Produits et services

Downloadable software and operating system software for safety, inspections, monitoring, testing, compliance, certification, validation, abstraction, error detection and correction, foundations, calibrating, controlling, training, system behavior adjustments, system isolations, critical system checks, safety logic, alerting, accessing and interpreting camera data, perception and inspection systems, simulation, system and environment modeling, hypervisors, contingency planning, and system guardrails; downloadable software for autonomous and semi-autonomous systems, smart spaces, and machines, namely, software providing guardrails, rules, logic, operational functionality, sensors, and compliance; downloadable software for inspection, compliance, training, and behavior modification of hardware, compute platforms, high performance computers, and GPUs; downloadable software comprising middleware, sensor, and abstraction layers, schedulers, communication libraries, and system frameworks for use in autonomous applications; downloadable software in the nature of full stack platforms for autonomous operations consisting of comprehensive safety systems and compliance, infrastructure, operating system software, and middleware; downloadable software for unifying edge computing into an integrated safety platform; downloadable software for implementing functional safety protocols in autonomous and semi-autonomous systems and smart spaces; downloadable software for real-time artificial intelligence inference at the network edge; downloadable software for managing and orchestrating safety-critical computing workloads; downloadable software for monitoring, diagnosing, and ensuring compliance with functional safety standards in computing systems; downloadable software for integrating safety-certified computing functions with artificial intelligence decision-making in real-time applications; downloadable software for developing, deploying, and managing safety-critical artificial intelligence applications at the network edge; downloadable software for real-time monitoring and enforcement of functional safety standards in artificial intelligence computing systems; downloadable software for training, simulating, and validating autonomous and semi-autonomous systems and smart spaces at scale; downloadable software for providing a safety evaluation framework for autonomous and semi-autonomous systems and smart spaces; downloadable software for building and documenting safety cases for autonomous and semi-autonomous systems and smart spaces; downloadable software incorporating tools and guidelines for evaluating functional safety of autonomous and semi-autonomous systems and smart spaces; downloadable software development kits (SDKs) for use in autonomous applications; downloadable machine learning software for enabling computers to learn to perform tasks autonomously; downloadable computer software using machine learning for developing predictive models; downloadable and recorded computer programs for monitoring the performance of computer systems for robotic, autonomous and semi-autonomous, and smart space systems; downloadable software for programming physical movements for use in robots; downloadable operating system software for robots; downloadable software for machine learning for use in robots; recorded software and operating system software for safety, inspections, monitoring, testing, compliance, certification, validation, abstraction, error detection and correction, foundations, calibrating, controlling, training, system behavior adjustments, system isolations, critical system checks, safety logic, alerting, accessing and interpreting camera data, perception and inspection systems, simulation, system and environment modeling, hypervisors, contingency planning, and system guardrails; recorded software for autonomous and semi-autonomous systems, smart spaces, and machines, namely, software providing guardrails, rules, logic, operational functionality, sensors, and compliance; recorded software for inspection, compliance, training, and behavior modification of hardware, compute platforms, high performance computers, and GPUs; recorded software comprising middleware, sensor, and abstraction layers, schedulers, communication libraries, and system frameworks for use in autonomous applications; recorded software in the nature of full stack platforms for autonomous operations consisting of comprehensive safety systems and compliance, infrastructure, operating system software, and middleware; recorded software for unifying edge computing into an integrated safety platform; recorded software for implementing functional safety protocols in autonomous and semi-autonomous systems and smart spaces; recorded software for real-time artificial intelligence inference at the network edge; recorded software for managing and orchestrating safety-critical computing workloads; recorded software for monitoring, diagnosing, and ensuring compliance with functional safety standards in computing systems; recorded software for integrating safety-certified computing functions with artificial intelligence decision-making in real-time applications; recorded software for developing, deploying, and managing safety-critical artificial intelligence applications at the network edge; recorded software for real-time monitoring and enforcement of functional safety standards in artificial intelligence computing systems; recorded software for training, simulating, and validating autonomous and semi-autonomous systems and smart spaces at scale; recorded software for providing a safety evaluation framework for autonomous and semi-autonomous systems and smart spaces; recorded software for building and documenting safety cases for autonomous and semi-autonomous systems and smart spaces; recorded software incorporating tools and guidelines for evaluating functional safety of autonomous and semi-autonomous systems and smart spaces; recorded software development kits (SDKs) for use in autonomous applications; recorded machine learning software for enabling computers to learn to perform tasks autonomously; recorded computer software using machine learning for developing predictive models; recorded software for programming physical movements for use in robots; recorded operating system software for robots; recorded software for machine learning for use in robots. Providing online non-downloadable software and operating system software for safety, inspections, monitoring, testing, compliance, certification, validation, abstraction, error detection and correction, foundations, calibrating, controlling, training, system behavior adjustments, system isolations, critical system checks, safety logic, alerting, accessing and interpreting camera data, perception and inspection systems, simulation, system and environment modeling, hypervisors, contingency planning, and system guardrails; providing online non-downloadable software for autonomous and semi-autonomous systems, smart spaces, and machines, namely, software for providing guardrails, rules, logic, operational functionality, sensors, and compliance; providing online non-downloadable software for inspection, compliance, training, and behavior modification of hardware, compute platforms, high performance computers, and GPUs; providing online non-downloadable software comprising middleware, sensor, and abstraction layers, schedulers, communication libraries, and system frameworks for use in autonomous applications; providing online non-downloadable software using artificial intelligence agents and modules for monitoring, controlling, testing, training, controlling, moving, and mobilizing autonomous machines; providing online non-downloadable software in the nature of full stack platforms for autonomous operations consisting of comprehensive safety systems and compliance, infrastructure, operating system software, and middleware; providing online non-downloadable software for unifying edge computing into an integrated safety platform; providing online non-downloadable software for implementing functional safety protocols in autonomous and semi-autonomous systems and smart spaces; providing online non-downloadable software for real-time artificial intelligence inference at the network edge; providing online non-downloadable software for managing and orchestrating safety-critical computing workloads; providing online non-downloadable software for monitoring, diagnosing, and ensuring compliance with functional safety standards in computing systems; providing online non-downloadable software for integrating safety-certified computing functions with artificial intelligence decision-making in real-time applications; providing online non-downloadable software for developing, deploying, and managing safety-critical artificial intelligence applications at the network edge; providing online non-downloadable software for real-time monitoring and enforcement of functional safety standards in artificial intelligence computing systems; providing online non-downloadable software for training, simulating, and validating autonomous and semi-autonomous systems and smart spaces at scale; providing online non-downloadable software for providing a safety evaluation framework for autonomous and semi-autonomous systems and smart spaces from operator assistance to autonomous operation; providing online non-downloadable software for building and documenting safety cases for autonomous and semi-autonomous systems and smart spaces; providing online non-downloadable software incorporating tools and guidelines for evaluating functional safety of autonomous and semi-autonomous systems and smart spaces; providing online non-downloadable software development kits (SDKs) for use in autonomous applications; technology consultation in the field of artificial intelligence (AI); providing information in the fields of technology and software development for autonomous systems and robotics via an on-line website; providing information relating to computer technology and programming for robotic, autonomous and semi-autonomous, and smart space systems via a website.

Produits et services

Downloadable computer software, namely, prebuilt, optimized software containers and inference microservices for deploying, running, integrating, scaling, and managing artificial intelligence and machine learning models; downloadable electronic data files featuring source code, text, and images for deploying, running, integrating, scaling, and managing artificial intelligence and machine learning models. Providing temporary use of online non-downloadable software, namely, prebuilt, optimized inference microservices for deploying, running, integrating, scaling, and managing artificial intelligence and machine learning models; providing online non-downloadable API software for artificial intelligence model inference.

Produits et services

Downloadable electronic data files featuring physical characteristics and operational parameters of robots for use in robotics research and simulation; downloadable data sets for developing, training, testing, and evaluating artificial intelligence models for use in robotics simulation, calibration, and system identification, namely, downloadable data sets featuring robot experiment telemetry, motion data, system-identification measurements, and operational parameters of robots for use in robotics research, robot simulation, simulation calibration, validation of physics-engine, actuator, and sensor models, and evaluation for robotics applications; downloadable data sets for the analyzing and reviewing of data featuring robot experiment telemetry, motion data, system-identification measurements, and operational parameters of robots for use in robotics research, robot simulation, simulation calibration, validation of physics-engine, actuator, and sensor models, and evaluation for robotics applications.

Produits et services

Downloadable computer software for training, deploying, and running artificial intelligence and machine learning models for robotics, autonomous vehicles, and vision AI agents; downloadable computer software development kits (SDKs) for creating, testing, and fine tuning world models and world foundation models; downloadable computer software libraries for synthetic data generation and processing of sensor data for training and evaluating physical AI models; downloadable machine learning (ML) models, foundation models, and pretrained neural network (NN) models, all for physical AI development in the fields of robotics, autonomous vehicles, and vision AI agents; downloadable electronic data files featuring source code, text, and images, all for physical AI development in the fields of robotics, autonomous vehicles, and vision AI agents. Providing temporary use of non-downloadable computer software for training, deploying, and running artificial intelligence and machine learning models for robotics, autonomous vehicles, and vision AI agents; providing temporary use of non-downloadable computer software for creating, testing, and fine tuning world models and world foundation models; providing temporary use of non-downloadable computer software for synthetic data generation and processing of sensor data for physical AI development; software as a service (SaaS) featuring software for training, deploying, and running artificial intelligence and machine learning models for robotics, autonomous vehicles, and vision AI agents; artificial intelligence as a service (AIaaS) services featuring software for training, deploying, and running artificial intelligence and machine learning models for robotics, autonomous vehicles, and vision AI agents; software as a service (SaaS) featuring software for creating, testing, and fine tuning world models and world foundation models; artificial intelligence as a service (AIaaS) services featuring software for creating, testing, and fine tuning world models and world foundation models; software as a service (SaaS) featuring software for synthetic data generation and processing of sensor data for physical AI development; artificial intelligence as a service (AIaaS) services featuring software for synthetic data generation and processing of sensor data for physical AI development.

Abrégé

Devices and methods are disclosed for power conversion using hybrid-switched capacitor converter (HSCC) devices. In at least one embodiment, an HSCC device includes a first power train, a second power train, and a coupling device. The first power train includes a first output terminal. The second power train includes a second output terminal. The coupling device includes, a first input terminal, a second input terminal and, a third output terminal. The first input terminal of the coupling device is connected to the first output terminal, and the second input terminal of the coupling device is connected to the second output terminal. Current from the first and second power trains induces coupling in the coupling device, enhancing performance across different states and various duty cycles.

Classes IPC  ?

• H02M 3/07 - Transformation d'une puissance d'entrée en courant continu en une puissance de sortie en courant continu sans transformation intermédiaire en courant alternatif par convertisseurs statiques utilisant des résistances ou des capacités, p. ex. diviseur de tension utilisant des capacités chargées et déchargées alternativement par des dispositifs à semi-conducteurs avec électrode de commande

Abrégé

Power supply and distribution architectures, e.g., for supplying and distributing power to a GPU accelerator array of a data center server. In at least one embodiment, a power supply and distribution architecture includes a power distribution board having both a power converter for converting a supply voltage of a power supply to an operating voltage of a GPU and a GPU power connector for transmitting power to the GPU at the operating voltage.

