Automated Error Detection System for Merchant Integration
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Solution Overview
Problem
Users often encounter technical issues during electronic transactions, leading to incomplete processes and lost opportunities due to integration errors and restrictive security settings, resulting in user dissatisfaction and missed sales for merchants.
Innovation Solution
An automated and semi-automated error detection and correction system that analyzes transactional data using issue detection models, filters issues with suppression rules, and provides communication channels for error reporting and suggested optimizations, allowing for automatic or user-approved corrections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated error detection and correction systems are implemented, then speed and accuracy in identifying integration errors improve, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary error detection and correction system that acts as a mediator between integrated systems. This system includes error detection models, suppression rules, and correction mechanisms that automatically identify and resolve integration errors without requiring complex manual intervention in the underlying systems.
Solution Approach 2:
The system implements self-service through automated error detection models that continuously monitor integration points, automatically apply suppression rules to filter false positives, and execute corrections without human intervention. The system serves itself by maintaining its own error detection capabilities and continuously improving through learned patterns.
2Reliability
If comprehensive error detection models are deployed, then reliability of electronic transactions improves, but loss of time in processing and analyzing data increases
Solution Approach 1:
The patent applies preliminary action by pre-configuring error detection models with suppression rules and correction protocols before errors occur. The system proactively monitors integration points and prepares correction mechanisms in advance, enabling rapid response when errors are detected without requiring time-consuming analysis during transaction execution.
Solution Approach 2:
The system implements continuous feedback loops where error detection models analyze transaction data, identify patterns, and automatically adjust suppression rules. This feedback mechanism reduces processing time by learning from past errors and improving detection accuracy, allowing the system to maintain high reliability while minimizing analysis time through adaptive optimization.
3Productivity
If automated correction mechanisms are implemented, then productivity of error resolution improves, but difficulty of detecting and measuring system state increases
Solution Approach 1:
The patent segments the error correction process into distinct modular components: error detection models that identify issues, suppression rules that filter false positives, and correction mechanisms that apply fixes. Each segment operates independently with clearly defined inputs and outputs, making system state monitoring manageable through localized observation points rather than requiring comprehensive system-wide analysis.
Data Source
AI summary
Methods, systems, and computer program products for detecting and correcting integration issues and errors between different computer systems are disclosed. For example, a computer-implemented method may include collecting transaction data for each one of a plurality of respective merchants where the transaction data is associated with service integration between computer systems associated with the respective merchants and one or more service provider computer systems, analyzing the transaction data across a plurality of respective issue detection models, generating for each one of the plurality of merchants action data corresponding to one or more of the respective issue detection models based on the analyzing, and providing each one of the plurality of merchants with the respective generated action data.