AI Module Interdependency Mapping for Accurate Dependency Documentation
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Solution Overview
Problem
Conventional computer-based systems lack the capability to accurately and effectively manage module dependencies, certifications, and versions during system maintenance and updates, leading to inefficiencies and potential outages due to missed documentation of dependencies and unclear software or hardware interactions.
Innovation Solution
A system utilizing an artificial intelligence model to determine module interdependencies, update dependencies, certifications, and versions, and identify redundant dependencies, thereby improving documentation accuracy and resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computer-based systems are used to manage module dependencies, then system operation is maintained, but documentation accuracy of module dependencies, certifications, and versions deteriorates leading to inefficiencies and potential outages
Solution Approach 1:
The patent replaces conventional manual or rule-based dependency tracking mechanisms with an artificial intelligence model that automatically analyzes code, identifies module interdependencies, and maintains documentation. This substitution enables precise automatic documentation of module dependencies, certifications, and versions without manual intervention, directly improving documentation accuracy while maintaining system reliability through automated verification.
2Productivity
If manual methods are used to track module dependencies, then system complexity is reduced, but productivity deteriorates due to time-consuming documentation and update processes
Solution Approach 1:
The AI model performs self-service by automatically analyzing module characteristics, identifying dependencies, and updating documentation without requiring manual system configuration or complex setup procedures. The system autonomously tracks module interdependencies, certifications, and versions, significantly improving documentation efficiency while the modular AI architecture keeps system complexity manageable through standardized interfaces and automated processes.
3Reliability
If comprehensive module dependency tracking is implemented, then reliability is improved, but computing resource usage increases
Solution Approach 1:
The AI model applies partial action by focusing its analysis on critical module dependencies and characteristics that directly impact system reliability, rather than uniformly processing all module attributes. The system intelligently prioritizes tracking high-impact dependencies and updates documentation only when significant changes occur, thereby improving dependency management reliability while optimizing computing resource usage by avoiding unnecessary processing of redundant information.
4Measurement precision
If advanced computational models are used for data analysis, then measurement precision of module interdependencies is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex computational model into distinct functional modules: code analysis module, dependency identification module, characteristic extraction module, and documentation update module. Each segment performs a specific function with well-defined inputs and outputs, improving interdependency detection accuracy through specialized processing while reducing overall system complexity through modular architecture that enables independent development, testing, and maintenance of each component.
Data Source
AI summary
Systems, computer program products, and methods are described herein for determining module interdependency using advanced computational models for data analysis and automated processing. The present disclosure is configured to receive a module characteristic from a system environment, wherein the system environment comprises a module; map the module characteristic, wherein mapping the module characteristic comprises comparing the module characteristic associated with the module with the module characteristic associated with the system environment; determine a weight for the module, wherein determining the weight comprises evaluating a significance level of the information produced by the module, analyzing a number of active modules contributing to a process, computing a weight value, and prioritizing the modules based on the weight value; determine, using an artificial intelligence module, a module update for the module characteristic, wherein the module update comprises updating the module characteristic; and implement the module update in the system environment.