Inferring Semantic Labels for Assistant Devices via Signal Analysis
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Solution Overview
Problem
Existing techniques for assigning labels to assistant devices in an ecosystem are inefficient, requiring manual user input or reliance on device proximity, and fail to accurately reflect device location or usage changes.
Innovation Solution
The implementation of semantic labels inferred from device-specific signals such as previous queries, commands, ambient noise, and user preferences, allowing for automatic or user-selected assignment of labels to assistant devices in a device topology representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual user input is used to assign labels to assistant devices, then users can specify devices accurately, but it requires multiple user interface inputs and increases operational complexity
Solution Approach 1:
The system automatically infers device labels by analyzing device-specific signals such as ambient noise, previously executed commands, and query patterns. This self-labeling mechanism eliminates the need for manual user input while maintaining accurate device identification, directly resolving the contradiction between label accuracy and ease of operation
Solution Approach 2:
The patent replaces the mechanical interaction of manual user input with an automated signal processing system. By substituting user interface operations with automated analysis of device signals (acoustic, textual, metadata), the system achieves accurate device labeling without requiring user interaction
2Ease of operation
If device proximity is used to determine device selection, then the system can automatically identify devices, but it fails to reflect device usage changes and location changes
Solution Approach 1:
The system dynamically updates device labels based on changing device-specific signals over time. By continuously monitoring commands executed, queries processed, and ambient noise patterns, the system adapts to device relocation and usage changes, resolving the contradiction between automatic identification and adaptability
Solution Approach 2:
The system uses feedback from device-specific signals (commands executed, queries processed, ambient noise) to continuously refine device label assignments. This feedback mechanism ensures that device labels remain accurate even when devices are moved or their usage patterns change, addressing the limitation of static proximity-based identification
3Measurement precision
If semantic labels are inferred from device-specific signals, then labels reflect actual device usage and location, but it requires processing multiple signal types
Solution Approach 1:
The system segments the label inference process by analyzing different signal types (ambient noise, commands, queries, metadata) separately and then integrating the results. This segmentation approach manages processing complexity while maintaining high label accuracy through multi-signal analysis
Data Source
AI summary
Implementations can identify a given assistant device from among a plurality of assistant devices in an ecosystem, obtain device-specific signal(s) that are generated by the given assistant device, process the device-specific signal(s) to generate candidate semantic label(s) for the given assistant device, select a given semantic label for the given semantic device from among the candidate semantic label(s), and assigning, in a device topology representation of the ecosystem, the given semantic label to the given assistant device. Implementations can optionally receive a spoken utterance that includes a query or command at the assistant device(s), determine a semantic property of the query or command matches the given semantic label to the given assistant device, and cause the given assistant device to satisfy the query or command.