(2024). Tracing the Flow of Knowledge From Science to Technology Using Deep Learning. Project Report for the Academic Research Programme of the EPO 2024.
This project aims to enhance the tracing of knowledge flows from scientific research to technology using advanced deep-learning techniques. By developing models such as Pat-SPECTER, and PaECTER, the project seeks to improve the accuracy of identifying connections between patents and scientific literature, surpassing the limitations of traditional citation-based analysis. Key findings include the analysis of the performance of Pat-SPECTER in predicting scientific citations for patents and the performance of PaECTER in predicting citations among patents. The evaluation process was made difficult by the incompleteness of the open-access database OpenAlex, which lacks abstracts for a portion of the scientific literature. Real-world tests demonstrated Pat-SPECTER’s effectiveness in identifying relevant prior art documents (patents and publications), improving the efficiency of prior art search. The project highlights the potential of advanced machine learning models and advances their use in tracing knowledge flows. It provides tools that can enhance patent examination processes, innovation tracking, and research and development strategies. These efforts help foster innovation by revealing the intricate connections between science and technology.
