NVIDIA Corporation patent applications on July 25th, 2024
Patent Applications by NVIDIA Corporation on July 25th, 2024
NVIDIA Corporation: 9 patent applications
NVIDIA Corporation has applied for patents in the areas of G06F9/30 (1), G06T7/70 (1), H04N23/73 (1), H04N21/258 (1), H04N21/234 (1) G06F9/30043 (1), G06N3/0464 (1), G06T11/00 (1), G06T13/40 (1), G06T15/06 (1)
With keywords such as: data, current, images, neural, detection, input, object, frame, network, and reference in patent application abstracts.
Patent Applications by NVIDIA Corporation
Inventor(s): Jeffrey Michael Pool of Durham NC (US) for nvidia corporation, Andrew Kerr of San Francisco CA (US) for nvidia corporation, John Tran of Denver CO (US) for nvidia corporation, Ming Y. Siu of Santa Clara CA (US) for nvidia corporation, Stuart Oberman of Sunnyvale CA (US) for nvidia corporation
IPC Code(s): G06F9/30, G06N20/00
CPC Code(s): G06F9/30043
Abstract: a method, computer readable medium, and processor are described herein for inline data inspection by using a decoder to decode a load instruction, including a signal to cause a circuit in a processor to indicate whether data loaded by a load instruction exceeds a threshold value. moreover, an indication of whether data loaded by a load instruction exceeds a threshold value may be stored.
Inventor(s): Ryan Marc Christian Benkert of Atlanta GA (US) for nvidia corporation, Shanshan Xu of Palo Alto CA (US) for nvidia corporation, Yifang Xu of San Jose CA (US) for nvidia corporation
IPC Code(s): G06N3/0464
CPC Code(s): G06N3/0464
Abstract: in various examples, machine learning data mining for autonomous or semi-autonomous systems and applications is described herein. systems and methods are disclosed that use neural networks to perform one or more data mining processes. for instance, a first neural network(s) may process input data (e.g., image data) to remove data samples (e.g., images) that are associated with a first object classification(s) and/or a second neural network(s) may process the input data to retrieve data samples (e.g., images) that are associated with a second classification(s). next, a third neural network(s) may process filtered input data (e.g., the input data not removed by the first neural network(s) and/or the input data retrieved by the second neural network(s)) to determine uncertainty classifications associated with the data samples and a fourth neural network(s) may process the filtered input data to determine final object classifications associated with the data samples.
Inventor(s): Yuval Atzmon of Hod Hasharon (IL) for nvidia corporation, Yoad Tewel of Tel Aviv-Yafo (IL) for nvidia corporation, Rinon Gal of Tel Aviv (IL) for nvidia corporation, Gal Chechik of Ramat Hasharon (IL) for nvidia corporation
IPC Code(s): G06T11/00, G06T7/10, G06V10/24
CPC Code(s): G06T11/00
Abstract: a text-to-image machine learning model takes a user input text and generates an image matching the given description. while text-to-image models currently exist, there is a desire to personalize these models on a per-user basis, including to configure the models to generate images of specific, unique user-provided concepts (via images of specific objects or styles) while allowing the user to use free text “prompts” to modify their appearance or compose them in new roles and novel scenes. current personalization solutions either generate images with only coarse-grained resemblance to the provided concept(s) or require fine tuning of the entire model which is costly and can adversely affect the model. the present description employs component locking and/or rank-one editing for personalization of text-to-image diffusion models, which can improve the fine-grained details of the concepts in the generated images, reduce the memory footprint update of the underlying model instead of full fine-tuning, and reduce adverse effects to the model.
Inventor(s): Chen Tessler of Tel Aviv (IL) for nvidia corporation, Gal Chechik of Tel Aviv (IL) for nvidia corporation, Yoni Kasten of Tel Aviv (IL) for nvidia corporation, Shie Mannor of Tel Aviv (IL) for nvidia corporation, Jason Peng of Vancouver (CA) for nvidia corporation
IPC Code(s): G06T13/40, G06N3/08, G06T13/80
CPC Code(s): G06T13/40
Abstract: a conditional adversarial latent model (calm) process can be used to generate reference motions from a set of original reference movements to create a library of new movements for an agent. the agent can be a virtual representation various types of characters, animals, or objects. the calm process can receive a set of reference movements and a requested movement. an encoder can be used to map the requested movement onto a latent space. a low-level policy can be employed to produce a series of latent space joint movements for the agent. a conditional discriminator can be used to provide feedback to the low-level policy to produce stationary distributions over the states of the agent. a high-level policy can be employed to provide a macro movement control over the low-level policy movements, such as providing direction in the environment. the high-level policy can utilize a reward or a finite-state machine function.
