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

Hanlin You, Mengjun Li, Keith W. Hipel, Jiang Jiang (), Bingfeng Ge and Hante Duan
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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

Scientometrics, 2017, vol. 111, issue 1, No 17, 297-315

Abstract: 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.

Keywords: Knowledge-transfer; Patent citation network; Time series model; Coherent light generator technology; Technology forecasting (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (14)

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DOI: 10.1007/s11192-017-2252-y

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