Bayesian Structure Learning and Visualization for Technology Analysis

Sangsung Park, Seongyong Choi and Sunghae Jun
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Sangsung Park: Department of Big Data and Statistics, Cheongju University, Chungbuk, Cheongju-si 28503, Korea
Seongyong Choi: Department of Computer Engineering, Inha University, Incheon 22212, Korea
Sunghae Jun: Department of Big Data and Statistics, Cheongju University, Chungbuk, Cheongju-si 28503, Korea

Sustainability, 2021, vol. 13, issue 14, 1-16

Abstract: To perform technology analysis, we usually search patent documents related to target technology. In technology analysis using statistics and machine learning algorithms, we have to transform the patent documents into structured data that is a matrix of patents and keywords. In general, this matrix is very sparse because its most elements are zero values. The data is not satisfied with data normality assumption. However, most statistical methods require the assumption for data analysis. To overcome this problem, we propose a patent analysis method using Bayesian structure learning and visualization. In addition, we apply the proposed method to technology analysis of extended reality (XR). XR technology is integrated technology of virtual and real worlds that includes all of virtual, augmented and mixed realities. This technology is affecting most of our society such as education, healthcare, manufacture, disaster prevention, etc. Therefore, we need to have correct understanding of this technology. Lastly, we carry out XR technology analysis using Bayesian structure learning and visualization.

Keywords: Bayesian structure learning; extended reality; technology analysis; sparse data; patent documents (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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