Virtual Assistant Notifications From Learned Home Automation Patterns
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Solution Overview
Problem
Home automation systems face challenges in integrating diverse devices due to different communication protocols and lack of easy interoperability, leading to complex setups and limited functionality.
Innovation Solution
A home automation system that includes a hub device and a controller using machine learning to analyze operational patterns and integrate with an audible virtual assistant, allowing for seamless communication and operation of various devices, such as motion sensors, light switches, and thermostats, while providing notifications and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple different communication protocols are used for diverse home automation devices, then device compatibility and functionality are improved, but system complexity and difficulty of integration increase
Solution Approach 1:
The patent employs a service aggregator that acts as an intermediary layer between diverse home automation devices using different communication protocols (WiFi, Zigbee, Z-Wave, Insteon, Itron, RadioRA2) and the user interface. This mediator translates and consolidates communications from multiple protocols into a unified application interface, enabling device compatibility without requiring complex direct integrations between each device pair.
Solution Approach 2:
The service aggregator provides universal functionality by consolidating multiple device-specific applications into a single unified application that can control and monitor various types of home automation devices regardless of their native communication protocol. This multi-functional approach allows one application to perform the roles of multiple protocol-specific applications.
2Ease of operation
If a service aggregator consolidates multiple applications and wireless adapters, then ease of operation is improved, but access to advanced hardware and software features is reduced
Solution Approach 1:
The system architecture segments functionality between the service aggregator (handling consolidation and ease of operation) and the underlying device layer (preserving advanced hardware and software capabilities). The aggregator provides simplified access while the individual devices maintain their full feature sets, allowing users to benefit from both simplicity and advanced functionality when needed.
3Ease of operation
If machine learning is used to determine operational patterns, then user experience and personalization are improved, but processing time and computational resources increase
Solution Approach 1:
The controller performs preliminary actions by continuously storing historical operational data for each home automation device and using machine learning to determine operational patterns in advance. This preliminary analysis enables the system to proactively generate audible notifications based on predicted user needs, such as greeting users upon waking or reminding them about medications, rather than waiting for explicit user requests.
4Reliability
If historical operational data is stored and analyzed, then notification accuracy and relevance are improved, but data storage requirements and processing complexity increase
Solution Approach 1:
The system implements self-service by automatically storing historical operational data and using machine learning to analyze patterns without requiring manual user input or configuration. The controller autonomously generates audible notifications based on learned patterns, reducing the need for users to manually program notification rules while maintaining high accuracy through continuous data collection and analysis.
Data Source
AI summary
A home automation (HA) system may include HA operation devices within a user living area and an audible virtual assistant device within the user living area. The HA system may also include an HA hub device to provide communications for the HA operation devices, and a controller. The controller may be configured to store historical operational data for each of the HA operation devices based upon a user within the user living area, and use machine learning to determine an operational pattern of at least one of the HA operation devices based upon the stored historical operational data. The controller may also be configured to monitor operation of the HA operation devices and cooperate with the audible virtual assistant device to generate an audible notification based upon a determined operation of the at least one HA operation device according to the operational pattern.