Commutative Hashing for Asynchronous Transaction Reconciliation
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Solution Overview
Problem
Existing transaction reconciliation systems in asynchronous digital transaction environments are resource-intensive and inefficient, particularly when dealing with large volumes of transactions, as they often require extensive computing and storage resources to detect failures and maintain logs or use hashing algorithms that are sensitive to transaction order.
Innovation Solution
A service provider system utilizing a commutative and associative hashing function to maintain aggregated hash values for transaction modules, allowing for efficient comparison and reconciliation without being affected by transaction order, thereby minimizing computing resource usage and enabling asynchronous communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional transaction reconciliation methods are used in asynchronous systems, then transaction failure detection can be achieved, but computing and storage resources are excessively consumed as transaction volumes scale
Solution Approach 1:
The patent extracts only the essential verification information (aggregated hash values) from the complete transaction data, storing and comparing only these condensed representations rather than full transaction logs. This extraction principle reduces storage requirements and comparison complexity while maintaining reconciliation accuracy.
Solution Approach 2:
The patent transforms transaction verification from comparing detailed transaction records to comparing aggregated hash values derived from those records. This parameter transformation changes the verification metric from raw transaction data to a condensed mathematical representation, reducing computational overhead while preserving failure detection capability.
2Measurement precision
If detailed transaction logs are maintained for reconciliation, then accurate failure detection is possible, but storage resources and processing time increase significantly
Solution Approach 1:
The patent performs preliminary aggregation of transaction data into hash values at the point of transaction processing. By pre-computing and storing these aggregated representations, the system eliminates the need for time-consuming full log comparisons during reconciliation, reducing processing time while maintaining detection accuracy.
Solution Approach 2:
The patent creates simplified copies (hash values) of the complete transaction information that preserve the essential verification properties. These hash copies enable rapid comparison and failure detection without requiring access to or processing of the full transaction logs, significantly reducing reconciliation time.
3Productivity
If asynchronous communication is implemented between transaction modules, then processing efficiency improves, but detecting when the second module fails to process transactions becomes more difficult
Solution Approach 1:
The patent implements a feedback mechanism where the first transaction module periodically requests and compares aggregated hash values from the second module. This feedback loop enables the first module to verify whether the second module has successfully processed transactions without blocking the asynchronous flow, making failure detection straightforward while preserving processing efficiency.
Data Source
AI summary
A system includes a first module that asynchronously communicates with a second module. The first module processes a set of digital transactions and transmits instructions for the second module to process the same set of digital transactions. The first module maintains a first aggregated hash value corresponding to the set of digital transactions that have been processed. The first aggregated has value is calculated using a commutative and associative hash function. The second module maintains a second aggregated hash value corresponding to a second set of digital transactions processed by the second module. The first and second aggregated hash values are compared to determine the second module processed the same digital transactions as the first module.