Network Analysis Device for Dynamic Traffic Routing

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Solution Overview

Problem

Existing communication systems face bottlenecks and reduced throughput due to network devices becoming overloaded with data, leading to inefficient resource utilization and potential system halts.

Innovation Solution

A communication system employing machine learning to dynamically route and reroute messages based on current message volume and bandwidth, using a network analysis device to classify traffic volume and provide routing recommendations, thereby optimizing resource utilization and preventing bottlenecks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If data transmission continues to network devices, then resource utilization improves, but network devices become overloaded and bottlenecks form

Engineering Contradiction:

Improvedata throughputVSAvoidsystem stability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The routing system dynamically adjusts routing decisions based on real-time network conditions. The network analysis device continuously monitors bandwidth information and traffic volume classifications, and the routing device reconfigures routing instructions accordingly. This dynamic adaptation prevents network devices from becoming overloaded while maintaining high data throughput, resolving the contradiction between productivity and reliability.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements a feedback mechanism where the network analysis device obtains metric information and bandwidth information, processes it through machine learning models to generate routing recommendations, and feeds these recommendations back to the routing device. This closed-loop feedback system enables the network to self-regulate traffic flow, preventing bottlenecks while maximizing resource utilization, thus maintaining both high throughput and system stability.

Inventive Principle:

Principle #23Feedback

2Productivity

If routing is static, then system complexity is low, but resource utilization is inefficient

Engineering Contradiction:

Improveresource utilizationVSAvoidrouting system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The network analysis device serves as an intermediary between the data source and the routing device. It collects metric information, processes it through machine learning models to generate routing recommendations, and provides these recommendations to the routing device. This intermediary layer enables intelligent, adaptive routing without requiring the routing device itself to be complex, thus improving resource utilization while keeping the routing system manageable.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces traditional rule-based or static routing mechanisms with machine learning-based intelligent routing. The machine learning models analyze traffic patterns and network conditions to generate optimized routing instructions, substituting mechanical or manual routing configuration with adaptive, data-driven decision-making. This substitution significantly improves resource utilization while the modular architecture keeps the overall system complexity manageable.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If network devices have fixed bandwidth, then device complexity is low, but system adaptability is poor

Engineering Contradiction:

Improvenetwork adaptabilityVSAvoidnetwork analysis complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system dynamically changes routing parameters based on network conditions. The network analysis device monitors bandwidth information and traffic volume, and the machine learning models adjust routing instructions accordingly. This parameter adaptation enables the network to respond to changing conditions without requiring physical changes to network devices, thus improving adaptability while keeping device complexity manageable through software-based solutions.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11784930B2Communication system with auto-routing and load balancing

Publication Date: 2023.10.10 BANK OF AMERICA CORP

AI summary

A network analysis device that is configured to obtain a traffic volume classification that is associated with a plurality of messages and bandwidth information that is associated with a plurality of network devices. The network analysis device is further configured to input the bandwidth information and the traffic volume classification into a machine learning model that outputs routing recommendations based on the bandwidth information and the traffic volume classification. The network analysis device is further configured to generate routing instructions based on the routing recommendations and to reconfigure a routing device based on the routing instructions.