Smart Home Hub Analytics via Cloud Query and Predictive Functions
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Solution Overview
Problem
Implementing smart home analytics is a difficult and time-consuming task, especially when a substantial number of smart objects or applications are involved, and existing user interfaces are error-prone and inefficient.
Innovation Solution
A smart home hub receives sensor information from multiple smart objects, generates events, and communicates with a cloud-native application runtime to execute functions via a cloud infrastructure, utilizing a cloud platform with built-in support for smart home analytics, including predictive analytics and multi-platform query libraries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If smart home analytics are implemented using existing user interfaces, then analysis capabilities can be provided, but the implementation becomes difficult and error-prone
Solution Approach 1:
The patent introduces a specialized analytics interface that acts as an intermediary between smart home devices and the analytics cloud platform. This intermediary layer provides standardized protocols and pre-configured analytics templates, eliminating the need for users to manually implement complex analytics logic and reducing implementation errors.
Solution Approach 2:
The system provides pre-configured analytics templates and pre-processed data structures that are prepared in advance. These templates include common smart home analytics scenarios already configured, allowing users to simply select and deploy them rather than implementing analytics from scratch, thereby improving reliability.
2Adaptability or versatility
If manual functions are implemented to control smart home operations, then customization is possible, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent implements copyable function templates that can be replicated and deployed across multiple smart home devices. Users can create a function once and copy it to other devices, significantly reducing implementation time while maintaining customization capabilities through parameter adjustment.
Solution Approach 2:
The system provides universal function templates that can be applied to multiple different smart home devices and scenarios. A single function template can serve multiple purposes across different device types, reducing the overall number of functions users need to implement manually.
3Adaptability or versatility
If a substantial number of smart objects are integrated into the smart home system, then system functionality increases, but implementation complexity and error rate increase
Solution Approach 1:
The patent segments the smart home system into standardized modules: device layer, protocol layer, data layer, and analytics layer. Each layer has defined interfaces and responsibilities, allowing numerous devices to be integrated systematically without overwhelming complexity. Devices are grouped by type and function, making management more tractable.
Solution Approach 2:
The system uses parameter-based configuration to manage device integration. Instead of hardcoding device-specific logic, the system adjusts parameters and settings to accommodate different device types. This allows the same integration framework to handle a substantial number of diverse devices through parameter variation rather than structural complexity.
Data Source
AI summary
A smart home hub may receive sensor information from a plurality of smart home objects (e.g., a washing machine, an electricity meter, etc.). The smart home hub may also generate smart home events based on the received sensor information. The sensor information may be received via a plurality of different communication protocols. An analytics cloud platform with built-in smart home support may receive smart home information from the smart home hub via an analytic querying protocol adapted to handle big data. The analytics cloud platform may process the received smart home information using a multi-platform query library and automatically analyze the processed data using predictive analytics. The analytics cloud platform may then arrange to display analysis results via an analytics cloud user interface.