# 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. - **Patent:** US 12376789 - **Original title:** Method of providing diagnostic information on Alzheimer's disease using brain network - **Owner:** Chosun University Industry Academic Cooperation Foundation - **Granted:** 2025 - **Status:** Active - **Times cited:** 0 - **Field:** ai_ml, biotech, software ## What it does 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. ## 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. ## 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. ## Real-world examples 1. Computer-aided diagnostic software for neurologists 2. Automated analysis tools for clinical research studies 3. Digital health platforms processing EEG data ## Why it matters 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. ## 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. **Full plain-English explainer:** https://patentbrief.org/patent/us/12376789/raptor-testing **Original patent:** https://patents.google.com/patent/US12376789 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [Detecting Alzheimer's Risk Early with a Blood Test and Brain Scan](https://patentbrief.org/patent/us/11193946/blood-biomarker-for-discerning-beta-amyloid-accumulation-in-brain) — This patent describes a two-step method to predict early Alzheimer's disease by first checking specific protein levels in a blood sample, and then, if certain markers are low, performing a brain scan to confirm amyloid beta buildup. - [Diagnosing Brain Diseases by Measuring a Protein in Blood or Urine](https://patentbrief.org/patent/us/20220283184/novel-diagnostic-marker-for-creutzfeldt-jakob-disease-and-alzheimers-disease) — This patent describes a method for diagnosing brain diseases like Alzheimer's and Creutzfeldt-Jakob disease by measuring the amount of a specific protein called beta-synuclein in non-spinal fluid samples such as blood or urine. - [Using a Blood Marker to Predict Concussion Risk and Severity](https://patentbrief.org/patent/us/12085565/sntf-is-a-blood-biomarker-for-the-diagnosis-and-prognosis-of-sports-related-conc) — This patent describes a method to diagnose and predict long-term issues from mild traumatic brain injury, like concussions, by measuring a specific protein fragment called SNTF in a blood sample. - [Checking if Brain Injury Drugs Work Using Saliva or Urine](https://patentbrief.org/patent/us/20200371093/prognostic-and-diagnostic-glycan-based-biomarkers-of-brain-damage) — This patent describes a method to determine if a drug for brain injury is effective by measuring specific sugar molecules in a patient's saliva or urine samples over time. - [AI System for Analyzing Medical Images Using Magnification-Aligned Transformers](https://patentbrief.org/patent/us/20260080696/method-and-system-for-analyzing-pathological-images-based-on-magnification-align) — This 2025 patent describes an AI system that analyzes medical images, like those from pathology slides, by using a special 'Transformer' model that understands how image details change with magnification.