NVIDIA Corporation patent applications on January 9th, 2025
Patent Applications by NVIDIA Corporation on January 9th, 2025
NVIDIA Corporation: 10 patent applications
NVIDIA Corporation has applied for patents in the areas of G06V10/764 (2), G06V20/56 (2), G06F18/214 (2), G06N3/08 (2), G06V10/82 (2) B25J9/1671 (1), G01R31/31727 (1), G06F16/27 (1), G06N20/00 (1), G06T7/11 (1)
With keywords such as: data, learning, machine, object, node, signal, content, frequency, lane, and direction in patent application abstracts.
Patent Applications by NVIDIA Corporation
Inventor(s): Carolyn Linjon Chen of El Cerrito CA (US) for nvidia corporation, Yashraj Shyam Narang of Seattle WA (US) for nvidia corporation, Fabio Tozeto Ramos of Seattle WA (US) for nvidia corporation, Dieter Fox of Seattle WA (US) for nvidia corporation
IPC Code(s): B25J9/16, B25J11/00, G01N15/00, G06F17/18, G06F18/214, G06F30/27, G06N3/08, G06N7/01, G06T7/00, G06T7/77, G06V10/764, G06V10/82, G06V20/56, G06V20/64
CPC Code(s): B25J9/1671
Abstract: apparatuses, systems, and techniques to identify at least one physical characteristic of materials from computer simulations of manipulations of materials. in at least one embodiment, physical characteristics are determined by comparing measured statistics of observed manipulations to simulations of manipulations using a simulator trained with a likelihood-free inference engine.
Inventor(s): Ofek Abadi of Nahariya (IL) for nvidia corporation
IPC Code(s): G01R31/317
CPC Code(s): G01R31/31727
Abstract: technologies directed to determine whether a frequency of a clock signal is outside a specified frequency range are described. one integrated circuit includes a signal generator circuit, a voltage divider circuit, and digital logic circuitry. the signal generator circuit generates phase signals from a clock signal. the voltage divider circuit converts a frequency of the clock signal to a voltage representing the frequency. the voltage divider circuit includes a first resistor and a first switched-capacitor structure to receive the phase signals. an average resistance of the first switched-capacitor structure is inversely proportional to the frequency of the clock signal. the digital logic circuitry can determine, using the voltage, whether the frequency is outside of a specified frequency range, and output an indication responsive to the frequency being outside the specified frequency range.
Inventor(s): Siamak Nazari of Mountain View CA (US) for nvidia corporation, Jonathan A. McDowell of Belfast (IE) for nvidia corporation, Nigel Kerr of Belfast (IE) for nvidia corporation
IPC Code(s): G06F16/27, G06F11/14
CPC Code(s): G06F16/27
Abstract: an initiating node (c) in a storage platform () receives a modification request () for changing an object (). the initiating node (c), using system configuration information (), identifies an owner node (a) and a backup node (b) for the object () and sends change data () to the owner node (a) and the backup node (b). the owner node (a) modifies the object () with the data () from the initiating node (c) and sends an update request () that does not include the data () to the backup node (b). the backup node (b) modifies a backup object () with data () from the initiating node (c).
Inventor(s): Eric Todd Brower of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): G06N20/00, G06F18/21, G06F18/214, G06N3/04, G06N3/08, G06Q50/26, G06V10/764, G06V20/52
CPC Code(s): G06N20/00
Abstract: in various examples, object detections of a machine learning model are leveraged to automatically generate new ground truth data for images captured at different perspectives. the machine learning model may generate a prediction of a detected object at the different perspective, and an object tracking algorithm may be used to track the object through other images in a sequence of images where the machine learning model may not have detected the object. new ground truth data may be generated as a result of the object tracking algorithms outputs, and the new ground truth data may be used to retrain or update the machine learning model, train a different machine learning model, or increase the robustness of a ground truth data set that may be used for training machine learning models from various perspectives.
Inventor(s): Ke CHEN of Sunvalle CA (US) for nvidia corporation, Nikolai SMOLYANSKIY of Seattle WA (US) for nvidia corporation, Alexey KAMENEV of Bellevue WA (US) for nvidia corporation, Ryan OLDJA of Redmond WA (US) for nvidia corporation, Tilman WEKEL of Sunnyvale CA (US) for nvidia corporation, David NISTER of Santa Clara CA (US) for nvidia corporation, Joachim PEHSERL of Lynnwood WA (US) for nvidia corporation, Ibrahim EDEN of Redmond WA (US) for nvidia corporation, Sangmin OH of San Jose CA (US) for nvidia corporation, Ruchi BHARGAVA of Redmond WA (US) for nvidia corporation
IPC Code(s): G06T7/11, G05D1/81, G06F18/22, G06F18/23, G06T5/50, G06T7/10, G06V10/44, G06V10/82, G06V20/56, G06V20/58
CPC Code(s): G06T7/11
Abstract: a deep neural network(s) (dnn) may be used to perform panoptic segmentation by performing pixel-level class and instance segmentation of a scene using a single pass of the dnn. generally, one or more images and/or other sensor data may be stitched together, stacked, and/or combined, and fed into a dnn that includes a common trunk and several heads that predict different outputs. the dnn may include a class confidence head that predicts a confidence map representing pixels that belong to particular classes, an instance regression head that predicts object instance data for detected objects, an instance clustering head that predicts a confidence map of pixels that belong to particular instances, and/or a depth head that predicts range values. these outputs may be decoded to identify bounding shapes, class labels, instance labels, and/or range values for detected objects, and used to enable safe path planning and control of an autonomous vehicle.
