Containerized Data Classification Engine for Automated Metadata Publishing

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Solution Overview

Problem

Existing data classification and automated metadata publishing systems are inefficient, leading to compliance issues, security risks, and difficulty in organizing sensitive information, often being platform-dependent and requiring manual input, which limits integration with modern infrastructures and increases complexity.

Innovation Solution

A containerized data classification and automated metadata publishing system (DCAMPS-C) that integrates data classification, metadata generation, and containerization technologies, using a data classification engine, automated metadata publishing service, and a YOLAGPT large language model to streamline data management, ensure compliance, and mitigate security risks, while eliminating the need for manual input and enhancing resource utilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual input is used for metadata generation, then system flexibility is maintained, but productivity decreases and human error increases

Engineering Contradiction:

Improvemanual input capabilityVSAvoidmetadata generation efficiency

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system enables self-service through automated metadata generation using AI/ML models that automatically classify data and generate metadata without requiring manual human input, thereby maintaining operational simplicity while dramatically improving productivity and eliminating human error

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical manual input process with an automated intelligent system using AI/ML algorithms that perform data classification and metadata generation autonomously, substituting human labor with intelligent automation to achieve both high productivity and accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If existing data classification systems are used, then data organization is achieved, but system complexity increases and integration with modern infrastructures becomes difficult

Engineering Contradiction:

Improvedata classification accuracyVSAvoidsystem integration complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system segments the data classification functionality into containerized microservices that can be independently deployed and integrated with modern infrastructures, reducing overall system complexity while maintaining classification accuracy through modular architecture

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a universal containerized platform that can integrate with multiple modern infrastructures (cloud environments, hybrid systems, edge computing) through standardized interfaces, enabling the system to serve multiple deployment scenarios without increasing complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Reliability

If platform-dependent systems are deployed, then specific platform optimization is achieved, but adaptability to different environments decreases

Engineering Contradiction:

Improveplatform-specific performanceVSAvoidcross-platform compatibility

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system achieves universality by containerizing the data classification platform, allowing it to run consistently across different environments (cloud, on-premises, edge) while maintaining optimized performance through platform-agnostic architecture and standardized interfaces

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Productivity

If automated metadata generation is implemented, then productivity increases, but measurement precision of data classification may decrease

Engineering Contradiction:

Improvemetadata generation speedVSAvoiddata classification accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent replaces manual metadata generation with AI/ML-based automated systems that use intelligent algorithms to achieve both high-speed processing and accurate classification, leveraging machine learning models trained on domain-specific data to maintain precision while automating the process

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system implements feedback mechanisms where classification results are continuously evaluated and used to refine AI/ML models, improving both accuracy and efficiency over time through iterative learning while maintaining automated high-speed operation

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12130821B1Containerized data classification and automated metadata publishing system

Publication Date: 2024.10.29 MUNDO SYSTEMS INC

AI summary

A containerized data classification and automated metadata publishing system (DCAMPS-C) is disclosed. DCAMPS-C is powered by a data classification engine. The containerized data classification and automated metadata publishing system hosts a data classification and automated metadata cloud application publishing service. DCAMPS-C offers a containerized solution that automates the data classification and metadata generation processes, eliminates the need for manual input, reduces the chances of human error, and ensures seamless integration with modern cloud and on-premise infrastructures for better resource utilization, scalability, and overall efficiency in managing digital content within an organization.