NVIDIA Corporation patent applications on February 13th, 2025
Patent Applications by NVIDIA Corporation on February 13th, 2025
NVIDIA Corporation: 7 patent applications
NVIDIA Corporation has applied for patents in the areas of B60R21/017 (1), G06F3/04815 (1), G06V10/82 (1), G06N3/092 (1), G06N3/126 (1) B60R21/017 (1), G05D1/0088 (1), G06F3/04845 (1), G06F9/4881 (1), G06N3/0895 (1)
With keywords such as: machine, solutions, functions, systems, worker, data, node, learning, occupant, and image in patent application abstracts.
Patent Applications by NVIDIA Corporation
Inventor(s): Atousa Torabi of Santa Clara CA (US) for nvidia corporation, Sakthivel Sivaraman of Santa Clara CA (US) for nvidia corporation, Niranjan Avadhanam of Saratoga CA (US) for nvidia corporation, Shagan Sah of Santa Clara CA (US) for nvidia corporation
IPC Code(s): B60R21/017, B60R21/01, B60R21/013, B60W50/00, B60W50/14, B60W60/00, G06N3/02
CPC Code(s): B60R21/017
Abstract: in various examples, systems and methods are disclosed that accurately identify driver and passenger in-cabin activities that may indicate a biomechanical distraction that prevents a driver from being fully engaged in driving a vehicle. in particular, image data representative of an image of an occupant of a vehicle may be applied to one or more deep neural networks (dnns). using the dnns, data indicative of key point locations corresponding to the occupant may be computed, a shape and/or a volume corresponding to the occupant may be reconstructed, a position and size of the occupant may be estimated, hand gesture activities may be classified, and/or body postures or poses may be classified. these determinations may be used to determine operations or settings for the vehicle to increase not only the safety of the occupants, but also of surrounding motorists, bicyclists, and pedestrians.
Inventor(s): Vito Magnanimo of Munich (DE) for nvidia corporation, Elliot Simon of San Diego CA (US) for nvidia corporation
IPC Code(s): G05D1/00
CPC Code(s): G05D1/0088
Abstract: in various examples, incremental booting of functions for autonomous systems and applications is described herein. systems and methods are disclosed that boot functions of a machine using different phases in order to more quickly provide at least a portion of the functions without compromising the safety of the machine. for example, during a first phase, resources associated with the machine may be allocated while the functions are initialized. additionally, at least a first portion of the functions, such as functions that do not compromise the safety of the machine if errors occur, may be made available for use by one or more users of the machine. next, during a second phase, at least a second portion of the functions, such as functions that may compromise the safety of the machine if errors occur, may be made available for use by the user(s) of the machine.
Inventor(s): Shie Mannor of Haifa (IL) for nvidia corporation, Gal Chechik of Ramat Hasharon (IL) for nvidia corporation
IPC Code(s): G06F3/04845, G06F3/04815
CPC Code(s): G06F3/04845
Abstract: apparatuses, systems, and techniques to identify one or more modifications to objects within an environment. in at least one embodiment, objects are identified in an image, based on extracted feedback information, using one or more machine learning models, for example, using direct and/or implicit feedback of user interaction with one or more objects in an environment.
Inventor(s): Binu Ramakrishnan of Fremont CA (US) for nvidia corporation, Soham Desai of Jacksonville FL (US) for nvidia corporation, Ayush Ambastha of Sunnyvale CA (US) for nvidia corporation, Aaron Kaloti of Santa Clara CA (US) for nvidia corporation
IPC Code(s): G06F9/48
CPC Code(s): G06F9/4881
Abstract: a system can include a memory and a processing device, operatively coupled to the memory, to perform operations including receiving a workload, selecting, from a set of worker nodes of a cluster environment, a worker node for scheduling of the workload, determining whether the worker node is valid, including determining whether the worker node is trusted, and in response to determining that the worker node is valid, scheduling the workload with the worker node.
20250053818. TEXT CLUSTERING PERFORMANCE EVALUATION_simplified_abstract_(nvidia corporation)
Inventor(s): Neilesh Chorakhalikar of San Jose CA (US) for nvidia corporation, Siddhant Mohan Kochrekar of New Brunswick NJ (US) for nvidia corporation, Arunachalam Arunachalam of Austin TX (US) for nvidia corporation, Bipin Prabhakar Todur of Santa Clara CA (US) for nvidia corporation, Deyang Liu of Shanghai (CN) for nvidia corporation
IPC Code(s): G06N3/0895, G06F40/30
CPC Code(s): G06N3/0895
Abstract: apparatuses, systems, and techniques generating one or more cluster performance evaluation metrics that allow for evaluation of the performance of an unsupervised natural language processing clustering algorithms to be used with unlabeled data. at least one embodiment pertains to methods of generating one or more cluster performance evaluation metrics based, at least in part, on one or more vectors generated by one or more neural networks to indicate a relationship among members of one or more clusters of data generated using the one or more data clustering algorithms, according to various novel techniques described herein.
Inventor(s): Eli Alexander Meirom of Haifa (IL) for nvidia corporation, Piotr Sielski of Lódz (PL) for nvidia corporation, Gal Chechik of Ramat Hasharon (IL) for nvidia corporation, Alexandre Fender of Winter Springs FL (US) for nvidia corporation, Shie Mannor of Haifa (IL) for nvidia corporation
IPC Code(s): G06N3/126, G06N3/092
CPC Code(s): G06N3/126
Abstract: a technique for solving combinatorial problems, such as vehicle routing for multiple vehicles integrates evolutionary algorithms and reinforcement learning. a genetic algorithm maintains a set of solutions for the problem and improves the solutions using mutation (modify a solution) and crossover (combine two solutions). the best solution is selected from the improved set of solutions. a system that integrates evolutionary algorithms, such as a genetic algorithm, and reinforcement learning comprises two components. a first component is a beam search technique for generating solutions using a reinforcement learning model. a second component augments a genetic algorithm using learning-based solutions that are generated by the reinforcement learning model. the learning-based solutions improve the diversity of the set which, in turn, improves the quality of the solutions computed by the genetic algorithm.
Inventor(s): Yuan-Hong LIAO of Toronto (CA) for nvidia corporation, David Jesus ACUNA MARRERO of Toronto (CA) for nvidia corporation, James LUCAS of Royston (GB) for nvidia corporation, Rafid MAHMOOD of Mississauga (CA) for nvidia corporation, Sanja FIDLER of Toronto (CA) for nvidia corporation, Viraj Uday PRABHU of Atlanta GA (US) for nvidia corporation
IPC Code(s): G06V10/82, G06V20/70
CPC Code(s): G06V10/82
Abstract: various examples relate to translating image labels from one domain (e.g., a synthetic domain) to another domain (e.g., a real-world domain) to improve model performance on real-world datasets and applications. systems and methods are disclosed that provide an unsupervised label translator that may employ a generative adversarial network (gan)-based approach. in contrast to conventional systems, the disclosed approach can employ a data-centric perspective that addresses systematic mismatches between datasets from different sources.
NVIDIA Corporation patent applications on February 13th, 2025