Computer-aided innovation for intelligent product design: a text mining and knowledge management approach

The fast-paced innovation and the growing need for user-centric products hold traditional design approaches against the wall in the Industry 4.0 era. This research establishes a unified Computer-Aided Innovation (CAI) framework based on text mining, ontology-based knowledge management, and TRIZ-based reasoning to support intelligent product design. The framework uses natural language processing to extract user requirements, technical problems, and potential contradictions from unstructured textual content sources such as product reviews, patents, and technical information. These insights are then structured in a TRIZ-compliant knowledge base to enable the rapid, transparent, and traceable generation of concepts. A smart wearable health device was used as the case study to evaluate the system’s performance, and the results showed that the ideation efficiency of all concepts was significantly improved, with all concepts produced in less than 20 minutes, and the results were balanced across novelty, feasibility, and usability metrics. Compared with traditional methods such as brainstorming and Quality Function Deployment (QFD), the proposed framework yielded richer insights, greater concept diversity, and more evidence-based recommendations. Despite these advantages, the approach appears sensitive to textual ambiguity, domain-specific terminology, and the long-term scalability of the ontology repository. Future research will focus on the following areas: leveraging multilingual data sources, combining generative AI with digital twin simulations for time-critical design exploration, and expanding the framework to other product domains. Overall, the proposed CAI framework is part of promoting systematic innovation by incorporating AI-assisted reasoning and structured knowledge representation in the early stages of product design.