Premises Automation Recommendations for Low-Input Home Control
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Solution Overview
Problem
Existing home automation systems can be overwhelming and time-consuming to manage, as they require manual input to control various aspects like lighting, music, and HVAC.
Innovation Solution
A computer/server system that stores current controlled state information for premises automation subsystems and generates recommendations based on user preferences, past behavior, and environmental factors, such as time of day, location, weather, and user responses to similar situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual control methods are used for each aspect of premises automation, then user control precision is improved, but user time consumption increases
Solution Approach 1:
The system automatically monitors premises state and generates recommendations without requiring manual user input for each control decision. The computer server system autonomously analyzes data from multiple sources and presents actionable recommendations, allowing the system to serve itself rather than requiring continuous user intervention.
Solution Approach 2:
The system implements a feedback loop where user responses to recommendations are collected and used to refine future recommendations. The system learns from user acceptance or rejection patterns, improving the precision of recommendations over time while maintaining reduced user time consumption through increasingly accurate automated suggestions.
2Loss of time
If automated recommendation systems are implemented, then user time consumption is reduced, but system complexity increases
Solution Approach 1:
The system is divided into distinct functional modules: data collection from multiple premises aspects, state analysis engine, recommendation generation module, and user interaction interface. This segmentation allows complex functions to be distributed across separate components, making the overall system more manageable despite its complexity.
Solution Approach 2:
The computer server system acts as an intermediary between the complex premises automation infrastructure and the user. It absorbs and manages the complexity of monitoring multiple subsystems and generating recommendations, presenting a simplified interface to users while handling sophisticated analysis in the background.
3Measurement precision
If comprehensive state monitoring is performed across multiple subsystems, then recommendation accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system employs a universal analysis framework that handles multiple types of premises data (lighting, temperature, security, entertainment) through a single integrated processing engine. This multi-functional approach improves recommendation accuracy by considering interactions across all subsystems while avoiding the complexity of separate specialized processors for each data type.
Data Source
AI summary
For each of a plurality of subsystems comprising a premises automation solution a corresponding current controlled state information is stored. Stored state information associated with the plurality of subsystems is used to generate and provide as output a recommendation to perform a recommended operation with respect to one or more of said subsystems.