AI Digital Component Styling Around Salient Image Regions
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Solution Overview
Problem
Current generative artificial intelligence models produce inaccurate and low-quality digital images, leading to wasteful consumption of computational resources and network bandwidth due to the generation and evaluation of sub-optimal components, and often result in occluded or unreadable content.
Innovation Solution
A system that employs machine learning models to detect salient regions in images, select appropriate text and interactive elements, and apply style features to ensure a cohesive and visually appealing design, using large language models to analyze subject matter and generate keywords for style selection, thereby dividing tasks into discrete operations to enhance quality and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If current generative AI models are used to create digital images, then image generation capability is provided, but image quality and accuracy deteriorate
Solution Approach 1:
The patent segments the image generation process into multiple specialized models: a base image generation model, a salient feature detection model, and a style feature application model. Each model focuses on a specific aspect of image quality, allowing for targeted optimization without requiring a complete redesign of the entire generation pipeline.
Solution Approach 2:
The system dynamically adjusts style features (color schemes, lighting, composition) based on detected salient features and subject matter analysis. By changing these parameters adaptively rather than using fixed generation settings, the system improves both image quality and accuracy while reducing the need for multiple generation attempts.
2Adaptability or versatility
If multiple digital components are generated and evaluated, then content variety is increased, but computational resource consumption increases
Solution Approach 1:
The patent performs preliminary detection of salient features and analysis of subject matter before generating the actual digital images. By preparing templates, identifying key elements, and determining style features in advance, the system reduces the computational burden during the actual image generation and evaluation phases, allowing for greater content variety with fewer resources.
3Loss of information
If text and interactive elements are overlaid on generated images, then information delivery is enhanced, but content readability deteriorates due to occlusion
Solution Approach 1:
The system applies different quality standards to different regions of the image. Salient features are preserved with high fidelity and protected from overlay placement, while non-salient background areas are suitable for text and interactive elements. This local differentiation ensures that information delivery is enhanced without compromising the readability of critical content.
4Productivity
If complex AI models are used for digital component generation, then generation capability is improved, but resource efficiency deteriorates
Solution Approach 1:
The patent divides the complex generation task into multiple simpler, specialized models. Instead of using one large complex model for all aspects, the system employs separate models for base image generation, feature detection, and style application. This segmentation maintains generation capability while significantly improving resource efficiency by allowing each model to operate at optimal complexity for its specific function.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automated digital component generation. In some aspects, a method includes obtaining digital content data for the digital component. The digital content data includes at least a base image of a subject of the digital component. A prompt that includes a description of the subject is obtained. The prompt is processed using a language model to generate one or more keywords related to the subject. A determination is made, based on the one or more keywords, one or more style features for the digital component. The digital component is generated by processing the digital content data based at least on the one or more determined style features. The generated digital component is distributed to one or more client devices.