NVIDIA Corporation patent applications on February 6th, 2025
Patent Applications by NVIDIA Corporation on February 6th, 2025
NVIDIA Corporation: 19 patent applications
NVIDIA Corporation has applied for patents in the areas of G06F9/50 (3), G06T15/06 (2), G06T17/00 (2), G06N20/00 (2), A63F13/355 (1) G06T15/06 (2), A63F13/355 (1), G06T11/001 (1), H04N21/23424 (1), G10L17/18 (1)
With keywords such as: data, application, state, systems, such, states, object, image, machine, and instance in patent application abstracts.
Patent Applications by NVIDIA Corporation
Inventor(s): Andrew Fear of Cedar Park TX (US) for nvidia corporation
IPC Code(s): A63F13/355, A63F13/358
CPC Code(s): A63F13/355
Abstract: in various examples, a user may access or acquire an application to download to the user's local computing device. upon accessing the application, a local instance of the application may begin downloading to the computing device, and the user may be given the option to play a cloud-hosted instance of the application. if the user selects to play a hosted instance of the application, the cloud-hosted instance of the application may begin streaming while the local instance of the application downloads to the user's computing device in the background. application state data may be stored and associated with the user during gameplay such that, once the local instance of the application has downloaded, the user may switch from the hosted instance of the application to the local instance to begin playing locally, with the application state information accounted for.
20250042413. CONTEXT-BASED STATE ESTIMATION_simplified_abstract_(nvidia corporation)
Inventor(s): Yuzhuo Ren of Sunnyvale CA (US) for nvidia corporation, Niranjan Avadhanam of Saratoga CA (US) for nvidia corporation
IPC Code(s): B60W40/09, G06V20/59, G06V40/16
CPC Code(s): B60W40/09
Abstract: state information can be determined for a subject that is robust to different inputs or conditions. for drowsiness, facial landmarks can be determined from captured image data and used to determine a set of blink parameters. these parameters can be used, such as with a temporal network, to estimate a state (e.g., drowsiness) of the subject. to improve robustness, an eye state determination network can determine eye state from the image data, without reliance on intermediate landmarks, that can be used, such as with another temporal network, to estimate the state of the subject. a weighted combination of these values can be used to determine an overall state of the subject. to improve accuracy, individual behavior patterns and context information can be utilized to account for variations in the data due to subject variation or current context rather than changes in state.
Inventor(s): Ayon SEN of Santa Clara CA (US) for nvidia corporation
IPC Code(s): B60W50/00, B62D15/02, G06T17/00, G06V10/764, G06V10/82, G06V20/56
CPC Code(s): B60W50/0097
Abstract: in various examples, a trailer angle may be estimated using one or more machine learning models to predict one or more keypoints on the center axis of the trailer drawbar (e.g., a keypoint representing the drawbar junction around which the drawbar pivots, one or more other keypoints along the center axis), back-projecting the predicted keypoint(s) onto a three-dimensional (3d) representation of the ground, and calculating the angle between the longitudinal axis of the towing vehicle and a line or ray formed by or fitted to the projected keypoints. the trailer angle may be estimated at any frame rate. for each frame, keypoints may be predicted from that frame and/or optical flow or some other type of feature tracking may be used to propagate predicted keypoint(s) from a preceding frame in lieu of predicting keypoint(s), and the resulting keypoint(s) may be used to estimate the trailer angle for that frame.
20250045094. GPU-INITITATED DATA ACCESS OF SCALED STORAGE_simplified_abstract_(nvidia corporation)
Inventor(s): Christopher J. Newburn of South Beloit IL (US) for nvidia corporation, Zaid Qureshi of Bronx NY (US) for nvidia corporation, Vikram Sharma Mailthody of Urbana IL (US) for nvidia corporation, Isaac Gelado of Pacifica CA (US) for nvidia corporation, Wen-Mei Hwu of Champaign IL (US) for nvidia corporation, Kiran Modukuri of San Ramon CA (US) for nvidia corporation, Harish Arora of Fremont CA (US) for nvidia corporation
IPC Code(s): G06F9/48, G06F9/50, G06F9/54
CPC Code(s): G06F9/485
Abstract: apparatuses, systems, and techniques to use parallel processing unit(s) (“ppu(s)”) to perform data access(es) in response to data access request(es). the data access(es) may be performed by accessing at least a first portion of data stored in at least a first location of data location(s) if the first location is on a first tier of a plurality of data tiers that is accessible by the ppu(s), and causing server interface(s) to access at least a second portion of the data stored in at least a second location of the data location(s) if the second location is on a second tier of the plurality of data tiers. the data access request(s) may be performed using an api. the data access(es) may be performed by client(s) and the server interface(s) may be implemented by server(s). the client(s) and server(s) may be implemented by node(s), and may be paused and migrated to other node(s) during execution time. the client(s) may implement synchronous and/or asynchronous interfaces.
