Pair-Wise Delta Compression Using AI File Pair Prediction

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Solution Overview

Problem

Conventional delta compression methods are inefficient, especially for large file sets, leading to increased computational expense and time, which is exacerbated by frequent software updates, resulting in higher bandwidth and storage requirements.

Innovation Solution

The use of deep learning models to predict and select file pairings with the smallest deltas for pair-wise delta compression, reducing the number of calculated deltas and optimizing for smaller file sizes and shorter compression times, thereby improving efficiency and compression ratios.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional delta compression methods are used on large file sets, then compression is performed, but computational expense and time increase significantly

Engineering Contradiction:

Improvecompression speedVSAvoidcomputational expense

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary actions by using deep learning models to predict and identify candidate file pairs with small deltas before actual compression. This pre-screening process filters out unlikely candidates, so that when compression is performed, only the most promising file pairs are processed, significantly reducing computational expense and time for large file sets

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces the traditional mechanical approach of calculating deltas for all possible file pairs with a deep learning-based predictive system. The neural network model learns patterns from file attributes and metadata to predict which file pairs are likely to have small deltas, substituting brute-force computation with intelligent prediction, thereby improving compression speed while reducing computational expense

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Manufacturing precision

If all possible file pairs are evaluated for delta compression, then optimal pairing is achieved, but time and computational resources are excessively consumed

Engineering Contradiction:

Improvepairing optimizationVSAvoidcompression time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system performs preliminary prediction using deep learning models to identify candidate file pairs before actual compression. By pre-evaluating file attributes and predicting which pairs are likely to have small deltas, the system achieves optimal pairing without having to calculate deltas for all possible file pairs, thus maintaining precision while reducing compression time

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent changes the approach from calculating actual delta values for all file pairs to using predicted similarity scores based on file attributes and metadata. This parameter change allows the system to identify optimal pairings efficiently by relying on predicted characteristics rather than exhaustive computation, achieving both optimization and time efficiency

Inventive Principle:

Principle #35Parameter changes

3Productivity

If deep learning models are used to predict file pairings, then compression efficiency is improved, but system complexity increases

Engineering Contradiction:

Improvecompression efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent introduces deep learning models as an intermediary between file attribute analysis and delta compression. The model acts as a mediator that translates file characteristics into predictions about which file pairs will have small deltas, enabling efficient compression without requiring the system to directly evaluate all possible pairs. This intermediary layer improves efficiency while managing complexity through modular architecture

Inventive Principle:

Principle #24Intermediary (Mediator)

4Adaptability or versatility

If frequent software updates are transmitted, then users receive latest versions, but bandwidth and storage requirements increase

Engineering Contradiction:

Improveupdate frequencyVSAvoidbandwidth and storage

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The system extracts and transmits only the essential difference data (deltas) between file versions rather than transmitting complete updated files. By using pair-wise delta compression to identify and extract only the minimal changes needed, the system enables frequent software updates to be transmitted and stored efficiently, reducing bandwidth and storage requirements while maintaining adaptability to deliver latest versions

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS12072845B2Systems and methods for pair-wise delta compression

Publication Date: 2024.08.27 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Some disclosed embodiments are directed to methods and systems for performing pair-wise delta compression. For example, systems obtain a set of files to be compressed into a single compressed file. The system identifies different attributes related to the set of files. For each file in the set of files, the system predicts an optimized set of candidate compression files and calculates a delta between each file in the optimized set and the target file corresponding to the optimized set. After identifying the smallest delta, the system compresses the selected pair of files associated with the smallest delta in order to generate the single compressed file for the set of files.