AI Transaction Visibility Framework for Enterprise System Monitoring
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Solution Overview
Problem
Conventional transaction data management approaches face challenges in tracking and tracing transactions end-to-end across multiple layers of enterprise systems, leading to inefficiencies in identifying and reprocessing problematic transactions.
Innovation Solution
A transaction visibility framework utilizing artificial intelligence to monitor transaction flows, detect problems, generate visualizations, and initiate automated actions across multiple layers of enterprise systems, enabling real-time tracking and tracing of transactions and identifying problem areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional transaction data management approaches are used, then system simplicity is maintained, but the ability to track and trace transactions end-to-end across multiple layers deteriorates
Solution Approach 1:
The patent introduces an intermediary transaction visibility framework that sits between the enterprise systems and the monitoring process. This framework includes a data collection layer that aggregates transaction data from multiple sources, a data processing layer that applies AI techniques to analyze the data, and a data presentation layer that generates visualizations. This intermediary structure enables comprehensive transaction tracking without requiring direct modification of each enterprise system, thus maintaining system simplicity while improving information visibility.
Solution Approach 2:
The transaction visibility framework is segmented into multiple independent layers: data collection, data processing, and data presentation. Each layer performs a specific function and can be developed, maintained, and scaled independently. This segmentation allows the system to handle complex multi-layer transaction tracking while keeping each component relatively simple and manageable.
2Productivity
If conventional approaches are used, then automation level is kept low, but the efficiency of identifying and reprocessing problematic transactions deteriorates
Solution Approach 1:
The system implements self-service capabilities where the AI-driven transaction visibility framework automatically identifies problematic transactions, determines their root causes, and initiates appropriate reprocessing actions without human intervention. The system monitors transaction flows, detects anomalies using AI techniques, and automatically triggers corrective actions, enabling the system to service itself and improve transaction processing efficiency through automation.
Solution Approach 2:
The framework establishes a feedback loop where transaction data is continuously collected, analyzed, and used to generate insights that trigger automated actions. The system monitors the results of these actions and uses the feedback to continuously improve its detection and response capabilities, creating a self-improving automated system that enhances productivity over time.
3Measurement precision
If detailed monitoring of transaction flows across multiple layers is implemented, then problem identification accuracy is improved, but the complexity of the monitoring system increases
Solution Approach 1:
The patent transitions from traditional single-dimensional transaction monitoring to a multi-dimensional analysis framework. It examines transactions across multiple layers (enterprise systems, integration layers, processing layers) and multiple dimensions (time, data flow, error types) simultaneously. This dimensional expansion enables precise problem identification by viewing transactions from multiple perspectives without proportionally increasing system complexity, as the framework processes these dimensions through integrated AI algorithms.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for transaction visibility framework implemented using artificial intelligence are provided herein. An example computer-implemented method includes monitoring data related to a transaction flow across multiple layers of an enterprise system; determining which of the multiple layers correspond to at least one detected problem within the transaction flow by applying artificial intelligence techniques to the data; generating a visualization of the transaction flow, wherein generating the visualization comprises generating a sequential view of the transaction flow, generating one or more parallel sub-transaction flows pertaining to one or more dependencies of the transaction flow, and producing a visual indication of the detected problem; determining, based on analyzing the generated visualization, one or more automated actions related to the detected problem and the determined layer of the enterprise system corresponding to the detected problem; and automatically initiating the one or more automated actions.