NVIDIA Corporation patent applications on February 27th, 2025
Patent Applications by NVIDIA Corporation on February 27th, 2025
NVIDIA Corporation: 17 patent applications
NVIDIA Corporation has applied for patents in the areas of G06V10/82 (3), G06N20/00 (2), G06T1/20 (1), H04W12/37 (1), G06V10/764 (1) G06N20/00 (2), B60R25/25 (1), B60W60/00253 (1), G01D21/00 (1), G05B13/0265 (1)
With keywords such as: data, image, based, sensor, vehicle, video, object, component, examples, and instructions in patent application abstracts.
Patent Applications by NVIDIA Corporation
Inventor(s): Rajath SHETTY of Sunnyvale CA (US) for nvidia corporation, Braeden Chance Syrnyk of Santa Clara CA (US) for nvidia corporation, Ratin Kumar of Cupertino CA (US) for nvidia corporation
IPC Code(s): B60R25/25, B60R25/01, B60R25/30, G06V10/26, G06V20/59, G06V40/12, G06V40/13, G06V40/14, H04N23/23
CPC Code(s): B60R25/25
Abstract: in various examples, infrared image data may be used to detect a subcutaneous characteristic(s) (e.g., a palm vein topology) of a person (e.g., a person requesting entry to a vehicle, a vehicle occupant) and authenticate the user based on the detected subcutaneous characteristic(s). for example, infrared image data representing one or more acquired subcutaneous characteristics (e.g., a topology of veins and/or other blood vessels in a region of the authenticating user's palm, hand, neck, forearm, face, fingertip, eye, etc.) may be generated. hand and/or palm detection may be applied to detect a region depicting the user's hand or palm, and that region (or some subset thereof) may be segmented to generate a representation of an acquired vein topology. the acquired vein topology may be compared with one or more reference vein topologies stored in a database to determine whether the acquired vein topology matches one of the reference vein topologies.
Inventor(s): Gary HICOK of Mesa AZ (US) for nvidia corporation, Michael COX of Menlo Park CA (US) for nvidia corporation, Miguel SAINZ of Palo Alto CA (US) for nvidia corporation, Martin HEMPEL of Mountain View CA (US) for nvidia corporation, Ratin KUMAR of Cupertino CA (US) for nvidia corporation, Timo ROMAN of Uusimaa (FI) for nvidia corporation, Gordon GRIGOR of San Francisco CA (US) for nvidia corporation, David NISTER of Bellevue WA (US) for nvidia corporation, Justin EBERT of Boulder CO (US) for nvidia corporation, Chin-Hsien SHIH of Saratoga CA (US) for nvidia corporation, Tony TAM of Redwood City CA (US) for nvidia corporation, Ruchi BHARGAVA of Redmond WA (US) for nvidia corporation
IPC Code(s): B60W60/00, G01C21/34, G01C21/36
CPC Code(s): B60W60/00253
Abstract: a system and method for an on-demand shuttle, bus, or taxi service able to operate on private and public roads provides situational awareness and confidence displays. the shuttle may include iso 26262 level 4 or level 5 functionality and can vary the route dynamically on-demand, and/or follow a predefined route or virtual rail. the shuttle is able to stop at any predetermined station along the route. the system allows passengers to request rides and interact with the system via a variety of interfaces, including without limitation a mobile device, desktop computer, or kiosks. each shuttle preferably includes an in-vehicle controller, which preferably is an ai supercomputer designed and optimized for autonomous vehicle functionality, with computer vision, deep learning, and real time ray tracing accelerators. an ai dispatcher performs ai simulations to optimize system performance according to operator-specified system parameters.
Inventor(s): Dae Jin Kim of San Jose CA (US) for nvidia corporation, Rajath Shetty of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): G01D21/00, G06T7/80
CPC Code(s): G01D21/00
Abstract: in various examples, interior sensor calibration for autonomous systems and applications is described herein. systems and methods are disclosed that may recalibrate sensors of a vehicle, such as sensors located within the interior of the vehicle, using one or more techniques. for instance, if a sensor is attached to a component within the interior of the vehicle, an additional sensor associated with the component may output data indicating the location and/or orientation of the component within the vehicle. the indicated location and/or orientation of the component may then be used to recalibrate the sensor with respect to a reference coordinate system of the vehicle. for a second example, the sensor may output data representing at least a feature located within the interior of the vehicle. the sensor may then again be recalibrated based at least on a portion of the data that represents the feature.
