Service method and device, equipment, storage medium and program product
By combining multimodal databases and natural language generation models, the problem of banking systems being unable to handle complex user interactions has been solved, achieving an efficient and seamless user service experience and business operations.
CN121858622APending Publication Date: 2026-04-14INDUSTRIAL AND COMMERCIAL BANK OF CHINA
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Patent Information
- Application Number
- CN202511807847.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-03
- Publication Date
- 2026-04-14
AI Technical Summary
Technical Problem
Existing banking systems are unable to effectively handle users' colloquial and complex expressions with multiple intentions, resulting in low efficiency in user interaction services.
Method used
It employs a multimodal database combined with a natural language generation model to determine the query vector by acquiring the user's query text, retrieve and generate response information, and handle complex interactions in a session state, supporting seamless business operations and human-computer interaction.
Benefits of technology
It enables complex interactive services, improves user experience and service efficiency, reduces system maintenance costs, and supports seamless integration of multi-turn dialogues and business operations.
✦ Generated by Eureka AI based on patent content.
Abstract
The embodiment of the invention provides a service method and device, equipment, a storage medium and a program product, and relates to the field of big data. The method comprises the steps of obtaining a query text of a user and determining a corresponding first query vector; corresponding query result information is determined in a multi-modal database according to the first query vector, the multi-modal database comprises a vector database constructed according to vectors corresponding to the unstructured data and a knowledge fragment library constructed according to the structured data, and the query result information comprises result vectors and result knowledge fragments; according to the query result information and historical query information, corresponding first reply information is generated through a natural language generation model, and the historical query information comprises a query text. According to the method, the capability of processing complex interaction is improved.
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