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A visualization tool of patent topic evolution using a growing cell structure neural network


This research used a cell structure map to visualize technological evolution and showed the developmental trend in a technological field. The basic concept was to organize patents into a map produced by growing cell structures. The map was then disassembled into clusters with similar contexts using the Girvan–Newman algorithm. Next, the continuity between clusters in two snapshots was identified and used as the base for establishing a trajectory in the technology. An analysis of patents in the flaw detection field found that the field was composed of several technological trajectories. Among them, ultrasonic flaw detection, wafer inspection and substrate inspection were relatively larger and more continuing technologies, while infrared thermography defect inspection has been an emerging topic in recent years. It is to be hoped that the map of technology constructed in this research provides insights into the history of technological evolution and helps explain the transition patterns through changes in cluster continuity. This can serve a reference point by experts who attempt to visualize the mapping of technological development or identify the latest focus of attention.

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The authors thank Professor Julie Callaert, the guest editor of Scientometrics, and two anonymous reviewers for constructive comments on an early version of this article. This research was partially supported by Taiwan Ministry of Science and Technology [MOST 104-2221-E-131-012].

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Correspondence to Ssu-Han Chen.

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Sung, HY., Yeh, HY., Lin, JK. et al. A visualization tool of patent topic evolution using a growing cell structure neural network. Scientometrics 111, 1267–1285 (2017).

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  • Technology map
  • Growing cell structures
  • Neural networks
  • Girvan–Newman algorithm
  • Natural language processing