Inventor(s): Elad Mentovich of Tel Aviv (IL) for nvidia corporation, Siddha Ganju of Santa Clara CA (US) for nvidia corporation, Jeff Whitmer of San Jose CA (US) for nvidia corporation, Ryan Albright of Beaverton OR (US) for nvidia corporation, Tahir Cader of Spokane Valley WA (US) for nvidia corporation, Ron Chao of San Diego CA (US) for nvidia corporation
IPC Code(s): G05B13/02, G06F1/20
CPC Code(s): G05B13/0265
Abstract: methods are described herein for manufacturing multi-component objects using artificial intelligence. the present invention may be directed to a method that includes determining an actual value of a first attribute of a first component of an object and determining, using a machine learning model and based on the actual value of the first attribute, an optimized value for a second attribute of a second component that is functionally interrelated to the first component in the object. the method may include selecting, from a plurality of second components each having a value for the second attribute within a tolerance range, a second component having the optimized value for the second attribute. the method may further include manufacturing the object using the first component and the selected second component.
Inventor(s): Riccardo Mariani of Porto Azzurro (IT) for nvidia corporation, Gary Hicok of Mesa AZ (US) for nvidia corporation
IPC Code(s): G05D1/00, G05D1/02
CPC Code(s): G05D1/0077
Abstract: in various examples, teleoperation architectures for safe control of machines are described. systems and methods are disclosed that use an end-to-end safety architecture that covers both a vehicle or machine and a remote system providing a control center, where the vehicle or machine is at least partly or temporarily configured for control by the remote system. in some examples, the end-to-end architecture uses a layered safety policy monitoring system, where the remote system uses first policies to ensure that operator commands are viable and the vehicle uses second policies to ensure that the operator commands are safe to perform (e.g., will not cause collisions with other objects). additionally, in some examples, the end-to-end architecture allows for the vehicle to perform minimum risk maneuvers, also referred to as “control fallbacks,” if problems were to occur.
Inventor(s): Maciej Piotr TYRLIK of Durham NC (US) for nvidia corporation, Ajay Sudarshan TIRUMALA of San Jose CA (US) for nvidia corporation, Shirish GADRE of Fremont CA (US) for nvidia corporation, Frank Joseph EATON of Austin TX (US) for nvidia corporation, Daniel Alan STIFFLER of Santa Clara CA (US) for nvidia corporation
IPC Code(s): G06F9/30, G06F9/38
CPC Code(s): G06F9/30065
Abstract: various techniques for accelerating dynamic programming algorithms are provided. for example, a fused addition and comparison instruction, a three-operand comparison instruction, and a two-operand comparison instruction are used to accelerate a needleman-wunsch algorithm that determines an optimized global alignment of subsequences over two entire sequences. in another example, the fused addition and comparison instruction is used in an innermost loop of a floyd-warshall algorithm to reduce the number of instructions required to determine shortest paths between pairs of vertices in a graph. in another example, a two-way single instruction multiple data (simd) floating point variant of the three-operand comparison instruction is used to reduce the number of instructions required to determine the median of an array of floating point values.
20250068501. FAILURE MODE CONSOLIDATION_simplified_abstract_(nvidia corporation)
Inventor(s): Sai Ram Dheeraj LOKAM of Gilroy CA (US) for nvidia corporation, Moslem DIDEHBAN of San Jose CA (US) for nvidia corporation, Frank NOHA of Dripping Springs TX (US) for nvidia corporation, Kevin RICH of San Diego CA (US) for nvidia corporation, Richard BRAMLEY of Santa Clara CA (US) for nvidia corporation
IPC Code(s): G06F11/07, G06F11/32
CPC Code(s): G06F11/079
Abstract: the present disclosure relates to collapsing a set of conditions during failure modes effects and diagnostic analysis. the set of conditions may include potential problems and issues corresponding to one or more system components included in a system. a mapping between individual conditions of the set of conditions and one or more system classes corresponding to one or more system level effects may be obtained. one or more collapsing instructions including one or more of the system components or one or more of the system classes of interest may be obtained. a subset of conditions, including one or more of the individual conditions, may be identified from the set of conditions based at least on the collapsing instructions. the set of conditions may be collapsed, and the subset of conditions may be un-collapsed for analysis.
20250068724. NEURAL NETWORK TRAINING TECHNIQUE_simplified_abstract_(nvidia corporation)
Inventor(s): Chong Yu of Shanghai (CN) for nvidia corporation
IPC Code(s): G06F21/55, G06V10/774, G06V10/776, G06V10/82
CPC Code(s): G06F21/554
Abstract: apparatuses, systems, and techniques to generate labels for images. in at least one embodiment, one or more labels of one or more images are generated based, at least in part, on an amount by which the one or more images were modified.
