IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v125y2020i3d10.1007_s11192-020-03710-3.html
   My bibliography  Save this article

Identification of promising inventions considering the quality of knowledge accumulation: a machine learning approach

Author

Listed:
  • Uijun Kwon

    (Seoul National University of Science and Technology)

  • Youngjung Geum

    (Seoul National University of Science and Technology)

Abstract

The identification of promising inventions is an important task in technology planning practice. Although several studies have been carried out using patent-based machine learning techniques, none of these have used the quality of knowledge accumulation as an input for identifying promising inventions, and have simply considered the number of backward citations as the link with previous knowledge. The current study therefore aims to fill this research gap by predicting promising inventions with patent-based machine learning, using the quality of knowledge accumulation as an important input variable. Eight criteria and 17 patent indicators are used as input variables, and patent forward citations are employed as the output variable. Six machine learning techniques are tested on 363,620 G06F patents filed between January 1990 and December 2009, and the results show that the quality of knowledge accumulation is the most important variable in predicting emerging inventions.

Suggested Citation

  • Uijun Kwon & Youngjung Geum, 2020. "Identification of promising inventions considering the quality of knowledge accumulation: a machine learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1877-1897, December.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:3:d:10.1007_s11192-020-03710-3
    DOI: 10.1007/s11192-020-03710-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-020-03710-3
    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/s11192-020-03710-3?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. Kyebambe, Moses Ntanda & Cheng, Ge & Huang, Yunqing & He, Chunhui & Zhang, Zhenyu, 2017. "Forecasting emerging technologies: A supervised learning approach through patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 236-244.
    2. Meyer, Martin, 2000. "Does science push technology? Patents citing scientific literature," Research Policy, Elsevier, vol. 29(3), pages 409-434, March.
    3. Suominen, Arho & Toivanen, Hannes & Seppänen, Marko, 2017. "Firms' knowledge profiles: Mapping patent data with unsupervised learning," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 131-142.
    4. Hanlin You & Mengjun Li & Keith W. Hipel & Jiang Jiang & Bingfeng Ge & Hante Duan, 2017. "Development trend forecasting for coherent light generator technology based on patent citation network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 297-315, April.
    5. Joung, Junegak & Kim, Kwangsoo, 2017. "Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data," Technological Forecasting and Social Change, Elsevier, vol. 114(C), pages 281-292.
    6. Mauricio Uriona-Maldonado & Leonardo Souza & Gregorio Varvakis, 2010. "Focus on practice service process innovation in the Brazilian electric energy sector," Service Business, Springer;Pan-Pacific Business Association, vol. 4(1), pages 77-88, March.
    7. 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.
    8. Stuart J. H. Graham & Alan C. Marco & Amanda F. Myers, 2018. "Patent transactions in the marketplace: Lessons from the USPTO Patent Assignment Dataset," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 27(3), pages 343-371, September.
    9. 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.
    10. Gautam Ahuja & Curba Morris Lampert, 2001. "Entrepreneurship in the large corporation: a longitudinal study of how established firms create breakthrough inventions," Strategic Management Journal, Wiley Blackwell, vol. 22(6‐7), pages 521-543, June.
    11. Azagra-Caro, Joaquín M. & Barberá-Tomás, David & Edwards-Schachter, Mónica & Tur, Elena M., 2017. "Dynamic interactions between university-industry knowledge transfer channels: A case study of the most highly cited academic patent," Research Policy, Elsevier, vol. 46(2), pages 463-474.
    12. Jean O. Lanjouw & Ariel Pakes & Jonathan Putnam, 1998. "How to Count Patents and Value Intellectual Property: The Uses of Patent Renewal and Application Data," Journal of Industrial Economics, Wiley Blackwell, vol. 46(4), pages 405-432, December.
    13. Tong, Xuesong & Frame, J. Davidson, 1994. "Measuring national technological performance with patent claims data," Research Policy, Elsevier, vol. 23(2), pages 133-141, March.
    14. Karki, M. M. S., 1997. "Patent citation analysis: A policy analysis tool," World Patent Information, Elsevier, vol. 19(4), pages 269-272, December.
    15. Harhoff, Dietmar & Scherer, Frederic M. & Vopel, Katrin, 2003. "Citations, family size, opposition and the value of patent rights," Research Policy, Elsevier, vol. 32(8), pages 1343-1363, September.
