Predicted Correction Data for Parallel Validation Bottlenecks

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Solution Overview

Problem

Conventional computing systems implementing high assurance software processes often operate serially, leading to inefficiencies due to the need for sequential validation of results, which hinders parallelization and increases bottlenecks, especially in critical infrastructure applications like aircraft safety and financial systems.

Innovation Solution

The system parallelizes tasks by using machine learning to detect errors and generate corrected data, allowing the processing and validation systems to operate concurrently, with the processing system sending modified requests to the validation system while awaiting validation results and confirming with the requesting system for accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional serial processing is used to ensure validation accuracy, then reliability is improved, but productivity deteriorates due to sequential operations

Engineering Contradiction:

Improvevalidation accuracyVSAvoidprocessing efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs preliminary error detection using machine learning models before validation, identifying and correcting potential errors in advance. This allows the validation system to receive pre-corrected data, enabling parallel processing while maintaining validation accuracy since errors are caught early in the workflow

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The processing system acts as an intermediary between the requesting system and validation system. It receives original data, detects errors using ML models, generates corrected data, and provides both original and corrected versions to the validation system for simultaneous validation, enabling parallel processing without compromising reliability

Inventive Principle:

Principle #24Intermediary (Mediator)

2Manufacturing precision

If serial validation is performed to ensure data accuracy, then manufacturing precision is improved, but loss of time increases due to sequential processing

Engineering Contradiction:

Improvedata accuracyVSAvoidvalidation time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

Error detection and correction are performed preliminarily before validation using machine learning models. The processing system identifies errors, generates corrected data, and prepares multiple versions (original and corrected) in advance, allowing the validation system to validate multiple versions simultaneously rather than sequentially, thus reducing validation time while maintaining data accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables continuous validation by providing the validation system with multiple data versions (original and corrected) that can be validated in parallel. This eliminates idle waiting time between validation steps, maintaining continuous useful action across the validation process while ensuring data accuracy through comprehensive validation

Inventive Principle:

Principle #20Continuity of useful action

3Productivity

If machine learning generated corrected data is sent to validation system, then productivity is improved through parallel processing, but reliability may deteriorate if corrected data is incorrect

Engineering Contradiction:

Improveparallel processing efficiencyVSAvoidcorrected data accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system implements feedback by sending both the original data and machine learning generated corrected data to the validation system for simultaneous validation. The validation results provide feedback on the accuracy of corrections, allowing the system to verify whether the ML-generated corrections are valid. This feedback mechanism ensures reliability while maintaining parallel processing efficiency

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system creates copies of the original data with corrections applied by machine learning models. Multiple copies (original and corrected versions) are sent to the validation system for parallel validation. This copying approach allows comprehensive verification of corrections without compromising the original data, ensuring reliability while enabling parallel processing of multiple data versions

Inventive Principle:

Principle #26Copying

Data Source

PatentUS11860723B2Systems and methods for parallelizing sequential processing requests using predicted correction data

Publication Date: 2024.01.02 CAPITAL ONE SERVICES LLC

AI summary

Methods and systems for parallelizing processes through the use of predicted data are described herein. A requesting system may send processing requests to a processing system. The processing system may need to have a third-party validation system validate the processing requests before proceeding. The processing system may detect errors and generate modified data that is predicted to resolve the errors in obtained processing requests. The modified data may be sent to the validation system in place of the original data that contained errors. While waiting for validation results from the validation system, the modified data may also be sent to the requesting system to confirm that it is correct. By sending the modified data to both the validation system and the requesting system for approval, increased efficiency and parallelization may be achieved.