09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
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.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
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.
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.
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.
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
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.
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
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.
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).
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
8.
APPLICATION PROGRAMMING INTERFACE TO INVALIDATE INFORMATION
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.
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
9.
APPLICATION PROGRAMMING INTERFACE TO SELECTIVELY ENABLE TEXTURE INTERPOLATION OPERATIONS
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.
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.
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.
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.
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
13.
UNCONDITIONALLY STABLE SCENE RECONSTRUCTION FOR AUGMENTED REALITY DISPLAYS USING DEPTH INFORMATION
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.
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).
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
15.
APPLICATION PROGRAMMING INTERFACE TO SHARE STORAGE
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.
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.
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.
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.
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
Disclosed are apparatuses, systems, and techniques that facilitates the efficient, automated comparison of computer graphics systems and algorithms. The techniques include comparing a first set of video artifacts and a second set of video artifacts. The sets of video artifacts may have been derived from respective videos generated by a respective computer graphics system. The techniques include determining, based on the comparing, that a difference between a first video artifact and a second video artifact satisfy a threshold criterion, indicating that the video artifacts have one or more similar characteristics. The techniques include causing a UI to play a first portion of the first video that includes the first video artifact and a second portion of the second video that includes the second video artifact.
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.
In various examples, audio-driven character animation using diffusion models is described herein. Systems and methods described herein may use a diffusion model that is trained to perform improved audio-driven character animation for real-time and/or near real-time applications. For instance, the diffusion model may be trained to encode and/or decode components of the face - such as the tongue, the eyes, the gums, and/or the like - separately when animating the character to provide realistic character animation. Additionally, such as during inference, the diffusion model may be configured to process segments of the audio during different processing stages by at least generating and storing state information that relates the processing stages. Furthermore, such as to further reduce the latency, the diffusion model may process data using a specific number of diffusion steps - such as ten or less diffusion steps -when generating denoised frames representing the animated character.
Apparatuses, systems, and techniques to enhance GPU programming through a compiler that supports a tile-based programming model. This model divides application data into tiles and maps these tiles to GPU threads, leveraging the specific architectural features of the GPU. In at least one embodiment, the compiler abstracts the complexities of GPU architecture, allowing the same code to be used across different GPU generations by adjusting the mapping process rather than modifying the application code itself. This approach ensures compatibility with new hardware without requiring low-level GPU instructions.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable software; downloadable software for managing,
routing, and sharing key-value (KV) cache data and memory
across compute, data nodes, and artificial intelligence
computing infrastructure; downloadable software for
hardware-accelerated key-value (KV) cache offloading, data
orchestration, and secure low-latency memory sharing for
infrastructure; downloadable software for operating systems
and firmware for optimizing data processing units (DPUs) and
network fabric, for hardware-encrypted cache retrieval, and
for memory management; downloadable software for offloading,
storing, and retrieving key-value (KV) cache data in
distributed computing environments; downloadable software
for extending graphic processing unit (GPU) and data
processing unit (DPU) memory capacity and reducing
recomputation in inference workloads; downloadable software
for optimizing and enabling data processing units (DPUs) and
memory tiers for managing and sharing key-value (KV) cache
across networks; downloadable software for
hardware-accelerated data integrity verification and
encryption in computing models; downloadable software
utilizing remote direct memory access (RDMA) protocols for
low-latency cache retrieval; downloadable software for
enabling memory sharing by multi-turn artificial
intelligence agents across computing pods; downloadable
software for inference workloads for converting network
flash storage into pod-level cache tiers; downloadable
software for extending effective graphics processing unit
(GPU) memory capacity, managing context windows, and
accelerating multi-turn AI agent inference; downloadable
software for building agentic key-value (KV) cache sharing
frameworks across the artificial intelligence pods;
downloadable software for extending effective graphic
processing unit (GPU) memory capacity, reducing
recomputation, and boosting tokens per second for
long-context, multi-turn agentic inference; downloadable
software for metadata management, data placement, cache
recall, cache pre-staging, tenant policy enforcement, and
data protection for artificial intelligence (AI)-native
key-value (KV) cache and context memory storage;
downloadable software development kits (SDKs); downloadable
software development tools; downloadable software featuring
libraries for software development; downloadable software
that assists computers in deploying parallel applications
and performing parallel computations; downloadable
artificial intelligence software; downloadable software for
facilitating data transmission, data collection, data
analysis, and decision-making; downloadable software for
data processing for use in data analysis, accessing and
editing large-scale data, interactive visual computing, and
maximizing graphics processing and performance; downloadable
software for data centers for use in collecting, analyzing
and reporting of computer network data; downloadable
software for the storage of electronic data; downloadable
software for managing large scale data centers, data
analysis, data storage, and storage system performance
analysis; downloadable software for enhancing computer
performance and for operation of integrated circuits,
micro-processors, GPUs, and DPUs; downloadable software for
managing computer networks; downloadable software for
deploying, distributing, configuring, and managing other
software; downloadable software for data and application
migration; downloadable electronic data files featuring
source code, text, and images; downloadable application
programming interfaces (APIs); downloadable computer
software libraries; downloadable software for orchestrating
distributed storage and retrieval of AI inference context
within agent-based systems; downloadable software for
optimizing data access patterns specific to AI-native
storage architectures; downloadable software for managing
persistent inference context as a primary data structure in
AI workflows; downloadable software using artificial
intelligence for enabling, integrating, managing,
distributing, and optimizing access to AI-native key-value
(KV) cache data within agent-based inference systems;
downloadable software for enabling, integrating, managing,
distributing, and optimizing access to AI-native key-value
(KV) cache data within agent-based inference systems;
downloadable software for storing, accessing, retrieving,
reusing, integrating, and sharing inference workload,
context, and session history, interactions, reasoning, and
agentic workflows utilizing AI-native key-value (KV) cache
management technologies; downloadable software for managing
agentic workflows in artificial intelligence (AI)
applications featuring dynamic cache management and storage
systems; downloadable application programming interfaces
(APIs) for storing, accessing, retrieving, reusing,
integrating, and sharing inference workload, context, and
session history, interactions, reasoning, and agentic
workflows utilizing AI-native key-value (KV) cache
management technologies; downloadable application
programming interfaces (APIs) for managing agentic workflows
in artificial intelligence (AI) applications featuring
dynamic cache management and storage systems; downloadable
software for data storage, database management, computer
hardware and infrastructure virtualization, networking,
collaboration, remote access, remote support, cloud
computing, data sharing, data security, access,
administration and management of computer applications and
computer hardware, and computer application distribution;
downloadable software for scalable application and data
management, and for migration of applications and data;
downloadable software for the collection, managing, editing,
organizing, modifying, transmission, and sharing of data and
information. Providing online non-downloadable software; providing online
non-downloadable software for managing, routing, and sharing
key-value (KV) cache data and memory across compute, data
nodes, and artificial intelligence computing infrastructure;
providing online non-downloadable software for
hardware-accelerated key-value (KV) cache offloading, data
orchestration, and secure low-latency memory sharing for
infrastructure; providing online non-downloadable software
for operating systems and firmware for optimizing data
processing units (DPUs) and network fabric, for
hardware-encrypted cache retrieval, and for memory
management; providing online non-downloadable software for
offloading, storing, and retrieving key-value (KV) cache
data in distributed computing environments; providing online
non-downloadable software for extending graphics processing
unit (GPU) and data processing units (DPU) memory capacity
and reducing recomputation in inference workloads; providing
online non-downloadable software for optimizing and enabling
data processing units (DPUs) and memory tiers for managing
and sharing key-value (KV) cache across networks; providing
online non-downloadable software for hardware-accelerated
data integrity verification and encryption in computing
models; providing online non-downloadable software utilizing
remote direct memory access (RDMA) protocols for low-latency
cache retrieval; providing online non-downloadable software
for enabling memory sharing by multi-turn artificial
intelligence agents across computing pods; providing online
non-downloadable software for inference workloads for
converting network flash storage into pod-level cache tiers;
