Geolocation-Based Background Generation for Scalable Object Images
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Solution Overview
Problem
Existing methods for generating object images on online platforms are costly and non-scalable, as they require physical photoshoots at multiple locations or manual image processing to create variations suitable for different user regions, failing to resonate with diverse user preferences.
Innovation Solution
An image generation server uses generative AI to create geolocation-based backgrounds by mapping geographic regions to context data, generating prompts with a text AI model, and producing synthetic images with background images using an image AI model, allowing for scalable and cost-effective image generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If physical photoshoots at multiple locations are conducted to create geolocation-based object images, then user engagement and personalization are improved, but operational costs and time consumption increase significantly
Solution Approach 1:
The patent uses generative AI to create synthetic copies of object images with geolocation-based backgrounds, replacing the need for physical photoshoots at multiple locations. The system generates realistic background images corresponding to different geographic regions and combines them with object images, achieving personalization at scale without the high costs and time consumption of traditional photography methods
Solution Approach 2:
The patent replaces the mechanical process of physical photoshoots (traveling to locations, setting up equipment, manual photography) with an automated AI-based system. The generative AI model automatically generates geolocation-specific backgrounds and composite images, substituting human labor and physical resources with computational processes that are faster and more cost-effective
2Adaptability or versatility
If manual image processing is used to create image variations for different user regions, then some level of personalization is achieved, but the process remains costly and non-scalable
Solution Approach 1:
The system enables self-service image generation through automated AI processing. The generative AI model automatically retrieves geolocation data, generates appropriate backgrounds, and creates composite images without requiring manual intervention for each image variation. This automates the previously manual process, making it scalable and reducing operational complexity
Solution Approach 2:
The patent changes the approach from manual parameter adjustment to automated parameter generation. Instead of manually selecting and adjusting image parameters for different regions, the system uses AI models that automatically generate appropriate backgrounds based on geolocation parameters, transforming the creative process into an automated computational task
3Productivity
If generative AI is used to automatically generate geolocation-based backgrounds, then productivity and cost-effectiveness improve, but the complexity of the image generation system increases
Solution Approach 1:
The patent segments the image generation system into distinct functional modules: a text generative AI model for prompt creation, an image generative AI model for background generation, and a composite image generation component. This modular architecture manages system complexity by dividing the overall process into specialized, independently manageable components, each handling a specific aspect of image generation
Data Source
AI summary
Systems and methods for generating geolocation-based images for a target object are provided. A geolocation module receives a set of geolocations associated with a geographic region of interest. Each geolocation of the set of geolocations is mapped to context data associated with the geolocation. A prompt generation module generates multiple prompts based on the set of geolocations and the context data. The prompt generation module comprises a first generative artificial intelligence (AI) model. An image generation module generates multiple synthetic images based on the multiple prompts. The image generation module comprises a second generative AI model. Each synthetic image depicts the target object in a background generated based on a prompt.