Virtual Assistant Shared Intent Merging for Multi-Person Dialog
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Solution Overview
Problem
Existing virtual personal assistant (VPA) systems struggle to accurately interpret and assist in multi-person dialog sessions, which are complex and often involve abstract language, non-verbal expressions, and evolving intentions, making it difficult for computing systems to understand and execute tasks effectively.
Innovation Solution
A VPA platform with a shared dialog understanding module that develops a semantic understanding of common intentions among multiple participants by analyzing natural language dialog inputs, even when the VPA is not directly involved in the conversation, using intent mapping and merging techniques to refine its understanding and create actionable items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a VPA system attempts to interpret and assist in multi-person dialog sessions, then the ability to provide virtual assistance is improved, but the complexity of accurately interpreting abstract language, non-verbal expressions, and evolving intentions increases
Solution Approach 1:
The system segments the complex multi-person dialog interpretation task into distinct components: individual intent detection for each participant, common intent identification, and action item extraction. This is achieved through separate processing modules that analyze dialog inputs independently before synthesizing shared understanding, thereby managing complexity while maintaining versatility.
Solution Approach 2:
The patent introduces an intermediary shared dialog context that mediates between individual participant intents and the final common intent determination. This intermediary structure allows the system to process multiple perspectives without direct conflict, enabling accurate interpretation of abstract language and evolving intentions through structured intermediate representation.
2Measurement precision
If the VPA analyzes natural language dialog inputs to develop semantic understanding, then the accuracy of intent detection is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of individual dialog inputs by detecting initial intents for each participant before synthesizing common intents. This preliminary action prepares structured intent representations in advance, allowing faster and more accurate common intent detection without requiring complete re-analysis of all dialog inputs, thus reducing processing time while maintaining precision.
Solution Approach 2:
The patent implements partial processing where the system focuses on detecting and merging relevant intents rather than analyzing every aspect of the dialog. By selectively processing only the necessary portions of dialog inputs that contribute to common intent determination, the system achieves accurate intent detection with reduced computational overhead and processing time.
3Productivity
If the VPA creates shared intents from individual intents, then the ability to execute actions based on common understanding is improved, but the complexity of intent mapping and merging increases
Solution Approach 1:
The system merges individual participant intents into shared common intents by identifying overlapping semantic content and consolidating complementary information. This merging process is structured through defined intent relationships and consolidation rules, enabling efficient creation of actionable shared intents without requiring complex custom mapping logic for each scenario, thereby improving productivity while managing complexity.
4Ease of operation
If the VPA operates without direct participation in the conversation, then the natural flow of multi-person dialog is preserved, but the ability to understand context and create actionable items decreases
Solution Approach 1:
The system employs feedback mechanisms where detected intents and created action items are continuously refined based on subsequent dialog inputs and participant responses. This feedback loop allows the VPA to improve context understanding accuracy over time while remaining a passive observer, maintaining natural dialog flow while progressively enhancing reliability through iterative learning from conversation patterns.
Data Source
AI summary
A computing system is operable as virtual personal assistant (VPA) to understand relationships between different instances of natural language dialog expressed by different people in a multi-person conversational dialog session. The VPA can develop a common resource, a shared intent, which represents the VPA's semantic understanding of at least a portion of the multi-person dialog experience. The VPA can store and manipulate multiple shared intents, and can alternate between different shared intents as the multi-person conversation unfolds. With the shared intents, the computing system can generate useful action items and present the action items to one or more of the participants in the dialog session.