A General Methodology for Technology Opportunity Discovery Based on Opportunity Evaluation and Optimization

Abstract

Technology opportunities are important drivers of technological advances. Consequently, methods for technology opportunity discovery (TOD) are proposed to discover new types of technology opportunities and to design criteria for defining and evaluating technology opportunities, providing R&D teams and innovators with a plethora of inventive ideas. However, current TOD methods have some common limitations. First, the criteria for defining technology opportunities are typically restrictive, thus may exclude some promising candidates. Second, most criteria for evaluating opportunities lack empirical evidence. In this article, we propose a general methodology for discovering technology opportunities that addresses these limitations. We create a less restrictive technology opportunity space (TOS), built evaluation models for each candidate by learning from historical data, and use optimization techniques to search the TOS for the best technology opportunities. We then implement the proposed methodology in a case study that discovered firm-specific technology opportunities in neural network technology. We present technology opportunities as connected subnetworks of subject–action–object based knowledge networks; designed industry-level, firm-specific and patent-specific evaluation criteria; use random forest to develop the evaluation model from historical patents; and apply ant colony optimization to find the best opportunities. The case shows the feasibility and effectiveness of the general methodology for TOD.