NVIDIA Corporation patent applications on April 18th, 2024
Patent Applications by NVIDIA Corporation on April 18th, 2024
NVIDIA Corporation: 15 patent applications
NVIDIA Corporation has applied for patents in the areas of G06V20/56 (4), G06N3/08 (4), G06F30/20 (3), G06V10/26 (3), G06V10/774 (2)
With keywords such as: data, intersection, vehicle, neural, frame, systems, learning, machine, location, and information in patent application abstracts.
Patent Applications by NVIDIA Corporation
20240123620.GRASP POSE PREDICTION_simplified_abstract_(nvidia corporation)
Inventor(s): Jonathan Tremblay of Redmond WA (US) for nvidia corporation, Stanley Thomas Birchfield of Sammamish WA (US) for nvidia corporation, Valts Blukis of Seattle WA (US) for nvidia corporation, Bowen Wen of Bellevue WA (US) for nvidia corporation, Dieter Fox of Seattle WA (US) for nvidia corporation, Taeyeop Lee of Daejeon (KR) for nvidia corporation
IPC Code(s): B25J9/16
Abstract: apparatuses, systems, and techniques to generate and select grasp proposals. in at least one embodiment, grasp proposals are generated and selected using one or more neural networks, based on, for example, a latent code corresponding to an object.
Inventor(s): Xianglong HAN of Palo Alto CA (US) for nvidia corporation, Ming CUI of San Jose CA (US) for nvidia corporation
IPC Code(s): B60W60/00, B60W30/18, B60W40/02, B60W50/14, G01C21/00, G08G1/01
Abstract: according to an aspect of an embodiment, operations may comprise accessing an hd map of a region comprising information describing an intersection of two or more roads and describing lanes of the two or more roads that intersect the intersection, automatically identifying constraints on the lanes at the intersection, automatically calculating, based on the constraints on the lanes at the intersection, lane connectivity for the intersection, displaying, on a user interface, the automatically calculated lane connectivity for the intersection, receiving, from a user through the user interface, confirmation that the automatically calculated lane connectivity for the intersection is an actual lane connectivity for the intersection, and adding the actual lane connectivity for the intersection to the information describing the intersection in the hd map.
20240126811.NEURAL NETWORKS TO INDICATE DATA DEPENDENCIES_simplified_abstract_(nvidia corporation)
Inventor(s): Marc Teva Law of Toronto (CA) for nvidia corporation, James Robert Lucas of Royston (GB) for nvidia corporation
IPC Code(s): G06F16/901
Abstract: apparatuses, systems, and techniques to indicate data dependencies. in at least one embodiment, one or more neural networks are used to generate one or more indicators of one or more data dependencies and one or more indicators of direction of the one or more data dependencies.
20240126940.MOMENTUM CONSERVATION IN PHYSICS ENGINES_simplified_abstract_(nvidia corporation)
Inventor(s): Kier Storey of Altrincham (GB) for nvidia corporation, Fengyun Lu of Altrincham (GB) for nvidia corporation
IPC Code(s): G06F30/20
Abstract: systems and methods herein address momentum conservation in physics engines using one or more processing units to simulate an articulated body based at least on an adjustment to a velocity that is associated with a root link of the articulated body, and using at least a change in momentum determined from one or more external forces separately from a change in momentum determined from one or more internal forces to conserve momentum within the system.
20240127041.CONVOLUTIONAL STRUCTURED STATE SPACE MODEL_simplified_abstract_(nvidia corporation)
Inventor(s): Jimmy Smith of Santa Clara CA (US) for nvidia corporation, Wonmin Byeon of Santa Cruz CA (US) for nvidia corporation, Shalini De Mello of San Francisco CA (US) for nvidia corporation
IPC Code(s): G06N3/0464, G06F17/16, G06N3/049
Abstract: systems and methods are disclosed related to a convolutional structured state space model (convssm), which has a tensor-structured state but a continuous-time parameterization and linear state updates. the linearity may be exploited to use parallel scans for subquadratic parallelization across the spatiotemporal sequence. the convssm effectively models long-range dependencies and, when followed by a nonlinear operation forms a spatiotemporal layer (convs5) that does not require compressing frames into tokens, can be efficiently parallelized across the sequence, provides an unbounded context, and enables fast autoregressive generation.
