Transaction Data Association for Credit Card Retrieval Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems for data storage and retrieval face challenges in efficiently handling and associating large volumes of information from multiple sources, particularly in the context of credit card transactions and tax calculations, without compromising speed and accuracy.
Innovation Solution
A system and method that utilizes a server system with a processor, communications interface, and input/output systems, along with a credit card reading machine and digital scanner to create and populate data tables, learn associations, and complete information using reference numbers and keywords, enabling efficient storage and retrieval of credit card data and tax calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large volumes of information from multiple sources are stored and processed, then data completeness and association accuracy are improved, but system complexity and processing time increase
Solution Approach 1:
The system segments information into structured data tables with distinct fields (creditor names, addresses, purchase descriptions, amounts, tax rates). This segmentation organizes large volumes of heterogeneous data from multiple sources into manageable, standardized units that can be efficiently processed and associated without overwhelming system complexity.
Solution Approach 2:
The system introduces an intermediary processing layer that learns associations between creditors and purchases from historical data. This intermediary layer (the association learning mechanism) mediates between raw input data and final retrieval operations, automatically establishing relationships without requiring complex manual configuration or direct matching of all data elements.
2Measurement precision
If comprehensive data association and completion is performed, then information accuracy is improved, but processing speed decreases
Solution Approach 1:
The system performs preliminary association learning by processing historical credit card transaction data to pre-establish relationships between creditors, purchases, and tax rates. This preliminary action creates a ready-made association database that enables rapid completion of new data entries without performing complex analysis in real-time, thus maintaining both accuracy and speed.
Solution Approach 2:
The system employs self-service mechanisms where the association learning process automatically completes partial information using learned patterns from historical data. When new credit card statements are processed, the system autonomously fills in missing creditor names, addresses, and tax rates based on previously learned associations, eliminating the need for manual data entry or complex real-time querying.
3Measurement precision
If manual data entry and verification is used, then data accuracy is improved, but time consumption and labor requirements increase
Solution Approach 1:
The system replaces manual data entry and verification with self-service automation. Credit card reading machines automatically extract transaction data, and the association learning mechanism autonomously completes missing information by referencing learned patterns from historical data. This self-service approach maintains high data accuracy through consistent application of learned associations while eliminating time-consuming manual operations.
Solution Approach 2:
The system substitutes mechanical manual data entry processes with automated electronic data extraction and processing. Credit card reading machines replace manual card swiping and data transcription, while computer-based association learning replaces manual verification and completion of creditor information, dramatically reducing time consumption while maintaining or improving accuracy through consistent automated application of learned patterns.
Data Source
AI summary
A system and method for storing and retrieving information when entering information from a variety of sources into a data table. The overall system includes a corresponding non-transitory computer storage media having instructions stored thereon which, when executed by a processor, performs the overall method steps. The overall method steps include creditor information that comes from a non-computer peripheral such as a credit card reading machine to read credit card information from a magnetic strip or a computer chip such as a Near Field Communication chip or NFC chip from credit cards that make-up the creditor information. The overall method also includes a digital cash register that utilizes a digital scanner to create a second table with creditors and purchasers.