A Predictive Model of Technology Transfer Using Patent Analysis

Jaehyun Choi,Dongsik Jang,Sunghae Jun,Sangsung Park

doi:10.3390/su71215809

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.

Highlights

It has become important to promote open innovation, since growing uncertainties in the world economy have seen a contraction of the technology market [1,2]
In order to test the validity of the proposed method, this research constructed a predictive model of technology transfer based on 1000 patents and their technology transfer results
This experiment constructed predictive models of technology transfers based on patent data from the categories of whole dataset, national research institute, public university, private university, collaborative research, and country, respectively

Summary

Introduction

It has become important to promote open innovation, since growing uncertainties in the world economy have seen a contraction of the technology market [1,2]. As patents have become important tools in creating economic profits, it is necessary to invest resources in establishing patent strategies [1] Many countries, such as the United States, the European Union, Japan, China, and Korea, have programs to protect intellectual property and to promote technology transfer and commercialization. Many countries are attempting to activate technology transfers by supporting patent offices and providing credit guarantee funds, but these efforts rarely contribute towards increasing the number of technology transfers This is because there are several problems in selecting core patents to commercialize. A patent with no technological potential could be estimated as a high-quality candidate for technology transfer and commercialization These faulty evaluations result in a waste of R&D resources such as time, effort, and cost. The predictive model is constructed by preprocessing patent data and performing social network analysis, linear regression analysis, and decision tree modeling

Objectives

Results

Conclusion