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.
Highlights
Visualization for TechnologyExtended reality (XR) technology, including virtual reality (VR), augmented reality (AR) and mixed reality (MR), has been rapidly developing in recent years and have a lot of influence on our society in various fields such as education, psychology, firefighting, culture and manufacturing [1,2,3,4,5,6]
We studied on a patent analysis method for XR technology analysis
To analyze the patent documents by statistics and machine learning algorithms, we have to preprocess the documents to make structured data which is a matrix of patents and keywords for rows and columns, respectively, because the analytical methods of statistical machine learning require structured data type for data analysis
Summary
Visualization for TechnologyExtended reality (XR) technology, including virtual reality (VR), augmented reality (AR) and mixed reality (MR), has been rapidly developing in recent years and have a lot of influence on our society in various fields such as education, psychology, firefighting, culture and manufacturing [1,2,3,4,5,6]. Most studies on technology analysis have been focused on patent documents related to target technology [7,8,9,10,11]. This is because a patent contains detailed results of the researched and developed technology [12]. Its element represents occurred frequency of each keyword in a patent document This structured data has several problems in terms of data analysis using statistical machine learning. One of them is data sparsity with a value of zero in many elements of the matrix [17,18] Due to this problem, the normality assumption of the data is not satisfied.
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