Voice Device Location via Sensor Fusion and Machine Learning
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Solution Overview
Problem
Existing voice-controlled devices lack the ability to accurately determine their location within a multi-room environment, leading to inefficient responses and interactions with users, as they rely solely on explicit user input or limited sensory data.
Innovation Solution
The implementation of a voice-controlled device that uses a combination of microphone-based audio signal analysis, additional sensors (like light and temperature sensors), and machine learning to identify its location by capturing and comparing environmental data to pre-defined room profiles, allowing it to adapt responses and interactions based on the determined location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If voice-controlled devices rely solely on explicit user input or limited sensory data, then the device complexity is reduced, but the location determination accuracy deteriorates
Solution Approach 1:
The patent combines multiple sensing modalities (microphone for audio analysis, light sensors, temperature sensors) with machine learning algorithms to create a comprehensive location determination system. This merging of diverse data sources enables accurate location identification without requiring complex dedicated hardware for each function.
Solution Approach 2:
The patent introduces machine learning models as intermediaries that process and interpret raw sensory data from multiple sources. These models act as mediators between the physical sensors and the location determination logic, transforming limited sensory inputs into accurate location predictions by comparing environmental data against pre-defined room profiles.
2Adaptability or versatility
If the device uses multiple sensors and machine learning to identify location, then the adaptability improves, but the use of energy increases
Solution Approach 1:
The patent pre-defines room profiles during system setup or initialization, storing characteristic environmental data patterns for each location. This preliminary action allows the machine learning model to quickly match current sensor readings against known profiles without requiring complex real-time computation, thereby reducing energy consumption during operation while maintaining high adaptability.
3Loss of information
If the device analyzes audio signals and environmental data to determine location, then the information completeness improves, but the processing time increases
Solution Approach 1:
The patent replaces traditional rule-based or threshold-based location determination mechanisms with machine learning models. These models efficiently process multiple environmental data streams (audio, light, temperature) simultaneously and make location predictions based on pattern recognition rather than sequential analysis, reducing processing time while maintaining comprehensive information utilization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the device to provide location-specific responses and interactions, enhancing user experience by accurately determining its environment and tailoring outputs to the context, such as playing music through a home theater system in a living room or providing cooking instructions in a kitchen.
Implementation Method 1
a microphone of the device captures sound and generates an audio signal based on the sound
Implementation Method 2
The device may include one or more sensors in addition to the microphone for the purpose of determining its location. For instance, the device may include a light sensor
Implementation Method 3
a temperature sensor, a camera or the like. The device may then gather data via the sensors and determine its location based on this data
Data Source
AI summary
Techniques for identifying a location of a voice-controlled device within an environment. After identifying a location of the device, the device may receive a voice command from a user within the environment and may determine a response to the command based in part on the location, may determine how to output a response based in part on the location or may determine how to interact with the user based in part on the location.