Electronic Device State Detection Using Global and Local Anomaly Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems lack effective methods for detecting anomalies in the behavior of electronic devices and responding to them in a timely and intelligent manner, particularly in environments with multiple devices.
Innovation Solution
A remote system generates global models based on aggregated data from various environments to identify expected behaviors of electronic devices, detects anomalies by comparing actual device behavior to these models, and sends notifications or instructions to change device states using voice-controlled devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a remote system monitors and detects anomalies in electronic device behavior, then system reliability and operational efficiency are improved, but device complexity and data processing requirements increase
Solution Approach 1:
A remote system acts as an intermediary between electronic devices and users, collecting state data from multiple devices, comparing it against global models to detect anomalies, and sending notifications or instructions back. This intermediary approach improves reliability through centralized monitoring without requiring complex anomaly detection logic in each individual device.
Solution Approach 2:
Global models are generated in advance from aggregated state data representing expected device behaviors across multiple environments. These pre-computed models enable rapid anomaly detection by simply comparing current device states against the predetermined expected behaviors, rather than requiring complex real-time analysis.
2Measurement precision
If global models are generated from aggregated data across multiple environments, then measurement precision and anomaly detection accuracy are improved, but loss of time for data aggregation and processing increases
Solution Approach 1:
The system performs preliminary data aggregation and model generation in advance, creating global models that represent expected device behaviors across multiple environments before actual anomaly detection begins. This pre-processing approach improves detection accuracy while reducing real-time processing requirements.
Solution Approach 2:
State data from multiple different environments and devices is aggregated and merged to create comprehensive global models. This combining of diverse data sources improves the precision and generalizability of anomaly detection by establishing broader patterns of expected behavior.
3Productivity
If the system sends notifications and instructions to change device states, then productivity and operational efficiency are improved, but ease of operation decreases due to automated control
Solution Approach 1:
The system implements feedback loops where anomaly detections trigger automated notifications and instructions to change device states. This automated feedback mechanism improves operational efficiency by enabling rapid response to anomalies without requiring manual user intervention for each issue.
Solution Approach 2:
The system provides self-service capabilities by automatically detecting anomalies and sending instructions to correct them without requiring constant user involvement. The electronic devices can autonomously respond to instructions from the remote system, improving productivity while reducing the operational burden on users.
Data Source
AI summary
This disclosure describes, in part, techniques for utilizing global models to generate local models for electronic devices in an environment, and techniques for utilizing the global models and/or the local models to provide notifications that are based on anomalies detected within the environment. For instance, a remote system may receive an identifier associated with an electronic device and identify a global model using the identifier. The remote system may then receive data indicating state changes of the electronic device and use the data and the global model to generate a local model associated with the electronic device. Using the global model and/or local model, the remote system can identify anomalies associated with the electronic device and, in response to identifying an anomaly, notify the user. The remote system can further cause the electronic device to change states after receiving a request from the user.