# Technology Commercialization Activation Model Using Imagification of Variables
## 3 Proposed Model
### Technology Commercialization Activation Model  
Patents contain detailed, diverse information about technologies that have been developed, but they require much analysis time and know-how to use them to commercialize technology. In addition, when quantitative analysis is used for technology commercialization, the performance is poor due to limitations in learnable variables and an insufficient number of technology transfers. The performance can be expected to improve if hidden patterns related to technology transfer are searched for effectively within the range of available patent information and reflected in the predictive model construction. Moreover, the lack of technology transfer learning data must also be addressed. The technology commercialization activation model proposed in this paper explores technology transfer patterns inherent in the data by imaging the preprocessed variables and reflecting them to improve the predictive model performance. The imaged data are augmented to solve the lack of learning data.  
Converting non-image data into image data makes it possible to search for potential information that has not yet been identified. It can also be used with models that analyze image data, such as CNNs. Therefore, non-image data is imaged and used for analysis in various fields such as gene and text analysis. In this paper, DeepInsight, which converts non-image data into image data, was applied to the patent information. Figure 2 shows the schematic of converting the patent information into image data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measure** & **Description** \\ \hline CH & Calculated as the ratio of the sum of dispersion between clusters and the dispersion within clusters for all clusters. The higher the value, the better the performance [41]. \\  & Calculated as the average similarity between each cluster and its most similar cluster, where the similarity is the ratio of the distance within the cluster to the distance between clusters. The lower the value, the better the performance [42]. \\ SS & A measure of how similar an object is to its own cluster compared to other clusters. The higher the value, the better the performance [43]. \\ SSE & The sum of the squared differences between each observation and its group’s mean. SSE is used with elbow plots to find the optimal number of clusters [44]. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measures for deriving the optimal number of clusters.  
Figure 2: Imagification process using DeepInsight.  
As shown in Figure 2, DeepInsight was applied to the preprocessed patent information, converting it into image data. The non-image data from the patent was normalized using Equation (2) and used with DeepInsight.  
\[\frac{x-x_{min}}{x_{max}-x_{min}}, \tag{2}\]  
where \(x_{min}\) is the minimum value of the data and \(x_{max}\) is the maximum value. Normalized values were projected into a two-dimensional space via tSNE, which uses a convex hull algorithm for projected spatial information to find the smallest rectangle that encloses all the data, rotate it, and map it to a pixel. The number of converted images is equal to the number of patents. Table 5 summarizes the parameters used for data image transformation.  
The comparison models are logistic regression (LR), K-nearest neighbor (KNN), decision tree (DT), random forest (RF), AdaBoost (AB), and a CNN. The comparison models learn non-image and image data, respectively, and compare the technology transfer prediction performance. Additionally, data augmentation was performed to prepare for situations with only a small amount of technology transfer learning data, and the learning and prediction results are also presented. Data augmentation uses the synthetic minority oversampling technique (SMOTE) and random oversampling (RS), which are primarily used in label imbalance problems. Furthermore, accuracy, precision, recall, and the F1-score were used to evaluate the model prediction performance in this paper.