# Patent Analysis Using Bayesian Data Analysis and Network Modeling
## 4 Experiments and Results
All \(p\)-values of Shapiro-Wilk and Kolmogorov-Smirnov tests were less than 0.0001. Thus, we found that the data were not satisfied with the normality assumption. This means that when data analysis was conducted from the point of view of frequentist using only the likelihood function of given data, we encountered the problem of performance degradation of analysis results. To overcome this problem, we needed the Bayesian approach, which uses not only likelihood but also prior. In general, most Bayesian modeling requires MCMC computing for sampling from posterior distribution. We also used the MCMC method in our BART modeling. In MCMC, the time it takes to converge to the posterior distribution is called the burn-in [33]. The samples generated before the burn-in are not used for BART modeling. This is because most of the samples extracted at the initial time deviate from the posterior distribution. Figure 12 shows the burn-in iterations in our MCMC computing. The \(x\)-axis and \(y\)-axis are number of MCMC iteration and trees acceptance, respectively. From the result of Figure 11, we found that the number of iterations for burn-in was about 250. Thus, we determined the burn-in time to be 250 in our experiments. Next, we visualized the result of important keyword selection of BART modeling, as shown in Figure 13. This plot shows the relative importance of explanatory keywords that affect drone technology. In Figure 13, our BART model selected 29 keywords that affect drone technology out of the 60 keywords. We can see that keyword _station_ had the most influence on drone technology. We confirmed that the keywords affect drone technology in the order of _network_, _areial_, _fuselag_ (fuselag), etc., after _station_. In this paper, the keyword drone was used for response variable, and the others except drone were used for explanatory variables. In our work, we can select the significantly explanatory keywords for response keyword by \begin{table}
\begin{tabular}{c c} \hline \hline
**Test Method** & \(p\)**-Value** \\ \hline Shapiro–[PERSON] & \textless{}0.0001 \\ \hline Kolmogorov–Smirnov & \textless{}0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Normality test results. Figure 12: Burn-in iteration for MCMC computing. various methods. We first performed the keyword visualization on the basis of word cloud, correlation networks with degrees to find the keywords that affect drone. Moreover, we carried out multiple liner regression and BART models to select the significant keywords to drone. Finally, we created the technology scenarios in Table 7 on the basis of all the results obtained in this paper, including the results of 29 keywords selected in Figure 12.  
We show technology scenarios with first, second, and third ranked technologies to affect the development of drone technology. The first scenario is defined as the technology to improve flight control and safety of the fuselage by collecting and analyzing visual information around the drone. This technology is based on 20 keywords as follows: _aircraft, automat, autonom, camera, data, direct, drive, fuselag, gear, image, network, plane, rotal, sensor, shaft, signal, space, stabil, tail,_ and wing. The second core technology for drone is the technology that collects surrounding information on the basis of sensor signals and uses it to generate and control power for flight. The keywords of _aerial, area, arrang, circuit, control, data, detect, flight, fuselag, machin, measur, monitor, motor, power, pressur, propel, remot, sensor, signal_, and _speed_ are composed of this technology. Each keyword becomes sub technology that constitutes the core technologies of drones. The last is the technology to control efficient battery storage and operation for drone flight on the basis of the 20 keywords of _aerial_, _aircraft, batteri, bottom, charg, control, electron, flight, ground, interfac, motor, power, pressur, speed, station, storage, system, water, weight_, and _wind_.