# Patent Analysis Using Bayesian Data Analysis and Network Modeling
## 1 Introduction  
Patent analysis is to analyze the patent documents related to target technology using data analysis methods based on statistics and machine learning. Moreover, the aim of patent analysis is to understand the target technology using the results of patent document analysis. In this paper, we selected drone technology, which is rapidly spreading recently, as a target technology for the proposed patent analysis method. A drone is an unmanned aerial vehicle that flies automatically or semi-automatically by a program without a pilot on board [1]. Many companies in the world are conducting various R&D activities to expand and commercialize drone system in diverse fields such as logistics, disaster relief, national defenses, and entertainment [2, 3, 4, 5, 6, 7]. [PERSON] et al. (2021) used drone remote sensing technology to enhance the sustainability of wolfram mining. They performed an effective exploration of the mine using the drone remote sensing technology [6]. In addition, [PERSON] (2018) studied on the use of drone technologies in robotics area. The author carried out the research on structure and mechanism of drone system [5]. As the demand of the public sector as well as the private continues to expand with the development of unmanned aerial vehicle technology, the commercial use of drones and the demand for personal flying vehicles are expected to increase [1, 8]. As the drone technology is increasingly coming into our daily lives, we need a deeper understanding of this technology. [PERSON] et al. (2020) emphasized technological importance of object detection and avoidance, management of air traffic, control, and security for the development of drone system. They also illustrated the convergence of drone technologies and markets in Europe [1]. In order to improve our understanding of drones, we have to conduct a drone technologyanalysis. For drone technology analysis, we use the patents related to drone technology searched from the world patent databases, because the patents contain most information about the developed technology [9, 10]. However, the studies that quantitatively analyze drone technology using statistical patent analysis have not yet been actively conducted. Thus, in this paper, we carried out drone patent analysis using statistical modeling for technology analysis of drone systems. For the patent analysis by statistics and machine learning algorithms, it is necessary to preprocess the patent documents for constructing structured data such as the table of relational databases [11, 12]. The table structure consists of row and column for observation (document) and variable (keyword), respectively. Thus, we built patent-keyword matrix as a structured data by text mining techniques. Using the structured patent data, we visualized the patent keywords by word cloud and correlation networks, and analyzed them by Bayesian additive regression trees (BART) for the drone patent analysis. From the visualization and analysis results of patent data related to drone technology, we identified the technological relationship of drone technology and used it for drone technology management. To this purpose, we finally created technology scenarios for drone technology development such as technology scenarios. The technology scenarios contribute to technological deployment of drones such as R&D planning, new product and service development, technological innovation, and technology forecasting of drone systems. To verify the performance and validity of our research, we showed how the proposed methodology can be applied to the practical domain using the patent documents collected from the patent databases such as the United States Patent and Trademark Office (USPTO), WIPS Corporation (WIPSON), and the Korea Intellectual Property Rights Information Service (KIPRIS) [13, 14, 15]. Our research contributes to the objective approach towards patent analysis using quantitative methods for technology analysis.