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2024 juillet (MACJ) 32
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Classe IPC
G06N 3/08 - Méthodes d'apprentissage 513
G06N 3/04 - Architecture, p.ex. topologie d'interconnexion 375
G06T 1/20 - Architectures de processeurs; Configuration de processeurs p.ex. configuration en pipeline 375
G06T 15/00 - Rendu d'images tridimensionnelles [3D] 367
G09G 5/00 - Dispositions ou circuits de commande de l'affichage communs à l'affichage utilisant des tubes à rayons cathodiques et à l'affichage utilisant d'autres moyens de visualisation 284
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En Instance 1 471
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1.

APPLICATION EXECUTION ALLOCATION USING MACHINE LEARNING

      
Numéro d'application 18094028
Statut En instance
Date de dépôt 2023-01-06
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corporation (USA)
Inventeur(s) Isaac, Ronald N.

Abrégé

Apparatuses, systems, and techniques for assigning execution of applications to various processing units using machine learning are disclosed herein. Usage data for an application to be executed using a computing system including an integrated processing unit and a discrete processing unit is identified. At least a portion of operations of the application to be executed using the integrated processing unit or the discrete processing unit based on the usage data and in view of at least one of one or more system performance metrics or one or more user experience metrics associated with executing the application using the integrated processing unit and the discrete processing unit.

Classes IPC  ?

  • G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p.ex. des interruptions ou des opérations d'entrée–sortie
  • G06N 20/00 - Apprentissage automatique

2.

IMAGE PROCESSING USING NEURAL NETWORKS, WITH IMAGE REGISTRATION

      
Numéro d'application 18150889
Statut En instance
Date de dépôt 2023-01-06
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corporation (USA)
Inventeur(s) Turkowski, Kenneth

Abrégé

In various examples, systems and methods are disclosed relating to registering image processing with image registration for image generation and content stream applications. Systems and methods are disclosed for registering portions of images that are modified to incorporate content or features, with references images from which the portions of the images are identified. The systems and methods can transform the modified portions to more realistically and precisely merge back into the reference images, such as for presentation as a content stream.

Classes IPC  ?

  • G06T 7/30 - Détermination des paramètres de transformation pour l'alignement des images, c. à d. recalage des images
  • G06T 3/00 - Transformation géométrique de l'image dans le plan de l'image
  • G06T 3/40 - Changement d'échelle d'une image entière ou d'une partie d'image
  • G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
  • G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux

3.

SCENARIO RECREATION THROUGH OBJECT DETECTION AND 3D VISUALIZATION IN A MULTI-SENSOR ENVIRONMENT

      
Numéro d'application 18612058
Statut En instance
Date de dépôt 2024-03-21
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Sriram, Parthasarathy
  • Kumar, Ratnesh
  • Aghdasi, Farzin
  • Toorians, Arman
  • Naphade, Milind
  • Biswas, Sujit
  • Kolar, Vinay
  • Pisupati, Bhanu
  • Bartholomew, Aaron

Abrégé

The present disclosure provides various approaches for smart area monitoring suitable for parking garages or other areas. These approaches may include ROI-based occupancy detection to determine whether particular parking spots are occupied by leveraging image data from image sensors, such as cameras. These approaches may also include multi-sensor object tracking using multiple sensors that are distributed across an area that leverage both image data and spatial information regarding the area, to provide precise object tracking across the sensors. Further approaches relate to various architectures and configurations for smart area monitoring systems, as well as visualization and processing techniques. For example, as opposed to presenting video of an area captured by cameras, 3D renderings may be generated and played from metadata extracted from sensors around the area.

Classes IPC  ?

  • G06V 20/54 - Trafic, p.ex. de voitures sur la route, de trains ou de bateaux
  • G06F 18/231 - Techniques hiérarchiques, c. à d. la division ou la fusion d'ensembles de manière à obtenir un dendrogramme
  • G06F 18/2413 - Techniques de classification relatives au modèle de classification, p.ex. approches paramétriques ou non paramétriques basées sur les distances des motifs d'entraînement ou de référence
  • G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p.ex. le suivi des coins ou des segments
  • G06T 7/292 - Suivi à plusieurs caméras
  • G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
  • G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
  • G06V 10/147 - Caractéristiques optiques de l’appareil qui effectue l’acquisition ou des dispositifs d’éclairage - Détails de capteurs, p.ex. lentilles de capteurs
  • G06V 10/20 - Prétraitement de l’image
  • G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
  • G06V 10/762 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant le regroupement, p.ex. de visages similaires sur les réseaux sociaux
  • G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
  • G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
  • G06V 20/52 - Activités de surveillance ou de suivi, p.ex. pour la reconnaissance d’objets suspects
  • G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes
  • G06V 20/62 - Texte, p.ex. plaques d’immatriculation, textes superposés ou légendes des images de télévision
  • G06V 40/20 - Mouvements ou comportement, p.ex. reconnaissance des gestes
  • H04N 23/90 - Agencement de caméras ou de modules de caméras, p. ex. de plusieurs caméras dans des studios de télévision ou des stades de sport

4.

DIGITALLY CONTROLLED UNIFIED RECEIVER FOR MULTI-RANK SYSTEM

      
Numéro d'application 18614490
Statut En instance
Date de dépôt 2024-03-22
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corp. (USA)
Inventeur(s)
  • Lee, Jiwang
  • Lee, Jaewon
  • Nee, Hsuche
  • Chiang, Po-Chien
  • Lo, Wen-Hung
  • Halfen, Michael Ivan
  • Dhir, Abhishek

Abrégé

A multi-rank circuit system includes multiple transmitters each switchably coupled to a first end of a shared input/output (IO) channel and a unified receiver coupled to a second end of the shared IO channel. The unified receiver is coupled to apply an analog reference voltage to set a differential output of the unified receiver, and further configured to apply a variable digital code to adjust the differential output according to a particular one of the transmitters that is switched to the shared IO channel.

Classes IPC  ?

  • H03K 19/1776 - Circuits logiques, c. à d. ayant au moins deux entrées agissant sur une sortie; Circuits d'inversion utilisant des éléments spécifiés utilisant des circuits logiques élémentaires comme composants disposés sous forme matricielle - Détails structurels des ressources de configuration pour les mémoires
  • H03K 19/17736 - Circuits logiques, c. à d. ayant au moins deux entrées agissant sur une sortie; Circuits d'inversion utilisant des éléments spécifiés utilisant des circuits logiques élémentaires comme composants disposés sous forme matricielle - Détails structurels des ressources de routage
  • H03K 19/17784 - Circuits logiques, c. à d. ayant au moins deux entrées agissant sur une sortie; Circuits d'inversion utilisant des éléments spécifiés utilisant des circuits logiques élémentaires comme composants disposés sous forme matricielle - Détails structurels pour l'adaptation des paramètres physiques pour la tension d'alimentation

5.

DISTANCE TO OBSTACLE DETECTION IN AUTONOMOUS MACHINE APPLICATIONS

      
Numéro d'application 18343291
Statut En instance
Date de dépôt 2023-06-28
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Yang, Yilin
  • Jujjavarapu, Bala Siva Sashank
  • Janis, Pekka
  • Ye, Zhaoting
  • Oh, Sangmin
  • Park, Minwoo
  • Herrera Castro, Daniel
  • Koivisto, Tommi
  • Nister, David

Abrégé

In various examples, a deep neural network (DNN) is trained to accurately predict, in deployment, distances to objects and obstacles using image data alone. The DNN may be trained with ground truth data that is generated and encoded using sensor data from any number of depth predicting sensors, such as, without limitation, RADAR sensors, LIDAR sensors, and/or SONAR sensors. Camera adaptation algorithms may be used in various embodiments to adapt the DNN for use with image data generated by cameras with varying parameters—such as varying fields of view. In some examples, a post-processing safety bounds operation may be executed on the predictions of the DNN to ensure that the predictions fall within a safety-permissible range.

Classes IPC  ?

  • G06N 3/08 - Méthodes d'apprentissage
  • B60W 30/14 - Régulateur d'allure
  • B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
  • G06F 18/214 - Génération de motifs d'entraînement; Procédés de Bootstrapping, p.ex. ”bagging” ou ”boosting”
  • G06V 10/762 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant le regroupement, p.ex. de visages similaires sur les réseaux sociaux
  • G06V 20/56 - Contexte ou environnement de l’image à l’extérieur d’un véhicule à partir de capteurs embarqués

6.

HYBRID LANGUAGE MODELS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS

      
Numéro d'application 18468086
Statut En instance
Date de dépôt 2023-09-15
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Bataev, Vladimir
  • Korostik, Roman
  • Shabalin, Evgenii
  • Lavrukhin, Vitaly Sergeyevich
  • Ginsburg, Boris

Abrégé

In various examples, first textual data may be applied to a first MLM to generate an intermediate speech representation (e.g., a frequency-domain representation), the intermediate audio representation and a second MLM may be used to generate output data indicating second textual data, and parameters of the second MLM may be updated using the output data and ground truth data associated with the first textual data. The first MLM may include a trained Text-To-Speech (TTS) model and the second MLM may include an Automatic Speech Recognition (ASR) model. A generator from a generative adversarial networks may be used to enhance an initial intermediate audio representation generated using the first MLM and the enhanced intermediate audio representation may be provided to the second MLM. The generator may include generator blocks that receive the initial intermediate audio representation to sequentially generate the enhanced intermediate audio representation.

Classes IPC  ?

  • G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels
  • G10L 15/065 - Adaptation

7.

TECHNIQUES FOR BALANCING DYNAMIC INFERENCING BY MACHINE LEARNING MODELS

      
Numéro d'application 18152528
Statut En instance
Date de dépôt 2023-01-10
Date de la première publication 2024-07-11
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s)
  • Clemons, Jason Lavar
  • Sreedhar, Kavya

Abrégé

Techniques are disclosed herein for allocating computational resources when executing trained machine learning models. The techniques include determining one or more available computational resources that are usable by one or more trained machine learning models to perform one or more tasks, allocating one or more computational resources to the one or more tasks based on the one or more available computational resources and one or more performance requirements associated with the one or more tasks, and causing the one or more trained machine learning models to perform the one or more tasks using the one or more computational resources allocated to the one or more tasks.

Classes IPC  ?

  • G06F 9/50 - Allocation de ressources, p.ex. de l'unité centrale de traitement [UCT]
  • G06N 20/00 - Apprentissage automatique

8.

ASYNCHRONOUS IN-SYSTEM TESTING FOR AUTONOMOUS SYSTEMS AND APPLICATIONS

      
Numéro d'application 18048952
Statut En instance
Date de dépôt 2022-10-24
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Kalva, Anitha
  • Wu, Jae
  • Sarangi, Shantanu
  • Chadalavada, Sailendra
  • Sonawane, Milind
  • Fang, Chen
  • Nerallapally, Abilash

Abrégé

Systems and methods are disclosed that relate to testing processing elements of an integrated processing system. A first system test may be performed on a first processing element of an integrated processing system. The first system test may be based at least on accessing a test node associated with the first processing element. The first system test may be accessed using a first local test controller. A second system test may be performed on a second processing element of the integrated processing system. The second system test may be based at least on accessing a second test node associated with the second processing element. The second system test may be accessed using a second local test controller.

Classes IPC  ?

  • B60W 50/02 - COMMANDE CONJUGUÉE DE PLUSIEURS SOUS-ENSEMBLES D'UN VÉHICULE, DE FONCTION OU DE TYPE DIFFÉRENTS; SYSTÈMES DE COMMANDE SPÉCIALEMENT ADAPTÉS AUX VÉHICULES HYBRIDES; SYSTÈMES D'AIDE À LA CONDUITE DE VÉHICULES ROUTIERS, NON LIÉS À LA COMMANDE D'UN SOUS-ENSEMBLE PARTICULIER - Détails des systèmes d'aide à la conduite des véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier pour préserver la sécurité en cas de défaillance du système d'aide à la conduite, p.ex. en diagnostiquant ou en palliant à un dysfonctionnement

9.

REMOVING ARTIFACTS USING DITHERING COMPENSATION IN IMAGE STREAMING SYSTEMS AND APPLICATIONS

      
Numéro d'application 18151653
Statut En instance
Date de dépôt 2023-01-09
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Russell, Andrew
  • Sundareson, Prabindh

Abrégé

In various examples, processing pipelines for removing artifacts from images are described herein. Systems and methods are disclosed that use one or more multi-pass techniques to identify and process areas in an image that have artifacts. For instance, using a series of forward passes, the image is processed to generate multiple levels of images, where the levels of images are used to identify at least areas of the original image that include artifacts and areas of the original image that include true color edges. Next, using a series of backward passes, processing is performed on color values associated with the areas that include artifacts to determine new color values for the pixels within the areas. In some examples, dithering may then be performed on the new color values to distribute errors in quantization across the pixels and remove the artifacts.

Classes IPC  ?

  • G06V 10/56 - Extraction de caractéristiques d’images ou de vidéos relative à la couleur
  • G06T 3/40 - Changement d'échelle d'une image entière ou d'une partie d'image
  • G06T 7/13 - Détection de bords
  • G06T 7/90 - Détermination de caractéristiques de couleur

10.

SOFTWARE PROGRAM ERROR TESTING FOR AUTONOMOUS SYSTEMS AND APPLICATIONS

      
Numéro d'application 18152666
Statut En instance
Date de dépôt 2023-01-10
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corporation (S)
Inventeur(s)
  • Nair, Saumya
  • Kini, Yogesh
  • Jain, Ashutosh
  • Gubba, Neeraja

Abrégé

One or more embodiments of the present disclosure relate to executing a software testing tool to identify function calls—internal and/or external—of software code and their corresponding errors. Once identified—such as during an information gathering operation—the error codes may be returned in place of actual outputs of the function during testing, and the downstream processing of the software as a result of the errors may be evaluated. As such, an automatic software testing tool may be implemented that not only identifies functions calls and corresponding errors, but also evaluates performance of the software in view of the various different error types associated with the function calls.

Classes IPC  ?

  • G06F 11/36 - Prévention d'erreurs en effectuant des tests ou par débogage de logiciel

11.

IMAGE STITCHING WITH SACCADE-BASED CONTROL OF DYNAMIC SEAM PLACEMENT FOR SURROUND VIEW VISUALIZATION

      
Numéro d'application 17969514
Statut En instance
Date de dépôt 2022-10-19
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Kristensen, Steen
  • Kiefhaber, Simon

Abrégé

In various examples, a stitched image may be generated from overlapping image frames using a dynamic seam placement that depends on scene content and/or other factors. Since an optimized seam placement may jump from a previous location from time slice to time slice, one or more constraints may be applied to limit the movement of dynamically placed seams such that any given seam moves gradually over time, limiting potential discontinuities in a visualization of the stitched image on a display. Eye tracking may be used to detect a saccade of a monitored person and/or detect that the monitored person is not looking at the display, and one or more of the constraints used to limit the movement of dynamically placed seams may be relaxed or lifted when the monitored person is experiencing a saccade and/or is looking away from the display.

