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Development trend forecasting for coherent light generator technology based on patent citation network analysis

Author

Listed:
  • Hanlin You

    (National University of Defense Technology)

  • Mengjun Li

    (National University of Defense Technology)

  • Keith W. Hipel

    (University of Waterloo)

  • Jiang Jiang

    (National University of Defense Technology)

  • Bingfeng Ge

    (National University of Defense Technology)

  • Hante Duan

    (National University of Defense Technology)

Abstract

A forecasting methodology for technology development trends is proposed based on a two-level network model consisting of knowledge-transfer among patents and patent subclasses, with the aim to confront the increasing complex challenge in technology investment and management. More specifically, the patents of the “coherent light generators” classification, granted from 1976 to 2014 by examiners of the United States Patent and Trademark Office, are collected and with which the first-level citation network is constructed first. Then, a new approach to assess patent importance from the perspective of topological structure is provided and the second-level citation network, which consists of patent subclasses, is produced with the evaluation results. Moreover, three assessment indices of the subclass citation network are abstracted as impact parameters for technology development trends. Finally, two typical time series models, the Bass and ARIMA model, are utilized and compared for development trend forecasting. Based on the results of evolution prediction and network analysis, the highlighted patent subclasses with more development potential are identified, and the correlation between technology development opportunity and topological structure of the patent citation network is discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:1:d:10.1007_s11192-017-2252-y
    DOI: 10.1007/s11192-017-2252-y
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    References listed on IDEAS

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    1. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    3. Comin, Diego & Mestieri, Martí, 2014. "Technology Diffusion: Measurement, Causes, and Consequences," Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 2, chapter 2, pages 565-622, Elsevier.
    4. Pao-Long Chang & Chao-Chan Wu & Hoang-Jyh Leu, 2010. "Using patent analyses to monitor the technological trends in an emerging field of technology: a case of carbon nanotube field emission display," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(1), pages 5-19, January.
    5. 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.
    6. Péter Érdi & Kinga Makovi & Zoltán Somogyvári & Katherine Strandburg & Jan Tobochnik & Péter Volf & László Zalányi, 2013. "Prediction of emerging technologies based on analysis of the US patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 225-242, April.
    7. Andrew Rodriguez & Byunghoon Kim & Mehmet Turkoz & Jae-Min Lee & Byoung-Youl Coh & Myong K. Jeong, 2015. "New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 565-581, May.
    8. J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 29(1), pages 1-27, March.
    9. Gress, Bernard, 2010. "Properties of the USPTO patent citation network: 1963-2002," World Patent Information, Elsevier, vol. 32(1), pages 3-21, March.
    10. Chang, Shann-Bin, 2012. "Using patent analysis to establish technological position: Two different strategic approaches," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 3-15.
    11. Xuezhao Wang & Yajuan Zhao & Rui Liu & Jing Zhang, 2013. "Knowledge-transfer analysis based on co-citation clustering," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 859-869, December.
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    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. Xu, Jianguo & Guo, Lixiang & Jiang, Jiang & Ge, Bingfeng & Li, Mengjun, 2019. "A deep learning methodology for automatic extraction and discovery of technical intelligence," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 339-351.
    4. Hanlin You & Mengjun Li & Jiang Jiang & Bingfeng Ge & Xueting Zhang, 2017. "Evolution monitoring for innovation sources using patent cluster analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 693-715, May.
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    7. Huang, Ying & Porter, Alan L. & Zhang, Yi & Lian, Xiangpeng & Guo, Ying, 2019. "An assessment of technology forecasting: Revisiting earlier analyses on dye-sensitized solar cells (DSSCs)," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 831-843.
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