Cloud Resource Code Generation for Cross-Environment IaC Deployment

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Solution Overview

Problem

The complexity of managing infrastructure as code (IaC) leads to errors and security vulnerabilities due to intricate codebases and the need for robust version control and documentation, especially in multi-team environments.

Innovation Solution

A system and method that utilizes an API to detect configuration data of cloud resources, generate code objects using language models, and deploy resources in another cloud environment, leveraging an IaC platform for unified management across different cloud providers.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual infrastructure management processes are used, then simplicity and ease of operation are maintained, but productivity and consistency deteriorate due to time-consuming manual tasks and human errors

Engineering Contradiction:

Improveinfrastructure deployment efficiencyVSAvoidcodebase complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent uses AI models to generate IaC code objects by copying and adapting from existing infrastructure configurations and templates. The system retrieves similar configurations from a database and uses AI to generate appropriate code objects, reducing the need to create new code from scratch and improving deployment efficiency while maintaining codebase manageability

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The patent introduces an AI-based code object generation system as an intermediary between infrastructure configurations and deployment processes. This intermediary automatically translates configuration data into executable IaC code objects, eliminating manual coding tasks and reducing human errors while maintaining systematic code management

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If automated infrastructure management is implemented, then productivity improves, but reliability deteriorates due to potential errors in automation processes

Engineering Contradiction:

Improveinfrastructure deployment efficiencyVSAvoiderror rate in code generation

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent implements feedback mechanisms where the AI model generates code objects that are then validated against existing configurations and templates. The system retrieves similar configurations from a database and uses AI to generate appropriate code objects, with the process iteratively improving accuracy based on feedback from configuration comparisons and validation checks

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent performs preliminary actions by retrieving similar configurations from a database before AI code generation. The system pre-processes and organizes existing infrastructure configurations into a searchable database, allowing the AI to generate more accurate code objects by referencing pre-validated templates and reducing errors before the actual code generation occurs

Inventive Principle:

Principle #10Preliminary action

3Reliability

If comprehensive version control and documentation practices are implemented, then reliability improves, but device complexity and ease of operation deteriorate due to additional processes required

Engineering Contradiction:

Improveinfrastructure configuration accuracyVSAvoidcodebase management difficulty

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The patent implements self-service mechanisms where the AI system automatically generates, validates, and manages IaC code objects without requiring extensive manual documentation and version control processes. The system autonomously retrieves configurations, generates code objects, and maintains consistency through automated database queries and AI processing, reducing the operational burden while maintaining reliability

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent changes the parameters of code generation by using AI models that can adapt to different infrastructure configurations and generate appropriate code objects dynamically. This parameter-driven approach allows the system to maintain high reliability through automated configuration matching and code generation, eliminating the need for manual documentation processes

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20260064378A1Techniques for generating code objects in a cloud computing environment

Publication Date: 2026.03.05 GO FIREFLY LTD

AI summary

A system method for generating a code object from a resource in a cloud computing environment for an infrastructure as code (IaC) platform, is presented. The method includes accessing an application programming interface (API) of a cloud computing environment; sending an API request to receive data pertaining to a plurality of resources deployed in the cloud computing environment; detecting configuration data of a unique resource of the plurality of resources in the received data; generating a code object based on the detected configuration data; and deploying a resource in another cloud computing environment based on the generated code object.