AI Interlocution Module for Group Dialog Turn Timing
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Solution Overview
Problem
Transitioning conversational AI agents from dyadic to group dialog environments is challenging, as determining when to converse is not trivial in group discussions involving multiple human participants.
Innovation Solution
A computer-implemented method and system that utilize an interlocution module trained with dialog content and turns to determine appropriate participation points for AI agents in group dialogues, considering current dialog context and response, using machine learning techniques like neural networks and transformers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI agent participates in group dialog, then user productivity and satisfaction improve, but determining appropriate participation timing becomes complex
Solution Approach 1:
The patent introduces an interlocution module as an intermediary component that mediates between the AI agent and the group dialog. This module analyzes dialog context, speaker roles, and conversation flow to determine appropriate participation timing, thereby resolving the complexity of direct AI-agent participation while maintaining productivity benefits
Solution Approach 2:
The system implements feedback mechanisms where the interlocution module continuously monitors dialog state and adjusts AI agent participation decisions based on real-time context analysis. This feedback loop enables dynamic determination of participation timing without requiring complex pre-programmed rules
2Measurement precision
If AI agent monitors dialog context continuously, then participation timing accuracy improves, but computational resources increase
Solution Approach 1:
The interlocution module employs periodic analysis of dialog context rather than continuous monitoring. It evaluates participation opportunities at strategically determined intervals and based on trigger events (e.g., topic transitions, question-answer patterns), achieving accurate timing determination while reducing computational overhead
Solution Approach 2:
The system performs preliminary analysis of dialog context and pre-identifies potential participation opportunities before actual intervention is needed. By preparing participation decisions in advance based on contextual cues, the system achieves high timing accuracy without requiring intensive real-time computation during critical moments
Data Source
AI summary
Provided are techniques for moderating Artificial Intelligence (AI) agent interlocution in group dialog environments. Under control of an interlocution module that has been trained with dialog content and dialog turns, an indication that interlocution is to be determined for a group dialog is received. Under control of the interlocution module, it is determined whether an AI agent is to participate in the group dialog based on a current dialog context and a dialog response. Under control of the interlocution module, in response to determining that the AI agent is to participate in the group dialog, the AI agent is triggered to post the dialog response to the group dialog.