providing online non-downloadable software for extending
effective graphics processing unit (GPU) memory capacity,
managing context windows, and accelerating multi-turn AI
agent inference; providing online non-downloadable software
for building agentic key-value (KV) cache sharing frameworks
across the artificial intelligence pods; providing online
non-downloadable software for extending effective graphic
processing unit (GPU) memory capacity, reducing
recomputation, and boosting tokens per second for
long-context, multi-turn agentic inference; providing online
non-downloadable software for metadata management, data
placement, cache recall, cache pre-staging, tenant policy
enforcement, and data protection for artificial intelligence
(AI)-native key-value (KV) cache and context memory storage;
software as a service (SaaS); cloud computing services
featuring software for use in artificial intelligence;
cloud-based supercomputing; 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 software that assists computers in
deploying parallel applications and performing parallel
computations; 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 data processing for use
in data analysis, accessing and editing large-scale data,
interactive visual computing, and maximizing graphics
processing and performance; providing online
non-downloadable software for data centers for use in
collecting, analyzing and reporting of computer network
data; providing online non-downloadable software for the
storage of electronic data; providing online
non-downloadable software for managing large scale data
centers, data analysis, data storage, and storage system
performance analysis; providing online non-downloadable
software for enhancing computer performance and for
operation of integrated circuits, micro-processors, GPUs,
and DPUs; providing online non-downloadable software for
managing computer networks; providing online
non-downloadable software for deploying, distributing,
configuring, and managing other software; providing online
non-downloadable software for data and application
migration; design and development of computer hardware and
software; cloud computing services; electronic storage of
data; computer software technical support services,
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;
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; software as a service (SaaS) featuring
software for managing agentic workflows in artificial
intelligence (AI) applications; providing temporary use of
non-downloadable software for enabling, integrating,
managing, distributing, and optimizing access to AI-native
key-value (KV) cache data within agent-based inference
systems; technical support services in the field of
AI-native storage infrastructure, namely, troubleshooting
performance of software-defined acceleration for storage,
networking, and security; design and development of custom
hardware and software solutions for managing persistent
inference context in AI-native data workflows; consulting
services related to the integration of agentic workflow
orchestration and AI-native storage systems; artificial
intelligence as a service (AIAAS) featuring software using
artificial intelligence for enabling, integrating, managing,
distributing, and optimizing access to AI-native key-value
(KV) cache data within agent-based inference systems;
infrastructure as a service (IAAS) being hosting software
for enabling, integrating, managing, distributing, and
optimizing access to AI-native key-value (KV) cache data
within agent-based inference systems; providing temporary
use of non-downloadable software for storing, accessing,
retrieving, reusing, integrating, and sharing inference
workload, context, and session history, interactions,
reasoning, and agentic workflows utilizing AI-native
key-value (KV) cache management technologies; software as a
service (SaaS) featuring software for managing agentic
workflows in artificial intelligence (AI) applications
featuring dynamic cache management and storage systems;
providing temporary use of non-downloadable software for use
as an application programming interfaces (APIs) for storing,
accessing, retrieving, reusing, integrating, and sharing
inference workload, context, and session history,
interactions, reasoning, and agentic workflows utilizing
AI-native key-value (KV) cache management technologies;
providing temporary use of non-downloadable software for use
as an application programming interfaces (APIs) for managing
agentic workflows in artificial intelligence (AI)
applications featuring dynamic cache management and storage
systems; providing online non-downloadable software for
accessing, browsing, sharing, defining, maintaining,
virtualizing and communicating information over computer
networks and servers; providing online non-downloadable
software for data storage, database management, computer
hardware and infrastructure virtualization, networking,
collaboration, remote access, remote support, cloud
computing, data sharing, data security, access,
administration and management of computer applications and
computer hardware, and computer application distribution;
providing online non-downloadable software for scalable
application and data management, for migration of
applications and data; providing online non-downloadable
software for the collection, managing, editing, organizing,
modifying, transmission, and sharing of data and
information.
A machine learning model (MLM) may be trained and evaluated. Attribute-based performance metrics may be analyzed to identify attributes for which the MLM is performing below a threshold when each are present in a sample. A generative neural network (GNN) may be used to generate samples including compositions of the attributes, and the samples may be used to augment the data used to train the MLM. This may be repeated until one or more criteria are satisfied. In various examples, a temporal sequence of data items, such as frames of a video, may be generated which may form samples of the data set. Sets of attribute values may be determined based on one or more temporal scenarios to be represented in the data set, and one or more GNNs may be used to generate the sequence to depict information corresponding to the attribute values.
G06V 40/16 - Visages humains, p. ex. parties du visage, croquis ou expressions
G06V 10/62 - Extraction de caractéristiques d’images ou de vidéos relative à une dimension temporelle, p. ex. extraction de caractéristiques axées sur le tempsSuivi de modèle
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 10/82 - 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
25.
METHOD TO ESTIMATE PROCESSING RATE REQUIREMENT FOR SAFE AV DRIVING TO PRIORITIZE RESOURCE USAGE
An estimation model utilizes simulations of an autonomous vehicle and objects detected near the automated vehicle to develop estimates of tolerable frame processing latency to develop real world frame processing latency estimates for similar driving conditions. An estimation model can a minimum tolerable latency for processing the frames of image data of an object detection camera on an autonomous vehicle using the object state data of the objects detected near the autonomous vehicle. An autonomous vehicle system process can determine if the processing latency of a sensor is greater than the modeled tolerable latency for that sensor, then a safety check is failed and an alert is sent. An autonomous vehicle system process can determine if the processing latency of a sensor is greater than the modeled tolerable latency for that sensor, then the hardware resources are prioritized to the processing for that sensor. An autonomous vehicle system process can determine if the processing latency of a sensor is greater than the modeled tolerable latency for that sensor, hardware performance may be increased.
B60W 50/06 - Détails des systèmes d'aide à la conduite des véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier pour améliorer la réponse dynamique du système d'aide à la conduite, p. ex. pour améliorer la vitesse de régulation, ou éviter le dépassement de la consigne ou l'instabilité
B60W 50/02 - Détails des systèmes d'aide à la conduite des véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier pour préserver la sécurité en cas de défaillance du système d'aide à la conduite, p. ex. en diagnostiquant ou en palliant à un dysfonctionnement
B60W 50/035 - Mise des unités de commande dans un état prédéterminé, p. ex. en donnant la priorité à des éléments d'actionnement particuliers
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
In various examples, systems and methods are disclosed that relate to programming multi-dimensional single instruction, multiple data (SIMD) processors (also referred to as an accelerator). In one example, a processor can obtain instructions to be performed by the accelerator. The processor can determine one or more operations to be performed by the accelerator based at least on the instructions and generate a set of accelerator instructions. In examples, the processor can then provide data associated with the accelerator instructions to cause the accelerator to perform at least a portion of the one or more operations.
G06F 15/80 - Architectures de calculateurs universels à programmes enregistrés comprenant un ensemble d'unités de traitement à commande commune, p. ex. plusieurs processeurs de données à instruction unique
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
Systems and methods disclosed herein are for a protected connector having protruding pins and a compliant mechanical trigger. The compliant mechanical trigger may include a fixed section and a compliant section which is around the fixed section. The compliant section may protect the pins. The compliant mechanical trigger may align with and couple to a receiving element of a circuit board. The compliant section may be moved relative to the fixed section by at least the receiving element. The movement may expose and allow the pins to couple to a protected receiving connector having the receiving element and to a surface contact pad of the circuit board.
H01R 12/73 - Dispositifs de couplage pour circuits imprimés rigides ou structures similaires se couplant avec la bordure des circuits imprimés rigides ou des structures similaires se raccordant à d'autres circuits imprimés rigides ou à des structures similaires
The present disclosure is directed towards heating wire configurations for imaging systems. The heating wire configurations can include a periodic sequence of quarter-circle arcs. The radius of the quarter-circle arcs can be determined at least partially using a coherence length of an optical wavelength of a light emitted by a light source. The light can be interacting with the quarter-circle arcs.
H05B 3/86 - Dispositions pour le chauffage spécialement adaptées à des surfaces transparentes ou réfléchissantes, p. ex. pour désembuer ou dégivrer des fenêtres, des miroirs ou des pare-brise de véhicules les conducteurs chauffants étant noyés dans le matériau transparent ou réfléchissant
H05B 3/26 - Éléments chauffants ayant une surface s'étendant essentiellement dans deux dimensions, p. ex. plaques chauffantes non flexibles le conducteur chauffant monté sur une base isolante
Systems and methods disclosed herein are for a connector having a lead section and a barrel section. The barrel section may be formed from a portion of the lead section and may include a pin and a spring. The pin may extend from or retract into the barrel section based in part on an interface with the spring. The pin may contact a surface contact pad on a circuit board to pass signals from the circuit board through the connector.
