US20250191262
2025-06-12
Physics
G06T11/60
The system described involves a data processing method for generating thematic variations in graphic design using artificial intelligence. It starts by receiving a user's request through a client device interface, where the user provides a textual prompt for image generation. A specialized unit then constructs a second prompt by extracting key elements such as an artifact and a theme from the initial text. These are appended to an instruction string that guides the generative model to select a suitable design template and generate an image by altering visual elements according to the theme while maintaining the original layout of the template.
AI technologies have revolutionized visual content creation, yet users often face challenges with control over AI-generated outputs. Despite detailed prompts, results may not align with user expectations, leading to frustration and inefficiency. Current systems lack mechanisms for users to effectively guide AI models in producing desired visual styles and compositions. The proposed method addresses these issues by enhancing user control and leveraging professionally crafted design templates.
The system leverages AI to improve graphic design outputs by offering thematic variations through design templates. It extracts user intent, artifacts, and themes from user data to customize templates that match contextual intent. This ensures that while the layout remains consistent, visual elements are adapted to suit user preferences. By preserving key design aspects such as layout, color, and typography, the system enhances user satisfaction and efficiency in content creation.
One significant advantage is the predictability and relevance of generated content, which closely aligns with user intent. The approach reduces computational resources by minimizing the need for repeated refinements. It introduces a pipeline for generating diverse design variations from existing designs based on runtime user input. The system also enriches user experience by suggesting thematic options, providing high-quality outputs that enhance creativity and productivity.
The system's capability to store image outputs as reusable templates offers long-term efficiency gains for users creating similar content in the future. This feature also benefits other users who can utilize these templates, saving time and effort in their projects. The described techniques are applicable across various platforms including cloud-based applications, collaboration tools, and web-enabled native applications, facilitating AI-assisted design across diverse environments.