NVIDIA Corporation patent applications on April 11th, 2024
Contents
- 1 Patent Applications by NVIDIA Corporation on April 11th, 2024
- 1.1 What specific problems do these NVIDIA patents aim to solve?
- 1.2 How could these technologies impact the future of autonomous vehicles and AI?
- 1.3 What are the potential commercial applications for these patents?
- 1.4 Are there any collaborations with other companies or academic institutions noted in these patents?
- 1.5 What future innovations could be built upon these patents?
Patent Applications by NVIDIA Corporation on April 11th, 2024
NVIDIA Corporation, a leader in advanced graphics processing units (GPUs) and artificial intelligence (AI) technologies, has been actively filing patent applications across several innovative fields. This summary provides insights into their recent patent activities focused on autonomous driving, neural network processing, and network communication improvements.
Patent Applications Overview
NVIDIA Corporation has applied for patents in diverse technological areas, notably in control systems for autonomous vehicles, scalable instruction architectures, and APIs for network enhancements. Their applications cover several International Patent Classification (IPC) codes, including:
G06F9/54 - relating to instruction processing in computers H04W24/02 - involving wireless communication networks B60W30/18 - pertaining to vehicle control systems Highlighted Patents
LANE CHANGE PLANNING AND CONTROL IN AUTONOMOUS MACHINES
Inventors: Zhenyi Zhang, Yizhou Wang, David Nister, Neda Cvijetic IPC Codes: B60W30/18, B60W30/095, B60W40/105 Abstract: Focuses on sensor-based environment representation for improving autonomous vehicle maneuvering, specifically during lane changes by evaluating longitudinal speed profiles. SCALARIZATION OF INSTRUCTIONS FOR SIMT ARCHITECTURES
Inventors: Aditya Avinash Atluri, Jack Choquette, Carter Edwards, Olivier Giroux IPC Codes: G06F9/38 Abstract: Describes techniques to adapt GPU instructions for efficient execution on serial processing units, enhancing the performance of Single Instruction, Multiple Threads (SIMT) architectures. DYNAMIC NEURAL NETWORK MODEL SPARSIFICATION
Inventors: Jose M. Alvarez Lopez, Pavlo Molchanov, Hongxu Yin IPC Codes: G06N3/082, G06N3/0495 Abstract: Innovations in dynamically adjusting the sparsity of neural networks during training phases to optimize performance and computational efficiency. Questions about NVIDIA Corporation's Patent Applications
What specific problems do these NVIDIA patents aim to solve?
These patents aim to enhance the efficiency and functionality of autonomous systems, improve GPU architectures for broader applications, and optimize network communications for emerging technologies like 5G.
How could these technologies impact the future of autonomous vehicles and AI?
By improving control systems and computational processes, NVIDIA's technologies could lead to safer, more efficient autonomous vehicles and more capable AI systems in various applications.
What are the potential commercial applications for these patents?
Commercial applications range from automotive industries adopting smarter, autonomous functions to IT infrastructures leveraging enhanced GPU capabilities for better data processing and network management.
Are there any collaborations with other companies or academic institutions noted in these patents?
The patents list multiple inventors, often suggesting collaborative efforts, although specific partnerships with institutions or other companies aren't detailed.
What future innovations could be built upon these patents?
Future innovations could include more advanced AI-driven traffic management systems, breakthroughs in virtual reality processing, and more robust AI models that can be trained with less computational overhead.
NVIDIA Corporation: 21 patent applications
NVIDIA Corporation has applied for patents in the areas of G06F9/54 (9), H04W24/02 (8), H04W24/10 (6), G06F9/547 (5), H04L41/0803 (4)
With keywords such as: network, data, device, said, subscription, radio, share, transport, analytic, and systems in patent application abstracts.
