Context-Aware Voice Control Without Wake Words
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Solution Overview
Problem
Existing cooperative intelligence systems require explicit wake-up words or phrases for functionality execution, limiting their responsiveness and efficiency in dynamic conversational contexts.
Innovation Solution
A system that monitors conversations to define system-directed commands through machine learning, associating conversational contexts without requiring explicit wake-up words, enabling automated functionality execution based on detected contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If explicit wake-up words or phrases are required for functionality execution, then system reliability is improved, but responsiveness and efficiency deteriorate
Solution Approach 1:
The system performs preliminary learning by monitoring conversations and defining system-directed commands with associated conversational contexts in advance. This allows the system to recognize and execute commands based on learned contexts without requiring explicit wake-up words during operation, thereby improving responsiveness while maintaining reliability through the pre-established command-context relationships
2Reliability
If explicit wake-up words or phrases are required for functionality execution, then system reliability is improved, but efficiency deteriorates
Solution Approach 1:
The system performs preliminary learning by monitoring conversations and defining system-directed commands with associated conversational contexts in advance. This allows the system to recognize and execute commands based on learned contexts without requiring explicit wake-up words during operation, thereby improving responsiveness while maintaining reliability through the pre-established command-context relationships
3Device complexity
If wake-up words or phrases are required for command execution, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The system performs preliminary learning by monitoring conversations and defining system-directed commands with associated conversational contexts in advance. This allows the system to recognize and execute commands based on learned contexts without requiring explicit wake-up words during operation, thereby improving responsiveness while maintaining reliability through the pre-established command-context relationships
Solution Approach 2:
The system monitors conversations and automatically defines system-directed commands and their associated contexts through machine learning, enabling the system to adapt to new conversational patterns and commands autonomously without requiring manual reconfiguration, thereby improving adaptability while maintaining manageable complexity
Data Source
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AI summary
A method, computer program product, and computing system for monitoring a plurality of conversations within a monitored space to generate a conversation data set; processing the conversation data set using machine learning to: define a system-directed command for an ACI system, and associate one or more conversational contexts with the system-directed command; detecting the occurrence of a specific conversational context within the monitored space, wherein the specific conversational context is included in the one or more conversational contexts associated with the system-directed command; and executing, in whole or in part, functionality associated with the system-directed command in response to detecting the occurrence of the specific conversational context without requiring the utterance of the system-directed command and/or a wake-up word / phrase.