# Technology Commercialization Activation Model Using Imagification of Variables
## 3 Proposed Model
### Data Description and Preprocessing  
In this study, we propose a technology commercialization activation model based on technology transfer prediction. To examine whether the proposed model can actually be used in the industrial field, patents were collected from the Database, and experiments were carried out. A total of 15,193 patents related to AI technology and registered with the USPTO were collected. The patents were filed between 1989 and 2016. Among these, 4137 cases of technology transfer occurred. Technology-transferred patents are cases transferred to another person. The transferred patent was checked on keywert, a paid patent information site [38]. Table 1 summarizes the characteristics of the experimental data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technological Field** & **DB** & **Application Period** & **Status** & **Number of Patents (Number Transferred)** \\ \hline Artificial Intelligence (AI) & USPTO & 1989–2016 & Registered & 15,193 (4137) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Experimental data. The technology transfer prediction model proposed in this paper was built by learning patent data. Therefore, both numerical and textual information from the patents were used. The numerical information used to build the model includes the indicators shown in Table 2.  
As shown in Table 2, indicators that measure various values were extracted and used to build the model. app_to_regi is the number of days elapsed from filing a patent application to registration. In general, applicants request priority examination to grant rights quickly for patents with high utility value. Thus, when the app_to_regi value is low, the utility value is high. all_claim_num is the number of claims, indicating the scope of the rights. Therefore, all_claim_num can be used as a measure of rights. applicant_num represents the number of applicants. Because it indicates whether multiple applicants are involved in the corresponding patent application, it can be used to measure the utility value. inventor_num \begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicator** & **Description** &
\begin{tabular}{c} **Measurable Value** \\ **Information** \\ \end{tabular} \\ \hline app\_to\_regi (days) & Time required from application to registration & Utility value \\ all\_claim\_num & Number of claims & Rights \\ applicant\_num & Number of applicants & Utility \\ inventor\_num & Number of inventors & Sustainable development \\ current\_owner\_num & Number of current rights owners & Market impact \\ IPC\_num & Number of IPC codes & Technology scalability \\ b\_citation\_num & Number of backward citations & Technology impact \\ f\_citation\_num & Number of forward citations & Technology impact \\ f\_man\_tion\_num & Number of family nations & Market impact \\ fam\_doc\_num & Number of family patents & Market impact \\ alone\_app\_yn & Sole application status (dummy) & Utility value \\ stand\_patent\_yn & Standard patent status (dummy) & Technology impact \\ lit\_yn & Litigation status (dummy) & Utility value \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Numerical information. Figure 1: Proposed model. represents the number of inventors who participated in a patent, so it can measure the sustainability of the development. current_owner_num represents the number of current patent holders, so it is a measure of the influence of the patent in the market. IPC_num is the number of technical field codes associated with the corresponding patent. A higher value means that the patent can be extended to various technologies. b_citation_num and f_citation_num are the numbers of backward and forward citations, respectively. In the backward citation, another patent cited the patent, and in the forward citation, the patent was cited by another patent. The citation degree of a patent can be used to measure the impact of a technology. fam_nation_num and fam_doc_num indicate the number of countries and patents included in family applications.