Research on patent-based cross-domain knowledge fusion methods driven by C-K theory

In order to obtain high-quality innovative concepts during the design process, designers often need to fusion multidisciplinary knowledge. However, cognitive fixation among designers and the technical distance between domains make it difficult to effectively apply cross-domain knowledge. To address these issues, this study proposes a C-K theory-driven systematic method for patent-based cross-domain knowledge fusion, aiming to enhance the feasibility of applying cross-domain knowledge in designer-led innovative design processes. First, a C&K space representation model is constructed to activate associations between concepts from different domains through seven types of conceptual relationships, enabling multi-level expression of cross-domain knowledge based on knowledge attributes. Second, knowledge intermediaries with different roles are selected based on the similarity of C&K space resources between the source and recommended domains, establishing corresponding retrieval paradigms to acquire intermediary domain knowledge. Then, a link prediction method is employed to construct an intermediary knowledge interaction network, recommending optimal cross-domain knowledge fusion paths from an attribute perspective to assist designers in overcoming decision paralysis in complex solution spaces. On this basis, a systematic cross-domain knowledge fusion innovation design process model is established, and the effectiveness of the method is verified using a garlic peeling machine as a case study.