Inventor(s): Urs Muller of Keyport NJ (US) for nvidia corporation, Mariusz Bojarski of Brooklyn NY (US) for nvidia corporation, Chenyi Chen of Fremont CA (US) for nvidia corporation, Bernhard Firner of Highland Park NJ (US) for nvidia corporation
IPC Code(s): G06N3/08, G06N20/00, G06V10/774, G06V20/56
Abstract: in various examples, a machine learning model—such as a deep neural network (dnn)—may be trained to use image data and/or other sensor data as inputs to generate two-dimensional or three-dimensional trajectory points in world space, a vehicle orientation, and/or a vehicle state. for example, sensor data that represents orientation, steering information, and/or speed of a vehicle may be collected and used to automatically generate a trajectory for use as ground truth data for training the dnn. once deployed, the trajectory points, the vehicle orientation, and/or the vehicle state may be used by a control component (e.g., a vehicle controller) for controlling the vehicle through a physical environment. for example, the control component may use these outputs of the dnn to determine a control profile (e.g., steering, decelerating, and/or accelerating) specific to the vehicle for controlling the vehicle through the physical environment.
Inventor(s): Annamarie Bair of Pittsburgh PA (US) for nvidia corporation, Hongxu Yin of San Jose CA (US) for nvidia corporation, Pavlo Molchanov of Mountain View CA (US) for nvidia corporation, Maying Shen of Fremont CA (US) for nvidia corporation, Jose Manuel Alvarez Lopez of Mountain View CA (US) for nvidia corporation
IPC Code(s): G06N3/082
Abstract: systems and methods are disclosed for improving natural robustness of sparse neural networks. pruning a dense neural network may improve inference speed and reduces the memory footprint and energy consumption of the resulting sparse neural network while maintaining a desired level of accuracy. in real-world scenarios in which sparse neural networks deployed in autonomous vehicles perform tasks such as object detection and classification for acquired inputs (images), the neural networks need to be robust to new environments, weather conditions, camera effects, etc. applying sharpness-aware minimization (sam) optimization during training of the sparse neural network improves performance for out of distribution (ood) images compared with using conventional stochastic gradient descent (sgd) optimization. sam optimizes a neural network to find a flat minimum: a region that both has a small loss value, but that also lies within a region of low loss.
20240127075.SYNTHETIC DATASET GENERATOR_simplified_abstract_(nvidia corporation)
Inventor(s): Shalini De Mello of San Francisco CA (US) for nvidia corporation, Christian Jacobsen of Ann Arbor MI (US) for nvidia corporation, Xunlei Wu of Cary NC (US) for nvidia corporation, Stephen Tyree of University City MO (US) for nvidia corporation, Alice Li of Santa Clara CA (US) for nvidia corporation, Wonmin Byeon of Santa Cruz CA (US) for nvidia corporation, Shangru Li of Philadelphia PA (US) for nvidia corporation
IPC Code(s): G06N3/0985
Abstract: machine learning is a process that learns a model from a given dataset, where the model can then be used to make a prediction about new data. in order to reduce the costs associated with collecting and labeling real world datasets for use in training the model, computer processes can synthetically generate datasets which simulate real world data. the present disclosure improves the effectiveness of such synthetic datasets for training machine learning models used in real world applications, in particular by generating a synthetic dataset that is specifically targeted to a specified downstream task (e.g. a particular computer vision task, a particular natural language processing task, etc.).
20240127409.DEPTH BASED IMAGE SHARPENING_simplified_abstract_(nvidia corporation)
Inventor(s): Pascal Gilcher of Koerborn (DE) for nvidia corporation
IPC Code(s): G06T5/73, G06T5/20, G06T7/50, G06T11/00
Abstract: pixel depth information is used to determine a weight to apply to neighboring pixels when using a sharpening filter. a difference between neighboring pixel depths is evaluated and pixels with pixel depths that exceed a threshold are given less weight than other pixels. a sharpening mask may be generated using adjusted pixel colors.
Inventor(s): Trung Pham of Santa Clara CA (US) for nvidia corporation, Berta Rodriguez Hervas of San Francisco CA (US) for nvidia corporation, Minwoo Park of Saratoga CA (US) for nvidia corporation, David Nister of Bellevue WA (US) for nvidia corporation, Neda Cvijetic of East Palo Alto CA (US) for nvidia corporation
IPC Code(s): G06T7/11, G05B13/02, G06F18/21, G06F18/24, G06N3/04, G06N3/08, G06T3/4046, G06T5/70, G06T11/20, G06V10/26, G06V10/34, G06V10/44, G06V10/82, G06V20/56, G06V30/19, G06V30/262
Abstract: in various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersection contention areas in an environment of a vehicle in real-time or near real-time. for example, a deep neural network (dnn) may be trained to compute outputs—such as signed distance functions—that may correspond to locations of boundaries delineating intersection contention areas. the signed distance functions may be decoded and/or post-processed to determine instance segmentation masks representing locations and classifications of intersection areas or regions. the locations of the intersections areas or regions may be generated in image-space and converted to world-space coordinates to aid an autonomous or semi-autonomous vehicle in navigating intersections according to rules of the road, traffic priority considerations, and/or the like.
