Data Repository Missing-Data Detection for Policy Compliance

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Solution Overview

Problem

Existing automated data processes in computing environments often fail to accurately manage digital data, leading to missing data issues that result in non-compliance and errors downstream, due to hardware failures, incorrect implementation, or coding errors, which can cause compliance issues and errors in downstream operations.

Innovation Solution

A missing data detection system that integrates with digital data repositories to classify and map digital content items against data policies, detecting missing data and generating notifications or modifying database operations to correct these issues.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated data processes are implemented to manage digital data, then productivity is improved, but reliability deteriorates due to hardware failures and coding errors

Engineering Contradiction:

Improvedata management efficiencyVSAvoiddata process accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent implements a feedback mechanism where the system continuously monitors data repository contents and compares actual data presence against expected data requirements. When discrepancies are detected (missing data), the system generates feedback signals that trigger corrective actions, such as notifying data sources or automatically retrieving missing data, thereby resolving the reliability issue while maintaining automated process efficiency

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs self-diagnosis and self-correction by automatically detecting missing data without external intervention. The monitoring component independently identifies data gaps, and the system autonomously initiates corrective measures such as generating notifications to data sources or retrieving missing data, reducing reliance on external manual processes while maintaining reliability

Inventive Principle:

Principle #25Self-service

2Ease of operation

If data processes are implemented without sufficient expertise, then ease of operation is improved, but manufacturing precision deteriorates due to incorrect implementation

Engineering Contradiction:

Improvedata process implementationVSAvoiddata categorization accuracy

Core Design Contradiction:

Ease of operationVSManufacturing precision

Solution Approach 1:

The patent introduces an intermediary monitoring system that acts as a mediator between data processes and data repository. This intermediary automatically verifies data categorization and placement accuracy without requiring expert intervention in the core data processes. The monitoring component detects mismatches between expected and actual data states, thereby maintaining precision while preserving ease of operation for the primary data management processes

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If comprehensive data monitoring is implemented to detect missing data, then reliability is improved, but device complexity increases

Engineering Contradiction:

Improvedata completenessVSAvoidmonitoring system structure

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent extracts the monitoring function as a separate, dedicated component independent from the core data management processes. This extraction allows the monitoring system to focus specifically on detecting missing data without the complexity of managing entire data workflows. The monitoring component independently compares expected data requirements against actual repository contents, achieving reliable detection while keeping the overall system architecture manageable through functional separation

Inventive Principle:

Principle #2Taking out (Extraction)

4Productivity

If automated correction of database operations is implemented, then productivity is improved, but difficulty of detecting and measuring increases

Engineering Contradiction:

Improvedata correction efficiencyVSAvoiderror detection complexity

Core Design Contradiction:

ProductivityVSDifficulty of detecting and measuring

Solution Approach 1:

The patent implements preliminary action by proactively detecting missing data before it causes downstream errors. The monitoring system continuously checks for data gaps and triggers corrective actions preemptively, rather than waiting for errors to manifest in downstream operations. This approach simplifies error detection by identifying issues at their source rather than dealing with complex downstream failure modes, thereby improving productivity while maintaining ease of error detection

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250363090A1Detecting missing data in a digital data repository according to data attributes and a set of digital data requirements

Publication Date: 2025.11.27 ONETRUST LLC

AI summary

Methods, systems, and non-transitory computer readable storage media are disclosed for detecting missing data in a digital data repository according to a set of digital data requirements and extracted data attributes for correcting database operations. The disclosed system utilizes a classifier model to classify digital content items via an integration with the digital data repository. The disclosed system generates mappings indicating that the digital content items correspond to digital data requirements of a data policy based on the classifications. The disclosed system utilizes the mappings to determine that one or more types of data are missing from the digital data repository as indicated by the digital data requirements. The disclosed system generate an indication of the data missing from the digital data repository for use in performing additional operations, such as modifying a database operation having access to the digital content items to prevent further errors.