# Patent Analysis Using Bayesian Data Analysis and Network Modeling
## 4 Experiments and Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Explanatory** & \(t\) & \(p\)**-Value** & **Explanatory** & \(t\) & \(p\)**-Value** \\ \hline (Intercept) & 43.988 & 0.0001 & plane & \(-7.298\) & 0.0001 \\ aerial & \(-26.258\) & 0.0001 & automat & \(-2.119\) & 0.0341 \\ control & \(-1.144\) & 0.2527 & wireless & \(-0.921\) & 0.357 \\ connect & \(-10.456\) & 0.0001 & charg & 1.945 & 0.0518 \\ devic & \(-0.285\) & 0.7759 & area & 4.639 & 0.0001 \\ system & 1.821 & 0.0686 & station & 11.029 & 0.0001 \\ flight & \(-0.587\) & 0.5574 & remot & 1.618 & 0.1057 \\ aircraft & \(-17.125\) & 0.0001 & measur & \(-1.655\) & 0.0979 \\ wing & \(-0.643\) & 0.5205 & gear & \(-2.839\) & 0.0045 \\ arrang & \(-4.414\) & 0.0001 & speed & \(-1.251\) & 0.211 \\ power & \(-2.567\) & 0.0103 & machin & \(-0.952\) & 0.3413 \\ data & 3.203 & 0.0014 & water & \(-2.597\) & 0.0094 \\ rotor & \(-4.426\) & 0.0001 & storage & \(-0.458\) & 0.6468 \\ rotat & \(-2.672\) & 0.0075 & image & \(-0.271\) & 0.0001 \\ motor & \(-2.273\) & 0.0231 & light & \(-2.157\) & 0.031 \\ drive & \(-2.837\) & 0.0045 & circuit & \(-3.514\) & 0.0004 \\ camera & 1.667 & 0.0956 & tail & \(-2.01\) & 0.0445 \\ signal & 3.379 & 0.0007 & space & \(-1.985\) & 0.0472 \\ detect & \(-0.994\) & 0.3202 & video & \(-0.67\) & 0.5025 \\ air & \(-4.641\) & 0.0001 & mobil & 0.131 & 0.8956 \\ time & \(-6.071\) & 0.0001 & network & 6.67 & 0.0001 \\ batteri & \(-3.209\) & 0.0013 & stabil & \(-2.956\) & 0.0031 \\ sensor & 2.424 & 0.0153 & gps & \(-1.282\) & 0.1997 \\ ground & \(-6.253\) & 0.0001 & interfac & 0.715 & 0.4748 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Testing results of drone keywords in linear model (response = drone). The larger the absolute \(t\)-statistic value of a keyword, the more it means that the keyword has more influence on drone technology development. Moreover, the sign of the value indicates the direction of technological influence. For example, if the sign of the value becomes negative, it exerts an influence in the opposite direction. Using the results of multiple regression, we carried out the patent keyword analysis using BART modeling. First, we had to select the number of trees in BART model. Figure 10 shows the root mean squared error (RMSE) by number of trees. We found that the RMSE value with number of trees of 30 was the lowest in Figure 10. Therefore, we determined the number of trees to be 30 for BART modeling. Before analyzing patent data analysis using BART model, we performed Q-Q plotting to check whether the data were satisfied with the normality assumption. Figure 11 illustrates the Q-Q plot of the patent keyword data. Figure 11: Q–Q plot of patent keyword data for BART modeling. Figure 10: RMSE for prediction performance by number of trees. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Explanatory** & \(t\) & \(p\)**-Value** & **Explanatory** & \(t\) & \(p\)**-Value** \\ \hline direct & 2.631 & 0.0085 & weight & \(-0.538\) & 0.5909 \\ bottom & \(-4.616\) & 0.0001 & electron & 0.685 & 0.4931 \\ shaft & \(-1.3\) & 0.1937 & pressur & \(-3.873\) & 0.0001 \\ propel & \(-0.234\) & 0.8151 & wind & \(-0.067\) & 0.9462 \\ fuselag & \(-4.189\) & 0.0001 & autonom & 7.722 & 0.0001 \\ monitor & \(-1.254\) & 0.21 & map & \(-1.416\) & 0.1569 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._We can visually check whether the data satisfies the normality assumption through the Q-Q plot results. That is, we identify how close the data are to normal distribution in Figure 10. The \(y\)-axis of the plot represents z-scores of data, and the \(x\)-axis is the corresponding quantile of normal distribution. Figure 10 shows the data points roughly are out of the diagonal line. Therefore, we carried out additional normality tests such as the Shapiro-Wilk and Kolmogorov-Smirnov tests [34]. The test results are shown in Table 6.  
As shown in Table 6, we performed two statistical tests to confirm whether the patent data used in this paper satisfied the normality assumption.