Forecasting emerging technologies: A supervised learning approach through patent analysis

Both private and public enterprises have great interest in prior knowledge of emerging technologies to enable them make strategic investments. Technology forecasting offers a relevant opportunity in this direction and is currently a hot upcoming area of research. However, accurate forecasting of emerging technologies is still problematic mainly due to absence labeled historical data to use in training of learners. Previous studies have approached the technological forecasting problem through unsupervised learning methods and, as such, are missing out on potential benefits of supervised learning approaches such as full automation. In this study, we propose a novel algorithm to automatically label data and then use the labeled data to train learners to forecast emerging technologies. As a case study, we used patent citation data provided by the United States Patent and Trademark Office to test and evaluate the proposed algorithm. The algorithm uses advanced patent citation techniques to derive useful predictors from patent citation data with a result of forecasting new technologies at least a year before they emerge. Our evaluation reveals that our proposed algorithm can retrieve as high as 70% of emerging technologies in a given year with high precision.