Predictive Modeling for University Technology Transfer Success for Automation and Robotics

Abstract

Technology transfer, particularly within the context of Industry 4.0, is a complicated process full of challenges. Integrating and commercializing advanced technologies of Industry 4.0, such as automation and robotics, into industry necessitates an in-depth understanding of the factors influencing effective technology transfer. This dissertation addresses these challenges and has three primary objectives. Firstly, it aims to identify and analyze existing gaps and challenges in the technology transfer process within Industry 4.0, focusing on understanding the factors contributing to effective technology transfer. Secondly, the dissertation investigates predictors to enhance the effectiveness of Technology Transfer Offices in managing Industry 4.0 technologies, particularly in automation and robotics. These predictors provide practical guidance and proven methods for technology transfer offices to enhance patent licensing success. Lastly, the dissertation intends to build a predictive model of patent licensing success specific to automation and robotics, targeting improving university technology transfer's patent portfolio management capabilities. This model aims to increase technology transfer office performance, increase technology commercialization, and facilitate self-sustainability through revenue generation. This dissertation employs a systematic literature review to incorporate existing knowledge, statistical analysis to explore patent variables and their relation with patent licensing, and supervised machine learning classification to develop a predictive model. These approaches enable an extensive investigation of the technology transfer process, facilitating the development of predictive models to promote innovation and enrich technology transfer effectiveness in the automation and robotics sectors within Industry 4.0. In conclusion, an Industry 4.0 Technology Transfer Model and Conceptual Framework are proposed, identifying novel predictors for automation and robotics patents, including independent claims, success rate of technology transfer office, and inventor experience. Additionally, a classification model is developed to predict patent licensing success, further contributing to the advancement of technology transfer in the Industry 4.0 era.