Classes IPC  ?

  • G06T 3/40 - Changement d'échelle d'une image entière ou d'une partie d'image
  • G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur

12.

HARDWARE LATENCY MONITORING FOR MEMORY DEVICE INPUT/OUTPUT REQUESTS

      
Numéro d'application 18592238
Statut En instance
Date de dépôt 2024-02-29
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Rasal, Shridhar
  • Duer, Oren
  • Kfir, Aviv
  • Mula, Liron

Abrégé

A system includes a hardware circuitry having a device coupled with one or more external memory devices. The device is to detect an input/output (I/O) request associated with an external memory device of the one or more external memory devices. The device is to record a first timestamp in response to detecting the I/O request transmitted to the external memory device. The device is further to detect an indication from the external memory device of a completion of the I/O request associated with the external memory device and record a second timestamp in response to detecting the indication. The device is also to determine a latency associated with the I/O request based on the first timestamp and the second timestamp.

Classes IPC  ?

  • G06F 3/06 - Entrée numérique à partir de, ou sortie numérique vers des supports d'enregistrement

13.

INTERSECTION POSE DETECTION IN AUTONOMOUS MACHINE APPLICATIONS

      
Numéro d'application 18615894
Statut En instance
Date de dépôt 2024-03-25
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Pham, Trung
  • Dou, Hang
  • Rodriguez Hervas, Berta
  • Park, Minwoo
  • Cvijetic, Neda
  • Nister, David

Abrégé

In various examples, live perception from sensors of a vehicle may be leveraged to generate potential paths for the vehicle to navigate an intersection in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as heat maps corresponding to key points associated with the intersection, vector fields corresponding to directionality, heading, and offsets with respect to lanes, intensity maps corresponding to widths of lanes, and/or classifications corresponding to line segments of the intersection. The outputs may be decoded and/or otherwise post-processed to reconstruct an intersection—or key points corresponding thereto—and to determine proposed or potential paths for navigating the vehicle through the intersection.

Classes IPC  ?

  • G01C 21/26 - Navigation; Instruments de navigation non prévus dans les groupes spécialement adaptés pour la navigation dans un réseau routier
  • G06N 3/04 - Architecture, p.ex. topologie d'interconnexion
  • G06N 3/08 - Méthodes d'apprentissage
  • G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
  • G06V 10/46 - Descripteurs pour la forme, descripteurs liés au contour ou aux points, p.ex. transformation de caractéristiques visuelles invariante à l’échelle [SIFT] ou sacs de mots [BoW]; Caractéristiques régionales saillantes
  • G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
  • G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
  • G06V 20/56 - Contexte ou environnement de l’image à l’extérieur d’un véhicule à partir de capteurs embarqués

14.

SYNTHETIC AUDIO-DRIVEN BODY ANIMATION USING VOICE TEMPO

      
Numéro d'application 18007867
Statut En instance
Date de dépôt 2021-11-08
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Tumanov, Evgeny Aleksandrovich
  • Korobchenko, Dmitry Aleksandrovich
  • Yuen, Simon
  • Margo, Kevin

Abrégé

In various examples, animations may be generated using audio-driven body animation synthesized with voice tempo. For example, full body animation may be driven from an audio input representative of recorded speech, where voice tempo (e.g., a number of phonemes per unit time) may be used to generate a 1D audio signal for comparing to datasets including data samples that each include an animation and a corresponding 1D audio signal. One or more loss functions may be used to compare the 1D audio signal from the input audio to the audio signals of the datasets, as well as to compare joint information of joints of an actor between animations of two or more data samples, in order to identify optimal transition points between the animations. The animations may then be stitched together—e.g., using interpolation and/or a neural network trained to seamlessly stitch sequences together—using the transition points.

Classes IPC  ?

  • G06T 13/20 - Animation tridimensionnelle [3D]
  • G06T 13/40 - Animation tridimensionnelle [3D] de personnages, p.ex. d’êtres humains, d’animaux ou d’êtres virtuels

15.

VERIFYING SECURITY FOR VIRTUAL MACHINES IN CLOUD STREAMING SYSTEMS AND APPLICATIONS

      
Numéro d'application 18151175
Statut En instance
Date de dépôt 2023-01-06
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Dunning, Lucien
  • Schneider, Seth
  • Swoboda, Dwayne
  • Mitic, Marko
  • Zabrocki, Adam

Abrégé

In examples, a VM may receive and aggregate a first attestation report corresponding to a In examples, a VM may receive and aggregate a first attestation report corresponding to a CPU and a second attestation report corresponding to a GPU. The aggregated data may be provided to an attestation service, which may verify the attestation reports indicate a TCB is to include the VM and GPU state data and is to isolate the GPU state data and the VM from an untrusted host OS. Based at least on the TCB being verified, the VM may perform one or more operations using the TCB. The TCB may include a trusted hypervisor to isolate the VM and GPU state data within the GPU(s) from the untrusted host OS. The trusted hypervisor may prevent the host OS from accessing device memory assigned to the VM based at least on controlling an IOMMU and/or second-level address translation (SLAT) used to access the data.

Classes IPC  ?

  • G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p.ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
  • A63F 13/73 - Autorisation des programmes ou des dispositifs de jeu, p.ex. vérification de l’authenticité

16.

WORKLOAD ASSIGNMENT TECHNIQUE

      
Numéro d'application 18094962
Statut En instance
Date de dépôt 2023-01-09
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corporation (USA)
Inventeur(s) Busato, Federico

Abrégé

Apparatuses, systems, and techniques to distribute workloads in parallel computing. In at least one embodiment, threads of a group are assigned equal numbers of items based, at least in part, on locations of non-zero values in a data structure that contains mostly zeros.

Classes IPC  ?

  • G06F 9/38 - Exécution simultanée d'instructions

17.

SYSTEMS AND METHODS FOR ITERATIVE AND ADAPTIVE OBJECT DETECTION

      
Numéro d'application 18094159
Statut En instance
Date de dépôt 2023-01-06
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corporation (USA)
Inventeur(s) Dwivedi, Shekhar

Abrégé

Apparatuses, systems, and techniques for improved object detection and/or segmentation are disclosed. In at least one embodiment, an object is detected in input data via iterative transformation and processing of the input data and aggregation of a result of each iteration into a combined object detection result.

Classes IPC  ?

  • G06T 7/11 - Découpage basé sur les zones
  • G06T 7/187 - Découpage; Détection de bords impliquant un étiquetage de composantes connexes
  • G06V 10/36 - Utilisation d’un opérateur local, c. à d. des moyens pour opérer sur des points d’image situés dans la proximité d’un point donné; Opérations de filtrage locales non linéaires, p.ex. filtrage médian
  • G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos

18.

SUBSURFACE SCATTERING FOR REAL-TIME RENDERING APPLICATIONS

      
Numéro d'application 18152320
Statut En instance
Date de dépôt 2023-01-10
Date de la première publication 2024-07-11
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Zhang, Tianyi
  • Taskov, Blagovest Borislavov

Abrégé

Light transport simulation algorithms or techniques may be used to generate a sample for a subsurface scattering of light, then a target function may be used to improve the sample. The target function may correspond to an amount of energy transported to the surface from within the object. The sample may be resampled using the sample and the target function to update a reservoir of samples. A resampled sample may be selected and used as a lighting sample for the subsurface scattering. Rather than using the resampled sample, it may be used with the target function to again update the reservoir and select another resampled sample from the updated reservoir. This may be performed for any number of iterations to determine the lighting sample for the frame. A backside lighting cache may be used in the ray tracing to determine lighting at the backside of the object.

Classes IPC  ?

19.

PARALLEL SELECTION OF FIFTH GENERATION (5G) NEW RADIO INFORMATION

      
Numéro d'application 18377605
Statut En instance
Date de dépôt 2023-10-06
Date de la première publication 2024-07-04
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Papadopoulou, Misel Myrto
  • Martin, Timothy James

Abrégé

Apparatuses, systems, and techniques to select fifth-generation (5G) new radio data. In at least one embodiment, a processor includes one or more circuits to select 5G new radio signal information in parallel.

Classes IPC  ?

  • H04L 1/00 - Dispositions pour détecter ou empêcher les erreurs dans l'information reçue
  • H03M 13/00 - Codage, décodage ou conversion de code pour détecter ou corriger des erreurs; Hypothèses de base sur la théorie du codage; Limites de codage; Méthodes d'évaluation de la probabilité d'erreur; Modèles de canaux; Simulation ou test des codes
  • H03M 13/11 - Détection d'erreurs ou correction d'erreurs transmises par redondance dans la représentation des données, c.à d. mots de code contenant plus de chiffres que les mots source utilisant un codage par blocs, c.à d. un nombre prédéterminé de bits de contrôle ajouté à un nombre prédéterminé de bits d'information utilisant plusieurs bits de parité

20.

PROGRAM FLOW MONITORING AND CONTROL OF AN EVENT-TRIGGERED SYSTEM

      
Numéro d'application 18603616
Statut En instance
Date de dépôt 2024-03-13
Date de la première publication 2024-07-04
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Boonstoppel, Peter Alexander
  • Cox, Michael
  • Perrin, Daniel

Abrégé

In various examples, a system is provided for monitoring and controlling program flow in an event-triggered system. A program (e.g., application, algorithm, routine, etc.) may be organized into operational units (e.g., nodes executed by one or more processors), each of which tasked with executing one or more respective events (e.g., tasks) within the larger program. At least some of the events of the larger program may be successively executed in a flow, one after another, using triggers sent directly from one node to the next. In addition, the system of the present disclosure may include a manager that may exchange communications with the nodes to monitor or assess a status of the system (e.g., determine when a node has completed an event) or to control or trigger a node to initiate an event.

Classes IPC  ?

  • G06F 9/48 - Lancement de programmes; Commutation de programmes, p.ex. par interruption
  • G06F 9/50 - Allocation de ressources, p.ex. de l'unité centrale de traitement [UCT]
  • G06F 9/54 - Communication interprogramme
  • G06F 11/30 - Surveillance du fonctionnement
  • G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p.ex. des interruptions ou des opérations d'entrée–sortie

21.

USING STABLE DIFFUSION TO GENERATE SEAMLESS CONTENT TILE SETS IN CONTENT GENERATION SYSTEMS AND APPLICATIONS

      
Numéro d'application 18149285
Statut En instance
Date de dépôt 2023-01-03
Date de la première publication 2024-07-04
Propriétaire Nvidia Corporation (USA)
Inventeur(s)
  • Greenen, Alex
  • Kraemer, Manuel

Abrégé

Approaches presented herein can utilize a network that learns to generate a set of content tiles that represent a type of content (e.g., texture) and satisfy a set of rules or boundary conditions. The network can be a diffusion network that learns or adapts to the boundary conditions over several iterations. An indication of a type of content, along with a set of noisy prior images, can then be provided as input to the trained diffusion network, which can generate a set of content images. The content images can then be placed using a random (or other) selection process, as long as each selection satisfies the respective boundary conditions. Such an approach enables a small number of content tiles to be used for a texture region with a repeatability or pattern that may not be obviously detectable by a typical human viewer.

Classes IPC  ?

  • G06T 11/00 - Génération d'images bidimensionnelles [2D]
  • G06T 5/00 - Amélioration ou restauration d'image
  • G06T 7/13 - Détection de bords
  • G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux

22.

OFFLOADING CONNECTION MANAGEMENT FOR NETWORK RESOURCES

      
Numéro d'application 18149466
Statut En instance
Date de dépôt 2023-01-03
Date de la première publication 2024-07-04
Propriétaire Nvidia Corporation (USA)
Inventeur(s) Gopalarathnam, Sudharsan Dhamal

Abrégé

Approaches in accordance with various illustrative embodiments provide for the management of active connections in a network environment. In particular, various embodiments implement keep alive functionality in components such as network processing units (NPUs) of network devices such as routers and switches, instead of host processors for those devices. When a status message is received, such as a hello message, the NPU can set or refresh a hit bit in a table entry for a given connection with a peer device. If a subsequent status message is not received within a keep alive interval of the last received status message, then the NPU can determine that the connection with the peer device is stale and can inform the host processor that the connection is no longer available for routing network traffic. The status messages are terminated in the NPU and prevented from being received and processed by the host processor.

Classes IPC  ?

  • H04L 45/02 - Mise à jour ou découverte de topologie

23.

SELECTING REPRESENTATIVE IMAGE VIEWS FOR 3D OBJECT MODELS IN SYNTHETIC CONTENT CREATION SYSTEMS AND APPLICATIONS

      
Numéro d'application 18147426
Statut En instance
Date de dépôt 2022-12-28
Date de la première publication 2024-07-04
Propriétaire Nvidia Corporation (USA)
Inventeur(s)
  • Foco, Marco
  • Kass, Michael
  • State, Gavriel
  • Rozantsev, Artem

Abrégé

Approaches presented herein provide for automatic generation of representative two-dimensional (2D) images for three-dimensional (3D) objects or assets. In generating these 2D images, a set of options is determined such as may relate to viewpoint or other parameters of a virtual camera. A set of sample points is determined from which to generate 2D images of a 3D model, for example, with 2D images being processed using a classifier to determine which of these images generates a classification with highest confidence or probability, individually or with respect to other classifications. The sample point for this selected image can then be used to select nearby sample points as part of a refinement or optimization process, where 2D images can again be generated and processed using a classifier to identify a 2D image with highest classification probability or confidence, which can be selected as representative of the 3D object or asset.

Classes IPC  ?

  • G06T 15/20 - Calcul de perspectives
  • G06T 7/55 - Récupération de la profondeur ou de la forme à partir de plusieurs images
  • G06T 11/00 - Génération d'images bidimensionnelles [2D]
  • G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo

24.

EMBEDDED SILICON-BASED DEVICE COMPONENTS IN A THICK CORE SUBSTRATE OF AN INTEGRATED CIRCUIT PACKAGE

      
Numéro d'application 18091943
Statut En instance
Date de dépôt 2022-12-30
Date de la première publication 2024-07-04
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Boja, Ronilo
  • Jain, Padam

Abrégé

An integrated circuit package including a package substrate including a monolithic core, the monolithic core having a first substrate side, a second substrate side opposite the first substrate side, a thickness in a range from 800 to 2000 microns and a through-cavity that passes through the first and second substrate sides. The package includes a device module, the device module having a first module side and a second module side opposite the first module side. The device module is embedded in the through-cavity, the first module side is aligned with the first substrate side, the second module side is aligned with the second substrate side, and the device module includes one or more silicon-based passive or silicon-based active device component. A method of manufacture of the integrated circuit package is also disclosed.

