Biometric Home Control Automation for Personalized Settings
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Solution Overview
Problem
Current home automation systems lack efficient and automated methods for regulating home environmental settings based on user preferences, requiring manual intervention and complex probabilistic analyses that are difficult for humans to perform accurately and efficiently.
Innovation Solution
A computer-implemented system utilizing machine learning components that analyze voice signatures and facial features to identify users and correlate preferences with environmental settings, such as lighting and temperature, to automatically regulate home conditions, along with voice and face recognition for authentication and communication with devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual intervention is used to regulate home environmental settings, then user control and customization are maintained, but system complexity and time consumption increase
Solution Approach 1:
The system enables automated self-service by using machine learning components to autonomously analyze user preferences, process biometric data, and regulate environmental settings without requiring manual user intervention. The system serves itself by automatically making decisions based on learned patterns and real-time biometric authentication.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing user preference data during training phases, and by pre-authenticating users through biometric scanning before environmental adjustments are made. This preparation work is done in advance to enable rapid automated response when users arrive at home.
2Productivity
If automated machine learning systems are implemented to regulate home settings, then efficiency and accuracy improve, but system complexity increases
Solution Approach 1:
The system is segmented into distinct functional modules: biometric authentication components (voice and face recognition), machine learning components (preprocessing, training, inference), and environmental control components. This segmentation allows each module to be optimized independently while working together to achieve high automation efficiency.
Solution Approach 2:
The system implements multi-functionality by integrating multiple authentication methods (voice and face recognition), multiple machine learning operations (preprocessing, training, inference) into a single unified platform that can handle various user preferences and environmental settings through one comprehensive system.
3Reliability
If biometric authentication is used for user identification, then security and personalization are enhanced, but processing time and computational requirements increase
Solution Approach 1:
The system uses periodic action by implementing real-time biometric scanning and continuous voice/face recognition during the authentication process. The machine learning model performs periodic inference operations to rapidly compare biometric data against stored user profiles, enabling secure authentication to complete quickly.
Solution Approach 2:
The system replaces traditional mechanical authentication methods (physical keys, manual codes) with biometric recognition systems that use acoustic and optical fields for voice and face analysis. This substitution enables more secure authentication while reducing physical interaction time.
Data Source
AI summary
Systems, computer-implemented methods and/or computer program products that facilitate automating home control are provided. In one embodiment a computer-implemented method comprises: using a voice recognition component to identify user identification by analyzing voice signatures; using a face recognition component to determine user identification by analyzing facial features; using an authentication component to verify user identification and authorize control access to functionality of one or more automated home control systems; using a communication component to facilitate communication between the one or more automated home control systems and one or more devices; using a service component to execute a set of functions based on authorized user commands and information communicated from the one or more devices; and using a machine learning component to learn user preferences by correlating a set of functions with the authorized users commands.