Conversational Home Automation Interface for Occupancy-Based Rule Learning
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Solution Overview
Problem
Existing home automation and security systems lack user-friendly communication platforms that allow for efficient customization and automation based on individual user preferences, leading to user dissatisfaction due to complexity and underutilization of system capabilities.
Innovation Solution
A method and apparatus for progressive profiling in home automation systems that utilize a graphical conversational user interface (G-CUI) to determine occupancy states and detect conditions, allowing users to grant permissions for changes in system settings through natural language interactions, generating rules based on user preferences and historical data for automated actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a security and automation system provides extensive customization and automation capabilities, then system functionality and user preferences alignment improve, but system complexity and difficulty of operation increase
Solution Approach 1:
The system performs progressive profiling automatically by observing user actions and preferences without requiring explicit user configuration. The system self-learns occupancy patterns, temperature preferences, lighting preferences, and device usage habits, then automatically generates automation rules and settings based on this learned data, eliminating the need for users to manually configure complex customization parameters
Solution Approach 2:
The system dynamically adjusts automation parameters based on learned user preferences and patterns. Instead of requiring users to set fixed parameters, the system continuously learns and adapts parameters such as occupancy detection thresholds, temperature setpoints, lighting schedules, and device activation conditions based on observed user behavior, allowing extensive adaptability without user-facing complexity
2Adaptability or versatility
If a security and automation system provides extensive customization capabilities, then system adaptability to user preferences improves, but ease of operation deteriorates
Solution Approach 1:
The system automatically profiles users by observing their actions and preferences without requiring users to manually configure settings. The system self-learns occupancy patterns, environmental preferences, and device usage habits, then automatically generates personalized automation rules, eliminating the need for users to navigate complex customization interfaces
Solution Approach 2:
The system performs preliminary profiling and rule generation automatically before users need any customization. By pre-analyzing user behavior patterns and pre-configuring automation rules based on observed preferences, the system delivers personalized functionality without requiring users to perform any configuration actions, making the system as easy to use as traditional non-customizable systems while providing advanced adaptability
3Productivity
If the system automatically performs actions based on occupancy detection, then productivity and system efficiency improve, but user control and permission management become more complex
Solution Approach 1:
The system implements a feedback-based permission management approach where it automatically requests user permission when detected conditions match learned patterns but the required action exceeds predefined authorization thresholds. The system learns from user responses to these permission requests, adapting its behavior and permission requirements based on feedback, which streamlines permission management over time while maintaining high system efficiency through automated decision-making for routine actions
Data Source
AI summary
A method including determining an occupancy state of a building relating to a user, detecting a condition of a component of the security and/or automation system associated with the home, displaying, based at least in part on the determined occupancy state of the user and the detected condition of the component, a message related to the user on a graphical conversational user interface (“G-CUI”). The message include a request for permission from the user to initiate a change associated with the component of the system. The method may include receiving, via the G-CUI, a response from the user including an instruction whether to grant permission to initiate the change based at least in part on the displaying, and generating a rule associated with the component and a future occupancy state of the home relating to the user based at least in part on the received response.