. "Technology familiarity, recombinant novelty, and breakthrough invention," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 24(6), pages 1215-1246.
    34. Kristina Dahlin & Deans M. Behrens, 2005. "When is an invention really radical? Defining and measuring technological radicalness," Post-Print hal-00480416, HAL.
    35. Xuefeng Wang & Pingping Ma & Ying Huang & Junfang Guo & Donghua Zhu & Alan L. Porter & Zhinan Wang, 2017. "Combining SAO semantic analysis and morphology analysis to identify technology opportunities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 3-24, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Juite Wang & Tzu-Yen Hsu, 2023. "Early discovery of emerging multi-technology convergence for analyzing technology opportunities from patent data: the case of smart health," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4167-4196, August.
    3. Kim, Juram & Hong, Suckwon & Kang, Yubin & Lee, Changyong, 2023. "Domain-specific valuation of university technologies using bibliometrics, Jonckheere–Terpstra tests, and data envelopment analysis," Technovation, Elsevier, vol. 122(C).
    4. Percia David, Dimitri & Maréchal, Loïc & Lacube, William & Gillard, Sébastien & Tsesmelis, Michael & Maillart, Thomas & Mermoud, Alain, 2023. "Measuring security development in information technologies: A scientometric framework using arXiv e-prints," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    5. Changyong Lee & Suckwon Hong & Juram Kim, 2021. "Anticipating multi-technology convergence: a machine learning approach using patent information," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 1867-1896, March.
    6. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. 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).
    12. 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).
    13. 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.
    14. Liang Chen & Shuo Xu & Lijun Zhu & Jing Zhang & Xiaoping Lei & Guancan Yang, 2020. "A deep learning based method for extracting semantic information from patent documents," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 289-312, October.
    15. Shin, Hyunjin & Woo, Hyun Goo & Sohn, Kyung-Ah & Lee, Sungjoo, 2023. "Comparing research trends with patenting activities in the biomedical sector: The case of dementia," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    16. 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).
    17. 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).

    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. 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.
    2. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    3. 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.
    4. 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.
    5. Ren, Haiying & Zhao, Yuhui, 2021. "Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks," Technovation, Elsevier, vol. 101(C).
    6. 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.
    7. Kathryn Rudie Harrigan & Maria Chiara DiGuardo, 2017. "Sustainability of patent-based competitive advantage in the U.S. communications services industry," The Journal of Technology Transfer, Springer, vol. 42(6), pages 1334-1361, December.
    8. Cammarano, Antonello & Michelino, Francesca & Lamberti, Emilia & Caputo, Mauro, 2017. "Accumulated stock of knowledge and current search practices: The impact on patent quality," Technological Forecasting and Social Change, Elsevier, vol. 120(C), pages 204-222.
    9. Changyong Lee & Suckwon Hong & Juram Kim, 2021. "Anticipating multi-technology convergence: a machine learning approach using patent information," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 1867-1896, March.
    10. Sun, Bixuan & Kolesnikov, Sergey & Goldstein, Anna & Chan, Gabriel, 2021. "A dynamic approach for identifying technological breakthroughs with an application in solar photovoltaics," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    11. Aharonson, Barak S. & Schilling, Melissa A., 2016. "Mapping the technological landscape: Measuring technology distance, technological footprints, and technology evolution," Research Policy, Elsevier, vol. 45(1), pages 81-96.
    12. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
    13. Barbieri, Nicolò & Marzucchi, Alberto & Rizzo, Ugo, 2020. "Knowledge sources and impacts on subsequent inventions: Do green technologies differ from non-green ones?," Research Policy, Elsevier, vol. 49(2).
    14. Hong, Suckwon & Kim, Juram & Woo, Han-Gyun & Kim, Young-Choon & Lee, Changyong, 2022. "Screening ideas in the early stages of technology development: A word2vec and convolutional neural network approach," Technovation, Elsevier, vol. 112(C).
    15. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    16. Kathryn Rudie Harrigan & Maria Chiara Guardo & Elona Marku, 2018. "Patent value and the Tobin’s q ratio in media services," The Journal of Technology Transfer, Springer, vol. 43(1), pages 1-19, February.
    17. 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).
    18. Arts, Sam & Hou, Jianan & Gomez, Juan Carlos, 2021. "Natural language processing to identify the creation and impact of new technologies in patent text: Code, data, and new measures," Research Policy, Elsevier, vol. 50(2).
    19. 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.
    20. 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.

    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:spr:scient:v:121:y:2019:i:2:d:10.1007_s11192-019-03224-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.