Quantum Machine Learning Weightage Inductors for Network Request Processing

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Solution Overview

Problem

Current systems face inefficiencies in processing incoming multi-channel network requests due to issues like invalid exposure controls and regression problems, leading to application failures and processing backlogs, necessitating a system that can analyze and execute these requests based on pre-generated channel weightages.

Innovation Solution

A system utilizing a quantum machine learning model with a quantum optimizer to determine weightage inductors for network requests, processing them based on customizable parameters, and storing these in a data repository for efficient execution, including parallel processing of repetitive requests by bypassing non-essential processes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional processing methods are used for multi-channel network requests, then the system can handle requests, but processing efficiency is low and application failures occur due to invalid exposure controls and regression problems

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidapplication failure rate

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary analysis of network requests using a quantum machine learning model to determine weightage inductors before full processing. This preliminary action identifies high-value requests that should be prioritized, preventing processing bottlenecks and failures before they occur by pre-categorizing requests based on their importance and characteristics.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system changes the processing parameters by introducing weightage inductors that dynamically adjust the processing priority and resource allocation for different requests. By modifying how requests are parameterized and weighted, the system optimizes processing efficiency while maintaining reliability through adaptive parameter adjustment based on request characteristics.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If all network requests are processed through complete validation processes, then processing accuracy is maintained, but processing time increases and backlogs form

Engineering Contradiction:

Improveprocessing accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system applies partial processing action by performing only essential validation steps on high-weightage requests identified by the quantum machine learning model. For prioritized requests, the system executes a streamlined validation process that maintains accuracy for critical fields while skipping non-essential checks, thereby reducing processing time without sacrificing necessary precision.

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The validation process is segmented into multiple stages based on request weightage. High-priority requests receive expedited validation with reduced checks, while lower-priority requests undergo complete validation. This segmentation allows the system to maintain processing accuracy for all requests while minimizing time loss for critical operations.

Inventive Principle:

Principle #1Segmentation

3Productivity

If quantum machine learning models are used to determine weightage inductors, then processing optimization is achieved, but computational complexity and resource requirements increase

Engineering Contradiction:

Improverequest processing optimizationVSAvoidquantum computing resource requirements

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system uses an intermediary classical computing layer that interfaces with the quantum machine learning model. The classical layer handles request intake, preprocessing, and result implementation, while the quantum model focuses specifically on calculating weightage inductors. This intermediary architecture allows the system to leverage quantum computing power for optimization while managing overall complexity through distributed computing responsibilities.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Reliability

If repetitive requests are processed through all validation processes, then complete validation is ensured, but processing efficiency decreases

Engineering Contradiction:

Improvevalidation completenessVSAvoidrepetitive request processing efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs preliminary identification of repetitive requests using the quantum machine learning model's weightage analysis. By recognizing patterns and identifying repetitive requests before full processing, the system can apply optimized validation paths that maintain necessary validation completeness while significantly improving processing efficiency for known request types.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20240330728A1System and method for analyzing and executing incoming multi-channel network requests based on pre-generated channel weightages

Publication Date: 2024.10.03 BANK OF AMERICA CORP

AI summary

Embodiments of the present invention provide a system for analyzing and executing incoming multi-channel network requests based on pre-generated channel weightages. The system is configured for receiving a network request from at least one network channel of a plurality of network channels, determining that the network request is a first time request, determining a weightage inductor for the network request via a quantum machine learning model executed via a quantum machine learning optimizer, assigning the weightage inductor to the network request and store the weightage inductor in a data repository, and processing the network request based on the weightage inductor by initiating a first set of processes.