Distributed Voice Control System for Smart Home Load Management
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Solution Overview
Problem
Current voice integration devices in smart home systems face limitations in scalability and efficiency, particularly in processing voice commands and ambient sound monitoring, leading to potential delays and inefficiencies in controlling electrical loads.
Innovation Solution
A distributed group of microphone devices that can learn and process voice commands and ambient sounds, with the ability to choose the best device for command transmission and utilize cloud-based machine learning to associate sounds with actions, allowing for local or cloud-based processing of voice commands and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a centralized voice service server processes all voice commands, then voice recognition accuracy is improved, but system latency and response time increase
Solution Approach 1:
The patent segments the voice processing system into distributed microphone devices, local edge processors, and cloud servers. Each device can independently process recognized voices locally, while complex tasks are offloaded to the cloud, enabling parallel processing and reducing centralized bottlenecks.
Solution Approach 2:
The system performs preliminary voice recognition and filtering at the edge devices before transmitting to the cloud. This preliminary processing reduces the amount of data requiring centralized processing and enables faster local responses for simple commands.
2Reliability
If multiple microphone devices are deployed throughout the home, then sound detection coverage and reliability are improved, but device complexity and coordination overhead increase
Solution Approach 1:
The patent merges the functionality of multiple microphone devices into a unified networked system where devices cooperate through standardized protocols. The system automatically selects the optimal microphone device based on signal quality, reducing the need for manual configuration and simplifying coordination.
Solution Approach 2:
Each microphone device autonomously performs self-calibration, signal quality assessment, and automatic selection for specific tasks. The system self-organizes by having devices independently evaluate their own capabilities and contribute to the collective function without centralized management overhead.
3Power
If all audio data is transmitted to the cloud for processing, then processing power and machine learning capabilities are improved, but network bandwidth consumption and energy usage increase
Solution Approach 1:
The system applies partial cloud processing by transmitting only necessary audio data segments to the cloud rather than all captured audio. Edge devices handle routine processing locally, sending only unrecognized or complex audio patterns to the cloud, reducing network traffic while maintaining access to advanced processing capabilities when needed.
Data Source
AI summary
A scalable, distributed load control system for home automation based on a network of microphones may include control devices (e.g., load control devices) that may include microphones for monitoring the system and communicating audio data to a cloud server for processing. The control devices of the load control system may receive a single voice command and may be configured to choose one of the load control devices to transmit the voice command to the cloud server. The load control devices may be configured to receive a voice command, control a connected load according to the voice command if the voice command is a validated command, and transmit the voice command to a voice service in the cloud if the voice command is not a validated command. The voice service to which the load control devices transmit audio data to may be selectable.