Device Emulation System for Application Upgrade Failure Prediction
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Solution Overview
Problem
Existing methods for managing application upgrades on computing devices fail to accurately predict and remediate failures due to the dynamic nature of upgrade states over time, often resulting in incomplete or unsuccessful upgrades.
Innovation Solution
A system and method that utilizes a predictor to generate future state predictions for failed application upgrades by processing live data through natural language processing and applying a future state prediction model, enabling remediation based on anticipated upgrade outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional application upgrade management methods are used, then the system structure remains simple, but the reliability of predicting and remediating upgrade failures deteriorates due to the dynamic nature of upgrade states
Solution Approach 1:
The patent introduces a device emulation system as an intermediary between the actual application upgrade process and the prediction/remediation system. This emulation system captures and replicates the dynamic upgrade states, allowing the predictor to analyze these states without interfering with the actual upgrade process. The intermediary handles the complexity of state tracking and transformation, isolating the core prediction logic from the dynamic environment.
Solution Approach 2:
The system creates a copy of the application upgrade environment through device emulation. Instead of directly monitoring and predicting on the live system, the patent replicates the upgrade states in an emulated environment, processes this copied data through natural language processing, and generates predictions. This copying approach allows for safe analysis and prediction without risking the actual system stability.
2Measurement precision
If real-time monitoring of application upgrade states is implemented, then the measurement precision of upgrade status improves, but the loss of time for processing and analyzing upgrade data increases
Solution Approach 1:
The system performs preliminary actions by capturing and storing upgrade state data in the device emulation system as it occurs during the actual upgrade process. This pre-capturing of state information allows for efficient later analysis without requiring real-time processing during critical upgrade moments. The emulation system prepares the data in advance, transforming it into a format suitable for natural language processing and prediction.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based state monitoring with natural language processing. Instead of using complex if-then rules to interpret upgrade states, the system transforms state data into natural language representations and uses NLP models to predict outcomes. This substitution reduces the computational complexity and time required for analysis while maintaining high measurement precision.
3Loss of information
If comprehensive data collection during application upgrades is performed, then the quantity of information for prediction improves, but the complexity of processing and managing this data increases
Solution Approach 1:
The system extracts only the essential and relevant features from the comprehensive upgrade data collected during the process. The device emulation system captures detailed state information, but the natural language processing component selectively extracts key elements needed for prediction, discarding redundant or less important data. This extraction approach maintains complete information for prediction while reducing processing complexity.
Solution Approach 2:
The patent transforms the raw comprehensive data into different parameter representations suitable for natural language processing. By changing the parameters from raw system states to natural language descriptions and structured representations, the system makes the data more manageable and easier to process while preserving all necessary information for accurate prediction and remediation.
Data Source
AI summary
A method for managing a client environment includes obtaining, by a predictor, a future state prediction request associated with an application upgrade on an emulation of a client device, wherein the application upgrade failed; in response to the future state prediction request: obtaining live data associated with the application upgrade; performing natural language processing on the live data to obtain processed live data; applying a future state prediction model to the processed live data to generate a future state prediction; and initiating the remediation of the application upgrade based on the future state prediction.