Knowledge base generation method and device of customer service robot, storage medium and equipment
By adopting knowledge base generation methods in customer service robots and using the inference and semantic expansion technology of large models, the problem that customer service robots cannot accurately solve user problems is solved, the Q&A hit rate and accuracy rate are improved, and the cost of manual customer service is reduced.
CN119692441APending Publication Date: 2025-03-25中邮人寿保险股份有限公司
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Patent Information
- Application Number
- CN202411600106.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-11
- Publication Date
- 2025-03-25
AI Technical Summary
Technical Problem
When dealing with user problems, customer service robots cannot give accurate answers or cannot solve the problem, resulting in a reduction in user experience and increasing the cost of manual customer service.
Method used
A knowledge base generation method of customer service robot is adopted to generate knowledge bases to improve the hit rate and accuracy of Q&A by obtaining customer service conversation data, data processing, large-model-based reasoning and semantic expansion.
Benefits of technology
It significantly improves the hit rate and accuracy of customer service robots, reduces the need for manual intervention, and reduces the cost of manual customer service.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention discloses a knowledge base generation method and device of a customer service robot, a storage medium and equipment. The method comprises the steps of obtaining customer service dialogue data; performing data processing on the customer service dialogue data to obtain target text information; reasoning the target text information based on a first preset large model to obtain a target problem; performing semantic extension on the target problem based on a preset generation large model to obtain a similar semantic problem sequence of the target problem; and generating a knowledge base according to the similar semantic question sequence of the target question and the question answers. Therefore, based on the understanding and reasoning capabilities of the large model, the method can accurately extract and generate problems which cannot be solved by the customer service robot, and fully amplify the knowledge base through semantic extension, thereby greatly improving the question and answer hit rate and accuracy of the customer service robot.
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