Duplicate Action Request Detection for Computing Resource Conservation
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Solution Overview
Problem
Unintended duplicate execution of electronic action requests consumes significant computing and user resources, affecting memory, processor, and human resources, and can adversely impact accounts or other interests.
Innovation Solution
A machine-learning-based system that trains a model to recognize patterns in historical action execution data to detect and remove duplicate requests, using techniques like clustering and pattern recognition, and optionally employs a time delay to allow for duplicate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If action execution requests are processed without duplicate detection, then processing speed is maintained, but computing resources (memory, processor, human resources) are unnecessarily consumed
Solution Approach 1:
The system performs preliminary duplicate detection by training a machine-learning model on historical action execution information before processing new requests. The model is trained to recognize patterns that indicate duplicate requests, allowing the system to identify and filter duplicates before they consume execution resources, thus preventing unnecessary resource consumption while maintaining processing efficiency
Solution Approach 2:
A machine-learning model serves as an intermediary between request reception and action execution. The model analyzes incoming requests against historical patterns and determines whether requests are duplicates, allowing the system to block duplicate execution without manual intervention while preserving legitimate requests for processing
2Measurement precision
If a machine-learning model is trained to detect duplicate requests, then duplicate detection accuracy is improved, but system complexity increases
Solution Approach 1:
The machine-learning model trains itself by consuming historical action execution information from the system's own operational history. The system automatically uses its accumulated data to improve the model's duplicate detection capabilities without requiring external training datasets or manual configuration, thereby improving detection accuracy while minimizing the complexity burden of external training infrastructure
Solution Approach 2:
The system dynamically adjusts the operational parameters of the machine-learning model based on performance feedback and changing request patterns. By modifying model parameters such as detection thresholds and pattern recognition weights, the system optimizes detection accuracy for different types of actions and request volumes without requiring complete model retraining, thus managing complexity while maintaining precision
Data Source
AI summary
A computing system for determining the presence of one or more duplicate action execution requests among multiple received action execution requests is disclosed. The system can train a machine-learning model on training data including historical action execution information associated with a plurality of historical actions executed by one or more action execution applications, to generate a trained a machine-learning model. The trained machine-learning model can subsequently identify duplicate requests among newly received action execution requests by recognizing patterns in associated action execution information received with the action execution requests. An action execution request that is determined to be a duplicate action execution request by the trained a machine-learning model can then be identified for removal or automatically removed from the system.