Generative Wheel Hub Display Content Model
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Solution Overview
Problem
Current technologies lack the ability to dynamically and efficiently generate customizable content for vehicle wheel displays, requiring extensive storage of pre-generated images and potentially leading to mechanical wear from physical manipulation of wheels.
Innovation Solution
A computing system utilizing a machine-learned generative model, such as a generative adversarial network, to generate content based on user input and wheel features, reducing the need for extensive data storage and minimizing mechanical wear by allowing dynamic customization without physical manipulation of wheels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-generated images are stored extensively for wheel displays, then content customization options increase, but storage requirements and system complexity increase
Solution Approach 1:
Instead of storing extensive pre-generated images, the patent uses a generative model that creates content on-demand based on text descriptions. The model copies and transforms existing wheel designs and features into new variations without requiring storage of all possible combinations, thus reducing storage needs while maintaining customization capability.
Solution Approach 2:
The system changes parameters by using a generative model that can create different wheel designs by modifying learned parameters from training data. This allows infinite customization options without storing each variant, as the model generates content by varying parameters within the learned distribution of wheel features.
2Adaptability or versatility
If physical manipulation of wheels is used for customization, then content variety increases, but mechanical wear increases
Solution Approach 1:
The patent replaces physical manipulation of wheels with a digital generative model. Instead of physically modifying or swapping wheel components to achieve customization, users provide text descriptions and the system generates appropriate wheel designs computationally, eliminating mechanical wear entirely while maintaining content variety.
Solution Approach 2:
The system creates virtual copies of wheel designs through the generative model rather than physically manipulating actual wheels. The model generates and displays customized wheel images without requiring physical contact or manipulation of the real wheels, thus preventing mechanical wear while providing diverse customization options.
3Quantity of substance
If a generative model is used to create content dynamically, then storage requirements decrease, but computational resource usage increases
Solution Approach 1:
The generative model is pre-trained on a comprehensive dataset of wheel images and features. This preliminary training allows the model to store learned patterns and relationships in its parameters rather than storing actual image data. During operation, the model generates content by applying these pre-learned patterns, reducing the need for extensive storage while requiring computational resources for inference.
Data Source
AI summary
Methods, computing systems, and technology for generative modeling of wheel hub display content are presented. A control circuit can: obtain user input data including a description of content to be presented via a display device positioned on a wheel of the vehicle; generate, using one or more models including a machine-learned generative model, the content based on the user input data; receive an output of the one or more models, the output including the generated content; and provide, for presentation via the display device positioned on the wheel of the vehicle, data indicative of the generated content. The machine-learned generative model can be trained to process the user input data and provide generated content that is: (i) based on the description of the content included in the user input data, and (ii) configured for presentation via the display device positioned on the wheel of the vehicle.