# Technology Commercialization Activation Model Using Imagification of Variables
###### Abstract  
Various institutions such as universities and corporations strive to commercialize technologies produced through R&D investment. The ideal way to commercialize technology is to transfer it, recognizing the value of the developed technology. Technology transfer is the transfer of technology from R&D entities, such as universities, research institutes, and companies, to others, with the advantage of spreading research results and maximizing cost efficiency. In other words, if enough technology is transferred, it can be commercialized. Although many institutions have various support measures to assist in transferring technology, there is no substitution for quantitative, objective methods. To solve this problem, this paper proposes a technology transfer prediction model based on the information found in patents. However, it is not realistic to include the information from all patents in the quantitative, objective method, so patterns related to technology transfer must be identified to select the appropriate patents that can be used in the predictive model. In addition, a method is needed to address the insufficient training data for the model. Training data are limited because some technology transfer information is not disclosed, and there is little technology transferred in new technology fields. The technology transfer prediction model proposed in this paper searches for hidden patterns related to technology transfer by imaging the patent information, which can also be applied to image analysis models. Furthermore, augmenting the data can solve the problem of the lack of learning data for technology transfer. To examine whether the proposed model can be used in real industries, we collected patents related to artificial intelligence technology registered in the United States and conducted experiments. The experimental results show that the models trained by imaging patent information performed excellently. Moreover, it was shown that  
Technology transfer is intended to increase the success rate of technology commercialization by spreading the R&D results to consumers and industries [7; 8; 9]. Forms of technology transfer include licensing, mergers and acquisitions (M&A), and the transfer of intellectual property rights [10]. Of these, the transfer of intellectual property rights, that is, the transfer of patents to others, is an important form of technology transfer that promotes commercialization [11; 12]. Transferring patents has the advantage of spreading research results and maximizing cost efficiency because the value of already-developed R&D results is recognized and transferred to others. Because of these advantages, many countries and institutions prefer to commercialize technology by transferring it rather than connecting R&D directly to commercialization. In other words, technology transfer has several advantages, such as the prevention of overlapping research, reduction of the R&D cost, and the possibility of professional commercialization, and it is the ideal way to commercialize technology [13]. A significant amount of technology must be transferred for commercialization, so some governments establish and operate support systems to increase the technology transfer success rate. Nevertheless, the success rate is still not high because of existing alternative technologies and market uncertainty. In addition, much time and money are consumed analyzing the value of patents to acquire, and the analysts' judgments are subjective [14]. Solving this problem requires a data-driven, quantitative, objective technology transfer analysis model [15; 16]. Existing quantitative technology transfer analysis models use various patent data information [17; 18; 19; 20; 21; 22]. However, in reality, quantitative models cannot use all the information in patents. Therefore, it is necessary to search effectively for patterns related to technology transfer in limited patent information and reflect them in the model.