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Analysis

1.20190295282Stereo 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.

2.WO/2022/133330ROBOT FLEET MANAGEMENT AND ADDITIVE MANUFACTURING FOR VALUE CHAIN NETWORKS
WO 23.06.2022
Int.Class G06Q 10/06
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QINFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
10Administration; Management
06Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
Appl.No PCT/US2021/064233 Applicant STRONG FORCE VCN PORTFOLIO 2019, LLC Inventor CELLA, Charles H.
A value chain network automation system includes a supply chain robotic fleet data set including attributes of a set of states and capabilities of a set of robotic systems in a supply chain for a set of goods. The system includes a demand intelligence robotic process automation data set including attributes of a set of states of a set of robotic process automation systems that undertake automation of a set of demand forecasting tasks for the set of goods. The system includes a coordination system that provides a set of robotic task instructions for the supply chain robotic fleet based on processing the supply chain robotic fleet data set and the demand intelligence robotic process automation data set to coordinate supply and demand for the set of goods.
3.2021401816ROBOT FLEET MANAGEMENT AND ADDITIVE MANUFACTURING FOR VALUE CHAIN NETWORKS
AU 23.06.2022
Int.Class G06Q 10/06
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
QINFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
10Administration; Management
06Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
Appl.No 2021401816 Applicant STRONG FORCE VCN PORTFOLIO 2019, LLC Inventor BLIVEN, Brent
A value chain network automation system includes a supply chain robotic fleet data set including attributes of a set of states and capabilities of a set of robotic systems in a supply chain for a set of goods. The system includes a demand intelligence robotic process automation data set including attributes of a set of states of a set of robotic process automation systems that undertake automation of a set of demand forecasting tasks for the set of goods. The system includes a coordination system that provides a set of robotic task instructions for the supply chain robotic fleet based on processing the supply chain robotic fleet data set and the demand intelligence robotic process automation data set to coordinate supply and demand for the set of goods.
4.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.
5.20220156946Supervised learning and occlusion masking for optical flow estimation
US 19.05.2022
Int.Class G06K 9/00
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
KGRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
9Methods or arrangements for recognising patterns
Appl.No 17510763 Applicant QUALCOMM Incorporated Inventor Jamie Menjay Lin

Systems and techniques are described for performing supervised learning (e.g., semi-supervised learning, self-supervised learning, and/or mixed supervision learning) for optical flow estimation. For example, a method can include obtaining an image associated with a sequence of images and generating an occluded image. The occluded image can include at least one of the image with an occlusion applied to the image and a different image of the sequence of images with the occlusion applied. The method can include determining a matching map based at least on matching areas of the image and the occluded image and, based on the matching map, determining a loss term associated with an optical flow loss prediction associated with the image and the occluded image. The loss term may include a matched loss and/or other loss. Based on the loss term, the method can include training a network configured to determine an optical flow between images.

6.WO/2022/104310SUPERVISED LEARNING AND OCCLUSION MASKING FOR OPTICAL FLOW ESTIMATION
WO 19.05.2022
Int.Class G06T 7/20
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
7Image analysis
20Analysis of motion
Appl.No PCT/US2021/072062 Applicant QUALCOMM INCORPORATED Inventor LIN, Jamie Menjay
Systems and techniques are described for performing supervised learning (e.g., semi-supervised learning, self-supervised learning, and/or mixed supervision learning) for optical flow estimation. For example, a method can include obtaining an image associated with a sequence of images and generating an occluded image. The occluded image can include at least one of the image with an occlusion applied to the image and a different image of the sequence of images with the occlusion applied. The method can include determining a matching map based at least on matching areas of the image and the occluded image and, based on the matching map, determining a loss term associated with an optical flow loss prediction associated with the image and the occluded image. The loss term may include a matched loss and/or other loss. Based on the loss term, the method can include training a network configured to determine an optical flow between images.
7.20240144103Value chain network planning using machine learning and digital twin simulation
US 02.05.2024
Int.Class G05D 1/00
GPHYSICS
05CONTROLLING; REGULATING
DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
1Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
Appl.No 18525831 Applicant Strong Force VCN Portfolio 2019, LLC Inventor Charles H. Cella

A VCN process may receive, by a value chain network digital twin, information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained to determine a task to be completed for the value chain network. A VCN process may provide at least one of an instruction for executing the task in the value chain network digital twin and a recommendation for executing the task in the value chain network digital twin.

8.2024220202SYSTEMS, METHODS, KITS, AND APPARATUSES FOR SPECIALIZED CHIPS FOR ROBOTIC INTELLIGENCE LAYERS
AU 13.03.2025
Int.Class G05D 101/15
GPHYSICS
05CONTROLLING; REGULATING
DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
101Details of software or hardware architectures used for the control of position
10using artificial intelligence techniques
15using machine learning, e.g. neural networks
Appl.No 2024220202 Applicant STRONG FORCE VCN PORTFOLIO 2019, LLC Inventor BLIVEN, Brent
A system may include a robotic control circuit configured to control one or more robotic functions of a robot. A system may include a plurality of sensors configured to collect data. A system may include a governance analysis circuit configured to analyze the data and select one or more governance frameworks based on the analyzed data. A system may include a governance model circuit configured to generate a model that applies the one or more governance frameworks to determine one or more governance actions, wherein the robotic control circuit is configured to control the one or more robotic functions in accordance with the one or more governance actions, wherein the robotic control circuit, the governance analysis circuit, and the governance model circuit are integrated on a single substrate.
9.2024220200SYSTEMS, 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.
10.20240144011SYSTEMS, METHODS, KITS, AND APPARATUSES FOR USING ARTIFICIAL INTELLIGENCE FOR INSTRUCTING SMART MACHINES IN VALUE CHAIN NETWORKS
US 02.05.2024
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 18525824 Applicant Strong Force VCN Portfolio 2019, LLC Inventor Charles H. Cella

A VCN process may receive information associated with a value chain network. A VCN process may provide the information to a set of Artificial Intelligence (AI)-based learning models, wherein at least one member of the set of AI-based learning models is trained to classify at least one of: an operating state, a fault condition, an operating flow, or a behavior of the value chain network and at least one member of the set of AI-based learning models is trained on the training data set to determine, upon receiving the classification of the at least one of: the operating state, the fault condition, the operating flow, or the behavior, a task to be completed for the value chain network. A VCN process may provide a computer code instruction set to a machine to execute the task to facilitate an improvement in the operation of the value chain network.