AI Software Upgrade System for Compatibility Conflict Resolution
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Solution Overview
Problem
Existing methods for software upgrades in computing environments often lead to compatibility issues and system malfunctions due to the inability to differentiate between impacted and non-impacted applications, resulting in application downtime and malfunctioning across network environments.
Innovation Solution
An automated and intelligent system that continuously monitors application log files using AI-based algorithms to determine which applications are impacted by software upgrades or compatibility issues, allowing for selective deployment to server environments or cloud environments with auto-configuration scripts to minimize disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software upgrades are performed across all applications, then system security and functionality are improved, but compatibility issues and system malfunctions occur
Solution Approach 1:
The patent segments applications into two distinct groups: impacted applications and non-impacted applications. This segmentation is achieved through AI-based analysis of application logs and compatibility data. Non-impacted applications receive software upgrades while impacted applications are excluded, thereby improving system security without introducing compatibility issues.
Solution Approach 2:
The system performs preliminary AI-based analysis before deploying software upgrades to identify which applications will be impacted by the upgrade. This preliminary action involves analyzing application logs, compatibility requirements, and software dependencies to predict potential compatibility issues before they occur, allowing proactive exclusion of affected applications from the upgrade process.
2Object-affected harmful factors
If software upgrades are performed selectively based on impact analysis, then compatibility issues are reduced, but system security improvement is limited
Solution Approach 1:
The system implements a feedback mechanism where AI algorithms continuously analyze application logs, performance metrics, and compatibility data to refine the identification of impacted versus non-impacted applications. This feedback loop ensures that the selective upgrade approach maintains high system security by accurately identifying which applications can safely receive upgrades while preventing compatibility issues.
Solution Approach 2:
The system enables applications to effectively self-identify their upgrade compatibility status through automated AI analysis of their own logs and configuration data. Applications that are determined to be non-impacted automatically receive upgrades without manual intervention, while impacted applications are automatically excluded, maintaining both security and compatibility.
3Measurement precision
If AI-based analysis is performed on all applications, then accurate identification of impacted applications is achieved, but processing time and computational resources increase
Solution Approach 1:
The system applies AI-based analysis selectively rather than uniformly to all applications. It focuses computational resources on analyzing applications that are more likely to be impacted based on initial screening criteria such as application type, criticality, and known compatibility requirements. This partial action approach maintains high identification accuracy while reducing overall processing time and computational resource consumption.
Solution Approach 2:
The system applies different levels of AI analysis intensity to different applications based on their characteristics. High-criticality applications or those with complex dependencies receive more thorough analysis, while simpler, low-risk applications receive streamlined analysis. This local quality approach ensures accurate identification of impacted applications without unnecessarily processing all applications at maximum analysis depth.
4Object-affected harmful factors
If manual assessment of each application is performed, then compatibility issues are avoided, but productivity and deployment speed decrease
Solution Approach 1:
The system replaces manual assessment with automated AI-based analysis that independently evaluates each application's compatibility with upcoming software upgrades. The AI algorithms automatically analyze application logs, configuration files, and dependency graphs to determine impact status, eliminating the need for manual human assessment while maintaining high accuracy in identifying compatibility issues.
Solution Approach 2:
The system substitutes the mechanical process of manual human assessment with automated computational AI analysis. Instead of human experts manually reviewing each application for compatibility, machine learning algorithms process application data at scale with superior speed and consistency, dramatically increasing deployment speed while maintaining or improving compatibility assessment accuracy.
Data Source
AI summary
A system is provided for automated and intelligent implementation of computing software upgrades. In particular, the system may continuously monitor computing application log files from one or more applications within a network environment and analyze the logs using one or more artificial intelligence-based algorithms recommended pathways for automatically upgrading the applications. Based on the upgrade pathways, the system may initiate a two-step application check to determine which applications may be impacted by the software upgrade and/or a software compatibility issue and which ones are not impacted. Applications that are impacted by the software upgrade and/or software compatibility issue may be instantiated in a cloud environment using an auto-configuration script, where the cloud environment may spool up containers as necessary to accommodate the deployed applications. In this way, the system provides an intelligent way to perform application upgrades and distribute computing load across multiple different computing environments.