Technology Opportunity Analysis for Creating Innovative Solutions: Applying Semi-supervised Topic Modelling on Patent Data

This paper introduces a technology opportunity analysis (TOA) framework designed to assist researchers in solving technological issues by leveraging insights from diverse domains. The methodology centers on the concept of functional approach and recombinant search, addressing technological issues of a specific domain by focusing on related functions realized in diverse reference fields. Beginning with the selection of a target technology and the collection of relevant data, the framework progresses to the extraction of reference fields and functions, utilizing patent classification codes and action-object structures of patent data. The zero-shot topic modeling module of BERTopic then clusters patent documents in a semi-supervised manner using reference field and function information, to provide technological insights of how different domains employ each function related to target technology. Next, the methodology leads to the evaluation process that prioritizes document clusters based on their potential value and technological feasibility. Finally, the document clusters with top scores are analyzed to extract detailed technological insights that can help solve the target technological issue. The proposed methodology was demonstrated with the case of miniaturization of wearable device. This study presents a data-driven TOA framework that fosters interdisciplinary collaboration, and extracts technology opportunities of high granularity using semi-supervised topic modeling technique.

Authors

Institutions

Publication Details

Published
2024-08-04
DOI
https://doi.org/10.23919/picmet64035.2024.10653159
Citations
2
Primary Topic
Intellectual Property and Patents
Type
article
Field-Weighted Citation Impact
1.46
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Technology Opportunity Analysis for Creating Innovative Solutions: Applying Semi-supervised Topic Modelling on Patent Data

Jinseob Kim, Sungjoo Lee
2 citations
Intellectual Property and Patents
1.46
article

Technology Opportunity Analysis for Creating Innovative Solutions: Applying Semi-supervised Topic Modelling on Patent Data

Jinseob Kim, Sungjoo Lee
article en
2 citations

Abstract

This paper introduces a technology opportunity analysis (TOA) framework designed to assist researchers in solving technological issues by leveraging insights from diverse domains. The methodology centers on the concept of functional approach and recombinant search, addressing technological issues of a specific domain by focusing on related functions realized in diverse reference fields. Beginning with the selection of a target technology and the collection of relevant data, the framework progresses to the extraction of reference fields and functions, utilizing patent classification codes and action-object structures of patent data. The zero-shot topic modeling module of BERTopic then clusters patent documents in a semi-supervised manner using reference field and function information, to provide technological insights of how different domains employ each function related to target technology. Next, the methodology leads to the evaluation process that prioritizes document clusters based on their potential value and technological feasibility. Finally, the document clusters with top scores are analyzed to extract detailed technological insights that can help solve the target technological issue. The proposed methodology was demonstrated with the case of miniaturization of wearable device. This study presents a data-driven TOA framework that fosters interdisciplinary collaboration, and extracts technology opportunities of high granularity using semi-supervised topic modeling technique.

Seoul National University (KR)
Industry, innovation and infrastructure
Openalex Percentile: Top 15%
Intellectual Property and Patents
1.46
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.