AI Conversation Engine Using Segmented Intent Extraction
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Solution Overview
Problem
Conventional automated conversation systems fail to provide timely and relevant responses, especially when dealing with complex requests, and cannot adapt to varying communication channels or user schedules, leading to unmet expectations in customer interactions.
Innovation Solution
An AI conversation system that uses machine learning models to extract intent from user messages, determine conversation states, and generate responses based on extracted entities and data, allowing for natural conversation experiences and handling complex requests across multiple communication channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional automated conversation systems are used, then basic information can be provided, but they fail to provide timely and relevant responses for complex requests
Solution Approach 1:
The system segments the conversation processing into distinct stages: intent extraction, entity extraction, conversation state determination, and response generation. Each stage is handled by specialized components (intent extraction model, entity extraction model, state transition model), allowing complex processing to be broken down into manageable functions that work together to produce relevant responses.
Solution Approach 2:
The patent introduces an intermediary conversation state management layer that mediates between raw user inputs and final responses. The state transition model acts as a mediator that processes intents and entities, determines appropriate conversation states, and coordinates response generation, enabling the system to handle complex requests through coordinated interaction between multiple components.
2Reliability
If more human resources are added to handle communications, then response quality improves, but cost increases
Solution Approach 1:
The conversation system performs self-service by automatically extracting intents, entities, and conversation states from user communications, then generating appropriate responses without human intervention. The machine learning models enable the system to autonomously process complex requests, manage conversation states, and provide timely responses that would otherwise require human agents, thereby reducing the need for additional headcount while maintaining response quality.
3Adaptability or versatility
If conventional systems are used, then simple requests can be handled, but they cannot adapt to varying communication channels or user schedules
Solution Approach 1:
The system achieves universality by designing a unified conversation management architecture that handles multiple communication channels (email, SMS, messaging apps) through the same intent extraction and state transition mechanisms. The single state transition model serves multiple purposes: tracking conversation progress, determining response timing based on user schedules, and adapting to different communication contexts, thereby providing multi-functionality without requiring separate systems for each channel.
Data Source
AI summary
Systems, methods, and devices of the various embodiments may provide an artificial intelligence (AI) conversation system, such as an AI driven virtual assistant, that can participate in automated conversations with users. The AI conversation system may be configured to respond to user inquiries or requests and implement conversations to achieve tasks.