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1 / 14,684
1.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.
2.2024220200SYSTEMS, 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.
3.20240144103Value 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.

4.20240144011SYSTEMS, 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.

5.2024220202SYSTEMS, 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.
6.WO/2025/050067SYSTEMS, METHODS, KITS, AND APPARATUSES FOR SPECIALIZED CHIPS FOR ROBOTIC INTELLIGENCE LAYERS
WO - 06.03.2025
Int.Class G06F 9/46
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
9Arrangements for program control, e.g. control units
06using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
46Multiprogramming arrangements
 Appl.No PCT/US2024/044898 Applicant STRONG FORCE VCN PORTFOLIO 2019, LLC Inventor CELLA, Charles H.
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.
7.20230252776Variable-Focus Dynamic Vision for Robotic System
US - 10.08.2023
Int.Class G06V 10/82
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
10Arrangements for image or video recognition or understanding
70using pattern recognition or machine learning
82using neural networks
 Appl.No 18180053 Applicant Strong Force VCN Portfolio 2019, LLC Inventor Charles Howard Cella

A dynamic vision system for a robotic system includes an optical assembly including a lens containing a liquid. The lens is deformable to generate variable focus for the lens. The optical assembly is configured to capture optical data. A robotic system is configured to simulate human or animal species capabilities having a control system configured to adjust one or more optical parameters. The one or more optical parameters modify the variable focus of the lens while the optical assembly captures current optical data relating to the robotic system. A processing system is configured to train a machine learning model to recognize an object relating to the robotic system from training data generated from the optical data captured by the optical assembly. The optical data includes the current optical data relating to the robotic system.

8.20230120318Dynamic Vision System for Robot Fleet Management
US - 20.04.2023
Int.Class G06V 10/774
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
10Arrangements for image or video recognition or understanding
70using pattern recognition or machine learning
77Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis or independent component analysis or self-organising maps ; Blind source separation
774Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
 Appl.No 18061396 Applicant Strong Force VCN Portfolio 2019, LLC Inventor Sava Marinkovich

A dynamic vision system for robot fleet management includes an optical assembly including a lens containing a liquid. The lens is deformable to generate variable focus for the lens. The optical assembly is configured to capture optical data. The dynamic vision system includes a robot fleet management platform having a control system configured to adjust one or more optical parameters. The one or more optical parameters modify the variable focus of the lens while the optical assembly captures current optical data relating to a robotic fleet. The dynamic vision system includes a processing system configured to train a machine learning model to recognize an object relating to the robotic fleet using training data generated from the optical data captured by the optical assembly. The optical data includes the current optical data relating to the robotic fleet.

9.20230123322Predictive Model Data Stream Prioritization
US - 20.04.2023
Int.Class G05B 13/04
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
04involving the use of models or simulators
 Appl.No 18061417 Applicant Strong Force VCN Portfolio 2019, LLC Inventor Charles H. Cella

A method for prioritizing predictive model data streams includes receiving, by a first device, a plurality of predictive model data streams. Each predictive model data stream includes a set of model parameters for a corresponding predictive model. Each predictive model is trained to predict future data values of a data source. The method includes prioritizing, by the first device, priorities to each of the plurality of predictive model data streams. The method includes selecting at least one of the predictive model data streams based on a corresponding priority. The method includes parameterizing, by the first device, a predictive model using the set of model parameters included in the selected predictive model data stream. The method includes predicting, by the first device, future data values of the data source using the parameterized predictive model.

10.2022274234SYSTEMS, METHODS, KITS, AND APPARATUSES FOR EDGE-DISTRIBUTED STORAGE AND QUERYING IN VALUE CHAIN NETWORKS
AU - 17.11.2022
Int.Class B33Y 50/02
BPERFORMING OPERATIONS; TRANSPORTING
33ADDITIVE MANUFACTURING TECHNOLOGY
YADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
50Data acquisition or data processing for additive manufacturing
02for controlling or regulating additive manufacturing processes
 Appl.No 2022274234 Applicant STRONG FORCE VCN PORTFOLIO 2019, LLC Inventor BUNIN, Andrew
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