Remote Device Application Activity Tracking and Corrective Action Generation
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Solution Overview
Problem
Existing computing devices face challenges in identifying and correcting bugs or shortcomings in scheduling and communication services, relying on limited observational and anecdotal information, which hinders performance improvement and user satisfaction.
Innovation Solution
A computerized system that analyzes application activity data from remote devices to identify challenges and generate corrective actions, such as feature introductions or graphical user interface modifications, to optimize application performance and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If observational and anecdotal information is used to identify bugs, then service improvement is limited, but the complexity of the system remains low
Solution Approach 1:
The system enables self-service by having remote devices automatically collect and transmit their own application activity data to the entity computer, eliminating the need for manual observation and reporting by users. This automated self-reporting mechanism improves service reliability while keeping the overall system architecture relatively simple.
Solution Approach 2:
The patent implements a feedback loop where application activity data is continuously collected from remote devices, analyzed by the entity computer to identify challenges, and used to generate corrective actions that are transmitted back to the remote devices. This automated feedback mechanism enables systematic identification and correction of service bugs, improving reliability without requiring complex manual intervention systems.
2Productivity
If application activity data is collected and analyzed from remote devices, then bug identification speed improves, but data processing complexity increases
Solution Approach 1:
The patent extracts only the necessary application activity data from remote devices that is relevant to identifying service challenges, rather than collecting all possible data. This selective extraction approach enables fast bug identification by focusing on critical metrics while keeping data processing complexity manageable through targeted data collection.
3Reliability
If corrective actions are automatically generated and transmitted, then service effectiveness improves, but system automation complexity increases
Solution Approach 1:
The system implements self-service by automatically generating corrective actions at the entity computer based on analyzed application activity data, and transmitting them to remote devices without requiring manual intervention. This automated corrective action generation and distribution improves service effectiveness while maintaining relatively simple automation through rule-based decision-making algorithms.
Data Source
AI summary
Systems, methods, and other embodiments associated with generating a corrective action data structure for a set of remote devices based upon corrective actions are described. In one embodiment, a method includes receiving application activity data from a plurality of remote devices. The application activity data is analyzed to determine a set of application activities associated with each of the plurality of remote devices, and the sets of application activities are analyzed to determine one or more challenges associated with a set of remote devices of the plurality of remote devices. Corrective actions are determined for each of the one or more challenges, and a corrective action data structure is generated for the set of remote devices based upon the corrective actions.