Inventor(s): Yaobin Ouyang of Shanghai (CN) for nvidia corporation, Nan Lin of Shanghai (CN) for nvidia corporation, Jacopo Pantaleoni of Berlin (DE) for nvidia corporation, Markus Kettunen of Urdorf (CH) for nvidia corporation, Shiqiu Liu of Santa Clara CA (US) for nvidia corporation
IPC Code(s): G06T15/06, G06T15/50
CPC Code(s): G06T15/06
Abstract: disclosed are apparatuses, systems, and techniques to render images with global illumination using efficient ray tracing, light source identification, and reservoir resampling that deploys temporal and spatial reservoirs.
Inventor(s): Zetong Yang of Rockville MD (US) for nvidia corporation, Zhiding Yu of Cupertino CA (US) for nvidia corporation, Ren Hao Wang of Toronto (CA) for nvidia corporation, Chris Choy of Los Angeles CA (US) for nvidia corporation, Anima Anandkumar of Pasadena CA (US) for nvidia corporation, Jose M. Alvarez Lopez of Mountain View CA (US) for nvidia corporation
IPC Code(s): G06V20/64, G06T7/50, G06T7/70, G06T7/80, G06V10/22, G06V10/82
CPC Code(s): G06V20/64
Abstract: 3d object detection is a computer vision task that generally detects (e.g. classifies and localizes) objects in 3d space from the 2d images or videos that capture the objects. current techniques used for 3d object detection rely on machine learning processes that learn to detect 3d objects from existing images annotated with high-quality 3d information including depth information generally obtained using lidar technology. however, due to lidar's limited measurable range, current machine learning solutions to 3d object detection do not support detection of 3d objects beyond the lidar range, which is needed for numerous applications, including autonomous driving applications where existing close or midrange 3d object detection does not always meet the safety-critical requirement of autonomous driving. the present disclosure provides for 3d object detection using a technique that supports long-range detection (i.e. detection beyond the lidar range).
Inventor(s): Malcolm GUTENBURG of San Francisco CA (US) for nvidia corporation, David HALEY of Beaverton OR (US) for nvidia corporation, Yunseok KIM of Pleasanton CA (US) for nvidia corporation, Amit KULKARNI of San Jose CA (US) for nvidia corporation
IPC Code(s): H01L23/00, H01L23/367, H01L25/18, H10B80/00
CPC Code(s): H01L24/26
Abstract: an electronic device comprises: a printed circuit board; an integrated circuit that is coupled to the printed circuit board on a first side and a thermal solution on a second side; a thermal interface material that is disposed between the integrated circuit and the thermal solution; and a first barrier seal that is disposed around a perimeter of the integrated circuit and is electrically insulative.
Inventor(s): Swapnil Jagdish Rathi of Pune (IN) for nvidia corporation, Viranjan Vishwasrao Pagar of Pune (IN) for nvidia corporation, Bhushan Rupde of Pune (IN) for nvidia corporation, Kaustubh Purandare of San Jose CA (US) for nvidia corporation
IPC Code(s): H04N21/234, H04N21/258
CPC Code(s): H04N21/23418
Abstract: systems and methods for improved media stream processing. in at least one embodiment, a media stream is assigned to either a hardware processing engine or software processing engine based on a performance state of an application server and one or more parameters of the media stream.
Inventor(s): Iuri FROSIO of Bergamo (IT) for nvidia corporation, Yazhou XING of Shenzhen (CN) for nvidia corporation, Chao LIU of Pittsburgh PA (US) for nvidia corporation, Anjul PATNEY of Kirkland WA (US) for nvidia corporation, Hongxu YIN of San Jose CA (US) for nvidia corporation, Amrita MAZUMDAR of San Francisco CA (US) for nvidia corporation, Jan KAUTZ of Lexington MA (US) for nvidia corporation
IPC Code(s): H04N23/73, H04N23/95
CPC Code(s): H04N23/73
Abstract: one or more embodiments include receiving one or more frames of a live video captured by a video capturing device, wherein the one or more frames include a current frame that is most-recently captured, identifying a set of reference frames included in the one or more frames based on at least the current frame, wherein each frame in the set of reference frames has a different exposure level relative to the current frame, determining, using one or more neural networks, a set of missing details for one or more regions of the current frame based on the set of reference frames, generating an updated version of the current frame based on the set of details, and outputting the updated version of the current frame in real-time.
- NVIDIA Corporation
- G06F9/30
- G06N20/00
- CPC G06F9/30043
- Nvidia corporation
- G06N3/0464
- CPC G06N3/0464
- G06T11/00
- G06T7/10
- G06V10/24
- CPC G06T11/00
- G06T13/40
- G06N3/08
- G06T13/80
- CPC G06T13/40
- G06T15/06
- G06T15/50
- CPC G06T15/06
- G06V20/64
- G06T7/50
- G06T7/70
- G06T7/80
- G06V10/22
- G06V10/82
- CPC G06V20/64
- H01L23/00
- H01L23/367
- H01L25/18
- H10B80/00
- CPC H01L24/26
- H04N21/234
- H04N21/258
- CPC H04N21/23418
- H04N23/73
- H04N23/95
- CPC H04N23/73
