IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v83y2014icp170-182.html
   My bibliography  Save this article

Patent keyword network analysis for improving technology development efficiency

Author

Listed:
  • Choi, Jinho
  • Hwang, Yong-Sik

Abstract

The methods of patent analysis are largely divided into network-based patent analysis and keyword-based morphological patent analysis. Both methods have their shortcomings: internal patent information composed of natural languages cannot be analyzed in the network-based patent analysis method, and the correlation between patents cannot be analyzed in the keyword-based morphological patent analysis method. In this research, we analyze the patents of Light Emitting Diode (LED) and wireless broadband fields via a method that incorporates both the network-based patent analysis and the keyword-based patent analysis methods. And by using network indices, we identify the characteristics of the patent keyword network, and also perform a trend analysis to discover how keywords play a significant role in network changes over time. The analysis results indicate that the patent keyword network is sporadic but clustered and shows a clear power law distribution. Further, the inflow keywords are highly likely to tie new connections with other keywords in the existing associated communities. Also, we confirm the fact that, as time passes, the top core keywords of a particular technology field continue to play an important role in the network and that also the rate of technological changes in wireless broadband field is faster than that of LED. Through the proposed analysis, researchers can easily grasp what technology keywords are important in the specific technology field and identify the relations between the essential technology elements; furthermore, this information can be utilized for developing new technologies by combining these technology elements extracted from community analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:tefoso:v:83:y:2014:i:c:p:170-182
    DOI: 10.1016/j.techfore.2013.07.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S004016251300156X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2013.07.004?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. Bagler, Ganesh, 2008. "Analysis of the airport network of India as a complex weighted network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 2972-2980.
    2. Bart Verspagen, 2007. "Mapping Technological Trajectories As Patent Citation Networks: A Study On The History Of Fuel Cell Research," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 10(01), pages 93-115.
    3. Wang, Mingyang & Yu, Guang & Yu, Daren, 2008. "Measuring the preferential attachment mechanism in citation networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(18), pages 4692-4698.
    4. Campbell, Richard S., 1983. "Patent trends as a technological forecasting tool," World Patent Information, Elsevier, vol. 5(3), pages 137-143.
    5. von Wartburg, Iwan & Teichert, Thorsten & Rost, Katja, 2005. "Inventive progress measured by multi-stage patent citation analysis," Research Policy, Elsevier, vol. 34(10), pages 1591-1607, December.
    6. 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.
    7. N. Vandewalle & F. Brisbois & X. Tordoir, 2001. "Non-random topology of stock markets," Quantitative Finance, Taylor & Francis Journals, vol. 1(3), pages 372-374, March.
    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. Ha, Sung Ho & Liu, Weina & Cho, Hune & Kim, Sang Hyun, 2015. "Technological advances in the fuel cell vehicle: Patent portfolio management," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 277-289.
    2. Huang, Ying & Li, Ruinan & Zou, Fang & Jiang, Lidan & Porter, Alan L. & Zhang, Lin, 2022. "Technology life cycle analysis: From the dynamic perspective of patent citation networks," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    3. Chi-Yo Huang & Liang-Chieh Wang & Ying-Ting Kuo & Wei-Ti Huang, 2021. "A Novel Analytic Framework of Technology Mining Using the Main Path Analysis and the Decision-Making Trial and Evaluation Laboratory-Based Analytic Network Process," Mathematics, MDPI, vol. 9(19), pages 1-24, October.
    4. Su, Hsin-Ning & Moaniba, Igam M., 2017. "Investigating the dynamics of interdisciplinary evolution in technology developments," Technological Forecasting and Social Change, Elsevier, vol. 122(C), pages 12-23.
    5. Lai, Kuei-Kuei & Chen, Yu-Long & Kumar, Vimal & Daim, Tugrul & Verma, Pratima & Kao, Fang-Chen & Liu, Ruirong, 2023. "Mapping technological trajectories and exploring knowledge sources: A case study of E-payment technologies," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    6. Hong Joo Lee & Hoyeon Oh, 2020. "A Study on the Deduction and Diffusion of Promising Artificial Intelligence Technology for Sustainable Industrial Development," Sustainability, MDPI, vol. 12(14), pages 1-15, July.
    7. Niemann, Helen & Moehrle, Martin G. & Frischkorn, Jonas, 2017. "Use of a new patent text-mining and visualization method for identifying patenting patterns over time: Concept, method and test application," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 210-220.
    8. Ying Huang & Donghua Zhu & Yue Qian & Yi Zhang & Alan L. Porter & Yuqin Liu & Ying Guo, 2017. "A hybrid method to trace technology evolution pathways: a case study of 3D printing," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 185-204, April.
    9. Roberto Fontana & Alessandro Nuvolari & Bart Verspagen, 2009. "Mapping technological trajectories as patent citation networks. An application to data communication standards," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 18(4), pages 311-336.
    10. Euiseok Kim & Yongrae Cho & Wonjoon Kim, 2014. "Dynamic patterns of technological convergence in printed electronics technologies: patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 975-998, February.
    11. Jang, Hyun Jin & Woo, Han-Gyun & Lee, Changyong, 2017. "Hawkes process-based technology impact analysis," Journal of Informetrics, Elsevier, vol. 11(2), pages 511-529.
    12. Hwang, Seonho & Shin, Juneseuk, 2019. "Extending technological trajectories to latest technological changes by overcoming time lags," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 142-153.
    13. Arianna Martinelli & Önder Nomaler, 2014. "Measuring knowledge persistence: a genetic approach to patent citation networks," Journal of Evolutionary Economics, Springer, vol. 24(3), pages 623-652, July.
    14. Adam B. Jaffe & Gaétan de Rassenfosse, 2017. "Patent citation data in social science research: Overview and best practices," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(6), pages 1360-1374, June.
    15. Higham, Kyle & Contisciani, Martina & De Bacco, Caterina, 2022. "Multilayer patent citation networks: A comprehensive analytical framework for studying explicit technological relationships," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    16. Momeni, Abdolreza & Rost, Katja, 2016. "Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 16-29.
    17. Wei Du & Yibo Wang & Wei Xu & Jian Ma, 2021. "A personalized recommendation system for high-quality patent trading by leveraging hybrid patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9369-9391, December.
    18. 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.
    19. Yoonki Rhee & Sejun Yoon & Hyunseok Park, 2022. "Exploring Knowledge Trajectories of Accounting Information Systems Using Business Method Patents and Knowledge Persistence-Based Main Path Analysis," Mathematics, MDPI, vol. 10(18), pages 1-22, September.
    20. 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.

    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:83:y:2014:i:c:p:170-182. 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.