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| Technical Opportunity Discovery Based on “Problem-Solution”Statements |
| Zhu Jianxin1,2, Bai Wentao1,2, Liu Ruinan1,2, Lin Chaoran1,2 |
1.School of Economics and Management, Harbin Engineering University, Harbin 150001
2.Key Laboratory of Big Data and Business Intelligence Technology, Ministry of Industry and Information Technology (Harbin Engineering University), Harbin 150001 |
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Abstract In the context of intensifying national technological competition, the ability to identify technological opportunities clearly and comprehensively is crucial for securing a strategic advantage. From the perspective of the theory of inventive problem solving (TRIZ), the core of technological opportunity discovery lies in the precise identification of technical problems and their corresponding solutions. However, existing approaches are constrained by limited global semantic analysis, the absence of systematic and iterative prompt optimization, and the inability of link prediction methods to fully capture and exploit complete semantic units. To address these limitations, this study proposes a novel framework for technological opportunity that ensures semantically explicit outputs and coherent, logically complete reasoning chains. First, the framework leverages the global semantic integration capabilities of large language models to extract technical object sentences, problem statements, and solution statements that are precise, generalizable, and semantically rich. Second, it introduces a prompt optimization strategy based on “multi-round iteration+multi-dimensional evaluation” to enhance the domain adaptability of prompts. Third, it integrates the TextRank algorithm with link relation statistics to identify high-value semantic units within technical solution statements. Specifically, TextRank is employed to filter salient information, while link relation statistics are incorporated to enable precise matching between problem statements and solution statements associated with technical objects. Concurrently, BERT, GraphSAGE, and the TPE algorithm are organically combined to ensure accurate prediction of link relationships. Experimental validation based on patent data from the aircraft engine domain demonstrates that the proposed framework effectively enhances semantic associations and strengthens the completeness of the logical chain in technological opportunity discovery. The findings provide valuable methodological support for innovators in identifying technological frontiers and guiding innovation practices.
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Received: 08 September 2025
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