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Analysis

1 / 5,058
1.20250064365A METHOD AND A SYSTEM FOR DETECTION OF EYE GAZE-PATTERN ABNORMALITIES AND RELATED NEUROLOGICAL DISEASES
US - 27.02.2025
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 17917181 Applicant INNODEM NEUROSCIENCES Inventor Etienne DE VILLERS-SIDANI

The present disclosure relates to a method and a system for detecting a neurological disease and an eye gaze-pattern abnormality related to the neurological disease of a user. The method comprises displaying stimulus videos on a screen of an electronic device and simultaneously filming with a camera of the electronic device to generate a video of the user's face for each one of the stimulus videos, each one of the stimulus videos corresponding to a task. The method further comprises providing a machine learning model for gaze predictions, generating the gaze predictions for each video frame of the recorded video, and determining features for each task to detect the neurological disease using a pre-trained machine learning model.

2.20220180975METHODS AND SYSTEMS FOR DETERMINING GENE EXPRESSION PROFILES AND CELL IDENTITIES FROM MULTI-OMIC IMAGING DATA
US - 09.06.2022
Int.Class G16B 40/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
30Unsupervised data analysis
 Appl.No 17553691 Applicant The Broad Institute, Inc. Inventor Aviv Regev

The present disclosure relates to systems and method of determining transcriptomic profile from omics imaging data. The systems and methods train machine learning methods with intrinsic and extrinsic features of a cell and/or tissue to define transcriptomic profiles of the cell and/or tissue. Applicants utilize a convolutional autoencoder to define cell subtypes from images of the cells.

3.WO/2022/232935A METHOD AND A SYSTEM FOR DETECTION OF EYE GAZE-PATTERN ABNORMALITIES AND RELATED NEUROLOGICAL DISEASES
WO - 10.11.2022
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 PCT/CA2022/050703 Applicant INNODEM NEUROSCIENCES Inventor DE VILLERS-SIDANI, Étienne
The present disclosure relates to a method and a system for detecting a neurological disease and an eye gaze-pattern abnormality related to the neurological disease of a user. The method comprises displaying stimulus videos on a screen of an electronic device and simultaneously filming with a camera of the electronic device to generate a video of the user's face for each one of the stimulus videos, each one of the stimulus videos corresponding to a task. The method further comprises providing a machine learning model for gaze predictions, generating the gaze predictions for each video frame of the recorded video, and determining features for each task to detect the neurological disease using a pre-trained machine learning model.
4.3177238A METHOD AND A SYSTEM FOR DETECTION OF EYE GAZE-PATTERN ABNORMALITIES AND RELATED NEUROLOGICAL DISEASES
CA - 05.11.2022
Int.Class A61B 3/113
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
3Apparatus for testing the eyes; Instruments for examining the eyes
10Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions
113for determining or recording eye movement
 Appl.No 3177238 Applicant INNODEM NEUROSCIENCES Inventor DE VILLERS-SIDANI, ETIENNE
The present disclosure relates to a method and a system for detecting a neurological disease and an eye gaze-pattern abnormality related to the neurological disease of a user. The method comprises displaying stimulus videos on a screen of an electronic device and simultaneously filming with a camera of the electronic device to generate a video of the user's face for each one of the stimulus videos, each one of the stimulus videos corresponding to a task. The method further comprises providing a machine learning model for gaze predictions, generating the gaze predictions for each video frame of the recorded video, and determining features for each task to detect the neurological disease using a pre-trained machine learning model.
5.20200364539Method of and system for evaluating consumption of visual information displayed to a user by analyzing user's eye tracking and bioresponse data
US - 19.11.2020
Int.Class G06N 3/04
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
3Computing arrangements based on biological models
02Neural networks
04Architecture, e.g. interconnection topology
 Appl.No 16940931 Applicant Oken Technologies, Inc. Inventor Victor Nikolaevich Anisimov

Method of analyzing eye tracking data for estimating user's cognitive and emotional level of consumption of visual information. A training machine learning model is trained using a data set containing gaze information of known training users, their known cognitive levels and their EEG signal measurements. A calibrating machine learning model is trained using a data set of calibrating visual information displayed to a user, calibrating gaze tracks of that user, calibrating actions data of that user, and calibrating session data related to the session environment. The device displays to that user a target visual information and records target eye tracking data of that user in response to consuming the target information. The recorded target eye tracking data is calibrated via the calibrating machine learning model. The calibrated target eye tracking data is fed into the training machine learning model, which estimates the cognitive levels of consumption of the target visual information of that user.

