Multi-modal intelligent interaction method based on large language model

By adopting a multimodal intelligent interaction method based on a large language model, the limitations of existing systems in interaction and analysis when dealing with complex financial investment problems are solved. This method enables multimodal data processing, task decomposition, and integration of professional tools, thereby improving the efficiency and accuracy of the system.

CN122264940APending Publication Date: 2026-06-23BEIYIN FINANCIAL TECH CO LTD
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Patent Information

Application Number
CN202610341001.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-19
Publication Date
2026-06-23

AI Technical Summary

Technical Problem

Existing robo-advisory systems suffer from problems such as limited interaction modality, limited task processing capabilities, low integration of professional knowledge, poor system scalability, and weak contextual understanding when dealing with complex financial investment issues, making it difficult to effectively handle multimodal data and complex analysis tasks.

Method used

It adopts a multimodal intelligent interaction method based on a large language model, which receives multimodal input, analyzes user intent and decomposes tasks, coordinates the execution of professional tools, integrates various financial analysis tools and services, manages the knowledge base for dynamic updates, and provides infrastructure support.

Benefits of technology

It improves the efficiency of handling complex financial analysis tasks, enhances the interactive experience, improves the accuracy of analysis and system scalability, reduces maintenance costs, and enhances the level of professionalism.

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Abstract

The invention discloses a multi-modal intelligent interaction method based on a large language model. The interaction method comprises the steps that multi-modal input of a user is received; the user intention is analyzed, a complex task is decomposed into a plurality of sub-tasks, and execution of each professional tool is coordinated; processing multiple types of input data; various financial professional analysis tools and services are integrated; a knowledge base of the system is managed and maintained, and dynamic updating and real-time retrieval of knowledge are carried out; and infrastructures required by system operation are provided. And through automatic task decomposition and tool calling, the workload of manual maintenance is reduced.
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