Development of a fundamental patent exploration methodology based on technology knowledge flow

This study proposes a methodology for identifying fundamental patents based on their technical content. To overcome the limitations of existing methods that rely on citation networks and bibliographic information, this study extracts the subject-action-object (SAO) structure from patent claims to define the technical knowledge flow. A technical knowledge network is then constructed by identifying technical problems through modularity analysis. Fundamental patents are assessed using five characteristics: pioneering nature, applicability, originality, marketability, and path-dependency within the network. The fundamental patent is identified by calculating a fundamental score through entropy-based weights. A case study of CO2 capture technology and wearable watch technologies demonstrates that this approach can more accurately identify fundamental patents from a technical and problem-solving perspective compared to traditional methods. Notably, the methodology effectively tracks the foundation of technological innovation by reflecting the semantic flow between technical problems and their solutions. This study integrates and redefines the characteristics of fundamental patents, overcoming the limitations of previous patent analysis by reducing dependence on expert input by classifying technical problems and analyzing technical knowledge flow. This methodology highlights how rapidly growing patent data can be effectively analyzed to identify fundamental patents, contributing to R&D strategy and technology management decision-making.

Authors

Institutions

Publication Details

Journal
EPJ Data Science
Published
2025-11-20
DOI
https://doi.org/10.1140/epjds/s13688-025-00598-y
Primary Topic
Intellectual Property and Patents
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development of a fundamental patent exploration methodology based on technology knowledge flow

Byungun Yoon, Sunhye Kim, Young‐Chul Song
EPJ Data Science
Intellectual Property and Patents
article

Development of a fundamental patent exploration methodology based on technology knowledge flow

Byungun Yoon, Sunhye Kim, Young‐Chul Song
article en

Abstract

This study proposes a methodology for identifying fundamental patents based on their technical content. To overcome the limitations of existing methods that rely on citation networks and bibliographic information, this study extracts the subject-action-object (SAO) structure from patent claims to define the technical knowledge flow. A technical knowledge network is then constructed by identifying technical problems through modularity analysis. Fundamental patents are assessed using five characteristics: pioneering nature, applicability, originality, marketability, and path-dependency within the network. The fundamental patent is identified by calculating a fundamental score through entropy-based weights. A case study of CO2 capture technology and wearable watch technologies demonstrates that this approach can more accurately identify fundamental patents from a technical and problem-solving perspective compared to traditional methods. Notably, the methodology effectively tracks the foundation of technological innovation by reflecting the semantic flow between technical problems and their solutions. This study integrates and redefines the characteristics of fundamental patents, overcoming the limitations of previous patent analysis by reducing dependence on expert input by classifying technical problems and analyzing technical knowledge flow. This methodology highlights how rapidly growing patent data can be effectively analyzed to identify fundamental patents, contributing to R&D strategy and technology management decision-making.

EPJ Data ScienceVol. 14(1)
Dongguk University (KR)
National Research Foundation, National Research Foundation of Korea
Openalex Percentile: Top 52%
Intellectual Property and Patents
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Development of a fundamental patent exploration methodology based on technology knowledge flow — Byungun Yoon, Sunhye Kim, et al. · EPJ Data Science (2025) | TGRS Research Map | TGRS