# Patent Analysis Using Bayesian Data Analysis and Network Modeling
## 2 Patent Data Analysis  
Big data are characterized by the volume and heterogeneity of data [16]. With the development of hardware as well as software, the size of data that can be stored in computer repositories is continuously increasing. The data included in big data consist of very diverse types such as numbers, texts, images, video, and symbols. The patent document data also have the same characteristics of big data [17, 18] because a patent document consists of various data types such as the title, inventor, application date, specification, figures, drawings, and citation information [9, 10, 19]. In addition, the number of patents applied and registered with the patent offices around the world is very large. Therefore, we can analyze patent document data using the existing big data analysis methods provided by statistics and machine learning algorithms. The analysis methods require a structured data type as input data before patent analysis. The data have a structure similar to table in relational database [20, 21]. The table is a matrix with patent documents and keywords for rows and columns, respectively. The element of this matrix is frequency of an occurred keyword in a patent document. We carried out technology analysis using this patent-keyword matrix. In this paper, we built this matrix using the collected patent documentsrelated drone technology and analyzed the matrix data for drone technology analysis. We show a comparison of existing studies related to patent data analysis in Table 1.  
In Table 1, we illustrate reference, technology field, methodology, and analysis purpose of previous research work for patent analysis. We found that the existing patent analyses were performed in various technology fields. Moreover, we knew that the purpose of technology analysis is widely distributed from R&D planning to technology forecasting. On the other hand, most technology analysis methodologies were based on text mining, statistics, and machine learning. Before conducting patent analysis on drone technology using the proposed method, we surveyed the drone technology on four perspectives: strength, weakness, opportunity, and threat (SWOT) from the previous research [1; 2; 3; 4; 5; 6; 7; 8]. On the basis of the SWOT analysis result, we extracted the drone technology scenarios from the result of drone patent analysis. Table 2 shows the result of SWOT analysis of drone technology. In the results of SWOT analysis, the strengths of drone technology are the technologies and infrastructures of aircraft manufacturing and communication and the willingness to develop drones at national level. On the other hand, the weaknesses of drone technology are the lack of intelligent software technology for drone operation and the exclusive drone technology focused on developed countries. Moreover, the opportunity of drone technology is based on continuous growth of drone market and expansion of drone services linked to other industries. Finally, the threat of drone technology is dependent on possibility of using drones that endanger human privacy and use of drones as weapons. Therefore, on the basis of the SWOT result shown in Table 2, we analyzed the patent data of drones. The