Smart Home Device Scheduling Based on User Absence Prediction
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Solution Overview
Problem
Traditional systems for controlling smart home devices require users to manually set timers and manage multiple devices, which is cumbersome and inefficient.
Innovation Solution
A system that uses machine learning models and user data, such as transaction card data, to determine when a user is away and will return home, categorizing smart devices by function, and automatically adjusts their schedules to save energy or prepare them for the user's return.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually set timers to control smart home devices, then device control is achievable, but user convenience deteriorates due to cumbersome tasks and multiple timer management
Solution Approach 1:
The system automatically detects user presence and absence, and autonomously adjusts smart home device schedules without requiring manual timer setup. The system serves itself by using sensor data and machine learning to make control decisions, eliminating the need for users to manage multiple timers while maintaining effective device control
Solution Approach 2:
The system predicts user return times using machine learning models and proactively prepares devices in advance. By analyzing patterns in user behavior and transaction data, the system pre-adjusts device schedules before the user actually returns home, eliminating the need for reactive manual timer management
2Ease of operation
If smart home devices operate continuously, then device availability is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts device operation schedules based on real-time user presence detection and predictive analytics. Instead of continuous operation, devices are activated only when needed based on predicted user return times, creating a flexible balance between availability and energy efficiency that adapts to changing conditions
Solution Approach 2:
The system changes operational parameters of smart home devices based on user absence duration predictions. When users are predicted to be away for extended periods, the system modifies device parameters such as operation timing, intensity, or state to reduce energy consumption while maintaining readiness for anticipated user return
3Measurement precision
If the system uses machine learning models and multiple data sources to predict user return times, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system employs a multi-functional machine learning platform that handles multiple data sources (sensor data, transaction data, calendar information) and performs various functions (presence detection, return time prediction, device control) through a unified architecture. This universal approach improves prediction accuracy while managing system complexity through integration rather than separate specialized systems
Data Source
AI summary
Disclosed embodiments may include a system for controlling smart home devices. The system may receive, from a router, a list of devices connected to a home network in a home. The system may determine, from the list of devices, a presence of one or more controllable smart devices, the one or more controllable smart devices operating on a schedule. The system may categorize the one or more controllable smart devices into function-based categories. The system may receive user data. The system may determine a duration of an absence of a user in the home based on the user data. The system may change the schedule of the one or more controllable smart devices based on the function-based category and the duration of the absence of the user.