Generative Neural Network Artifact Interface

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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.

Innovation Solution

A novel user interface and method utilizing a generative neural network with a selector to visually explore latent spaces, allowing continuous or discrete updating of output artifacts based on weighted contributions from multiple source artifacts, enabling the creation of new artifacts from diverse sources in a fluid and intuitive manner.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

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 becomes complex and the process lacks fluidity and intuitive feedback

Engineering Contradiction:

Improveautomation of artifact creationVSAvoiduser interface complexity

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical interaction interfaces (buttons, sliders, menus) with a neural network-based system that processes artifacts directly. The neural network automatically learns and applies transformations, substituting the complex mechanical user interface with an intelligent system that provides natural, fluid interaction through artifact-to-artifact generation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by allowing artifacts to serve as their own instructions. The input artifact automatically guides the neural network to generate the appropriate output artifact without requiring users to manually configure complex parameters or navigate complex interfaces. The artifact itself contains the information needed to drive the generation process.

Inventive Principle:

Principle #25Self-service

2Productivity

If traditional image generation tools are used, then new images can be generated from existing images, but the process requires iterative steps and lacks continuous feedback

Engineering Contradiction:

Improveartifact generation capabilityVSAvoidtime for iterative steps

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent implements continuous action by enabling real-time, continuous updates of the output artifact as the input artifact changes. The neural network processes artifacts continuously without requiring discrete iterative steps, providing immediate feedback and allowing users to explore the generation space fluidly by simply moving the input artifact around.

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The system performs preliminary action by pre-training the neural network on large datasets of artifact pairs before deployment. This preliminary training phase allows the network to learn transformation patterns in advance, so that during actual use, artifact generation occurs instantly without requiring iterative refinement steps during the user interaction phase.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If existing artifact generation tools are used, then new artifacts can be created, but the tools are restricted to a single type of artifact and lack versatility

Engineering Contradiction:

Improveartifact type flexibilityVSAvoidsystem adaptability

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements universality by designing a single neural network system that can handle multiple types of artifacts (images, videos, audio, 3D models) through a unified architecture. The same core generation mechanism works across different artifact types, eliminating the need for separate specialized tools for each artifact type while maintaining high versatility.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Productivity

If traditional tools are used to modify existing artifacts, then new artifacts can be created, but the contribution of source artifacts remains unclear

Engineering Contradiction:

Improveartifact creation efficiencyVSAvoidclarity of source contribution

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The patent implements feedback by providing the neural network with the original input artifact alongside the generated output artifact. This allows the system to learn and preserve the relationship between source and result, ensuring that the contribution of source artifacts remains clear and traceable. The feedback loop maintains information about source provenance while enabling efficient generation.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11921997B2User interfaces and methods for generating a new artifact based on existing artifacts

Publication Date: 2024.03.05 NVIDIA CORP

AI summary

User interfaces and methods are disclosed. In some embodiments, a plurality of source artifacts is displayed. A selector is operable to indicate a selected set of the source artifacts. An output artifact is displayed having an output attribute that represents a combination of source attributes from the source artifacts in the selected set. An amount of contribution to the first output attribute by respective ones of the source artifacts in the first selected set is based on a coordinate of the selector relative to coordinates of the source attributes in the first selected set.