Characterizing Patent Assignees by Their Structural Positions Relative to a Field’s Evolutionary Trajectory
Chung-Huei Kuan,
Jia-Tian Lin and
Dar-Zen Chen
Journal of Informetrics, 2021, vol. 15, issue 4
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.
Keywords: Patent citation network; Patent assignee; Knowledge structure; Structural position; Evolutionary trajectory; Main path (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1751157721000584
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:15:y:2021:i:4:s1751157721000584
DOI: 10.1016/j.joi.2021.101187
Access Statistics for this article
Journal of Informetrics is currently edited by Leo Egghe
More articles in Journal of Informetrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().