Patent Applications by NVIDIA Corporation
Inventor(s): Zhenyi Zhang of Los Altos CA (US) for nvidia corporation, Yizhou Wang of San Jose 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): B60W60/00, B60W30/095, B60W30/18, B60W40/105
Abstract: in various examples, sensor data may be collected using one or more sensors of an ego-vehicle to generate a representation of an environment surrounding the ego-vehicle. the representation may include lanes of the roadway and object locations within the lanes. the representation of the environment may be provided as input to a longitudinal speed profile identifier, which may project a plurality of longitudinal speed profile candidates onto a target lane. each of the plurality of longitudinal speed profiles candidates may be evaluated one or more times based on one or more sets of criteria. using scores from the evaluation, a target gap and a particular longitudinal speed profile from the longitudinal speed profile candidates may be selected. once the longitudinal speed profile for a target gap has been determined, the system may execute a lane change maneuver according to the longitudinal speed profile.
Inventor(s): Aditya Avinash Atluri of Redmond WA (US) for nvidia corporation, Jack Choquette of Palo Alto CA (US) for nvidia corporation, Carter Edwards of Campbell CA (US) for nvidia corporation, Olivier Giroux of Santa Clara CA (US) for nvidia corporation, Praveen Kumar Kaushik of Bengaluru (IN) for nvidia corporation, Ronny Krashinsky of Portola Valley CA (US) for nvidia corporation, Rishkul Kulkarni of Austin TX (US) for nvidia corporation, Konstantinos Kyriakopoulos of Weinsberg (DE) for nvidia corporation
IPC Code(s): G06F9/38
Abstract: apparatuses, systems, and techniques to adapt instructions in a simt architecture for execution on serial execution units. in at least one embodiment, a set of one or more threads is selected from a group of active threads associated with an instruction and the instruction is executed for the set of one or more threads on a serial execution unit.
Inventor(s): Joseph Boccuzzi of Kingwood TX (US) for nvidia corporation, Lopamudra Kundu of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): G06F9/54, H04W24/02
Abstract: apparatuses, systems, and techniques including apis, subscription services, and controllers to enable one or more fifth generation new radio (5g-nr) networks to share information. for example, a processor comprising one or more circuits can perform an api or subscription service to cause a device in a radio access network (ran) to share its analytic data with a device in a transport network, and said device in said transport network can use said analytic data to adjust its network settings to improve performance.
Inventor(s): Joseph Boccuzzi of Kingwood TX (US) for nvidia corporation, Lopamudra Kundu of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): G06F9/54, H04W24/02
Abstract: apparatuses, systems, and techniques including apis, subscription services, and controllers to enable one or more fifth generation new radio (5g-nr) networks to share information. for example, a processor comprising one or more circuits can perform an api or subscription service to cause a device in a radio access network (ran) to share its analytic data with a device in a transport network, and said device in said transport network can use said analytic data to adjust its network settings to improve performance.
Inventor(s): Joseph Boccuzzi of Kingwood TX (US) for nvidia corporation, Lopamudra Kundu of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): G06F9/54, H04W24/02, H04W24/10
Abstract: apparatuses, systems, and techniques including apis, subscription services, and controllers to enable one or more fifth generation new radio (5g-nr) networks to share information. for example, a processor comprising one or more circuits can perform an api or subscription service to cause a device in a radio access network (ran) to share its analytic data with a device in a transport network, and said device in said transport network can use said analytic data to adjust its network settings to improve performance.
Inventor(s): Joseph Boccuzzi of Kingwood TX (US) for nvidia corporation, Lopamudra Kundu of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): G06F9/54, H04L41/0803
Abstract: apparatuses, systems, and techniques including apis, subscription services, and controllers to enable one or more fifth generation new radio (5g-nr) networks to share information. for example, a processor comprising one or more circuits can perform an api or subscription service to cause a device in a radio access network (ran) to share its analytic data with a device in a transport network, and said device in said transport network can use said analytic data to adjust its network settings to improve performance.
