Human-Machine Collaborative Conversation System Using Multi-Dimensional Semantic Vectors
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Solution Overview
Problem
Current human-machine collaborative conversation interaction systems face challenges such as low service efficiency, significant variations in service quality due to professionalism and complexity, and high training costs for customer service agents, as intelligent conversation bots struggle to fully meet service requirements in various industries.
Innovation Solution
A system that processes conversation data to extract structural information, generates semantic representation vectors, and determines semantic transfer relationships to match service requirements, enabling accurate service processing and assistance through a conversational pre-training layer, conversation representation learning layer, and service layer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If intelligent conversation bots are used to assist customer service agents, then service efficiency can be improved, but the bots cannot fully meet service requirements due to technology limitations and scenario complexity
Solution Approach 1:
The patent segments the conversation understanding task into multiple dimensions: topic dimension (identifying main subject), sentence dimension (analyzing semantic relationships), and phrase dimension (extracting key information). This multi-dimensional segmentation enables the system to comprehensively analyze conversation data and accurately match service requirements, resolving the contradiction between efficiency improvement and reliable requirement fulfillment.
2Reliability
If human customer service agents handle complex service problems, then service quality can be maintained, but service efficiency decreases due to need to query knowledge base and historical cases
Solution Approach 1:
The patent replaces the mechanical process of human agents manually querying knowledge bases and historical cases with an intelligent system that automatically performs multi-dimensional semantic analysis. The system extracts topic information, analyzes sentence semantics, and identifies key phrases to directly match service requirements, substituting manual information retrieval with automated semantic processing to improve efficiency while maintaining quality.
3Reliability
If junior customer service agents are trained to master standard service processes, then service quality can be improved, but training period and cost increase significantly
Solution Approach 1:
The patent enables the system to perform self-service through automated semantic analysis and reasoning capabilities. The intelligent system independently analyzes conversation data across multiple dimensions, extracts service requirements, and provides assistance without requiring extensive human training. This self-service capability allows junior agents to effectively handle complex cases with minimal training, reducing training time while maintaining service quality.
Data Source
AI summary
A system for human-machine collaborative conversation interaction includes one or more processors configured to execute instructions to cause the system to perform operations including: outputting, according to conversation data to be processed, structural information of the conversation data, wherein the conversation data comprises multiple turns of conversation; obtaining, according to the structural information, a semantic representation vector carrying phrase-dimensional semantic information, sentence-dimensional semantic information and topic-dimensional semantic information corresponding to the conversation data; obtaining semantic transfer relationships between each turn of conversation according to the semantic representation vector; and determining, according to the semantic representation vector and the semantic transfer relationships, conversation data matching service requirements so as to perform preset service processing through the determined conversation data.