20240127519.SCALABLE CONTACT-RICH SIMULATION_simplified_abstract_(nvidia corporation)
Inventor(s): Kier Storey of Altrincham (GB) for nvidia corporation, Fengyun Lu of Altrincham (GB) for nvidia corporation
IPC Code(s): G06T13/20, G06K9/62, G06T15/00, G06T15/04, G06T17/20
Abstract: systems and methods herein address scalable contact-rich simulation in physics engines using one or more processing units to simulate movement between at least two objects in a simulation, the movement based at least on a plurality of sets of reduced points obtained from an iterative reduction using one or more threshold criteria, the iterative reduction applied to a plurality of points associated with at least one contact between the depictions.
Inventor(s): Karthick Sekkappan of Pune (IN) for nvidia corporation, Aurobinda Maharana of Chinchwad (IN) for nvidia corporation
IPC Code(s): G06V10/26, G06T7/269, G06V10/56, G06V10/60, H04N19/139
Abstract: systems and methods estimate occluded pixels in frames of a video sequence. optical flow data is received to determine a validity for forward and backward flow vectors for a common pixel location in a first frame and a second frame that are temporally next to one another. occlusion information for the first frame determines pixels that are hidden in the second frame with respect to playback from the first frame to the second frame. occlusion information for the second frame determines pixels that are hidden in the first frame with respect to playback from the second frame to the first frame.
Inventor(s): Cheng-Ping HSIEH of La Jolla CA (US) for nvidia corporation, Subhankar GHOSH of Santa Clara CA (US) for nvidia corporation, Boris GINSBURG of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): G10L13/00, G10L17/02
Abstract: in various examples, one or more text-to-speech machine learning models may be customized or adapted to accommodate new or additional speakers or speaker voices without requiring a full re-training of the models. for example, a base model may be trained on a set of one or more speakers and, after training or deployment, the model may be adapted to support one or more other speakers. to do this, one or more additional layers (e.g., adapter layers) may be added to the model, and the model may be re-trained or updated—e.g., by freezing parameters of the base model while updating parameters of the adapter layers—to generate an adapted model that can support the one or more original speakers of the base model in addition to the one or more additional speakers corresponding to the adapter layers.
Inventor(s): Siddha Ganju of Santa Clara CA (US) for nvidia corporation, Elad Mentovich of Tel Aviv (IL) for nvidia corporation, Michael Balint of Nashville TN (US) for nvidia corporation, Eitan Zahavi of Zichron Yaakov (IL) for nvidia corporation, Michael Sabotta of Cypress TX (US) for nvidia corporation, Michael Norman of Redwood City CA (US) for nvidia corporation, Ryan Wells of Raleigh NC (US) for nvidia corporation
IPC Code(s): H04L67/60, G06F11/30, H04L41/16
Abstract: a method includes receiving, using a processing device, a first condition associated with an operation at a data center, where the operation at the data center pertains to a first location at the data center, the first location corresponding to a first parameter value. the method further includes providing the first condition as an input to a machine learning model. the method also includes performing one or more reinforcement learning techniques using the machine learning model to cause the machine learning model to output an indication of a final location associated with the operation, where the final location corresponds to a final parameter value that is closer to a target than the first parameter value corresponding to the first location at the data center.
Inventor(s): Mauro Belgiovine of Cambridge MA (US) for nvidia corporation, Christopher Hans Dick of San Jose CA (US) for nvidia corporation
IPC Code(s): H04W16/28, H04B17/318
Abstract: apparatuses, systems, and techniques to identify and select a wireless signal beam. in at least one embodiment, a wireless signal beam is identified and selected using a determined angle of arrival of one or more wireless signals at a base station or ue.
- NVIDIA Corporation
- B25J9/16
- Nvidia corporation
- B60W60/00
- B60W30/18
- B60W40/02
- B60W50/14
- G01C21/00
- G08G1/01
- G06F16/901
- G06F30/20
- G06N3/0464
- G06F17/16
- G06N3/049
- G06N3/08
- G06N20/00
- G06V10/774
- G06V20/56
- G06N3/082
- G06N3/0985
- G06T5/73
- G06T5/20
- G06T7/50
- G06T11/00
- G06T7/11
- G05B13/02
- G06F18/21
- G06F18/24
- G06N3/04
- G06T3/4046
- G06T5/70
- G06T11/20
- G06V10/26
- G06V10/34
- G06V10/44
- G06V10/82
- G06V30/19
- G06V30/262
- G06T13/20
- G06K9/62
- G06T15/00
- G06T15/04
- G06T17/20
- G06T7/269
- G06V10/56
- G06V10/60
- H04N19/139
- G10L13/00
- G10L17/02
- H04L67/60
- G06F11/30
- H04L41/16
- H04W16/28
- H04B17/318
