Garage Door Opener with Adaptive Closing Logic
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Solution Overview
Problem
Conventional garage door opening systems are unable to adequately accommodate varying usage scenarios, often leaving the door open longer than necessary for quick errands and closing it too early for situations where a person needs to return, due to their fixed timing settings.
Innovation Solution
A garage door opener system equipped with sensors and trainable logic that uses machine learning to recognize and respond to household activities, adjusting the timing for closing the door based on learned usage patterns, including radar and respiration sensors, cameras, and machine learning algorithms to predict optimal door opening durations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a garage door opener system uses a fixed predetermined time to close the door after opening, then the system is simple to operate, but it cannot adequately accommodate varying usage scenarios such as quick errands versus immediate departure
Solution Approach 1:
The system dynamically adjusts the door closing time based on real-time sensor inputs and learned user behavior patterns. Instead of using a fixed predetermined time, the system continuously adapts the timing parameter to match actual usage scenarios, allowing it to accommodate both quick errands and immediate departures effectively
Solution Approach 2:
The system employs machine learning algorithms that automatically learn and adapt to user behavior patterns without requiring manual programming or user intervention. The system serves itself by continuously improving its timing accuracy through observed usage data, eliminating the need for users to manually configure different timing settings for different scenarios
2Ease of operation
If the garage door remains open for a longer fixed time, then it accommodates quick errands, but it increases security risks by leaving the door open longer than necessary for immediate departures
Solution Approach 1:
The system uses multiple sensors to continuously monitor the garage environment and provide feedback to the control system. This feedback mechanism allows the system to detect when a vehicle has truly departed versus when the user is performing a quick errand, enabling real-time adjustment of the door closing time to balance convenience and security
Solution Approach 2:
The system changes the time parameter dynamically based on sensor inputs and learned patterns. Instead of maintaining a constant open duration, the system adjusts the closing time parameter in real-time to match the actual usage scenario, thereby accommodating quick errands when needed while minimizing security risks during immediate departures
3Reliability
If the garage door closes shortly after departure, then security is improved, but it closes too early for users who need to return after quick errands
Solution Approach 1:
The system automatically learns to distinguish between immediate departures and errand runs by analyzing sensor data and user behavior patterns over time. This self-learning capability allows the system to improve its security timing without manual intervention, automatically adapting to accommodate return trips when they occur while maintaining tight security for true departures
Solution Approach 2:
The system prepares for potential return trips by maintaining an extended monitoring period after vehicle departure is detected. During this preliminary phase, the system continues to monitor for user return while preparing to close the door, allowing it to secure the garage promptly for true departures while still accommodating unexpected return trips
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
The system effectively adjusts the garage door closing time to match the user's behavior, ensuring the door remains open long enough for intended activities and closes promptly when not needed, enhancing security and convenience by accommodating various usage patterns.
Implementation Method 1
uses machine learning to recognize and respond to household activities, adjusting the timing for closing the door based on learned usage patterns, including radar and respiration sensors
Implementation Method 2
including radar and respiration sensors, cameras, and machine learning algorithms to predict optimal door opening durations
Data Source
AI summary
A garage door opener system includes a motor, a sensor, a controller, and a network interface. The motor is coupled to the garage door and is configured to selectively raise the garage door and lower the garage door. The sensor is configured to monitor the doorway and distinguish between an object entering and exiting the doorway. The controller is coupled to the motor and is configured to generate signals that cause the motor to lower the garage door into a closed position based on signals received from the sensor and whether the object is entering or exiting the doorway. The network interface is configured to operatively couple the controller to a server via a network. The network interface further provides the server with information obtained from the sensor and receives a table comprising durations that the server customized based upon the received information.