Anticipatory Device Update System Using Contextual Performance Correlation
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Solution Overview
Problem
Conventional computing technologies delay communication of hardware and software update information to users, requiring additional user effort and understanding, and do not effectively monitor device performance for anticipatory updates.
Innovation Solution
A system that receives contextual and performance information from computing devices to correlate and send configuration updates, including recommendations for upgrades or updates based on inferred relationships between the information, using a service component that communicates configuration information to improve device performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional technologies delay communication of update information to users, then device complexity is reduced, but user experience deteriorates and update timeliness is reduced
Solution Approach 1:
The system performs preliminary actions by continuously monitoring device performance metrics (CPU usage, memory consumption, disk I/O, network bandwidth) and contextual information (device configuration, installed applications, operating system version) in advance. This allows the system to identify performance issues before they significantly impact user experience and proactively communicate update information to users, eliminating delays in update communication while maintaining manageable system complexity through automated monitoring and analysis.
2Loss of information
If conventional technologies require additional user effort and understanding, then ease of operation deteriorates, but information accuracy is improved
Solution Approach 1:
The system implements self-service by automatically collecting device performance data, analyzing contextual information, identifying performance bottlenecks, and generating personalized update recommendations without requiring user intervention. The system autonomously correlates performance metrics with device configuration and application data, processes the information through analysis algorithms, and presents actionable recommendations to users, thereby maintaining information accuracy while completely eliminating the need for additional user effort or specialized understanding.
Solution Approach 2:
The system acts as an intermediary between complex device performance data and users by introducing an automated analysis layer that translates raw performance metrics into understandable recommendations. This intermediary process automatically correlates performance information with contextual data, identifies root causes of performance issues, and communicates findings in user-friendly formats, preserving information accuracy while removing the burden of user interpretation and analysis.
3Productivity
If conventional technologies do not monitor device performance for anticipatory updates, then productivity is reduced, but device complexity is reduced
Solution Approach 1:
The system performs preliminary monitoring of device performance metrics continuously in the background, collecting data on CPU usage, memory consumption, disk I/O operations, and network bandwidth utilization. By proactively gathering and analyzing this performance information along with contextual data (device configuration, installed applications, OS version), the system can identify performance degradation patterns early and trigger update communications before users experience significant issues, thereby improving update efficiency without requiring complex user-facing monitoring interfaces.
Solution Approach 2:
The system implements automated feedback loops by continuously monitoring device performance metrics, comparing current performance against historical data and performance thresholds, and automatically initiating update processes when performance degradation is detected. This feedback mechanism correlates performance information with contextual information, identifies performance issues, and triggers appropriate update actions, improving productivity through automated responsiveness while managing system complexity through standardized monitoring and analysis protocols.
Data Source
AI summary
The subject disclosure relates to techniques for monitoring contextual and performance information of a device for anticipatorily sending update information to the device. An interface component can receive, from the client, contextual information indicating an operating environment of the client and performance information that is associated with one or more operations being performed by the client based on the operating environment, and send, based on correlation information, update information to the client. Further, a service component can to infer a relationship between the contextual information and the performance information to obtain the correlation information. In other embodiments, a client can populate a cache with portion(s) of the contextual information to obtain cached information, and send at least a portion of the cached information to a system including one or more aspects of the service component.