Intelligent customer service intention layered identification method oriented to multi-modal interaction

Through topological data analysis and causal inference, high-order semantic correlation characteristics and causal relationship diagrams of multimodal data are constructed, combined with self-supervised learning to optimize feature fusion, the problem of insufficient intention recognition accuracy and generalization capabilities of intelligent customer service systems in multimodal interaction scenarios is solved, and more efficient multimodal data fusion and intention recognition are achieved.

CN120296458AActive Publication Date: 2025-07-11KEXUN JIALIAN INFORMATION TECH CO LTD
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Patent Information

Application Number
CN202510776286.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-11
Publication Date
2025-07-11
Estimated Expiration
2045-06-11

AI Technical Summary

Technical Problem

The existing intelligent customer service system is difficult to effectively integrate multimodal data in multimodal interaction scenarios, and lacks modeling of causal relationships between modal features, resulting in a decrease in intention recognition accuracy and insufficient generalization ability of model, especially under the condition of few samples.

Method used

Topological data analysis, causal inference, self-supervised contrast learning and spectral clustering methods are used to construct high-order semantic correlation features and explicit causal relationship diagrams of multimodal data, combined with cross-modal self-supervised consistency regularization method, feature fusion and model training are optimized to achieve accurate hierarchical recognition of intentions.

Benefits of technology

It significantly improves the depth and accuracy of multimodal information fusion, enhances the robustness and generalization capabilities of the model, improves the accuracy of intention recognition and user service experience, and is suitable for various practical multimodal service scenarios.

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Abstract

The invention discloses an intelligent customer service intention layered recognition method oriented to multi-modal interaction. The method comprises the following steps: acquiring and preprocessing three modal data of a text, a voice and an image; constructing a high-dimensional topological structure of modal data by using a topological data analysis method, and carrying out feature fusion; a causal relationship graph of modal features is constructed through a structural equation model, and causal contribution significant features are screened; a cross-modal self-supervision consistency regularization method is adopted, and an intention recognition model is trained under the condition of extremely few supervision data; and realizing multi-level layered classification output of the intention based on a spectral clustering method. The intention recognition accuracy and generalization performance of the intelligent customer service system are remarkably improved, and the method is suitable for complex multi-modal interaction scenes.
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