H01R 12/73 - Dispositifs de couplage pour circuits imprimés rigides ou structures similaires se couplant avec la bordure des circuits imprimés rigides ou des structures similaires se raccordant à d'autres circuits imprimés rigides ou à des structures similaires
H01R 13/03 - Contacts caractérisés par le matériau, p. ex. matériaux de plaquage ou de revêtement
H01R 43/26 - Appareils ou procédés spécialement adaptés à la fabrication, l'assemblage, l'entretien ou la réparation de connecteurs de lignes ou de collecteurs de courant ou pour relier les conducteurs électriques pour engager ou séparer les deux pièces d'un dispositif de couplage
Disclosed are systems and techniques for efficient communication of broadcast node records. The techniques include generating one or more record to be consumed by one or more threads. The one or more records are associated with a queue task descriptor. The techniques further include storing the one or more records in a records queue. The techniques further include causing the one or more threads to execute based on the queue task descriptor. A first queue task descriptor index is provided to a first thread of the one or more threads. The first queue task descriptor index corresponds to a first record of the one or more records.
Apparatuses, systems, and techniques for texture synthesis from small input textures in images using convolutional neural networks. In at least one embodiment, one or more convolutional layers are used in conjunction with one or more transposed convolution operations to generate a large textured output image from a small input textured image while preserving global features and texture, according to various novel techniques described herein.
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/54 - Extraction de caractéristiques d’images ou de vidéos relative à la texture
G06V 10/776 - ValidationÉvaluation des performances
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
32.
NATURAL LANGUAGE PROCESSING APPLICATIONS USING MULTIPLE ENCODER AND JOINT DECODER ARCHITECTURES
Approaches presented herein may be used to generate a text output of an input speech signal using an encoder from a plurality of encoders and a joint decoder associated with the plurality of encoders. The encoder may be selected based at least on a label associated with the input speech signal identifying the encoder from the plurality of encoders.
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.
Apparatuses, systems, and techniques are presented to generate image or video content. In at least one embodiment, one or more neural networks are used to generate one or more time-lapsed images of a second object based, at least in part, on one or more images of a first object.
G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
G06F 18/2413 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur les distances des motifs d'entraînement ou de référence
G06N 3/047 - Réseaux probabilistes ou stochastiques
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
G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient
G06N 3/088 - Apprentissage non supervisé, p. ex. apprentissage compétitif
G06T 5/50 - Amélioration ou restauration d'image utilisant plusieurs images, p. ex. moyenne ou soustraction
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 10/764 - 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
In various examples, determining point bounding shapes for systems and applications is described herein. Systems and methods are disclosed that determine a bounding shape (e.g., a minimum-area bounding rectangle) for an object and/or a point set associated with the object. In some examples, to determine the bounding shape, the systems and methods may rotate points from the point set to include various orientations. The systems and methods may then determine a respective bounding shape for one or more (e.g., each) orientation of the points and use the bounding shapes to determine a final bounding shape for the points. For example, the bounding shape that is associated with the smallest area may be selected for the points. The bounding shape may then be rotated by an angle in order to determine a final bounding shape for the object.
Systems and methods in accordance with the present disclosure can implement a parallel processing system, such as a graphics processing unit (GPU)-based system, to generate solutions to complex computational problems. Aspects of this technical solution can retrieve a plurality of solutions each representing a plurality of values in a multi-dimensional space, allocate, to one or more processing units associated with the one or more circuits and having a parallelized configuration, one or more of the plurality of solutions, modify, by the one or more processing units according to the parallelized configuration, at least one value of the one or more solutions allocated to the one or more processing units, to determine a plurality of modified solutions, and output, from the plurality of modified solutions, according to one or more criteria indicating a diversity among the plurality of modified solutions, a selected solution.
Apparatuses, systems, and techniques to train a machine-learned model. In at least one embodiment, a plurality of training clients each obtain an exclusive right to update a model in turn, and each client trains said model with training data not accessible to other training clients.
Systems and methods disclosed herein are for a connector having a ground cage receptacle which may be around at least a barrel section of the connector. The barrel section may include a pin and a spring to transmit a data signal. The ground cage receptacle may couple to a receiving cage receptacle of a circuit board to provide a ground reference for the data signal. The pin may extend from or retract into the barrel section based in part at least one stop feature associated with a receiving cage receptacle. The stop feature may hold the ground cage receptacle at a predetermined position with respect to the receiving cage receptacle. The stop feature may allow the pin to contact a signal surface contact pad on the circuit board for transmission of the data signal to the circuit board.
H01R 13/6594 - Caractéristiques ou dispositions spécifiques de raccordement du blindage aux organes conducteurs le blindage étant monté sur une carte de circuits imprimés et raccordé à des organes conducteurs
H01R 12/71 - Dispositifs de couplage pour circuits imprimés rigides ou structures similaires
39.
POSE DETERMINATION USING ONE OR MORE NEURAL NETWORKS
Apparatuses, systems, and techniques are presented to determine a pose of an object. In at least one embodiment, a network is trained to predict a pose of an autonomous object based, at least in part, on only one image of the autonomous object.
Apparatuses, systems, and techniques to transform data sets, such as matrices representing layers of neural networks, to increase sparsity and/or other characteristics of said data sets to improve performance in computations, such as neural network computations. In at least one embodiment, one or more subsets of data in one or more sets of data are rearranged as part of a process to increase sparsity in said one or more sets of data to satisfy one or more one or more structural sparsity constraints.
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
In various examples, an environment visualization pipeline may determine whether to generate or otherwise enable a visualization using an environmental modeling pipeline that models an environment as a 3D bowl or using an environmental modeling pipeline that models the environment using some other 3D representation, such as a detected 3D surface topology. The determination may made based on various factors, such as ego-machine state, (e.g., one or more detected features indicative of a designated operational scenario, proximity to a detected object, speed of ego-machine, etc.), estimated image quality of a corresponding environment visualization, and/or other factors. Accordingly, an environment around an ego-machine, such as a vehicle, robot, and/or other type of object, may be visualized in systems such as parking visualization systems, Surround View Systems, and/or others.
B60W 50/14 - Moyens d'information du conducteur, pour l'avertir ou provoquer son intervention
G06T 7/50 - Récupération de la profondeur ou de la forme
G06T 17/20 - Description filaire, p. ex. polygonalisation ou tessellation
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 10/98 - Détection ou correction d’erreurs, p. ex. en effectuant une deuxième exploration du motif ou par intervention humaineÉvaluation de la qualité des motifs acquis
G06V 20/56 - Contexte ou environnement de l’image à l’extérieur d’un véhicule à partir de capteurs embarqués
The disclosed method for eye tracking includes processing an image using a trained machine learning model that detects a face within the image and predicts an identity of the face, determining at least one of an eye gaze or a point of regard based on the face and the identity of the face, and performing, via a computing device, at least one action based on the at least one of the eye gaze or the point of regard.
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
A63F 13/213 - Dispositions d'entrée pour les dispositifs de jeu vidéo caractérisées par leurs capteurs, leurs finalités ou leurs types comprenant des moyens de photo-détection, p. ex. des caméras, des photodiodes ou des cellules infrarouges
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06V 10/24 - Alignement, centrage, détection de l’orientation ou correction de l’image
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
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 40/16 - Visages humains, p. ex. parties du visage, croquis ou expressions
An autonomous driving system could create or exacerbate a hazardous driving situation due to incorrect machine learning, algorithm design, sensor limitations, environmental conditions or other factors. This technology presents solutions that use machine learning to detect when the autonomous driving system is in this state e.g., erratic or reckless driving and other behavior, in order to take remedial action to prevent a hazard such as a collision.
B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
B60W 50/08 - Interaction entre le conducteur et le système d'aide à la conduite
G06F 18/2413 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur les distances des motifs d'entraînement ou de référence
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
44.
LOCALIZATION OF VERTEX ATTRIBUTES, CONSTANTS AND LOCAL MEMORY IN A MULTI-CHIP GPU
Methods and systems are described for localizing writes of certain graphics pipeline attributes, localizing certain page pool and root table version buffers, and/or localizing of local memory in order to improve the latency of write operations and/or to reduce the inter-chip traffic in a multi-chip graphics processing unit (GPU) that uses a common unified memory. The described localizing may be implemented over a coarse-grained interleaving of the virtual memory on the locally connected memories of each GPU in the multi-chip GPU.
In various examples, systems and methods are disclosed relating to generating animatable characters or avatars. The system can assign a plurality of first elements of a three-dimensional (3D) model of a subject to a plurality of locations on a surface of the subject in an initial pose. Further, the system can assign a plurality of second elements to the plurality of first elements, each second element of the plurality of second elements having an opacity corresponding to a distance between the second element and the surface of the subject. Further, the system can update the plurality of second elements based at least on a target pose for the subject and one or more attributes of the subject to determine a plurality of updated second elements. Further, the system can render a representation of the subject based at least on the plurality of updated second elements.
