Data Reconciliation System Using Intermediary Mediator
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Solution Overview
Problem
Existing technologies face challenges in reconciling inconsistent data records across different data tables, which reduces the accuracy in user verification and validation, and data verification and validation.
Innovation Solution
The system implements an unconventional data reconciliation technique that identifies corresponding data records, transforms them into a unified format, groups records belonging to the same user, and assigns a unique entity ID, performing these operations in multiple iterations for each data key in each data table.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data records from different data sources are compared directly, then the process is simple and fast, but the accuracy in user verification and validation is reduced due to format inconsistencies
Solution Approach 1:
The patent introduces an intermediary data reconciliation system that acts as a mediator between different data sources. This system transforms and standardizes data records from multiple sources into a unified format before comparison, enabling accurate user verification without direct complex comparisons between inconsistent formats. The intermediary handles format transformations, data normalization, and reconciliation operations.
Solution Approach 2:
The patent applies parameter changes by transforming data records from various formats into a standardized format. The system modifies parameters such as data structure, format, and representation to ensure consistency across different data sources. This includes converting different date formats, name formats, and other data representations into a unified parameter set that enables accurate comparison and verification.
2Reliability
If data records are transformed into a unified format through reconciliation, then verification accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by performing data transformation and standardization operations in advance, before the actual verification process. The data reconciliation system pre-processes incoming data records from multiple sources, converting them into a unified format and storing the reconciled data for future verification operations. This preliminary processing eliminates the need for repeated transformations during verification, reducing overall processing time.
Solution Approach 2:
The patent applies periodic action by implementing batch processing cycles where data reconciliation operations are performed at scheduled intervals. The system collects data records from multiple sources, performs bulk transformation and standardization operations, and updates the verified database periodically. This approach balances processing overhead with verification needs, reducing computational burden compared to real-time processing of every individual record.
3Loss of information
If multiple data tables from different sources are reconciled, then complete user profiles are formed, but the system complexity and difficulty of detecting corresponding records increase
Solution Approach 1:
The patent implements universality by creating a standardized data model that can accommodate and represent information from multiple different data sources. The unified data structure serves multiple functions: it stores data from various sources, enables cross-source comparison, supports user verification, and maintains data integrity. This universal format eliminates the need for source-specific processing logic and simplifies the detection of corresponding records across different tables.
Solution Approach 2:
The patent uses an intermediary reconciliation layer that bridges multiple data tables from different sources. This intermediary system identifies corresponding records by comparing standardized fields across sources, resolves conflicts, and consolidates information into complete user profiles. The mediator handles the complexity of cross-table matching, making the process more manageable than direct comparison of raw data from multiple sources.
Data Source
AI summary
A system assigns a unique prefix to a data table. The system generates a set of document identifications (IDs) for a set of data records included in the data table. The system generates a data key that corresponds to a combination of two or more columns of the data table. The system identifies one or more data records that share a key value with each other, where the key value is associated with the data key. The system determines that the one or more data records belong to a user based at least on identifying that the one or more data records share the key value with each other. The system assigns an entity ID to the identified one or more data records in response to determining that the one or more data records belong to the user.