AI Path Selection for Distributed Register Latency

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Solution Overview

Problem

Conventional network management of database systems in enterprise environments is inefficient and complex, particularly when using distributed registers, leading to longer execution times and resource overutilization due to the lack of mechanisms to identify optimal execution paths for read and write operations.

Innovation Solution

A computer-based system utilizing AI-driven models to dynamically classify and select nodes for data transactions based on input load, workload, historical execution time, node availability, backup capacity, and latency needs, along with a multi-model data repository to optimize data routing and storage in a distributed register environment.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional network management of database systems is used, then system simplicity is maintained, but transaction execution time increases and resource utilization becomes inefficient

Engineering Contradiction:

Improvetransaction execution speedVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary component (path selection system with AI models) that mediates between the distributed register nodes and applications. This intermediary analyzes multiple parameters (load, latency, historical data) and selects optimal execution paths, thereby improving transaction speed without requiring changes to the fundamental distributed register architecture or node operations.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces conventional mechanical/rule-based path selection mechanisms with AI-driven models. These models use machine learning to predict optimal execution paths based on historical data and current system state, substituting static routing rules with dynamic, intelligent decision-making that adapts to changing conditions without increasing operational complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If distributed registers are used for data management, then data reliability and availability are improved, but transaction latency increases

Engineering Contradiction:

Improvedata availabilityVSAvoidtransaction latency

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent implements preliminary action by pre-calculating and storing optimal execution paths using AI models that analyze historical transaction data and system performance metrics. When a transaction is initiated, the system retrieves pre-determined optimal paths rather than calculating them in real-time, significantly reducing transaction latency while maintaining data availability across the distributed register.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent applies dynamics by making the path selection process adaptive and dynamic. The AI models continuously learn from new transaction data and system conditions, dynamically adjusting execution path recommendations. This allows the system to respond to changing load conditions, node availability, and performance characteristics, reducing latency without compromising the reliability guarantees of the distributed register.

Inventive Principle:

Principle #15Dynamics

3Productivity

If optimal execution path identification is implemented, then transaction performance is improved, but system complexity and resource overhead increase

Engineering Contradiction:

Improvetransaction throughputVSAvoidcomputational resource utilization

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The patent applies partial action by implementing path optimization selectively rather than for all transactions. The AI models analyze transaction characteristics and apply optimization intelligence to transactions where it provides the most benefit, while using simpler routing for less critical operations. This partial application of complex optimization reduces overall computational overhead while maintaining high transaction throughput for priority operations.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS11615074B2System and methods for intelligent path selection of enhanced distributed processors

Publication Date: 2023.03.28 BANK OF AMERICA CORP

AI summary

The present invention provides systems, methods, and computer program products for a novel system using machine learning and artificially intelligent neural networks to generate, manage, and store data, and also intelligently and effectively manage the routing of data between one or more nodes within a distributed register environment in a dynamic fashion. The invention provides a computer-based system for executing read and write operations between applications involving distributed registers, but is not specifically limited to such embodiments.