## 4 Technology Development Strategy of TFIFT Systems
### Patient Map Development and Identification of Vacant Technology Fields
#### 4.1.2 Identification of Vacant Technology Fields  
Subsequently, a GTM-based patent map was developed by analyzing patent documents for subclass vectors to detect patent vacuums. Prior to developing this map, we need to define the parameter of map size [37]. Since there are no rules that determine this parameter, we performed sensitivity experiments that changed the map's size to select it. The sensitivity analysis conducted was based on (1) 4 \(\times\) 4, (2) 6 \(\times\) 6, (3) 8 \(\times\) 8, and (4) 10 \(\times\) 10 to determine the size of the patent map. As a result, the number of patent vacuums was too small to be identified and defined in (1) 4 \(\times\) 4 because there were less than five patent vacuums, and more than 63 patent vacuums were analyzed in (3) 8 \(\times\) 8 and (4) 10 \(\times\) 10, making it impossible to define the patent vacuums. Thus, a patent map analysis was conducted based on (3) 6 \(\times\) 6, where 10 patent spaces were derived. Figure 5 shows the developed GTM-based patent map on which all patent documents are mapped, allowing patent vacuums to be clearer and automatically discovered. A total of 10 patent vacuums were detected.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **B61D** & **B65D** & **B65G** & **B60P** & \(\cdots\) & **G05B** \\ \hline Patent 1 & 0 & 1 & 0 & 0 & \(\cdots\) & 0 \\ Patent 2 & 0 & 0 & 1 & 0 & \(\cdots\) & 0 \\ Patent 3 & 0 & 0 & 1 & 0 & \(\cdots\) & 1 \\ Patent 4 & 0 & 0 & 1 & 0 & \(\cdots\) & 0 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\ddots\) & \(\vdots\) \\ Patent & 1536 & 1 & 0 & 0 & 0 & \(\cdots\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The format of the international patent classification (IPC) vectors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Load** & **Control** & **Connect** & **Vibrat** & \(\cdots\) & **Gas** \\ \hline Patent 1 & 1 & 0 & 1 & 0 & \(\cdots\) & 1 \\ Patent 2 & 1 & 1 & 0 & 0 & \(\cdots\) & 0 \\ Patent 3 & 0 & 0 & 1 & 0 & \(\cdots\) & 0 \\ Patent 4 & 0 & 0 & 0 & 1 & \(\cdots\) & 0 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\ddots\) & \(\vdots\) \\ Patent & 1536 & 1 & 1 & 0 & 0 & \(\cdots\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The format of the significant keywords’ vectors.  
Patent vacuums can be converted to subclass vectors on the patent map using GTM\({}^{\prime}\)s inverse mapping algorithm. However, subclass vectors are converted to continuous values, not binary values. In order to analyze the subclass corresponding to the patent vacuum, it is necessary to determine the threshold value, which must be determined in consideration of the research purpose and the distribution of data. In this study, the threshold was set to 0.4. If the vector value of the subclass is 0.4 or more, it is displayed as \"1\", and if it is less than 0.4, it is displayed as \"0\". Based on this, the subclasses corresponding to the patent vacuum are classified as shown in Table 3. The patent vacuum can be interpreted in the vacant technical field of the TFTFT systems.  
As a result of analyzing the patent vacuums, no subclasses were found in the four patent vacuums (patent vacuums 2, 3, 6, and 7). Finally, we identified six patent vacuums and their subclasses in each patent space.