Classes IPC  ?

• G06F 1/26 - Alimentation en énergie électrique, p. ex. régulation à cet effet
• H05K 7/14 - Montage de la structure de support dans l'enveloppe, sur cadre ou sur bâti

Abrégé

Apparatuses, systems, and techniques to identify a first one or more fifth generation new radio (5G NR) signal pairs having one or more quality characteristics based, at least in part, on neural network weight information received from a plurality of different sources. In at least one embodiment, neural network weight information can be aggregated to generate one or more neural networks to identify the first one or more 5G NR signal pairs.

Classes IPC  ?

• 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
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/082 - Méthodes d'apprentissage modifiant l’architecture, p. ex. par ajout, suppression ou mise sous silence de nœuds ou de connexions

Abrégé

Systems and methods are disclosed related to collaborative texture filtering. In at least one embodiment, in a graphics processing unit (GPU) executes a plurality of processing threads organized into waves. In at least one embodiment, redundant texel fetches are identified and one or more threads are reassigned to fetch unique texels through collaboration.

Classes IPC  ?

• G06T 15/04 - Mappage de texture
• G06T 1/20 - Architectures de processeursConfiguration de processeurs p. ex. configuration en pipeline
• G06T 5/20 - Amélioration ou restauration d'image utilisant des opérateurs locaux

Abrégé

Apparatuses, systems, and techniques related to computing intensity images from infrared (IR) wavelength data and visibility wavelength data. In at least one embodiment, each IR image corresponding to the IR wavelength data is fused with a luminance (luma) image computed from corresponding the visibility wavelength data to produce IR/luma intensity images.

Classes IPC  ?

• G06T 7/00 - Analyse d'image

Abrégé

Apparatuses, systems, and techniques related to a perception neural network model determining controls for an image signal processor (ISP). In at least one embodiment, the perception neural network model extracts attributes and semantic features from an image to produce perception data. The perception data is used to determine one or more ISP controls to enhance a portion of the image.

Classes IPC  ?

• G06T 5/60 - Amélioration ou restauration d'image utilisant l’apprentissage automatique, p. ex. les réseaux neuronaux
• G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
• G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
• G06V 10/77 - Traitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions 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 10/82 - 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

Abrégé

Systems and methods to use weight information corresponding to a neural network, wherein weight information of at least a first one or more layers of the neural network is modified to compensate for deactivating one or more second layers of the neural network. The neural network may be a residual neural network.

Classes IPC  ?

• G06N 3/082 - Méthodes d'apprentissage modifiant l’architecture, p. ex. par ajout, suppression ou mise sous silence de nœuds ou de connexions
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
• G06V 30/18 - Extraction d’éléments ou de caractéristiques de l’image

Abrégé

In various examples, apparatuses, systems, software, and methods are described for selecting decoders to decode messages from encoders in a wireless network. For example, a base station includes a processor that uses a machine learning model to select a decoder from a plurality of decoders to decode an encoded representation of information received from an encoder in a user equipment (UE) device.

Classes IPC  ?

• H04L 1/00 - Dispositions pour détecter ou empêcher les erreurs dans l'information reçue
• H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle

Abrégé

In various examples, one or more neural networks are to be performed by a number of processors. The neural network may be reconfigured during inferencing such that the number of processors that start the inferencing is different from the number of processors that finish the inferencing.

Classes IPC  ?

• G06N 5/04 - Modèles d’inférence ou de raisonnement
• G06F 40/40 - Traitement ou traduction du langage naturel

Abrégé

In various examples, methods for training machine learning models to predict a binding affinity between molecules in a biological complex using supervised and unsupervised learning techniques includes generating an unlabeled training data set based on random perturbation applied to biological complexes included in a labeled training data set, wherein: the labeled training data set includes a plurality of samples of biological complexes, and each respective sample of a biological complex is associated with a calculated binding affinity between molecules in the respective sample; training a predictive model to predict binding affinity of a first molecule to a second molecule based on a first loss associated with the labeled training data set and a second loss associated with the unlabeled training data set; and deploying the trained predictive model.

Classes IPC  ?

• G06N 3/088 - Apprentissage non supervisé, p. ex. apprentissage compétitif

Abrégé

Systems and methods are disclosed that extracting material information using one or more video diffusion models. For example, embodiments of the present disclosure may process one or more images and a text prompt using a first video diffusion model to generate a rendered video of an object. For instance, the one or more images may be based on a three-dimensional (3D) geometry of the object and lighting conditions and the rendered image may include frames that show different viewpoints of the object. Subsequently, a differentiable renderer may be used to process the rendered video to generate a material data structure for the object, which indicates material information for points from the 3D geometry of the object. The material information may indicate physical-based rendering (PBR) characteristics such as an albedo, roughness, and/or metallic characteristic of the points on the object.

Classes IPC  ?

• G06T 15/50 - Effets de lumière
• G06T 13/20 - Animation tridimensionnelle [3D]
• G06T 15/04 - Mappage de texture
• G06T 15/20 - Calcul de perspectives
• G06T 17/20 - Description filaire, p. ex. polygonalisation ou tessellation

Abrégé

Apparatuses, systems, and techniques to implement cooperative thread groups. In at least one embodiment, two or more groups of synchronized threads are synchronized and executed in parallel for efficient execution on a GPU.

Classes IPC  ?

• G06F 9/52 - Synchronisation de programmesExclusion mutuelle, p. ex. au moyen de sémaphores
• G06F 9/54 - Communication interprogramme

Abrégé

Devices, systems, and techniques for assessing and managing security risk scenarios associated with an artificial intelligence (AI) model executed by a primary computing system. The techniques include generating, based on a set of security-related data associated with an AI model, a library comprising a set of identified ai security risk scenarios associated with the AI model. The techniques include classifying, based on a set of AI model criteria, a first identified security risk scenario of the set of identified security risk scenarios into a first category of a set of categories to generate a first classified security risk scenario. The techniques include causing a simulated security attack associated with the computing system to be created based on the first classified security risk scenario.

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

Abrégé

In various examples, surface features such as lane lines or road boundaries may be detected and 2D or 3D polylines representing their shape may be regressed. For example, a set of predefined vertical, slanted, and/or curved anchors may be used to represent corresponding lines (e.g., in a top-down view or bird's eye view). For each anchor, extracted features may be sampled along the anchor and processed using one or more DNNs to generate global (e.g., anchor-level) predictions such as an anchor-level existence or confidence score and/or local (e.g., per-point) predictions for each of a set of sampled points along the anchor such as regressed polylines and per-point classifications. The detected surface features may be used by one or more control components such as a drive or control stack for localization, safe planning, and control of an autonomous vehicle, semi-autonomous vehicle, machine, robot, and/or other object or machine type.

Classes IPC  ?

• 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 10/75 - Organisation de procédés de l’appariement, p. ex. comparaisons simultanées ou séquentielles des caractéristiques d’images ou de vidéosApproches-approximative-fine, p. ex. approches multi-échellesAppariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexteSélection des dictionnaires
• 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

Abrégé

In various examples, images of geometrically refined parametric surfaces may be denoised using motion vectors computed in 2D screen space and/or using locations of corresponding control points. Surfaces in a scene may be geometrically refined (e.g., subdivided or tessellated), an image of the scene may be ray traced or rasterized, and motion vectors may be computed in 2D screen space and/or using corresponding control points on the control cages for geometrically refined parametric surfaces. In some embodiments with displaced surfaces, motion vectors are computed by comparing 3D locations on a rendered (displaced) surface or by comparing corresponding 3D locations on the limit surface, and displacement may be determined using the applicable displacement function (e.g., a displacement map, texture, procedural math function, some combination thereof) or by measuring or deriving it from approximated locations on the displaced and limit surfaces. As such, the image may be denoised using the motion vectors.

Classes IPC  ?

• G06T 5/70 - DébruitageLissage
• G06T 15/06 - Lancer de rayon
• G06T 15/10 - Effets géométriques

Abrégé

Disclosed are apparatuses, systems, and techniques that train and use trained language models to assist users with complex systems installation, troubleshooting, and/or maintenance. The techniques include receiving, via a user interface (UI), a natural language (NL) query associated with one or more malfunction indicators indicative of a malfunction state of a system, providing, to a language model (LM) trained using a documentation associated with the system, an input having a prompt that is based at least on the NL query. The techniques further include receiving, from the LM, a response to the NL query, the response having one or more instructions associated with resolution of the malfunction state of the system and causing the UI to display the response.

Classes IPC  ?

• G06F 11/07 - Réaction à l'apparition d'un défaut, p. ex. tolérance de certains défauts
• G06F 40/284 - Analyse lexicale, p. ex. segmentation en unités ou cooccurrence
• G06F 40/40 - Traitement ou traduction du langage naturel

Abrégé

Apparatuses, systems, and techniques are presented to generate panoramas from individual images. In at least one embodiment, one or more generative neural networks are used to generate a spherical panoramic image using features extracted from a single input image.

Classes IPC  ?

• G06T 3/4038 - Création de mosaïques d’images, p. ex. composition d’images planes à partir de sous-images planes
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/088 - Apprentissage non supervisé, p. ex. apprentissage compétitif
• G06T 3/4046 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement utilisant des réseaux neuronaux
• G06T 17/10 - Description de volumes, p. ex. de cylindres, de cubes ou utilisant la GSC [géométrie solide constructive]

Abrégé

Various examples, systems, and methods are disclosed relating to orthogonal triangle strips. A first computing system can execute one or more operations to obtain an approximation of a three-dimensional (3D) curve including a plurality of linear segments. The first computing system can execute one or more operations to determine two quadrilateral primitives (quads) for each of the plurality of linear segments using a plurality of triangle primitives, wherein each of the two quads includes two of the plurality of triangle primitives, wherein the two quads corresponding to each linear segment of the plurality of linear segments are angled relative to one another and extend along the corresponding linear segment. The first computing system can execute one or more operations to render the two quads for each of the plurality of linear segments.

Classes IPC  ?

• G06T 15/80 - Ombrage

Abrégé

Apparatuses, systems, and techniques to provisioning of pipelines for efficient training, retraining, configuring, deploying, and using machine learning models for inference in user-specific platforms.

Classes IPC  ?

• G06N 3/08 - Méthodes d'apprentissage
• G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
• G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
• G10L 17/18 - Réseaux neuronaux artificielsApproches connexionnistes

Abrégé

Approaches presented herein relate to active messaging for parallel processing architectures, such as utilizing active messages (AMs) for direct graphics processing unit (GPU)-to-GPU communications. Gadgets may be launched across multiple GPUs for specific types of message handling. When a gadget is launched, the gadget may run across multiple GPUs to send messages between the different GPUs handling a given task. In operation, a gadget may include multiple handlers, and may be executed for specific tasks through completion prior to shutting down. In this manner, multiple gadgets may be launched sequentially to execute different selected operations across the GPUs.

Classes IPC  ?

• G06F 9/54 - Communication interprogramme

Abrégé

In various examples, apparatuses, systems, software, and methods are described for executing a Riding Peaks algorithm in parallel using multiple threads (e.g., GPU threads) to allocate sub-bands to user equipment device (UE)s. Examples of UEs include mobile phones, in-machine infotainment devices, smart devices, security systems, and/or other wireless-capable devices. For example, a processor executing an algorithm that allocates sub-bands to UEs by using threads (e.g., GPU threads) operating concurrently to identify peaks and assign sub-bands.

