Interactive 3D Vehicle Model Recommendation System
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Solution Overview
Problem
Existing vehicle purchasing tools are limited by rigid search filters that fail to accommodate user preferences, making it difficult for customers to effectively identify suitable vehicles.
Innovation Solution
A computer-implemented method and system that uses interactive three-dimensional vehicle models, machine learning algorithms, and user feedback to generate personalized vehicle recommendations by comparing user-defined vehicle features with pre-stored vehicle data, providing a more tailored search experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rigid predefined search filters are used, then the search tool structure is simple and easy to operate, but it fails to encompass user preferences and provides poor recommendation accuracy
Solution Approach 1:
The system transitions from static predefined filters to dynamic interactive customization. Users can interactively modify vehicle features (color, size, type, etc.) through a graphical interface, and the system dynamically generates updated three-dimensional models and performs real-time image comparisons to provide personalized recommendations, thereby achieving both accuracy and adaptability
Solution Approach 2:
The system allows users to change multiple vehicle parameters simultaneously (color, dimensions, vehicle type, features) rather than being constrained by fixed filter categories. This parameter-based customization approach enables precise matching of user preferences while maintaining system manageability through structured data handling
2Adaptability or versatility
If interactive customization of vehicle features is enabled, then user preference matching is improved, but the system complexity increases due to three-dimensional model generation and machine learning algorithms
Solution Approach 1:
The system uses two-dimensional images as simplified representations of three-dimensional vehicle models for the comparison process. By generating and comparing 2D images rather than performing complex 3D model operations, the system achieves versatile customization while controlling computational complexity through image-based processing
Solution Approach 2:
The trained machine learning algorithm acts as an intermediary between user customization inputs and recommendation outputs. The algorithm processes the complex task of comparing customized vehicle features against the database, translating user preferences into matched recommendations and thereby managing system complexity through intelligent automation
3Ease of operation
If machine learning algorithms are used for vehicle comparison, then recommendation personalization is enhanced, but processing time and computational resources increase
Solution Approach 1:
The system pre-generates multiple two-dimensional images from each three-dimensional vehicle model in the database before user interaction. This preliminary preparation allows the machine learning algorithm to perform comparisons on already-processed images rather than generating models in real-time, significantly reducing processing time while maintaining personalized recommendation quality
Solution Approach 2:
The system extracts only the necessary two-dimensional image representations from three-dimensional models for comparison purposes. By extracting and processing only the essential visual features rather than analyzing complete 3D models, the system achieves personalized recommendations with reduced computational overhead and faster processing
Data Source
AI summary
A computer-implemented method for providing a vehicle recommendation may include obtaining characteristic data of a user-defined vehicle based on one or more interactive activities of a user with a first three-dimensional model of a model vehicle; generating a second three-dimensional model of the user-defined vehicle based on the first three-dimensional model and the characteristic data; obtaining one or more images of the second three-dimensional model; generating a selection of one or more pre-stored vehicles based on a comparison between the one or more images of the second three-dimensional model and pre-stored image data of the one or more pre-stored vehicles other than the user-defined vehicle; obtaining user feedback data based on the selection of the one or more pre-stored vehicles; generating the vehicle recommendation for the user based on the user feedback data; and transmitting, to a device associated with the user, a notification indicative of the vehicle recommendation.