Edge-Node Server for Local Transaction Processing
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Solution Overview
Problem
Current resource allocation methods for merchant channels, such as online stores and brick and mortar locations, lead to inefficient use and waste of computing resources, particularly during peak demand, resulting in potential customer access denials or delayed responses.
Innovation Solution
Implementing an edge-node server architecture that processes transactions locally, reducing the need for central server resources by using edge-nodes with AI to anticipate customer trajectories and pre-fetch necessary data, thereby enabling local transaction processing without relying on distant computing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If resources are allocated to handle peak demand, then customer service reliability is improved, but resource waste occurs during non-peak periods
Solution Approach 1:
The patent implements dynamic resource allocation where computing resources are not statically assigned but dynamically adjusted based on real-time demand conditions. Edge nodes can scale resources up during peak demand and scale down during non-peak periods, allowing the system to maintain reliability when needed while avoiding resource waste during low-utilization periods.
Solution Approach 2:
The patent segments the centralized computing system into distributed edge nodes that can independently manage their own resource allocation. This segmentation allows different parts of the system to operate at different resource levels simultaneously - some edge nodes may be at full capacity during local peak demand while others operate at lower levels, eliminating the need for all resources to be provisioned for the absolute peak of any single location.
2Loss of energy
If resources are reduced to eliminate waste, then resource efficiency is improved, but customer access reliability deteriorates during peak demand
Solution Approach 1:
The patent employs predictive analytics and machine learning models that analyze historical data and current trends to anticipate peak demand periods before they occur. This preliminary action allows the system to pre-allocate and pre-position computing resources at edge nodes before demand spikes, ensuring reliability is maintained while avoiding the need for permanent over-provisioning of resources.
Solution Approach 2:
The patent implements continuous feedback loops that monitor actual demand patterns, resource utilization metrics, and customer access outcomes. This feedback information is used to dynamically adjust resource allocation decisions in real-time, allowing the system to efficiently respond to changing conditions and maintain reliability without wasting resources during low-demand periods.
3Device complexity
If centralized processing is used, then resource management is simplified, but network traffic and computational burden on central systems increase
Solution Approach 1:
The patent segments the centralized processing architecture into distributed edge nodes that can independently process transactions locally. This segmentation eliminates the need for all transaction data to traverse the network to a central system, dramatically reducing network traffic and computational burden on central infrastructure while maintaining coordinated resource management through standardized protocols and interfaces.
Solution Approach 2:
The patent introduces edge nodes as intermediary computing resources between customers and the centralized system. These intermediaries handle local processing and resource management autonomously, reducing the direct computational burden on central systems and minimizing network traffic while still enabling centralized oversight and coordination when needed through standardized communication protocols.
Data Source
AI summary
This application describes apparatus and methods for distributing computing resources using edge-computing. Apparatus may include an edge node that is positioned in a target geographic region. The edge-node may include information needed to process transactions that occur locally in the target geographic region. The edge-node may process the transaction without communicating with a central server. The edge-node may support processing transactions on behalf of a variety of financial institutions and merchant processing systems. Processing the transaction locally may reduce computational resources typically required by region server to process high volumes of transactions. The edge-nodes would process transactions locally, close to a location of at least one party to the transaction.