Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks

Discovering and seizing technology opportunities is key to innovation at all levels. However, there are several open issues in the existing research into the discovery of technology opportunities, such as the insufficient specification of technology opportunities, defining the features of opportunities in a way that may lead to the exclusion of some valuable opportunities, and a lack of empirical support for evaluation criteria. This study proposes a new approach to technology opportunity discovery that attempts to address these issues. Our approach uses patents as a data source and constructs domain knowledge networks (DKNs) automatically based on the syntactic dependencies of technological words. We represent technology opportunities as connected subnetworks within DKNs and use a regression analysis of historical patents to obtain significant variables that affect the value of technology opportunities. These are then used to form an objective function for searching for and interpreting optimal opportunities. Ant colony optimization is applied to discover the optimal set of technology opportunities. The feasibility and effectiveness of the proposed approach are demonstrated by empirical research into a technology for measuring mechanical vibrations or sound waves by electromagnetic means.

Automated summary

This study proposes a new approach to technology opportunity discovery, using patents as data source and constructing domain knowledge networks (DKNs) based on syntactic dependencies. Regression analysis is used to obtain significant variables, forming an objective function for searching optimal opportunities. Ant colony optimization is applied for discovering the best set of technology opportunities.