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Analysis

1 / 195
1.20210397895INTELLIGENT LEARNING SYSTEM WITH NOISY LABEL DATA
US - 23.12.2021
Int.Class G06K 9/62
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KGRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for recognising patterns
62Methods or arrangements for pattern recognition using electronic means
 Appl.No 16946465 Applicant INTERNATIONAL BUSINESS MACHINES CORPORATION Inventor Yang SUN

Various embodiments are provided for providing machine learning with noisy label data in a computing environment using one or more processors in a computing system. A label corruption probability of noisy labels may be estimated for selected data from a dataset using temporal inconsistency in a machine model prediction during a training operation in a neural network.

2.20180018757TRANSFORMING PROJECTION DATA IN TOMOGRAPHY BY MEANS OF MACHINE LEARNING
US - 18.01.2018
Int.Class G06T 3/40
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
3Geometric image transformations in the plane of the image
40Scaling of whole images or parts thereof, e.g. expanding or contracting
 Appl.No 15646119 Applicant Kenji SUZUKI Inventor Kenji SUZUKI

A method and system for transforming low-quality projection data into higher quality projection data, using of a machine learning model. Regions are extracted from an input projection image acquired, for example, at a reduced x-ray radiation dose (lower-dose), and pixel values in the region are entered into the machine learning model as input. The output of the machine learning model is a region that corresponds to the input region. The output information is arranged to form an output high-quality projection image. A reconstruction algorithm reconstructs high-quality tomographic images from the output high-quality projection images. The machine learning model is trained with matched pairs of projection images, namely, input lower-quality (lower-dose) projection images together with corresponding desired higher-quality (higher-dose) projection images. Through the training, the machine learning model learns to transform lower-quality (lower-dose) projection images to higher-quality (higher-dose) projection images. Once trained, the trained machine learning model does not require the higher-quality (higher-dose) projection images anymore. When a new lower-quality (low radiation dose) projection image is entered, the trained machine learning model would output a region similar to its desired region, in other words, it would output simulated high-quality (high-dose) projection images where noise and artifacts due to low radiation dose are substantially reduced, i.e., a higher image quality. The reconstruction algorithm reconstructs simulated high-quality (high-dose) tomographic images from the output high-quality (high-dose) projection images. With the simulated high-quality (high-dose) tomographic images, the detectability of lesions and clinically important findings can be improved.

3.20250185986METHODS FOR PROVIDING MORE EFFECTIVE COMPRESSION THERAPY
US - 12.06.2025
Int.Class A61B 5/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
 Appl.No 18974202 Applicant Wound Pros Technology, Inc. Inventor Daniel Lee Hallman

This disclosure provides a method for providing more effective compression therapy, for example, by including pre and post volume plethysmography as indicators in addition to Ankle-Brachial Index (ABI)/Toe-Brachial Index (TBI) and pulse volume recording (PVR) to evaluate safety and effectiveness of compression therapy.

4.3062071NEURAL NETWORK BASED TRANSLATION OF NATURAL LANGUAGE QUERIES TO DATABASE QUERIES
CA - 22.11.2018
Int.Class G06F 16/245
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
20of structured data, e.g. relational data
24Querying
245Query processing
 Appl.No 3062071 Applicant SALESFORCE.COM, INC. Inventor ZHONG, VICTOR
A computing system uses neural networks to translate natural language queries to database queries. The computing system uses a plurality of machine learning based models, each machine learning model for generating a portion of the database query. The machine learning models use an input representation generated based on terms of the input natural language query, a set of columns of the database schema, and the vocabulary of a database query language, for example, structured query language SQL. The plurality of machine learning based models may include an aggregation classifier model for determining an aggregation operator in the database query, a result column predictor model for determining the result columns of the database query, and a condition clause predictor model for determining the condition clause of the database query. The condition clause predictor is based on reinforcement learning.
5.WO/2025/123027METHODS FOR PROVIDING MORE EFFECTIVE COMPRESSION THERAPY
WO - 12.06.2025
Int.Class A61B 5/026
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
02Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
026Measuring blood flow
 Appl.No PCT/US2024/059202 Applicant WOUND PROS TECHNOLOGY, INC. Inventor HALLMAN, Daniel
This disclosure provides a method for providing more effective compression therapy, for example, by including pre and post volume plethysmography as indicators in addition to Ankle-Brachial index (ABI) / Toe-Brachial Index (TBI) and pulse volume recording (PVR) to evaluate safety and effectiveness of compression therapy.
6.WO/2025/111275METHODS AND SYSTEMS WITH INTEGRATED VASCULAR ASSESSMENT
WO - 30.05.2025
Int.Class A61B 5/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
 Appl.No PCT/US2024/056536 Applicant WOUND PROS GROUP MANAGEMENT, INC. Inventor RELEFORD, Bill
This disclosure provides methods and systems for providing a patient-specific wound care plan for a patient based on vascular assessment. The disclosed methods and systems integrate a vascular diagnostic tool with Electronic Health Record (EHR) systems, leveraging advanced automation and artificial intelligence (Al) to generate patient-specific treatment plans based on real-time vascular assessments. This integration addresses the need for seamless data transfer and enhanced clinical efficiency in wound care, particularly for patients requiring consistent vascular monitoring.
7.20250166778METHODS AND SYSTEMS WITH INTEGRATED VASCULAR ASSESSMENT
US - 22.05.2025
Int.Class G16H 20/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
20ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
30relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
 Appl.No 18952675 Applicant Wound Pros Technology, Inc. Inventor Bill J. Releford

