Detecting Mission Changes in Conversations
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Solution Overview
Problem
Detecting mission changes in conversations is challenging for algorithms, as user intentions can be unclear, especially when syntactically similar utterances express different desires, and existing hand-crafted heuristic rules are labor-intensive and limited to narrow circumstances.
Innovation Solution
A data-driven, machine-learning based approach is employed to identify mission changes by training a model on labeled examples and using it to guide conversations, leveraging a database of non-conversational interactions to recognize when users are continuing or starting new missions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hand-crafted heuristic rules are used to detect mission changes, then detection can be achieved within narrow circumstances, but the approach requires labor-intensive hand-crafting for each set of narrow circumstances and lacks scalability
Solution Approach 1:
The system uses machine learning models that automatically learn mission change patterns from conversation data without requiring manual rule creation. The model self-adjusts to different contexts and scenarios, eliminating the need for labor-intensive hand-crafting of heuristic rules for each narrow circumstance.
Solution Approach 2:
The machine learning-based detection system provides a universal solution that works across multiple contexts and conversation types simultaneously. A single model can detect mission changes in various scenarios (shopping, information seeking, task completion) without requiring separate hand-crafted rules for each context, thus achieving scalability.
2Ease of operation
If syntactically similar utterances are processed using traditional methods, then processing is straightforward, but different user intentions cannot be distinguished
Solution Approach 1:
The system moves beyond traditional syntactic analysis by incorporating semantic and contextual dimensions. Machine learning models analyze utterances in multiple dimensions including conversation history, user behavior patterns, and contextual cues, enabling distinction between different intentions even when syntax is identical.
Solution Approach 2:
The system uses feedback from conversation outcomes and user responses to continuously improve intention recognition. By analyzing whether detected missions lead to successful outcomes or user corrections, the model refines its ability to distinguish between similar utterances with different intentions.
Data Source
AI summary
Methods, systems, and computer programs are presented for detecting a mission changes in a conversation. A user utterance from a user device is received. The user utterance is part of a conversation with an intelligent assistant. The conversation includes preceding user utterances in pursuit of a first mission. It is determined that the user utterance indicates a mission change from the first mission to a second mission based on an application of a machine-learned model to the user utterance and the preceding user utterances. The machine-learned model has been trained repeatedly with past utterances of other users over a time period, the determining based on a certainty of the indication satisfying a certainty threshold. Responsive to the determining that the user utterance indicates the mission change from the first mission to a second mission, a reply to the user utterance is generated to further the second mission rather than the first mission.