Intelligent Interaction Service System Using Intent and Vector Retrieval
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Solution Overview
Problem
Existing methods for users to obtain enterprise services are inefficient, lack understanding of user needs, leading to low matching accuracy and poor user experience, particularly in manual selection and customer service interactions.
Innovation Solution
A construction method for an intelligent interaction service system involving intent recognition components, information retrieval components, vector databases, and generating models to analyze user interactions, determine user intents, and provide accurate and comprehensive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual selection method is used for users to obtain enterprise services, then users can independently select services, but the efficiency is low
Solution Approach 1:
The system enables users to independently obtain services through intelligent interaction without manual selection. Users input natural language queries, and the system automatically understands user intent, retrieves relevant services, and presents them for selection, transforming manual browsing into an automated self-service process that improves efficiency while reducing time investment
Solution Approach 2:
The patent replaces the mechanical manual selection process with an intelligent system comprising intent recognition components, information retrieval components, and generating models. This substitution automates the service selection process by using natural language processing and vector database matching to understand user needs and retrieve appropriate services, significantly improving productivity while minimizing time loss
2Adaptability or versatility
If customer service assistant interaction is used, then users can obtain services through intelligent interaction, but the interaction method is single
Solution Approach 1:
The patent segments the interaction system into multiple specialized components: intent recognition components for understanding user needs, information retrieval components for finding services, and generating models for creating responses. This segmentation allows each component to specialize in specific tasks, enabling diverse interaction methods while comprehensively capturing user needs information that a single assistant model might miss
Solution Approach 2:
The system creates a universal intelligent interaction platform that handles multiple types of user queries and service requests through a unified architecture. The intent recognition component can identify various user intents, the information retrieval component can search across different service categories, and the generating model can produce diverse response types, making the system adaptable to various interaction scenarios while maintaining comprehensive user needs understanding
3Loss of information
If staff service interaction is used, then users can obtain comprehensive service information, but the cost is high
Solution Approach 1:
The patent creates virtual copies of staff service capabilities through the intelligent interaction system. The generating model produces responses that mimic comprehensive staff service information delivery, while the information retrieval component accesses service databases that replicate staff knowledge. This copying approach provides complete service information without requiring actual staff involvement, significantly reducing service delivery costs while maintaining information completeness
Solution Approach 2:
The system enables users to obtain comprehensive service information through automated self-service interaction. The intelligent system independently retrieves and presents complete service information without requiring staff intervention, transforming what would be a high-cost staff service into a low-cost automated service that maintains full information completeness
4Measurement precision
If traditional service matching is used, then simple service queries can be handled, but the matching accuracy is low
Solution Approach 1:
The patent transforms the service matching process from traditional keyword-based matching to vector-based semantic matching. By converting service descriptions and user queries into vector representations in a high-dimensional space, the system measures similarity through vector distance calculations. This parameter change from discrete keyword matching to continuous vector space matching dramatically improves matching accuracy while the modular architecture manages the increased computational complexity
Data Source
AI summary
The present application relates to a construction method of an intelligent interaction service system and a website intelligent interaction method and device, which involve, through obtaining a user identity, determining historical multimodal interaction data and a user type that are corresponding to the user identity; obtaining first modal interaction data sent by a user, and determining a user interaction mode according to the historical multimodal interaction data and the first modal interaction data; performing an intent recognition analysis on current interaction data according to an intent recognition component to obtain a user intent label, and transforming the user intent label into a user intent vector; performing a retrieve and match on the user intent vector based on a vector database group to obtain user interaction result information, and sending the user interaction result information corresponding to the user type to a human-computer interaction interface in the user interaction mode.