Subscriber-Specific ML Classification for Data Governance Adaptation

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Solution Overview

Problem

Existing data handling and governance systems face challenges in managing decentralized and complex storage architectures, particularly in identifying and securing sensitive information, which complicates data governance and compliance.

Innovation Solution

A data handling and governance platform that interfaces with subscriber-agnostic machine learning models, allowing subscribers to adapt these models to subscriber-specific needs by training with custom data samples, and replacing them if efficacy metrics are met, enabling intelligent data classification and governance.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If decentralized and complex storage architectures are used, then data storage flexibility is improved, but data governance and security management difficulty increases

Engineering Contradiction:

Improvedata storage flexibilityVSAvoiddata governance complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces a centralized data governance platform as an intermediary between decentralized storage systems and security compliance requirements. This platform provides unified access points for data classification, sensitivity detection, and governance policy enforcement across distributed storage architectures, thereby maintaining storage flexibility while simplifying governance management.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The data governance platform implements universal functionality to handle multiple data types, storage locations, and compliance requirements through a single integrated system. The platform can classify different data sensitivities, enforce various governance policies, and interface with diverse storage architectures simultaneously, reducing overall system complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Reliability

If traditional on-premises data storage is used, then data control is improved, but identifying and managing sensitive information difficulty increases

Engineering Contradiction:

Improvedata controlVSAvoidsensitive information identification difficulty

Core Design Contradiction:

ReliabilityVSDifficulty of detecting and measuring

Solution Approach 1:

The patent replaces manual mechanical processes of data identification and sensitivity assessment with automated machine learning-based detection systems. The platform uses trained models to automatically identify sensitive information patterns, classify data types, and determine governance requirements, significantly improving detection capability while maintaining data control.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The data governance platform implements self-service capabilities where the system automatically detects, classifies, and manages sensitive information without requiring continuous manual intervention. The platform self-updates its classification models and governance policies based on feedback and changing requirements, maintaining high detection accuracy while reducing operational complexity.

Inventive Principle:

Principle #25Self-service

3Device complexity

If centralized cloud-based storage architectures are used, then data governance is improved, but data security and compliance risk management difficulty increases

Engineering Contradiction:

Improvedata governance structureVSAvoiddata security and compliance risk

Core Design Contradiction:

Device complexityVSObject-affected harmful factors

Solution Approach 1:

The patent implements comprehensive feedback mechanisms that continuously monitor data access, classification accuracy, and compliance status. The system uses this feedback to dynamically adjust security policies, retrain classification models, and update risk assessments, thereby adapting to evolving security threats and compliance requirements in centralized cloud environments.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The data governance platform performs preliminary actions by pre-classifying data upon ingestion, pre-establishing governance policies, and pre-configuring security measures before data is stored or accessed. This proactive approach ensures that security and compliance risks are addressed before they materialize, reducing the impact of potential breaches or non-compliance issues.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12567000B2Systems and methods for subscriber-based adaptation of production-implemented machine learning models of a service provider using a training application

Publication Date: 2026.03.03 DRYVIQ INC

AI summary

A system and method for accelerating an adaptation of one or more machine learning models of a data handling and governance service includes implementing a data handling and governance platform digitally accessible by a target subscriber of the data handling and governance service, wherein the data handling and governance platform interfaces with a plurality of distinct subscriber-agnostic digital content machine learning classification models of the data handling and governance service; identifying a target subscriber-agnostic digital content machine learning classification model; adapting the target subscriber-agnostic digital content machine learning classification model to a subscriber-specific digital content machine learning classification model based on a training of the target subscriber-agnostic digital content machine learning classification model with at least one training corpus comprising subscriber-specific training data samples; and implementing the subscriber-specific digital content machine learning classification model in a production mode of operation for the target subscriber.