Methods and systems are described for localizing render targets in a multi-chip graphics processing unit (GPU) thereby reducing the inter-chip traffic and improving the latency of certain rendering. The localization techniques provide for storing surfaces of different bit densities on the same locally connected memory, and enabling access to the different surfaces in a similar manner. The described localizing may be implemented over an interleaving of the virtual memory on the locally connected memories of each die in the multi-chip GPU.
Apparatuses, systems, and techniques to transform addresses of information in storage. In at least one embodiment, a compiler identifies and transforms a portion of a dynamic address in an intermediate representation during compiling in order to optimize software performance at runtime.
Hardware-accelerated fused mixed precision floating point instructions include built-in precision conversion capabilities to eliminate overhead associated with switching between precisions. Such mixed precision operations that can be used to accelerate probability distribution function operations like SoftMax and Flash Attention, and/or for accelerated conversion to MX microscaling formats in other contexts. Using the new mixed precision instructions reduces the instruction count in half in example SoftMax/Flash Attention implementations.
G06F 7/483 - Calculs avec des nombres représentés par une combinaison non linéaire de nombres codés, p. ex. nombres rationnels, système de numération logarithmique ou nombres à virgule flottante
G06F 9/30 - Dispositions pour exécuter des instructions machines, p. ex. décodage d'instructions
G06N 3/06 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone
G06F 7/48 - 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
G06F 7/556 - 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 de fonctions logarithmiques ou exponentielles
G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
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
G06N 5/04 - Modèles d’inférence ou de raisonnement
Apparatuses, systems, and methods to perform an application programming interface (API) to indicate one or more graph code portions to be re-performed as a result of one or more graph code conditions being evaluated. In at least one embodiment, a GPU performs this evaluation without additional processing by a central processing unit (CPU).
Apparatuses, systems, and techniques to identify performance anomalies in cloud-based applications. In at least one embodiment, one or more natural language processing (NLP) neural networks are used to identify one or more performance anomalies associated with one or more cloud-based applications based, at least in part, on textual feedback from one or more users of the one or more cloud-based applications.
Various examples, systems, and methods relate to facilitating memory use by a hardware accelerator. A system can check whether an on-chip memory region has already been set up for a given use. If it has, the system can reuse it. If not, the system can allocate a new region and connect it to a larger off-chip memory. The system can determine whether to load data into on-chip memory or save data back to off-chip memory based on various rules.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable software; recorded software; preinstalled software; computer software; downloadable software development kits (SDKs); downloadable software development tools; downloadable software featuring libraries for software development; downloadable software for parallel computing; downloadable compiler software; downloadable software for developing software applications on graphics processing units (GPUs); downloadable software that assists computers in deploying parallel applications and performing parallel computations; downloadable software for use as an application programming interface (API) for use in building software applications; 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, interactive visual computing, design of information graphics, and maximizing graphics processing and performance; 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, 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, natural language generation, statistical learning, supervised learning, unsupervised learning, data mining, predictive analytics, business intelligence, and computer vision; downloadable software for the storage of electronic data; downloadable software for managing large scale data centers, data analysis, data storage, and storage system performance analysis; downloadable software, namely, knowledge-based artificial intelligence platforms, data analytics platforms, and automation platforms; downloadable software for enhancing computer performance and for operation of integrated circuits, semiconductors, computer chipsets, micro-processors, GPUs, and data processing units (DPUs); 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 software using artificial intelligence (AI) for simulating natural conversation; Downloadable chatbot software using large language models (LLMs); Downloadable software using large language models (LLMs) for computer software code generation; Downloadable software using large language models (LLMs) for language translation; Downloadable software for programming physical movements for use in robots; Downloadable software using artificial intelligence (AI) for speech recognition for use in robots; 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, 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; downloadable data set for the collection, transmission, management, analyzing, reviewing and displaying of data, text, images, audio, video and multimedia content; 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; Downloadable and recorded computer software for building, deploying, and managing autonomous software agents; downloadable and recorded computer software for executing long-running automated processes and agent-based workflows; downloadable and recorded computer software for orchestrating autonomous artificial intelligence agents in computing environments; downloadable and recorded computer software providing a secure execution environment for autonomous agents; downloadable and recorded software for monitoring and controlling the lifecycle of automated software agents Providing online non-downloadable software; software as a service (SaaS); cloud computing services featuring software for use in artificial intelligence; cloud-based supercomputing; providing online non-downloadable software development kits (SDKs); providing online non-downloadable software development tools; providing online non-downloadable software for parallel computing; providing online non-downloadable compiler software; providing online non-downloadable software for developing software applications on graphics processing units (GPUs); providing online non-downloadable software featuring libraries for software development; providing online non-downloadable software that assists computers in deploying parallel applications and performing parallel computations; providing online non-downloadable software for use as an application programming interface (API) for use in building software applications; 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, 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 for the storage of electronic data; providing online non-downloadable software for managing large scale data centers, data analysis, data storage, and storage system performance analysis; 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 data processing units (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; design and development of computer hardware and software; cloud computing services; electronic storage of data; 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; Writing of computer software for others; 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; Artificial intelligence as a service (AIAAS) featuring software using artificial intelligence for writing custom algorithms and implementing algorithms into dataflows; Infrastructure as a service (IAAS) being hosting software for operating virtual servers for use by others; Hosting of computer platforms; Design and development of artificial intelligence (AI) software on an outsourcing basis; Artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence for creating and integrating computer models; Providing temporary use of online non-downloadable chatbot software using large language models (LLMs); Technology consultation in the field of artificial intelligence (AI); Application service provider (ASP), namely, hosting computer software applications of others; 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; Providing temporary use of non-downloadable computer software for building, deploying, and managing autonomous software agents; providing temporary use of non-downloadable computer software for executing long-running automated processes and agent-based workflows; providing temporary use of non-downloadable computer software for orchestrating autonomous artificial intelligence agents in computing environments; providing temporary use of non-downloadable computer software for providing a secure execution environment for autonomous agents; providing temporary use of non-downloadable computer software for monitoring and controlling the lifecycle of automated software agents; software as a service (SaaS) featuring software for building, deploying, and managing autonomous software agents; software as a service (SaaS) featuring software for executing long-running automated processes and agent-based workflows; software as a service (SaaS) featuring software for orchestrating autonomous artificial intelligence agents in computing environments; software as a service (SaaS) featuring software for providing a secure execution environment for autonomous agents; software as a service (SaaS) featuring software for monitoring and controlling the lifecycle of automated software agents; artificial intelligence as a service (AIaaS) featuring software for building, deploying, and managing autonomous software agents; artificial intelligence as a service (AIaaS) featuring software for executing long-running automated processes and agent-based workflows; artificial intelligence as a service (AIaaS) featuring software for orchestrating autonomous artificial intelligence agents in computing environments; artificial intelligence as a service (AIaaS) featuring software for providing a secure execution environment for autonomous agents; artificial intelligence as a service (AIaaS) featuring software for monitoring and controlling the lifecycle of automated software agents; Agents as a Service (AaaS) featuring software for building, deploying, and managing autonomous software agents; Agents as a Service (AaaS) featuring software for executing long-running automated processes and agent-based workflows; Agents as a Service (AaaS) featuring software for orchestrating autonomous artificial intelligence agents in computing environments; Agents as a Service (AaaS) featuring software for providing a secure execution environment for autonomous agents; Agents as a Service (AaaS) featuring software for monitoring and controlling the lifecycle of automated software agents; research and development of computer software for building, deploying, and managing autonomous software agents; research and development of computer software for executing long-running automated processes and agent-based workflows; research and development of computer software for orchestrating autonomous artificial intelligence agents in computing environments; research and development of computer software for providing a secure execution environment for autonomous agents; research and development of computer software for monitoring and controlling the lifecycle of automated software agents
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Computer hardware, namely, computer storage systems for
extending GPU memory with network-attached flash storage
optimized for artificial intelligence inference workloads;
