AI Cloud Architecture Diagram Generation

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Designing cloud-native application architectures without comprehensive knowledge of the platform's unique requirements often results in substandard products with reliability, security, and performance issues, relying heavily on human expertise and consuming significant time, resources, and capital.

Innovation Solution

An AI-driven system that uses natural language processing and machine learning to automatically identify application components, extract dependencies, and generate architecture diagrams, reducing manual errors and variability, and enabling efficient cloud infrastructure provisioning.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If cloud architecture is designed manually by system architects, then architecture quality and reliability are improved, but time consumption and resource costs increase significantly

Engineering Contradiction:

Improvearchitecture qualityVSAvoidtime consumption

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical process of architecture design with an automated AI-based system. The system uses natural language processing to parse requirements, machine learning models to generate architecture diagrams, and automated component selection to replace human architects' manual work, thereby reducing time consumption while maintaining design quality

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service architecture generation by automatically processing user requirements and generating complete architecture diagrams without human intervention. The automated system serves itself by using AI models to interpret requirements, select components, and produce architecture designs that would traditionally require expert architects

Inventive Principle:

Principle #25Self-service

2Productivity

If cloud architecture is designed without comprehensive platform knowledge, then design speed is improved, but architecture reliability and security deteriorate

Engineering Contradiction:

Improvedesign speedVSAvoidarchitecture reliability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The AI-based system provides universal architecture generation capabilities that work across different cloud platforms and application types. The system incorporates comprehensive platform knowledge within its trained models, enabling it to generate reliable architectures for various cloud environments without requiring designers to have specialized knowledge of each platform

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system acts as an intermediary between user requirements and cloud platform implementation. It translates high-level requirements into platform-specific architecture designs by using trained AI models that understand both requirement semantics and cloud platform characteristics, ensuring reliability without requiring users to have deep platform knowledge

Inventive Principle:

Principle #24Intermediary (Mediator)

3Adaptability or versatility

If manual architecture design processes are used, then flexibility in handling unique requirements is improved, but consistency and standardization deteriorate

Engineering Contradiction:

ImproveflexibilityVSAvoidconsistency

Core Design Contradiction:

Adaptability or versatilityVSStability of the object's composition

Solution Approach 1:

The system maintains flexibility by allowing dynamic adjustment of architecture parameters based on specific requirements while ensuring consistency through standardized processing. The AI model can adapt its output to match different requirement scenarios while following consistent architectural patterns and best practices learned during training

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system segments the architecture design process into distinct, standardized steps: requirement parsing, component selection, relationship identification, and diagram generation. Each segment follows standardized procedures while the overall system adapts to unique requirements, achieving both consistency in process and flexibility in outcome

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20240345897A1System and method for cloud architecture design and diagram generation using artificial intelligence

Publication Date: 2024.10.17 ACCENTURE GLOBAL SOLUTIONS LTD

AI summary

Intelligent AI-based systems and methods of generating architecture diagrams for cloud computing-based infrastructures. The system can ingest and process requirement data and identify intents associated with the software. Based on the classifications of the requirement data, the system can automatically extract dependencies between software layers and microservices in order to identify the most appropriate components for the application. In some embodiments, validation can be performed in which a digital twin model is implemented. Implementation of such as system can eliminate manual errors and variability based on human skill sets, as well as enable risk-free testing of the architecture based on the application goals.