Payment Terminal Routing Module for Low-Latency Network Selection
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Solution Overview
Problem
Existing payment terminals face challenges in determining optimal payment network routing due to high processing latency and limited computational capacity, which complicates real-time transaction processing and is exacerbated by the need for machine learning analytical solutions.
Innovation Solution
A dynamic routing module in the payment terminal, updated via a server using machine learning, determines optimal payment networks based on predefined algorithms and parameters, reducing processing latency by embedding the module within the terminal to minimize remote processing delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning analytical solutions are used to determine optimal routing networks, then routing optimization is improved, but processing time and computational capacity requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing routing optimization parameters in a lookup table during off-peak times or through batch processing. During real-time transactions, the system queries this pre-computed data rather than performing complex machine learning calculations immediately, thus achieving optimized routing decisions within the required 20-30ms latency constraint.
Solution Approach 2:
The patent introduces an intermediary layer between the payment terminal and the full machine learning system. This intermediary consists of a simplified routing decision module that uses pre-processed data and heuristics to make rapid routing decisions locally, while only complex machine learning models run on centralized servers during batch processing or model training phases.
2Measurement precision
If complex scenario analysis is performed within the payment terminal, then routing decision accuracy is improved, but terminal processing capacity is exceeded
Solution Approach 1:
The patent extracts the computationally intensive machine learning models from the payment terminal and places them on centralized servers. The terminal retains only lightweight routing decision logic and pre-processed data structures, significantly reducing its processing burden while maintaining accurate routing decisions through the extracted intelligence.
Solution Approach 2:
The system creates simplified copies of complex routing analysis results in the form of lookup tables and pre-computed routing guidelines. These copies are stored in the terminal's local memory and can be quickly queried during transactions, providing accurate routing decisions without requiring the terminal to perform the full complex analysis.
3Speed
If real-time routing determination is implemented, then transaction processing speed is improved, but computational resources are consumed
Solution Approach 1:
The system applies partial action by performing only the necessary computational steps required for real-time routing decisions. Instead of executing full machine learning models, the system uses simplified lookup tables and pre-computed data structures that require minimal computational resources, enabling fast routing decisions while conserving terminal energy and processing capacity.
Data Source
Figure 1A~1B
Figure 2A~2B
Figure 2C
AI summary
A payment terminal comprises a dynamic routing module configured to receive a payment transaction having a card profile, and to determine an optimal payment network for the received payment transaction based on a parameter, wherein the payment terminal uses the optimal payment network to process the payment transaction.