Voice Trigger Matching for Long-Tail Command Recognition
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Solution Overview
Problem
Existing interactive assistant modules struggle to recognize long-tail voice commands due to prescriptive grammars that lack flexibility, often requiring manual association creation and failing to handle colloquialisms and dialects, leading to unrecognized voice-based triggers.
Innovation Solution
Implement a descriptive linguistics library that leverages associations between voice-based triggers and responsive actions across a user population, using syntactic and semantic similarity measures to automatically match user commands, and adapt to regional dialects and contexts through user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If prescriptive linguistic approaches are used to develop grammars for interactive assistant modules, then the system structure is simple and easy to implement, but the recognition accuracy for long-tail voice commands deteriorates
Solution Approach 1:
The patent copies successful trigger-action associations from a population of users and applies them to individual users. Instead of manually creating grammars for each user, the system replicates proven associations across users, automatically adapting to long-tail commands that have been successfully used by others.
Solution Approach 2:
The system enables self-service by automatically learning and adapting to user-specific colloquialisms and regional variations through feedback mechanisms. The interactive assistant module自行 adjusts its trigger associations based on user interactions without requiring manual grammar programming for each user.
2Reliability
If manual association creation is required for voice-based triggers, then the system maintains precise control over trigger-action mappings, but the operation complexity and time consumption increase
Solution Approach 1:
The system performs self-service by automatically creating and maintaining trigger associations through population-level data. Individual users benefit from pre-established associations without manually creating them, while the system autonomously manages the association database through continuous learning from user feedback.
Solution Approach 2:
The system performs preliminary action by pre-establishing trigger associations based on population-wide usage patterns before individual users need them. This advance preparation eliminates the need for manual association creation at the user level while ensuring reliable mappings are already in place.
3Device complexity
If existing grammars are used with limited flexibility, then the system complexity remains low, but the adaptability to user-specific colloquialisms and regional variations deteriorates
Solution Approach 1:
The patent implements dynamics by making the trigger associations adaptive and evolving rather than static. The system dynamically adjusts associations based on user feedback and population-level patterns, allowing it to adapt to colloquialisms and regional variations without requiring complex predefined grammars for each variation.
Solution Approach 2:
The system achieves universality by using a single flexible association framework that serves multiple functions: it handles standard commands, colloquialisms, and regional variations through a unified mechanism. This population-level approach makes the system multi-functional without increasing individual user device complexity.
4Reliability
If clarification requests are frequently issued to handle unrecognized commands, then the system ensures accurate understanding of user intent, but the response time and user experience deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where user responses to triggered actions are collected and used to refine future associations. This feedback loop enables the system to learn from user corrections and preferences, improving intent understanding accuracy over time without requiring explicit clarification requests for each interaction.
Solution Approach 2:
The system performs preliminary action by pre-establishing associations based on population-level patterns before individual users encounter ambiguous situations. This advance preparation reduces the need for clarification requests by having relevant triggers already configured, thereby reducing response time while maintaining understanding accuracy.
Data Source
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AI summary
In various implementations, upon receiving a given voice command from a user, a voice-based trigger may be selected from a library of voice-based triggers previously used across a population of users. The library may include association(s) between each voice-based trigger and responsive action(s) previously performed in response to the voice-based trigger. The selecting may be based on a measure of similarity between the given voice command and the selected voice-based trigger. One or more responsive actions associated with the selected voice-based trigger in the library may be determined. Based on the one or more responsive actions, current responsive action(s) may be performed by the client device. Feedback associated with performance of the current responsive action(s) may be received from the user and used to alter a strength of an association between the selected voice-based trigger and the one or more responsive actions.