Quantitative approaches for detecting emerging technologies

Abstract

The rapid pace of globalization of science and technology increased the potential for high-impact technical capabilities to emerge in diverse technical, socio-economic, and geographic areas. Simultaneously, scientific literature, patents and other technology indicators have been producing and accumulating at an increasing rate. Their exponential growth is creating a wealth of information about technology development in science, technology and innovation (STI) data sources.

Empirical approaches and proxies based on citation networks, scientific publication and patent analysis are used extensively in STI studies. However, non-unique coordination of classification schemes onto specific product/market in STI related databases (in particular patent data sources) presents difficulties in delineating boundaries of information related to an emerging technology. Moreover, the conventional scientometric approaches that built upon the characteristics of citation networks may fall short in capturing the technology’s early development. Simply because citation data require considerable amount of time to be generated. Moreover, given the current debates on the importance of interaction between science and technology (S&T) that supports technological progress, the ability to evaluate the content-relatedness between science and technology outputs (patents and publications) is useful. Existing scientometric approaches used to track S&T relationship, such as analysis of non-patent literature (NPL) or author-inventor matching offer a narrow window for technology/industry level studies.

This work seeks to address these challenges by providing empirical approaches developed based on a synergy of natural language processing, text analytics and machine learning techniques. Firstly, a systematic literature review is conducted which presents a state-of-the-art in utilizing advanced text analytics in STI research. Secondly, a semantic approach is proposed to classify relevant patent data to a particular technology area. Thirdly, an alternative approach to citation methods is presented that detect topical overlap between science and technology relying on patent and publication abstracts. In addition, a cloud-based online tool is developed that allows users to monitor science and technology development evidenced by patent and publication data. The designed cloud-based tool can automate the process of patent landscape visualization, scientific literature mapping and provides an independent interface for comparing patent and paper trends on a specific subject. Finally, an empirical framework is suggested that combines several STI data sources to project the future industrial application of a new scientific breakthrough. To demonstrate the performance of proposed methods and empirical approaches, this research presents case studies in different technological domains.