Machine-Learned Vehicle Item Graphics for Online Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Online vehicle purchasing lacks personalized assistance from knowledgeable salespersons, resulting in unsophisticated and untailored presentation of vehicle items, leading to sub-optimal selections or missed opportunities for desirable items.
Innovation Solution
A computer-implemented system using a machine learning model trained on user data and vehicle item selections to generate graphics for vehicle items based on user preferences and history, filtering and displaying only high-probability items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic electronically generated menus are used for online vehicle purchasing, then the system complexity is reduced and ease of operation is improved, but the adaptability to user preferences and personal circumstances deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting user data and training the machine learning model in advance, before the actual vehicle purchasing process. This allows the system to have personalized recommendations ready when users interact with the interface, resolving the contradiction by preparing adaptability beforehand while maintaining simple operation during use.
Solution Approach 2:
The machine learning model enables the system to serve itself by automatically generating personalized vehicle item recommendations without requiring manual configuration or salesperson intervention. The model self-adjusts based on user data, providing adaptability while keeping the interface simple and easy to operate.
2Adaptability or versatility
If all available vehicle items are presented to users, then the adaptability and completeness of information is improved, but the loss of user time increases due to navigating through excessive options
Solution Approach 1:
The system extracts and presents only the most relevant vehicle items to each user based on their preferences and circumstances, rather than showing all available items. The machine learning model identifies and extracts the subset of items with highest relevance scores, reducing user time loss while maintaining adaptability through personalized selection.
Solution Approach 2:
The presentation of vehicle items is customized for each user based on their local characteristics and preferences. The system applies different quality levels of recommendation to different users, with higher relevance items prioritized for each individual user, thus reducing their information search time while maintaining high adaptability.
3Adaptability or versatility
If a machine learning model is trained on user data and vehicle item selections, then the adaptability to user preferences is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The machine learning model serves multiple functions: it analyzes user preferences, predicts vehicle item relevance, and generates personalized recommendations. This multi-functionality justifies the increased complexity by providing comprehensive adaptability across different user interactions and vehicle item types through a single unified system.
4Adaptability or versatility
If personalized recommendations are generated using machine learning, then the adaptability to user preferences is improved, but the loss of time for data processing and model generation increases
Solution Approach 1:
The machine learning model is trained on historical user data and vehicle item selections in advance, before actual recommendations are needed. This preliminary training allows the system to generate fast, personalized recommendations during user interactions, reducing real-time processing time while maintaining high adaptability through pre-learned patterns.
Data Source
AI summary
A computer-implemented method of generating a graphic for a vehicle item may include: causing a user device to display a user interface indicative of one or more vehicles; receiving, from the user device, vehicle selection information, the vehicle selection information indicative of a vehicle selected by a user; obtaining, from a database, user data corresponding to the user; generating, using a machine learning model, a first score corresponding to a first vehicle item based on the user data; determining whether the first score exceeds a first predetermined score threshold; generating, in response to a determination that the first score exceeds the first predetermined score threshold, a first graphic indicative of the first vehicle item; and causing the user device to display the first graphic via the user interface.