Classes IPC  ?

  • H01L 23/31 - Capsulations, p.ex. couches de capsulation, revêtements caractérisées par leur disposition
  • H01L 21/683 - Appareils spécialement adaptés pour la manipulation des dispositifs à semi-conducteurs ou des dispositifs électriques à l'état solide pendant leur fabrication ou leur traitement; Appareils spécialement adaptés pour la manipulation des plaquettes pendant la fabrication ou le traitement des dispositifs à semi-conducteurs ou des dispositifs électriques à l'état solide ou de leurs composants pour le maintien ou la préhension
  • H01L 21/768 - Fixation d'interconnexions servant à conduire le courant entre des composants distincts à l'intérieur du dispositif
  • H01L 21/786 - Fabrication ou traitement de dispositifs consistant en une pluralité de composants à l'état solide ou de circuits intégrés formés dans ou sur un substrat commun avec une division ultérieure du substrat en plusieurs dispositifs individuels pour produire des dispositifs qui consistent chacun en un seul élément de circuit le substrat étant autre chose qu'un corps semi-conducteur, p.ex. un corps isolant
  • H01L 23/00 - DISPOSITIFS À SEMI-CONDUCTEURS NON COUVERTS PAR LA CLASSE - Détails de dispositifs à semi-conducteurs ou d'autres dispositifs à l'état solide
  • H01L 23/36 - Emploi de matériaux spécifiés ou mise en forme, en vue de faciliter le refroidissement ou le chauffage, p.ex. dissipateurs de chaleur
  • H01L 23/498 - Connexions électriques sur des substrats isolants
  • H01L 25/10 - Ensembles consistant en une pluralité de dispositifs à semi-conducteurs ou d'autres dispositifs à l'état solide les dispositifs étant tous d'un type prévu dans le même sous-groupe des groupes , ou dans une seule sous-classe de , , p.ex. ensembles de diodes redresseuses les dispositifs ayant des conteneurs séparés

25.

WATERMARKING FOR SPEECH IN CONVERSATIONAL AI AND COLLABORATIVE SYNTHETIC CONTENT GENERATION SYSTEMS AND APPLICATIONS

      
Numéro d'application 18148226
Statut En instance
Date de dépôt 2022-12-29
Date de la première publication 2024-07-04
Propriétaire Nvidia Corporation (USA)
Inventeur(s) Ginsburg, Boris

Abrégé

Approaches presented herein provide for insertion of watermarks into synthesized content, such as audio content that may include synthesized speech to appear to be spoken by a digital avatar in a 3D virtual environment. A Text-to-Speech (TTS) generator, such as a trained neural network, can be used to produce synthetic speech audio, which can have an audio watermark inserted therein. This watermark can be detected by a process of a collaborative content generation platform, for example, and an indication can be provided that the content contains synthesized speech. The presence of the audio watermark will generally not be detectable by the human ear during presentation. To make it difficult to remove or modify the watermark, the watermark can be generated using a key or other unique piece of data known only to authorized entities.

Classes IPC  ?

  • G10L 19/018 - Mise en place d’un filigrane audio, c. à d. insertion de données inaudibles dans le signal audio
  • G10L 13/04 - Procédés d'élaboration de parole synthétique; Synthétiseurs de parole - Détails des systèmes de synthèse de la parole, p.ex. structure du synthétiseur ou gestion de la mémoire

26.

ALLOCATING RADIO RESOURCES USING ARTIFICIAL INTELLIGENCE

      
Numéro d'application 18066127
Statut En instance
Date de dépôt 2022-12-14
Date de la première publication 2024-07-04
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Huang, Yan
  • Delfeld, James
  • Gao, Yuan
  • Lin, Xingqin
  • Casas, Christian Ibars

Abrégé

Apparatuses, systems, and techniques to allocate one or more compute resources to a user device. In at least one embodiment, one or more circuits cause one or more compute resources to be allocated to two or more fifth-generation (5G) radio access network (RAN) cells based, at least in part, on interference between the two or more 5G RAN cells.

Classes IPC  ?

  • H04L 5/00 - Dispositions destinées à permettre l'usage multiple de la voie de transmission
  • H04W 72/12 - Planification du trafic sans fil

27.

VISION-BASED SOUND SIMULATION FOR CORRECTING ACOUSTICS AT A LOCATION

      
Numéro d'application 18147915
Statut En instance
Date de dépôt 2022-12-29
Date de la première publication 2024-07-04
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Stengel, Michael
  • Lopes, Ward
  • Kim, Joohwan
  • Luebke, David

Abrégé

The disclosure provides a method for audio calibration that uses audio simulation and reconstructed surface information from images or video recordings along with recorded sound. The surface component of the method introduces knowledge that enables audio wave propagation simulation for a particular location. Using the simulation results the sound distribution can be optimized. For example, unwanted audio reflection and occlusion can be recognized and resolved. In one example, the disclosure provides a method for improving acoustics at a location that includes: (1) generating a geometric model of a location using visual data obtained from the location, wherein the location includes an audio system, and (2) simulating, using the geometric model, movement of sound waves in the location that originate from the audio system. The disclosure also provides a computer system, a computer program product, and a mobile computing device that include features for improving acoustics at a location.

Classes IPC  ?

  • H04S 7/00 - Dispositions pour l'indication; Dispositions pour la commande, p.ex. pour la commande de l'équilibrage
  • G01S 17/86 - Combinaisons de systèmes lidar avec des systèmes autres que lidar, radar ou sonar, p.ex. avec des goniomètres
  • G01S 17/89 - Systèmes lidar, spécialement adaptés pour des applications spécifiques pour la cartographie ou l'imagerie

28.

MANAGEMENT OF ARTIFICIAL INTELLIGENCE RESOURCES IN A DISTRIBUTED RESOURCE ENVIRONMENT

      
Numéro d'application 18149248
Statut En instance
Date de dépôt 2023-01-03
Date de la première publication 2024-07-04
Propriétaire Nvidia Corporation (USA)
Inventeur(s)
  • Wyman, J
  • Nahar, Pritish
  • Groff, Dana

Abrégé

Approaches presented herein provide for the management of artificial intelligence (AI)-related resources in a distributed resource environment, such as may be used to support accelerated machine learning (ML) applications on behalf of different users. Management functionality can be provided using an AI manager, such as a management service, that can determine the requirements, capabilities, and limitations of various available AI-related components, such as those of a plurality of AI models, engines, and accelerators, as well as the hardware (e.g., graphics processing units (GPUs)) that run or make up these AI-related resources. An AI manager can determine a selection and configuration of resources that is not only appropriate for use with a specific AI model, but that can also be optimized for factors such as throughput, resource utilization, and inference latency. An AI manager can ensure compatibility of resources and configuration, and can enforce access control to models and data.

Classes IPC  ?

  • G06N 5/04 - Modèles d’inférence ou de raisonnement
  • G06N 20/00 - Apprentissage automatique

29.

POINT-LEVEL SUPERVISION FOR VIDEO INSTANCE SEGMENTATION

      
Numéro d'application 18395198
Statut En instance
Date de dépôt 2023-12-22
Date de la première publication 2024-07-04
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Yu, Zhiding
  • Huang, Shuaiyi
  • Huang, De-An
  • Lan, Shiyi
  • Radhakrishnan, Subhashree
  • Alvarez Lopez, Jose M.
  • Anandkumar, Anima

Abrégé

Video instance segmentation is a computer vision task that aims to detect, segment, and track objects continuously in videos. It can be used in numerous real-world applications, such as video editing, three-dimensional (3D) reconstruction, 3D navigation (e.g. for autonomous driving and/or robotics), and view point estimation. However, current machine learning-based processes employed for video instance segmentation are lacking, particularly because the densely annotated videos needed for supervised training of high-quality models are not readily available and are not easily generated. To address the issues in the prior art, the present disclosure provides point-level supervision for video instance segmentation in a manner that allows the resulting machine learning model to handle any object category.

Classes IPC  ?

  • G06T 7/12 - Découpage basé sur les bords
  • G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
  • G06V 20/70 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène Étiquetage du contenu de scène, p.ex. en tirant des représentations syntaxiques ou sémantiques

30.

MIXED PHASE THERMAL INTERFACE MATERIAL ASSEMBLY WITH HIGH THERMAL CONDUCTIVITY AND LOW INTERNAL CONTACT RESISTANCE

      
Numéro d'application 18149617
Statut En instance
Date de dépôt 2023-01-03
Date de la première publication 2024-07-04
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Jain, Padam
  • Boja, Ronilo

Abrégé

An IC package including an IC and a TIM assembly located on the IC. The TIM assembly includes a lid defining a compartment, a mixed-phase material located in the compartment, the mixed-phase material including nanostructures, and a liquid metal occupying open spaces in the compartment that are not occupied by the nanostructures. A method of manufacturing an IC package, including providing the IC and placing the TIM assembly on the IC. A computer having one or more circuits that include the IC package.

Classes IPC  ?

  • H01L 23/427 - Refroidissement par changement d'état, p.ex. caloducs
  • H01L 21/48 - Fabrication ou traitement de parties, p.ex. de conteneurs, avant l'assemblage des dispositifs, en utilisant des procédés non couverts par l'un uniquement des groupes
  • H01L 23/433 - Pièces auxiliaires caractérisées par leur forme, p.ex. pistons

31.

BEHAVIOR PLANNING FOR AUTONOMOUS VEHICLES IN YIELD SCENARIOS

      
Numéro d'application 18602802
Statut En instance
Date de dépôt 2024-03-12
Date de la première publication 2024-07-04
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Yang, Fangkai
  • Nister, David
  • Wang, Yizhou
  • Aviv, Rotem
  • Ng, Julia
  • Henke, Birgit
  • Lee, Hon Leung
  • Shi, Yunfei

Abrégé

In various examples, a yield scenario may be identified for a first vehicle. A wait element is received that encodes a first path for the first vehicle to traverse a yield area and a second path for a second vehicle to traverse the yield area. The first path is employed to determine a first trajectory in the yield area for the first vehicle based at least on a first location of the first vehicle at a time and the second path is employed to determine a second trajectory in the yield area for the second vehicle based at least on a second location of the second vehicle at the time. To operate the first vehicle in accordance with a wait state, it may be determined whether there is a conflict between the first trajectory and the second trajectory, where the wait state defines a yielding behavior for the first vehicle.

Classes IPC  ?

  • B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
  • B60W 30/18 - Propulsion du véhicule
  • G08G 1/0967 - Systèmes impliquant la transmission d'informations pour les grands axes de circulation, p.ex. conditions météorologiques, limites de vitesse

32.

Multi-level and multi-label content classification using unsupervised and ensemble machine learning techniques

      
Numéro d'application 16793351
Numéro de brevet 12026626
Statut Délivré - en vigueur
Date de dépôt 2020-02-18
Date de la première publication 2024-07-02
Date d'octroi 2024-07-02
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s)
  • Pardeshi, Siddhant Prakash
  • Kothari, Pranit P.
  • Gaikwad, Vinayak Vilas

Abrégé

Apparatuses, systems, and techniques to classify content. In at least one embodiment, a mixture of neural networks each trained to generate labels for various types of input are used to automatically generate appropriate content labels given an input.

Classes IPC  ?

33.

DISCONTINUOUS TRANSMISSION DETECTION

      
Numéro d'application 18087714
Statut En instance
Date de dépôt 2022-12-22
Date de la première publication 2024-06-27
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Huang, Yan
  • Tang, Xiangguo
  • Delfeld, James Hansen
  • Ibars Casas, Christian

Abrégé

Apparatuses, systems, and techniques to detect whether fifth-generation new radio (5G-NR) devices are transmitting data. In at least one embodiment, a receiver detects whether 5G-NR devices are transmitting 5G-NR signals based on a number of 5G-NR devices sharing a same time and frequency.

Classes IPC  ?

  • H04W 52/02 - Dispositions d'économie de puissance
  • H04W 72/044 - Affectation de ressources sans fil sur la base du type de ressources affectées
  • H04W 72/1263 - Jumelage du trafic à la planification, p.ex. affectation planifiée ou multiplexage de flux
  • H04W 72/21 - Canaux de commande ou signalisation pour la gestion des ressources dans le sens ascendant de la liaison sans fil, c. à d. en direction du réseau

34.

BI-DIRECTIONAL OPTICAL COMMUNICATION MODULES AND CABLES

      
Numéro d'application 18088877
Statut En instance
Date de dépôt 2022-12-27
Date de la première publication 2024-06-27
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s) Seyedi, Ashkan

Abrégé

Apparatuses, devices, modules, cables, and systems are provided for bi-directional optical communication. An example module includes a substrate, a first band pass filter, a first optical transmitter, and a first optical receiver each supported by the substrate. The first optical transmitter is communicably coupled with the first band pass filter and configured to generate optical signals having a first wavelength. The first optical receiver is communicably coupled with the first band pass filter and configured to receive optical signals having a second wavelength. The first band pass filter passes optical signals received from the first optical transmitter having the first wavelength into an optical communication medium and directs optical signals received from the optical communication medium having the second wavelength into the first optical receiver.

Classes IPC  ?

  • H04B 10/25 - Dispositions spécifiques à la transmission par fibres

35.

DETERMINING OBJECT ASSOCIATIONS USING MACHINE LEARNING IN AUTONOMOUS SYSTEMS AND APPLICATIONS

      
Numéro d'application 18146671
Statut En instance
Date de dépôt 2022-12-27
Date de la première publication 2024-06-27
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Sajjan, Neeraj
  • Kocamaz, Mehmet
  • Parikh, Parthiv

Abrégé

In various examples, systems and methods are disclosed relating to determining associations between objects represented in sensor data and predicted states of the objects in multi-sensor systems such as autonomous or semi-autonomous vehicle perception systems. Systems and methods are disclosed that employ neural network models, such as multi-layer perceptron (MLP) models or other deep neural network (DNN) models, in learning association costs between sensor measurements and predicted states of objects. During training, the systems and methods can generate data for updating parameters of the neural network models such that, during deployment, the neural network models can receive sensor data and predicted states, and provide corresponding association costs.

Classes IPC  ?

36.

