How AI Generates Images Based on Style and Content Cues
This patent describes an artificial intelligence system that creates new images by understanding both what the image should show (like a cat) and how it should look (like a high-quality photo), using a special internal layer to blend these instructions.
Original patent title: “Generative adversarial network for generating images”
This patent describes an artificial intelligence system that creates new images by understanding both what the image should show (like a cat) and how it should look (like a high-quality photo), using a special internal layer to blend these instructions. Granted to Naver in 2024 with 41 claims and 3 forward citations, and it is expected to expire in 2040.
Coverage
What does this patent actually cover?
This patent details a generative adversarial network (GAN) that creates synthetic images. It uses two main parts: a generator neural network and a discriminator neural network. The generator takes a random 'noise vector' and a pair of 'conditioning variables' as input (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1). One variable describes 'semantic information,' like what objects are in the image, and the other describes 'aesthetic information,' such as its quality or style (Claim 1). A key part of the generator is a 'mixed-conditional batch normalization layer' which normalizes the data flow within the network (AbstractabstractA short summary at the front of the patent describing the invention. Not legally binding.Read more →, Claim 1). This layer uses the semantic and aesthetic conditioning variables to adjust its internal parameters, allowing it to precisely control how the generated image reflects both content and style (Abstract, Claim 1). For example, you could ask it to generate a 'dog' (semantic) that looks 'beautiful' (aesthetic).
The gap
What does this patent NOT cover?
- Does not cover image generation systems that only use semantic information without also incorporating aesthetic information as a distinct conditioning variable.
- Does not cover GANs that generate images without using a 'mixed-conditional batch normalization layer' that specifically adjusts its parameters based on both semantic and aesthetic inputs.
- Does not cover image generation where the conditioning variables are not applied via an 'affine transformation' to compute the normalization layer's parameters.
- Does not cover image generation where the aesthetic information is not provided as 'continuous information in the form of histogram score distributions' (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1).
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The clever part is how the patent uses a 'mixed-conditional batch normalization layer' within the generator. This layer dynamically adjusts its internal scaling and shifting parameters based on both semantic (what it is) and aesthetic (how it looks) instructions, allowing the AI to blend these two types of guidance seamlessly when creating an image.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
AI art generators that allow users to specify style and content
Tools for generating synthetic datasets with controlled visual properties for training other AIs
Image editing software offering style transfer or content-aware generation features
Virtual try-on applications where clothing style and fit are controlled
Why it matters
The bigger picture
This technology is important because it allows for more precise control over AI-generated images. Instead of just asking an AI to create 'a dog,' this system enables users to specify 'a beautiful dog' or 'a low-quality dog,' giving creators fine-grained control over both content and style. This level of control is crucial for applications where specific visual characteristics are desired, moving beyond simple object generation to nuanced artistic or functional outputs.
Filed
September 17, 2020
Granted
November 12, 2024
Market context
Who's building on this
Companies in this space
Naver Corp, the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is a major South Korean internet company that invests heavily in AI research and development, including advanced image generation and understanding. Other companies like Google, Meta, and Adobe, along with numerous AI startups, are actively developing and deploying generative AI models that incorporate various forms of conditional control for image and media creation.
Market impact
This patent contributes to the growing field of controllable generative AI, which has significantly impacted digital content creation. It enables more sophisticated AI tools for artists, designers, and developers, allowing them to create custom images with specific stylistic and thematic properties. This capability has fueled the rise of AI art platforms and advanced content generation services, expanding the market for AI-powered creative tools.
Claim 1 — Plain English
What this patent covers
This patent details a generative adversarial network (GAN) that creates synthetic images. It uses two main parts: a generator neural network and a discriminator neural network. The generator takes a random 'noise vector' and a pair of 'conditioning variables' as input (Claim 1). One variable describes 'semantic information,' like what objects are in the image, and the other describes 'aesthetic information,' such as its quality or style (Claim 1). A key part of the generator is a 'mixed-conditional batch normalization layer' which normalizes the data flow within the network (Abstract, Claim 1). This layer uses the semantic and aesthetic conditioning variables to adjust its internal parameters, allowing it to precisely control how the generated image reflects both content and style (Abstract, Claim 1). For example, you could ask it to generate a 'dog' (semantic) that looks 'beautiful' (aesthetic).
The clever bit
The clever part is how the patent uses a 'mixed-conditional batch normalization layer' within the generator. This layer dynamically adjusts its internal scaling and shifting parameters based on both semantic (what it is) and aesthetic (how it looks) instructions, allowing the AI to blend these two types of guidance seamlessly when creating an image.
What it does not cover
- Does not cover image generation systems that only use semantic information without also incorporating aesthetic information as a distinct conditioning variable.