Classes IPC  ?

• H04B 17/309 - Mesure ou estimation des paramètres de qualité d’un canal
• G06F 9/30 - Dispositions pour exécuter des instructions machines, p. ex. décodage d'instructions
• G06F 9/38 - Exécution simultanée d'instructions, p. ex. pipeline ou lecture en mémoire
• G06T 1/20 - Architectures de processeursConfiguration de processeurs p. ex. configuration en pipeline

Abrégé

In various examples, towing- or generally external object transportation-detection for autonomous and/or semi-autonomous systems and applications is described herein. Systems and methods are disclosed that use one or more sensors to automatically determine when a machine is towing an object. For instance, sensor data obtained using the sensor(s)-such as image data, LiDAR data, RADAR data, ultrasonic data, and/or the like-may be used to determine information related to the object and/or the machine. This information may then be used to determine that the machine is towing the object, such as when the object remains a substantially similar distance from the machine when moving and/or when the object includes one or more specific types of classifications. The systems of methods may then perform one or more operations.

Classes IPC  ?

• B60W 50/14 - Moyens d'information du conducteur, pour l'avertir ou provoquer son intervention
• B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes

Abrégé

In various examples, a modular (e.g., control, drive, software, software/hardware) stack may evaluate candidate trajectory(ies) by predicting the subsequent scene or world state that would result from using the candidate trajectory(ies), and quantifying a measure of the quality, safety, and/or favorability of the predicted subsequent state. If the quality, safety, and/or favorability of the estimated subsequent state satisfies a designated threshold (e.g., the candidate trajectory is predicted to result in a favorable, safe, desirable, and/or beneficial future state), the candidate trajectory may be used to control navigation. If not, a determination may be made to disengage autonomous navigation, execute a safety procedure, and/or a parameterized optimization may be used to generate an optimized trajectory using an optimized planner to iteratively refine a trajectory predicted to lead to the most desirable state—or at least a state beyond a threshold desirability.

Classes IPC  ?

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

Abrégé

One embodiment of a method for determining object poses includes receiving first sensor data and second sensor data, where the first sensor data is associated with a first modality, and the second sensor data is associated with a second modality that is different from the first modality, and performing one or more iterative operations to determine a pose of an object based on one or more comparisons of (i) one or more renderings of a three-dimensional (3D) representation of the object in the first modality with the first sensor data, and (ii) one or more renderings of the 3D representation of the object in the second modality with the second sensor data.

Classes IPC  ?

• G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
• G06T 15/04 - Mappage de texture

Abrégé

Apparatuses, systems, and techniques are presented to compress data. In at least one embodiment, one or more images are generated based, at least in part, upon one or more compressed images and image enhancement data generated using one or more neural networks.

Classes IPC  ?

• G06T 9/00 - Codage d'image
• G06T 5/60 - Amélioration ou restauration d'image utilisant l’apprentissage automatique, p. ex. les réseaux neuronaux
• G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques

Abrégé

Apparatuses, systems, and techniques to perform matrix multiply-acumulate operations with block scaling of specified portions of input matrices by scale factors. In at least one embodiment, an input matrix is multiplied by a scaled matrix that is generated by multiplying elements of a second input matrix by one or more corresponding scale factors stored in tensor memory.

Classes IPC  ?

• G06F 17/16 - Calcul de matrice ou de vecteur

Abrégé

Apparatuses, systems, and techniques to perform matrix multiply-accumulate operations with block scaling of specified portions of input matrices by scale factors. In at least one embodiment, an input matrix is multiplied by a scaled matrix that is generated by multiplying elements of a second input matrix by one or more corresponding scale factors stored in one or more registers.

Classes IPC  ?

• G06F 17/16 - Calcul de matrice ou de vecteur

Abrégé

Apparatuses, systems, and techniques are presented to generate image or video content representing at least one point of view. In at least one embodiment, one or more neural networks are used to generate one or more images of one or more objects from a first point of view based at least in part upon one or more images of the one or more objects from a second point of view.

Classes IPC  ?

• G06T 9/00 - Codage d'image
• G06N 3/045 - Combinaisons de réseaux
• G06T 1/20 - Architectures de processeursConfiguration de processeurs p. ex. configuration en pipeline

Abrégé

Apparatuses, systems, and techniques to generate complete depictions of objects based on a partial depiction of the object. In at least one embodiment, an image of a complete object is generated by one or more neural networks, based on an image of a portion of the object, using an encoder of the one or more neural networks trained using training data generated from output of a decoder of the one or more neural networks.

Classes IPC  ?

• G06T 9/00 - Codage d'image
• G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
• G06N 3/08 - Méthodes d'apprentissage
• G06T 7/11 - Découpage basé sur les zones

Abrégé

In various examples, systems and methods related to depth-assisted 3D detection are provided. The techniques described herein may be used to monitor a state of a vehicle occupant with high precision using depth-assisted 3D detection. Sensors may be used to capture image data and depth data that includes features of the vehicle occupant (e.g., eyes, head, hands, etc.). The 2D image coordinates from the image data may be converted to 3D coordinates in a global coordinate system using depth data and a 3D occupant model, which may be personalized to the vehicle occupant. Precise values may be determined based on the 3D coordinates in a global coordinate system, and the state of features of the vehicle occupant may be estimated based on these values. The values and/or state of features of the vehicle occupant may be used to detect drowsiness, distraction, and/or other conditions and provide safe vehicle operation.

Classes IPC  ?

• G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p. ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
• G06T 7/593 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir d’images stéréo
• G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques

Abrégé

In various examples, machine learning model agent-based analysis of debugging reports for circuit design systems and applications is disclosed. A multi-file report analysis agent may analyze the original structure of individual timing reports to construct a structured report database for the set of reports. Based on the individual reports incorporated into the structured report database, the report analysis agent may construct a TDRG that connects the individual reports based on relationships. An MCMM planner agent may decompose the user-provided evaluation task inputs into sub-tasks for multiple corners and modes and call a TDRG traversal agent to resolve individual sub-tasks, as indicated by a mode plan. The TDRG traversal agent prompts expert report agents to retrieve information from the structural report database based on traversing the TDRG. An expert report agent may generate data retrieval code that may be executed to query the specified report from the structured report database.

Classes IPC  ?

• G06F 30/3315 - Vérification de la conception, p. ex. simulation fonctionnelle ou vérification du modèle utilisant une analyse temporelle statique [STA]
• G06F 30/27 - Optimisation, vérification ou simulation de l’objet conçu utilisant l’apprentissage automatique, p. ex. l’intelligence artificielle, les réseaux neuronaux, les machines à support de vecteur [MSV] ou l’apprentissage d’un modèle

Abrégé

In various examples, systems and methods for root cause failure identification using multi-stage retrieval-augmented generation are provided. A multi-stage retrieval-augmented generation system may include first model(s) that may extract a first context from test log(s) including a test failure based on first prompt(s) and information retrieved from first database(s). The multi-stage retrieval-augmented generation system may also include second model(s) that may extract a second context from product log(s) corresponding to the test log(s) based on the first context extracted from the test log(s), second prompt(s), and information retrieved from second database(s). Operation(s) for addressing the test failure may be performed based on the second context extracted from the product log(s) includes generating a report that identifies a root cause of at least one test failure, generating proposed change(s) to a test script for the automated testing, and/or generating proposed change(s) to source code for the product under test.

Classes IPC  ?

• G06F 11/3668 - Test de logiciel

Abrégé

A system includes a first cooling loop to cool one or more first components of a server and exchange first heat with an ambient environment via a first heat exchanger. The first cooling loop can exchange heat directly with the ambient environment without a cooling distribution unit.

Classes IPC  ?

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

Abrégé

Apparatuses, systems, and techniques to facilitate storage allocation. In at least one embodiment, an application programming interface is used to execute instructions to cause storage to be allocated based on an indication of whether the storage may be modified.

Classes IPC  ?

• G06F 13/16 - Gestion de demandes d'interconnexion ou de transfert pour l'accès au bus de mémoire
• G06T 1/20 - Architectures de processeursConfiguration de processeurs p. ex. configuration en pipeline

Abrégé

Apparatuses, processors, computing systems, devices, non-transitory computer medium, and/or methods for using one or more neural networks to identify a number of wireless devices communicating with one or more base stations based, at least in part, on communication signals, data structures, or information received by one or more base stations. For example, a processor is to use one or more neural networks to identify a number of wireless devices communicating with one or more base stations based, at least in part, on communication signals received by the one or more base stations.

Classes IPC  ?

• H04W 74/0833 - Procédures d’accès aléatoire, p. ex. avec accès en 4 étapes
• H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
• H04W 56/00 - Dispositions de synchronisation

Abrégé

Apparatuses, systems, and techniques are presented schedule operations to be performed. In at least one embodiment, two or more data partitions of a neural network are caused to be automatically scheduled to be performed by two or more corresponding logical processors in parallel.

Classes IPC  ?

• G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
• G06N 3/02 - Réseaux neuronaux