6.12274503Myopia ocular predictive technology and integrated characterization system
US - 15.04.2025
Int.Class A61B 3/14
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
3Apparatus for testing the eyes; Instruments for examining the eyes
10Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions
14Arrangements specially adapted for eye photography
 Appl.No 18778027 Applicant COGNITIVECARE INC. Inventor Venkata Narasimham Peri

According to an embodiment, disclosed is a system comprising a processor wherein the processor is configured to receive an input data comprising an image of an ocular region of a user, clinical data of the user, and external factors; extract, using an image processing module comprising adaptive filtering techniques, ocular characteristics, combine, using a multimodal fusion module, the input data to determine a holistic health embedding; detect, based on a machine learning model and the holistic health embedding, a first output comprising likelihood of myopia, and severity of myopia; predict, based on the machine learning model and the holistic health embedding, a second output comprising an onset of myopia and a progression of myopia in the user; and wherein the machine learning model is a pre-trained model; and wherein the system is configured for myopia prognosis powered by multimodal data.

7.20220369923METHOD AND A SYSTEM FOR DETECTION OF EYE GAZE-PATTERN ABNORMALITIES AND RELATED NEUROLOGICAL DISEASES
US - 24.11.2022
Int.Class A61B 3/113
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
3Apparatus for testing the eyes; Instruments for examining the eyes
10Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions
113for determining or recording eye movement
 Appl.No 17308439 Applicant INNODEM NEUROSCIENCES Inventor Etienne DE VILLERS-SIDANI

The present disclosure relates to a method and a system for detecting a neurological disease and an eye gaze-pattern abnormality related to the neurological disease of a user. The method comprises displaying stimulus videos on a screen of an electronic device and simultaneously filming with a camera of the electronic device to generate a video of the user's face for each one of the stimulus videos, each one of the stimulus videos corresponding to a task. The method further comprises providing a machine learning model for gaze predictions, generating the gaze predictions for each video frame of the recorded video, and determining features for each task to detect the neurological disease using a pre-trained machine learning model.

8.WO/2024/039288SYSTEM AND METHOD FOR USER COMPETENCY ASSESSMENT IN MARINE NAVIGATION
WO - 22.02.2024
Int.Class G06V 40/20
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
40Recognition of biometric, human-related or animal-related patterns in image or video data
20Movements or behaviour, e.g. gesture recognition
 Appl.No PCT/SG2023/050525 Applicant SINGAPORE POLYTECHNIC Inventor VIRDI, Satinder Singh
The present disclosure generally relates to a computerized method and a computer device for assessing user competency in marine navigation. The method comprises: receiving eye tracking data measured from a user, the eye tracking data comprising a video and eye gaze data of the user's perspective during marine navigation; locating a region of interest in each of a plurality of image frames of the video based on the eye gaze data; feeding the regions of interest to a machine learning model; determining, from the regions of interest and machine learning model, a set of marine navigation tasks that the user is performing; and generating assessment data comprising the video for an assessor to assess the user's competency in the marine navigation tasks.
9.20220366568Adaptive eye tracking machine learning model engine
US - 17.11.2022
Int.Class G06T 7/20
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
20Analysis of motion
 Appl.No 17319891 Applicant NVIDIA Corporation Inventor Nuri Murat Arar

In various examples, an adaptive eye tracking machine learning model engine (“adaptive-model engine”) for an eye tracking system is described. The adaptive-model engine may include an eye tracking or gaze tracking development pipeline (“adaptive-model training pipeline”) that supports collecting data, training, optimizing, and deploying an adaptive eye tracking model that is a customized eye tracking model based on a set of features of an identified deployment environment. The adaptive-model engine supports ensembling the adaptive eye tracking model that may be trained on gaze vector estimation in surround environments and ensemble based on a plurality of eye tracking variant models and a plurality of facial landmark neural network metrics.

10.20220001893Motion sickness detection system for autonomous vehicles
US - 06.01.2022
Int.Class A61B 3/113
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
3Apparatus for testing the eyes; Instruments for examining the eyes
10Objective types, i.e. instruments for examining the eyes independent of the patients perceptions or reactions
113for determining or recording eye movement
 Appl.No 16920410 Applicant QUALCOMM Incorporated Inventor Robert Tartz

Techniques described herein include detecting a degree of motion sickness experienced by a user within a vehicle. A suitable combination of physiological data (heart rate, heart rate variability parameters, blood volume pulse, oxygen values, respiration values, galvanic skin response, skin conductance values, and the like), eye gaze data (e.g., images of the user), vehicle motion data (e.g., accelerometer, gyroscope data indicative of vehicle oscillations) may be utilized to identify the degree of motion sickness experienced by the user. One or more autonomous actions may be performed to prevent an escalation in the degree of motion sickness experienced by the user or to ameliorate the degree of motion sickness currently experienced by the user.

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