ML Version Control for Digital Objects

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Solution Overview

Problem

Computer-based version control systems often inaccurately flag false differences among digital objects due to their complexity, leading to resource-intensive and inefficient operations.

Innovation Solution

A version control system utilizing a categorization machine learning model to generate structured representations of digital objects and applying natural language techniques to produce pseudo-instructions, which are used to validate differences between versions, sending auto-validation reports to client devices with compliant or non-compliant messages.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional version control systems compare digital objects directly, then they can detect differences between versions, but they flag numerous false or spurious differences due to the complexity of digital objects

Engineering Contradiction:

Improvedifference detection accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces an intermediary representation layer (structured representation with extracted data items) between the original digital objects and the comparison process. This intermediary structure standardizes complex digital objects into comparable formats, enabling accurate difference detection while reducing false positives caused by direct comparison of unstructured or semi-structured data

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent transforms digital objects by extracting specific data items and organizing them into a standardized structured representation. This parameter transformation converts complex, variable-format digital objects into a consistent comparison framework, allowing for precise difference identification without being affected by superficial formatting variations

Inventive Principle:

Principle #35Parameter changes

2Reliability

If version control systems perform comprehensive comparison of digital objects, then they can identify all differences, but the process becomes resource intensive

Engineering Contradiction:

Improvevalidation accuracyVSAvoidcomputational resources

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The patent extracts only the relevant data items from digital objects into a structured representation, comparing only these extracted elements rather than performing comprehensive comparison of entire digital objects. This selective extraction approach maintains validation accuracy for critical data while significantly reducing computational resource requirements

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent segments digital objects into discrete data items within a structured representation, allowing for targeted comparison of specific elements. This segmentation enables the system to focus computational resources on comparing individual data items rather than processing entire digital objects, reducing overall resource consumption while maintaining reliability

Inventive Principle:

Principle #1Segmentation

3Adaptability or versatility

If version control systems use complex comparison algorithms, then they can handle digital object complexity, but the systems become inaccurate and produce false differences

Engineering Contradiction:

Improvehandling digital object complexityVSAvoiddifference identification accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent applies parameter transformation by converting complex digital objects into a standardized structured representation with defined data items and relationships. This transformation maintains the system's ability to handle digital object complexity while improving accuracy by eliminating false differences that arise from comparing unstandardized formats

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent creates a universal structured representation framework that can accommodate various types of digital objects through a common comparison interface. This universal structure enables accurate difference identification across different digital object types without requiring complex type-specific comparison logic, thereby improving measurement precision while maintaining adaptability

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11416700B1Computer-based systems configured for machine learning version control of digital objects and methods of use thereof

Publication Date: 2022.08.16 AMERICAN EXPRESS TRAVEL RELATED SERVICES CO INC

AI summary

In some embodiments, a version control system receives a first version of a digital object and a second version of the digital object. The version control system extracts data from the first version of the digital object, and data from the second version of the digital object. The version control system utilizes a categorization machine learning model to generate structured representations of the first digital object and a second digital object. The version control system identifies differences between the two digital objects and utilizes a natural language technique to produce pseudo-instructions representing an update request. The version control system validates differences between the two digital objects and sends an auto-validation report to a client computing device when the differences are compliant with the pseudo-instructions.