Neural Occupancy Automation for Adaptive Device Control
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Solution Overview
Problem
Current home automation systems lack the ability to learn from user actions and adapt to individual needs, making them inefficient and limited in scalability, as they require explicit user input or programming for device control.
Innovation Solution
A neural network-based system, referred to as the 'brain device,' that learns from user actions and experiences to predict and control occupiable space devices, adapting to specific needs without requiring extensive setup or programming, using a brain-inspired multi-layer neural network with plastic connectivity between neurons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional home automation systems use explicit programming or remote control methods, then device control functionality is achieved, but user convenience deteriorates due to additional time-consuming steps
Solution Approach 1:
The system performs automation control autonomously by learning user behavior patterns and automatically executing device control actions without requiring user intervention. The neural network analyzes sensor data and user actions to predict and execute control commands, making the system serve itself rather than requiring continuous user programming or remote control operations.
Solution Approach 2:
The system pre-learns user behavior patterns and preferences during an initial training phase, storing this knowledge in the neural network. This preliminary learning enables the system to automatically execute appropriate control actions without requiring users to program rules in advance or manually control devices during operation.
2Ease of manufacture
If home automation systems require extensive programming or rule creation, then device control capability is achieved, but ease of setup deteriorates due to time and effort requirements
Solution Approach 1:
The system automatically configures itself by observing and learning from user actions during normal operation. Instead of requiring users to manually program device control rules, the neural network autonomously analyzes sensor inputs and user behaviors to build the automation logic, making the setup process effortless for users.
Solution Approach 2:
The system continuously monitors user actions and device states, using this feedback to refine and update the neural network's understanding of user preferences. This ongoing learning process allows the system to improve its automation capabilities automatically without requiring users to revisit programming or configuration settings.
3Adaptability or versatility
If simple rule-based automation is implemented, then basic device control is achieved, but scalability deteriorates when deploying to multiple devices in large spaces
Solution Approach 1:
The neural network provides a universal control framework that can handle any number of devices and sensor types through a single unified architecture. Instead of requiring separate programming for each device or rule combination, the system processes all device control decisions through the same learning-based approach, enabling seamless scalability from small to large installations.
Solution Approach 2:
The neural network acts as an intermediary layer between raw sensor data and device control commands, abstracting away the complexity of individual device protocols and control logic. This intermediary processing layer enables the system to scale to multiple devices without proportionally increasing programming complexity, as the neural network handles the coordination automatically.
4Productivity
If traditional machine learning algorithms are used, then pattern recognition capability is achieved, but computational efficiency deteriorates due to sequential processing limitations
Solution Approach 1:
The system implements dynamic parallel processing where multiple neural network computations occur simultaneously rather than sequentially. The spiking neural network architecture allows concurrent processing of multiple sensor inputs and control decisions, dramatically improving computational efficiency and reducing the processing power required compared to traditional sequential machine learning approaches.
Data Source
AI summary
Provided herein is a system for occupiable space automation using neural networks that delivers scalable and more intelligent occupiable space automation that can continuously learn from user actions and experiences and adapt to specific needs of each individual occupiable space. The occupiable space automation control system is built based on brain inspired multi-layer neural network with plastic connectivity between neurons. The occupiable space automation control system is configured to (a) adaptively predict previously learned activity patterns and (b) alert about potentially harmful or undesired activity patterns of the plurality of periphery devices based on response events of the plurality of artificial neurons and coupling strengths of the plurality of synapses. The occupiable space automation control system is configured to automatically operate the at least one controller based on the predicted activity pattern and/or provide user alerts based on a detected harmful activity pattern.