Generative Neural Network Interface for Artifact Creation
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Solution Overview
Problem
Existing image generation tools are limited in their ability to intuitively and fluidly create new artifacts from existing ones, lacking feedback during the selection process, requiring iterative steps, and being restricted to a single type of artifact, with unclear contributions of source image sliders to the generated image.
Innovation Solution
A user interface and method utilizing a generative neural network with a novel user interface that allows for the selection and weighting of multiple source artifacts in a visual and intuitive manner, enabling continuous or discrete updating of output artifacts, and supporting multiple types of source attributes, allowing users to explore latent spaces and generate new artifacts based on selected attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional application software is used to create new artifacts from existing artifacts, then the creation process can be automated to some extent, but the user interface lacks intuitiveness and fluidity, requiring iterative steps without clear feedback
Solution Approach 1:
The patent implements real-time feedback mechanisms where the output artifact continuously updates as users interact with source artifact selections. The system provides visual feedback showing how selected source artifacts and their attributes contribute to the generated output, eliminating the iterative guesswork of traditional software. This is achieved through a neural network that processes user selections and immediately displays the resulting artifact with clear attribution of source contributions.
Solution Approach 2:
The system performs preliminary analysis of source artifacts by extracting and organizing their attributes before the user makes selections. This pre-processing allows the interface to present curated attribute options and predict potential output artifacts in advance, making the creation process more intuitive. The neural network is pre-trained on large datasets, enabling it to anticipate user intentions and provide relevant suggestions before explicit commands are given.
2Adaptability or versatility
If multiple source artifacts are selected to create diverse output artifacts, then the versatility of the system increases, but the complexity of managing and weighting multiple sources increases
Solution Approach 1:
The patent segments the complex task of managing multiple source artifacts into distinct attribute dimensions. Each source artifact is analyzed and broken down into specific attributes (e.g., style, content, color, composition), allowing users to select and weight individual attributes rather than managing entire artifacts. This segmentation simplifies the interface while maintaining the ability to create diverse outputs by combining attributes from multiple sources.
Solution Approach 2:
The neural network acts as an intermediary that automatically manages the complexity of combining multiple source artifacts. It receives user selections of source artifacts and attributes, then autonomously performs the complex task of weighting, blending, and synthesizing them into a coherent output. This intermediary handles the mathematical and computational complexity, presenting a simplified interface to users while maintaining high versatility.
3Manufacturing precision
If the system provides detailed control over attribute contributions, then the precision of artifact generation improves, but the interface becomes more complex and less intuitive
Solution Approach 1:
The patent introduces a new dimension of control by organizing attributes hierarchically across multiple levels. Users can control artifacts at a high level by selecting entire source artifacts, then drill down into specific attribute dimensions (style, content, color, etc.), and finally adjust individual attribute weights. This multi-dimensional approach allows precise control without overwhelming the user, as each level builds upon the previous one with increasing granularity.
Data Source
AI summary
User interfaces, methods and structures are described for intuitively and fluidly creating new artifacts from existing artifacts and for exploring latent spaces in a visual manner. In example embodiments, source artifacts are displayed along with a selector. The selector is operable to indicate a selected set of the source artifacts by establishing a selection region that includes portions of one or more of the source artifacts displayed. Source vectors are associated with the source artifacts in the selected set. One or more resultant vectors are determined based on the source vectors, and an output artifact is generated based on the one or more resultant vectors.