ML-Based Dynamic Transaction Timers for Adaptive Request Processing

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Solution Overview

Problem

Distributed computing systems face inefficiencies due to statically defined values that do not adapt to changing conditions, leading to suboptimal performance in processing data transactions.

Innovation Solution

A machine learning system using a trained neural network dynamically adjusts the duration of timers in a distributed transaction processing system based on changing conditions, allowing each identifier to have its own time duration adjusted accordingly.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If static timer values are used in a distributed transaction processing system, then system complexity is reduced and ease of operation is improved, but system adaptability deteriorates and processing efficiency decreases

Engineering Contradiction:

Improveease of operationVSAvoidsystem adaptability

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The patent implements dynamic timer values that automatically adjust based on real-time system conditions such as transaction volume, network latency, and server load. The system transitions from static configuration to dynamic adaptation, allowing timer durations to change continuously in response to environmental factors, thereby resolving the contradiction between operational simplicity and system adaptability

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system incorporates feedback mechanisms that monitor system performance metrics and use this information to adjust timer values. By continuously observing system state and adapting timer configurations based on observed conditions, the system achieves both ease of operation (automatic adjustment) and high adaptability (response to changing conditions)

Inventive Principle:

Principle #23Feedback

2Device complexity

If static timer values are used in a distributed transaction processing system, then device complexity is reduced, but productivity and processing efficiency deteriorate

Engineering Contradiction:

Improvedevice complexityVSAvoidprocessing efficiency

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The system implements self-service capabilities where the timer configuration automatically optimizes itself based on monitored system conditions. The system serves itself by detecting performance bottlenecks and adjusting timer values without external intervention, thereby improving processing efficiency while maintaining manageable complexity through automated self-configuration

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent employs dynamic timer adjustment mechanisms that adapt processing times based on real-time system state. This dynamic approach enables the system to optimize productivity under varying loads while keeping the underlying complexity managed through systematic adaptation rather than manual configuration

Inventive Principle:

Principle #15Dynamics

3Adaptability or versatility

If dynamic timer values are implemented in a distributed transaction processing system, then system adaptability and processing efficiency are improved, but device complexity and configuration complexity increase

Engineering Contradiction:

Improvesystem adaptabilityVSAvoiddevice complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system uses feedback loops to monitor performance metrics and automatically adjust timer values based on observed system state. This feedback-driven approach enables high adaptability while managing complexity through automated decision-making algorithms that respond to system conditions without requiring complex manual configuration

Inventive Principle:

Principle #23Feedback

4Productivity

If dynamic timer values are implemented in a distributed transaction processing system, then execution quality and processing efficiency are improved, but configuration complexity and ease of operation worsen

Engineering Contradiction:

Improveexecution qualityVSAvoidease of operation

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The system implements self-service automation where timer configurations are automatically optimized based on system conditions. This eliminates the need for manual configuration while maintaining high execution quality, thereby improving productivity while preserving ease of operation through automated self-configuration capabilities

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20260072725A1Systems and methods of determining dynamic timers using machine learning

Publication Date: 2026.03.12 NASDAQ INC

AI summary

Dynamic timers are determined using machine learning. The timers are used to control the amount of time that new data transaction requests wait before being processed by a data transaction processing system. The timers are adjusted based on changing conditions within the data transaction processing system. The dynamic timers may be determined using machine learning inference based on feature values calculated as a result of the changing conditions.