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Exploring key licensing technologies and trends of public research institutions through technology patent networks

Shu-Hao Chang

Science and Public Policy2026https://doi.org/10.1093/scipol/scag012article
AJG 2ABDC C
Weight
0.50

What the paper says

Abstract This study analysed licensed patents of public research institutions (PRIs) and investigated the key licensing technologies and trends of these institutions from the perspective of technology patent networks. A total of 661 licensed patents granted to PRIs between 2019 and 2023 were examined; patent information was obtained from the United States Patent and Trademark Office. The results revealed that technologies identified as key players are measurement, chemical or physical processes and relevant apparatus, and additive manufacturing. Furthermore, optics and semiconductors are critical bridging technologies across different technological sectors. In addition, this study examined specific trends of policy relevance and identified additive manufacturing and optics have emerged as prominent key technologies in recent years, indicating that light- and laser-based optical and manufacturing technologies are critical for future innovation. The findings aim to identify the key technologies of these institutions and provide governments with a reference for allocating research and development subsidies.

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https://doi.org/https://doi.org/10.1093/scipol/scag012

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@article{shu-hao2026,
  title        = {{Exploring key licensing technologies and trends of public research institutions through technology patent networks}},
  author       = {Shu-Hao Chang},
  journal      = {Science and Public Policy},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1093/scipol/scag012},
}

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Exploring key licensing technologies and trends of public research institutions through technology patent networks

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.