How Computers Use Brain Maps to Diagnose Alzheimer's Disease
A method for classifying stages of Alzheimer's disease by turning brain scan data into mathematical graphs and using machine learning to identify patterns.
Original patent title: “Method of providing diagnostic information on Alzheimer's disease using brain network”
A method for classifying stages of Alzheimer's disease by turning brain scan data into mathematical graphs and using machine learning to identify patterns. Granted to Chosun University Industry Academic Cooperation Foundation in 2025 with 5 claims.
Key facts
Coverage
What does this patent actually cover?
The patent describes a computational pipeline that turns raw brain scan data—like EEG or MRI—into a mathematical representation of how different brain regions communicate. First, it extracts data from a scan and builds a 'brain network graph,' where brain regions are nodes and their connections are edges. It then uses a technique called node2vec to turn this complex graph into a simplified list of numbers, known as a feature vector. Finally, it uses machine learning algorithms like Support Vector Machines to analyze these vectors and classify whether a patient is healthy, has mild cognitive impairment, or has Alzheimer's disease.
The gap
What does this patent NOT cover?
- Does not cover the physical hardware or medical devices used to capture the initial EEG, fMRI, or PET scans.
- Does not cover diagnostic methods that rely solely on clinical interviews or memory tests without the graph-based computational analysis.
- Does not cover the specific medical treatment or drug intervention prescribed after a diagnosis is made.
- Does not cover general-purpose machine learning algorithms that are not specifically applied to the described brain network graph methodology.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
What made this novel
The innovation lies in applying node2vec—a technique usually used to map social networks or web links—to the structural and functional connections of the human brain to detect disease progression.
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
Computer-aided diagnostic software for neurologists
Automated analysis tools for clinical research studies
Digital health platforms processing EEG data
Why it matters
The bigger picture
As the global population ages, early and accurate diagnosis of neurodegenerative diseases is critical. By standardizing how we turn brain imaging into data that computers can 'read,' this approach helps move Alzheimer's diagnosis from subjective clinical observation toward more objective, data-driven analysis.
Filed
February 4, 2022
Granted
August 5, 2025
Market context
Who's building on this
Companies in this space
Academic research institutions and specialized medical AI startups are the primary drivers of this field. Chosun University remains a key player in developing these specific graph-based diagnostic frameworks.
Market impact
This patent contributes to the growing field of digital biomarkers, where software analysis of medical data becomes a core component of clinical decision-making. It enables developers to build more standardized diagnostic tools that could eventually be integrated into hospital information systems.
Claim 1 — Plain English
What this patent covers
The patent describes a computational pipeline that turns raw brain scan data—like EEG or MRI—into a mathematical representation of how different brain regions communicate. First, it extracts data from a scan and builds a 'brain network graph,' where brain regions are nodes and their connections are edges. It then uses a technique called node2vec to turn this complex graph into a simplified list of numbers, known as a feature vector. Finally, it uses machine learning algorithms like Support Vector Machines to analyze these vectors and classify whether a patient is healthy, has mild cognitive impairment, or has Alzheimer's disease.
The clever bit
The innovation lies in applying node2vec—a technique usually used to map social networks or web links—to the structural and functional connections of the human brain to detect disease progression.
What it does not cover
- Does not cover the physical hardware or medical devices used to capture the initial EEG, fMRI, or PET scans.
- Does not cover diagnostic methods that rely solely on clinical interviews or memory tests without the graph-based computational analysis.
- Does not cover the specific medical treatment or drug intervention prescribed after a diagnosis is made.
- Does not cover general-purpose machine learning algorithms that are not specifically applied to the described brain network graph methodology.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
PatentBrief Score
Impact Score
Early stage
Citation count
0/40
No citations yet
Claim breadth
3/20
Moderate scope
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
$26K – $84K
Midpoint $53K · 15.6 yr remaining · industry ×2.2
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
The original legal language
Original claims
5 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
KWON, G. (2025). How Computers Use Brain Maps to Diagnose Alzheimer's Disease (U.S. Patent No. 12,376,789). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/12376789/raptor-testing
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="US12376789"></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
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 Computers Use Brain Maps to Diagnose Alzheimer's Disease cover?
A method for classifying stages of Alzheimer's disease by turning brain scan data into mathematical graphs and using machine learning to identify patterns.
Who owns patent US 12376789?
Chosun University Industry Academic Cooperation Foundation owns this patent, granted in 2025.
When does this patent expire?
This patent is expected to expire on August 5, 2045, when the invention enters the public domain.
What problem does this patent solve?
As the global population ages, early and accurate diagnosis of neurodegenerative diseases is critical. By standardizing how we turn brain imaging into data that computers can 'read,' this approach helps move Alzheimer's diagnosis from subjective clinical observation toward more objective, data-driven analysis.
What does this patent NOT cover?
Does not cover the physical hardware or medical devices used to capture the initial EEG, fMRI, or PET scans.
Patent monitoring