Real-Time AI Issue Detection and Response for Domain Conversations
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Solution Overview
Problem
Participants in domain-specific conversations may face issues that they are unable to identify or address promptly, leading to potential loss of credibility and inefficiencies.
Innovation Solution
Utilizing artificial intelligence models, particularly domain-specific sentiment analysis and passage ranking models, to detect negative sentiments and rank relevant passages in real-time, enabling generation of responses that mitigate the identified issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a participant manually identifies and researches domain-specific issues during a conversation, then the response may be accurate and well-researched, but the time delay becomes unacceptable and credibility is lost
Solution Approach 1:
The system performs preliminary actions by continuously monitoring and analyzing conversation statements in real-time using AI models. The sentiment analysis model proactively detects domain-specific issues as they arise, and the passage ranking model pre-loads and ranks relevant domain knowledge passages, so that when an issue is identified, the response can be generated immediately without manual research delay.
2Reliability
If the participant performs manual research to provide accurate guidance on domain-specific issues, then the quality of guidance improves, but the delay in providing guidance becomes unacceptable
Solution Approach 1:
The system replaces the mechanical manual research process with automated AI models. The sentiment analysis model automatically identifies domain-specific issues from conversation statements, and the passage ranking model automatically retrieves and ranks relevant domain knowledge from the knowledge base, eliminating the need for manual research while maintaining high guidance quality and reducing response time to real-time.
3Speed
If the participant responds to domain-specific issues without proper identification and research, then the response time is fast, but the credibility is lost due to inability to handle issues
Solution Approach 1:
The system introduces AI models as intermediaries between the conversation participant and the domain knowledge base. The sentiment analysis model acts as an intermediary to accurately identify domain-specific issues from conversation statements, and the passage ranking model serves as an intermediary to retrieve relevant domain knowledge, enabling the participant to provide credible, well-informed responses at real-time speeds without manual research.
Data Source
AI summary
Techniques are described herein that are capable of using AI to perform real-time domain-specific issue detection and response generation. A domain-specific negative sentiment is detected in a statement of a first user from a domain-specific conversation between the first user and a second user in real-time during the domain-specific conversation using a first AI model. An indication of the domain-specific negative sentiment is converted into a query describing a domain-specific issue. Passages from domain-specific documents are ranked to provide relevancy ranks, which represent relevancies of the passages regarding mitigation of the domain-specific issue, using a second AI model. A subset of the passages is identified such that the relevancy rank of each passage in the subset satisfies a relevancy criterion. A response to the query is generated using the subset of the passages and presented to the second user. The response specifies a mitigating factor that mitigates the domain-specific issue.