ROBUST, EFFICIENT MULTIPROCESSOR-COPROCESSOR INTERFACE

      
Numéro d'application 18596106
Statut En instance
Date de dépôt 2024-03-05
Date de la première publication 2024-06-27
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Babich, Jr., Ronald Charles
  • Burgess, John
  • Choquette, Jack
  • Karras, Tero
  • Laine, Samuli
  • Llamas, Ignacio
  • Muthler, Gregory
  • Newhall, Jr., William Parsons

Abrégé

Systems and methods for an efficient and robust multiprocessor-coprocessor interface that may be used between a streaming multiprocessor and an acceleration coprocessor in a GPU are provided. According to an example implementation, in order to perform an acceleration of a particular operation using the coprocessor, the multiprocessor: issues a series of write instructions to write input data for the operation into coprocessor-accessible storage locations, issues an operation instruction to cause the coprocessor to execute the particular operation; and then issues a series of read instructions to read result data of the operation from coprocessor-accessible storage locations to multiprocessor-accessible storage locations.

Classes IPC  ?

  • G06F 9/30 - Dispositions pour exécuter des instructions machines, p.ex. décodage d'instructions
  • G06F 9/38 - Exécution simultanée d'instructions
  • G06F 9/48 - Lancement de programmes; Commutation de programmes, p.ex. par interruption
  • G06F 15/163 - Communication entre processeurs
  • G06T 1/20 - Architectures de processeurs; Configuration de processeurs p.ex. configuration en pipeline
  • G06T 1/60 - Gestion de mémoire

37.

SIGNALING OVER RC-DOMINATED TRANSMISSION LINES

      
Numéro d'application 18086351
Statut En instance
Date de dépôt 2022-12-21
Date de la première publication 2024-06-27
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Poulton, John
  • Song, Sanquan
  • Chen, Xi
  • Turner, Walker
  • Nishi, Yoshinori
  • Wilson, John M.

Abrégé

The disclosure provides a signaling link that overcomes or at least reduces the limitations of RC-dominated signaling wires, improving both the bandwidth and the power consumption of signaling circuits. Both an AC and a DC signaling link are disclosed. In one example, a signaling link is provided that includes: (1) a transmitter including a passive equalizer, (2) an over-terminated receiver, and (3) a lossy channel having a first end connected to the passive equalizer and a second end connected to the receiver, wherein the lossy channel has a channel characteristic impedance that is lower than a terminating impedance of the passive equalizer and a termination impedance of the receiver.

Classes IPC  ?

  • H04B 3/04 - Systèmes à ligne de transmission - Détails Égalisation

38.

APPLICATION PROGRAMMING INTERFACE TO GENERATE PACKAGING INFORMATION

      
Numéro d'application US2023084442
Numéro de publication 2024/137417
Statut Délivré - en vigueur
Date de dépôt 2023-12-15
Date de publication 2024-06-27
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s)
  • Boccuzzi, Joseph
  • Kundu, Lopamudra

Abrégé

Apparatuses, systems, and techniques including APIs to enable one or more fifth generation new radio (5G-NR) network components to write, read, send, transmit, load, or otherwise obtain packaging, synchronization, and/or management information. For example, a processor comprising one or more circuits to perform an application programming interface (API) to cause fifth generation new radio (5G-NR) packaging, synchronization, or management information to be indicated to one or more accelerators.

Classes IPC  ?

  • G06F 9/50 - Allocation de ressources, p.ex. de l'unité centrale de traitement [UCT]
  • G06F 9/54 - Communication interprogramme

39.

APPLICATION PROGRAMMING INTERFACE TO LOAD SYNCHRONIZATION INFORMATION

      
Numéro d'application US2023084444
Numéro de publication 2024/137419
Statut Délivré - en vigueur
Date de dépôt 2023-12-15
Date de publication 2024-06-27
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s)
  • Boccuzzi, Joseph
  • Kundu, Lopamudra

Abrégé

Apparatuses, systems, and techniques including APIs to enable one or more fifth generation new radio (5G-NR) network components to write, read, send, transmit, load, or otherwise obtain packaging, synchronization, and/or management information. For example, a processor comprising one or more circuits to perform an application programming interface (API) to cause fifth generation new radio (5G-NR) packaging, synchronization, or management information to be indicated to one or more accelerators.

Classes IPC  ?

  • G06F 9/50 - Allocation de ressources, p.ex. de l'unité centrale de traitement [UCT]
  • G06F 9/54 - Communication interprogramme

40.

DUAL-MODE DATACENTER COOLING SYSTEM

      
Numéro d'application 18085129
Statut En instance
Date de dépôt 2022-12-20
Date de la première publication 2024-06-27
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Shahi, Pardeep
  • Heydari, Ali

Abrégé

A system includes one or more first cooling loops to cool one or more first components within one or more servers having a first power density, and one or more second cooling loops to cool one or more second components within the one or more servers having a second power density. The system can flow first coolant to cold plates to cool high-power server components and flow second coolant to cool low-power server components by immersion cooling.

Classes IPC  ?

  • H05K 7/20 - Modifications en vue de faciliter la réfrigération, l'aération ou le chauffage

41.

ADAPTING FORWARD ERROR CORRECTION (FEC) OR LINK PARAMETERS FOR IMPROVED POST-FEC PERFORMANCE

      
Numéro d'application 18112406
Statut En instance
Date de dépôt 2023-02-21
Date de la première publication 2024-06-27
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Aziz, Pervez Mirza
  • Balan, Vishnu
  • Rathi, Rohit

Abrégé

Technologies for optimizing post-FEC bit error rate performance of a Forward Error Correction (FEC) system are described. A controller is coupled to an FEC circuit and a receiver circuit. The controller receives FEC symbol error data from the receiver circuit and determines, using the FEC symbol error data, a post-FEC correlated performance metric indicative of an estimated post-FEC BER of the FEC circuit. The controller adjusts, based on the post-FEC correlated performance metric, at least one of a FEC parameter of the FEC circuit or a link parameter of the receiver circuit to decrease the estimated post-FEC BER. This improves the post-FEC BER performance of the FEC circuit.

Classes IPC  ?

  • H04L 1/20 - Dispositions pour détecter ou empêcher les erreurs dans l'information reçue en utilisant un détecteur de la qualité du signal
  • H04L 1/00 - Dispositions pour détecter ou empêcher les erreurs dans l'information reçue

42.

METHOD AND APPARATUS FOR DERIVING NETWORK CONFIGURATION AND STATE FOR PHYSICAL NETWORK FABRIC SIMULATION

      
Numéro d'application 18164972
Statut En instance
Date de dépôt 2023-02-06
Date de la première publication 2024-06-27
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Basavaraja, Rohith
  • Prasad V, Vinay
  • Ramamurthy, Sharath

Abrégé

Systems and methods are described for collecting configuration data associated with one or more devices of a network, in association with a configuration of the network. The systems and methods include validating the configuration of the network. Validating the configuration includes determining a stability status associated with the network and the configuration. The systems and methods include generating a data record corresponding to the configuration of the network and storing the data record to a data repository. The data record includes the configuration data and results associated with validating the configuration of the network. The systems and methods include generating a second configuration and simulating the second network based on the second configuration. The second configuration includes the one or more devices, one or more second devices included in the data repository, or both.

Classes IPC  ?

  • H04L 41/0853 - Récupération de la configuration du réseau; Suivi de l’historique de configuration du réseau en recueillant activement des informations de configuration ou en sauvegardant les informations de configuration
  • H04L 41/0859 - Récupération de la configuration du réseau; Suivi de l’historique de configuration du réseau en conservant l'historique des différentes générations de configuration ou en revenant aux versions de configuration précédentes
  • H04L 41/0869 - Validation de la configuration au sein d'un élément de réseau

43.

REAL-TIME RENDERING WITH IMPLICIT SHAPES

      
Numéro d'application 18412228
Statut En instance
Date de dépôt 2024-01-12
Date de la première publication 2024-06-27
Propriétaire Nvidia Corporation (USA)
Inventeur(s)
  • Takikawa, Towaki Alan
  • Litalien, Joey
  • Yin, Kangxue
  • Kreis, Karsten Julian
  • Loop, Charles
  • Mcguire, Morgan
  • Fidler, Sanja

Abrégé

Systems and methods are described for rendering complex surfaces or geometry. In at least one embodiment, neural signed distance functions (SDFs) can be used that efficiently capture multiple levels of detail (LODs), and that can be used to reconstruct multi-dimensional geometry or surfaces with high image quality. An example architecture can represent complex shapes in a compressed format with high visual fidelity, and can generalize across different geometries from a single learned example. Extremely small multi-layer perceptrons (MHLPs) can be used with an octree-based feature representation for the learned neural SDFs.

Classes IPC  ?

  • G06T 15/08 - Rendu de volume
  • G06T 15/00 - Rendu d'images tridimensionnelles [3D]
  • G06T 17/00 - Modélisation tridimensionnelle [3D] pour infographie

44.

HIERARCHICAL NETWORK FOR STACKED MEMORY SYSTEM

      
Numéro d'application 18438139
Statut En instance
Date de dépôt 2024-02-09
Date de la première publication 2024-06-27
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Dally, William James
  • Gray, Carl Thomas
  • Keckler, Stephen W.
  • O'Connor, James Michael

Abrégé

A hierarchical network enables access for a stacked memory system including or more memory dies that each include multiple memory tiles. The processor die includes multiple processing tiles that are stacked with the one or more memory die. The memory tiles that are vertically aligned with a processing tile are directly coupled to the processing tile and comprise the local memory block for the processing tile. The hierarchical network provides access paths for each processing tile to access the processing tile's local memory block, the local memory block coupled to a different processing tile within the same processing die, memory tiles in a different die stack, and memory tiles in a different device. The ratio of memory bandwidth (byte) to floating-point operation (B:F) may improve 50× for accessing the local memory block compared with conventional memory. Additionally, the energy consumed to transfer each bit may be reduced by 10×.

Classes IPC  ?

  • G06F 3/06 - Entrée numérique à partir de, ou sortie numérique vers des supports d'enregistrement

45.

REMOTE PROMISE AND REMOTE FUTURE FOR DOWNSTREAM COMPONENTS TO UPDATE UPSTREAM STATES

      
Numéro d'application 18598022
Statut En instance
Date de dépôt 2024-03-07
Date de la première publication 2024-06-27
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Olson, Ryan
  • Demoret, Michael
  • Richardson, Bartley

Abrégé

Technologies for enabling downstream components to update upstream states in streaming pipelines are described. One method of a first computing device receives a remote promise object assigned to a first serialized object from a second computing device in the data center over a network fabric. The remote promise object uniquely identifies a first contiguous block of the first serialized object stored in a memory associated with the second computing device. The method obtains contents of the first contiguous block and sends contents of a second serialized object back to the second computing device to release the remote promise object.

Classes IPC  ?

  • G06F 15/173 - Communication entre processeurs utilisant un réseau d'interconnexion, p.ex. matriciel, de réarrangement, pyramidal, en étoile ou ramifié
  • G06F 9/30 - Dispositions pour exécuter des instructions machines, p.ex. décodage d'instructions
  • H04L 67/025 - Protocoles basés sur la technologie du Web, p.ex. protocole de transfert hypertexte [HTTP] pour la commande à distance ou la surveillance à distance des applications
  • H04L 67/1097 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau pour le stockage distribué de données dans des réseaux, p.ex. dispositions de transport pour le système de fichiers réseau [NFS], réseaux de stockage [SAN] ou stockage en réseau [NAS]

46.

APPLICATION PROGRAMMING INTERFACE TO GENERATE SYNCHRONIZATION INFORMATION

      
Numéro d'application US2023084441
Numéro de publication 2024/137416
Statut Délivré - en vigueur
Date de dépôt 2023-12-15
Date de publication 2024-06-27
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s)
  • Boccuzzi, Joseph
  • Kundu, Lopamudra

Abrégé

Apparatuses, systems, and techniques including APIs to enable one or more fifth generation new radio (5G-NR) network components to write, read, send, transmit, load, or otherwise obtain packaging, synchronization, and/or management information. For example, a processor comprising one or more circuits to perform an application programming interface (API) to cause fifth generation new radio (5G-NR) packaging, synchronization, or management information to be indicated to one or more accelerators.

Classes IPC  ?

  • G06F 9/50 - Allocation de ressources, p.ex. de l'unité centrale de traitement [UCT]
  • G06F 9/54 - Communication interprogramme

47.

APPLICATION PROGRAMMING INTERFACE TO WRITE INFORMATION

      
Numéro d'application US2023084443
Numéro de publication 2024/137418
Statut Délivré - en vigueur
Date de dépôt 2023-12-15
Date de publication 2024-06-27
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s)
  • Boccuzzi, Joseph
  • Kundu, Lopamudra

Abrégé

Apparatuses, systems, and techniques including APIs to enable one or more fifth generation new radio (5G-NR) network components to write, read, send, transmit, load, or otherwise obtain packaging, synchronization, and/or management information. For example, a processor comprising one or more circuits to perform an application programming interface (API) to cause fifth generation new radio (5G-NR) packaging, synchronization, or management information to be indicated to one or more accelerators.

Classes IPC  ?

  • G06F 9/50 - Allocation de ressources, p.ex. de l'unité centrale de traitement [UCT]
  • G06F 9/54 - Communication interprogramme

48.

APPLICATION PROGRAMMING INTERFACE TO READ INFORMATION

      
Numéro d'application US2023084445
Numéro de publication 2024/137420
Statut Délivré - en vigueur
Date de dépôt 2023-12-15
Date de publication 2024-06-27
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s)
  • Boccuzzi, Joseph
  • Kundu, Lopamudra

Abrégé

Apparatuses, systems, and techniques including APIs to enable one or more fifth generation new radio (5G-NR) network components to write, read, send, transmit, load, or otherwise obtain packaging, synchronization, and/or management information. For example, a processor comprising one or more circuits to perform an application programming interface (API) to cause fifth generation new radio (5G-NR) packaging, synchronization, or management information to be indicated to one or more accelerators.

Classes IPC  ?

  • G06F 9/50 - Allocation de ressources, p.ex. de l'unité centrale de traitement [UCT]
  • G06F 9/54 - Communication interprogramme

49.

APPLICATION PROGRAMMING INTERFACE TO GENERATE DATA PACKETS

      
Numéro d'application US2023084447
Numéro de publication 2024/137421
Statut Délivré - en vigueur
Date de dépôt 2023-12-15
Date de publication 2024-06-27
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s)
  • Boccuzzi, Joseph
  • Kundu, Lopamudra

Abrégé

Apparatuses, systems, and techniques including APIs to enable one or more fifth generation new radio (5G-NR) network components to write, read, send, transmit, load, or otherwise obtain packaging, synchronization, and/or management information. For example, a processor comprising one or more circuits to perform an application programming interface (API) to cause fifth generation new radio (5G-NR) packaging, synchronization, or management information to be indicated to one or more accelerators.

