Feedback
Settings

Settings

Goto Application

48,518 results
Offices all Languages en Stemming true Single Family Member false Include NPL false
RSS feed can only be generated if you have a WIPO account

Save query

A private query is only visible to you when you are logged-in and can not be used in RSS feeds

Query Tree

Refine Options

Offices
All
Specify the language of your search keywords
Stemming reduces inflected words to their stem or root form.
For example the words fishing, fished,fish, and fisher are reduced to the root word,fish,
so a search for fisher returns all the different variations
Returns only one member of a family of patents
Include Non-Patent literature in results

Full Query

AIapplicationfieldArtsAndHumanities

Side-by-side view shortcuts

General
Go to Search input
CTRL + SHIFT +
Go to Results (selected record)
CTRL + SHIFT +
Go to Detail (selected tab)
CTRL + SHIFT +
Go to Next page
CTRL +
Go to Previous page
CTRL +
Results (First, do 'Go to Results')
Go to Next record / image
/
Go to Previous record / image
/
Scroll Up
Page Up
Scroll Down
Page Down
Scroll to Top
CTRL + Home
Scroll to Bottom
CTRL + End
Detail (First, do 'Go to Detail')
Go to Next tab
Go to Previous tab

Analysis

1 / 4,852
1.20220180975METHODS 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.

2.12274503Myopia 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.

3.WO/2023/059663SYSTEMS AND METHODS FOR ASSESSMENT OF BODY FAT COMPOSITION AND TYPE VIA IMAGE PROCESSING
WO - 13.04.2023
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/US2022/045706 Applicant THE BROAD INSTITUTE, INC. Inventor KHERA, Amit
The subject matter disclosed herein relates to utilizing the silhouette of an individual to measure body fat volume and distribution. Particular examples relates to providing a system, a computer-implemented method, and a computer program product to utilize a binary outline, or silhouette, to predict the individual's fat depot volumes with machine learning models.
4.20210397895INTELLIGENT 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.

5.20210201205METHOD AND SYSTEM FOR DETERMINING CORRECTNESS OF PREDICTIONS PERFORMED BY DEEP LEARNING MODEL
US - 01.07.2021
Int.Class G06N 20/10
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
10using kernel methods, e.g. support vector machines
 Appl.No 16793173 Applicant Wipro Limited Inventor Arindam Chatterjee

The disclosure relates to method and system for determining correctness of predictions performed by deep learning model. The method includes extracting a neuron activation pattern of a layer of the deep learning model with respect to the input data, and generating an activation vector based on the extracted neuron activation pattern. The method further includes determining the correctness of the prediction performed by the deep learning model with respect to the input data using a prediction validation model and based on the activation vector. The prediction validation model is a machine learning model that has been generated and trained using training activation vectors derived from correctly predicted test dataset and incorrectly predicted test dataset of the deep learning model. The method further includes providing the correctness of the prediction performed by the deep learning model with respect to the input data for subsequent rendering or subsequent processing.

6.12112752Cohort determination in natural language processing
US - 08.10.2024
Int.Class G10L 15/22
GPHYSICS
10MUSICAL INSTRUMENTS; ACOUSTICS
LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
15Speech recognition
22Procedures used during a speech recognition process, e.g. man-machine dialog
 Appl.No 17688279 Applicant Amazon Technologies, Inc. Inventor Rahul Gupta

Devices and techniques are generally described for cohort determination in natural language processing. In various examples, a first natural language input to a natural language processing system may be determined. The first natural language input may be associated with a first account identifier. A first machine learning model may determine first data representing one or more words of the first natural language input. A second machine learning model may determine second data representing one or more acoustic characteristics of the first natural language input. Third data may be determined, the third data including a predicted performance for processing the first natural language input by the natural language processing system. The third data may be determined based on the first data representation and the second data representation.

7.WO/2022/155555SYSTEMS AND METHODS FOR DERIVING HEALTH INDICATORS FROM USER-GENERATED CONTENT
WO - 21.07.2022
Int.Class G16H 50/20
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
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
20for computer-aided diagnosis, e.g. based on medical expert systems
 Appl.No PCT/US2022/012645 Applicant MY LUA LLC Inventor CONWARD, Michael
The present disclosure relates to systems and methods for generating priority lists and/or predictions or identifications of root causes of acute or chronic conditions. In one exemplary embodiment, a method comprises aggregating data corresponding to a plurality of individuals, the data comprising, for each individual, user-generated content and/or biometric data; generating, from a machine learning model that utilizes the aggregated user-generated content and/or biometric data as input, one or more of a priority list for the plurality of individuals, or, for each individual, a prediction, diagnosis, or identification of one or more root causes of one or more acute or chronic conditions of the individual.
8.20200311520Training machine learning model
US - 01.10.2020
Int.Class G06T 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
 Appl.No 16369135 Applicant International Business Machines Corporation Inventor Shiwan Zhao

Techniques are provided for training machine learning model. According to one aspect, a training data is received by one or more processing units. The machine learning model is trained based on the training data, wherein the training comprises: optimizing the machine learning model based on stochastic gradient descent (SGD) by adding a dynamic noise to a gradient of a model parameter of the machine learning model calculated by the SGD.

9.WO/2018/199997AUDIO CLASSIFCATION WITH MACHINE LEARNING MODEL USING AUDIO DURATION
WO - 01.11.2018
Int.Class G06F 15/18
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
15Digital computers in general; Data processing equipment in general
18in which a program is changed according to experience gained by the computer itself during a complete run; Learning machines
 Appl.No PCT/US2017/030213 Applicant HEWLETT-PACKARD DEVELOPMENT COMPANY, L.P. Inventor BHARITKAR, Sunil
An audio signal classifier including a feature extractor to extract metadata from an audio signal, the metadata defining a plurality of features of the audio signal, the feature extractor to generate a feature vector including selected features of the audio signal, the selected features including a duration of the audio signal, and each selected feature having a feature value. A machine learning model trained to classify the audio signal as one of a plurality of audio signal classes based on the feature vector. The machine learning model to provide a plurality of class values based on the feature values, each class value corresponding to one of the plurality of audio signal classes, the plurality of class values together indicating the class of the audio signal.
10.3563251AUDIO CLASSIFCATION WITH MACHINE LEARNING MODEL USING AUDIO DURATION
EP - 06.11.2019
Int.Class H04S 7/00
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
SSTEREOPHONIC SYSTEMS
7Indicating arrangements; Control arrangements, e.g. balance control
 Appl.No 17907333 Applicant HEWLETT PACKARD DEVELOPMENT CO Inventor BHARITKAR SUNIL
An audio signal classifier including a feature extractor to extract metadata from an audio signal, the metadata defining a plurality of features of the audio signal, the feature extractor to generate a feature vector including selected features of the audio signal, the selected features including a duration of the audio signal, and each selected feature having a feature value. A machine learning model trained to classify the audio signal as one of a plurality of audio signal classes based on the feature vector. The machine learning model to provide a plurality of class values based on the feature values, each class value corresponding to one of the plurality of audio signal classes, the plurality of class values together indicating the class of the audio signal.
# -