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| Construction of Generalized “Technology-Function” Matrix Incorporating TRIZ Knowledge |
| Xi Chongjun1,2,3,4, Zhao Yajuan1,2, Zhang Ting1,2, Lyu Lucheng1,2 |
1.National Science Library, Chinese Academy of Sciences, Beijing 100190
2.Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190
3.Faculty of Arts and Humanities, Sorbonne University, Paris 75017
4.Study Group on Methods of Sociological Analysis of the Sorbonne (GEMASS), National Centre for Scientific Research, Paris 75017 |
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Abstract As a core tool for patent analysis, the “technology-function” matrix is pivotal in revealing the mapping relationships between technical approaches and functional effects, identifying research and development hotspots, and predicting industrial gaps. This study proposes a method for constructing a generalized “technology-function” matrix that integrates patent and paper data while incorporating Theory of Inventive Problem Solving (TRIZ) knowledge to overcome the limitation of single-dimensional analysis in conventional tools. First, the “technology-function” matrix model is constructed based on the “problem-method” framework, thus expanding the functional dimension into a multilayered structure comprising research objects (object, category) and research objectives (issue-E1, problem-E2, question-E3) while classifying the technical dimension into a progressive system comprising theoretical level (theory), methodological level(method) , and technical level (technology). By introducing TRIZ theory, the highest level of the functional dimension adopts a classification system comprising parameter, structural, and resource attributes, while the highest level of the technical dimension corresponds to solution tools such as inventive principles, standard solutions, and effect libraries. In terms of construction methodology, large language models are utilized to extract technology-function pairs from patent specifications and paper abstracts. This realizes the hierarchical mapping of technical terms to inventive principles and functional terms to engineering parameters through semantic analysis, thus ultimately forming a quantifiable multilevel matrix. Finally, considering patent and paper data in three fields—micro electro mechanical systems (MEMS) seismic detectors, power battery thermal management and safety control, and intelligent driving perception and decision control systems—as examples, a case analysis of a generalized “technology-function” matrix is conducted, where 39 engineering parameters and 40 inventive principles of TRIZ theory are combined. The results show that this model effectively achieves cross-domain correlation between papers and patents as well as identifies explicit technology layouts while uncovering potential innovation pathways, thus providing decision support with both academic depth and industrial application value for the technological roadmap planning of strategic emerging industries. This study offers significant theoretical and practical implications for improving the national innovation system.
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Received: 27 August 2025
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