    16. Robert J. W. Tijssen & Martijn S. Visser & Thed N. van Leeuwen, 2002. "Benchmarking international scientific excellence: Are highly cited research papers an appropriate frame of reference?," Scientometrics, Springer;Akadémiai Kiadó, vol. 54(3), pages 381-397, July.
    17. Jaehyun Choi & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Predictive Model of Technology Transfer Using Patent Analysis," Sustainability, MDPI, vol. 7(12), pages 1-21, December.
    18. Julie Callaert & Bart Van Looy & Arnold Verbeek & Koenraad Debackere & Bart Thijs, 2006. "Traces of Prior Art: An analysis of non-patent references found in patent documents," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 3-20, October.
    19. Scott Shane, 2001. "Technological Opportunities and New Firm Creation," Management Science, INFORMS, vol. 47(2), pages 205-220, February.
    20. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    21. 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.
    22. Bessen, James, 2008. "The value of U.S. patents by owner and patent characteristics," Research Policy, Elsevier, vol. 37(5), pages 932-945, June.
    23. Youngjung Geum & Moon-Soo Kim & Sungjoo Lee, 2017. "Service Technology: Definition and Characteristics Based on a Patent Database," Service Science, INFORMS, vol. 9(2), pages 147-166, June.
    24. 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.
    25. Mark Schankerman, 1991. "How Valuable is Patent Protection? Estimates by Technology Field Using Patent Renewal Data," CEP Discussion Papers dp0046, Centre for Economic Performance, LSE.
    26. Mark Schankerman, 1991. "How Valuable is Patent Protection? Estimates By Technology Field Using Patent Renewal Data," NBER Working Papers 3780, National Bureau of Economic Research, Inc.
    27. Verhoeven, Dennis & Bakker, Jurriën & Veugelers, Reinhilde, 2016. "Measuring technological novelty with patent-based indicators," Research Policy, Elsevier, vol. 45(3), pages 707-723.
    28. Lanjouw, Jean O & Pakes, Ariel & Putnam, Jonathan, 1998. "How to Count Patents and Value Intellectual Property: The Uses of Patent Renewal and Application Data," Journal of Industrial Economics, Wiley Blackwell, vol. 46(4), pages 405-432, December.
    29. Ju, Yonghan & Sohn, So Young, 2015. "Patent-based QFD framework development for identification of emerging technologies and related business models: A case of robot technology in Korea," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 44-64.
    30. Marco, Antonio De & Scellato, Giuseppe & Ughetto, Elisa & Caviggioli, Federico, 2017. "Global markets for technology: Evidence from patent transactions," Research Policy, Elsevier, vol. 46(9), pages 1644-1654.
    31. 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.
    32. 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.
    33. Lori Rosenkopf & Atul Nerkar, 2001. "Beyond local search: boundary‐spanning, exploration, and impact in the optical disk industry," Strategic Management Journal, Wiley Blackwell, vol. 22(4), pages 287-306, April.
    34. Jean O. Lanjouw & Mark Schankerman, 1999. "The Quality of Ideas: Measuring Innovation with Multiple Indicators," NBER Working Papers 7345, National Bureau of Economic Research, Inc.
    35. 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.
    36. Schoenmakers, Wilfred & Duysters, Geert, 2010. "The technological origins of radical inventions," Research Policy, Elsevier, vol. 39(8), pages 1051-1059, October.
    37. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    38. Huang, Mu-Hsuan & Yang, Hsiao-Wen & Chen, Dar-Zen, 2015. "Increasing science and technology linkage in fuel cells: A cross citation analysis of papers and patents," Journal of Informetrics, Elsevier, vol. 9(2), pages 237-249.
    39. Marc Gruber & Dietmar Harhoff & Karin Hoisl, 2013. "Knowledge Recombination Across Technological Boundaries: Scientists vs. Engineers," Management Science, INFORMS, vol. 59(4), pages 837-851, April.
    40. Jean O. Lanjouw & Mark Schankerman, 2004. "Patent Quality and Research Productivity: Measuring Innovation with Multiple Indicators," Economic Journal, Royal Economic Society, vol. 114(495), pages 441-465, 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. Libo Sheng & Dongqing Lyu & Xuanmin Ruan & Hongquan Shen & Ying Cheng, 2023. "The association between prior knowledge and the disruption of an article," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4731-4751, August.
    2. Zehra Taşkın, 2021. "Forecasting the future of library and information science and its sub-fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1527-1551, February.
    3. Li Yao & He Ni, 2023. "Prediction of patent grant and interpreting the key determinants: an application of interpretable machine learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 4933-4969, September.
    4. Hu, Zewen & Zhou, Xiji & Lin, Angela, 2023. "Evaluation and identification of potential high-value patents in the field of integrated circuits using a multidimensional patent indicators pre-screening strategy and machine learning approaches," Journal of Informetrics, Elsevier, vol. 17(2).

