Data Subject Access Request Prioritization via Metadata Analysis

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current systems face challenges in efficiently prioritizing and managing data subject access requests, particularly in ensuring compliance with privacy and security policies, due to the complexity of handling personal data across multiple locations and the need for cost-effective and timely fulfillment of requests.

Innovation Solution

A computer-implemented method that receives and prioritizes data subject access requests based on metadata such as request type, location, current events, and requestor status, adjusting the prioritization level accordingly and digitally storing it for efficient processing and fulfillment.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual processing methods are used for data subject access requests, then flexibility in handling complex cases is maintained, but processing time and operational costs increase significantly

Engineering Contradiction:

Improverequest processing efficiencyVSAvoidrequest fulfillment time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system enables automatic self-service processing of data subject access requests through machine learning models that autonomously classify requests, retrieve relevant data, and generate responses without human intervention for routine cases, thereby improving productivity while maintaining timely fulfillment

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary classification and routing of requests based on their characteristics before full processing begins, using metadata analysis and machine learning to pre-determine handling priorities and required resources, reducing overall processing time while maintaining efficiency

Inventive Principle:

Principle #10Preliminary action

2Productivity

If automated systems are implemented to increase processing speed, then productivity improves, but system complexity and implementation costs increase

Engineering Contradiction:

Improverequest processing throughputVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The automated system is segmented into distinct functional modules including request intake, classification, data retrieval, response generation, and quality assurance layers. Each module operates independently with well-defined interfaces, reducing overall system complexity while maintaining high processing throughput through modular architecture

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces intermediary components such as standardized data models, abstraction layers, and protocol translators that simplify interactions between different system components and external systems, reducing implementation complexity while enabling automated high-volume processing

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If comprehensive data retrieval is performed for all requests, then compliance accuracy is ensured, but processing time and resource consumption increase

Engineering Contradiction:

Improvecompliance fulfillment accuracyVSAvoiddata retrieval time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs partial data retrieval by initially fetching only the most relevant and frequently accessed data elements based on request classification, then progressively retrieving additional data only if needed for compliance verification, thereby ensuring accuracy while minimizing retrieval time and resource consumption

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system performs preliminary identification of required data sources and filtering criteria before actual data retrieval begins, using request metadata and classification results to pre-determine the scope of data collection, ensuring compliance accuracy while reducing unnecessary retrieval operations

Inventive Principle:

Principle #10Preliminary action

4Reliability

If multiple data locations are searched to ensure complete data retrieval, then data completeness is improved, but system complexity and processing overhead increase

Engineering Contradiction:

Improvedata retrieval completenessVSAvoiddata location management complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system implements a universal data location registry that serves multiple functions including storing data source metadata, defining retrieval protocols, managing access permissions, and tracking data lineage across all locations. This multi-functional approach ensures complete data retrieval while reducing the complexity of managing multiple data locations through a single unified interface

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS10706174B2Data processing systems for prioritizing data subject access requests for fulfillment and related methods

Publication Date: 2020.07.07 ONETRUST LLC

AI summary

In various embodiments, a data subject request fulfillment system may be adapted to prioritize the processing of data subject access requests based on metadata of the data subject access request. For example, the system may be adapted for: (1) in response to receiving a data subject access request, obtaining metadata regarding the location from which the data subject access request is being made; (2) using the metadata to determine whether a priority of the data subject access request should be adjusted based on the obtained metadata; and (3) in response to determining that the priority of the data subject access request should be adjusted based on the obtained metadata, adjusting the priority of the data subject access request.