# Patent Analysis Using Bayesian Data Analysis and Network Modeling
## 3 Proposed Method
### Keyword Visualization  
In this paper, we used the R data language and packages for preprocessing patent data by text mining [23; 24]. R provides programming environment for data analysis and  
Figure 3: Structured data of drone patent documents. Figure 2: Constructing structured data using text mining. visualization [24]. Moreover, the package \(tm\) is a popular R package for text mining [22; 23]. We used this package to create structured data through preprocessing of the drone patent documents in Figure 2. Next, we analyzed the structured data using visualization and BART modeling. The proposed method for drone technology analysis is composed of two approaches. The first approach is the keyword visualization with word cloud and correlation networks. The second is statistical modeling based on multiple linear regression and BART models. We first carried out word cloud to show the relative strength of patent keywords representing drone technology. A word cloud is a method to visualize the intensity of keyword as the size of text on the cloud shape [25; 26]. Using the word cloud result, we selected the keywords that had a strong influence on drone technology development. Next, we performed correlation analysis on keywords. The correlation coefficient between keywords \(X\) and \(Y\) is defined as (1) [20]. \[\text{Cor}(X,Y)=\frac{\sum_{i=1}^{n}\bigl{(}X_{i}-\overline{X}\bigr{)}\bigl{(}Y _{i}-\overline{Y}\bigr{)}}{(n-1)S_{X}S_{Y}} \tag{1}\]  
where \(\overline{X}\) and \(\overline{Y}\) are mean values of occurred frequency values of \(X\) and \(Y\), respectively. \(n\) is the number of collected patent documents. Moreover, \(S_{X}\) and \(S_{Y}\) are standard deviations of \(X\) and \(Y\), respectively. We obtained correlation coefficient values for all keyword pairs and used them for visualization of correlation networks. This was based on graph data structure consisting of node and edge. The node and edge were keyword and connection between two keywords, respectively. In the proposed correlation networks, the edge is connected when the correlation coefficient value between two keywords is greater than or equal to the threshold. Figure 4 illustrates the structure of correlation networks. In Figure 4, the network contains three nodes (keywords)--\(X\), \(Y\), and \(Z\). The edge of \(X\) and \(Y\) is connected because the correlation coefficient value between \(X\) and \(Y\) is greater than or equal to the threshold value. For the same reason, the keywords \(Y\) and \(Z\) are also connected. However, the edge of keywords \(X\) and \(Z\) is not connected because their correlation coefficient value is less than the threshold. To find the technological structure for drone technology, we perform the visualization of correlation networks using all patent keywords. The threshold value is predetermined before visualization. In our experiments, we compared the visualization results according to the change of threshold values. In addition, we considered only undirected graph in our networks. The undirected graph has a bidirectional network structure that connects two keywords to each other. Thus, the degree of the keyword with undirected graph is counted as twice the number of other linked keywords because the degree is the sum of indegree and outdegree. The degree of node \(v\) is defined as Equation (2). \[\text{degree}(v,G)=N^{+}(v)+N^{-}(v) \tag{2}\]  
where \(G\) is the network including node \(v\). \(N^{+}(v)\) and \(N^{-}(v)\) are indegree and outdegree of \(v\), that is, the indegree represents the number of connecting lines coming from other nodes in the same network to node v. Conversely, the outdegree illustrates the number  
Figure 4: Correlation network structure. of connecting lines from node \(v\) to the other nodes. Therefore, the degree of node \(v\) is calculated as the sum of indegree and outdegree.