A predictive model of technology transfer using patent analysis

Jaehyun Choi, Dong Sik Jang, Sunghae Jun, Sangsung Park

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

The rapid pace of technological advances creates many difficulties for R&D practitioners in analyzing emerging technologies. Patent information analysis is an effective tool in this situation. Conventional patent information analysis has focused on the extraction of vacant, promising, or core technologies and the monitoring of technological trends. From a technology management perspective, the ultimate purpose of R&D is technology commercialization. The core of technology commercialization is the technology transfer phase. Although a great number of patents are filed, publicized, and registered every year, many commercially relevant patents are filtered through registration processes that examine novelty, creativity, and industrial applicability. Despite the efforts of these selection processes, the number of patents being transferred is low when compared with total annual patent registrations. To deal with this problem, this study proposes a predictive model for technology transfer using patent analysis. In the predictive model, patent analysis is conducted to reveal the quantitative relations between technology transfer and a range of variables included in the patent data.

Original languageEnglish
Pages (from-to)16175-16195
Number of pages21
JournalSustainability (Switzerland)
Volume7
Issue number12
DOIs
Publication statusPublished - 2015

Keywords

  • Machine learning algorithms
  • Patent analysis
  • Predictive model
  • Statistics
  • Sustainable management of technology
  • Technology transfer

ASJC Scopus subject areas

  • Management, Monitoring, Policy and Law
  • Renewable Energy, Sustainability and the Environment
  • Geography, Planning and Development

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