Inventor(s): Ravi kumar Boddeti of Hyderabad (IN) for nvidia corporation, Vinayak Pore of Pune (IN) for nvidia corporation, Hassane Samir Azar of Los Altos CA (US) for nvidia corporation, Prashant Sohani of Pune IN (US) for nvidia corporation
IPC Code(s): G06F21/57, G06F21/53, H04W12/37
CPC Code(s): G06F21/577
Abstract: in various examples, machine learning of encoding parameter values for a network is performed using a video encoder. feedback associated with streaming video encoded by a video encoder over a network may be applied to an mlm(s). using such feedback, the mlm(s) may predict a value(s) of an encoding parameter(s). the video encoder may then use the value to encode subsequent video data for the streaming. by using the video encoder in training, the mlm(s) may learn based on actual encoded parameter values of the video encoder. the mlm(s) may be trained via reinforcement learning based on video encoded by the video encoder. a rewards metric(s) may be used to train the mlm(s) using data generated or applied to the physical network in which the mlm(s) is to be deployed and/or a simulation thereof. penalty metric(s) (e.g., the quantity of dropped frames) may also be used to train the mlm(s).
Inventor(s): Chong YU of Shanghai (CN) for nvidia corporation
IPC Code(s): G06N20/00, G06V10/764
CPC Code(s): G06N20/00
Abstract: in various examples, a technique for processing sensor data includes generating, using a machine learning model and based on a first sensor data instance, a first set of confidences for a set of output types and a first adversarial confidence that represents a likelihood that the first sensor data instance is adversarial. the technique also includes determining that the first sensor data instance is adversarial based on the first adversarial confidence. the technique further includes transmitting a first indication that the first sensor data instance is adversarial to one or more downstream components such that the one or more downstream components perform one or more operations based at least on the indication.
Inventor(s): Ajay Uday MANDLEKAR of Cupertino CA (US) for nvidia corporation, Caelan Reed GARRETT of Seattle WA (US) for nvidia corporation, Danfei XU of Atlanta GA (US) for nvidia corporation, Dieter FOX of Seattle WA (US) for nvidia corporation
IPC Code(s): G06N20/00
CPC Code(s): G06N20/00
Abstract: in various examples, systems and methods are disclosed relating to training machine learning models using human demonstration of segments of a task, where other segments of the task are performed by a planning method, such as a task and motion planning (tamp) system. a method may include segmenting a task to be performed by a robot into segments, determining a first set of instructions of a plurality of sets of instructions for operating the robot to perform a first objective of a first segment, determining that the plurality of sets of instructions is inadequate to perform a second objective of a second segment, receiving from a user device a second set of instructions for operating the robot for the second segment following an end of the first segment, and updating a machine learning model for controlling the robot using the second set of instructions for the second segment.
20250069191. SYNTHETIC BRACKETING FOR EXPOSURE CORRECTION_simplified_abstract_(nvidia corporation)
Inventor(s): Iuri Frosio of Bergamo (IT) for nvidia corporation, Mayoore Selvarasa Jaiswal of Bothell WA (US) for nvidia corporation, Jan Kautz of Lexington MA (US) for nvidia corporation, Jianyuan Min of Santa Clara CA (US) for nvidia corporation
IPC Code(s): G06T5/50, H04N23/743
CPC Code(s): G06T5/50
Abstract: systems and methods are disclosed related to synthetic bracketing for exposure correction. a deep learning based method and system produces a set of differently exposed images from a single input image. the images in the set may be combined to produce an output image with improved global and local exposure compared with the input image. an image encoder applies learned parameters to each input image to generate a set of image features including local exposure estimates for each of two or more regions of the input image and a low resolution latent representation of the input image. a decoder receives the local exposure estimates, the latent representation, and target enhancements that are processed to generate synthesized transformations. when applied to the input image, the synthesized transformations produce the set of transformed images. each transformed image is a version of the input image synthesized to correspond to a respective target enhancement.
Inventor(s): Ruthie D. Lyle of Durham NC (US) for nvidia corporation
IPC Code(s): G06T13/40, G06T19/00
CPC Code(s): G06T13/40
Abstract: apparatuses, systems, and techniques simulating physical properties of real-world objects. data for a real-world object is collected. the collected data indicates one or more physical characteristics of the real-world object. a three-dimensional (3d) object is created based on some portion of the collected data. the 3d object has a format that is compatible with a 3d graphics platform. physics simulation data associated with the 3d object moving within the 3d graphics platform is obtained. image rendering data associated with the 3d object moving within the 3d graphics platform is stored with the computer system. the image rendering data is based at least in part on the obtained physics simulation data.
