# Technology Commercialization Activation Model Using Imagification of Variables
## 4 Experimental Results  
Various models were used to evaluate the technology transfer prediction performance of the technology commercialization activation model proposed in this paper, and the results were compared. To evaluate the technology transfer prediction performance of the proposed technology commercialization activation model, actual registered patents were collected, and experiments were conducted. A total of 15,193 patents related to AI technology were collected. Of these, 12,154 were used as the training data, and the remaining 3039 were used for the test data. Table 6 summarizes the characteristics of the training and test datasets. Table 6 shows there were 8845 (72.77%) non-transferred and 3309 (27.23%) transferred patents in the training dataset. In addition, the test dataset contained 2211 (72.75%) non-transferred and 828 (27.25%) transferred patents. The image data had a size of 120 \(\times\) 120, consisting of three channels (RGB). \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Data**} & **Number of Patents** & **Number of Labels** \\  & **(Size: Height \(\times\) Width \(\times\) Channel)** & **(0:Non-Transferred, \(\mathbf{\hat{z}}\)Transferred)** \\ \hline \multirow{2}{*}{Non-image data} & Training data & 12,154 & 0.8845 \\  & & 1:3309 \\  & Test data & 3039 & 0.2211 \\ \hline \multirow{2}{*}{Image data} & Training data & 12,154 (120 \(\times\) 120 \(\times\) 3) & 0.8845 \\  & & 1:3309 \\ \cline{1-1}  & Test data & 3039 (120 \(\times\) 120 \(\times\) 3) & 0.2211 \\ \cline{1-1} \cline{2-3}  & & 1:828 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Characteristics of the training and test datasets. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & **Value** \\ \hline Dimensionality reduction method & tSNE \\ Pixel size & 120 \(\times\) 120 \\ Channel & 3 (RGB) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Imagification parameters. Text information was extracted from the patent abstract, and the documents were embedded in 384 dimensions using the pre-trained BERT. Next, the elementary technology was identified by applying K-means clustering to the embedded patent. The optimal K value was then selected by comparing the values derived for each measure, as shown in Table 7.  
The areas colored in yellow in Table 7 show the optimal values derived for each measure. For CH and DB, it was found that K values of two and three, respectively, were appropriate. For SS and SSE, a K value of five was found to be appropriate. Based on these results, the collected patent data were grouped into five elementary technology clusters in this study. Table 8 defines the high-frequency words and elementary technology by cluster. As shown in Table 8, the basic technology labels identified in patents were converted into dummy variables and merged with preprocessed quantitative information to be used with DeepInsight. Figure 3 shows an example of a converted patent information image. Figure 3 shows the difference in the patent information image based on whether or not the technology was transferred. The technology transfer prediction performance was compared by training the models on the derived patent information images using the parameters shown in Table 9.  
We trained the models shown in Table 9 using non-image and image data and compared the technology transfer prediction performance. Table 10 summarizes their prediction performance by data type. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**K**} & \multicolumn{4}{c}{**Measure**} \\ \cline{2-5}  & **CH** & **DB** & **SS** & **SSE** \\ \hline
2 & 782.671 & 4.315 & 0.047 & 183,495.098 \\
3 & 676.904 & 3.042 & 0.058 & 177,159.764 \\
4 & 635.130 & 3.258 & 0.063 & 171,663.809 \\
5 & 580.094 & 3.149 & 0.064 & 167,377.781 \\
6 & 516.671 & 3.587 & 0.044 & 164,899.573 \\
7 & 473.446 & 3.640 & 0.043 & 162,545.410 \\
8 & 438.377 & 3.609 & 0.045 & 160,513.242 \\
9 & 409.199 & 3.638 & 0.046 & 158,727.356 \\ \hline \hline \end{tabular}
\end{table}