computer hardware, namely, computer hardware for
accelerating data storage and retrieval in artificial
intelligence inference systems; computer hardware, namely,
computer hardware featuring integrated processors for
Software-Defined acceleration of storage, networking, and
security functions; computer hardware, namely, hardware
accelerators for facilitating distributed key-value cache
management in artificial intelligence computing systems;
computer hardware for enabling implementation of reference
architectures for artificial intelligence (AI)-native
storage infrastructure, context memory storage, and
distributed key-value cache management for AI inference
systems; computer servers, namely, storage servers and
storage enclosures for managing context memory in artificial
intelligence computing pods; data storage devices, namely,
computer storage platforms comprising computer hardware and
software sold as a unit for storing, managing, and routing
key-value cache data for artificial intelligence inference
computing systems; data storage devices, namely, data
storage apparatus for storing and managing inference context
data in artificial intelligence computing systems;
downloadable software for managing, routing, storing,
retrieving, sharing, and transferring key-value cache data
and inference context across artificial intelligence
compute, networking, and storage systems; downloadable
software, namely, software libraries, application
programming interfaces (APIs), and software development kits
(SDKs) for implementing reference architectures for
artificial intelligence (AI)-native storage infrastructure
and context memory storage systems; downloadable software
for enabling implementation of reference architectures for
artificial intelligence (AI)-native storage infrastructure,
context memory storage, and distributed key-value cache
management for AI inference systems; computer software,
namely, device drivers for managing, routing, storing,
retrieving, sharing, and transferring key-value cache data
and inference context across artificial intelligence
compute, networking, and storage systems; downloadable
software development kits (SDKs) for managing, routing,
storing, retrieving, sharing, and transferring key-value
cache data and inference context across artificial
intelligence compute, networking, and storage systems;
downloadable electronic publications in the nature of
technical specifications, reference architecture documents,
implementation guides, and technical manuals in the field of
artificial intelligence (AI)-native storage infrastructure,
context memory storage, and management of key-value cache
data and inference context for AI inference systems;
downloadable application programming interfaces (APIs) for
managing, routing, storing, retrieving, sharing, and
transferring key-value cache data and inference context
across artificial intelligence compute, networking, and
storage systems; downloadable computer software libraries
for managing, routing, storing, retrieving, sharing, and
transferring key-value cache data and inference context
across artificial intelligence compute, networking, and
storage systems; computer hardware for accelerating
artificial intelligence (AI) inference workflows; computer
hardware featuring integrated processors for
Software-Defined acceleration of storage, networking, and
security functions; hardware accelerators for facilitating
distributed ai-native key-value (kv) cache management;
hardware and downloadable software sold as a unit for
managing agentic workflows and treating inference context as
a persistent data type. Providing temporary use of online non-downloadable software
for managing, routing, storing, retrieving, sharing, and
transferring key-value cache data and inference context
across artificial intelligence compute, networking, and
storage systems; providing online non-downloadable software
for the storage of electronic data; providing online
non-downloadable software for managing large scale data
storage and storage system performance analysis; electronic
storage of data; computer software technical support
services, technical information and technical support
regarding software patches, upgrades and updates; providing
information relating to computer technology; providing
information relating to computer technology, namely,
providing technical information, consulting, design, and
development services relating to reference architectures for
artificial intelligence (AI)-native storage infrastructure,
context memory storage, and distributed key-value cache
management for AI inference systems; providing information
relating to computer technology, namely, providing support
services for implementing artificial intelligence
(AI)-native storage reference architectures; providing
information relating to computer technology, namely,
troubleshooting and optimization of Software-Defined
acceleration for storage, networking, security, and
inference context management; technology consultation in the
field of artificial intelligence (AI); providing computer
hardware and software information online; 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; software as a service (SaaS) featuring software for
managing agentic workflows in artificial intelligence (AI)
applications; providing temporary use of non-downloadable
software for managing, distributing, and optimizing access
to ai-native key-value (kv) cache data within agent-based
inference systems; technical support services in the field
of ai-native storage infrastructure, namely, troubleshooting
performance of Software-Defined acceleration for storage,
networking, and security; design and development of custom
hardware and software solutions for managing persistent
inference context in ai-native data workflows; consulting
services related to the integration of agentic workflow
orchestration and ai-native storage systems.
54.
3D PERCEPTION AND MULTI-SENSOR OBJECT TRACKING FOR MONITORED ENVIRONMENTS USING MACHINE LEARNING
In various embodiments, 3D perception and multi-sensor object tracking for monitored environments using machine learning are provided. A 3D perception-based real-time location system is disclosed that tracks multiple objects within a monitored area based on 3D behavior data from multiple image sensors, producing 3D tracking solutions for detected entities moving within the environment. The system may leverage synchronized 3D behavior data computed from optical image sensors grouped into sensor pods. The system may include a machine learning-based tracking framework that includes a spatial aggregation model and a temporal tracking model. The spatial aggregation model aggregates 3D perception data across the sensor pods, while the temporal tracking model refines object associations across time. The spatial aggregation model and/or the temporal tracking model may comprise a graph neural network (GNN). In some embodiments, a machine learning-based tracking framework may comprise a state management system to augment inputs into the temporal tracking model.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/52 - Activités de surveillance ou de suivi, p. ex. pour la reconnaissance d’objets suspects
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
55.
PIN JUNCTION WAVEGUIDES FOR HANDLING HIGH OPTICAL POWER
An optical device includes a semiconductor waveguide. The semiconductor waveguide includes waveguide core, a first semiconductor region at a first side of the waveguide core, a second semiconductor region at a second side of the waveguide core opposite the first side. The optical device also includes wiring that electrically connects the first semiconductor region to the second semiconductor region.
G02F 1/025 - Dispositifs ou dispositions pour la commande de l'intensité, de la couleur, de la phase, de la polarisation ou de la direction de la lumière arrivant d'une source lumineuse indépendante, p. ex. commutation, ouverture de porte ou modulationOptique non linéaire pour la commande de l'intensité, de la phase, de la polarisation ou de la couleur basés sur des éléments à semi-conducteurs ayant des barrières de potentiel, p. ex. une jonction PN ou PIN dans une structure de guide d'ondes optique
56.
GENERALIZED USER INPUT COLLECTION FOR AGENT WORKFLOWS
An artificial intelligence (AI) agent system receives requests for user input from various workflows during execution. A user input manager identifies a registered callback handler associated with a specific user interface front-end, which corresponds to a particular interaction modality. The manager transmits the request to this front-end, which processes the input according to its modality and returns the user's input. The user input manager then routes the response to the original workflows, allowing the AI agent system to continue execution with the user's input.
An AI agent system processes multiple AI agent workflows created with different frameworks, providing standardized abstractions and formatting functions. The toolkit generates instrumentation data and performance efficiency metrics for the workflows based on execution events, and outputs these metrics in a standardized format, allowing for efficient comparison and analysis of the workflows' performance.
Apparatuses, systems, and techniques for performing statistical calculations during data transport in graphics processing units are disclosed herein. The system may include a statistical reduction circuit coupled to a data transport circuit. The statistical reduction circuit performs statistical reduction operations on data elements during transfer from tensor memory or directly from matrix multiply-accumulate arrays to register files. The statistical reduction circuit may compute running statistical values as each data element is transferred, eliminating separate computational passes. The system may include not-a-number handling circuitry that implements filtering modes for wide reduction operations. The system may support statistical operations such as determination of a maximum value, a minimum value, and absolute value variants, while processing multiple numeric formats. The integrated statistical computation capability can reduce latencies and eliminate register file bandwidth conflicts during statistical processing through concurrent data movement and statistical computation.
Processors, systems, and techniques to cause a compiler to partition a tile of a tile program into a plurality of sub-tiles and perform tile program operations on the sub-tiles in a determined order. In at least one embodiment a compiler is to partition a tile of a tile program into a plurality of sub-tiles, wherein the compiler determines a size of the sub-tiles based, at least in part, on one or more accelerator hardware characteristics. The compiler is further to generate a plurality of instruction groups to be performed on corresponding sub-tiles of the plurality of sub-tiles, wherein the instruction groups are based, at least in part, on a tile operation to be performed on the tile of the tile program, determine an order to perform individual instruction groups of the plurality of instruction groups, and generate code to cause individual instruction groups to be performed in the determined order.
Apparatuses, systems, and techniques to perform training and finetuning to increase accuracy of neural networks. In at least one embodiment, a processor performs a neural network training by generating a clone of the neural network to generate training data through minifinetuning which may then be used to train the neural network, facilitating an increase in domain-specific knowledge while also preventing degeneralization.
Apparatuses, systems, and techniques for generating a response in response to a user query using physical AI agents. In at least one embodiment, one or more processors comprise circuitry to generate a task plan from a user query, the task plan comprising workflow to generate response to the query; execute the task plan by using a neural network to select different agents to generate information to perform different operations in the task plan; and generate a response based on execution of the task plan.
