Query Interpretation Prioritization for Multi-Domain Virtual Assistants
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Solution Overview
Problem
Virtual assistants often provide inappropriate responses to potentially ambiguous natural language expressions due to multiple domains providing high scores, failing to accurately interpret user intent.
Innovation Solution
A method and system that weights and prioritizes different domain interpretations based on metadata such as client ID, vendor ID, and user context to select the most appropriate domain for interpreting expressions, using a cloud-based multi-domain natural language understanding platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the virtual assistant computes scores for multiple domains to handle potentially ambiguous expressions, then the versatility and coverage of domains is improved, but the accuracy of interpreting user intent deteriorates when multiple domains provide high scores
Solution Approach 1:
The patent applies parameter changes by modifying the scoring mechanism to incorporate multiple factors beyond simple expression matching. The system adjusts scores based on domain metadata, user context, and expression characteristics, transforming the static scoring into a dynamic multi-parameter evaluation that resolves ambiguities by weighing different parameters differently.
Solution Approach 2:
The system implements dynamics by making domain selection adaptive rather than static. The scoring and selection process dynamically adjusts based on real-time factors including user profile, context, and expression analysis, allowing the virtual assistant to flexibly prioritize domains based on current situational factors rather than fixed rules.
2Ease of operation
If the virtual assistant uses simple score computation for domain selection, then the ease of operation is improved, but the reliability of responses deteriorates when ambiguous expressions are encountered
Solution Approach 1:
The patent applies segmentation by dividing the complex domain selection process into distinct modular components: expression analysis, domain scoring, metadata retrieval, context analysis, and final selection. This segmentation allows each component to be optimized independently while working together to achieve both simplicity in implementation and reliability in outcome.
Solution Approach 2:
The system uses an intermediary scoring mechanism that mediates between simple expression matching and complex context-based selection. The scored domain list serves as an intermediary structure that organizes multiple factors into a manageable ranking, making the complex decision process appear simple from the user's perspective while maintaining high reliability through sophisticated underlying logic.
3Ease of manufacture
If the virtual assistant provides unified treatment for all domains, then the ease of manufacture is improved, but the adaptability to different device contexts deteriorates
Solution Approach 1:
The patent applies universality by creating a multi-functional domain selection framework that can serve multiple purposes: it works across different device types, supports various expression styles, and adapts to different user contexts. The same core mechanism handles diverse scenarios from music requests to weather queries, providing both ease of manufacture through a unified approach and adaptability through configurable parameters.
Data Source
AI summary
The present invention extends to methods, systems, and computer program products for interpreting queries according to preferences. Multi-domain natural language understanding systems can support a variety of different types of clients. Queries can be received and interpreted across one or more domains. Preferred query interpretations can be identified and query responses provided based on any of: domain preferences, preferences indicated by an identifier, or (e.g., weighted) scores exceeding a threshold.