17455101. GENERATION OF GRAPHICS FOR VEHICLE ITEMS simplified abstract (Capital One Services, LLC)
Contents
GENERATION OF GRAPHICS FOR VEHICLE ITEMS
Organization Name
Inventor(s)
Qiaochu Tang of Frisco TX (US)
Jeremy Jaylee Huang of Plano TX (US)
Michelle Emamdie of Saint Augustine FL (US)
GENERATION OF GRAPHICS FOR VEHICLE ITEMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17455101 titled 'GENERATION OF GRAPHICS FOR VEHICLE ITEMS
Simplified Explanation
The abstract describes a computer-implemented method for generating a graphic representation of a vehicle item based on user data and vehicle selection information. The method involves displaying a user interface with vehicle options, receiving the user's selection, obtaining user data from a database, and using a machine learning model to generate a score for the selected vehicle item. If the score exceeds a predetermined threshold, a graphic representation of the vehicle item is generated and displayed.
- Method involves generating a graphic representation of a vehicle item based on user data and vehicle selection information.
- User interface displays vehicle options and allows the user to make a selection.
- User data is obtained from a database to personalize the generated graphic.
- A machine learning model is used to generate a score for the selected vehicle item.
- If the score exceeds a predetermined threshold, a graphic representation of the vehicle item is generated and displayed.
Potential Applications
- Automotive industry: Can be used by car manufacturers or dealerships to provide personalized graphics of vehicle items to potential customers.
- E-commerce platforms: Can enhance the shopping experience by generating graphics of vehicle items based on user preferences and data.
- Online car configurators: Can assist users in visualizing different vehicle options based on their preferences and data.
Problems Solved
- Personalization: The method uses user data to generate personalized graphics, enhancing the user experience.
- Decision-making: By providing visual representations of vehicle items, the method helps users make informed decisions.
- Efficiency: The use of a machine learning model streamlines the process of generating graphics based on user data.
Benefits
- Enhanced user experience: Users can visualize vehicle items based on their preferences and data, making the process more engaging and informative.
- Personalization: The generated graphics are tailored to the user's preferences and data, increasing the relevance and appeal.
- Streamlined process: The use of a machine learning model automates the generation of graphics, saving time and effort for users and businesses.
Original Abstract Submitted
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.