Produits et services

Computer hardware; computer software; downloadable software development kits (SDKs); downloadable and recorded artificial intelligence models and large language models (LLMs), namely pre-trained neural network models for generating, processing, understanding and analyzing natural language, speech, text, images, video, audio, software code and structured and unstructured data, including generative artificial intelligence models, multimodal models, computer vision models, speech and audio models, recommendation models, embedding models and other machine learning and foundation models; downloadable and recorded computer software featuring artificial intelligence, machine learning and deep learning; downloadable and recorded computer software for the collection, transmission, management, analyzing, reviewing and displaying of data, text, images, audio, video and multimedia content; downloadable and recorded computer software for developing, running and analyzing algorithms that are able to learn to analyze, classify, and take actions in response to exposure to data; downloadable and recorded computer software for developing, training, fine-tuning, testing, evaluating, deploying and monitoring artificial intelligence models, large language models (LLMs) and other machine learning and generative artificial intelligence models, including multimodal, computer vision, speech, recommendation and embedding models; downloadable and recorded computer software for use in generating and executing automated and autonomous processes and tasks; downloadable and recorded computer software for generating and executing autonomous tasks in response to exposure to data using machine learning and artificial intelligence; downloadable and recorded computer software that uses artificial intelligence and machine learning to generate, process, understand and analyze speech, text, images, videos, sounds, software code and tasks; downloadable and recorded software for facilitating interaction, communication, and actions between humans and artificial intelligence chatbots and agents and between artificial intelligence chatbots and agents; downloadable and recorded computer software for developing, customizing, and managing computer systems, databases, mobile and computer software applications, client interfaces, frameworks, and templates; downloadable and recorded computer software for use as an autonomous, on-machine agent that automates and executes tasks using artificial intelligence; downloadable and recorded computer software for facilitating interaction with third-party application programming interfaces (APIs) and platforms; downloadable and recorded computer software, namely, customizable software toolkits that enable extensibility through user-defined functions, and support API requests, deployments, and other tasks; downloadable and recorded computer software for executing commands using a shell and for integration with and deployment of large language models (LLMs) and other artificial intelligence models, including generative artificial intelligence models, multimodal models, computer vision models, speech and audio models, recommendation engines, embedding models and other machine learning and foundation models; downloadable and recorded computer software for using artificial intelligence for configuring, generating, and running interactive simulations; downloadable and recorded computer software for creating simulations using natural language, images, video, and audio data; downloadable electronic publications in the nature of technical documentation, model cards, white papers, instruction manuals, user guides and reference manuals in the field of artificial intelligence, machine learning, large language models (LLMs), generative artificial intelligence models, multimodal models and related software platforms; downloadable chatbot software for simulating conversations, analyzing images, sound and video, summarizing text, creating content, generating code, brainstorming, trip planning, and answering queries; downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; downloadable chatbot software for providing information from searchable indexes and databases of information, including text, images, videos, software algorithms, mathematical equations, electronic documents, and databases; computer hardware and recorded software for the autonomous driving of motor vehicles; downloadable software in the nature of vehicle operating system software; downloadable software for autonomous vehicle operation, navigation, steering, calibration, and management; computer hardware and recorded software for use with vehicle cameras; downloadable computer programs and downloadable software for artificial intelligence (AI), machine learning, and deep learning for use in connection with operating autonomous vehicles, systems, devices; recorded software and computer hardware for use in connection with and for operating autonomous vehicles, systems, devices; computer hardware and recorded software for operating vehicle cameras; computer hardware for operating autonomous vehicles. Providing temporary use of non-downloadable computer software; providing online software platforms; software as a service (SaaS); artificial intelligence as a service (AIAAS); software as a service (SaaS) featuring computer software platforms for building, training, fine-tuning, testing, evaluating, deploying, scaling and monitoring artificial intelligence models and large language models (LLMs) and other machine learning and generative artificial intelligence models, including multimodal models, computer vision models, speech and audio models, recommendation models, embedding models and other foundation models; software as a service (SaaS) and providing temporary use of non-downloadable computer software for developing, training, fine-tuning, testing, evaluating, deploying and monitoring artificial intelligence models, large language models (LLMs) and other machine learning and generative artificial intelligence models, including multimodal, computer vision, speech, recommendation and embedding models; software as a service (SaaS) and providing temporary use of non-downloadable computer software for the collection, transmission, management, analyzing, reviewing and displaying of data, text, images, audio, video and multimedia content; software as a service (SaaS) and providing temporary use of non-downloadable computer software for developing, running and analyzing algorithms that are able to learn to analyze, classify, and take actions in response to exposure to data; software as a service (SaaS) and providing temporary use of non-downloadable computer software for use in generating and executing automated and autonomous processes and tasks; software as a service (SaaS) and providing temporary use of non-downloadable computer software for generating and executing autonomous tasks in response to exposure to data using machine learning and artificial intelligence; software as a service (SaaS) and providing temporary use of non-downloadable computer software that uses artificial intelligence and machine learning to generate, process, understand and analyze speech, text, images, videos, sounds, software code and tasks; software as a service (SaaS) and providing temporary use of non-downloadable computer software for facilitating interaction, communication, and actions between humans and artificial intelligence chatbots and agents and between artificial intelligence chatbots and agents; software as a service (SaaS) and providing temporary use of non-downloadable computer software for developing, customizing, and managing computer systems, databases, mobile and computer software applications, client interfaces, frameworks, and templates; software as a service (SaaS) and providing temporary use of non-downloadable computer software as an autonomous, on-machine agent that automates and executes tasks using artificial intelligence; software as a service (SaaS) and providing temporary use of non-downloadable computer software for facilitating interaction with third-party APIs and platforms; software as a service (SaaS) and providing temporary use of non-downloadable open-source computer software, namely, customizable software toolkits that enable extensibility through user-defined functions, and supporting API requests, deployments, and other tasks; providing temporary use of non-downloadable artificial intelligence models and large language models (LLMs) via online interfaces and application programming interfaces (APIs) for generating, processing, understanding and analyzing natural language, speech, text, images, video, audio, software code and structured and unstructured data, including via generative artificial intelligence models, multimodal models, computer vision models, speech and audio models, recommendation models, embedding models and other machine learning and foundation models; software as a service (SaaS) and providing temporary use of non-downloadable computer software for executing commands using a shell and for integration with and deployment of large language models (LLMs) and other artificial intelligence models, including generative artificial intelligence models, multimodal models, computer vision models, speech and audio models, recommendation engines, embedding models and other machine learning and foundation models; software as a service (SaaS) and providing temporary use of non-downloadable computer software for using artificial intelligence for configuring, generating, and running interactive simulations; software as a service (SaaS) and providing temporary use of non-downloadable computer software for creating simulations using natural language, images, video, and audio data; providing technical information relating to computer software, namely, artificial intelligence, machine learning, and software development via a website; providing computer software consulting services; technical support services, namely, troubleshooting in the nature of diagnosing and repairing computer software problems; research, development and evaluation of large language models and data sets; research, design and development of computer programs and software; software as a service (SaaS) and providing temporary use of non-downloadable chatbot software for simulating conversations, analyzing images, sound and video, summarizing text, creating content, generating code, brainstorming, trip planning, and answering queries; software as a service (SaaS) and providing temporary use of non-downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; software as a service (SaaS) and providing temporary use of non-downloadable chatbot software for providing information from searchable indexes and databases of information, including text, images, videos, software algorithms, mathematical equations, electronic documents, and databases; Artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence for developing data science models; Artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence for creating and integrating computer models; providing online non-downloadable software for the autonomous driving of motor vehicles; providing online non-downloadable software for autonomous vehicle navigation, steering, calibration, and management; providing online non-downloadable software used for data analytics in the field of transportation; providing temporary use of non-downloadable data sets in the field of machine perception and autonomous driving technology; providing information about autonomous-vehicle and machine-perception research via a website; research, design, and development in the field of artificial intelligence; research, design, and development in the field of autonomous technology; research, design, and development of vehicle software; technological, scientific and research services in the field of robotics, self-driving car and autonomous vehicle technology; providing virtual computer systems and environments through cloud computing for the purpose of training self-driving cars, autonomous vehicles and robots.

Abrégé

Apparatuses, systems, and techniques are presented to determine distance for one or more objects. In at least one embodiment, a disparity network is trained to determine distance data from input stereoscopic images using a loss function that includes at least one of a gradient loss term and an occlusion loss term.

Classes IPC  ?

• G06N 3/08 - Méthodes d'apprentissage
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion

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

Abrégé

Apparatuses, systems, and techniques are to transfer data based, at least in part, on a computational graph. In at least one embodiment, a processor causes a compiler to generate instructions to prefetch one or more data values from dynamic random access memory (DRAM) into an in-processor cache based, at least in part, on a computational graph.

Classes IPC  ?

• G06F 12/0862 - Adressage d’un niveau de mémoire dans lequel l’accès aux données ou aux blocs de données désirés nécessite des moyens d’adressage associatif, p. ex. mémoires cache avec pré-lecture
• G06F 12/1072 - Traduction d’adresse décentralisée, p. ex. dans des systèmes de mémoire partagée distribuée

Abrégé

Apparatuses, systems, and techniques for caching compiled shader programs in a cloud computing environment. A request for a compiled shader program associated with an initial rendering operation is received from a client device. Based on the request, at least one of a shader key representing a hardware or software state of the client device, or a shader value derived from source code of the compiled shader program is obtained. The shader key or shader value is provided as input to a trained model. From the trained model, an indication of additional compiled shader programs and corresponding confidence levels that each will be requested within a time window is obtained. The compiled shader program and additional compiled shader programs having confidence levels satisfying confidence criteria are retrieved from a shader cache and provided to the client device.

Classes IPC  ?

• G06F 8/41 - Compilation
• G06F 8/60 - Déploiement de logiciel
• G06F 8/71 - Gestion de versions Gestion de configuration
• G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
• G06N 20/00 - Apprentissage automatique
• G06T 1/20 - Architectures de processeursConfiguration de processeurs p. ex. configuration en pipeline
• G06T 1/60 - Gestion de mémoire
• H04L 67/00 - Dispositions ou protocoles de réseau pour la prise en charge de services ou d'applications réseau
• H04L 67/1095 - Réplication ou mise en miroir des données, p. ex. l’ordonnancement ou le transport pour la synchronisation des données entre les nœuds du réseau
• H04L 67/568 - Stockage temporaire des données à un stade intermédiaire, p. ex. par mise en antémémoire

Abrégé

Apparatuses, systems, and techniques to execute CUDA programs. In at least one embodiment, an application programming interface is performed to determine a scheduling policy of one or more blocks of one or more threads.

Classes IPC  ?

• G06F 9/48 - Lancement de programmes Commutation de programmes, p. ex. par interruption
• G06F 8/41 - Compilation
• G06F 9/30 - Dispositions pour exécuter des instructions machines, p. ex. décodage d'instructions
• G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
• G06F 9/52 - Synchronisation de programmesExclusion mutuelle, p. ex. au moyen de sémaphores
• G06F 9/54 - Communication interprogramme

Abrégé

One embodiment of a method for controlling a robot includes performing a plurality of simulations of a robot interacting with one or more objects represented by one or more signed distance functions (SDFs), where performing the plurality of simulations comprises reducing a number of contacts between the one or more objects that are being simulated, and updating one or more parameters of a machine learning model based on the plurality of simulations to generate a trained machine learning model.

Classes IPC  ?

• B25J 9/16 - Commandes à programme

Abrégé

In various examples, a visualization of an environment may be generated using a Panini projection that is optimized based on detected scene content. For example, image data of an environment may be perspective projected (e.g., using a rectilinear projection) to generate a reference projection image, which may be analyzed to detect the presence of vanishing points and/or horizontal lines (e.g., in a central region). The image data of the environment may be projected using a Panini projection that is optimized based on distances to detected objects, the absence of a detected vanishing point, and/or the presence of a detected horizontal line to generate a Panini projection image. In some embodiments, vertical compression is applied to the Panini projection image to correct for distortion of horizontal lines (e.g., based on the presence of a detected horizontal line).

Classes IPC  ?

• G06T 17/05 - Modèles géographiques
• G06T 5/80 - Correction géométrique
• G06T 7/536 - Récupération de la profondeur ou de la forme à partir des effets de perspective, p. ex. en utilisant des points de fuite
• G06T 15/20 - Calcul de perspectives

Abrégé

In various examples, datasets for training computer vision and/or other artificial intelligence (AI) models may be automatically generated using AI-based techniques. For instance, the systems and methods of the present disclosure may crawl network resources (e.g., webpages) that include information associated with an interactive application (e.g., a wiki page for a gaming application) to obtain “seed” images depicting objects from the interactive application, as well as information (e.g., metadata) corresponding to the seed images. In some examples, a multimodal language model may be used to process the seed images and corresponding information. Based on the processing, the systems and methods may assign one or more labels to the seed images. The labeled seed images may then be used to expand the training dataset by processing and identifying additional images that depict similar objects to those in the labeled seed images.

Classes IPC  ?

• G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions 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 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
• G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
• 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 30/10 - Reconnaissance de caractères
• G06V 30/413 - Classification de contenu, p. ex. de textes, de photographies ou de tableaux
• G06V 30/42 - Reconnaissance des formes à partir d’images axée sur les documents basées sur le type de document

Abrégé

Neural networks, and in particular deep neural networks, are traditionally resource intensive as they require execution of a significant number of computations in order to generate an output. In an effort to accelerate neural networks, quantization has been employed in which the input/output of certain operations are scaled. However, the degree to which a neural network can be quantized is limited because current scaling methods, which do not ensure consistent scaling for operands, cannot be applied to the commonly used element-wise operations (e.g. addition and subtraction). The present disclosure provides scaling of element-wise operations in a neural network by using scale factors that are shared between operands, thus allowing for greater quantization of neural networks.

