Early identification of emerging technologies: A machine learning approach using multiple patent indicators

• Published in: Technological forecasting & social change 2018-02, Vol.127, p.291-303
• Main Authors: Lee, Changyong, Kwon, Ohjin, Kim, Myeongjung, Kwon, Daeil
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Citation analysis; Early identification; Emerging technologies; Forecasting; Identification methods; Indicators; Machine learning; Machine learning models; Multiple patent indicators; Neural networks; Pharmaceutical industry; Studies; Technological change; Technological forecasting; Technology; Technology forecasting
• ISSN: 0040-1625; 1873-5509
• DOI: 10.1016/j.techfore.2017.10.002

Patent citation analysis is considered a useful tool for identifying emerging technologies. However, the outcomes of previous methods are likely to reveal no more than current key technologies, since they can only be performed at later stages of technology development due to the time required for patents to be cited (or fail to be cited). This study proposes a machine learning approach to identifying emerging technologies at early stages using multiple patent indicators that can be defined immediately after the relevant patents are issued. For this, first, a total of 18 input and 3 output indicators are extracted from the United States Patent and Trademark Office database. Second, a feed-forward multilayer neural network is employed to capture the complex nonlinear relationships between input and output indicators in a time period of interest. Finally, two quantitative indicators are developed to identify trends of a technology's emergingness over time. Based on this, we also provide the practical guidelines for implementation of the proposed approach. The case of pharmaceutical technology shows that our approach can facilitate responsive technology forecasting and planning. [Display omitted] •Proposing a machine learning approach to identifying emerging technologies at early stages•Defining 18 input and 3 output variables from the United States Patent and Trademark Office database•Employing feed-forward multilayer neural networks to capture nonlinear relationships between input and output variables•Developing two quantitative indicators to identify trends of a technology's emergingness