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

TL;DR

This paper reviews how patent data has been used in engineering design research, emphasizing recent advances in AI and data science to develop new design methods, tools, and strategies, and suggests future research directions.

Contribution

It provides a comprehensive survey and categorization of patent data-driven design research, highlighting recent advances and future opportunities in the field.

Findings

Patent data is valuable for engineering design research due to its size and information richness.

Recent AI and data science techniques enable new data-driven design methods and tools.

The review identifies promising future directions for patent data-driven design research.

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.