Data Enrichment via Tokenized Matching and Reference Sources
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Solution Overview
Problem
Entities face inefficiencies and data integrity issues when reconciling multiple incomplete records, and there is a risk of exposing personally identifiable information during the process of matching and reconciling data records.
Innovation Solution
The system employs data matching, profiling, masking, consolidating, and enriching processes using tokenization and encryption, with Reference Sources to corroborate and fill in missing data attributes, ensuring data integrity and reducing manual efforts by utilizing a third-party Consolidation Platform for analysis and enrichment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual processes are used to combine and reconcile incomplete data records, then data enrichment can be achieved, but the process becomes inefficient and time-consuming
Solution Approach 1:
The patent introduces a third-party Consolidation Platform as an intermediary that receives tokenized data from multiple entities, performs matching and reconciliation operations, and returns enriched data without entities directly sharing their raw data. This mediator approach enables automated data enrichment while preserving entity autonomy and security requirements.
Solution Approach 2:
The patent replaces manual mechanical processes of data reconciliation with automated computational systems that use tokenization, hashing, and algorithmic matching. The system automatically identifies related records, matches them across entities, and consolidates data without human intervention, dramatically improving efficiency.
2Measurement precision
If entities share raw data to reconcile records, then data matching accuracy improves, but exposure of personally identifiable information increases
Solution Approach 1:
The patent extracts the essential matching characteristics from raw data by creating tokens and hashes that capture identifying features without exposing actual personally identifiable information. Entities send only tokenized representations of their data to the Consolidation Platform, enabling matching while removing sensitive information from the sharing process.
Solution Approach 2:
The Consolidation Platform serves as a trusted intermediary that receives tokenized data, performs matching operations, and returns results without entities ever exposing their raw PII to each other. The platform enables accurate matching through cryptographic techniques while maintaining strict data privacy boundaries.
3Reliability
If multiple incomplete records are reconciled manually, then data integrity can be maintained through careful review, but the process becomes complex and error-prone
Solution Approach 1:
The patent implements self-service automation where the Consolidation Platform autonomously performs data matching, consolidation, and quality assessment without requiring manual review. The system automatically identifies related records, resolves conflicts using predefined rules and algorithms, and generates consolidated outputs, reducing both complexity and human error.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously assesses data quality, validates matching results, and adjusts its reconciliation processes based on outcomes. Data quality scores and validation feedback loops ensure integrity while the automated feedback system manages complexity by providing clear guidance and automatic correction.
Data Source
AI summary
The present disclosure is directed to systems and methods for the enrichment of data via matching, identifying composite data records, and utilizing Reference Source datasets. In one example aspect, Customer data is tokenized and then subsequently transmitted to a third-party Consolidation Platform. The Customer tokens may comprise multiple token records, wherein the multiple token records are displayed in the form of a bitmap. The bitmap may indicate which attributes in a Customer record may be present or absent. The composited Customer token records may then be matched to a Reference Source token set, wherein the matching analysis identifies missing data attributes in the Customer token set that the Customer may or may not already possess. The missing data attributes may be populated and/or updated in a Customer environment based on the Reference Source token set. In other example aspects, a data quality score may be assigned to each data attribute.