Feedback
Settings

Settings

Goto Application

22,770 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

AIapplicationfieldLifeAndMedicalSciencesDrugDiscovery

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 / 2,277
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.
4.20210097682Disease characterization and response estimation through spatially-invoked radiomics and deep learning fusion
US - 01.04.2021
Int.Class G06T 7/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
 Appl.No 17038934 Applicant Case Western Reserve University Inventor Anant Madabhushi

Embodiments discussed herein facilitate training and/or employing a combined model employing machine learning and deep learning outputs to generate prognoses for treatment of tumors. One example embodiment can extract radiomic features from a tumor and a peri-tumoral region; provide the intra-tumoral and peri-tumoral features to two separate machine learning models; provide the segmented tumor and peri-tumoral region to two separate deep learning models; receive predicted prognoses from each of the machine learning models and each of the deep learning models; provide the predicted prognoses to a combined machine learning model; and receive a combined predicted prognosis for the tumor from the combined machine learning model.

5.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.
6.WO/2023/141277SYSTEMS AND METHODS FOR SKIN BIOMOLECULAR PROFILE ASSESSMENT USING ARTIFICIAL INTELLIGENCE
WO - 27.07.2023
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/US2023/011249 Applicant VANDERBILT UNIVERSITY Inventor MARASCO, Christina, C.
Skin biomolecular profile assessment methods and systems that can analyze the molecular composition of the skin using molecular-level, user-specific data to assess an individual's skin state and/or disease state are described herein. An example method includes receiving skin data associated with a subject, where the skin data includes a biomolecular profile. The method also includes inputting the skin data into a trained artificial intelligence (AI) model and receiving, from the trained AI model, a skin care prediction.
7.20210241178COMPUTATIONALLY DERIVED CYTOLOGICAL IMAGE MARKERS FOR PREDICTING RISK OF RELAPSE IN ACUTE MYELOID LEUKEMIA PATIENTS FOLLOWING BONE MARROW TRANSPLANTATION IMAGES
US - 05.08.2021
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 17167438 Applicant Case Western Reserve University Inventor Anant Madabhushi

Embodiments discussed herein facilitate determination of risk of relapse of AML post-transplant. One example embodiment is a method, comprising: accessing a digital whole slide image (WSI) comprising a post-transplant bone marrow aspirate from a patient that has acute myeloid leukemia (AML); segmenting one or more myeloblasts on the digital WSI; extracting one or more features from the segmented one or more myeloblasts; providing the one or more features extracted from the segmented one or more myeloblasts to a trained machine learning model; and receiving, from the trained machine learning model, an indication of a risk of relapse of the AML.

8.WO/2018/017467DISTRIBUTED MACHINE LEARNING SYSTEMS, APPARATUS, AND METHODS
WO - 25.01.2018
Int.Class G06N 99/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
99Subject matter not provided for in other groups of this subclass
 Appl.No PCT/US2017/042356 Applicant NANTOMICS, LLC Inventor SZETO, Christopher
A distributed, online machine learning system is presented. Contemplated systems include many private data servers, each having local private data. Researchers can request that relevant private data servers train implementations of machine learning algorithms on their local private data without requiring de-identification of the private data or without exposing the private data to unauthorized computing systems. The private data servers also generate synthetic or proxy data according to the data distributions of the actual data. The servers then use the proxy data to train proxy models. When the proxy models are sufficiently similar to the trained actual models, the proxy data, proxy model parameters, or other learned knowledge can be transmitted to one or more non-private computing devices. The learned knowledge from many private data servers can then be aggregated into one or more trained global models without exposing private data.
9.20220202373SYSTEMS AND METHODS OF USING MACHINE LEARNING TO DETECT AND PREDICT EMERGENCE OF AGITATION BASED ON SYMPATHETIC NERVOUS SYSTEM ACTIVITIES
US - 30.06.2022
Int.Class A61B 5/00
AHUMAN NECESSITIES
61MEDICAL OR VETERINARY SCIENCE; HYGIENE
BDIAGNOSIS; SURGERY; IDENTIFICATION
5Measuring for diagnostic purposes ; Identification of persons
 Appl.No 17698407 Applicant BioXcel Therapeutics, Inc. Inventor Frank D. YOCCA

In some embodiments, a method includes receiving first physiological data of sympathetic nervous system activity and establishing a baseline value of at least one physiological parameter by training at least one machine learning model using the first physiological data. The method further includes receiving, from a first monitoring device attached to a subject, second physiological data of sympathetic nervous system activity in the subject. Using the at least one machine learning model and based on the baseline value of at least one physiological parameter, the method includes analyzing the second physiological data to predict an agitation episode of the subject and sending a signal to a second monitoring device to notify of the prediction of the agitation episode of the subject such that treatment can be provided to the subject to decrease sympathetic nervous system activity in the subject.

10.2017300259Distributed machine learning systems, apparatus, and methods
AU - 25.01.2018
Int.Class G06N 99/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
99Subject matter not provided for in other groups of this subclass
 Appl.No 2017300259 Applicant Nant Holdings IP, LLC Inventor Benz, Stephen Charles
A distributed, online machine learning system is presented. Contemplated systems include many private data servers, each having local private data. Researchers can request that relevant private data servers train implementations of machine learning algorithms on their local private data without requiring de-identification of the private data or without exposing the private data to unauthorized computing systems. The private data servers also generate synthetic or proxy data according to the data distributions of the actual data. The servers then use the proxy data to train proxy models. When the proxy models are sufficiently similar to the trained actual models, the proxy data, proxy model parameters, or other learned knowledge can be transmitted to one or more non-private computing devices. The learned knowledge from many private data servers can then be aggregated into one or more trained global models without exposing private data.
# -