A hardware mechanism at each dielet of a multi-dielet processing system is aware of engine page-table binds at all the dielets, thereby providing accurate traffic notifications to software (e.g., a unified virtual memory driver) for on-demand page-migration between system memory and GPU memory. The mechanism broadcasts binding information to access counters on each dielet so the access counters are able to correlate engines requesting memory access with bound virtual memory pages and generate corresponding informative notifications. A flexible multi-dielet counter clear capability enables software to clear access counters.
Apparatuses, systems, and techniques to cause a first tensor to be translated into a second tensor according to a tensor map without storing information about a memory transaction corresponding to the translation. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to cause a first tensor to be translated into a second tensor according to a tensor map without storing information about one or more memory transactions corresponding to the translation.
G06F 9/30 - Dispositions pour exécuter des instructions machines, p. ex. décodage d'instructions
G06F 3/06 - Entrée numérique à partir de, ou sortie numérique vers des supports d'enregistrement
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
In various examples, a path perception ensemble is used to produce a more accurate and reliable understanding of a driving surface and/or a path there through. For example, an analysis of a plurality of path perception inputs provides testability and reliability for accurate and redundant lane mapping and/or path planning in real-time or near real-time. By incorporating a plurality of separate path perception computations, a means of metricizing path perception correctness, quality, and reliability is provided by analyzing whether and how much the individual path perception signals agree or disagree. By implementing this approach—where individual path perception inputs fail in almost independent ways—a system failure is less statistically likely. In addition, with diversity and redundancy in path perception, comfortable lane keeping on high curvature roads, under severe road conditions, and/or at complex intersections, as well as autonomous negotiation of turns at intersections, may be enabled.
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 1/617 - Sécurité ou protection, p. ex. définition de zones de protection autour d’obstacles ou évitement de zones dangereuses
G05D 1/648 - Exécution d’une tâche au sein d’une zone ou d’un espace de travail, p. ex. nettoyage
In various examples, systems and methods are disclosed relating to context-aware error concealment to improve inference accuracy are disclosed. A system can identify a frame of a video stream and determine that the frame comprises corrupted or lost data. The system can generate a corrected frame by applying an error concealment function selected based at least on a location of the corrupted or lost data in the frame and a region of interest in the frame.
H04N 19/895 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le pré-traitement ou le post-traitement spécialement adaptés pour la compression vidéo mettant en œuvre des procédés ou des dispositions de détection d'erreurs de transmission au niveau du décodeur combiné à la dissimulation d’erreurs
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
H04N 19/136 - Caractéristiques ou propriétés du signal vidéo entrant
H04N 19/167 - Position dans une image vidéo, p. ex. région d'intérêt [ROI]
H04N 19/172 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant une image, une trame ou un champ
H04N 19/184 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant des bits, p. ex. de flux vidéo compressé
H04N 19/42 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques caractérisés par les détails de mise en œuvre ou le matériel spécialement adapté à la compression ou à la décompression vidéo, p. ex. la mise en œuvre de logiciels spécialisés
66.
GLOBAL ILLUMINATION USING SHARED LIGHTING CONTRIBUTIONS FOR INTERACTIONS IN PATH TRACING
Disclosed approaches provide for interactions of secondary rays of light transport paths in a virtual environment to share lighting contributions when determining lighting conditions for a light transport path. Interactions may be shared based on similarities in characteristics (e.g., hit locations), which may define a region in which interactions may share lighting condition data. The region may correspond to a texel of a texture map and lighting contribution data for interactions may be accumulated to the texel spatially and/or temporally, then used to compute composite lighting contribution data that estimates radiance at an interaction. Approaches are also provided for reprojecting lighting contributions of interactions to pixels to share lighting contribution data from secondary bounces of light transport paths while avoiding potential over blurring.
A device includes receiver logic to receive frames over a link, datalink logic coupled to the receiver logic to process the frames, and control logic coupled to the receiver logic and the datalink logic. The control logic is to determine whether a first frame indicates that a first subsequent frame is a low-power frame type for which the datalink logic consumes less power than when processing a second frame type, and to cause the datalink logic to process the first subsequent frame as the low-power frame type by enabling a clock gate for at least a portion of the datalink logic used to process the low-power frame type, responsive to determining that the first frame indicates that the first subsequent frame is the low-power frame type.
Apparatuses, systems, and techniques to perform one or more APIs. In at least one embodiment, a processor is to perform an API to indicate a number of 5G-NR cells that are able to be performed concurrently by one or more processors; a processor is to perform an API to indicate whether one or more processors are able to perform a first number of 5G-NR cells concurrently; a processor comprising one or more circuits is to perform an API to indicate whether one or more resources of one or more processors are allocated to perform 5G-NR cells; and/or a processor comprises one or more circuits to perform an API to indicate one or more techniques to be used by one or more processors in performing one or more 5G-NR cells.
H04L 67/61 - Ordonnancement ou organisation du service des demandes d'application, p. ex. demandes de transmission de données d'application en utilisant l'analyse et l'optimisation des ressources réseau requises en tenant compte de la qualité de service [QoS] ou des exigences de priorité
H04L 67/63 - Ordonnancement ou organisation du service des demandes d'application, p. ex. demandes de transmission de données d'application en utilisant l'analyse et l'optimisation des ressources réseau requises en acheminant une demande de service en fonction du contenu ou du contexte de la demande
H04W 4/40 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p. ex. communication véhicule-piétons
H04W 24/02 - Dispositions pour optimiser l'état de fonctionnement
In various examples, associating object detections for sensor data processing for autonomous and semi-autonomous systems and applications is described herein. Systems and methods described herein may group object detections (e.g., echoes, etc.) that are detected using multiple sensors (e.g., ultrasonic sensors, sonar sensors, etc.) and then use the groupings to process the object detections to perform one or more tasks, such as object or feature detection. In some examples, the object detections are grouped using one or more configurations, such as an order associated with analyzing sensor data generated using the sensors and/or threshold distances associated with determining that object detections are associated with the same object. Additionally, a respective group may be generated for one or more (e.g., each) detected object such that the locations of the object surrounding the machine may be determined.
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
B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
G01C 21/00 - NavigationInstruments de navigation non prévus dans les groupes
G05D 1/246 - Dispositions pour déterminer la position ou l’orientation utilisant des cartes d’environnement, p. ex. localisation et cartographie simultanées [SLAM]
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/77 - 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
In various examples, systems and methods are disclosed for a two-dimensional (2D) dynamic bucket pre-processor that enhances the efficiency of machine learning model training on GPUs. The 2D dynamic bucket pre-processor includes a bucket stratification optimizer that configures a 2D bucket buffer for performing stratified 2D bucketing by organizing data into first-tier and second-tier sub-buckets, and a batch size optimizer to identify optimal batch sizes that fit into GPU memory. During training of a machine learning model, micro-batches comprise data served from selected sub-buckets to a model training platform. Organizing data into first and second tiers based on sequence length dimensions minimizes padding and ensures efficient GPU processing. In some embodiments, the 2D dynamic bucket pre-processor may perform 2D bucketing as a dynamic process that streams data into the 2D bucket buffer, as micro-batches of previously bucketed data are being served to execute training steps of a model training process.
A hybrid control stack may be implemented with classical and E2E signal paths with independent feature extractors or backbone networks. A classical planner in the classical signal path may be used to generate a first candidate trajectory or control action, a neural planner in the E2E signal path may be used to generate a second, and any suitable technique may be used to determine which to use (e.g., based on an applicable navigational domain). To free up resources to run both signal paths on the same SoC(s), either or both networks may be simplified or downscaled using any suitable technique. The present techniques may be used to transition from a decomposed control stack to an E2E control stack, adapt an existing decomposed control stack into a hybrid control stack, train the hybrid control stack, and/or control navigation of autonomous vehicles, semi-autonomous vehicles, robots, and/or other object or machine types.
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
72.
SYSTEMS AND METHODS FOR AUTOMATIC EVALUATION, MONITORING, AND ROOT CAUSE ANALYSIS FOR GENERATIVE ARTIFICIAL INTELLIGENCE APPLICATIONS
In various examples, systems and methods are provided for automatic evaluation, monitoring, and root cause analysis for generative AI applications. Quality metric(s) may be generated for a generative AI application based at least on output(s) of the generative AI application during evaluation. Log(s) for component(s) of the generative AI application may be generated during evaluation and comprise data flow, processing steps, and performance metric(s) associated with the generative AI application. Indication(s) of a root cause of a regression for the generative AI application may be generated using model(s) based at least on the quality metric(s) for the generative AI application and the log(s) for the component(s) of the generative AI application. Operation(s) for addressing the regression for the generative AI application may be performed based at least on the indication(s) of the root cause of the regression for the generative AI application.