    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. 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.
    2. 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).
    3. 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.
    4. Manuel Acosta & Daniel Coronado & Esther Ferrándiz & Manuel Jiménez, 2022. "Effects of knowledge spillovers between competitors on patent quality: what patent citations reveal about a global duopoly," The Journal of Technology Transfer, Springer, vol. 47(5), pages 1451-1487, October.
    5. 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.
    6. Jungpyo Lee & So Young Sohn, 2017. "What makes the first forward citation of a patent occur earlier?," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 279-298, October.
    7. Martin Kalthaus, 2020. "Knowledge recombination along the technology life cycle," Journal of Evolutionary Economics, Springer, vol. 30(3), pages 643-704, July.
    8. Ugo Rizzo & Nicolò Barbieri & Laura Ramaciotti & Demian Iannantuono, 2020. "The division of labour between academia and industry for the generation of radical inventions," The Journal of Technology Transfer, Springer, vol. 45(2), pages 393-413, April.
    9. Antonio Messeni Petruzzelli & Daniele Rotolo & Vito Albino, 2014. "Determinants of Patent Citations in Biotechnology: An Analysis of Patent Influence Across the Industrial and Organizational Boundaries," SPRU Working Paper Series 2014-05, SPRU - Science Policy Research Unit, University of Sussex Business School.
    10. Leila Tahmooresnejad & Catherine Beaudry, 2019. "Capturing the economic value of triadic patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 127-157, January.
    11. 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).
    12. Appio, Francesco Paolo & Baglieri, Daniela & Cesaroni, Fabrizio & Spicuzza, Lucia & Donato, Alessia, 2022. "Patent design strategies: Empirical evidence from European patents," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    13. Hur, Wonchang & Oh, Junbyoung, 2021. "A man is known by the company he keeps?: A structural relationship between backward citation and forward citation of patents," Research Policy, Elsevier, vol. 50(1).
    14. Caviggioli, Federico & De Marco, Antonio & Montobbio, Fabio & Ughetto, Elisa, 2020. "The licensing and selling of inventions by US universities," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    15. Noh, Heeyong & Lee, Sungjoo, 2020. "What constitutes a promising technology in the era of open innovation? An investigation of patent potential from multiple perspectives," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    16. Grimaldi, Michele & Cricelli, Livio & Di Giovanni, Martina & Rogo, Francesco, 2015. "The patent portfolio value analysis: A new framework to leverage patent information for strategic technology planning," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 286-302.
    17. 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.
    18. Nicolas van Zeebroeck, 2007. "Patents only live twice: a patent survival analysis in Europe," Working Papers CEB 07-028.RS, ULB -- Universite Libre de Bruxelles.
    19. Yun, Siyeong & Song, Kisik & Kim, Chulhyun & Lee, Sungjoo, 2021. "From stones to jewellery: Investigating technology opportunities from expired patents," Technovation, Elsevier, vol. 103(C).
    20. Mohd Shadab Danish & Pritam Ranjan & Ruchi Sharma, 2021. "Identification of “Valuable” Technologies via Patent Statistics in India: An Analysis Based on Renewal Information," BASE University Working Papers 13/2021, BASE University, Bengaluru, India.

    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:125:y:2020:i:3:d:10.1007_s11192-020-03710-3. 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.