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Analysis

1 / 213
1.20180018757TRANSFORMING 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.

2.20230320642SYSTEMS AND METHODS FOR TECHNIQUES TO PROCESS, ANALYZE AND MODEL INTERACTIVE VERBAL DATA FOR MULTIPLE INDIVIDUALS
US - 12.10.2023
Int.Class A61B 5/16
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
16Devices for psychotechnics; Testing reaction times
 Appl.No 18130947 Applicant The Trustees of Columbia University in the City of New York Inventor Baihan Lin

Disclosed are methods, systems, and other implementations for processing, analyzing, and modelling psychotherapy data. The implementations include a method for analyzing psychotherapy data that includes obtaining transcript data representative of spoken dialog in one or more psychotherapy sessions conducted between a patient and a therapist, extracting speech segments from the transcript data related to one or more of the patient or the therapist, applying a trained machine learning topic model process to the extracted speech segments to determine weighted topic labels representative of semantic psychiatric content of the extracted speech segments, and processing the weighted topic labels to derive a psychiatric assessment for the patient.

3.20210397895Intelligent learning system with noisy label data
US - 23.12.2021
Int.Class G06F 16/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
 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.

4.20220261994Machine 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.

5.2025283474MACHINE LEARNING FOR OTITIS MEDIA DIAGNOSIS
AU - 15.01.2026
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 2025283474 Applicant Otonexus Medical Technologies, Inc. Inventor CAMERON, Caitlin
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
6.20230135553AI-managed additive manufacturing for value chain networks
US - 04.05.2023
Int.Class G05B 17/02
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
17Systems involving the use of models or simulators of said systems
02electric
 Appl.No 17942061 Applicant Strong Force VCN Portfolio 2019, LLC Inventor Charles Howard Cella

A distributed manufacturing network information technology system includes a cloud-based additive manufacturing management platform with a user interface, connectivity facilities, data storage facilities, and monitoring facilities. The distributed manufacturing network information technology system includes a set of applications for enabling the additive manufacturing management platform to manage a set of distributed manufacturing network entities. The distributed manufacturing network information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

7.20190147849Natural language generation based on user speech style
US - 16.05.2019
Int.Class G10L 13/02
GPHYSICS
10MUSICAL INSTRUMENTS; ACOUSTICS
LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
13Speech synthesis; Text to speech systems
02Methods for producing synthetic speech; Speech synthesisers
 Appl.No 15811204 Applicant GM GLOBAL TECHNOLOGY OPERATIONS LLC Inventor Gaurav Talwar

A system and method of generating a natural language generation (NLG) output, wherein the method includes: receiving speech signals from a user at a microphone of a client device; determining a requested communication goal and at least one inputted communication value based on the received speech signals; determining to use a static natural language generation (NLG) template or a dynamic NLG template to generate an NLG output, wherein the determination of whether to use a static NLG template or a dynamic NLG template is made using a neural network NLG template selection process; selecting an NLG template after the determination of whether to use a static NLG template or a dynamic NLG template; and generating an NLG output based on the selected NLG template.

8.20230342597USING 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.

9.WO/2020/232460MACHINE LEARNING TECHNICS WITH SYSTEM IN THE LOOP FOR OIL & GAS TELEMETRY SYSTEMS
WO - 19.11.2020
Int.Class E21B 47/12
EFIXED CONSTRUCTIONS
21EARTH OR ROCK DRILLING; MINING
BEARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
47Survey of boreholes or wells
12Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling
 Appl.No PCT/US2020/042010 Applicant ONESUBSEA IP UK LIMITED Inventor JARROT, Arnaud
A telemetry system is provided. The telemetry system includes a transmitter configured to convert digital bits representative of oil and gas operations into an analog signal and to transmit the analog signal via a communications channel. The telemetry system further includes a receiver configured to receive the analog signal and to convert the analog signal into output digital bits via an encoder, wherein the receiver comprises one or more receiver components trained via machine learning to process the analog signals for improved communications.
10.2020273469Machine learning technics with system in the loop for oil and gas telemetry systems
AU - 19.11.2020
Int.Class E21B 47/12
EFIXED CONSTRUCTIONS
21EARTH OR ROCK DRILLING; MINING
BEARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
47Survey of boreholes or wells
12Means for transmitting measuring-signals or control signals from the well to the surface, or from the surface to the well, e.g. for logging while drilling
 Appl.No 2020273469 Applicant Onesubsea IP UK Limited Inventor CROUX, Arnaud
A telemetry system is provided. The telemetry system includes a transmitter configured to convert digital bits representative of oil and gas operations into an analog signal and to transmit the analog signal via a communications channel. The telemetry system further includes a receiver configured to receive the analog signal and to convert the analog signal into output digital bits via an encoder, wherein the receiver comprises one or more receiver components trained via machine learning to process the analog signals for improved communications.
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