Inventor(s): Jorge Albericio Latorre of Brooklyn NY (US) for nvidia corporation, Chong Yu of Shanghai (CN) for nvidia corporation
IPC Code(s): G06F9/50, G06F17/16
CPC Code(s): G06F9/5027
Abstract: apparatuses, systems, and methods to enable matrix multiplication acceleration by modifying an input to apply sparsity through sparse activation filtering. in at least one embodiment, a neural network modifies pixels within an image through sparse activation filtering to enable use of one or more matrix multiplication acceleration units to perform a sparse patch embedding operation.
20250045589. MULTI-GPU TRAINING OF NEURAL NETWORKS_simplified_abstract_(nvidia corporation)
Inventor(s): Vasudevan Rengasamy of Redmond WA (US) for nvidia corporation, Sukru Burc Eryilmaz of Santa Clara CA (US) for nvidia corporation, Marcin Chochowski of Warsaw (PL) for nvidia corporation
IPC Code(s): G06N3/084
CPC Code(s): G06N3/084
Abstract: apparatuses, systems, and techniques to train a neural network on multiple graphics processing units (gpus). in at least one embodiment, the neural network may be trained in parallel based, at least in part, on two or more randomly selected, similarly-sized portions of one or more datasets.
Inventor(s): Shekhar Dwivedi of Santa Clara CA (US) for nvidia corporation, Rahul Choudhury of Livermore CA (US) for nvidia corporation
IPC Code(s): G06N5/04, G06N20/00
CPC Code(s): G06N5/04
Abstract: apparatuses, systems, and frameworks for provisioning of efficient pipelines capable of multi-model inference and data processing, including streaming data applications. the disclosed techniques allow efficient deployment and execution of multiple machine learning using pluggable inference and data processing backends by users without specialized developer experience.
Inventor(s): Shekhar Dwivedi of Santa Clara CA (US) for nvidia corporation, Rahul Choudhury of Livermore CA (US) for nvidia corporation
IPC Code(s): G06N20/00
CPC Code(s): G06N20/00
Abstract: apparatuses, systems, and techniques for efficient profiling, scheduling, and batch execution of multiple machine learning models (mlms). efficient batch execution includes obtaining execution metrics characterizing expected utilization of computational resources by the mlms, and generating at least one batch queue having one or more mlm batches of mlms with a combined expected utilization not exceeding a threshold utilization, and initiating parallel execution of the mlms using the generated mlm batches.
20250045892. VARIATIONAL INFERENCING BY A DIFFUSION MODEL_simplified_abstract_(nvidia corporation)
Inventor(s): Morteza Mardani of Santa Clara CA (US) for nvidia corporation, Jiaming Song of San Carlos CA (US) for nvidia corporation, Jan Kautz of Lexington MA (US) for nvidia corporation, Arash Vahdat of San Mateo CA (US) for nvidia corporation
IPC Code(s): G06T7/00, G06T3/40, G06T5/70, G06T5/73, G06T5/77
CPC Code(s): G06T7/0002
Abstract: diffusion models are machine learning algorithms that are uniquely trained to generate high-quality data from an input lower-quality data. for example, they can be trained in the image domain, for example, to perform specific image restoration tasks, such as inpainting (e.g. completing an incomplete image), deblurring (e.g. removing blurring from an image), and super-resolution (e.g. increasing a resolution of an image), or they can be trained to perform image rendering tasks, including 2d-to-3d image generation tasks. however, current approaches to training diffusion models only allow the models to be optimized for a specific task such that they will not achieve high-quality results when used for other tasks. the present disclosure provides a diffusion model that uses variational inferencing to approximate a distribution of data, which allows the diffusion model to universally solve different tasks without having to be re-trained specifically for each task.
Inventor(s): Alexander Popov of Kirkland WA (US) for nvidia corporation, Nikolai Smolyanskiy of Seattle WA (US) for nvidia corporation, Ruchita Bhargava of Redmond WA (US) for nvidia corporation, Ibrahim Eden of Redmond WA (US) for nvidia corporation, Amala Sanjay Deshmukh of Redmond WA (US) for nvidia corporation, Ryan Oldja of Issaquah WA (US) for nvidia corporation, Ke Chen of Mountain View CA (US) for nvidia corporation, Sai Krishnan Chandrasekar of Santa Clara CA (US) for nvidia corporation, Minwoo Park of Santa Clara CA (US) for nvidia corporation
IPC Code(s): G06T7/73
CPC Code(s): G06T7/74
Abstract: in various examples, systems and methods are disclosed relating to real-time multiview map generation using neural networks. a system can receive sensors images of an environment, such as images from one or more camera, radar, lidar, and/or ultrasound sensors. the system can process the sensor images using one or more neural networks, such as neural networks implementing attention structures, to detect features in the environment such as lane lines, lane dividers, wait lines, or boundaries. the system can represent the features in various views, including top-down/bird's eye view representations. the system can provide the representations for operations including map generation, map updating, perception, and object detection.
