Machine-Learning Response Correlation for User-Specific Options

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Solution Overview

Problem

Existing online interaction models fail to provide user-specific responses, requiring users to exert significant effort in seeking and comparing options, with complex interactions like vehicle or property purchases being particularly challenging due to technical limitations and lack of direct comparison capabilities.

Innovation Solution

A machine-learning model trained on historical interactions is used to evaluate and optimize responses from providers based on user-specific data, including prequalification and interaction parameters, to identify optimal options and present them to users.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If users seek out and compare options online themselves, then they can see more options, but they must exert significant effort and have less assurance in selecting the best option

Engineering Contradiction:

Improvenumber of optionsVSAvoiduser effort

Core Design Contradiction:

Quantity of substanceVSEase of operation

Solution Approach 1:

The system performs self-service by automatically querying multiple providers and evaluating their responses using machine learning models, eliminating the need for users to manually seek out and compare options. The system serves itself by autonomously navigating the complex comparison process that would otherwise require significant user effort.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The machine learning model acts as an intermediary between the user and multiple providers. It receives user-specific data, evaluates responses from various providers, and determines optimal responses without requiring direct user involvement in the comparison process, thus reducing user effort while maintaining access to multiple options.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If providers receive user-specific data, then they can provide tailored responses, but data exposure risks increase

Engineering Contradiction:

Improveuser-specific responsesVSAvoiddata exposure risk

Core Design Contradiction:

Adaptability or versatilityVSObject-affected harmful factors

Solution Approach 1:

The machine learning model serves as a mediator that receives and processes user-specific data without requiring direct transmission to individual providers. The system evaluates responses from providers based on the user profile generated from the data, allowing tailored responses while minimizing direct data exposure to multiple providers simultaneously.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system segments the data processing function by creating a centralized user profile that encapsulates user-specific information. This profile is then used to evaluate provider responses without requiring raw user data to be transmitted to each provider, thereby segmenting data exposure risks and reducing overall data exposure while maintaining adaptability.

Inventive Principle:

Principle #1Segmentation

3Ease of operation

If online interaction models are used, then convenience is improved, but technical limitations prevent direct comparison capabilities for complex interactions

Engineering Contradiction:

ImproveconvenienceVSAvoidtechnical limitations

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system replaces manual mechanical comparison processes with automated machine learning evaluation. Instead of users manually comparing complex options from multiple providers, the machine learning model automatically evaluates provider responses against user-specific criteria, overcoming technical limitations while maintaining online convenience.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the parameters of interaction by transforming raw user-specific data into a structured user profile that can be used to evaluate provider responses. This parameter transformation enables the system to handle complex interactions that would otherwise be difficult to compare online, maintaining convenience while overcoming technical limitations through sophisticated data processing.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250315441A1Systems and methods for correlating responses to user-specific data

Publication Date: 2025.10.09 CAPITAL ONE SERVICES LLC

AI summary

A technique for correlating responses to user-specific data may include obtaining user-specific data having an item parameter and an interaction parameter set by the user; generating a user-specific score based on prequalification and interaction data; generating a classification of the user based on the score; identifying entities providing an item corresponding to the parameter; transmitting at least a portion of the user-specific data and the classification of the user to the plurality of entities; receiving responses from the plurality of entities, each response including parameters for a proposed interaction with the user in which at least one parameter is responsive to the user-specific data; determining an optimal response by inputting the user-specific data and the responses into a machine-learning model trained on historical interactions between users and entities; and causing a user interface of a user device to display a visual indication of the optimal response.