Classes IPC  ?

• G06N 3/08 - Méthodes d'apprentissage
• G06N 5/04 - Modèles d’inférence ou de raisonnement

Abrégé

Apparatuses, systems, and techniques to edit data elements of a spherical image. In at least one embodiment, two or more software threads are used to perform two or more fast Fourier transforms (FFTs) on two or more pixels of one or more spherical images.

Classes IPC  ?

• G06F 17/14 - Transformations de Fourier, de Walsh ou transformations d'espace analogues

Abrégé

Apparatuses, systems, methods, and techniques to process a work request (e.g., a Work Queue Element (WQE)) using graphics processing unit (GPU) resources. In at least one embodiment, the GPU detects one or more work requests to be processed, retrieves the work request(s), and processes the work request(s) in parallel.

Classes IPC  ?

• G06T 1/20 - Architectures de processeursConfiguration de processeurs p. ex. configuration en pipeline
• G06F 9/54 - Communication interprogramme

Abrégé

In various examples, techniques for neural rendering of characters using attention masks are described herein. Systems and methods described herein may use various techniques with regard to neural rendering (and/or any other rendering techniques) to render smooth, stable, and/or quality interactive characters. For instance, in some examples, an attention mask may be used to stabilize the motion outside of a specific region of a character-such as a mouth region of the character-that may be caused by audio inputs processed using one or more neural networks. Additionally, in some examples, an attention mask may be used to stabilize motion outside of other specific regions of the character-such as eye regions of the character-that may be caused by eye movement inputs processed using the neural network(s). As such, the masks may be used to control the motion that is caused by the different inputs.

Classes IPC  ?

• G06T 13/40 - Animation tridimensionnelle [3D] de personnages, p. ex. d’êtres humains, d’animaux ou d’êtres virtuels
• G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
• G06T 7/90 - Détermination de caractéristiques de couleur
• G06T 17/00 - Modélisation tridimensionnelle [3D] pour infographie

Produits et services

Computer hardware; downloadable software; downloadable software for system safety, cybersecurity, inspections, monitoring, testing, compliance, certification, validation, abstraction, data management, workflows, inter-process communication, scheduling, error detection and correction, foundations, calibrating, controlling, training, system behavior adjustments, system isolations, critical system checks, system communications, hardware integration, artificial intelligence (AI) perception, safety logic, accessing and interpreting camera data, hypervisors, contingency planning, and system guardrails; downloadable software for operating system safety, cybersecurity, inspections, monitoring, testing, compliance, certification, validation, abstraction, data management, workflows, inter-process communication, scheduling, error detection and correction, foundations, calibrating, controlling, training, system behavior adjustments, system isolations, critical system checks, system communications, hardware integration, artificial intelligence (AI) perception, safety logic, accessing and interpreting camera data, hypervisors, contingency planning, and system guardrails; downloadable software for autonomous systems and machines, namely, software providing guardrails, rules, logic, operational functionality, scheduling, monitoring, controls, communications, sensors, testing, and compliance; downloadable software for integration, monitoring, inspection, compliance, control, scheduling, training, and behavior modification of hardware, compute platforms, high performance computers, computer processors, GPUs, machines, robots, autonomous systems and machines, and autonomous vehicles; downloadable software comprising middleware, sensor, and abstraction layers, schedulers, communication libraries, system frameworks for use in autonomous applications; downloadable software using artificial intelligence agents and modules for monitoring, controlling, testing, training, moving, and mobilizing autonomous machines, robots, robotaxis, and autonomous vehicles; downloadable software in the nature of full stack platforms for autonomous operations consisting of comprehensive safety systems, monitoring, and compliance, infrastructure, operating system software, middleware, software applications; downloadable software for unifying real-time artificial intelligence processing, functional safety monitoring, and edge computing into an integrated safety platform; downloadable software for implementing functional safety protocols in autonomous and semi-autonomous systems; downloadable software for real-time artificial intelligence inference at the network edge; downloadable software for managing and orchestrating safety-critical computing workloads; downloadable software for monitoring, diagnosing, and ensuring compliance with functional safety standards in computing systems; downloadable software for integrating safety-certified computing functions with artificial intelligence decision-making in real-time applications; downloadable software for developing, deploying, and managing safety-critical artificial intelligence applications at the network edge; downloadable software for real-time monitoring and enforcement of functional safety standards in artificial intelligence computing systems; downloadable software for training, simulating, and validating autonomous vehicle systems at scale; downloadable software for providing a safety evaluation framework for autonomous driving systems from driver assistance to autonomous operation; downloadable software for building and documenting safety cases for autonomous and semi-autonomous vehicle systems; downloadable software for cloud-based development infrastructure enabling autonomous vehicle training, simulation, and validation; downloadable software for real-time artificial intelligence inference in autonomous vehicles; downloadable software incorporating tools and guidelines for evaluating functional safety of autonomous driving systems; recorded software; preinstalled software; downloadable software development kits (SDKs); downloadable software development tools; downloadable software featuring libraries for software development; downloadable artificial intelligence software; downloadable software for facilitating data transmission, data collection, data analysis, and decision-making; downloadable software for simulation, modeling and data processing for use in data visualization, data analysis, data mining, data interpretation, predictive analytics, accessing and editing large-scale data; downloadable software for use in general purpose computation, manipulation of collections of data, data analysis, image content analysis, information analysis, data transformation, data input/output, communications, speech recognition, graphics display, modeling and testing for use in the fields of artificial intelligence, deep learning, high performance computing, distributed computing, virtualization and machine learning; downloadable software for data management, analytics and pattern and activity recognition; downloadable software for artificial intelligence, machine learning, deep learning, statistical learning, supervised learning, unsupervised learning, data mining, predictive analytics, business intelligence, and computer vision; downloadable software, namely, knowledge-based artificial intelligence platforms, data analytics platforms, and automation platforms; downloadable software for deploying, distributing, configuring, testing, installing, upgrading, updating, customizing, debugging, and managing other software; downloadable software for data and application migration; downloadable electronic data files featuring source code, text, and images; downloadable electronic publications; downloadable computer software using machine learning for creating data models; downloadable machine learning software for enabling computers to learn to perform tasks autonomously; downloadable computer software using machine learning for developing predictive models; downloadable and recorded computer programs for monitoring the performance of computer systems; downloadable operating system software for robots; downloadable software for machine learning for use in robots; downloadable application programming interfaces (APIs); downloadable computer software libraries; downloadable data sets for generating, processing, understanding and analyzing natural language, speech, text, images, video, audio, software code and structured and unstructured data, namely, data sets for training, testing, validating, evaluating, and monitoring artificial intelligence models for autonomous vehicles, robots, autonomous machines, and safety-critical physical AI systems, including generative artificial intelligence models, multimodal models, computer vision models, speech and audio models, recommendation models, embedding models and other machine learning and foundation models; downloadable data sets for generating and executing autonomous tasks in response to exposure to data using machine learning and artificial intelligence for training, testing, validating, evaluating, and monitoring artificial intelligence models for autonomous vehicles, robots, autonomous machines, and safety-critical physical AI systems; downloadable data set for the collection, transmission, management, analyzing, reviewing and displaying of data, text, images, audio, video and multimedia content for training, testing, validating, evaluating, and monitoring artificial intelligence models for autonomous vehicles, robots, autonomous machines, and safety-critical physical AI systems; downloadable data sets for developing, training, fine-tuning, testing, evaluating, deploying and monitoring artificial intelligence models, large language models (LLMs) and other machine learning and generative artificial intelligence models, including multimodal, computer vision, speech, recommendation and embedding models for autonomous vehicles, robots, autonomous machines, and safety-critical physical AI systems. Providing online non-downloadable software; providing online non-downloadable software for system safety, cybersecurity, inspections, monitoring, testing, compliance, certification, validation, abstraction, data management, workflows, inter-process communication, scheduling, error detection and correction, foundations, calibrating, controlling, training, system behavior adjustments, system isolations, critical system checks, system communications, hardware integration, artificial intelligence (AI) perception, safety logic, accessing and interpreting camera data, hypervisors, contingency planning, and system guardrails; providing online non-downloadable software for operating system safety, cybersecurity, inspections, monitoring, testing, compliance, certification, validation, abstraction, data management, workflows, inter-process communication, scheduling, error detection and correction, foundations, calibrating, controlling, training, system behavior adjustments, system isolations, critical system checks, system communications, hardware integration, artificial intelligence (AI) perception, safety logic, accessing and interpreting camera data, hypervisors, contingency planning, and system guardrails; providing online non-downloadable software for autonomous systems and machines, namely, software for providing guardrails, rules, logic, operational functionality, scheduling, monitoring, controls, communications, sensors, testing, and compliance; providing online non-downloadable software for integration, monitoring, inspection, compliance, control, scheduling, training, and behavior modification of hardware, compute platforms, high performance computers, computer processors, GPUs, machines, robots, autonomous systems and machines, and autonomous vehicles; providing online non-downloadable software comprising middleware, sensor, and abstraction layers, schedulers, communication libraries, system frameworks for use in autonomous applications; providing online non-downloadable software using artificial intelligence agents and modules for monitoring, controlling, testing, training, moving, and mobilizing autonomous machines, robots, robotaxis, and autonomous vehicles; providing online non-downloadable software for unifying real-time artificial intelligence processing, functional safety monitoring, and edge computing into an integrated safety platform; providing online non-downloadable software for implementing functional safety protocols in autonomous and semi-autonomous systems; providing online non-downloadable software for real-time artificial intelligence inference at the network edge; providing online non-downloadable software for managing and orchestrating safety-critical computing workloads; providing online non-downloadable software for monitoring, diagnosing, and ensuring compliance with functional safety standards in computing systems; providing online non-downloadable software for integrating safety-certified computing functions with artificial intelligence decision-making in real-time applications; providing online non-downloadable software for developing, deploying, and managing safety-critical artificial intelligence applications at the network edge; providing online non-downloadable software for real-time monitoring and enforcement of functional safety standards in artificial intelligence computing systems; providing online non-downloadable software for training, simulating, and validating autonomous vehicle systems at scale; providing online non-downloadable software for providing a safety evaluation framework for autonomous driving systems from driver assistance to autonomous operation; providing online non-downloadable software for building and documenting safety cases for autonomous and semi-autonomous vehicle systems; providing online non-downloadable software for cloud-based development infrastructure enabling autonomous vehicle training, simulation, and validation; providing online non-downloadable software for real-time artificial intelligence inference in autonomous vehicles; providing online non-downloadable software incorporating tools and guidelines for evaluating functional safety of autonomous driving systems; software as a service (SaaS); cloud computing services featuring software for use in artificial intelligence monitoring, compliance, safety, training, control, behavior modification, and assessment; providing online non-downloadable software development kits (SDKs); providing online non-downloadable software development tools; providing online non-downloadable software featuring libraries for software development; providing online non-downloadable artificial intelligence software; providing online non-downloadable software for facilitating data transmission, data collection, data analysis, and decision-making; providing online non-downloadable software for simulation, modeling and data processing for use in data visualization, data analysis, data mining, data interpretation, predictive analytics, accessing and editing large-scale data, interactive visual computing, design of information graphics, and maximizing graphics processing and performance; providing online non-downloadable software for use in general purpose computation, manipulation of collections of data, data analysis, image content analysis, information analysis, data transformation, data input/output, communications, speech recognition, graphics display, modeling and testing for use in the fields of artificial intelligence, deep learning, high performance computing, distributed computing, virtualization and machine learning; providing online non-downloadable software for data management, analytics and pattern and activity recognition; providing online non-downloadable software for artificial intelligence, machine learning, deep learning, natural language generation, statistical learning, supervised learning, unsupervised learning, data mining, predictive analytics, business intelligence, and computer vision; providing online non-downloadable software, namely, knowledge-based artificial intelligence platforms, data analytics platforms, and automation platforms; providing online non-downloadable software for enhancing computer performance and for operation of integrated circuits, semiconductors, computer chipsets, micro-processors, GPUs, and dpus; providing online non-downloadable software for deploying, distributing, configuring, testing, installing, upgrading, updating, customizing, debugging, and managing other software; providing online non-downloadable software for data and application migration; computer software technical support services, technical information and technical support regarding software patches, upgrades and updates; advanced product research in the field of artificial intelligence; providing information relating to computer technology; providing information relating to computer programming; providing information relating to computer programs; providing computer hardware and software information online; platform as a service (PaaS) services; infrastructure as a service (IaaS); artificial intelligence as a service (AIaaS) services; hosting of computer platforms; artificial intelligence as a service (AIaaS) services featuring software using artificial intelligence for integrating computer models; research in the field of autonomous car technology; technology consultation in the field of artificial intelligence (AI); application service provider (ASP), namely, hosting computer software applications of others; scientific and technological services and research and design relating thereto; quality control and authentication services; providing information in the fields of technology and software development via an on-line website; providing information relating to computer technology and programming via a website.

