AI Graphic Text Editing With Context-Aware Design Matching
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Solution Overview
Problem
Existing AI-based design content creation systems lack the ability to manually edit mis-spelled words or unintended texts in AI-generated graphic design images, requiring users to use external tools with a steep learning curve, which is inefficient and time-consuming.
Innovation Solution
An AI graphic design text editing assistant that identifies textual areas in a graphic design image, determines design context attributes, and provides an editable text box for manual editing, while recommending new text designs based on context, using a large multimodal model like GPT-4V or Dall-E.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If external image editing tools are used to edit text in AI-generated images, then text editing capability is achieved, but time consumption and operational complexity increase
Solution Approach 1:
The patent combines the AI image generation model and the text editing functionality into a single integrated system. The editing interface is embedded within the generation platform, allowing users to edit text without switching to external tools. This merging eliminates the need to upload images to separate applications and directly processes edits on the generated content, significantly reducing time loss while maintaining full text editing capability.
Solution Approach 2:
The patent introduces an intermediary text editing interface that sits between the user and the AI generation model. This interface allows users to select and edit specific text regions in generated images through a simple text box, acting as a mediator that translates user text inputs into image modifications without requiring users to master complex external editing software.
2Ease of operation
If external image editing tools are used to edit text in AI-generated images, then text editing capability is achieved, but learning complexity increases
Solution Approach 1:
By merging the text editing functionality into the AI generation platform itself, the patent eliminates the need for users to learn separate external editing tools. The editing interface uses the same simple interaction paradigm as the generation process, maintaining consistency and reducing the overall learning curve while preserving full text editing capability.
Solution Approach 2:
The intermediary text editing interface simplifies the interaction by providing a straightforward text box for editing, which mediates between the user's simple text input and the complex image processing operations. This mediator abstracts away the complexity of image manipulation, allowing users with minimal training to effectively edit text in generated images.
3Extent of automation
If AI generation model is used to create graphic design images, then design content creation is automated, but text editing capability is lost
Solution Approach 1:
The patent merges the automated AI generation capabilities with manual text editing functionality into a unified system. The same AI model that generates the images also processes text editing requests, allowing the system to maintain high automation for content creation while simultaneously providing easy text editing through an integrated interface.
Solution Approach 2:
The patent implements a dynamic system that adapts its processing mode based on user input. When users interact with the text editing interface, the system dynamically switches from pure generation mode to editing mode, adjusting its operations to handle text modifications while maintaining the underlying AI-generated content. This dynamic behavior allows seamless transitions between automated creation and manual editing.
Data Source
AI summary
A data processing system implements receiving a user marking of a textual area in a graphic design image; constructing a prompt including the image, the marking, and instructions to a generative model to identify character(s) in the area, to determine design context attribute(s) of the character(s) with respect to the image, and to create a new image based on one changed design context attribute, the attribute(s) including a character design and semantics of the character(s), and a position of the area in the image; providing the prompt to the model and receive the character(s), the attribute(s), and the new image; providing the character(s), the attribute(s), and the new image to a client device; and causing the client device to display at least one of the new image or an editable text box over the area in the image, the box showing the character(s) based on the attribute(s).