Centralized Data Retrieval Framework for Unstructured Personal Information
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Solution Overview
Problem
Companies face difficulties in efficiently retrieving and presenting personal data stored across multiple applications in a machine-readable and human-readable format, due to the unstructured nature of the data and the resource-intensive process of configuring each application for data retrieval.
Innovation Solution
A centralized data retrieval framework using an Information Retrieval Framework (IRF) generates a data model for each application, retrieves metadata to fetch attachments from a database, and presents the data in a readable format, allowing for automated and efficient retrieval and presentation of personal data to data subjects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If each application is individually modified to retrieve data, then data retrieval capability is improved, but device complexity and resource consumption increase significantly
Solution Approach 1:
The patent introduces a centralized data retrieval service as an intermediary component that mediates between the user interface and multiple applications. This service handles all data retrieval operations centrally, eliminating the need to modify each application individually. The intermediary service translates user requests into application-specific queries and aggregates results, thereby improving ease of operation while maintaining manageable system complexity.
Solution Approach 2:
The centralized data retrieval service performs multiple functions: it manages data retrieval from different applications, formats data in various structures (machine-readable and human-readable), handles user authentication, and manages data aggregation. This universal service replaces the need for separate retrieval mechanisms in each application, reducing overall system complexity while enhancing operational ease.
2Measurement precision
If each application develops a specific framework for data retrieval, then data retrieval accuracy is improved, but productivity decreases due to resource intensity
Solution Approach 1:
The patent merges the data retrieval functionality of multiple applications into a single centralized service. This service maintains knowledge of data structures and retrieval methods for all connected applications, enabling accurate retrieval without requiring each application to develop its own framework. The merged approach improves productivity by eliminating redundant development efforts while preserving retrieval accuracy through centralized expertise.
Solution Approach 2:
The centralized service performs preliminary actions by pre-configuring retrieval methods for each connected application during the service setup phase. This preliminary configuration includes understanding data schemas, query structures, and access patterns for all applications. When a retrieval request arrives, the service can immediately execute optimized queries without performing setup work at request time, thereby improving both accuracy and productivity.
3Quantity of substance
If unstructured data is retrieved from multiple applications, then data completeness is improved, but ease of operation deteriorates due to formatting complexity
Solution Approach 1:
The patent applies local quality by providing different data formats tailored to different user needs and consumption contexts. The centralized service can output data in machine-readable formats (such as JSON, XML, or CSV) for automated processing, and in human-readable formats (such as formatted text reports or visualizations) for user consumption. Each data recipient receives data optimized for their specific requirements, maintaining ease of operation while ensuring data completeness.
Solution Approach 2:
The service changes the parameters of data presentation by offering multiple format options and allowing users to specify their preferred output format. It can transform unstructured data from various applications into consistent, standardized formats while preserving all necessary information. This parameter flexibility ensures that complete data is delivered in a form that is easy for the specific user to operate with and consume.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for the centralized retrieval of personal data about a data subject across a plurality of applications. The data subject may request the retrieval of personal data from a company. To retrieve the personal data, a data model may be created for each application having personal data about the data subject. Each application may store personal data in the form of attachments. The data model may be in tabular form and store virtual representations of the attachments. Metadata for the attachments may be retrieved using the virtual representations of the attachments. The attachment metadata may then be used to retrieve the attachments. The attachments may then be provided to the data subject for download. The personal data may be provided to the data subject in both machine-readable and human-readable form to comply with data privacy regulations.