Abstract

Patent data have been utilized for engineering design research for long because of its large and expanding size, wide variety, and 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 and to enable advanced understanding of design science based on the patent database. Herein, we survey the patent-for-design literature categorized by their contributions to design research and practice, including design theories, methods, tools, and strategies, as well as the forms of patent data and the data science methods in respective studies. Based on systematic review and analysis, our review sheds light on promising future research directions for the field.

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