Inventor(s): Rev Lebaredian of Los Gatos CA (US) for nvidia corporation, Michael Kass of San Jose CA (US) for nvidia corporation, Brian Harris of Santa Clara CA (US) for nvidia corporation, Andrey Shulzhenko of Santa Clara CA (US) for nvidia corporation, Dmitry Duka of Santa Clara CA (US) for nvidia corporation
IPC Code(s): G06T19/20
CPC Code(s): G06T19/20
Abstract: a content management system may maintain a scene description that represents a 3d virtual environment and a publish/subscribe model in which clients subscribe to content items that correspond to respective portions of the shared scene description. when changes are made to content, the changes may be served to subscribing clients. rather than transferring entire descriptions of assets to propagate changes, differences between versions of content may be exchanged, which may be used construct updated versions of the content. portions of scene description may reference other content items and clients may determine whether to request and load these content items for lazy loading. content items may be identified by uniform resource identifiers (uris) used to reference the content items. the content management system may maintain states for client connections including for authentication, for the set of subscriptions in the publish/subscribe model, and for their corresponding version identifiers.
Inventor(s): Xianchao Wu of Tokyo (JP) for nvidia corporation, Yi Dong of Lexington MA (US) for nvidia corporation, Scott Nunweiler of Yokohama (JP) for nvidia corporation
IPC Code(s): G10L15/06, G10L13/047, G10L15/16
CPC Code(s): G10L15/063
Abstract: disclosed are systems and techniques for training machine learning models. the techniques include providing a first data of a first modality as input to a first machine learning model to obtain a first output of a second modality, providing the first output of the second modality as input to a second machine learning model to obtain a second output of the first modality, providing the first data as input to a third machine learning model to obtain a first tensor, providing the second output as input to the third machine learning model to obtain a second tensor, calculating a first loss based on a comparison between the first tensor and the second tensor, and causing the first machine learning model to be modified based on the first loss.
Inventor(s): Ofek Abadi of Nahariya (IL) for nvidia corporation
IPC Code(s): H04L5/14
CPC Code(s): H04L5/1423
Abstract: a system includes a series of first/second transceivers mutually coupled over data lanes as bidirectional transceivers, and first/second control logic coupled to the first/second transceivers, respectively. an encoding of bit inversions by the first/second control logic causes: first pair of transceivers coupled over a first data lane to transmit non-inverted bits in a first direction and a second direction over the first data lane; second pair of transceivers coupled over a second data lane to transmit inverted bits in a first direction but not a second direction over the second data lane; third pair of transceivers coupled over a third data lane to transmit inverted bits in the second direction but not the first direction over the third data lane; and fourth pair of transceivers coupled over a fourth data lane to transmit inverted bits in the first direction and the second direction over the fourth data lane.
Inventor(s): Elena AGOSTINI of Rome (IT) for nvidia corporation, Itai GEFFEN of Tel Aviv (IL) for nvidia corporation
IPC Code(s): H04L47/10, H04L49/00
CPC Code(s): H04L47/10
Abstract: in various examples, a technique for performing digital signal processing includes receiving, at a kernel executing on a parallel-processing unit (ppu), a first plurality of packets from a network interface. the technique also includes extracting, by the kernel, one or more portions of a first digital signal from the first plurality of packets and converting, by the kernel, the one or more portions of the first digital signal into a second digital signal using one or more signal-processing techniques. the technique further includes transmitting, by the kernel, the second digital signal within a second plurality of packets to the network interface, wherein the second plurality of packets is further forwarded over the network interface to a remote client.
Inventor(s): Ante Jukic of Culver City CA (US) for nvidia corporation, Jagadeesh Balam of Campbell CA (US) for nvidia corporation, Boris Ginsburg of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): H04S7/00, G10L25/30
CPC Code(s): H04S7/302
Abstract: approaches presented herein provide for identification of sound from a sound source relative to an array of microphones of a potentially unknown configuration using, in part, differences in the audio signals received by the microphones. in at least one embodiment, audio signals are captured using an array of microphones and audio features are extracted from those signals. the audio features can be processed using a first neural network to generate a feature vector representing a spatial location of an audio source with respect to the plurality of microphones, where the spatial location is inferred based on audio differences and independent of an availability of information indicating a physical configuration of the plurality of microphones. the feature vector can be provided to a task-specific model to perform at least one audio-related task based in part on the spatial location.
- NVIDIA Corporation
- B25J9/16
- B25J11/00
- G01N15/00
- G06F17/18
- G06F18/214
- G06F30/27
- G06N3/08
- G06N7/01
- G06T7/00
- G06T7/77
- G06V10/764
- G06V10/82
- G06V20/56
- G06V20/64
- CPC B25J9/1671
- Nvidia corporation
- G01R31/317
- CPC G01R31/31727
- G06F16/27
- G06F11/14
- CPC G06F16/27
- G06N20/00
- G06F18/21
- G06N3/04
- G06Q50/26
- G06V20/52
- CPC G06N20/00
- G06T7/11
- G05D1/81
- G06F18/22
- G06F18/23
- G06T5/50
- G06T7/10
- G06V10/44
- G06V20/58
- CPC G06T7/11
- G06T19/20
- CPC G06T19/20
- G10L15/06
- G10L13/047
- G10L15/16
- CPC G10L15/063
- H04L5/14
- CPC H04L5/1423
- H04L47/10
- H04L49/00
- CPC H04L47/10
- H04S7/00
- G10L25/30
- CPC H04S7/302