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http://dx.doi.org/10.9708/jksci.2022.27.08.143

Patent Keyword Analysis using Gamma Regression Model and Visualization  

Jun, Sunghae (Dept. of Big Data and Statistics, Cheongju University)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.8, 2022 , pp. 143-149 More about this Journal
Abstract
Since patent documents contain detailed results of research and development technologies, many studies on various patent analysis methods for effective technology analysis have been conducted. In particular, research on quantitative patent analysis by statistics and machine learning algorithms has been actively conducted recently. The most used patent data in quantitative patent analysis is technology keywords. Most of the existing methods for analyzing the keyword data were models based on the Gaussian probability distribution with random variable on real space from negative infinity to positive infinity. In this paper, we propose a model using gamma probability distribution to analyze the frequency data of patent keywords that can theoretically have values from zero to positive infinity. In addition, in order to determine the regression equation of the gamma-based regression model, two-mode network is constructed to visualize the technological association between keywords. Practical patent data is collected and analyzed for performance evaluation between the proposed method and the existing Gaussian-based analysis models.
Keywords
Patent Keyword; Generalized Linear Model; Gaussian Distribution; Gamma Distribution; Patent Analysis;
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