Inventor(s): Tianshi CAO of Toronto (CA) for nvidia corporation, Kangxue YIN of Toronto (CA) for nvidia corporation, Nicholas Mark Worth SHARP of Seattle WA (US) for nvidia corporation, Karsten Julian KREIS of Vancouver (CA) for nvidia corporation, Sanja FIDLER of Toronto (CA) for nvidia corporation
IPC Code(s): G06T11/00, G06T5/00, G06T15/20
CPC Code(s): G06T11/001
Abstract: aspects of this technical solution can obtain, according to a plurality of cameras oriented toward the surface of a three-dimensional (3d) model having a surface including a two-dimensional (2d) texture model, input according to corresponding views from the plurality of cameras of the 2d texture model on the surface of the 3d model, and generate, according to the input and according to a model configured to generate a two-dimensional (2d) image, an output including a 2d texture for the 3d model, the output responsive to receiving an indication of the 3d model and the 2d texture.
Inventor(s): Rev Lebaredian of Los Gatos CA (US) for nvidia corporation, Simon Yuen of Playa Vista CA (US) for nvidia corporation, Santanu Dutta of Sunnyvale CA (US) for nvidia corporation, Jonathan Michael Cohen of Mountain View CA (US) for nvidia corporation, Ratin Kumar of Cupertino CA (US) for nvidia corporation
IPC Code(s): G06T13/00, G10L13/08, G10L15/08, H04L51/02
CPC Code(s): G06T13/00
Abstract: in various examples, a virtually animated and interactive agent may be rendered for visual and audible communication with one or more users with an application. for example, a conversational artificial intelligence (ai) assistant may be rendered and displayed for visual communication in addition to audible communication with end-users. as such, the ai assistant may leverage the visual domain—in addition to the audible domain—to more clearly communicate with users, including interacting with a virtual environment in which the ai assistant is rendered. similarly, the ai assistant may leverage audio, video, and/or text inputs from a user to determine a request, mood, gesture, and/or posture of a user for more accurately responding to and interacting with the user.
Inventor(s): Gregory MUTHLER of Chapel Hill NC (US) for nvidia corporation, John BURGESS of Austin TX (US) for nvidia corporation, Magnus ANDERSSON of Lund (SE) for nvidia corporation, Timo VIITANEN of Uusimaa (FI) for nvidia corporation, Levi OLIVER of Cambridge MA (US) for nvidia corporation
IPC Code(s): G06T15/06, G06T15/00, G06T17/00
CPC Code(s): G06T15/06
Abstract: an alternate root tree or graph structure for ray and path tracing enables dynamic instancing build time decisions to split any number of geometry acceleration structures in a manner that is developer transparent, nearly memory storage neutral, and traversal efficient. the resulting traversals only need to partially traverse the acceleration structure, which improves efficiency. one example use reduces the number of false positive instance acceleration structure to geometry acceleration structure transitions for many spatially separated instances of the same geometry.
Inventor(s): Gregory MUTHLER of Chapel Hill NC (US) for nvidia corporation, John BURGESS of Austin TX (US) for nvidia corporation
IPC Code(s): G06T15/06, G06F9/50, G06T3/4007, G06T9/00, G06T15/08, G06T17/10
CPC Code(s): G06T15/06
Abstract: ray tracing hardware accelerators supporting motion blur and moving/deforming geometry are disclosed. for example, dynamic objects in an acceleration data structure are encoded with temporal and spatial information. the hardware includes circuitry that test ray intersections against moving/deforming geometry by applying such temporal and spatial information. such circuitry accelerates the visibility sampling of moving geometry, including rigid body motion and object deformation, and its associated moving bounding volumes to a performance similar to that of the visibility sampling of static geometry.
Inventor(s): Joonhwa Shin of Santa Clara CA (US) for nvidia corporation, Fangyu Li of San Jose CA (US) for nvidia corporation, Zheng Liu of Los Altos CA (US) for nvidia corporation, Kaustubh Purandare of San Jose CA (US) for nvidia corporation
IPC Code(s): G06V20/40, G06F18/28, G06N3/044, G06N3/08, G06T7/20, G06T7/70, G06V10/22, G06V10/25, G06V10/94
CPC Code(s): G06V20/41
Abstract: apparatuses, systems, and techniques for real-time persistent object tracking for intelligent video analytics systems are provided. a first object is tracked in an environment depicted by a first set of images. one or more predicted future states of the first object in the environment are obtained. a second object is detected in the environment depicted by a second set of images. a number of images of the second set of images exceeds a threshold number of images. a determination is made of whether a current state of the second object corresponds to at least one of the predicted future states of the first object. responsive to a determination that a current state of the second object corresponds to at least one of the predicted future states of the first object, state data for the first object is updated based on the determined current state of the second object.
