Machine Learning Model Training Using Access Token Data

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Solution Overview

Problem

The implementation of artificial intelligence models to generate recommended user interface templates is hindered by the lack of training data, particularly when using access tokens with variable properties, as users may have limited experience with the account's user interface.

Innovation Solution

The system relies on an alternative data stream for training machine learning models, using data on how users interact with access tokens to generate customized user interface templates, rather than relying on data specific to the account's user interface.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If artificial intelligence models are used to generate recommended user interface templates, then user interface customization and navigability are improved, but the lack of training data (especially when users have limited experience with the account's interface) prevents proper model training

Engineering Contradiction:

Improveuser interface navigabilityVSAvoidtraining data availability

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

Instead of training the AI model on user interface interaction data (the conventional approach), the patent inverts the approach by training the model on access token usage data. This alternative data stream becomes the foundation for generating recommended user interface templates, allowing the system to overcome the scarcity of UI-specific training data while still achieving effective interface customization.

Inventive Principle:

Principle #13The other way round (Inversion)

2Reliability

If access tokens with variable properties are used to securely allow temporary access to account content, then security and access control are improved, but users have limited experience with the account's user interface, reducing available training data

Engineering Contradiction:

Improveaccess control securityVSAvoiduser interface experience data

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The patent uses access token data as an intermediary to bridge the gap between security requirements and AI model training needs. Rather than requiring direct user interface interaction data from users with limited account experience, the system leverages access token usage patterns as a proxy to train the AI model, enabling it to generate appropriate interface recommendations without compromising security or requiring extensive user experience data.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250173411A1Systems and methods for using machine learning models to organize and select access-restricted components for accessed using user-specific access tokens with variable properties

Publication Date: 2025.05.29 CAPITAL ONE SERVICES LLC

AI summary

Systems and methods for providing variable and temporary access to account content for a user in a secured manner through the use of an access token with variable properties are described. The systems and methods provide improved navigability to account content accessed via the access token through the customization of user interfaces. For example, the system and methods may generate user interface templates that comprise a recommended selection and organization of user input fields and/or user interface pages.