Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/31444
Title: Technology Forecasting using Topic-Based Patent Analysis
Authors: Kim, Gab Jo
Park, Sang Sung
Jang, Dong Sik
Keywords: Patent analysis;Technology cluster;K-means clustering;Latent Dirichlet allocation
Issue Date: May-2015
Publisher: NISCAIR-CSIR, India
Abstract: The number of patents with critical information related to various technologies is increasing by the day. This trend has led corporations and countries to consider patent analysis as an important element in their analysis methodology for research and development. The present study seeks to determine and forecast vacant technology with considerable development potential through an analysis of patents. In order to identify a vacant technology cluster, the unstructured patent documents need to be structured into groups of similar technologies by using k-means clustering. Furthermore, silhouette width, Davies-Bouldin Index (DBI), and Pseudo F are used for enhancing reliability of determining the optimal number of clusters. From each technology cluster, a generative topic model, latent Dirichlet allocation (LDA), is adopted to extract latent topics specifically for examination of technologies. Renewable energy patents from the United States Patent and Trademark Office (USPTO) are analyzed for the case study, which verifies the proposed methodology.
Page(s): 265-270
URI: http://hdl.handle.net/123456789/31444
ISSN: 0975-1084 (Online); 0022-4456 (Print)
Appears in Collections:JSIR Vol.74(05) [May 2015]

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