Pairing Algorithm Training With Third-Party Input and Data Reduction

Solution Overview

Problem

Existing digital platforms face challenges in optimizing user matching, reducing storage space and processing resources, and ensuring privacy and security while enhancing the user experience.

Innovation Solution

A system and method for pairing individuals using a neural network trained with third-party input, where positive and negative matches are used to create and refine a pairing algorithm, reducing storage and processing needs by intelligent data deletions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If a neural network is trained using traditional methods with all available user data, then matching accuracy is improved, but storage space and processing resources are consumed

Engineering Contradiction:

Improvematching accuracyVSAvoidstorage space

Core Design Contradiction:

Measurement precisionVSQuantity of substance

Solution Approach 1:

The patent extracts only the essential features and preferences from user profiles that are relevant for matching, rather than storing and processing complete user data. This extraction approach maintains matching accuracy while significantly reducing storage requirements by keeping only the critical data elements needed for the neural network to function effectively.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent applies different data retention strategies to different types of data. Essential matching-related features are preserved in detail, while non-essential personal information is aggregated or removed. This local quality approach ensures that the neural network receives sufficient information for accurate matching without storing unnecessary data that would consume storage space.

Inventive Principle:

Principle #3Local quality

2Measurement precision

If a neural network is trained using all available user data, then matching accuracy is improved, but processing resources are consumed

Engineering Contradiction:

Improvematching accuracyVSAvoidprocessing resources

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent extracts only the essential features and preferences from user profiles that are relevant for matching, rather than storing and processing complete user data. This extraction approach maintains matching accuracy while significantly reducing storage requirements by keeping only the critical data elements needed for the neural network to function effectively.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent applies different data retention strategies to different types of data. Essential matching-related features are preserved in detail, while non-essential personal information is aggregated or removed. This local quality approach ensures that the neural network receives sufficient information for accurate matching without storing unnecessary data that would consume storage space.

Inventive Principle:

Principle #3Local quality

3Measurement precision

If user data is retained for improved matching, then matching quality is improved, but privacy and security risks increase

Engineering Contradiction:

Improvematching qualityVSAvoidprivacy and security risks

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The patent extracts only the essential features and preferences from user profiles that are relevant for matching, rather than storing and processing complete user data. This extraction approach maintains matching accuracy while significantly reducing storage requirements by keeping only the critical data elements needed for the neural network to function effectively.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent converts the potential harm of data retention (privacy and security risks) into a benefit by selectively retaining only essential matching-related data while removing or aggregating sensitive personal information. This approach transforms the data minimization requirement from a limitation into a security advantage, reducing the attack surface for privacy breaches while maintaining matching quality.

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

Data Source

PatentUS20260044731A1Systems and methods for matching individuals and/or training a pairing algorithm based on third party input

Publication Date: 2026.02.12 SHAABAN AHMED FAROUK

AI summary

Systems and methods for matching individuals with each other are disclosed herein. In an embodiment, a method of matching individuals with each other includes creating a profile pair using a plurality of inputs from a first user regarding input characteristic preferences and the plurality of inputs from second user regarding input characteristics, receiving positive or negative selections regarding the profile pair from a plurality of third party users, training the pairing algorithm using the positive or negative selections, and using the trained pairing algorithm to reduce the plurality of pairs in the database.