Feedback
Settings

Settings

Goto Application

16,315 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

AIfunctionalapplicationsRobotics

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 / 1,632
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.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.
3.20230278227Device and method for training a machine learning model to derive a movement vector for a robot from image data
US - 07.09.2023
Int.Class G06T 7/70
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
70Determining position or orientation of objects or cameras
 Appl.No 18174803 Applicant Robert Bosch GmbH Inventor Oren Spector

A method for training a machine learning model to derive a movement vector for a robot from image data. The method includes acquiring images from a perspective of an end-effector of the robot, forming training image data elements from the acquired images, generating augmentations of the training image data elements, training an encoder network using contrastive loss and training a neural network to reduce a loss between movement vectors output by the neural network in response to embedding outputs provided by the encoder network and respective ground truth movement vectors.

4.20190251694Atlas-based segmentation using deep-learning
US - 15.08.2019
Int.Class G06T 7/174
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
10Segmentation; Edge detection
174involving the use of two or more images
 Appl.No 15896895 Applicant Elekta, Inc. Inventor Xiao Han

Techniques for enhancing image segmentation with the integration of deep learning are disclosed herein. An example method for atlas-based segmentation using deep learning includes: applying a deep learning model to a subject image to identify an anatomical feature, registering an atlas image to the subject image, using the deep learning segmentation data to improve a registration result, generating a mapped atlas, and identifying the feature in the subject image using the mapped atlas. Another example method for training and use of a trained machine learning classifier, in an atlas-based segmentation process using deep learning, includes: applying a deep learning model to an atlas image, training a machine learning model classifier using data from applying the deep learning model, estimating structure labels of areas of the subject image, and defining structure labels by combining the estimated structure labels with labels produced from atlas-based segmentation on the subject image.

5.20200401397Intelligent software agent to facilitate software development and operations
US - 24.12.2020
Int.Class G06F 8/70
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
8Arrangements for software engineering
70Software maintenance or management
 Appl.No 16450104 Applicant Hartford Fire Insurance Company Inventor Renoi Thomas, Jr.

Some embodiments may facilitate software development and operations for an enterprise. A communication input port may receive information associated with a software continuous integration/deployment pipeline of the enterprise. An intelligent software agent platform, coupled to the communication input port, may listen for a trigger indication from the software continuous integration/deployment pipeline. Responsive to the trigger indication, the intelligent software agent platform may apply system configuration and rule layer information to extract software log data and apply a machine learning model to the extracted software log data to generate a pipeline health check analysis report. The pipeline health check analysis report may include, for example, an automatically generated prediction associated with future operation of the software continuous integration/deployment pipeline. The intelligent software agent platform may then facilitate transmission of the pipeline health check analysis report via a communication output port and a distributed communication network.

6.20210141635Intelligent software agent to facilitate software development and operations
US - 13.05.2021
Int.Class G06F 8/70
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
8Arrangements for software engineering
70Software maintenance or management
 Appl.No 17154184 Applicant Hartford Fire Insurance Company Inventor Renoi Thomas

Some embodiments may facilitate software development and operations for an enterprise. A communication input port may receive information associated with a software continuous integration/deployment pipeline of the enterprise. An intelligent software agent platform, coupled to the communication input port, may listen for a trigger indication from the software continuous integration/deployment pipeline. Responsive to the trigger indication, the intelligent software agent platform may apply system configuration information and rule layer information to extract software log data and apply a machine learning model to the extracted software log data to generate a pipeline health check analysis report. The pipeline health check analysis report may include, for example, an automatically generated prediction associated with future operation of the software continuous integration/deployment pipeline. The intelligent software agent platform may then facilitate transmission of the pipeline health check analysis report via a communication output port and a distributed communication network.

7.4163833DEEP NEURAL NETWORK MODEL DESIGN ENHANCED BY REAL-TIME PROXY EVALUATION FEEDBACK
EP - 12.04.2023
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 22186944 Applicant INTEL CORP Inventor CUMMINGS DANIEL J
The present disclosure is related to artificial intelligence (AI), machine learning (ML), and Neural Architecture Search (NAS) technologies, and in particular, to Deep Neural Network (DNN) model engineering techniques that use proxy evaluation feedback. The DNN model engineering techniques discussed herein provide near real-time feedback on model performance via low-cost proxy scores without requiring continual training and/or validation cycles, iterations, epochs, etc. In conjunction with the proxy-based scoring, semi-supervised learning mechanisms are used to map proxy scores to various model performance metrics. Other embodiments may be described and/or claimed.
8.20180361514Machine learning for weldment classification and correlation
US - 20.12.2018
Int.Class B23K 31/12
BPERFORMING OPERATIONS; TRANSPORTING
23MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
KSOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
31Processes relevant to this subclass, specially adapted for particular articles or purposes, but not covered by any single one of main groups B23K1/-B23K28/193
12relating to investigating the properties, e.g. the weldability, of materials
 Appl.No 15627867 Applicant Lincoln Global, Inc. Inventor Badri K. Narayanan

Embodiments of systems and methods for characterizing weldments are disclosed. One embodiment includes a method of generating an algorithm for classifying weldments as meeting or not meeting a specification. Training data is read by a machine learning system. The training data includes cross-sectional images of training weldments, truth data indicating whether the training weldments meet the specification or not, and training weld data associated with creating the training weldments. The machine learning system trains up an algorithm using the training data such that the resultant algorithm can classify a subsequent test weldment as meeting the specification or not meeting the specification when a cross-sectional image of the test weldment and test weld data used to create the test weldment are read and processed by the classification algorithm as trained.

9.20200111005Trusted neural network system
US - 09.04.2020
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 16226286 Applicant SRI International Inventor Shalini Ghosh

In general, the disclosure describes techniques for facilitating trust in neural networks using a trusted neural network system. For example, described herein are multi-headed, trusted neural network systems that can be trained to satisfy one or more constraints as part of the training process, where such constraints may take the form of one or more logical rules and cause the objective function of at least one the heads of the trusted neural network system to steer, during machine learning model training, the overall objective function for the system toward an optimal solution that satisfies the constraints. The constraints may be non-temporal, temporal, or a combination of non-temporal and temporal. The constraints may be directly compiled to a neural network or otherwise used to train the machine learning model.

10.WO/2019/160850ATLAS-BASED SEGMENTATION USING DEEP-LEARNING
WO - 22.08.2019
Int.Class G06T 7/11
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
10Segmentation; Edge detection
11Region-based segmentation
 Appl.No PCT/US2019/017626 Applicant ELEKTA, INC. Inventor HAN, Xiao
Techniques for enhancing image segmentation with the integration of deep learning are disclosed herein. An example method for atlas-based segmentation using deep learning includes: applying a deep learning model to a subject image to identify an anatomical feature, registering an atlas image to the subject image, using the deep learning segmentation data to improve a registration result, generating a mapped atlas, and identifying the feature in the subject image using the mapped atlas. Another example method for training and use of a trained machine learning classifier, in an atlas-based segmentation process using deep learning, includes: applying a deep learning model to an atlas image, training a machine learning model classifier using data from applying the deep learning model, estimating structure labels of areas of the subject image, and defining structure labels by combining the estimated structure labels with labels produced from atlas-based segmentation on the subject image.
# -