Inventor(s): Joseph Boccuzzi of Kingwood TX (US) for nvidia corporation, Lopamudra Kundu of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): G06F9/54, H04L41/0803
Abstract: apparatuses, systems, and techniques including apis, subscription services, and controllers to enable one or more fifth generation new radio (5g-nr) networks to share information. for example, a processor comprising one or more circuits can perform an api or subscription service to cause a device in a radio access network (ran) to share its analytic data with a device in a transport network, and said device in said transport network can use said analytic data to adjust its network settings to improve performance.
20240119267.GENERATING NEURAL NETWORKS_simplified_abstract_(nvidia corporation)
Inventor(s): Slawomir Kierat of Warsaw (PL) for nvidia corporation, Piotr Karpinski of Warsaw (PL) for nvidia corporation, Mateusz Sieniawski of Warsaw (PL) for nvidia corporation, Pawel Morkisz of San Jose CA (US) for nvidia corporation, Szymon Migacz of Santa Clara CA (US) for nvidia corporation, Linnan Wang of Pleasanton CA (US) for nvidia corporation, Chen-Han Yu of Mountain House CA (US) for nvidia corporation, Satish Salian of Santa Clara CA (US) for nvidia corporation, Ashwath Aithal of Fremont CA (US) for nvidia corporation, Alexandru Fit-Florea of Los Altos Hills CA (US) for nvidia corporation
IPC Code(s): G06N3/04, G06N3/08
Abstract: apparatuses, systems, and techniques to selectively use one or more neural network layers. in at least one embodiment, one or more neural network layers are selectively used based on, for example, one or more iteratively increasing neural network performance metrics.
20240119291.DYNAMIC NEURAL NETWORK MODEL SPARSIFICATION_simplified_abstract_(nvidia corporation)
Inventor(s): Jose M. Alvarez Lopez of Mountain View CA (US) for nvidia corporation, Pavlo Molchanov of Mountain View CA (US) for nvidia corporation, Hongxu Yin of San Jose CA (US) for nvidia corporation, Maying Shen of Santa Clara CA (US) for nvidia corporation, Lei Mao of San Jose CA (US) for nvidia corporation, Xinglong Sun of Menlo Park CA (US) for nvidia corporation
IPC Code(s): G06N3/082, G06N3/0495
Abstract: machine learning is a process that learns a neural network model from a given dataset, where the model can then be used to make a prediction about new data. in order to reduce the size, computation, and latency of a neural network model, a compression technique can be employed which includes model sparsification. to avoid the negative consequences of pruning a fully pretrained neural network model and on the other hand of training a sparse model in the first place without any recovery option, the present disclosure provides a dynamic neural network model sparsification process which allows for recovery of previously pruned parts to improve the quality of the sparse neural network model.
Inventor(s): Hongxu YIN of San Jose CA (US) for nvidia corporation, Wonmin BYEON of Santa Cruz CA (US) for nvidia corporation, Jan KAUTZ of Lexington MA (US) for nvidia corporation, Divyam MADAAN of Brooklyn NY (US) for nvidia corporation, Pavlo MOLCHANOV of Mountain View CA (US) for nvidia corporation
IPC Code(s): G06N20/00
Abstract: one embodiment of a method for training a first machine learning model having a different architecture than a second machine learning model includes receiving a first data set, performing one or more operations to generate a second data set based on the first data set and the second machine learning model, wherein the second data set includes at least one feature associated with one or more tasks that the second machine learning model was previously trained to perform, and performing one or more operations to train the first machine learning model based on the second data set and the second machine learning model.
Inventor(s): Michael Hemmer of Saarbruecken (DE) for nvidia corporation
IPC Code(s): G06T7/50, G06T17/00, G06T19/20
Abstract: approaches presented herein provide systems and methods for determining duplicate objects within an interaction environment. connectivity information for an object may be used to map a set of three linearly independent vectors corresponding to a transform applied to the object. these three linearly independent vectors may be used to form canonical forms of first and second objects to determine whether the first object and the second object are duplicates or near-duplicates. copies of duplicate or near-duplicate objects may then be deleted from the interaction environment and represented by a common object to which one or more additional transforms are applied.
