Smart Assistant Intent Evaluation Without Wake Words
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Solution Overview
Problem
Conventional smart assistant computing systems rely on predefined wake words to determine user intent, which can disrupt the natural flow of human-device interaction, especially in multi-user scenarios where distinguishing between user interactions and general conversation is challenging.
Innovation Solution
The system evaluates user intent by receiving recorded speech and detecting attention indicators from images of the user environment, using a trained command recognition model to estimate command confidence and classify user intent without relying on wake words.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predefined wake words are used to determine user intent, then the system can reliably activate only when called upon, but the natural flow of human-device interaction is disrupted and false activations occur in multi-user scenarios
Solution Approach 1:
The patent combines multiple data sources (audio speech recognition, visual attention indicators, and contextual information) into a unified intent evaluation system. The intent evaluation module integrates these diverse data types to make comprehensive intent determination, resolving the contradiction by maintaining reliable activation while improving interaction naturalness through multi-modal fusion.
Solution Approach 2:
The system performs multiple functions simultaneously: it processes audio commands, analyzes visual attention indicators, evaluates contextual factors, and determines user intent. This multi-functional approach allows the system to maintain reliable activation detection while naturally handling various interaction scenarios without requiring predefined wake words.
2Measurement precision
If the system processes multiple data sources for intent evaluation, then user intent can be accurately determined in multi-user environments, but system complexity increases
Solution Approach 1:
The patent segments the intent evaluation process into distinct functional modules: audio processing module, visual processing module, contextual analysis module, and intent evaluation module. Each module handles specific data types independently before integrating results, which reduces overall system complexity while maintaining high intent detection accuracy through specialized processing.
Solution Approach 2:
The intent evaluation module acts as an intermediary that receives and processes information from multiple data sources (audio, visual, contextual) before producing the final intent determination. This intermediary structure simplifies the system architecture by providing a centralized coordination point that manages the complexity of multi-source data integration.
Data Source
AI summary
A method for user intent evaluation includes receiving recorded speech of a human user. One or more attention indicators are detected in an image of the human user. Using a trained command recognition model, a command confidence is estimated indicating a confidence that the recorded human speech includes a command for a smart assistant computing system. Based at least in part on detecting the one or more attention indicators, and the command confidence exceeding a command confidence threshold, the human user is classified as intending to interact with the smart assistant computing system.