Patent Data for Engineering Design: A Critical Review and Future Directions

Abstract

Patent data have long been used for engineering design research because of its large and expanding size, and widely varying massive amount of design information contained in patents. Recent advances in artificial intelligence and data science present unprecedented opportunities to develop data-driven design methods and tools, as well as advance design science, using the patent database. Herein, we survey and categorize the patent-for-design literature based on its contributions to design theories, methods, tools, and strategies, as well as the types of patent data and data-driven methods used in respective studies. Our review highlights promising future research directions in patent data-driven design research and practice.

arXiv commentAccepted by JCISE

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Cited by in corpus (3)

• AutoTRIZ: Automating Engineering Innovation with TRIZ and Large Language Models
• Natural Language Processing in the Patent Domain: A Survey
• Technology Fitness Landscape for Design Innovation: A Deep Neural Embedding Approach Based on Patent Data