Dynamic Content Generation With Brand Style Transfer
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Solution Overview
Problem
Creating marketing content is time-consuming due to the need for extensive communication between marketing and design teams, and customizing content for individual consumers or groups requires significant effort, limiting the amount of customized content that can be produced in a given timeframe.
Innovation Solution
A system for style-based dynamic content generation that utilizes a seed image, entity style data, and text items to automatically generate content variants by placing text items into bounding boxes based on entity design data and rendering them with entity style data, using machine learning models like GANs and copyspace models to adjust and apply typography and iconography.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual design team generation of marketing content images is used, then customization quality and brand consistency are improved, but content creation speed and productivity deteriorate
Solution Approach 1:
The system creates a digital copy of brand design guidelines and applies them automatically to generate multiple content variants. The style transfer model copies brand-specific visual characteristics from reference images and applies them to generated content, enabling automated production while maintaining brand consistency and design quality without manual intervention for each piece of content.
Solution Approach 2:
The patent replaces the manual mechanical process of designers creating and modifying images with an automated system combining GANs for image generation and style transfer models for applying brand guidelines. This substitution of mechanical design work with computational processes enables high-speed content creation while preserving the quality standards that would otherwise require manual design team involvement.
2Manufacturing precision
If extensive communication between marketing and design teams is required, then content customization quality is improved, but time consumption and production duration increase
Solution Approach 1:
The system performs preliminary action by encoding brand design guidelines into a style transfer model in advance. This pre-processing of design requirements allows the system to automatically apply brand-specific styles to generated content without requiring real-time communication or iterative reviews between marketing and design teams during the content creation process, significantly reducing production duration while maintaining quality.
Solution Approach 2:
The marketing content generation system becomes self-service by automatically applying brand guidelines through the style transfer model without requiring external design team input. The system independently generates and styles content according to pre-loaded brand parameters, eliminating the need for extensive communication loops between teams and reducing overall production time.
3Adaptability or versatility
If customizing marketing content for individual consumers is performed manually, then personalization quality is improved, but effort required and complexity increase
Solution Approach 1:
The system segments the content generation process into independent components: base image generation via GAN, style transfer for brand consistency, and parameter-based personalization. This segmentation allows individual consumer customization to be applied as a separate, automated layer on top of the base content, enabling high personalization quality without increasing overall system complexity since each component operates independently with clear interfaces.
Solution Approach 2:
The system enables personalization through parameter changes rather than complex redesigns. By adjusting specific parameters (such as text content, color accents, or layout variations) within the generated content framework, the system can quickly adapt content for individual consumers. This parameter-based approach maintains high personalization quality while avoiding the complexity of manual customization processes.
Data Source
AI summary
Systems, devices, and techniques are disclosed for style-based dynamic content generation. A seed image, entity design data, entity style data, and text items may be received. Bounding boxes that identify areas of the seed image for the placement of the text items may be generated for the seed image. Variant images may be generated from the seed image, the entity design data, and the entity style data. The variant images may be generated by placing text items in the bounding boxes based on the entity design data and rendering text of the text items using the entity style data.