20240119664.STREAMING A COMPRESSED LIGHT FIELD_simplified_abstract_(nvidia corporation)
Inventor(s): Michael Stengel of Cupertino CA (US) for nvidia corporation, Alexander Majercik of San Francisco CA (US) for nvidia corporation, Ben Boudaoud of Efland NC (US) for nvidia corporation, Morgan McGuire of Williamstown MA (US) for nvidia corporation
IPC Code(s): G06T15/50, G06T15/04, G06T15/06, H04L67/131, H04N19/46
Abstract: a remote device utilizes ray tracing to compute a light field for a scene to be rendered, where the light field includes information about light reflected off surfaces within the scene. this light field is then compressed utilizing one or more video compression techniques that implement temporal reuse, such that only differences between the light field for the scene and a light field for a previous scene are compressed. the compressed light field data is then sent to a client device that decompresses the light field data and uses such data to obtain the light field for the scene at the client device. this light field is then used by the client device to compute global illumination for the scene. the global illumination may be used to accurately render the scene at the mobile device, resulting in a realistic scene that is presented by the mobile device.
Inventor(s): Zoran Nikolic of Sugarland TX (US) for nvidia corporation, Eric Viscito of Shelburne VT (US) for nvidia corporation
IPC Code(s): G06V10/75, G06V10/62, G06V20/56
Abstract: in various examples, techniques for using hardware feature trackers in autonomous or semi-autonomous systems are described. systems and methods are disclosed that use a processor(s) to determine flow vectors associated with pixel locations in a first image. the systems also use the processor(s) to determine a location of a feature point in a second image based at least on one or more of the flow vectors and a subpixel location of the feature point in the first image. in some examples, the processor(s) may include an optical flow accelerator (ofa) that includes a hardware unit storing a lookup table that is used to determine the location of the feature point in the second image. in some examples, the processor(s) may include an ofa to determine the flow vectors and a vision processor to determine the location of the feature point in the second image.
Inventor(s): Nithin Rao Koluguri of San Jose CA (US) for nvidia corporation, Taejin Park of San Jose CA (US) for nvidia corporation, Boris Ginsburg of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): G10L15/16, G06N3/08, G10L15/06
Abstract: disclosed are apparatuses, systems, and techniques that may use machine learning for implementing speaker recognition, verification, and/or diarization. the techniques include applying a neural network (nn) to a speech data to obtain a speaker embedding representative of an association between the speech data and a speaker that produced the speech. the speech data includes a plurality of frames and a plurality of channels representative of spectral content of the speech data. the nn has one or more blocks of neurons that include a first branch performing convolutions of the speech data across the plurality of channels and across the plurality of frames and a second branch performing convolutions of the speech data across the plurality of channels. obtained speaker embeddings may be used for various tasks of speaker identification, verification, and/or diarization.
Inventor(s): Joseph Boccuzzi of Kingwood TX (US) for nvidia corporation, Lopamudra Kundu of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): H04L41/14, H04L41/0823
Abstract: apparatuses, systems, and techniques including apis, subscription services, and controllers to enable one or more fifth generation new radio (5g-nr) networks to share information.
Inventor(s): Joseph Boccuzzi of Kingwood TX (US) for nvidia corporation, Lopamudra Kundu of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): H04L41/14, H04L41/0823
Abstract: for example, a processor comprising one or more circuits can perform an api or subscription service to cause a device in a radio access network (ran) to share its analytic data with a device in a transport network, and said device in said transport network can use said analytic data to adjust its network settings to improve performance.
