Self-Learned Container Templates for Secure Image Generation

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Solution Overview

Problem

Developers face challenges in building optimal container images and containers for efficient and secure deployment, lacking expertise and time, leading to inefficiencies and security issues, and often relying on outdated guidance.

Innovation Solution

A containerization engine with modules like Project Container Template Generation, Template Match and Command file Generation, and Runtime Execution, which automatically analyze source code, generate templates, and correct errors to optimize container deployment.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If developers manually build container images and containers, then they can have control over the deployment process, but it requires substantial time and expertise acquisition

Engineering Contradiction:

Improvedeployment securityVSAvoidexpertise acquisition time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs self-learning by automatically analyzing source code, identifying dependencies, and generating optimized container templates without requiring developer expertise in containerization. The engine learns from multiple sample projects and autonomously creates deployment configurations.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The containerization engine acts as an intermediary between the source code and the container deployment process. It translates developer code into optimized container configurations, eliminating the need for developers to directly learn container deployment mechanisms while ensuring security and efficiency.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If developers use optimal container construction solutions, then deployment efficiency and security are improved, but it requires substantial resource expenditure for expertise

Engineering Contradiction:

Improvedeployment efficiencyVSAvoidresource expenditure

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The engine autonomously analyzes source code, identifies security vulnerabilities, and generates optimized container configurations without requiring human experts. It self-corrects errors and continuously improves through learning from sample projects, eliminating the need for expensive expert intervention.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system replaces the manual mechanical process of expert developers analyzing and constructing container images with an automated AI-driven engine. This substitution eliminates human resource expenditure while maintaining or improving deployment efficiency and security through systematic analysis and learning.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Ease of manufacture

If developers rely on outdated guidance for containerization, then the process is simpler, but security vulnerabilities and inefficiencies increase

Engineering Contradiction:

Improvecontainer construction simplicityVSAvoidsecurity vulnerabilities

Core Design Contradiction:

Ease of manufactureVSObject-affected harmful factors

Solution Approach 1:

The engine continuously learns from sample projects and feedback, automatically updating its knowledge base with the latest security best practices and optimization techniques. This ensures that container constructions are based on current, secure methods rather than outdated guidance, while maintaining simplicity through automated processes.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system automatically stays current with security vulnerabilities and best practices by learning from multiple sample projects and continuously updating its templates. Developers simply provide source code, and the engine handles security analysis and optimization without requiring them to track or update guidance manually.

Inventive Principle:

Principle #25Self-service

4Adaptability or versatility

If custom container templates are created for each project, then deployment is optimized, but the complexity of template creation increases

Engineering Contradiction:

Improvedeployment optimizationVSAvoidtemplate creation complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The engine creates universal container templates that can be applied across multiple projects with different technologies and requirements. By learning from diverse sample projects, it generates adaptable templates that work universally while automatically adjusting to specific project needs, eliminating the need for complex custom template creation for each project.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system automatically analyzes each project's source code and generates optimized container templates specific to that project's requirements. This self-service approach creates custom-optimized templates without requiring developers to manually create or configure complex template structures, achieving adaptability through automation rather than manual customization.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20260030022A1Containerization using self-learned container templates

Publication Date: 2026.01.29 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include analyzing input source code and building a project instance based on the input source code. The operations may include matching a project instance to a template and generating, automatically, a command file based on the template.