Capsule Neural Network Identity Authentication

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Solution Overview

Problem

There is a need for a system that can generate user identity-based encrypted data files using capsule neural networks to prevent identity misappropriation.

Innovation Solution

The system retrieves images associated with a user from entity databases, generates a consolidated image, trains capsule neural networks with this image, and then uses these networks to validate real-time images associated with new applications, thereby authenticating users and preventing identity misappropriation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional authentication methods are used, then the system is simpler to implement, but identity misappropriation cannot be effectively prevented

Engineering Contradiction:

Improveidentity authentication reliabilityVSAvoidauthentication system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical/authentication systems with capsule neural networks, a sophisticated AI-based system that can analyze facial features and detect identity misappropriation. This substitution enables reliable identity verification while maintaining system manageability through automated processing.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces capsule neural networks as an intermediary between the user and the authentication system. This intermediary layer processes and validates identity information, providing a buffer that enhances security while simplifying the overall authentication workflow through intelligent automation.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If capsule neural networks are implemented for identity validation, then identity misappropriation is prevented, but the processing time increases

Engineering Contradiction:

Improveidentity authentication reliabilityVSAvoidauthentication processing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent performs preliminary training of capsule neural networks using consolidated images before actual authentication. This pre-processing step prepares the system in advance, so that during actual authentication, the validation process is faster and more efficient, reducing the time loss while maintaining high reliability.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If multiple images are consolidated for training, then the authentication accuracy improves, but the data processing complexity increases

Engineering Contradiction:

Improveidentity validation accuracyVSAvoidimage processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent merges multiple images associated with a user into a single consolidated image for training the capsule neural network. This combining approach improves validation accuracy by providing comprehensive facial data while managing processing complexity through systematic integration of multiple image sources.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS12314363B2System and method for generating user identity based encrypted data files using capsule neural networks to prevent identity misappropriation

Publication Date: 2025.05.27 BANK OF AMERICA CORP

AI summary

Embodiments of the present invention provide a system for generating user identity based encrypted data files using capsule neural networks to prevent identity misappropriation. The system is configured for retrieving at least two images associated with a user from one or more entity databases, generating a consolidated image of the at least two images via an image coupling application, providing the consolidated image to capsule neural networks for training, receiving a real-time image associated with the user, wherein the real-time image is associated with a new application, providing the real-time image to the capsule neural networks, validating the real-time image via the capsule neural network, and determining if the validation is successful to authenticate the user.