Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents

Won Sang Lee, Eun Jin Han and So Young Sohn

Technological Forecasting and Social Change, 2015, vol. 100, issue C, 317-329

Abstract: Understanding technology convergence became crucial for pursuing innovation and economic growth. This paper attempts to predict the pattern of technology convergence by jointly applying the Association Rule and Link Prediction to entire IPCs related to triadic patents filed during the period from 1955 to 2011. We further use a topic model to discover emerging areas of the predicted technology convergence. The results show that the medical area is in the center of convergence, and we predict that technologies for treating respiratory system/blood/sense disorders are associated with the technologies of genetic engineering/peptide/heterocyclic compounds. After eliminating the majority of convergence, we found the convergence pattern among activating catalysts, printing, advanced networking, controlling devices, secured communication with in-memory system, television system with pattern recognition, and image processing and analyzing technologies. The results of our study are expected to contribute to firms that seek new innovative technological domain.

Keywords: Technology convergence; Link prediction; Association rule; Big data; Triadic patents; Topic model (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (58)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:100:y:2015:i:c:p:317-329

DOI: 10.1016/j.techfore.2015.07.022

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