Personalized Branding with Prompt Adaptation in Large Language Models and Visual Language Models

Smart overview of the Invention

The described data processing system focuses on generating personalized content by utilizing a brand kit. The system processes natural language prompts from users to create or modify content within a design application. It determines if the user intends to apply a brand kit and, if not available, automatically generates one. This brand kit is then used in conjunction with generative models to produce personalized content that aligns with the user's brand identity.

Background

Design applications are widely used for creating high-quality content such as social media posts, advertisements, and more. These applications often require users to maintain a consistent brand identity, typically achieved through a brand kit. A brand kit includes assets like logos, color palettes, and fonts that represent the visual identity of a brand. However, creating these kits manually can be cumbersome and not all users are aware of the tools available for this purpose.

System Functionality

The system employs a processor and memory to execute operations based on user inputs. Upon receiving a natural language prompt, it uses a trained language model to predict if the user intends to use a brand kit. If so, it retrieves or generates the necessary brand kit and creates intermediate content using model-specific prompts sent to various generative models. The intermediate content is then customized into personalized content using the brand kit.

Methodology

The methodology involves analyzing user prompts through a prompt construction layer which determines the need for branding in content creation. It checks for an existing brand kit and applies it automatically or generates a new one if necessary. This approach leverages language models to construct prompts for generative models without needing to retrain them. This reduces the time and resources typically required for model training or fine-tuning.

Technical Benefits

By using prompt adaptation techniques with large language models and visual language models, the system offers an efficient solution for automated branding in content creation. The use of a prompt construction layer ensures that generative models can produce content reflective of a brand's visual identity without extensive retraining. This not only streamlines the branding process but also conserves computational resources.