Inventor(s): Ilia Fedorov of Moskva (RU) for nvidia corporation, Dmitry Korobchenko of Yerevan (AM) for nvidia corporation
IPC Code(s): G10L15/06, G10L15/16
CPC Code(s): G10L15/063
Abstract: in various examples, determining emotion sequences for speech in conversational ai systems and applications is described herein. systems and methods are disclosed that use one or more first machine learning models to determine a sequence of emotional states associated with audio data representing speech. to use the first machine learning model(s), the systems and methods may train the first machine learning model(s) using one or more second machine learning models, where the second machine learning model(s) is trained to determine scores indicating accuracies associated with sequences of emotional states. for instance, the second machine learning model(s) may be trained to determine the scores using audio data representing speech, sequences of emotional states associated with the speech, and indications of which sequences of emotional states better represent the speech as compared to other sequences of emotional states.
Inventor(s): Amy Rose of Durham NC (US) for nvidia corporation, Andrew James Woodard of Reading (GB) for nvidia corporation, Benjemin Thomas Waine of Reading (GB) for nvidia corporation
IPC Code(s): G10L17/18, H04M3/56
CPC Code(s): G10L17/18
Abstract: apparatuses, systems, and techniques to selectively suppress noise in a conference call. in at least one embodiment, one or more neural networks may be used to identify a source of a noise component and cause a notification to be sent, informing a user of the noise source to confirm or decline suppression of the noise component.
Inventor(s): Andrew Fear of Cedar Park TX (US) for nvidia corporation, YuCheng Liu of Santa Clara CA (US) for nvidia corporation
IPC Code(s): H04N21/234, H04N21/431, H04N21/442
CPC Code(s): H04N21/23424
Abstract: approaches presented herein provide systems and methods for determining different states for distributed computing processes based on information acquired from underlying hardware. different tasks may be executed by hardware for a given distributed computing process having a certain hardware configuration and for a given application. telemetry information may be acquired to identify different states according to the telemetry information independent from underlying application engines. thereafter, identification of different application states enables prediction of time periods between various application states, which may provide opportunities for additional processing tasks, such as providing supplemental content.
20250048532. THREE DIMENSIONAL CIRCUIT MOUNTING STRUCTURES_simplified_abstract_(nvidia corporation)
Inventor(s): Joey Cai of Shenzhen (CN) for nvidia corporation, Tiger Yan of SHEN ZHEN (CN) for nvidia corporation, Zhu Hao of Shenzhen City (CN) for nvidia corporation, Yi Dinghai of Shenzhen (CN) for nvidia corporation
IPC Code(s): H05K1/02
CPC Code(s): H05K1/0203
Abstract: a circuit board includes chip die mounted on a three dimensional rectangular structure, a three dimensional triangular prism structure, or a combination thereof. a ball grid array for the chip die mounted on any such three dimensional structure is interposed between the three dimensional structure and the circuit board itself.
NVIDIA Corporation patent applications on February 6th, 2025
- NVIDIA Corporation
- A63F13/355
- A63F13/358
- CPC A63F13/355
- Nvidia corporation
- B60W40/09
- G06V20/59
- G06V40/16
- CPC B60W40/09
- B60W50/00
- B62D15/02
- G06T17/00
- G06V10/764
- G06V10/82
- G06V20/56
- CPC B60W50/0097
- G06F9/48
- G06F9/50
- G06F9/54
- CPC G06F9/485
- G06F17/16
- CPC G06F9/5027
- G06N3/084
- CPC G06N3/084
- G06N5/04
- G06N20/00
- CPC G06N5/04
- CPC G06N20/00
- G06T7/00
- G06T3/40
- G06T5/70
- G06T5/73
- G06T5/77
- CPC G06T7/0002
- G06T7/73
- CPC G06T7/74
- G06T11/00
- G06T5/00
- G06T15/20
- CPC G06T11/001
- G06T13/00
- G10L13/08
- G10L15/08
- H04L51/02
- CPC G06T13/00
- G06T15/06
- G06T15/00
- CPC G06T15/06
- G06T3/4007
- G06T9/00
- G06T15/08
- G06T17/10
- G06V20/40
- G06F18/28
- G06N3/044
- G06N3/08
- G06T7/20
- G06T7/70
- G06V10/22
- G06V10/25
- G06V10/94
- CPC G06V20/41
- G10L15/06
- G10L15/16
- CPC G10L15/063
- G10L17/18
- H04M3/56
- CPC G10L17/18
- H04N21/234
- H04N21/431
- H04N21/442
- CPC H04N21/23424
- H05K1/02
- CPC H05K1/0203
