AI Dependency Detection for Computing Environment Adaptation
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Solution Overview
Problem
Entities face challenges in identifying and adapting their documents and computer programs to comply with frequently changing rules and regulations across different geographical regions, which is difficult to achieve with existing systems.
Innovation Solution
A deep learning-based system for dynamic intra-system component dependency detection and computing environment adaption, utilizing a receiver, data processor, and artificially-intelligent engine to analyze dependencies, simulate parameter and feature combinations, and implement necessary changes to ensure compliance with new guidelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual methods are used to identify compliance with modified rules and regulations, then flexibility and adaptability are maintained, but time consumption and productivity are significantly increased
Solution Approach 1:
The patent replaces manual compliance checking (mechanical human analysis) with an automated deep learning system that uses natural language processing and dependency graphs to analyze rule modifications and identify impacted documentation and code, thereby maintaining adaptability while dramatically improving productivity
Solution Approach 2:
The patent introduces a deep learning system as an intermediary between rule modifications and compliance verification, which automatically analyzes the semantic meaning of rules, builds dependency graphs, and identifies impacted elements, serving as a bridge that maintains human-like adaptability while achieving automated efficiency
2Measurement precision
If comprehensive impact analysis is performed on all dependencies, then measurement precision and reliability are improved, but device complexity and computational resources are increased
Solution Approach 1:
The patent segments the compliance verification process into distinct components: rule modification detection, dependency graph construction, impact analysis, and recommendation generation. This segmentation allows each component to be optimized independently while maintaining overall system precision without excessive complexity
Solution Approach 2:
The patent performs preliminary actions by building a dependency graph of the codebase before impact analysis, and by pre-processing rule modifications to extract key elements. This preliminary structuring enables more efficient and accurate impact detection while reducing the complexity of the actual analysis phase
Data Source
AI summary
A system for continuous system adaption comprising a receiver, a data processor and an artificially-intelligent (“AI”) engine. The receiver may receive a structure map of an in-use computing environment. The structure map may include details relating to feature sets and dependencies between features sets. The receiver may receive a new set of guidelines for implementation in the in-use computing environment. The data processor may process the new set of guidelines and the structure map into a vocabulary. The AI engine may extract vectorized features from the vocabulary and categorize the features as impacted or unimpacted. The AI engine may simulate, using a quantum simulator, parameter and feature combinations to stabilize the impacted features when evaluated alongside the new set of guidelines. The AI engine may, based on the simulation, output a set of changes to be implemented to the features within the in-use computing environment.