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Characterizing Patent Assignees by Their Structural Positions Relative to a Field’s Evolutionary Trajectory

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  • Kuan, Chung-Huei
  • Lin, Jia-Tian
  • Chen, Dar-Zen

Abstract

This study characterizes and classifies the assignees of a technology field’s patents through quantitatively determining their structural positions against a trajectory epitomizing the field’s knowledge evolution. By considering that these patents’ citation network embodies a knowledge structure for the technology field, and assuming that a series of mainstream (MS) patents constitute the evolutionary trajectory, each non-MS patent is identified to be at one of the following positions: forward and backward reachable (FBR), backward reachable only (BRO), forward reachable only (FRO), and unreachable (UR), based on their reachability with the MS patents. The assignees are then associated with five positioning attributes, which are the shares of their patents at respective positions. With precise definitions using these quantitative attributes, assignees of the technology field are classified into exactly one of the distinctly positioned categories, namely trendsetters, contributors, absorbers, bystanders, and reinforcers, or one of the multiply positioned categories of mixed characteristics. These categories can be geometrically interpreted and the assignees’ positions can be visualized in a three-dimensional positioning space. This study then uses U.S. biochip patents and evolutionary trajectory derived by main path analysis (MPA) to observe how the proposed method work.

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  • Kuan, Chung-Huei & Lin, Jia-Tian & Chen, Dar-Zen, 2021. "Characterizing Patent Assignees by Their Structural Positions Relative to a Field’s Evolutionary Trajectory," Journal of Informetrics, Elsevier, vol. 15(4).
    • Handle: RePEc:eee:infome:v:15:y:2021:i:4:s1751157721000584
    DOI: 10.1016/j.joi.2021.101187
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    2. Yu, Dejian & Yan, Zhaoping, 2023. "Main path analysis considering citation structure and content: Case studies in different domains," Journal of Informetrics, Elsevier, vol. 17(1).

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