This disclosure provides methods and systems for providing a patient-specific wound care plan for a patient based on vascular assessment. The disclosed methods and systems integrate a vascular diagnostic tool with Electronic Health Record (EHR) systems, leveraging advanced automation and artificial intelligence (AI) to generate patient-specific treatment plans based on real-time vascular assessments. This integration addresses the need for seamless data transfer and enhanced clinical efficiency in wound care, particularly for patients requiring consistent vascular monitoring.

8.20220261994Machine learning for otitis media diagnosis
US - 18.08.2022
Int.Class A61B 1/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
1Instruments for performing medical examinations of the interior of cavities or tubes of the body by visual or photographical inspection, e.g. endoscopes; Illuminating arrangements therefor
 Appl.No 17738656 Applicant OtoNexus Medical Technologies, Inc. Inventor Charlie Corredor

Disclosed herein are systems and methods for classifying a tympanic membrane by using a classifier. The classifier is a machine learning algorithm. A method for classifying a tympanic membrane includes steps of: receiving, from an interrogation system, one or more datasets relating to the tympanic membrane; determining a set of parameters from the one or more datasets, wherein at least one parameter of the set of parameters is related to a dynamic property or a static position of the tympanic membrane; and outputting a classification of the tympanic membrane based on a classifier model derived from the set of parameters. The classification comprises one or more of a state, a condition, or a mobility metric of the tympanic membrane.

9.WO/2025/106583METHODS AND SYSTEMS FOR DISCRIMINATING BETWEEN CLONAL HEMATOPOIESIS-DERIVED AND TUMOR-DERIVED VARIANTS
WO - 22.05.2025
Int.Class C12Q 1/6886
CCHEMISTRY; METALLURGY
12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
QMEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
1Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
68involving nucleic acids
6876Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
6883for diseases caused by alterations of genetic material
6886for cancer
 Appl.No PCT/US2024/055782 Applicant THE JOHNS HOPKINS UNIVERSITY Inventor CANZONIERO, Jenna
This disclosure describes novel methods and systems for discriminating between tumor-origin variants and clonal hematopoiesis (CH)-derived variants across solid tumors and NGS sequencing platforms. The disclosed methods and systems utilize a machine learning model that integrates fragment-level, vanant-level, and patient-level features, allowing for predicting tumor vs. CH variant origin using plasma-only sequencing without additional white blood cell (WBC) or tumor sequencing. The ability to identify bona fide tumor- derived variants in plasma-only sequencing fills a critical need in the clinical implementation of liquid biopsy-guided cancer therapy by reducing misinterpretation due to CH contamination. The methods and systems as disclosed have improved precision and reliability' of prediction utilizing circulating-tumor DNA (ctDNA) for clinical prognosis purposes.
10.20230342597USING MACHINE LEARNING TO EXTRACT SUBSETS OF INTERACTION DATA FOR TRIGGERING DEVELOPMENT ACTIONS
US - 26.10.2023
Int.Class G06N 3/08
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
08Learning methods
 Appl.No 17660260 Applicant Truist Bank Inventor Rachna Behl

A system for guiding interactions with a user device includes a computer generating a predictive model during training of a machine learning program utilizing at least one neural network. A training data set utilized during the training of the machine learning program includes a personal data set of each of a plurality of first users. The predictive model predicts a probability of a second user associated with the user device interacting with a first product and/or service. The predicting of the probability including the predictive model correlating a personal data set of the second user to the personal data set of at least one first user. The computer sends a communication to the user device of the second user including content relating to the first product and/or service when the predicted probability meets or exceeds a threshold value.

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