Discourse Tree Navigation for Intent-Aligned Dialogue Management
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Solution Overview
Problem
Autonomous agents using machine learning models struggle to separate topic structure and navigation structure, leading to incomplete or irrelevant information presentation, and fail to capture the relevance of different text parts, resulting in random utterances and difficulty in accomplishing tasks.
Innovation Solution
A computing device creates a discourse tree from a body of text, determines a main topic, and navigates through fragments based on user interest and discourse tree configuration, providing relevant information through mixed initiative dialogue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If autonomous agents use machine learning models to manage dialogue, then they can answer questions and engage in conversation, but they cannot separate topic structure and navigation structure, resulting in random utterances and incomplete information presentation
Solution Approach 1:
The patent segments the dialogue management system into two distinct structures: a topic structure that organizes subject matter hierarchically, and a navigation structure that tracks user intent and dialogue flow. This segmentation allows the system to maintain separate representations for what is being discussed (topics) and how the conversation is progressing (navigation), thereby preventing information loss while preserving dialogue management capabilities.
2Adaptability or versatility
If autonomous agents navigate through text fragments based on user interest, then they can provide relevant information, but they fail to capture the relevance of different text parts, resulting in random utterances
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors user responses and adjusts the navigation path through the discourse tree accordingly. User feedback (explicit or implicit) is used to refine the selection of text fragments, ensuring that subsequent utterances are relevant to user interest while maintaining logical coherence. This feedback loop prevents random utterances by grounding each response in the structured topic-n navigation framework.
3Loss of information
If the system presents all relevant information from the discourse tree, then completeness is improved, but the complexity of information presentation increases
Solution Approach 1:
The patent employs dynamic information presentation where the system adapts the level of detail and structure of information based on user needs and dialogue context. Rather than statically presenting all information from the discourse tree, the system dynamically selects and organizes content along the navigation path, adjusting presentation complexity in real-time to balance completeness with usability.
Data Source
AI summary
Disclosed techniques relate to managing a dialogue between a user device and an autonomous agent. For instance, a computing device creates a discourse tree from a body of text that includes fragments. The discourse tree includes a root node, nonterminal nodes, and terminal nodes. Each nonterminal node represents a rhetorical relationship between two of the fragments and each terminal node is associated with one of the fragments. The computing device determines a main topic of the body of text from the discourse tree. The computing device provides the main topic to the user device, and in response, receives a user utterance. The computing device determines an intent from the user utterance and navigates the discourse tree to locate relevant information consistent with the intent.