A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool

Abstract

Radical novelty is one of the key characteristics of emerging technologies. This characteristics makes emerging technologies as a quite different from established technologies. From the perspective of radical novelty, some studies consider patents with little similarity in terms of key concepts and contents to existing patents as candidate emerging technologies. However, existing research remains in examining small-scale patents for evaluating candidate emerging technologies due to the lack of data-processing capacity—the recent rising of deep learning methods may help in this. This study, therefore, develops a novel deep learning based framework for identifying emerging technologies by combining a technological impact evaluation using patents and a social impact evaluation using website articles. Using a large scale multi-source dataset including 129,694 patents and 35,940 website articles, this paper applies the framework to investigate the case of computerized numerical control machine tool technology, through which the framework is validated. The results show that 16,131 patents out of 129,694 patents are considered as candidate emerging technologies, and 192 patents out of 16,131 patents are identified as emerging technologies through the evaluation of technology impact and social impact. This implies that these candidate emerging technologies can evolve to emerging technologies, though not all of them—we need deep learning method to scrutinize a larger scale multi-source data to identify rather a small number of potential emerging technologies. The proposed framework can also be extended to explore other disciplinary multi-source data for strategic decision support in identifying emerging technologies.