# Patent Analysis Using Bayesian Data Analysis and Network Modeling
## 4 Experiments and Results  
To show the validity and performance of proposed drone technology analysis, we collected and analyzed the patent documents related to drone technology. First, we retrieved the patent documents of drone technology from the popular patent databases, USPTO and WIPSON [13; 14]. The searched patents include title, abstract, applicant, inventor, nation, international patent classification (IPC) code, claims, and citation information. There are many resources for describing technology in patent documents. In this paper, we selected the abstracts of issued patents. Of course, patent analysis on drone technology is possible using various features as well as abstracts. Our patent data analysis is focused on the keyword data extracted from the patent documents. The valid patent data contains 60,311 documents. We performed our experiments in computational framework with 16 GB and Intel core i7 1.99 GHz for RAM and CPU, respectively. In addition, we used R data language and its packages for our patent data analysis [22; 23; 24; 32]. First, we constructed the patent-keyword matrix and extracted 85 technology keywords from the matrix using the text-mining techniques. We used the papers, articles, and domain experts for extracting the keywords related to drone technology. Figure 6 shows the word cloud of drone technology keywords. We used this figure to find the keywords with high influence on drone technology. In the preprocessing process of text mining, we removed the suffixes of each keyword for the efficiency of structured data generation. For example, the keyword _devic_ is the result of removing the suffix from device. From the result of word cloud, we found the keywords with high frequency such as _serial, control, connect, device, system, flight, aircraft, wing, arrange, power,_ and _data_. They influence on the  
Figure 5: Correlation network structure. Figure 6: Word cloud of drone technology keywords. development of drone technology. Table 3 illustrates the top 60 keywords representing the drone technology. Since it is difficult to obtain meaningful analysis results if the frequency of keywords is too small, we selected the top 60 keywords with high frequency as shown in Table 3. This was also shown in Figure 2. The first row shows the keywords with high ranked frequencies, and the second row illustrates the keywords with relatively low frequencies. Because keywords were extracted through the preprocessing of text mining, some keywords have suffixes omitted. Using the top 60 keywords, we constructed the keyword correlation networks according to the correlation coefficient. Figure 7 shows the keyword correlation network with correlation coefficient value larger than 0.2. In Figure 6, most keywords are isolated, but some keywords are connected each other. We found five connecting components in this figure. The keywords _motor_, _shaft_, and _rotat_ were connected around _shaft_. We knew that the role of the shaft between the rotation of the drone propeller and the motor is important. In addition, the four keywords _flight_, _system_, _control_, and _remot_ were connected around _control_. That is, the flight of drone and drone system are dependent on the remote control. The keywords _aerial_ and _aircraft_ are connected, and _ground_ and _station_ are also connected. Finally, _fuselag_ and _tail_ are connected via _wing_. The technology development of fuselag is based on the technologies of wing and tail of drone. Next, we built the correlation network with the value of correlation coefficient larger than 0.15 in Figure 8. We found six components from the network in Figure 8. The