Classification Microservice for Dynamic Transaction Workflow Routing
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Solution Overview
Problem
Existing transaction processing systems are complex and monolithic, requiring re-coding and re-deployment for workflow changes, leading to inefficiencies, missed deadlines, and additional costs due to suboptimal or incomplete transaction processing.
Innovation Solution
A transaction exchange platform using a streaming data platform and microservices, with classification microservices that leverage machine-learning models to determine alternative workflows for transactions, allowing automatic reconfiguration and processing through dynamic, flexible workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a monolithic payment processing system is used to accommodate multiple transaction types and workflows, then the system can handle diverse transaction requirements, but the system complexity increases and any workflow change requires re-coding, re-compiling, and re-deploying large software components
Solution Approach 1:
The patent segments the monolithic payment processing system into multiple independent microservices, each responsible for specific transaction types or workflow steps. This allows individual services to be modified, deployed, and scaled independently, reducing overall system complexity while maintaining versatility across different transaction types.
Solution Approach 2:
The patent creates a universal workflow engine that can orchestrate different transaction types through a common interface and standardized data models. This allows the system to handle diverse transactions (payments, refunds, transfers, etc.) through a unified architecture rather than separate specialized systems.
2Ease of manufacture
If the payment processing system is divided into modules for different transactions, then changes to single workflow steps can be made more easily, but problems with individual workflow steps can grind the whole approval process to a halt
Solution Approach 1:
The patent implements retry logic, fallback mechanisms, and error handling at each microservice level to prevent single points of failure from cascading through the entire workflow. Compensation transactions and idempotency guarantees ensure that partial failures can be recovered from without grinding the whole process to a halt.
Solution Approach 2:
The patent introduces an event-driven architecture with message queues and workflow orchestration as intermediaries between microservices. This allows decoupled communication where individual service failures can be buffered, retried, or handled by fallback mechanisms without directly impacting the entire workflow chain.
3Productivity
If transactions are processed through fixed workflows, then the processing path is predictable and manageable, but some transactions may be processed using suboptimal workflows resulting in missed deadlines, additional costs, and inefficiency
Solution Approach 1:
The patent implements dynamic workflow selection where the system can determine at runtime which workflow path to take based on transaction characteristics, current system state, and performance metrics. This allows the system to adapt to changing conditions and select optimal processing paths rather than following rigid predetermined workflows.
Solution Approach 2:
The patent incorporates feedback mechanisms that monitor workflow performance, transaction outcomes, and system metrics to continuously optimize routing decisions. Machine learning models and analytics can learn from historical data to predict which workflows are most likely to succeed for given transaction types, improving overall productivity while maintaining flexibility.
Data Source
AI summary
Aspects described herein may relate to a transaction exchange platform using a streaming data platform (SDP) and microservices to process transactions in accordance with corresponding workflows. The transaction exchange platform may receive transactions from origination sources, which may be added to the SDP as transaction objects. Microservices on the transaction exchange platform may interact with the transaction objects based on configured workflows associated with the transactions. Further, the microservices may leverage machine-learning models to determine whether transaction objects may be more effectively processed using alternative or secondary workflows. Processing on the transaction exchange platform may facilitate clearing and settlement of transactions. Some aspects may provide for dynamic and flexible reconfiguration of workflows.


