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

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Date: Nov. 2015
Publisher: Elsevier B.V.
Document Type: Report
Length: 243 words

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Abstract :

Keywords Technology convergence; Link prediction; Association rule; Big data; Triadic patents; Topic model Highlights * Entire triadic patents are analyzed using open-source big data technology. * Potential future convergences among technologies are predicted by using Adamic/Adar similarity between IPC codes. * With LDA, we found that peptides, viruses, proteins, and DNA constitute topics where convergences were predicted. 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. Author Affiliation: Department of Information and Industrial Engineering, Yonsei University, South Korea * Corresponding author. Article History: Received 28 May 2014; Revised 26 May 2015; Accepted 23 July 2015 Byline: Won Sang Lee, Eun Jin Han, So Young Sohn [] (*)

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Gale Document Number: GALE|A631472756