Interactive Agent Exit Condition Recovery via Sequence Labeling
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Solution Overview
Problem
Existing interactive agents lack mechanisms to detect and predict user dissatisfaction during conversations, leading to potential abandonment of the dialogue.
Innovation Solution
A system and method that continuously monitor conversations between users and agents, identifying exit conditions by comparing message labels to a sequence model, and sending recovery messages to redirect the conversation and prevent user exit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the interactive agent follows a standard conversation flow without monitoring mechanisms, then the conversation structure remains simple, but the user may become dissatisfied and abandon the dialogue
Solution Approach 1:
The system performs preliminary labeling of conversation turns and compares them against exit condition sequences before the user actually exits. By predicting exit conditions in advance and sending recovery messages proactively, the system prevents user abandonment rather than reacting after the fact. This resolves the contradiction by maintaining simple conversation flow while adding predictive monitoring capability.
Solution Approach 2:
The system continuously monitors the conversation sequence, compares actual turns against predefined exit condition sequences, and provides feedback by sending recovery messages when potential exit conditions are detected. This feedback loop maintains user engagement without requiring complex real-time intervention, resolving the contradiction between engagement reliability and system complexity.
2Loss of time
If the system continuously monitors and analyzes each conversation turn, then user exit conditions can be detected early, but the processing time and computational resources increase
Solution Approach 1:
Exit condition sequences are predefined and prepared in advance before conversations occur. The system labels conversation turns and compares them against these pre-prepared sequences, avoiding the need for complex real-time analysis. This reduces computational power requirements while maintaining fast detection capability.
Solution Approach 2:
The conversation is segmented into discrete turns that are labeled and compared against segmented exit condition sequences. This segmentation allows for efficient processing by breaking down the continuous conversation stream into manageable units that can be quickly matched against predefined patterns, reducing both processing time and computational power requirements.
3Reliability
If the interactive agent sends recovery messages frequently to prevent exit, then user engagement improves, but the conversation becomes less natural and more intrusive
Solution Approach 1:
The system sends recovery messages only when predefined exit condition sequences are detected, not continuously or frequently. By using pre-defined exit conditions as triggers, the system maintains conversation continuity only when necessary, preserving naturalness while preventing abandonment. This resolves the contradiction by making intervention conditional rather than frequent.
Data Source
AI summary
A method, computer program product and computer system for recovering from a likely exit condition in an electronic conversation with a user and interactive agent is provided. A processor retrieves one or more messages from a conversation between a user and an interactive agent. A processor determines at least one label for the one or more messages between a user and an interactive agent. A processor compares the at least one label for the one or more messages to a sequence model. A processor identifies the one or more messages from the conversation indicate an exit condition for the user based on the comparison of the at least one label for the one or more messages to the sequence model. A processor sends a recovery message to the user to redirect the conversation.