Progressive Occupant Profiling for Personalized Home Automation
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Solution Overview
Problem
Premises automation systems often perform actions that occupants do not want or perform them differently than desired, leading to inefficiencies and dissatisfaction with the current automation and security technologies.
Innovation Solution
A computer-implemented method for progressive profiling in home automation systems that analyzes data related to occupants and their actions, allowing for personalized automation by offering to perform actions based on observed conditions and receiving confirmation from the occupants, with the ability to simulate communication manner and learn language characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automation systems perform actions automatically without user input, then productivity and automation efficiency are improved, but the system may perform actions that occupants do not want or perform them differently than desired, leading to loss of information about user preferences
Solution Approach 1:
The system implements feedback by analyzing user responses to offers and using this information to update occupancy profiles. The system learns from user confirmations or rejections of automated actions, continuously improving its understanding of user preferences while maintaining high automation efficiency.
Solution Approach 2:
The system performs preliminary actions by proactively offering to perform tasks before users explicitly request them. By analyzing patterns in user behavior and environment data, the system anticipates user needs and presents automated action options in advance, allowing users to confirm or reject these pre-planned actions.
2Adaptability or versatility
If the system collects and analyzes extensive data about occupants to improve personalization, then adaptability to user preferences is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments data collection and processing into modular components: data collection from multiple sources, pattern analysis through machine learning models, profile generation, and action recommendation. This segmentation allows the system to handle complex data processing tasks through distributed, manageable modules rather than a monolithic complex system.
Solution Approach 2:
The system introduces an intermediary profiling layer that sits between raw data collection and automated action execution. This intermediary profile stores learned user preferences and serves as a mediator that translates complex data patterns into actionable automation decisions, reducing the complexity of direct data-to-action mappings.
3Productivity
If the system proactively offers automated actions to occupants, then automation efficiency is improved, but this may increase the quantity of information and communications the occupant receives
Solution Approach 1:
The system applies partial action by selectively offering automation only for specific tasks and conditions rather than attempting to automate all possible actions. Offers are generated based on analyzed user patterns and current context, presenting a curated subset of relevant automation opportunities rather than overwhelming users with comprehensive action lists.
Data Source
AI summary
A computer-implemented method for progressive profiling in a home automation system is described. Data related to a premises and an occupant of the premises may be analyzed and one or more observations may be made based on the analysis of the data. Upon detecting one or more conditions associated with the one or more observations made, an offer to perform one or more actions may be communicated to the occupant.