Systems and methods for implementing media synchronization for cloud processing are disclosed. A system can encode a plurality of video frames to generate a plurality of encoded frames, where the video frames are associated with corresponding audio. The system can update a respective header of at least one of the plurality of encoded frames to include encoded audio generated from the corresponding audio. The system can transmit one or more network packets including the plurality of encoded frames as part of a video stream. This approach ensures that audio and video data remain synchronized by embedding the audio data within the video frames, thereby reducing the likelihood of desynchronization due to network latency or packet loss.
H04N 19/172 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant une image, une trame ou un champ
G06T 17/00 - Modélisation tridimensionnelle [3D] pour infographie
H04N 19/70 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques caractérisés par des aspects de syntaxe liés au codage vidéo, p. ex. liés aux standards de compression
In various examples, the embodiments disclosed herein describe a 3D perception-based machine learning framework for generating behavior data for detected objects (e.g., objects, persons, animals, machines, etc.) using multi-view optical image data. The framework processes multi-view optical image data from multiple camera sensors, and neural network-based spatial-temporal processing, to generate object behavior data that may be used to facilitate accurate real-time multi-target multi-camera (MTMC) object tracking across a monitored environment. The framework may comprise one or more machine learning models that input multi-view image sensor data and infer behavior data that may include, but is not limited to, 3D bounding shapes, instance features, and/or 3D Re-Identification (ReID) feature embeddings that may be used for assigning an object ID and for extending tracking of detected objects within the monitored environment. To generate an ReID feature embedding, an ReID module may aggregate features from different camera views based on visibility scores.
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
A server tray is provided that includes a sensing element and a controller circuit. The sensing element is configured to detect initiation of a power supply removal. The controller circuit is configured to initiate a controlled power down sequence for the server tray responsive to a defined eject state being detected by the sensing element.
According to an aspect of the present disclosure, a power management module configured for powering a server tray is provided. In an example embodiment, the power management module includes a housing; a busbar connector extending from a first side of the housing; power management circuitry; and a tray connector accessible via a second side of the housing. The power management circuitry includes at least one fuse component, and is in electrical communication with the busbar connector. The tray connector is configured to provide electrical power to the server tray.
In various examples, methods, systems, processors, and/or machine-readable mediums implement a conversational artificial intelligence (AI) that obtains results from other conversational AIs, and generates output based at least on those results. In at least one embodiment, an orchestrator conversational AI receives input(s), and uses one or more strategies to obtain one or more results from one or more expert conversational AIs based in part on one or more inputs received by the orchestrator conversational AI, and determines at least one response to the one or more inputs based at least on a portion of the one or more results.
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
78.
METHODS AND SYSTEMS FOR AUTOMATED SIMULATION ACTIVITY AND SYNTHETIC DATA GENERATION
Apparatuses, systems, and techniques for automated activity simulation and synthetic data generation are provided. Bounding box data for a first object in an environment is obtained. First simulation operations are performed with respect to an asset in an environment in accordance with a simulation of the asset in the environment. Contextual data representing a context of the simulation environment and historical data representing historical simulation events associated with the asset are obtained. The contextual data and the historical simulation data are provided as an input to an artificial intelligence (AI) model. An action associated with the asset in view of contextual data and the historical simulation data, and second simulation operations associated with the action are extracted from output(s) of the AI model. The second simulation operations are performed to simulate the action with respect to the asset in the environment in accordance with the simulation.
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
A63F 13/57 - Simulations de propriétés, de comportement ou de déplacement d’objets dans le jeu, p. ex. calcul de l’effort supporté par un pneu dans un jeu de course automobile
In various examples, a compiled code for execution of instructions on a host device including a graphics processing unit (GPU) and/or a central processing unit (GPU) is generated from a higher-level or intermediate representation. The code generation system can iteratively process functions of the input instructions by selecting an appropriate code generation function and selecting an appropriate variant of the code generation function. The variant of the code generation function can be selected based on a value of an attribute for a variable input to the function. A visitor configuration is used to cause the appropriate variant to be selected. By propagating values for a binding attribute of variables, statically defined code can be eliminated from the compiled output. In addition, memory allocation and layout can be optimized by propagating values for minimum, maximum, and/or divisibility attributes.
Examples of a coldplate for thermal management of electronic components are disclosed. In an example, a coldplate apparatus includes a body having a top structure and a sidewall, wherein the top structure and the sidewall form a receiving channel configured to be disposed over an electronic component, wherein the sidewall of the body includes a first opening, a second opening, and a sidewall conduit, wherein the sidewall conduit is configured to allow liquid to flow between the first opening and the second opening, and wherein the top structure includes a plurality of topside conduits that are configured to allow liquid to flow between the first opening and the second opening.
In various examples, generative artificial intelligence (AI) models may be used to automatically generate state keys (or “identifiers”) associated with an interactive application. For instance, the systems and methods of the present disclosure may use the AI models to generate the state keys corresponding to essential features of an application. The generated state keys may be structured as key-value pairs, enabling automated application state tracking by mapping values of the essential features to their respective state identifier keys during application runtime. In some examples, the systems and methods of the present disclosure may generate the state keys in a number of ways, such as by using a pretrained AI model with intrinsic knowledge of the application, using a generic AI model to infer the essential features by analyzing a plurality of screenshots captured during application runtime and/or by processing application documentation, wikis, or manuals.
G06F 40/166 - Édition, p. ex. insertion ou suppression
A63F 13/67 - Création ou modification du contenu du jeu avant ou pendant l’exécution du programme de jeu, p. ex. au moyen d’outils spécialement adaptés au développement du jeu ou d’un éditeur de niveau intégré au jeu en s’adaptant à ou par apprentissage des actions de joueurs, p. ex. modification du niveau de compétences ou stockage de séquences de combats réussies en vue de leur réutilisation
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
In various examples, techniques for routing queries in multi-backend artificial intelligence systems is described herein. Systems and methods described herein may include a frontend that uses various routing techniques to select backends for routing queries. For instance, the frontend may use at least a keyword routing technique that routes queries to backends based at least on identifiers (e.g., names, codenames, etc.) included in the queries, a classification routing technique that routes queries to backends based at least on performing text classification (e.g., using one or more models, embedding comparisons, etc.), and/or a serialized routing technique that routes queries to backends based at least on an order (e.g., either static or dynamic). In some examples, the backends may be configured to initially respond to the frontend to indicate whether the backends accept or reject queries, such that the frontend is able to reroute queries if needed.
Apparatuses, systems, and techniques to calculate efficiency metrics associated with performing operations of a software program a first type of processor compared to a second type of processor. In at least one embodiment efficiency metrics associated with performing operations are used to determine whether the operation is to be executed by the first processor type or by the second processor type.
In various examples, a game development testing platform is presented that leverages visual language models (VLMs) to identify and analyze bugs within video gameplay. The platform processes captured gameplay video to produce keyframes, representing unique and significant frames. Keyframes may be selected based on computing embedding vectors and dissimilarity scores for individual video frames. The platform may perform keyframe chunking to produce keyframe packets for VLM evaluation. The VLM, provided with keyframe packets and context description prompts, detects bugs within the gameplay video. Additionally, audio inputs may be incorporated for comprehensive analysis. The VLM outputs a structured data object listing detected bugs, which may be consolidated into a comprehensive bug report by a Large Language Model (LLM). This report, augmented with annotated gameplay videos, facilitates efficient bug tracking and resolution and may be integrated with a game developer's bug tracking systems to streamline the debugging process.
Apparatuses, systems, and techniques to cause one or more neural networks to perform one or more prefill operations on a token of a set of tokens before the set of tokens is completely generated. In at least one embodiment, a processor comprises circuitry to cause one or more first neural networks to begin generating a set of tokens and cause one or more second neural networks to perform one or more prefill operations on a token of the set of tokens before the set of tokens is completely generated.
Apparatuses, systems, and techniques to compile computer program code to cause one or more memory accelerators to be used to access a plurality of memory locations. In at least one embodiment, computer program code is compiled to cause one or more memory accelerators to be used to access a plurality of memory locations based on, for example, an indication of a layout of the plurality of memory locations in the computer program code.