Abrégé

Apparatuses, systems, and techniques to decode codewords encoded using linear block codes. In at least one embodiment, a codeword encoded using a linear block code is decoded by at least using one or more interleavers to generate a plurality of permutations based, at least in part, on automorphisms of the linear block code and the codeword, wherein at least one interleaver of the one or more interleavers is to be re-used multiple times as part of generating the plurality of permutations.

Classes IPC  ?

• H03M 13/27 - Codage, décodage ou conversion de code pour détecter ou corriger des erreursHypothèses de base sur la théorie du codageLimites de codageMéthodes d'évaluation de la probabilité d'erreurModèles de canauxSimulation ou test des codes utilisant des techniques d'entrelaçage
• H03M 13/37 - Méthodes ou techniques de décodage non spécifiques à un type particulier de codage prévu dans les groupes

Abrégé

Two and three dimensional video diffusion models configured via training to be equivariant to spatial transformations of the input by training the model using warped noise, where equivariance of the models is learned using a conventional video diffusion loss function modified to replace independent noise inputs with warped noise inputs.

Classes IPC  ?

• G06T 15/04 - Mappage de texture
• G06T 15/20 - Calcul de perspectives
• G06T 17/20 - Description filaire, p. ex. polygonalisation ou tessellation

Abrégé

Systems and methods for a datacenter cooling system are disclosed. In at least one embodiment, a secondary cooling loop interfaces with a primary cooling loop and includes at least one processor that is adapted with a first proportional-integral-derivative (PID) controller to control a first flow controller, which can cause a flow rate for a secondary coolant from a coolant distribution unit (CDU) to a plurality of second flow controllers, and where the plurality of second flow controllers are associated with a plurality of second PID controllers and the flow rate is based in part of on feedback from the plurality of second PID controllers.

Classes IPC  ?

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

Abrégé

A system for manufacturing an object includes a non-transitory storage device and a processor coupled to the non-transitory storage device. The processor is configured to determine a value of an attribute of a first component of the object; determine, using a machine learning model and based on the value, a manufacturing parameter for performing a manufacturing operation with respect to a second component that is functionally interrelated with the first component; and provide instructions for manufacturing the object by performing the manufacturing operation according to the manufacturing parameter to generate the second component.

Classes IPC  ?

• G05B 13/02 - Systèmes de commande adaptatifs, c.-à-d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques
• G06F 1/20 - Moyens de refroidissement

Abrégé

In various examples, systems and method are provided for generation of ground truth gaze data for training in-cabin monitoring systems and applications. A gaze target projector mounted to a known position inside a cabin may be used to project a gaze target onto an interior surface of the cabin. Because a beam of light may be used to produce the projected gaze target, the projected gaze target may be displayed at a projection point on the surface of the cabin interior, even if the surface at the projection point is curved, small, or an irregular shape. Three-dimensional coordinates of a projected gaze target in the cabin coordinate system may be determined and used to label image data that is captured as a projected gaze target is selectively projected onto an interior surface of the cabin and a test occupant's gaze is directed at the projected gaze target.

Classes IPC  ?

• G06V 20/59 - Contexte ou environnement de l’image à l’intérieur d’un véhicule, p. ex. concernant l’occupation des sièges, l’état du conducteur ou les conditions de l’éclairage intérieur
• B60W 40/08 - 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 conducteurs ou aux passagers
• G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur

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

Abrégé

Apparatuses, systems, and techniques to train neural networks to perform image processing tasks. In at least one embodiment, one or more second neural networks are used to train one or more first neural networks based, at least in part, on a first object type in one or more images and a second object type in the one or more images, in parallel.

Classes IPC  ?

• G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
• G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
• G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/08 - Méthodes d'apprentissage
• G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos

Abrégé

A device includes memory to store a first session key and a second session key different from the first session key. A transmitter (TX) physical layer is to transmit frames to a second device over a link and a receiver (RX) physical layer is to receive frames from the second device over the link. A TX datalink logic is coupled to the TX physical layer and the memory, the TX datalink logic to cause encryption of frame data transmitted to the second device using the first session key. A RX datalink logic is coupled to the RX physical layer and the memory, the RX datalink logic to cause decryption of frame data received from the second device using the second session key.

Classes IPC  ?

• H04L 9/12 - Dispositifs de chiffrement d'émission et de réception synchronisés ou initialisés d'une manière particulière

Abrégé

In various examples, metadata-based image harmonization for image stitching systems and applications are disclosed. Systems and methods are disclosed that preprocess images with respect to rendering parameters, with the effect of blending those parameters at a border between images to facilitate a smooth rendering when those images are stitched together. An image signal processing (ISP) parameter harmonization function may input metadata parameters associated with a set of images to match and blend one or more of the rendering parameters across an overlapping border between images prior to applying those images to a stitching algorithm. A scaling of the metadata parameter may be performed using a parameter gain function. Pixels in both images located along the border are adjusted to the same boundary metadata parameter value, and smoothed based on the parameter gain function. A discontinuity in rendering parameters is avoided, substantially avoiding corresponding artifacts in the resulting stitched image.

Classes IPC  ?

• G06V 10/10 - Acquisition d’images
• G06T 3/4015 - Démosaïquage d’images, p. ex. matrices de filtres colorés [CFA] ou matrices de Bayer
• G06T 5/70 - DébruitageLissage
• G06T 5/80 - Correction géométrique
• G06T 5/92 - Modification de la plage dynamique d'images ou de parties d'images basée sur les propriétés globales des images
• G06V 10/60 - Extraction de caractéristiques d’images ou de vidéos relative aux propriétés luminescentes, p. ex. utilisant un modèle de réflectance ou d’éclairage

Abrégé

This specification describes techniques for implementing matrix multiply and add (MMA) operations in graphics processing units (GPU)s and other processors. The implementations provide for a plurality of warps of threads to collaborate in generating the result matrix by enabling each thread to share its respective register files to be accessed by the datapaths associated with other threads in the group of warps. A state machine circuit controls a MMA execution among the warps executing on asynchronous computation units. A group MMA (GMMA) instruction provides for a descriptor to be provided as parameter where the descriptor may include information regarding size and formats of input data to be loaded into shared memory and/or the datapath.

Classes IPC  ?

• G06F 9/30 - Dispositions pour exécuter des instructions machines, p. ex. décodage d'instructions
• G06F 7/544 - Méthodes ou dispositions pour effectuer des calculs en utilisant exclusivement une représentation numérique codée, p. ex. en utilisant une représentation binaire, ternaire, décimale utilisant des dispositifs n'établissant pas de contact, p. ex. tube, dispositif à l'état solideMéthodes ou dispositions pour effectuer des calculs en utilisant exclusivement une représentation numérique codée, p. ex. en utilisant une représentation binaire, ternaire, décimale utilisant des dispositifs non spécifiés pour l'évaluation de fonctions par calcul
• G06F 9/38 - Exécution simultanée d'instructions, p. ex. pipeline ou lecture en mémoire
• G06F 17/16 - Calcul de matrice ou de vecteur

Abrégé

Approaches presented herein provide systems and methods for generating three-dimensional (3D) objects using compressed data as an input. One or more models may learn from a hash table of latent features to map different features to a reconstruction domain, using a hash function as part of a learned process. A 3D shape for an object may be encoded to a multi-layered grid and represented by a series of embeddings, where given point within the grid May be interpolated based on the embeddings for a given layer of the multi-layered grid. A decoder may then be trained to use the embeddings to generate an output object.

Classes IPC  ?

• G06T 17/20 - Description filaire, p. ex. polygonalisation ou tessellation
• G06T 3/4007 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement basé sur l’interpolation, p. ex. interpolation bilinéaire

Abrégé

In various examples, a technique for generating speed change decisions for a mobile robot includes identifying, using one or more maps of a physical environment, one or more obstacles associated with one or more portions of a path of the mobile robot in the physical environment. The technique also includes generating, based at least on the one or more obstacles, one or more speed constraints, each speed constraint specifying a speed limit for a respective portion of the path. The technique further includes generating one or more speed change decisions specifying actions to be performed by the mobile robot to cause a speed profile of the mobile robot to satisfy the one or more speed constraints.

Classes IPC  ?

• B25J 9/16 - Commandes à programme
• B25J 13/08 - Commandes pour manipulateurs au moyens de dispositifs sensibles, p. ex. à la vue ou au toucher

Abrégé

Technology related to calibrating synchronization of a data signal to a forwarded clock signal is described. A circuit includes a receiver circuit and a clock-to-data synchronization circuit. The receiver circuit receives a data signal and includes two inverters and two resistors. The clock-to-data synchronization circuit receives the data signal and the clock signal and determines an indication of synchronization between the data and clock signals. The clock-to-data synchronization circuit adjusts, using the indication, a parameter of the receiver circuit to synchronize the data signal and the clock signal.

Classes IPC  ?

• G06F 1/12 - Synchronisation des différents signaux d'horloge
• G06F 1/10 - Répartition des signaux d'horloge
• G06F 13/16 - Gestion de demandes d'interconnexion ou de transfert pour l'accès au bus de mémoire
• G06F 13/42 - Protocole de transfert pour bus, p. ex. liaisonSynchronisation

Abrégé

The disclosed method for generating a first depth map for a first frame of a video includes performing one or more operations to generate a first intermediate depth map based on the first frame and a second frame preceding the first frame within the video, performing one or more operations to generate a second intermediate depth map based on the first frame, and performing one or more operations to combine the first intermediate depth map and the second intermediate depth map to generate the first depth map.

