Patent Text Mining

Patent Text Mining is a computational analysis technique that extracts, processes, and interprets textual information from patent documents to uncover technical, legal, and competitive intelligence, distinguishing itself from general text mining by its focus on the unique structure, language, and semantic content of patent literature.

578

Publications

26.5K

Citations

1.4K

Authors

549

Institutions

Publications per year

2017–2026

244

Authors

1.4K

  1. AJ
    Amy J.C. Trappey

    National Tsing Hua University, Taiwan

    15 publications · 719 citations

    14

    h-index

  2. CV
    Charles V. Trappey

    National Yang Ming Chiao Tung University, Taiwan

    14 publications · 695 citations

    13

    h-index

  3. BY
    Byungun Yoon

    Dongguk University, South Korea

    12 publications · 1.2K citations

    12

    h-index

  4. JY
    Janghyeok Yoon

    Konkuk University, South Korea

    12 publications · 896 citations

    12

    h-index

  5. MG
    Martin G. Moehrle

    University of Bremen, Germany

    11 publications · 582 citations

    11

    h-index

1–5 of 1.4K

Venues

  1. Scientometrics

    48 publications · 2.3K citations

    28

    h-index

  2. World Patent Information

    56 publications · 2.5K citations

    26

    h-index

  3. Technological Forecasting and Social Change

    24 publications · 1.6K citations

    23

    h-index

  4. Expert Systems with Applications

    22 publications · 1.8K citations

    21

    h-index

  5. Sustainability

    12 publications · 363 citations

    12

    h-index

1–5