Inventor(s): Joseph Boccuzzi of Kingwood TX (US) for nvidia corporation, Lopamudra Kundu of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): H04L67/51, G06F9/54
Abstract: apparatuses, systems, and techniques including apis, subscription services, and controllers to enable one or more fifth generation new radio (5g-nr) networks to share information. for example, a processor comprising one or more circuits can perform an api or subscription service to cause a device in a radio access network (ran) to share its analytic data with a device in a transport network, and said device in said transport network can use said analytic data to adjust its network settings to improve performance.
Inventor(s): Joseph Boccuzzi of Kingwood TX (US) for nvidia corporation, Lopamudra Kundu of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): H04L67/51, G06F9/54
Abstract: apparatuses, systems, and techniques including apis, subscription services, and controllers to enable one or more fifth generation new radio (5g-nr) networks to share information. for example, a processor comprising one or more circuits can perform an api or subscription service to cause a device in a radio access network (ran) to share its analytic data with a device in a transport network, and said device in said transport network can use said analytic data to adjust its network settings to improve performance.
Inventor(s): Joseph Boccuzzi of Kingwood TX (US) for nvidia corporation, Lopamudra Kundu of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): H04W8/26, H04L67/133, H04W24/10
Abstract: apparatuses, systems, and techniques including apis, subscription services, and controllers to enable one or more fifth generation new radio (5g-nr) networks to share information. for example, a processor comprising one or more circuits can perform an api or subscription service to cause a device in a radio access network (ran) to share its analytic data with a device in a transport network, and said device in said transport network can use said analytic data to adjust its network settings to improve performance.
Inventor(s): Joseph Boccuzzi of Kingwood TX (US) for nvidia corporation, Lopamudra Kundu of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): H04W8/26, H04L67/133, H04W24/10
Abstract: apparatuses, systems, and techniques including apis, subscription services, and controllers to enable one or more fifth generation new radio (5g-nr) networks to share information. for example, a processor comprising one or more circuits can perform an api or subscription service to cause a device in a radio access network (ran) to share its analytic data with a device in a transport network, and said device in said transport network can use said analytic data to adjust its network settings to improve performance.
Inventor(s): Joseph Boccuzzi of Kingwood TX (US) for nvidia corporation, Lopamudra Kundu of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): H04L41/5009, H04L41/14
Abstract: apparatuses, systems, and techniques including apis, subscription services, and controllers to enable one or more fifth generation new radio (5g-nr) networks to share information. for example, a processor comprising one or more circuits can perform an api or subscription service to cause a device in a radio access network (ran) to share its analytic data with a device in a transport network, and said device in said transport network can use said analytic data to adjust its network settings to improve performance.
Inventor(s): Joseph Boccuzzi of Kingwood TX (US) for nvidia corporation, Lopamudra Kundu of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): H04W24/02, H04W28/02
Abstract: apparatuses, systems, and techniques including apis, subscription services, and controllers to enable one or more fifth generation new radio (5g-nr) networks to share information.
Inventor(s): Joseph Boccuzzi of Kingwood TX (US) for nvidia corporation, Lopamudra Kundu of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): H04W24/02, H04W28/02
Abstract: for example, a processor comprising one or more circuits can perform an api or subscription service to cause a device in a radio access network (ran) to share its analytic data with a device in a transport network, and said device in said transport network can use said analytic data to adjust its network settings to improve performance.
- G06F9/54
- H04W24/02
- B60W30/18
- NVIDIA Corporation
- B60W60/00
- B60W30/095
- B60W40/105
- Nvidia corporation
- G06F9/38
- H04W24/10
- H04L41/0803
- G06N3/04
- G06N3/08
- G06N3/082
- G06N3/0495
- G06N20/00
- G06T7/50
- G06T17/00
- G06T19/20
- G06T15/50
- G06T15/04
- G06T15/06
- H04L67/131
- H04N19/46
- G06V10/75
- G06V10/62
- G06V20/56
- G10L15/16
- G10L15/06
- H04L41/14
- H04L41/0823
- H04L67/51
- H04W8/26
- H04L67/133
- H04L41/5009
- H04W28/02
- G06F9/547
