Machine Learning Model for Generic Vehicle Recommendations

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Solution Overview

Problem

Current search engines fail to deliver high-quality information when users make requests using generic language, particularly in the context of automobiles, leading to inaccurate or irrelevant results.

Innovation Solution

A machine learning model trained on a corpus of automobile reviews is used to associate generic language with specific automobile features and models, enabling the generation of targeted recommendations based on user inputs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If a machine learning model is trained on automobile reviews to associate generic language with specific features, then the accuracy of vehicle recommendations is improved, but the device complexity increases

Engineering Contradiction:

Improveaccuracy of vehicle recommendationsVSAvoidcomplexity of machine learning model

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The machine learning model is trained in advance on a corpus of automobile reviews to learn the relationship between generic language and specific vehicle features. This preliminary training enables the system to automatically interpret generic user requests without requiring complex real-time processing, thus improving recommendation accuracy while managing system complexity.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces a machine learning model as an intermediary component that bridges generic user language and specific vehicle features. The model acts as a mediator that translates ambiguous generic terms into precise feature mappings based on patterns learned from review data, resolving the contradiction between simplicity and accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If multiple search fields are required to capture specific user preferences, then the measurement precision of user intent is improved, but the ease of operation deteriorates

Engineering Contradiction:

Improveprecision of user intentVSAvoidease of making requests

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system employs a single search field that serves multiple functions: capturing generic user preferences, translating them through the machine learning model, and retrieving specific vehicle recommendations. This universal interface eliminates the need for multiple specialized search fields while maintaining precision in understanding user intent.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The machine learning model automatically performs the task of interpreting generic language and mapping it to specific features without requiring users to manually specify multiple search parameters. The system serves itself by using the trained model to bridge the gap between generic input and precise results, improving ease of operation while maintaining measurement precision.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20240193669A1Offering automobile recommendations from generic features learned from natural language inputs

Publication Date: 2024.06.13 CAPITAL ONE SERVICES LLC

AI summary

Various embodiments are generally directed to techniques to provide specific vehicle recommendations to generic user requests. A method for providing the specific vehicle recommendation includes: receiving a generic automobile request from a user, applying a machine learning model (MLM) trained by a corpus of reviews to the received request, and generating, by the MLM, a recommendation for at least one specific automobile feature based on the generic automobile request.