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| Research on Fine-Grained Technology Opportunity Analysis Based on Patent Text Mining |
| Wu Keye1,2, Sun Jianjun1,2, Xie Ziyue1 |
1.School of Information Management, Nanjing University, Nanjing 210023
2.Laboratory for Data Intelligence and Cross-Innovation of Nanjing University, Nanjing 210023 |
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Abstract In the new round of scientific and technological revolution and industrial transformation, the strategic position of technology opportunity analysis in R&D management and corporate decision-making is growing. However, the accuracy of technology opportunity analysis based on traditional link prediction indicators has reached a bottleneck, the stubborn expertise can hardly cope with the dynamics and complexity of technological innovation, and fine-grained technical opportunity identification and analysis are difficult to realize. As a result, this study proposes a fine-grained technical opportunity analysis framework based on patent text mining that combines patent text mining and the graph neural network link prediction method and divides technology opportunity analysis into three research subtasks: knowledge network construction and evolution analysis, element link prediction and technology opportunity assessment, and screening. An empirical study in the field of computer vision shows that the knowledge network built using multi-dimensional keyword features can fully present the knowledge panorama of cross-fields, and the combination of complex network indicators and time series can further reveal the context of technological development and provide direction for subsequent technological opportunity analysis guidance. The BERT model combined with the graph neural network method is suitable for the knowledge element link prediction task of each technology life cycle, and it shows higher accuracy and robustness than traditional prediction indicators. Following a comparison and evaluation with multi-source technical reports, it is confirmed that the nine technical opportunities based on this framework are in line with the current development of computer vision technology and have practical R&D value.
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Received: 16 September 2022
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