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Analysis

1 / 2,072
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.
3.20200342307Swarm fair deep reinforcement learning
US - 29.10.2020
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 16395187 Applicant International Business Machines Corporation Inventor Aaron K. Baughman

Fair deep reinforcement learning is provided. A microstate of an environment and reaction of items in a plurality of microstates within the environment are observed after an agent performs an action in the environment. Semi-supervised training is utilized to determine bias weights corresponding to the action for the microstate of the environment and the reaction of the items in the plurality of microstates within the environment. The bias weights from the semi-supervised training are merged with non-bias weights using an artificial neural network. Over time, it is determined where bias is occurring in the semi-supervised training based on merging the bias weights with the non-bias weights in the artificial neural network. A deep reinforcement learning model that decreases reliance on the bias weights is generated based on determined bias to increase fairness.

4.20230278227Device 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.

5.WO/2024/226801SYSTEMS, METHODS, KITS, AND APPARATUSES FOR GENERATIVE ARTIFICIAL INTELLIGENCE, GRAPHICAL NEURAL NETWORKS, TRANSFORMER MODELS, AND CONVERGING TECHNOLOGY STACKS IN VALUE CHAIN NETWORKS
WO - 31.10.2024
Int.Class G06F 30/27
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
30Computer-aided design
20Design optimisation, verification or simulation
27using machine learning, e.g. artificial intelligence, neural networks, support vector machines or training a model
 Appl.No PCT/US2024/026275 Applicant STRONG FORCE VCN PORTFOLIO 2019, LLC Inventor CELLA, Charles H.
A system may execute, by a generative artificial intelligence system, generative artificial intelligence algorithms trained on value chain network data. A system may receive input data including at least one of images, video, audio, text, programmatic code, and data, process the input data using the generative artificial intelligence algorithms to generate output content, wherein the output content includes at least one of structured prose, images, video, audio content, software source code, formatted data, algorithms, definitions, and context-specific structures, and generate an internal state of the generative artificial intelligence system, including a set of weights and/or biases as a result of prior processing. A system may provide the generated output content to a user interface for presentation to a user.
6.4163833DEEP 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.
7.20200111005Trusted 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.

8.20190295282Stereo depth estimation using deep neural networks
US - 26.09.2019
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 16356439 Applicant NVIDIA Corporation Inventor Nikolai Smolyanskiy

Various examples of the present disclosure include a stereoscopic deep neural network (DNN) that produces accurate and reliable results in real-time. Both LIDAR data (supervised training) and photometric error (unsupervised training) may be used to train the DNN in a semi-supervised manner. The stereoscopic DNN may use an exponential linear unit (ELU) activation function to increase processing speeds, as well as a machine learned argmax function that may include a plurality of convolutional layers having trainable parameters to account for context. The stereoscopic DNN may further include layers having an encoder/decoder architecture, where the encoder portion of the layers may include a combination of three-dimensional convolutional layers followed by two-dimensional convolutional layers.

9.20240118667MITIGATING REALITY GAP THROUGH TRAINING A SIMULATION-TO-REAL MODEL USING A VISION-BASED ROBOT TASK MODEL
US - 11.04.2024
Int.Class G05B 13/02
GPHYSICS
05CONTROLLING; REGULATING
BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
13Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
02electric
 Appl.No 17767675 Applicant GOOGLE LLC Inventor Kanishka Rao

Implementations disclosed herein relate to mitigating the reality gap through training a simulation-to-real machine learning model (“Sim2Real” model) using a vision-based robot task machine learning model. The vision-based robot task machine learning model can be, for example, a reinforcement learning (“RL”) neural network model (RL-network), such as an RL-network that represents a Q-function.

10.2024220200SYSTEMS, METHODS, KITS, AND APPARATUSES FOR GENERATIVE ARTIFICIAL INTELLIGENCE, GRAPHICAL NEURAL NETWORKS, TRANSFORMER MODELS, AND CONVERGING TECHNOLOGY STACKS IN VALUE CHAIN NETWORKS.
AU - 21.11.2024
Int.Class G06N 20/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
20Machine learning
 Appl.No 2024220200 Applicant STRONG FORCE VCN PORTFOLIO 2019, LLC Inventor BUNIN, Andrew
A system may execute, by a generative artificial intelligence system, generative artificial intelligence algorithms trained on value chain network data. A system may receive input data including at least one of images, video, audio, text, programmatic code, and data, process the input data using the generative artificial intelligence algorithms to generate output content, wherein the output content includes at least one of structured prose, images, video, audio content, software source code, formatted data, algorithms, definitions, and context-specific structures, and generate an internal state of the generative artificial intelligence system, including a set of weights and/or biases as a result of prior processing. A system may provide the generated output content to a user interface for presentation to a user.
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