Classes IPC  ?

  • G06F 9/50 - Allocation de ressources, p.ex. de l'unité centrale de traitement [UCT]
  • G06F 9/54 - Communication interprogramme
  • H04W 88/08 - Dispositifs formant point d'accès

50.

EGO TRAJECTORY PLANNING WITH RULE HIERARCHIES FOR AUTONOMOUS VEHICLES

      
Numéro d'application 18335028
Statut En instance
Date de dépôt 2023-06-14
Date de la première publication 2024-06-20
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Veer, Sushant
  • Leung, Karen
  • Cosner, Ryan
  • Chen, Yuxiao
  • Pavone, Marco

Abrégé

Autonomous vehicles (AVs) may need to contend with conflicting traveling rules and the AV controller would need to select the least objectionable control action. A rank-preserving reward function can be applied to trajectories derived from a rule hierarchy. The reward function can be correlated to a robustness vector derived for each trajectory. Thereby the highest ranked rules would result in the highest reward, and the lower ranked rules would result in lower reward. In some aspects, one or more optimizers, such as a stochastic optimizer can be utilized to improve the results of the reward calculation. In some aspects, a sigmoid function can be applied to the calculation to smooth out the step function used to calculate the robustness vector. The preferred trajectory selected using the results from the reward function can be communicated to an AV controller for implementation as a control action.

Classes IPC  ?

  • B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
  • B60W 40/02 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés aux conditions ambiantes

51.

REPLICATING PHYSICAL ENVIRONMENTS AND GENERATING 3D ASSETS FOR SYNTHETIC SCENE GENERATION

      
Numéro d'application 18066135
Statut En instance
Date de dépôt 2022-12-14
Date de la première publication 2024-06-20
Propriétaire Nvidia Corporation (USA)
Inventeur(s)
  • Foco, Marco
  • Bódis-Szomorú, András
  • Deutsch, Isaac
  • Rozantsev, Artem
  • Shelley, Michael
  • State, Gavriel
  • Wang, Jiehan
  • Hu, Anita
  • Lafleche, Jean-Francois

Abrégé

Approaches presented herein can provide for the automatic generation of a digital representation of an environment that may include multiple objects of various object types. An initial representation (e.g., a point cloud) of the environment can be generated from registered image or scan data, for example, and objects in the environment can be segmented and identified based at least on that initial representation. For objects that are recognized based on these segmentations, stored accurate representations can be substituted for those objects in the representation of the environment, and if no such model is available then a mesh or other representation of that object can be generated and positioned in the environment. A result can then include a 3D representation of a scene or environment in which objects are identified and segmented as individual objects, and representations of the scene or environment can be viewed, and interacted with, through various viewports, positions, and perspectives.

Classes IPC  ?

  • G06T 17/20 - Description filaire, p.ex. polygonalisation ou tessellation
  • G06T 7/33 - Détermination des paramètres de transformation pour l'alignement des images, c. à d. recalage des images utilisant des procédés basés sur les caractéristiques
  • G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux

52.

EFFICIENT FREQUENCY-BASED AUDIO RESAMPLING FOR USING NEURAL NETWORKS

      
Numéro d'application 18068187
Statut En instance
Date de dépôt 2022-12-19
Date de la première publication 2024-06-20
Propriétaire Nvidia Corporation (USA)
Inventeur(s)
  • Jjoshi, Suchitra Mandar
  • Nyayate, Mihir Manohar
  • Gode, Nitin Mahesh

Abrégé

Systems and methods described relate to the enhancement of audio, such as through machine learning-based audio super-resolution processing. An efficient resampling approach can be used for audio data received at a lower frequency than is needed for an audio enhancement neural network. This audio data can be converted into the frequency domain using, and once in the frequency domain (e.g., represented using a spectrogram) this lower frequency data can be resampled to provide a frequency-based representation that is at the target input resolution for the neural network. To keep this resampling process lightweight, the upper frequency bands can be padded with zero value entries (or other such padding values). This resampled, higher frequency spectrogram can be provided as input to the neural network, which can perform an enhancement operation such as audio upsampling or super-resolution.

Classes IPC  ?

  • G10L 21/14 - Transformation en information visible en affichant l’information du domaine fréquentiel
  • G10L 21/0232 - Traitement dans le domaine fréquentiel
  • G10L 25/30 - Techniques d'analyses de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par la technique d’analyse utilisant des réseaux neuronaux

53.

APPLICATION PROGRAMMING INTERFACE TO GENERATE DATA PACKETS

      
Numéro d'application 18083544
Statut En instance
Date de dépôt 2022-12-18
Date de la première publication 2024-06-20
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Boccuzzi, Joseph
  • Kundu, Lopamudra

Abrégé

Apparatuses, systems, and techniques including APIs to enable one or more fifth generation new radio (5G-NR) network components to write, read, send, transmit, load, or otherwise obtain packaging, synchronization, and/or management information. For example, a processor comprising one or more circuits to perform an application programming interface (API) to cause fifth generation new radio (5G-NR) packaging, synchronization, or management information to be indicated to one or more accelerators.

Classes IPC  ?

  • H04W 28/06 - Optimisation, p.ex. compression de l'en-tête, calibrage des informations
  • G06F 9/54 - Communication interprogramme

54.

APPLICATION PROGRAMMING INTERFACE TO GENERATE PACKAGING INFORMATION

      
Numéro d'application 18083545
Statut En instance
Date de dépôt 2022-12-18
Date de la première publication 2024-06-20
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Boccuzzi, Joseph
  • Kundu, Lopamudra

Abrégé

Apparatuses, systems, and techniques including APIs to enable one or more fifth generation new radio (5G-NR) network components to write, read, send, transmit, load, or otherwise obtain packaging, synchronization, and/or management information. For example, a processor comprising one or more circuits to perform an application programming interface (API) to cause fifth generation new radio (5G-NR) packaging, synchronization, or management information to be indicated to one or more accelerators.

Classes IPC  ?

  • H04W 48/18 - Sélection d'un réseau ou d'un service de télécommunications

55.

APPLICATION PROGRAMMING INTERFACE TO GENERATE SYNCHRONIZATION INFORMATION

      
Numéro d'application 18083546
Statut En instance
Date de dépôt 2022-12-18
Date de la première publication 2024-06-20
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Boccuzzi, Joseph
  • Kundu, Lopamudra

Abrégé

Apparatuses, systems, and techniques including APIs to enable one or more fifth generation new radio (5G-NR) network components to write, read, send, transmit, load, or otherwise obtain packaging, synchronization, and/or management information. For example, a processor comprising one or more circuits to perform an application programming interface (API) to cause fifth generation new radio (5G-NR) packaging, synchronization, or management information to be indicated to one or more accelerators.

Classes IPC  ?

  • G06F 1/12 - Synchronisation des différents signaux d'horloge
  • G06F 9/54 - Communication interprogramme

56.

APPLICATION PROGRAMMING INTERFACE TO LOAD SYNCHRONIZATION INFORMATION

      
Numéro d'application 18083547
Statut En instance
Date de dépôt 2022-12-18
Date de la première publication 2024-06-20
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Boccuzzi, Joseph
  • Kundu, Lopamudra

Abrégé

Apparatuses, systems, and techniques including APIs to enable one or more fifth generation new radio (5G-NR) network components to write, read, send, transmit, load, or otherwise obtain packaging, synchronization, and/or management information. For example, a processor comprising one or more circuits to perform an application programming interface (API) to cause fifth generation new radio (5G-NR) packaging, synchronization, or management information to be indicated to one or more accelerators.

Classes IPC  ?

  • H04L 69/28 - Minuteurs ou mécanismes de chronométrage utilisés dans les protocoles
  • G06F 9/52 - Synchronisation de programmes; Exclusion mutuelle, p.ex. au moyen de sémaphores
  • H04W 56/00 - Dispositions de synchronisation

57.

POLICY-BASED PROCESSING OF AUTHENTICATION REQUESTS USING LOCATION DATA FOR CLOUD-HOSTED SYSTEMS AND APPLICATIONS

      
Numéro d'application 18085226
Statut En instance
Date de dépôt 2022-12-20
Date de la première publication 2024-06-20
Propriétaire NVIDIA Corporation (USA)
Inventeur(s) Batni, Dhruva Lakshmana Rao

Abrégé

Disclosed are apparatuses, systems, and techniques that improve efficiency and decrease latency of processing of authorization requests by cloud-based access servers that evaluate access rights to access various cloud-based services. The techniques include but are not limited to using location tracking data to predict that a user is to move from an area served by a first access point of the cloud-based services to an area served by a second access point of the cloud-based services. The techniques further include proactively providing policy data and policy dependencies to the second access point to minimize latency of processing of access requests generated by the user.

Classes IPC  ?

58.

APPLICATION PROGRAMMING INTERFACE TO STORE INFORMATION IN FIFTH GENERATION NEW RADIO (5G-NR) STATICALLY-SIZED LINKED STORAGE

      
Numéro d'application CN2022138418
Numéro de publication 2024/124376
Statut Délivré - en vigueur
Date de dépôt 2022-12-12
Date de publication 2024-06-20
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s) Wu, Jinyou

Abrégé

Apparatuses, systems, and techniques to perform and facilitate lock-free data sharing between processes performing computations in fifth generation (5G) new radio (NR) wireless communication. In at least one embodiment, 5G-NR information to be shared between one or more processes is stored by one or more statically-sized regions of linked storage locations in response to an application programming interface (API).

Classes IPC  ?

  • G06F 9/46 - Dispositions pour la multiprogrammation
  • H04W 48/08 - Distribution d'informations relatives aux restrictions d'accès ou aux accès, p.ex. distribution de données d'exploration

59.

APPLICATION PROGRAMMING INTERFACE TO WRITE INFORMATION

      
Numéro d'application 18083548
Statut En instance
Date de dépôt 2022-12-18
Date de la première publication 2024-06-20
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Boccuzzi, Joseph
  • Kundu, Lopamudra

Abrégé

Apparatuses, systems, and techniques including APIs to enable one or more fifth generation new radio (5G-NR) network components to write, read, send, transmit, load, or otherwise obtain packaging, synchronization, and/or management information. For example, a processor comprising one or more circuits to perform an application programming interface (API) to cause fifth generation new radio (5G-NR) packaging, synchronization, or management information to be indicated to one or more accelerators.

Classes IPC  ?

  • H04W 28/16 - Gestion centrale des ressources; Négociation de ressources ou de paramètres de communication, p.ex. négociation de la bande passante ou de la qualité de service [QoS Quality of Service]
  • G06F 9/54 - Communication interprogramme
  • G06T 1/20 - Architectures de processeurs; Configuration de processeurs p.ex. configuration en pipeline
  • H04W 52/54 - Aspects de signalisation des instructions TPC, p.ex. structure de trame

60.

APPLICATION PROGRAMMING INTERFACE TO READ INFORMATION

      
Numéro d'application 18083549
Statut En instance
Date de dépôt 2022-12-18
Date de la première publication 2024-06-20
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Boccuzzi, Joseph
  • Kundu, Lopamudra

Abrégé

Apparatuses, systems, and techniques including APIs to enable one or more fifth generation new radio (5G-NR) network components to write, read, send, transmit, load, or otherwise obtain packaging, synchronization, and/or management information. For example, a processor comprising one or more circuits to perform an application programming interface (API) to cause fifth generation new radio (5G-NR) packaging, synchronization, or management information to be indicated to one or more accelerators.

Classes IPC  ?

  • H04L 67/133 - Protocoles pour les appels de procédure à distance [RPC]
  • G06T 1/20 - Architectures de processeurs; Configuration de processeurs p.ex. configuration en pipeline

61.

OBJECT POSE ESTIMATION

      
Numéro d'application 18244050
Statut En instance
Date de dépôt 2023-09-08
Date de la première publication 2024-06-20
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Pavone, Marco
  • Yang, Heng

Abrégé

Apparatuses, systems, and techniques to obtain prediction set(s) (e.g., region(s)) for keypoint prediction(s) based at least in part on data associated with an object, compute a set of candidate poses for the object based at least in part on the prediction set(s), and estimate an estimated object pose based at least in part on the set of candidate poses. The estimated object pose may be used to move a device. For example the estimated object pose may be used to provide collision-free motion generation for a real-world or virtual device (e.g., a robot, an autonomous machine, or a semi-autonomous machine). In at least one embodiment, at least a portion of the object pose estimation and/or at least a portion of the collision-free motion generation is performed in parallel.

Classes IPC  ?

  • B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
  • B60W 50/00 - COMMANDE CONJUGUÉE DE PLUSIEURS SOUS-ENSEMBLES D'UN VÉHICULE, DE FONCTION OU DE TYPE DIFFÉRENTS; SYSTÈMES DE COMMANDE SPÉCIALEMENT ADAPTÉS AUX VÉHICULES HYBRIDES; SYSTÈMES D'AIDE À LA CONDUITE DE VÉHICULES ROUTIERS, NON LIÉS À LA COMMANDE D'UN SOUS-ENSEMBLE PARTICULIER - Détails des systèmes d'aide à la conduite des véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier
  • G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques

62.

REAL-TIME OBJECT TRACKING USING MOTION AND VISUAL CHARACTERISTICS FOR INTELLIGENT VIDEO ANALYTICS SYSTEMS

      
Numéro d'application 18542389
Statut En instance
Date de dépôt 2023-12-15
Date de la première publication 2024-06-20
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Shin, Joonhwa
  • Li, Fangyu
  • Verjus, Hugo Maxence
  • Liu, Zheng
  • Purandare, Kaustubh

Abrégé

A first visual appearance descriptor associated with a first object in an environment is obtained based on a first set of images of a first time period. The first object is subsequently absent from the environment in a second set of images of a second time period. A second visual appearance descriptor associated with a second object is obtained based on a third set of images, of a third time period subsequent to the second time period. A compound similarity metric between the first and second objects is obtained in view of visual appearance similarity and motion similarity metrics. The visual appearance similarity metric corresponds to a degree of similarity between the first and second visual appearance descriptors. An identifier associated with the second object is updated to correspond to an identifier associated with the first object in response to determining that the compound similarity metric meets a threshold value.

Classes IPC  ?

  • G06T 7/20 - Analyse du mouvement
  • G06V 10/74 - Appariement de motifs d’image ou de vidéo; Mesures de proximité dans les espaces de caractéristiques

63.

DYNAMICALLY REDUCING LATENCY IN PROCESSING PIPELINES

      
Numéro d'application 18594099
Statut En instance
Date de dépôt 2024-03-04
Date de la première publication 2024-06-20
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Li, Sau Yan Keith
  • Schneider, Seth
  • Robson, Cody
  • Nordskog, Lars
  • Hansen, Charles
  • Dimitrov, Rouslan

Abrégé

A weighted average execution time associated with each execution stage of a plurality of execution stages used to process a plurality of frames in parallel is obtained. The processing of each of the plurality of frames is performed at each of the plurality of execution stages in a sequential order, starting with an initial execution stage and continuing with each subsequent execution stage. A first largest weighted average execution time associated with one of the plurality of execution stages is determined. A delay to the initial execution stage prior to processing a first next frame is applied. The delay is determined based on the first largest weighted average execution time.

Classes IPC  ?

  • G06T 1/20 - Architectures de processeurs; Configuration de processeurs p.ex. configuration en pipeline
  • G06F 9/38 - Exécution simultanée d'instructions
  • G06F 9/48 - Lancement de programmes; Commutation de programmes, p.ex. par interruption

64.

FREQUENCY-LOCKED AND PHASE-LOCKED LOOP-BASED CLOCK GLITCH DETECTION FOR SECURITY

      
Numéro d'application 18595042
Statut En instance
Date de dépôt 2024-03-04
Date de la première publication 2024-06-20
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Song, Sanquan
  • Tell, Stephen G.
  • Nedovic, Nikola

Abrégé

A glitch detection device includes an oscillator to generate multiple local clocks of multiple different phases and a sampling circuit to oversample, using the multiple local clocks, a system clock to generate multiple samples of the system clock. The device further includes a glitch detector to monitor a variation in pulse width of the system clock based on counting the multiple samples and to report a glitch in response to detecting a variation in the pulse width that exceeds a threshold value.

Classes IPC  ?

  • H03L 7/099 - Commande automatique de fréquence ou de phase; Synchronisation utilisant un signal de référence qui est appliqué à une boucle verrouillée en fréquence ou en phase - Détails de la boucle verrouillée en phase concernant principalement l'oscillateur commandé de la boucle
  • H03L 7/089 - Commande automatique de fréquence ou de phase; Synchronisation utilisant un signal de référence qui est appliqué à une boucle verrouillée en fréquence ou en phase - Détails de la boucle verrouillée en phase concernant principalement l'agencement de détection de phase ou de fréquence y compris le filtrage ou l'amplification de son signal de sortie le détecteur de phase ou de fréquence engendrant des impulsions d'augmentation ou de diminution

65.

ALLOCATING RADIO RESOURCES USING ARTIFICIAL INTELLIGENCE

      
Numéro d'application US2023084133
Numéro de publication 2024/130034
Statut Délivré - en vigueur
Date de dépôt 2023-12-14
Date de publication 2024-06-20
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s)
  • Huang, Yan
  • Delfeld, James
  • Gao, Yuan
  • Lin, Xingqin
  • Casas, Christian Ibars

Abrégé

Apparatuses, systems, and techniques to allocate one or more compute resources to a user device. In at least one embodiment, one or more circuits cause one or more compute resources to be allocated to two or more fifth-generation (5G) radio access network (RAN) cells based, at least in part, on interference between the two or more 5G RAN cells.

Classes IPC  ?

  • H04W 72/23 - Canaux de commande ou signalisation pour la gestion des ressources dans le sens descendant de la liaison sans fil, c. à d. en direction du terminal
  • H04W 72/54 - Critères d’affectation ou de planification des ressources sans fil sur la base de critères de qualité
  • H04W 72/541 - Critères d’affectation ou de planification des ressources sans fil sur la base de critères de qualité en utilisant le niveau d’interférence

66.

APPLICATION PROGRAMMING INTERFACE TO ALLOCATE FIFTH GENERATION NEW RADIO (5G-NR) STATICALLY-SIZED LINKED STORAGE

      
Numéro d'application CN2022138416
Numéro de publication 2024/124375
Statut Délivré - en vigueur
Date de dépôt 2022-12-12
Date de publication 2024-06-20
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s) Wu, Jinyou

Abrégé

Apparatuses, systems, and techniques to perform and facilitate lock-free data sharing between processes performing computations in fifth generation (5G) new radio (NR) wireless communication. In at least one embodiment, one or more statically-sized regions of linked storage locations are pre-allocated, in response to an application programming interface (API), to store 5G-NR information to be shared between one or more processes.

Classes IPC  ?

  • G06F 12/08 - Adressage ou affectation; Réadressage dans des systèmes de mémoires hiérarchiques, p.ex. des systèmes de mémoire virtuelle

67.

APPLICATION PROGRAMMING INTERFACE TO DEALLOCATE FIFTH GENERATION NEW RADIO (5G-NR) STATICALLY-SIZED LINKED STORAGE

      
Numéro d'application 18229298
Statut En instance
Date de dépôt 2023-08-02
Date de la première publication 2024-06-13
Propriétaire NVIDIA Corporation (USA)
Inventeur(s) Wu, Jinyou

Abrégé

Apparatuses, systems, and techniques to perform and facilitate lock-free data sharing between processes performing computations in fifth generation (5G) new radio (NR) wireless communication. In at least one embodiment, one or more statically-sized regions of linked storage locations are deallocated, in response to an application programming interface (API), to free memory used to store 5G-NR information to be shared between one or more processes.

Classes IPC  ?

  • H04W 8/00 - Gestion de données relatives au réseau
  • H04W 76/30 - Libération de la connexion

68.

APPLICATION PROGRAMMING INTERFACE TO STORE INFORMATION IN FIFTH GENERATION NEW RADIO (5G-NR) STATICALLY-SIZED LINKED STORAGE

      
Numéro d'application 18229525
Statut En instance
Date de dépôt 2023-08-02
Date de la première publication 2024-06-13
Propriétaire NVIDIA Corporation (USA)
Inventeur(s) Wu, Jinyou

Abrégé

Apparatuses, systems, and techniques to perform and facilitate lock-free data sharing between processes performing computations in fifth generation (5G) new radio (NR) wireless communication. In at least one embodiment, 5G-NR information to be shared between one or more processes is stored by one or more statically-sized regions of linked storage locations in response to an application programming interface (API).

Classes IPC  ?

  • H04L 67/1097 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau pour le stockage distribué de données dans des réseaux, p.ex. dispositions de transport pour le système de fichiers réseau [NFS], réseaux de stockage [SAN] ou stockage en réseau [NAS]
  • H04L 67/1001 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau pour accéder à un serveur parmi une pluralité de serveurs répliqués

69.

APPLICATION PROGRAMMING INTERFACE TO INVALIDATE INFORMATION IN FIFTH GENERATION NEW RADIO (5G-NR) STATICALLY-SIZED LINKED STORAGE

      
Numéro d'application 18229562
Statut En instance
Date de dépôt 2023-08-02
Date de la première publication 2024-06-13
Propriétaire NVIDIA Corporation (USA)
Inventeur(s) Wu, Jinyou

Abrégé

Apparatuses, systems, and techniques to perform and facilitate lock-free data sharing between processes performing computations in fifth generation (5G) new radio (NR) wireless communication. In at least one embodiment, 5G-NR information stored by one or more statically-sized regions of linked storage locations is to be invalidated in response to an application programming interface (API).

Classes IPC  ?

70.

LUMINANCE-PRESERVING AND TEMPORALLY STABLE DALTONIZATION

      
Numéro d'application 18491993
Statut En instance
Date de dépôt 2023-10-23
Date de la première publication 2024-06-13
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Ebelin, Johan Pontus
  • Crassin, Cyril
  • Akenine-Möller, Tomas Guy

Abrégé

It is difficult for people with color vision deficiency (CVD) to distinguish between certain colors, e.g., reds and greens may be indistinguishable, causing a loss of information. Image recoloring, daltonization, techniques aim to improve the experience for people with CVD. Preserving luminance between the original image as seen by a person with normal color vision and someone with a CVD assists in preserving image appearance. Conventional algorithms attempt to daltonize images by exploiting the content of the image itself. While this is a suitable idea for an image in isolation, temporal inconsistencies (e.g., flickering) occur when applied to a stream of images, as a color c could be mapped to a color a in one frame and b in another. In contrast, the luminance-preserving technique operates on pixels and provides a consistent mapping and therefore is temporally stable.

Classes IPC  ?

  • G06T 11/00 - Génération d'images bidimensionnelles [2D]
  • G06T 7/90 - Détermination de caractéristiques de couleur

71.

OBJECT DETECTION AND DETECTION CONFIDENCE SUITABLE FOR AUTONOMOUS DRIVING

      
Numéro d'application 18582358
Statut En instance
Date de dépôt 2024-02-20
Date de la première publication 2024-06-13
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Koivisto, Tommi
  • Janis, Pekka
  • Kuosmanen, Tero
  • Roman, Timo
  • Sarathy, Sriya
  • Zhang, William
  • Assaf, Nizar
  • Tracey, Colin

Abrégé

In various examples, detected object data representative of locations of detected objects in a field of view may be determined. One or more clusters of the detected objects may be generated based at least in part on the locations and features of the cluster may be determined for use as inputs to a machine learning model(s). A confidence score, computed by the machine learning model(s) based at least in part on the inputs, may be received, where the confidence score may be representative of a probability that the cluster corresponds to an object depicted at least partially in the field of view. Further examples provide approaches for determining ground truth data for training object detectors, such as for determining coverage values for ground truth objects using associated shapes, and for determining soft coverage values for ground truth objects.

Classes IPC  ?

  • G01S 7/41 - DÉTERMINATION DE LA DIRECTION PAR RADIO; RADIO-NAVIGATION; DÉTERMINATION DE LA DISTANCE OU DE LA VITESSE EN UTILISANT DES ONDES RADIO; LOCALISATION OU DÉTECTION DE LA PRÉSENCE EN UTILISANT LA RÉFLEXION OU LA RERADIATION D'ONDES RADIO; DISPOSITIONS ANALOGUES UTILISANT D'AUTRES ONDES - Détails des systèmes correspondant aux groupes , , de systèmes selon le groupe utilisant l'analyse du signal d'écho pour la caractérisation de la cible; Signature de cible; Surface équivalente de cible
  • B60W 50/00 - COMMANDE CONJUGUÉE DE PLUSIEURS SOUS-ENSEMBLES D'UN VÉHICULE, DE FONCTION OU DE TYPE DIFFÉRENTS; SYSTÈMES DE COMMANDE SPÉCIALEMENT ADAPTÉS AUX VÉHICULES HYBRIDES; SYSTÈMES D'AIDE À LA CONDUITE DE VÉHICULES ROUTIERS, NON LIÉS À LA COMMANDE D'UN SOUS-ENSEMBLE PARTICULIER - Détails des systèmes d'aide à la conduite des véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier
  • G01S 7/48 - DÉTERMINATION DE LA DIRECTION PAR RADIO; RADIO-NAVIGATION; DÉTERMINATION DE LA DISTANCE OU DE LA VITESSE EN UTILISANT DES ONDES RADIO; LOCALISATION OU DÉTECTION DE LA PRÉSENCE EN UTILISANT LA RÉFLEXION OU LA RERADIATION D'ONDES RADIO; DISPOSITIONS ANALOGUES UTILISANT D'AUTRES ONDES - Détails des systèmes correspondant aux groupes , , de systèmes selon le groupe
  • G01S 13/86 - Combinaisons de systèmes radar avec des systèmes autres que radar, p.ex. sonar, chercheur de direction
  • G01S 13/931 - Radar ou systèmes analogues, spécialement adaptés pour des applications spécifiques pour prévenir les collisions de véhicules terrestres
  • G01S 17/931 - Systèmes lidar, spécialement adaptés pour des applications spécifiques pour prévenir les collisions de véhicules terrestres
  • G06F 16/35 - Groupement; Classement
  • G06F 18/21 - Conception ou mise en place de systèmes ou de techniques; Extraction de caractéristiques dans l'espace des caractéristiques; Séparation aveugle de sources
  • G06F 18/214 - Génération de motifs d'entraînement; Procédés de Bootstrapping, p.ex. ”bagging” ou ”boosting”
  • G06F 18/23 - Techniques de partitionnement
  • G06F 18/2413 - Techniques de classification relatives au modèle de classification, p.ex. approches paramétriques ou non paramétriques basées sur les distances des motifs d'entraînement ou de référence
  • G06N 3/044 - Réseaux récurrents, p.ex. réseaux de Hopfield
  • G06N 3/045 - Combinaisons de réseaux
  • G06N 3/047 - Réseaux probabilistes ou stochastiques
  • G06N 3/048 - Fonctions d’activation
  • G06N 3/084 - Rétropropagation, p.ex. suivant l’algorithme du gradient
  • G06N 20/00 - Apprentissage automatique
  • G06V 10/20 - Prétraitement de l’image
  • G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p.ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersections; Analyse de connectivité, p.ex. de composantes connectées
  • G06V 10/46 - Descripteurs pour la forme, descripteurs liés au contour ou aux points, p.ex. transformation de caractéristiques visuelles invariante à l’échelle [SIFT] ou sacs de mots [BoW]; Caractéristiques régionales saillantes
  • G06V 10/762 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant le regroupement, p.ex. de visages similaires sur les réseaux sociaux
  • G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p.ex. des objets vidéo
  • G06V 10/77 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source
  • G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
  • G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p.ex. véhicules ou piétons; Reconnaissance des objets de la circulation, p.ex. signalisation routière, feux de signalisation ou routes

72.

OPTIMIZING INTERMEDIATE OUTPUT ACCUMULATION OF PARALLEL PROCESSING OPERATIONS IN STREAMING AND LATENCY-SENSITIVE APPLICATIONS

      
Numéro d'application 18077942
Statut En instance
Date de dépôt 2022-12-08
Date de la première publication 2024-06-13
Propriétaire NVIDIA Corporation (USA)
Inventeur(s) Wachowicz, Dominik

Abrégé

Disclosed are apparatuses, systems, and techniques for efficient parallel execution of multiple processes in real-time streaming and latency-sensitive applications. The techniques include but are not limited to executing in parallel multiple processing threads, storing data output by the multiple processing threads in respective accumulation buffers, and applying an aggregation function to the stored data to generate an aggregated data.

Classes IPC  ?

73.

FALLBACK MECHANISM FOR AUTO WHITE BALANCING

      
Numéro d'application 18078670
Statut En instance
Date de dépôt 2022-12-09
Date de la première publication 2024-06-13
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Taylor, Douglas
  • Khemka, Animesh
  • Dujardin, Eric

Abrégé

Improved fallback mechanisms for auto white balancing are presented. In at least one embodiment, white balance correction factors produced by a first white balance technique are blended with white balance correction factors produced by a second white balance technique based on a confidence level in the white balance correction factors produced by the first white balance technique.

Classes IPC  ?

  • H04N 23/88 - Chaînes de traitement de la caméra; Leurs composants pour le traitement de signaux de couleur pour l'équilibrage des couleurs, p.ex. circuits pour équilibrer le blanc ou commande de la température de couleur
  • G01J 1/10 - Photométrie, p.ex. posemètres photographiques par comparaison avec une lumière de référence ou avec une valeur électrique de référence
  • G06T 7/90 - Détermination de caractéristiques de couleur

74.

DOMAIN-CUSTOMIZABLE MODELS FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS

      
Numéro d'application 18064125
Statut En instance
Date de dépôt 2022-12-09
Date de la première publication 2024-06-13
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Dong, Yi
  • Wu, Xianchao

Abrégé

In various examples, systems and methods are disclosed that train a machine learning model(s)—such as a large language model (LLM)—for one or more specific domains. In some embodiments, the machine learning model(s) may include at least a base model(s) as well as additional parts, such as additional layers, associated with the domains for which the machine learning model(s) is being trained. As such, the parts of the machine learning model(s) may be trained separately, such that training data associated with a domain is used to train a part of the machine learning model(s) that is associated with the domain without training the other part(s) of the machine learning model(s). The systems and methods may then use these parts when deploying the machine learning model(s), such as by activating and/or deactivating parts based on the input data being processed.

Classes IPC  ?

  • G06N 5/043 - Systèmes experts distribués; Tableaux noirs
  • G06F 40/40 - Traitement ou traduction du langage naturel

75.

DISTURBANCE COMPENSATION USING CONTROL SYSTEMS FOR AUTONOMOUS SYSTEMS AND APPLICATIONS

      
Numéro d'application 18065484
Statut En instance
Date de dépôt 2022-12-13
Date de la première publication 2024-06-13
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Nasir, Mohammed
  • Murali, Vishal
  • Sun, Yue

Abrégé

The present disclosure relates to determining an observed state of a system based at least on sensor data generated using one or more sensors of the system. The present disclosure further relates to generating disturbance data based at least on comparing an estimated state of the system with the observed state of the system. The present disclosure further relates to updating one or more disturbance terms of a state space formulation based at least on the disturbance data. The present disclosure further relates to generating, based at least on the state space formulation, a control command that directs one or more operations of the system according to plan data indicative of a plan for completing one or more tasks of the system.

Classes IPC  ?

  • B60W 30/18 - Propulsion du véhicule
  • B60W 40/10 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés au mouvement du véhicule

76.

BRIGHTNESS BASED CHROMATICITY WEIGHTING FOR IMPROVED ILLUMINANT COLOR ESTIMATION FOR AUTO WHITE BALANCING

      
Numéro d'application 18078667
Statut En instance
Date de dépôt 2022-12-09
Date de la première publication 2024-06-13
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Taylor, Douglas
  • Khemka, Animesh

Abrégé

Apparatuses, systems, and techniques for white balancing an image are presented. In at least one embodiment, a chromaticity-based weighting function is determined based at least on an estimated scene brightness of the image and applied to exclude or minimize the impact of large colored portions or objects within an image when estimating an illuminant color.

Classes IPC  ?

  • H04N 23/88 - Chaînes de traitement de la caméra; Leurs composants pour le traitement de signaux de couleur pour l'équilibrage des couleurs, p.ex. circuits pour équilibrer le blanc ou commande de la température de couleur
  • G06T 7/90 - Détermination de caractéristiques de couleur
  • H04N 9/77 - Circuits pour le traitement l'un par rapport à l'autre des signaux de luminance et de chrominance, p.ex. ajustement de la phase du signal de luminance par rapport au signal de couleur, correction différentielle du gain ou de la phase
  • H04N 23/71 - Circuits d'évaluation de la variation de luminosité
  • H04N 23/76 - Circuits de compensation de la variation de luminosité dans la scène en agissant sur le signal d'image

77.

APPLICATION PROGRAMMING INTERFACE TO ALLOCATE FIFTH GENERATION NEW RADIO (5G-NR) STORAGE

      
Numéro d'application 18229288
Statut En instance
Date de dépôt 2023-08-02
Date de la première publication 2024-06-13
Propriétaire NVIDIA Corporation (USA)
Inventeur(s) Wu, Jinyou

Abrégé

Apparatuses, systems, and techniques to perform and facilitate lock-free data sharing between processes performing computations in fifth generation (5G) new radio (NR) wireless communication. In at least one embodiment, one or more statically-sized regions of linked storage locations are pre-allocated, in response to an application programming interface (API), to store 5G-NR information to be shared between one or more processes.

Classes IPC  ?

78.

NEURAL VECTOR FIELDS FOR 3D SHAPE GENERATION

      
Numéro d'application 18361587
Statut En instance
Date de dépôt 2023-07-28
Date de la première publication 2024-06-13
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Hao, Zekun
  • Liu, Ming-Yu
  • Mallya, Arun Mohanray

Abrégé

Synthesis of high-quality 3D shapes with smooth surfaces has various creative and practical use cases, such as 3D content creation and CAD modeling. A vector field decoder neural network is trained to predict a generative vector field (GVF) representation of a 3D shape from a latent representation (latent code or feature volume) of the 3D shape. The GVF representation is agnostic to surface orientation, all dimensions of the vector field vary smoothly, the GVF can represent both watertight and non-watertight 3D shapes, and there is a one-to-one mapping between a predicted 3D shape and the ground truth 3D shape (i.e., the mapping is bijective). The vector field decoder can synthesize 3D shapes in multiple categories and can also synthesize 3D shapes for objects that were not included in the training dataset. In other words, the vector field decoder is also capable of zero-shot generation.

Classes IPC  ?

  • G06T 19/20 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie Édition d'images tridimensionnelles [3D], p.ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties

79.

APPLICATION MANAGEMENT PLATFORM FOR HYPER-CONVERGED CLOUD INFRASTRUCTURES

      
Numéro d'application 18416320
Statut En instance
Date de dépôt 2024-01-18
Date de la première publication 2024-06-13
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Vijaywargiya, Vishvesh
  • Adithya V, Lalit
  • Duraisamy, Krishnan
  • Rajani, Rohit
  • Vadlamudi, Gopi
  • Stock, Andrew
  • Pelavin, Alexander
  • Mishra, Shivam
  • Kotian, Prathik

Abrégé

An application management platform comprising at least a packaging and bundling component, a deployment management component, and an update component. The packaging and bundling component versions, packages, and bundles a plurality of infrastructure components for a remote data center. The deployment management component provisions one or more nodes of the remote data center with the plurality of infrastructure components for an application. The update component monitors available updates to one or more of the plurality of infrastructure components used by the remote data center and facilitates update of the one or more of the plurality of infrastructure components at the remote data center.

Classes IPC  ?

80.

TRANSFORMERS AS NEURAL RENDERERS

      
Numéro d'application 18516625
Statut En instance
Date de dépôt 2023-11-21
Date de la première publication 2024-06-13
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Wang, Yue
  • Pavone, Marco

Abrégé

Apparatuses, systems, and techniques to use one or more machine learning processes to obtain a set of feature values based at least in part on a set of locations along a ray that intersects an object. A color value is obtained based at least in part on the set of feature values. A view of the object may be generated using the color value. A path of motion may be determined based at least in part on the color value and used to cause a device to move.

Classes IPC  ?

81.

RELIABLE LINK MANAGEMENT FOR A HIGH-SPEED SIGNALING INTERCONNECT

      
Numéro d'application 18587111
Statut En instance
Date de dépôt 2024-02-26
Date de la première publication 2024-06-13
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Kumar, Seema
  • Chadha, Ish

Abrégé

A device includes receiver circuitry to receive incoming signals on a clock lane and data lanes and detection circuitry. The detection circuitry is to monitor the incoming signals on the clock lane, and determine that an incoming pattern of the incoming signals on the clock lane does not correspond to a clock pattern associated with communicating data on the data lanes. The detection circuitry is to initiate a power-down sequence in response to determining that the incoming pattern does not correspond to the clock pattern.

Classes IPC  ?

  • H04L 7/00 - Dispositions pour synchroniser le récepteur avec l'émetteur

82.

APPLICATION PROGRAMMING INTERFACE TO DEALLOCATE FIFTH GENERATION NEW RADIO (5G-NR) STATICALLY-SIZED LINKED STORAGE

      
Numéro d'application CN2022138042
Numéro de publication 2024/119497
Statut Délivré - en vigueur
Date de dépôt 2022-12-09
Date de publication 2024-06-13
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s) Wu, Jinyou

Abrégé

Apparatuses, systems, and techniques to perform and facilitate lock-free data sharing between processes performing computations in fifth generation (5G) new radio (NR) wireless communication. In at least one embodiment, one or more statically-sized regions of linked storage locations are deallocated, in response to an application programming interface (API), to free memory used to store 5G-NR information to be shared between one or more processes.

Classes IPC  ?

  • G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p.ex. pour le traitement simultané de plusieurs programmes

83.

APPLICATION PROGRAMMING INTERFACE TO INVALIDATE INFORMATION IN FIFTH GENERATION NEW RADIO (5G-NR) STATICALLY-SIZED LINKED STORAGE

      
Numéro d'application CN2022138053
Numéro de publication 2024/119499
Statut Délivré - en vigueur
Date de dépôt 2022-12-09
Date de publication 2024-06-13
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s) Wu, Jinyou

Abrégé

Apparatuses, systems, and techniques to perform and facilitate lock- free data sharing between processes performing computations in fifth generation (5G) new radio (NR) wireless communication. In at least one embodiment, 5G-NR information stored by one or more statically-sized regions of linked storage locations is to be invalidated in response to an application programming interface (API).

Classes IPC  ?

  • G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p.ex. pour le traitement simultané de plusieurs programmes

84.

GENERATING VARIATIONAL DIALOGUE RESPONSES FROM STRUCTURED DATA FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS

      
Numéro d'application 18061027
Statut En instance
Date de dépôt 2022-12-02
Date de la première publication 2024-06-06
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Mahabaleshwarkar, Ameya Sunil
  • Wang, Zhilin
  • Olabiyi, Oluwatobi

Abrégé

In various examples, systems and methods are disclosed relating to generating dialogue responses from structured data for conversational artificial intelligence (AI) systems and applications. Systems and methods are disclosed for training or updating a machine learning model—such as a deep neural network—for deployment using structured data from dialogues of multiple domains. The systems and methods can generate responses to users to provide a more natural user experience, such as by generating alternative outputs that vary in syntax with respect to how the outputs incorporate data used to respond to user utterances, while still accurately providing information to satisfy requests from users.

Classes IPC  ?

85.

GENERATING GLOBAL HIERARCHICAL SELF-ATTENTION

      
Numéro d'application 18130648
Statut En instance
Date de dépôt 2023-04-04
Date de la première publication 2024-06-06
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Hatamizadeh, Ali
  • Heinrich, Gregory
  • Yin, Hongxu
  • Alvarez Lopez, Jose Manuel
  • Kautz, Jan
  • Molchanov, Pavlo

Abrégé

Apparatuses, systems, and techniques of using one or more machine learning processes (e.g., neural network(s)) to process data (e.g., using hierarchical self-attention). In at least one embodiment, image data is classified using hierarchical self-attention generated using carrier tokens that are associated with windowed subregions of the image data, and local attention generated using local tokens within the windowed subregions and the carrier tokens.

Classes IPC  ?

  • G06N 3/0455 - Réseaux auto-encodeurs; Réseaux encodeurs-décodeurs
  • G06N 3/0464 - Réseaux convolutifs [CNN, ConvNet]
  • G06N 3/08 - Méthodes d'apprentissage

86.

GENERATING COMPLETE THREE-DIMENSIONAL SCENE GEOMETRIES USING MACHINE LEARNING

      
Numéro d'application 18339936
Statut En instance
Date de dépôt 2023-06-22
Date de la première publication 2024-06-06
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s)
  • Zhang, Dongsu
  • Kar, Amlan
  • Williams, Francis
  • Gojcic, Zan
  • Kreis, Karsten
  • Fidler, Sanja

Abrégé

In various examples, a technique for performing three-dimensional (3D) scene completion includes determining an initial representation of a first 3D scene. The technique also includes executing a machine learning model to generate a first update to the initial representation at a previous time step and a second update to the initial representation at a current time step, wherein the second update is generated based at least on a threshold applied to a set of predictions corresponding to the first update. The technique also includes generating a 3D model of the 3D scene based at least on the second update to the initial representation.

Classes IPC  ?

  • G06T 17/10 - Description de volumes, p.ex. de cylindres, de cubes ou utilisant la GSC [géométrie solide constructive]

87.

ALIAS-FREE TAGGED ERROR CORRECTING CODES FOR MACHINE MEMORY OPERATIONS

      
Numéro d'application 18485132
Statut En instance
Date de dépôt 2023-10-11
Date de la première publication 2024-06-06
Propriétaire NVIDIA Corp. (USA)
Inventeur(s)
  • Sullivan, Michael B.
  • Hassan, Mohamed Tarek Bnziad Mohamed
  • Jaleel, Aamer

Abrégé

Implicit Memory Tagging (IMT) mechanisms utilizing alias-free memory tags that enable hardware-assisted memory tagging without incurring storage overhead above those incurred by conventional tagging mechanisms, while providing enhanced data integrity and memory security. The IMT mechanisms enhance the utility of error correcting codes (ECCs) to test memory tags in addition to the traditional utility of ECCs for detecting and correcting data errors and enable a finer granularity of memory tagging than many conventional approaches.

Classes IPC  ?

  • G06F 11/10 - Détection ou correction d'erreur par introduction de redondance dans la représentation des données, p.ex. en utilisant des codes de contrôle en ajoutant des chiffres binaires ou des symboles particuliers aux données exprimées suivant un code, p.ex. contrôle de parité, exclusion des 9 ou des 11

88.

DISTRIBUTION OF QUANTUM STATE VECTOR ELEMENTS ACROSS NETWORK DEVICES IN QUANTUM COMPUTING SIMULATION

      
Numéro d'application 18526829
Statut En instance
Date de dépôt 2023-12-01
Date de la première publication 2024-06-06
Propriétaire NVIDIA Corporation (USA)
Inventeur(s) Morino, Shinya

Abrégé

Aspects of this technical solution can identify, based at least on a representation of a quantum computing circuit, a first node of a topology of a computing platform configured to simulate at least a portion of the quantum computing circuit, compute a first metric indicating a first latency including the first node, the first latency based at least on a portion of the topology including the first node, select a second node of the topology having a second metric indicating a second latency less than the first latency, the second latency based at least on a portion of the topology including the second node, and simulate the quantum computing circuit on the computing platform using the second node.

Classes IPC  ?

  • G06N 10/20 - Modèles d’informatique quantique, p.ex. circuits quantiques ou ordinateurs quantiques universels
  • G06N 10/40 - Réalisations ou architectures physiques de processeurs ou de composants quantiques pour la manipulation de qubits, p.ex. couplage ou commande de qubit

89.

EPOCH-BASED MECHANISM FOR PROVIDING DATA INTEGRITY AND RELIABILITY IN A MESSAGING SYSTEM

      
Numéro d'application 18074362
Statut En instance
Date de dépôt 2022-12-02
Date de la première publication 2024-06-06
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Klenk, Benjamin
  • Davis, Al
  • Dennison, Larry Robert

Abrégé

Messaging protocols used by components in a messaging system to exchange messages conventionally use a reliability mechanism to ensure that each message sent by a sender is received, without compromise, by the intended receiver. Typically, this reliability mechanism involves use of a returned acknowledgement message to the message sender, with automatic retransmission of the message by the sender when the acknowledgement message is not received (e.g. within a defined timeframe). However, existing acknowledgement-based reliability mechanisms require that a sender identifier be included in the message header, which increases the overhead of the message. The present disclosure provides an epoch-based reliability mechanism that allows the sender identifier to be omitted from the message header to minimize overhead and maximize the efficient use of the available bandwidth.

Classes IPC  ?

  • G06F 21/64 - Protection de l’intégrité des données, p.ex. par sommes de contrôle, certificats ou signatures

90.

DATASET GENERATION USING LARGE LANGUAGE MODELS

      
Numéro d'application 18075942
Statut En instance
Date de dépôt 2022-12-06
Date de la première publication 2024-06-06
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Nagaraju, Divija
  • Parisien, Christopher

Abrégé

Disclosed are systems and techniques that may generate datasets for training task-oriented dialogue systems. The techniques include generating natural language queries by selecting a template query, sampling one or more tokens from a data store of domain-specific tokens, modifying the selected template query using the one or more sampled tokens to generate a query prompt, and using a natural language generative machine-learning model to generate, based on the query prompt, a respective natural language query of the subset of the plurality of natural language queries, and causing the generated plurality of natural language queries to be provided to a machine-learning model training engine configured to train, using the generated plurality of natural language queries, a conversational machine-learning model to perform a domain-specific conversational task.

Classes IPC  ?

  • G06F 40/56 - Génération de langage naturel
  • G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence

91.

APPLICATION PROGRAMMING INTERFACE TO INDICATE ACCELERATOR ERROR HANDLERS

      
Numéro d'application US2023081208
Numéro de publication 2024/118526
Statut Délivré - en vigueur
Date de dépôt 2023-11-27
Date de publication 2024-06-06
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s)
  • Ravi, Karthik Raghavan
  • Jain, Ashutosh
  • Suresh, Rahul

Abrégé

Apparatuses, systems, and techniques to execute one or more application programming interfaces (APIs) to perform one or more operations for one or more accelerators within a heterogeneous processor. In at least one embodiment, one or more processors are to perform one or more instructions in response to one or more APIs to indicate one or more functions to be performed in response to one or more errors from one or more accelerators within a heterogeneous processor.

Classes IPC  ?

  • G06F 11/07 - Réaction à l'apparition d'un défaut, p.ex. tolérance de certains défauts

92.

APPLICATION PROGRAMMING INTERFACE TO CAUSE PERFORMANCE OF ACCELERATOR OPERATIONS

      
Numéro d'application US2023081356
Numéro de publication 2024/118609
Statut Délivré - en vigueur
Date de dépôt 2023-11-28
Date de publication 2024-06-06
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s)
  • Ravi, Karthik Raghavan
  • Jain, Ashutosh
  • Suresh, Rahul

Abrégé

Apparatuses, systems, and techniques to execute one or more application programming interfaces (APIs) to perform one or more operations for one or more accelerators within a heterogeneous processor. In at least one embodiment, one or more processors are to perform one or more instructions in response to one or more APIs to indicate one or more operations in a sequence of operations to be performed by one or more accelerators within a heterogeneous processor.

Classes IPC  ?

  • G06F 9/48 - Lancement de programmes; Commutation de programmes, p.ex. par interruption
  • G06F 9/50 - Allocation de ressources, p.ex. de l'unité centrale de traitement [UCT]
  • G06F 9/54 - Communication interprogramme

93.

SLOT FILLING USING A ZERO SHOT MODEL FOR CONVERSATIONAL AI SYSTEMS AND APPLICATIONS

      
Numéro d'application 18074394
Statut En instance
Date de dépôt 2022-12-02
Date de la première publication 2024-06-06
Propriétaire NVIDIA CORPORATION (USA)
Inventeur(s)
  • Das, Shubhadeep
  • Lee, Yi-Hui
  • Olabiyi, Oluwatobi
  • Wang, Zhilin

Abrégé

In various examples, a technique for slot filling includes receiving a natural language sentence from a user and identifying a first mention span included in the natural language sentence. The technique also includes determining, using a first machine learning model, that the first mention span is associated with a first slot class included in a set of slot classes based on a set of slot class descriptions corresponding to the set of slot classes.

Classes IPC  ?

  • G06F 40/56 - Génération de langage naturel
  • G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence

94.

PREPROCESSING DATA USING A NETWORK INTERFACE

      
Numéro d'application 18076221
Statut En instance
Date de dépôt 2022-12-06
Date de la première publication 2024-06-06
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Ihsani, Alvin
  • Arazi, Shaul
  • Agostini, Elena
  • Tasinga, Penn
  • Lacey, Jr., Carl Everett
  • Groff, Dana
  • Levi, Dotan David
  • Wasko, Wojciech
  • Nath, Vishwesh
  • Alle, Sachidanand

Abrégé

Methods and systems for obtaining data having a first format, converting the data to a second format, storing the converted data in memory accessible by at least one parallel processing unit, and processing the converted data stored in the memory using the at least one parallel processing unit.

Classes IPC  ?

  • G06N 5/04 - Modèles d’inférence ou de raisonnement
  • G16H 30/20 - TIC spécialement adaptées au maniement ou au traitement d’images médicales pour le maniement d’images médicales, p.ex. DICOM, HL7 ou PACS

95.

GENERATING A MOTION PLAN TO POSITION AT LEAST A PORTION OF A DEVICE WITH RESPECT TO A REGION

      
Numéro d'application 18120864
Statut En instance
Date de dépôt 2023-03-13
Date de la première publication 2024-06-06
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Hermans, Tucker Ryer
  • Pavlasek, Jana
  • Tozeto Ramos, Fabio

Abrégé

Apparatuses, systems, and techniques to perform inference to determine a trajectory based at least in part on a loss function including a cost associated with an amount of divergence between a set of terminal states and a set of goal states within a goal region.

Classes IPC  ?

  • G05D 1/02 - Commande de la position ou du cap par référence à un système à deux dimensions
  • G05D 1/00 - Commande de la position, du cap, de l'altitude ou de l'attitude des véhicules terrestres, aquatiques, aériens ou spatiaux, p.ex. pilote automatique

96.

DETERMINING INTENTS AND RESPONSES USING MACHINE LEARNING IN CONVERSATIONAL AI SYSTEMS AND APPLICATIONS

      
Numéro d'application 18173622
Statut En instance
Date de dépôt 2023-02-23
Date de la première publication 2024-06-06
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Das, Shubhadeep
  • Bhattacharya, Sumit Kumar
  • Olabiyi, Oluwatobi

Abrégé

In various examples, hybrid models for determining intents in conversational AI systems and applications are disclosed. Systems and methods are disclosed that use a machine learning model(s) and a data file(s) that associates requests (e.g., questions) with responses (e.g., answers) in order to generate final responses to requests. For instance, the machine learning model(s) may determine confidence scores that indicate similarities between the requests from the data file(s) and an input request represented by text data. The data file(s) is then used to determine, based on the confidence scores, one of the responses that is associated with one of the requests that is related to the input request. Additionally, the response may then used to generate a final response to the input request.

Classes IPC  ?

97.

VISION TRANSFORMER FOR IMAGE GENERATION

      
Numéro d'application 18222725
Statut En instance
Date de dépôt 2023-07-17
Date de la première publication 2024-06-06
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Hatamizadeh, Ali
  • Song, Jiaming
  • Kautz, Jan
  • Vahdat, Arash

Abrégé

Apparatuses, systems, and techniques to generate images. In at least one embodiment, one or more machine learning models generate an output image based, at least in part, on calculating attention scores using time embeddings.

Classes IPC  ?

  • G06T 5/00 - Amélioration ou restauration d'image
  • G06T 1/20 - Architectures de processeurs; Configuration de processeurs p.ex. configuration en pipeline
  • G06T 7/00 - Analyse d'image

98.

POLICY PLANNING USING BEHAVIOR MODELS FOR AUTONOMOUS SYSTEMS AND APPLICATIONS

      
Numéro d'application 18354892
Statut En instance
Date de dépôt 2023-07-19
Date de la première publication 2024-06-06
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Chen, Yuxiao
  • Karkus, Peter
  • Ivanovic, Boris
  • Weng, Xinshuo
  • Pavone, Marco

Abrégé

In various examples, policy planning using behavior models for autonomous and semi-autonomous systems and applications is described herein. Systems and methods are disclosed that determine a policy for navigating a vehicle, such as a semi-autonomous vehicle or an autonomous vehicle (or other machine), where the policy allows for multistage reasoning that leverages future reactive behaviors of one or more other objects. For instance, a first behavior model (e.g., a trajectory tree) may be generated that represents candidate trajectories for the vehicle and one or more second behavior models (e.g., one or more scenario trees) may be generated that respectively represent future behaviors of the other object(s). The first behavior model and the second behavior model(s) may then be processed, such as in a closed-loop simulation based on a realistic data-driven traffic model, to determine the policy for navigating the vehicle.

Classes IPC  ?

  • B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes

99.

HYBRID DIFFERENTIABLE RENDERING FOR LIGHT TRANSPORT SIMULATION SYSTEMS AND APPLICATIONS

      
Numéro d'application 18441486
Statut En instance
Date de dépôt 2024-02-14
Date de la première publication 2024-06-06
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Chen, Wenzheng
  • Litalien, Joey
  • Gao, Jun
  • Wang, Zian
  • Fuji Tsang, Clement Tse Tsian Christophe Louis
  • Khamis, Sameh
  • Litany, Or
  • Fidler, Sanja

Abrégé

In various examples, information may be received for a 3D model, such as 3D geometry information, lighting information, and material information. A machine learning model may be trained to disentangle the 3D geometry information, the lighting information, and/or material information from input data to provide the information, which may be used to project geometry of the 3D model onto an image plane to generate a mapping between pixels and portions of the 3D model. Rasterization may then use the mapping to determine which pixels are covered and in what manner, by the geometry. The mapping may also be used to compute radiance for points corresponding to the one or more 3D models using light transport simulation. Disclosed approaches may be used in various applications, such as image editing, 3D model editing, synthetic data generation, and/or data set augmentation.

Classes IPC  ?

  • G06T 15/06 - Lancer de rayon
  • G06T 15/50 - Effets de lumière
  • G06T 19/20 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie Édition d'images tridimensionnelles [3D], p.ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties

100.

SIMULATING REALISTIC TEST DATA FROM TRANSFORMED REAL-WORLD SENSOR DATA FOR AUTONOMOUS MACHINE APPLICATIONS

      
Numéro d'application 18442753
Statut En instance
Date de dépôt 2024-02-15
Date de la première publication 2024-06-06
Propriétaire NVIDIA Corporation (USA)
Inventeur(s)
  • Hong, Jesse
  • Muller, Urs
  • Firner, Bernhard
  • Yang, Zongyi
  • Daw, Joyjit
  • Nister, David
  • Valenti, Roberto Giuseppe Luca
  • Aviv, Rotem

Abrégé

In various examples, sensor data recorded in the real-world may be leveraged to generate transformed, additional, sensor data to test one or more functions of a vehicle—such as a function of an AEB, CMW, LDW, ALC, or ACC system. Sensor data recorded by the sensors may be augmented, transformed, or otherwise updated to represent sensor data corresponding to state information defined by a simulation test profile for testing the vehicle function(s). Once a set of test data has been generated, the test data may be processed by a system of the vehicle to determine the efficacy of the system with respect to any number of test criteria. As a result, a test set including additional or alternative instances of sensor data may be generated from real-world recorded sensor data to test a vehicle in a variety of test scenarios.

Classes IPC  ?

  • G01M 17/007 - Véhicules à roues ou à chenilles
  • B60W 30/08 - Anticipation ou prévention de collision probable ou imminente
  • B60W 30/12 - Maintien de la trajectoire dans une voie de circulation
  • B60W 30/14 - Régulateur d'allure
  • B60W 50/00 - COMMANDE CONJUGUÉE DE PLUSIEURS SOUS-ENSEMBLES D'UN VÉHICULE, DE FONCTION OU DE TYPE DIFFÉRENTS; SYSTÈMES DE COMMANDE SPÉCIALEMENT ADAPTÉS AUX VÉHICULES HYBRIDES; SYSTÈMES D'AIDE À LA CONDUITE DE VÉHICULES ROUTIERS, NON LIÉS À LA COMMANDE D'UN SOUS-ENSEMBLE PARTICULIER - Détails des systèmes d'aide à la conduite des véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier
  • B60W 50/04 - COMMANDE CONJUGUÉE DE PLUSIEURS SOUS-ENSEMBLES D'UN VÉHICULE, DE FONCTION OU DE TYPE DIFFÉRENTS; SYSTÈMES DE COMMANDE SPÉCIALEMENT ADAPTÉS AUX VÉHICULES HYBRIDES; SYSTÈMES D'AIDE À LA CONDUITE DE VÉHICULES ROUTIERS, NON LIÉS À LA COMMANDE D'UN SOUS-ENSEMBLE PARTICULIER - Détails des systèmes d'aide à la conduite des véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier pour surveiller le fonctionnement du système d'aide à la conduite
  • B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
  • G06F 11/36 - Prévention d'erreurs en effectuant des tests ou par débogage de logiciel
  • G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
  • G06V 20/56 - Contexte ou environnement de l’image à l’extérieur d’un véhicule à partir de capteurs embarqués
  • G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
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