Voice Interactive Service Activation via Acoustic Feature Classification
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Solution Overview
Problem
Existing automated assistant systems rely on linguistic cues, which can be burdensome and ineffective for users, especially those impaired, elderly, or in need of health monitoring, as they require specific keywords or phrases for activation, limiting interaction and health monitoring efficiency.
Innovation Solution
A system that uses non-linguistic vocal features to classify user states and activate personal assistance services, including health-related services, by extracting acoustic features from user-generated sounds to provide timely and context-aware interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linguistic cues (keywords or phrases) are used for activation, then the system can identify user intent, but the interaction becomes burdensome and ineffective for impaired or elderly users
Solution Approach 1:
The patent replaces the mechanical/linguistic system (keyword-based activation) with an acoustic system (non-linguistic vocal feature analysis). Instead of requiring users to speak specific words or phrases, the system analyzes acoustic characteristics of speech such as pitch, tone, and vocal quality to detect user state and activate services automatically.
Solution Approach 2:
The system enables self-service by automatically detecting user state through acoustic analysis and activating appropriate services without requiring explicit user commands. The system serves itself by using the user's natural speech patterns as both the trigger and the information source for service activation.
2Productivity
If non-linguistic vocal features are analyzed, then monitoring frequency increases and latency reduces, but processing complexity increases
Solution Approach 1:
The patent extracts only the essential acoustic features needed for user state detection from the full speech signal, rather than processing the entire audio stream in detail. This selective extraction of relevant features (such as pitch contours, spectral characteristics, and temporal patterns) reduces processing complexity while maintaining high monitoring frequency.
Solution Approach 2:
The system performs partial analysis by focusing on specific acoustic dimensions that are most indicative of user state, rather than comprehensively analyzing all aspects of the speech signal. This partial action approach enables continuous monitoring without the computational burden of full-signal processing.
3Loss of time
If continuous audio monitoring is implemented, then health information delivery latency reduces, but energy consumption increases
Solution Approach 1:
The patent implements periodic analysis of acoustic features rather than continuous full-signal processing. The system analyzes speech during active communication periods and uses these periodic assessments to trigger health monitoring services, reducing energy consumption while maintaining timely detection of user state changes.
Solution Approach 2:
The system performs preliminary acoustic analysis during natural speech interactions to prepare user state information in advance. By extracting and storing acoustic features during regular communication, the system reduces latency for health information delivery without requiring continuous dedicated monitoring, thus lowering energy consumption.
Data Source
AI summary
The present invention provides a system for activating personal assistance services. The system includes an audio data collector adapted to collect a sample of speech, a processing module, and a service activator couple to an output device. The processing module further includes an audio feature extractor that extracts a plurality of acoustic features from the sample of speech, and a classification unit that classifies a status of a user from the plurality of acoustic features. The Service activator activates a personal assistance service according to the status of the user classified by the classification unit.