Data-Driven Intelligence on Innovation and Competition paper is published on Information Systems Management
Technology positions of firms may determine their competitive advantages and innovation capabilities. While a tangible understanding of technology positions can inform competitive intelligence, they are heterogeneous, intangible and difficult to analyze. We introduce network visualization and analytics to assess and compare the technology positions of firms for data-driven strategic insights on innovation and competition.
TechNet (http://www.tech-net.org) consists of >4 million technology-related terms and their semantic relations. The terms are retrieved from unstructured texts in ~6 million USPTO patents and their pairwise semantic relations are calculated using a neural network language model. TechNet outperforms Google Knowledge Graph, ConceptNet, and WordNet for engineering technical text analysis. It can serve as an infrastructure to support a wide range of AI applications in the context of engineering and technology.
Our team won the "Best Paper Award" at 31st International Conference on Design Theory & Methodology, Anaheim, California, for a new method to evaluate design concepts using machine learning & natural language processing
Professor Jianxi Luo received SUTD's "Excellence in Research" Award, 2018