20250069358. INFERENCING USING NEURAL NETWORKS_simplified_abstract_(nvidia corporation)
Inventor(s): Tushar Santosh Khinvasara of Pune (IN) for nvidia corporation, Kaustubh Shriharsh Purandare of San Jose CA (US) for nvidia corporation
IPC Code(s): G06V10/20, G06N3/045, G06N5/04, G06T1/20, G06V20/64
CPC Code(s): G06V10/255
Abstract: apparatuses, systems, and techniques to use data obtained from an inferred object to determine whether to re-infer the same inferred object. in at least one embodiment, one or more objects are identified in one or more images. a size of the one or more objects in one image is compared to a size of the one or more objects in another image. the one or more objects are re-inferenced based, at least in part, on the comparison.
Inventor(s): Haiguang Wen of Edison NJ (US) for nvidia corporation, Bernhard Firner of Highland Park NJ (US) for nvidia corporation, Mariusz Bojarski of Lincroft NJ (US) for nvidia corporation, Zongyi Yang of Eatontown NJ (US) for nvidia corporation, Urs Muller of Keyport NJ (US) for nvidia corporation
IPC Code(s): G06V10/82, G06N3/08, G06T7/70, G06T9/00, G06V10/25, G06V10/50, G06V10/52, G06V20/56
CPC Code(s): G06V10/82
Abstract: in examples, image data representative of an image of a field of view of at least one sensor may be received. source areas may be defined that correspond to a region of the image. areas and/or dimensions of at least some of the source areas may decrease along at least one direction relative to a perspective of the at least one sensor. a downsampled version of the region (e.g., a downsampled image or feature map of a neural network) may be generated from the source areas based at least in part on mapping the source areas to cells of the downsampled version of the region. resolutions of the region that are captured by the cells may correspond to the areas of the source areas, such that certain portions of the region (e.g., portions at a far distance from the sensor) retain higher resolution than others.
Inventor(s): Pankaj Ratnakar Kadtan of Pune (IN) for nvidia corporation, Vishesh Gupta of Pune (IN) for nvidia corporation
IPC Code(s): G06V20/58, G06V10/22, G06V10/82
CPC Code(s): G06V20/58
Abstract: apparatuses, systems, and techniques to track object movements in a video. in at least one embodiment, a rate, at which one or more neural networks is to identify one or more objects within the video, may be adjusted based, at least in part on one or more user-selected portions of a video frame.
Inventor(s): Omer Wolkovitz of Kfar Saba (IL) for nvidia corporation, Ofek Abadi of Nahariya (IL) for nvidia corporation
IPC Code(s): H04B1/44, H03K17/56, H04B1/401
CPC Code(s): H04B1/44
Abstract: a system includes a transmission driver coupled to a channel, a capacitor coupled in series to the channel, and a receiver coupled to the channel. the receiver includes a front-end circuit to detect, as data, transitions in voltage over the channel, the front-end circuit including an activation switch. a voltage swing detector is coupled between the channel and the activation switch. the voltage swing detector detects a voltage swing in the voltage that satisfies one of a first threshold value or a second threshold value and causes, in response to the detection, the activation switch to one of open or close, respectively.
NVIDIA Corporation patent applications on February 27th, 2025
- NVIDIA Corporation
- B60R25/25
- B60R25/01
- B60R25/30
- G06V10/26
- G06V20/59
- G06V40/12
- G06V40/13
- G06V40/14
- H04N23/23
- CPC B60R25/25
- Nvidia corporation
- B60W60/00
- G01C21/34
- G01C21/36
- CPC B60W60/00253
- G01D21/00
- G06T7/80
- CPC G01D21/00
- G05B13/02
- G06F1/20
- CPC G05B13/0265
- G05D1/00
- G05D1/02
- CPC G05D1/0077
- G06F9/30
- G06F9/38
- CPC G06F9/30065
- G06F11/07
- G06F11/32
- CPC G06F11/079
- G06F21/55
- G06V10/774
- G06V10/776
- G06V10/82
- CPC G06F21/554
- G06F21/57
- G06F21/53
- H04W12/37
- CPC G06F21/577
- G06N20/00
- G06V10/764
- CPC G06N20/00
- G06T5/50
- H04N23/743
- CPC G06T5/50
- G06T13/40
- G06T19/00
- CPC G06T13/40
- G06V10/20
- G06N3/045
- G06N5/04
- G06T1/20
- G06V20/64
- CPC G06V10/255
- G06N3/08
- G06T7/70
- G06T9/00
- G06V10/25
- G06V10/50
- G06V10/52
- G06V20/56
- CPC G06V10/82
- G06V20/58
- G06V10/22
- CPC G06V20/58
- H04B1/44
- H03K17/56
- H04B1/401
- CPC H04B1/44