Classes IPC  ?

• G06T 7/50 - Récupération de la profondeur ou de la forme
• G06T 7/20 - Analyse du mouvement
• 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

Abrégé

Apparatuses, systems, and techniques are presented to reconstruct one or more images. In at least one embodiment, one or more objects in an image are caused to be generated based, at least in part, on applying one or more offsets to a motion of the one or more objects relative to one or more prior images.

Classes IPC  ?

• G06T 3/4007 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement basé sur l’interpolation, p. ex. interpolation bilinéaire
• G06T 3/40 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement
• G06T 7/20 - Analyse du mouvement

Abrégé

In various examples, Artificial Intelligence (AI)-based systems and methods may be used to autonomously manage the movement of items in a variety of environments, such as a retail space. For instance, the systems and methods of the present disclosure may (e.g., using one or more AI models) continuously update a virtual representation (e.g., digital twin) of an environment based on images depicting the environment. The virtual representation of the environment may be used to track locations of various objects in real time. In some examples, the systems of the present disclosure may use the virtual representation of the environment to select optimal machines for moving items, select optimal paths for the machines to follow, etc. Additionally, the systems of the present disclosure may use 3D models of items in the virtual representation to determine grasps and/or control commands for manipulators of the machines to pick up the items.

Classes IPC  ?

• G05B 19/4155 - Commande numérique [CN], c.-à-d. machines fonctionnant automatiquement, en particulier machines-outils, p. ex. dans un milieu de fabrication industriel, afin d'effectuer un positionnement, un mouvement ou des actions coordonnées au moyen de données d'un programme sous forme numérique caractérisée par le déroulement du programme, c.-à-d. le déroulement d'un programme de pièce ou le déroulement d'une fonction machine, p. ex. choix d'un programme
• G06F 30/13 - Conception architecturale, p. ex. conception architecturale assistée par ordinateur [CAAO] relative à la conception de bâtiments, de ponts, de paysages, d’usines ou de routes
• G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte

Abrégé

In computer graphics, texture refers to a type of surface, including the material characteristics, that can be applied to an object in an image. In some implementations, a texture may be represented as an image that can be placed on a three-dimensional (3D) model of an object to give surface details to the 3D object. To reduce a size of textures (e.g. for storage and transmission), textures may be compressed. However, current decompression methods that execute in software shader code are costly, especially where high compression ratios are involved. The present disclosure provides for a software-assisted hardware cache for texture decompression, which can be used to access already decompressed textures thereby reducing a number of decompressions required to be performed.

Classes IPC  ?

• G06T 1/60 - Gestion de mémoire
• G06T 9/00 - Codage d'image
• G06T 15/00 - Rendu d'images tridimensionnelles [3D]
• G06T 15/04 - Mappage de texture

Abrégé

In various examples, Artificial Intelligence (AI)-based systems and methods may be used to autonomously manage intellectual property (IP)-related submissions. For example, a system(s) may obtain project-related information from one or more sources and use one or more AI models (e.g., a multimodal AI model(s)) to automatically generate a document representative of an invention submission. In some instances, the AI model(s) may identify missing details related to the invention submission and perform one or more operations to obtain those missing details. For instance, the AI model(s) may search through additional sources of information and/or initiate conversations with relevant individuals (e.g., inventors) to collect the missing information. Additionally, in some examples, the system(s) may automatically escalate certain tasks and take proactive actions, such as initiating a review or approving an invention submission responsive to learning that a public disclosure is approaching.

Classes IPC  ?

• G06Q 50/18 - Services juridiques
• H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel

Abrégé

Various examples, systems, and methods are disclosed relating to contextual filtering for vision language model (VLM) processing. A system can detect a motion between a first frame of image data and a second frame of image data. A system can determine a first similarity score between the first frame and the second frame and a second similarity score between the second frame and a context for processing of the second frame. A system can generate a combined score. A system can process the second frame using one or more neural networks responsive to determining that one or more frame selection criteria are satisfied based at least on the combined score.

Classes IPC  ?

• 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 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
• G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
• G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos
• G06V 20/20 - ScènesÉléments spécifiques à la scène dans les scènes de réalité augmentée
• G06V 20/52 - Activités de surveillance ou de suivi, p. ex. pour la reconnaissance d’objets suspects
• G06V 20/70 - Étiquetage du contenu de scène, p. ex. en tirant des représentations syntaxiques ou sémantiques

Abrégé

Apparatuses, systems, and techniques for generating a binary to be deployed to multiple processors. In at least one embodiment, a compiler is to generate a binary file comprising instructions to be performed on one or more processors, wherein the instructions included in the binary file are generated from a set of intermediate representation instructions limited to those having compatibility with a classification of processors indicated to the compiler.

Classes IPC  ?

• G06F 8/52 - Binaire à binaire

Abrégé

In various examples, techniques for neural rendering of characters using operating states are described herein. Systems and methods described herein may use various techniques with regard to neural rendering (and/or any other rendering techniques) to render smooth, stable, and/or quality interactive characters. For instance, a presentation of a character may operate in different operating states, such as an idle state where the character is not interacting, a transition state where the character is preparing to interact, and an interactive state where the character is interacting. To maintain a smooth transition between the operating states, poses associated with the character may be maintained, even when transitioning switches from recorded frames to newly generated frames. In some examples, the idle state and the transition state may use the recorded frames while the interactive state uses the newly generated frames to improve the performance of the presentation.

Classes IPC  ?

• G06T 13/40 - Animation tridimensionnelle [3D] de personnages, p. ex. d’êtres humains, d’animaux ou d’êtres virtuels
• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
• G06T 17/00 - Modélisation tridimensionnelle [3D] pour infographie
• 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

Abrégé

Bi-directional optical communication modules, cables, computing systems, and interconnect modules are provided. An example module includes a substrate supporting a first optical transmitter configured to generate a first optical signal at a first wavelength, a first optical receiver configured to receive a second optical signal at a second wavelength distinct from the first wavelength, and a band pass filter communicably coupled to the first optical transmitter, the first optical receiver, and an optical port. The band pass filter passes the first optical signal to the optical port and directs the second optical signal from the optical port into the first optical receiver, enabling simultaneous bi-directional transmission over a single optical fiber between modules coupled to host devices.

Classes IPC  ?

• H04B 10/25 - Dispositions spécifiques à la transmission par fibres
• H04B 10/40 - Émetteurs-récepteurs

Abrégé

Apparatuses, systems, and techniques including application programming interfaces (APIs), compilers, and instructions to allocate a number of registers per thread, to deallocate a number of allocated registers for a thread, and to allocate a number of registers per function. For example, a processor performs an API to indicate a number of registers to allocate to a thread when performing matrix multiplication operations on a graphics processing unit (GPU). As another example, a processor performs an API to indicate a number of registers to allocate to a function.

Classes IPC  ?

• G06F 9/38 - Exécution simultanée d'instructions, p. ex. pipeline ou lecture en mémoire
• G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
• G06F 9/30 - Dispositions pour exécuter des instructions machines, p. ex. décodage d'instructions

Abrégé

Various embodiments enable a robot, or other autonomous or semi-autonomous device or system, to receive data involving the performance of a task in the physical world. The data can be provided as input to a perception network to infer a set of percepts about the task, which can correspond to relationships between objects observed during the performance. The percepts can be provided as input to a plan generation network, which can infer a set of actions as part of a plan. Each action can correspond to one of the observed relationships. The plan can be reviewed and any corrections made, either manually or through another demonstration of the task. Once the plan is verified as correct, the plan (and any related data) can be provided as input to an execution network that can infer instructions to cause the robot, and/or another robot, to perform the task.

Classes IPC  ?

• G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
• B25J 9/16 - Commandes à programme
• G05B 13/00 - Systèmes de commande adaptatifs, c.-à-d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé
• G06N 3/08 - Méthodes d'apprentissage
• G06T 1/00 - Traitement de données d'image, d'application générale
• G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
• 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. utilisant des pilotes automatiques
• G05D 1/227 - Transfert de la commande entre la commande à distance et la commande embarquéeTransfert de la commande entre plusieurs dispositions de commande à distance

Abrégé

Apparatuses, systems, and techniques to perform an application programming interface (API) to add one or more graph nodes to a software graph, wherein the API is to cause a child graph node to be added to a software graph based, at least in part, on a dependency type indicated by the API. In at least one embodiment, one or more nodes are added to a graph in accordance to one or more dependency types.

Classes IPC  ?

• G06T 1/20 - Architectures de processeursConfiguration de processeurs p. ex. configuration en pipeline
• G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
• G06F 9/54 - Communication interprogramme

Abrégé

Apparatuses, systems, and methods are to cause an image denoising filter to identify noise within an image to be performed. In at least one embodiment, an image denoising filter identifies noise within an image noise based, at least in part, on whether a plurality of concurrent comparisons of pixel depths within the image exceed a threshold value.

Classes IPC  ?

• G06T 5/70 - DébruitageLissage
• G06T 1/20 - Architectures de processeursConfiguration de processeurs p. ex. configuration en pipeline
• G06T 5/20 - Amélioration ou restauration d'image utilisant des opérateurs locaux
• G06T 5/40 - Amélioration ou restauration d'image utilisant des techniques d'histogrammes
• G06T 7/50 - Récupération de la profondeur ou de la forme

Abrégé

Apparatuses, systems, and techniques of identifying one or more distances to one or more objects. In at least one embodiment, one or more distances to one or more objects are identified based, at least in part on one or more first optical rays of a first image depicting an objects and one or more second optical rays a second images depicting the object. In at least one embodiment, one or more machine learning processes (e.g., neural network(s)) are used to identify a correspondence ray to determine distances to one or more objects.

Classes IPC  ?

• G06T 7/593 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir d’images stéréo
• G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
• H04N 13/00 - Systèmes vidéo stéréoscopiquesSystèmes vidéo multi-vuesLeurs détails
• H04N 13/128 - Ajustement de la profondeur ou de la disparité
• H04N 13/232 - Générateurs de signaux d’images utilisant des caméras à images stéréoscopiques utilisant un seul capteur d’images 2D utilisant des lentilles du type œil de mouche, p. ex. dispositions de lentilles circulaires
• H04N 13/268 - Générateurs de signaux d’images avec conversion d’images monoscopiques en images stéréoscopiques au moyen du rendu basé sur une image de profondeur

Abrégé

An imaging system comprises a camera and one or more processors. The one or more processors are to process a plurality of channels associated with color information in a color space of an image captured by the camera to generate a second plurality of channels associated with corrected color information in a corrected color space. The one or more processors are further to determine a plurality of luminance values based at least in part on the second plurality of channels and an exposure value of the camera, and generate an updated version of the image using the plurality of luminance values.

Classes IPC  ?

• H04N 9/797 - Traitement des signaux de télévision en couleurs en combinaison avec l'enregistrement pour enregistrer le signal dans plusieurs canaux, la largeur de bande de chaque canal étant inférieure à la largeur de bande du signal
• G06T 11/10 -
• H04N 1/60 - Correction ou commande des couleurs
• H04N 23/70 - Circuits de compensation de la variation de luminosité dans la scène
• H04N 23/80 - Chaînes de traitement de la caméraLeurs composants

Abrégé

Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, ray tracing is caused to be selectively performed on one or more objects within a three-dimensional (3D) environment.

