Electronic Transaction Status Detection via Reduced-Memory Message Grouping
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Solution Overview
Problem
Analyzing the status of electronic transactions requires significant memory and processing resources due to the large volume of electronic messages involved, particularly in real-time payment transactions, leading to inefficiencies and high costs.
Innovation Solution
A system that identifies and groups related electronic messages using common markers, determines missing messages indicating incomplete transactions, and generates notifications to notify senders or receivers, thereby reducing the need for extensive memory and processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all electronic messages are stored and analyzed to determine transaction status, then measurement precision is improved, but memory resources and processing power are significantly consumed
Solution Approach 1:
The patent segments the large set of electronic messages into smaller subsets based on transaction identifiers, message types, and temporal relationships. By dividing the message analysis into manageable segments rather than processing all messages simultaneously, the system reduces memory requirements while maintaining accurate transaction status determination through systematic analysis of each segment.
Solution Approach 2:
The patent extracts only the relevant messages needed for transaction status determination by filtering out unnecessary messages using criteria such as message type indicators, transaction ID matching, and temporal filters. This extraction process removes irrelevant data from the analysis set, reducing memory consumption while preserving the precision needed to accurately determine transaction status.
2Measurement precision
If all electronic messages are sorted and analyzed to determine transaction status, then measurement precision is improved, but processor power is significantly consumed
Solution Approach 1:
The patent performs preliminary filtering and organization of electronic messages before the main analysis phase. By pre-sorting messages according to transaction identifiers, message types, and temporal sequences, and by pre-identifying relevant message subsets, the system reduces the computational burden during the actual status determination process, thereby reducing processor power consumption while maintaining analysis accuracy.
Solution Approach 2:
The patent extracts only the essential messages required for transaction status determination by applying filters based on message type indicators, transaction ID matching, and temporal criteria. This extraction eliminates unnecessary sorting and analysis of irrelevant messages, significantly reducing processor power requirements while preserving the precision needed for accurate status determination.
3Productivity
If memory resources are increased to handle growing transaction volumes, then productivity is improved, but operational costs increase
Solution Approach 1:
The patent segments the transaction message set into smaller, manageable groups based on transaction identifiers and message types. This segmentation allows the system to process multiple transactions concurrently using limited memory resources, effectively increasing transaction processing capacity without requiring proportional increases in memory allocation, thereby improving productivity while controlling operational costs.
Solution Approach 2:
The patent extracts and processes only the relevant messages for each transaction using filtering criteria such as message type indicators and transaction ID matching. This selective extraction enables the system to handle growing transaction volumes with fixed or minimal memory resources by focusing computational efforts only on necessary data, thus improving productivity without increasing memory costs.
Data Source
AI summary
Disclosed herein is a system that can, using reduced memory and processor resources, receive a plurality of electronic messages associated with a number of previously executed electronic transactions, analyze data fields of the electronic messages, and identify electronic messages that share a common marker as being related to each other and to a particular electronic transaction. The system can also extract electronic messages as a message group and, by identifying the latest message of the electronic messages in the message group using dates of transmission of the electronic messages, determine that the latest message does not indicate completion of the related electronic transaction. The related electronic transaction may then be designated as an incomplete transaction, and a notification of the same may be generated.