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Patent keyword network analysis for improving technology development efficiency

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. Campbell, Richard S., 1983. "Patent trends as a technological forecasting tool," World Patent Information, Elsevier, vol. 5(3), pages 137-143.
    3. 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.
    4. 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.
    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)

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