Classes IPC  ?

• G06T 15/06 - Lancer de rayon
• G06N 3/045 - Combinaisons de réseaux
• G06N 3/08 - Méthodes d'apprentissage
• G06T 15/08 - Rendu de volume
• G06T 15/50 - Effets de lumière

Abrégé

Apparatuses, systems, and techniques to facilitate selective use of one or more texture interpolation operations. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to selectively enable one or more texture interpolation operations.

Classes IPC  ?

• G06T 15/04 - Mappage de texture
• G06F 9/54 - Communication interprogramme
• G06T 1/20 - Architectures de processeursConfiguration de processeurs p. ex. configuration en pipeline
• G06T 1/60 - Gestion de mémoire

Abrégé

Disclosed are apparatuses, systems, and techniques that improve efficiency and quality of data streaming in time-sensitive network communications. The techniques include but are not limited to proactive replacement of packets in network communications that use forward error correction techniques. Proactive replacement of packets that have been lost or can potentially become lost reduces network latency and increases the number of timely communicated data messages.

Classes IPC  ?

• H04L 1/00 - Dispositions pour détecter ou empêcher les erreurs dans l'information reçue
• H04L 1/16 - Dispositions pour détecter ou empêcher les erreurs dans l'information reçue en utilisant un canal de retour dans lesquelles le canal de retour transporte des signaux de contrôle, p. ex. répétition de signaux de demande

Abrégé

In various examples, an environment surrounding an ego-object is visualized using an adaptive 3D bowl that models the environment with a shape that changes based on distance (and direction) to one or more representative point(s) on detected objects. Distance (and direction) to detected objects may be determined using 3D object detection or a top-down 2D or 3D occupancy grid, and used to adapt the shape of the adaptive 3D bowl in various ways (e.g., by sizing its ground plane to fit within the distance to the closest detected object, fitting a shape using an optimization algorithm). The adaptive 3D bowl may be enabled or disabled during each time slice (e.g., based on ego-speed), and the 3D bowl for each time slice may be used to render a visualization of the environment (e.g., a top-down projection image, a textured 3D bowl, and/or a rendered view thereof).

Classes IPC  ?

• G06T 3/4038 - Création de mosaïques d’images, p. ex. composition d’images planes à partir de sous-images planes
• B60W 30/06 - Manœuvre automatique de stationnement
• B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
• 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
• G06T 15/20 - Calcul de perspectives
• G06T 17/20 - Description filaire, p. ex. polygonalisation ou tessellation
• G06T 19/20 - Édition d'images tridimensionnelles [3D], p. ex. modification de formes ou de couleurs, alignement d'objets ou positionnements de parties
• G06V 10/10 - Acquisition d’images
• G06V 20/56 - Contexte ou environnement de l’image à l’extérieur d’un véhicule à partir de capteurs embarqués
• G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p. ex. véhicules ou piétonsReconnaissance des objets de la circulation, p. ex. signalisation routière, feux de signalisation ou routes
• H04N 5/262 - Circuits de studio, p. ex. pour mélanger, commuter, changer le caractère de l'image, pour d'autres effets spéciaux

Abrégé

Apparatuses, systems, and techniques for bounding area planning using a congestion prediction model. Placement data associated with cells of an IC design is identified. A graph based at least on the identified placement data is generated. The graph is provided as input to a machine learning model. The machine learning model is trained to predict, based at least on a given graph associated with cells according to a respective IC design, a congestion level for cells at one or more bounding areas of a respective IC design. One or more outputs of the machine learning model are obtained. The one or more outputs include congestion data indicating a congestion level for a first bounding area of the IC design. Cells are caused to be placed within at least one region, of the IC design, corresponding to the first bounding area.

Classes IPC  ?

• G06N 5/022 - Ingénierie de la connaissanceAcquisition de la connaissance

Abrégé

Apparatuses, systems, and techniques to cause information to be invalidated in a second cache location after information is stored in a first cache location. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to cause information to be invalidated in a second cache location after information is stored in a first cache location.

Classes IPC  ?

• G06F 12/0811 - Systèmes de mémoire cache multi-utilisateurs, multiprocesseurs ou multitraitement avec hiérarchies de mémoires cache multi-niveaux
• G06F 12/0804 - Adressage d’un niveau de mémoire dans lequel l’accès aux données ou aux blocs de données désirés nécessite des moyens d’adressage associatif, p. ex. mémoires cache avec mise à jour de la mémoire principale
• G06F 12/0891 - Adressage d’un niveau de mémoire dans lequel l’accès aux données ou aux blocs de données désirés nécessite des moyens d’adressage associatif, p. ex. mémoires cache utilisant des moyens d’effacement, d’invalidation ou de réinitialisation

Abrégé

Apparatuses, systems, and techniques to share portions of storage across operating system instances. In at least one embodiment, one or more APIs are performed to cause a portion of storage used by a first operation system instance to be shared with a second operation system instance using a handle identifying the portion of storage.

Classes IPC  ?

• G06F 9/54 - Communication interprogramme

Abrégé

In various examples, a conversational artificial intelligence (AI) platform uses structured data and unstructured data to generate responses to queries from users. In an example, if data for a response to a query is not stored in a structured data structured, the conversational AI platform searches for the data in an unstructured data structure.

Classes IPC  ?

• G06F 16/3329 - Formulation de requêtes en langage naturel
• G06F 16/334 - Exécution de requêtes
• G06F 16/38 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
• G10L 13/08 - Analyse de texte ou génération de paramètres pour la synthèse de la parole à partir de texte, p. ex. conversion graphème-phonème, génération de prosodie ou détermination de l'intonation ou de l'accent tonique
• G10L 15/08 - Classement ou recherche de la parole

Abrégé

Apparatuses, systems, and techniques to blend two or more images based on confidence values of objects within said two or more images. In at least one embodiment, one or more confidence values in one or more images are generated using one or more neural networks that are used, for example, to blend two or more images to be displayed.

Classes IPC  ?

• G06T 5/50 - Amélioration ou restauration d'image utilisant plusieurs images, p. ex. moyenne ou soustraction
• G06T 5/70 - DébruitageLissage

Abrégé

Embodiments of the present disclosure relate to real-time neural appearance models. Using a neural decoder, scenes are rendered in real-time with complex material appearance previously reserved for offline use. Learned hierarchical textures representing the material properties are encoded as latent codes. When a ray is cast and intersects with geometry in the scene, the intersection point is mapped to one of the latent codes. The latent code is interpreted using neural decoders, which produce reflectance values and importance-sampled directions that can be used to determine a pixel color.

Classes IPC  ?

• G06T 15/06 - Lancer de rayon
• 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
• G06T 7/40 - Analyse de la texture
• G06T 15/50 - Effets de lumière

Abrégé

In accordance with the disclosure, an inductor may be formed over a semiconductor substrate of one or both dies in a face-to-face die arrangement while reducing the parasitic capacitance between the inductor and the adjacent die. In disclosed embodiments, a semiconductor device may include a void (e.g., an air gap) between the inductor and the adjacent die to reduce the parasitic capacitance between the inductor and the adjacent die. The void may be formed in the die that includes the inductor and/or the adjacent die. In some respects, the void may be etched in interface layers (e.g., comprising bump pads and dielectric material) between the semiconductor dies, and may extend along the length of the inductor.

Classes IPC  ?

• H10W 70/60 -
• H10D 1/20 - Inducteurs
• H10W 20/20 -
• H10W 20/40 -
• H10W 70/63 -
• H10W 72/20 -
• H10W 72/90 -
• H10W 80/00 -
• H10W 90/00 -
• H10W 90/20 -
• H10W 90/26 -

Abrégé

A system is described having one or more processing devices that prepare a work queue entry (WQE), transmit the WQE or provide a doorbell indication indicating the WQE is available, and, in response to determining an execution failure has occurred, perform at least one additional step to facilitate completion of an operation associated with the WQE.

Classes IPC  ?

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

Abrégé

In various examples, a three-dimensional (3D) intersection structure may be predicted using a deep neural network (DNN) based on processing two-dimensional (2D) input data. To train the DNN to accurately predict 3D intersection structures from 2D inputs, the DNN may be trained using a first loss function that compares 3D outputs of the DNN—after conversion to 2D space—to 2D ground truth data and a second loss function that analyzes the 3D predictions of the DNN in view of one or more geometric constraints—e.g., geometric knowledge of intersections may be used to penalize predictions of the DNN that do not align with known intersection and/or road structure geometries. As such, live perception of an autonomous or semi-autonomous vehicle may be used by the DNN to detect 3D locations of intersection structures from 2D inputs.

Classes IPC  ?

• B60W 30/18 - Propulsion du véhicule
• G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
• G06N 3/08 - Méthodes d'apprentissage
• 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 10/764 - 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
• G06V 20/64 - Objets tridimensionnels

Abrégé

Embodiments of the present disclosure are directed to the proactive re-routing of network traffic upon a failure of a link or node of the network in a manner that reduces overall performance degradation. As described herein, embodiments use a host or other node of the network to detect and react to the failure instead of a switch since the host or node can do so more quickly than the switch. Once the host or node detects a failure, that host or node can cause communications between the nodes to switch from one node, such as packet spray routing, for example, to another node, such as Equal Cost Multi-Path (ECMP) routing, for example. Packets intended for the failed node can be re-routed to other nodes and the communications can be returned to the original mode.

Classes IPC  ?

• H04L 45/28 - Routage ou recherche de routes de paquets dans les réseaux de commutation de données en utilisant la reprise sur incident de routes
• H04L 45/00 - Routage ou recherche de routes de paquets dans les réseaux de commutation de données
• H04L 45/24 - Routes multiples

Abrégé

Apparatuses, systems, and techniques to identify one or more decoding techniques to be used based, at least in part, on one or more requests from one or more users. In at least one embodiment, a processor comprises processing circuitry to identify one or more decoding techniques to generate one or more responses to one or more requests based, at least in part, on one or more constraints, and cause one or more computing resources to perform the one or more decoding techniques to generate the one or more responses to the one or more requests.

Classes IPC  ?

• G06F 9/38 - Exécution simultanée d'instructions, p. ex. pipeline ou lecture en mémoire
• G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]

Abrégé

A mesh that includes a virtual object to be rendered for display by a client device is generated by execution of software that lacks access to a rendering pipeline of the client device and based at least in part on a first pose generated by a client device. The mesh is caused to be rendered according to a second pose by providing the mesh and color data to the rendering pipeline via an application programming interface call.

Classes IPC  ?

• G06T 19/00 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie
• G06F 9/54 - Communication interprogramme
• G06T 15/20 - Calcul de perspectives

Abrégé

In various examples, systems and methods are disclosed relating to managing confidential containers implementing graphics processing unit (GPU) workloads are disclosed. A system can initialize a virtual machine corresponding to a confidential computing environment. The system can generate a mapping for at least one physical component interface to the virtual machine. The system can update a configuration setting(s) of a container to be executed within the virtual machine based on the mapping and a container-device interface (CDI). The system can execute the container within the confidential computing environment of the virtual machine according to the configuration setting(s).

Classes IPC  ?

• G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation