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Abstract

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.

Keywords

Computer-aided innovation (CAI) Text mining Natural language processing Ontology-based knowledge management Smart product design Idea generation

Article Details

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Ganesh, S. K. ., Ghosh, P. ., Tiwari, A. S. ., Deore, B. S. ., Hirapara, J. ., Hema, K. ., & Misra, D. D. . (2025). Computer-aided innovation for intelligent product design: a text mining and knowledge management approach . Future Technology, 5(1), 278–289. Retrieved from https://fupubco.com/futech/article/view/622
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References

  1. R. Du, J. Sun, R. Miao, and D. Zhang, "AI-Aided Resource Mining Method for Idealization-Driven Product Innovation," in Proc. Int. TRIZ Future Conf., Cham, Switzerland: Springer Nature, Oct. 2024, pp. 147–164.
  2. H. Rong, W. Liu, J. Li, and Z. Zhou, "Product innovation design process combined Kano and TRIZ with AD: Case study," PLoS One, vol. 19, no. 3, p. e0296980, 2024.
  3. O. Abramov, "Use of AI in the TRIZ Innovation Process: A TESE-Based Forecast," in Proc. Int. TRIZ Future Conf., Cham, Switzerland: Springer Nature, Oct. 2024, pp. 165–174.
  4. M. Iuga and S. Brad, "Integrating generative AI with TRIZ for evolutionary product design," in Proc. Int. TRIZ Future Conf., Cham, Switzerland: Springer Nature, Oct. 2024, pp. 32–49.
  5. A. R. Durmaz et al., "MaterioMiner--An ontology-based text mining dataset for extraction of process-structure-property entities," arXiv:2408.0, 2024.
  6. Z. Wang et al., "A new multifractal-based deep learning model for text mining," Inf. Process. Manag., vol. 61, no. 1, p. 103561, 2024.
  7. U. F. Dimlo, V. Rupesh, and Y. Raju, "The dynamics of natural language processing and text mining under emerging artificial intelligence techniques," Int. J. Syst. Assur. Eng. Manag., vol. 15, no. 9, pp. 4512–4526, 2024.
  8. Ayele, Workneh Y., and Gustaf Juell-Skielse. "A Systematic Literature Review about Idea Mining: The Use of Machine-driven Analytics to Generate Ideas." arXiv preprint arXiv:2202.12826 (2022).
  9. Ghidalia, Sarah, et al. "Combining machine learning and ontology: A systematic literature review." arXiv preprint arXiv:2401.07744 (2024).
  10. L. Chen et al., "An artificial intelligence approach for interpreting creative combinational designs," J. Eng. Des., vol. 36, no. 5–6, pp. 920–947, 2025.
  11. Buehler, Markus J. "Accelerating scientific discovery with generative knowledge extraction, graph-based representation, and multimodal intelligent graph reasoning." Machine Learning: Science and Technology 5.3 (2024): 035083.
  12. Farrell, Maxwell J., et al. "The changing landscape of text mining: a review of approaches for ecology and evolution." Proceedings B 291.2027 (2024): 20240423.
  13. Ghane, Mostafa, et al. "Semantic TRIZ feasibility in technology development, innovation, and production: A systematic review." Heliyon 10.1 (2024). DOI: 10.1016/j.heliyon.2023.e23775
  14. Bae, Suyeon, Mingyu Jeon, and Hoi Ri Moon. "Text Mining in MOF Research: From Manual Curation to Large Language Model-Based Automation." Chemical Communications 2025, 61, 11083-11094. DOI: 10.1039/D5CC02511G
  15. Liang, Xinxin, et al. "A survey of LLM-augmented knowledge graph construction and application in complex product design." Procedia CIRP 128 (2024): 870-875.
  16. Haridy, Shaimaa, et al. "An ontology development methodology based on ontology-driven conceptual modeling and natural language processing: tourism case study." Big Data and Cognitive Computing 7.2 (2023): 101.
  17. S. Pan et al., "Unifying large language models and knowledge graphs," IEEE TKDE, vol. 36, no. 7, pp. 3580–3599, 2024.
  18. Mihindukulasooriya, Nandana, et al. "Text2kgbench: A benchmark for ontology-driven knowledge graph generation from text." International semantic web conference. Cham: Springer Nature Switzerland, 2023.(pp. 247-265). https://doi.org/10.1007/978-3-031-47243-5_14
  19. L. Zhong et al., "A comprehensive survey on automatic knowledge graph construction," ACM Comput. Surv., vol. 56, no. 4, pp. 1–62, 2023.
  20. Z. Zhao et al., "A Survey of Knowledge Graph Construction Using Machine Learning," CMES, vol. 139, no. 1, 2024.
  21. L. Feng, Y. Niu, and J. Wang, "Development of Morphology Analysis-Based Technology Roadmap…," Appl. Sci., vol. 10, p. 8498, 2020.
  22. J. Wątróbski, "Ontology learning methods from text," Procedia Comput. Sci., vol. 176, pp. 3356–3368, 2020.
  23. Y. Borgianni et al., "The process for individuating TRIZ Inventive Principles," Des. Sci., vol. 7, p. e12, 2021.
  24. Tan, Qian, and Hongxiu Li. "Application of Computer Aided Design in Product Innovation and Development: Practical Examination on Taking the Industrial Design Process." IEEE Access 12 (2024): 85622-85634.
  25. Jiang, Shuo, and Jianxi Luo. "Autotriz: Artificial ideation with triz and large language models." International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Vol. 88377. American Society of Mechanical Engineers, 2024.
  26. Szczepanik, Kamil, and Jarosław A. Chudziak. "TRIZ Agents: a multi-agent LLM approach for TRIZ-based innovation." arXiv preprint arXiv:2506.18783 (2025).
  27. Chen, Liuqing, et al. "TRIZ-GPT: An LLM-augmented method for problem-solving." International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Vol. 88407. American Society of Mechanical Engineers, 2024.
  28. Dewulf, Simon, and Peter RN Childs. "Innovation logic: benefits of a TRIZ-like mind in AI using text analysis of patent literature." International TRIZ Future Conference. Cham: Springer Nature Switzerland, 2023.
  29. M. Freddi et al., "Integration Between AI and TRIZ for Technical Innovation," in Proc. Int. Conf. Italian Association of Design Methods and Tools for Industrial Engineering, Springer, Sept. 2024, pp. 115–124.
  30. Iqbal, Muhammad Saqib, et al. "Leveraging AI and TRIZ for sustainable innovation in advanced manufacturing." Discover Applied Sciences 7.6 (2025): 589.