- Does not cover GANs that generate images without using a 'mixed-conditional batch normalization layer' that specifically adjusts its parameters based on both semantic and aesthetic inputs.
- Does not cover image generation where the conditioning variables are not applied via an 'affine transformation' to compute the normalization layer's parameters.
- Does not cover image generation where the aesthetic information is not provided as 'continuous information in the form of histogram score distributions' (Claim 1).
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Moderate
Citation count
12/40
Early citations
Claim breadth
20/20
Very broad protection
Recency
20/20
Granted within 5 years
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$77K – $246K
Midpoint $154K · 14.1 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
41 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Murray, N. (2024). How AI Generates Images Based on Style and Content Cues (U.S. Patent No. 12,141,700). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12141700/generative-adversarial-network-for-generating-images
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
Embed
Add this patent to your site
Drop this plain-English patent card into any blog post or article — free, no signup. It always links back to the full breakdown here.
<div data-patentlens-widget data-patent-number="US12141700"></div> <script src="https://patentbrief.org/embed.js" async></script>
Stay in the loop
Get a weekly digest of new patents.
One email per week. No spam. Unsubscribe anytime.
Keep exploring
Related patents you should know
US 4683195 · 1987
How to Make Billions of Copies of a DNA Segment
This patent describes the Polymerase Chain Reaction (PCR), a method to rapidly create many copies of a specific piece of DNA or RNA, enabling its detection and analysis.
Cetus Corp
US 8697359 · 2014
How to Edit Genes in Human Cells Using an Engineered CRISPR System
This patent describes an engineered CRISPR-Cas9 system for precisely cutting DNA in eukaryotic cells to change how genes work, opening the door for gene editing in complex organisms.
Massachusetts Institute of Technology
US 7657849 · 2010
How the iPhone's Slide-to-Unlock Gesture Works
Apple's 2010 patent describes unlocking a device by dragging a specific graphical image across the touchscreen along a predefined path, a gesture that became iconic with the original iPhone.
Apple Inc
US 4733665 · 1988
How Doctors Implant a Permanent Stent Using a Balloon
This patent describes the method for placing a permanent, expandable wire mesh tube inside a blood vessel or other body tube using a balloon-tipped catheter to widen it and keep it open.
Expandable Grafts Partnership
US 4965188 · 1990
How to Make Many Copies of a DNA Piece with Heat
This patent describes the Polymerase Chain Reaction (PCR) method, a technique to make millions of copies of a specific DNA segment using a heat-resistant enzyme and repeated temperature changes.
Cetus Corp
US 4235871 · 1980
How to Encapsulate Active Materials in Lipid Bubbles Efficiently
This patent describes a method for trapping biologically active substances inside tiny, multi-layered fat bubbles called liposomes, using a specific water-in-oil emulsion and gel-forming process to improve how much material gets captured.
Individual
Semantically similar
You might also find these interesting
US 12282696 · 2025 · Yeda Research and Development Co
How AI Transfers Visual Styles Between Images While Keeping Structure
US 20230297446 · Capital One Services
How to Train AI Models with Fake Data Using Generative Networks
US 10964084 · 2021 · Adobe
Creating Realistic Character Animations with AI Networks
US 11615208 · 2023 · Capital One Services LLC
How Cloud Systems Automatically Create and Train AI Data Models
More to explore
More in AI & Machine Learning
US 10452978 · 2019 · Google LLC
How AI Models Understand Language Using 'Attention'
US 6523026 · 2003 · Huntsman International LLC
How Computers Find Hidden Connections Between Different Fields of Knowledge
US 11615208 · 2023 · Capital One Services LLC
How Cloud Systems Automatically Create and Train AI Data Models
US 10402750 · 2019 · Facebook Inc
How Facebook Uses Deep Learning to Predict What You Might Like
New to patents?
Common Questions
Frequently Asked Questions
What does How AI Generates Images Based on Style and Content Cues cover?
This patent describes an artificial intelligence system that creates new images by understanding both what the image should show (like a cat) and how it should look (like a high-quality photo), using a special internal layer to blend these instructions.
Who owns patent US 12141700?
Naver owns this patent, granted in 2024.
When does this patent expire?
This patent is expected to expire on September 17, 2040, when the invention enters the public domain.
What is patent US 12141700 cited by?
This patent has been cited by 3 later patents that build on its ideas.
What problem does this patent solve?
This technology is important because it allows for more precise control over AI-generated images. Instead of just asking an AI to create 'a dog,' this system enables users to specify 'a beautiful dog' or 'a low-quality dog,' giving creators fine-grained control over both content and style. This level of control is crucial for applications where specific visual characteristics are desired, moving beyond simple object generation to nuanced artistic or functional outputs.
What does this patent NOT cover?
Does not cover image generation systems that only use semantic information without also incorporating aesthetic information as a distinct conditioning variable.
Patent monitoring