Disclosed are apparatuses, systems, and methods, for generative artificial intelligence incident response and management of IT systems. The systems and methods may, based on real time identification of an incident using event data from one or more event data resources of a plurality of event data resources, identify a plurality of entities associated with the one or more event data resources to address the incident in real time. The system may then provide a potential solution for resolving the incident to the plurality of entities on a generated communication channel. By monitoring one or more entity actions associated with the incident, the system and methods may provide an updated potential solution to address the incident in real time based on the one or more entity actions, event data from the one or more data resources, and historical incident data from the plurality of historical incidents associated with the incident.
In various examples, techniques for video metadata generation using video segmentation and data fusion are described herein. For instance, systems and methods described herein may segment a video may into video segments using one or more factors that are configured to enhance the processing of the individual video segments. For example, the video segments may be processed—such as by using one or more processing components and/or parallel processing—to generate instances of metadata associated with the video segments. As described herein, an instance of metadata may represent at least identifiers, spatio-temporal information, and/or visual information associated with objects as represented by a video segment. The systems and methods described herein may then analyze the instances of metadata to track objects between the video segments (e.g., perform object reassociation) and generate fused metadata associated with the video.
System and method for compiling software program according to efficiency metrics.The method comprises: calculating one or more efficiency metrics to indicate relative efficiency of performing one or more operations of a software program by a first type of processor compared to performing of the software program by a second type of processor; and compiling the software program according to the calculated one or more efficiency metrics.
Processors, systems, and techniques to generate schedules of operations for tile-based programs. In at least one embodiment, one or more operation schedules are generated to indicate an order in which to perform one or more operations, where the one or more operation schedules are based on one or more sequences of atom-level operations generated from one or more tile-level operations.
Apparatuses, systems, and techniques to compile computer program code to cause one or more memory accelerators to be used to access a plurality of memory locations. In at least one embodiment, computer program code is compiled to cause one or more memory accelerators to be used to access a plurality of memory locations based on, for example, an indication of a layout of the plurality of memory locations in the computer program code.
Apparatuses, systems, and techniques for performing statistical calculations during data transport in graphics processing units are disclosed herein. The system may include a statistical reduction circuit coupled to a data transport circuit. The statistical reduction circuit performs statistical reduction operations on data elements during transfer from tensor memory or directly from matrix multiply-accumulate arrays to register files. The statistical reduction circuit may compute running statistical values as each data element is transferred, eliminating separate computational passes. The system may include not-a-number handling circuitry that implements filtering modes for wide reduction operations. The system may support statistical operations such as determination of a maximum value, a minimum value, and absolute value variants, while processing multiple numeric formats. The integrated statistical computation capability can reduce latencies and eliminate register file bandwidth conflicts during statistical processing through concurrent data movement and statistical computation.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable software; recorded software; preinstalled
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development kits (SDKs); downloadable software development
tools; downloadable software featuring libraries for
software development; downloadable software for parallel
computing; downloadable compiler software; downloadable
software for developing software applications on GPUs, CPUs,
and dpus; downloadable software that assists computers in
deploying parallel applications and performing parallel
computations; downloadable software for use as an
application programming interface (API) for use in building
software applications; 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,
interactive visual computing, design of information
graphics, and maximizing graphics processing and
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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
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computer software using machine learning for creating data
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downloadable computer software using machine learning for
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computer programs for monitoring the performance of computer
systems; downloadable and recorded computer software for
optimizing and improving performance of graphics processing
unit (GPU) applications using evolutionary algorithms,
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online non-downloadable software development tools;
providing online non-downloadable software for parallel
computing; providing online non-downloadable compiler
software; providing online non-downloadable software for
developing software applications on GPUs, CPUs, and dpus;
providing online non-downloadable software featuring
libraries for software development; providing online
non-downloadable software that assists computers in
deploying parallel applications and performing parallel
computations; providing online non-downloadable software for
use as an application programming interface (API) for use in
building software applications; 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,
CPUs, 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
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use of non-downloadable computer software for optimizing and
improving performance of graphics processing unit (GPU)
applications using evolutionary algorithms, machine
learning, and automated search techniques; providing
temporary use of non-downloadable computer software for
optimizing graphics processing unit (GPU) application
performance; software as a service (SaaS) featuring software
for optimizing and improving performance of graphics
processing unit (GPU) applications using evolutionary
algorithms, machine learning, and automated search
techniques; artificial intelligence as a service (AIaaS)
services featuring software for optimizing and improving
performance of graphics processing unit (GPU) applications
using evolutionary algorithms, machine learning, and
automated search techniques.
Apparatuses, systems, and techniques to transform addresses of information in storage. In at least one embodiment, a compiler identifies and transforms a portion of a dynamic address in an intermediate representation during compiling in order to optimize software performance at runtime.
G06F 12/06 - Adressage d'un bloc physique de transfert, p. ex. par adresse de base, adressage de modules, extension de l'espace d'adresse, spécialisation de mémoire
95.
OCCLUSION DETECTION FOR AUTONOMOUS AND SEMI-AUTONOMOUS SYSTEMS AND APPLICATIONS
In various examples, occlusion detection using occupancy maps for autonomous and/or semi-autonomous systems and applications is described herein. Systems and methods are disclosed that generate a two-dimensional (2D) occupancy map—such as a 2D occupancy grid—of an environment and then use ray tracing with regard to the 2D occupancy map to identify one or more occluded areas of the environment. In some examples, the 2D occupancy map may be generated using 3D occupancy detections which are determined using the fused image data and RADAR data. Additionally, the occluded area(s) of the environment may be identified by connecting points associated with the ray tracing to generate a polygon (and/or any other shape) indicating an area of the environment that is not occluded. In some examples, the systems and methods may generate a map—such as an occlusion map—indicating the occluded area(s) of the environment.
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
G01C 21/00 - NavigationInstruments de navigation non prévus dans les groupes
G06V 10/80 - Fusion, c.-à-d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
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 20/58 - 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
96.
SENSOR FUSION FOR OBJECT DETECTION IN AUTONOMOUS AND SEMI-AUTONOMOUS SYSTEMS AND APPLICATIONS
In various examples, multi-modal fusion networks for occupancy detection in autonomous and/or semi-autonomous systems and applications is described herein. Systems and methods are disclosed that use a multi-modal fusion network to perform object detection based on image data obtained using one or more image sensors along with RADAR data obtained using one or more RADAR sensors of a machine. For instance, the image data may be processed to generate one or more first features and the RADAR data may be processed to generate one or more second features. The first feature(s) may then be combined (e.g., fused, etc.) with the second feature(s) to generate one or more fused features. Additionally, the fused feature(s) may be processed using one or more neural networks to determine information associated with objects located within an environment, such as three-dimensional (3D) occupancy detections and/or classifications associated with the 3D occupancy detections.
G06V 10/80 - Fusion, c.-à-d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
G01S 13/89 - Radar ou systèmes analogues, spécialement adaptés pour des applications spécifiques pour la cartographie ou la représentation
G06V 10/40 - Extraction de caractéristiques d’images ou de vidéos
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 20/58 - 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
97.
OCCLUSION DETECTION FOR AUTONOMOUS AND SEMI-AUTONOMOUS SYSTEMS AND APPLICATIONS
In various examples, occlusion detection using occupancy maps for autonomous and/or semi-autonomous systems and applications is described herein. Systems and methods are disclosed that generate an occupancy map—such as a two-dimensional (2D) occupancy grid and/or a 3D occupancy map—of an environment and then use ray tracing with regard to the occupancy map to identify one or more occluded areas of the environment. In some examples, the occupancy map may be generated using 3D occupancy detections that are determined using the fused image data and RADAR data. Additionally, the occluded area(s) of the environment may be identified based at least on projecting rays within the occupancy map to whether points of the occupancy map are occluded. In some examples, the systems and methods may generate a map—such as an occlusion map and/or an occlusion grid-indicating the occluded area(s) of the environment.
Apparatuses, systems, and techniques for shared memory optimization through circular memory buffers are disclosed herein. The system may include an address generation unit to generate addresses for a single matrix operation that processes data spanning non-contiguous buffers in shared memory. The address generation unit may store a first buffer address and a second buffer address and coordinate access to both buffers for the single matrix operation. The system may include boundary detection logic to determine when the single matrix operation requires data from multiple buffers. The system may support matrix operations that process multiple precision formats that require a predetermined amount of data per operation. The predetermined amount may be a non-integer multiple of a buffer storage capacity. The address generation unit can enable optimal memory bandwidth utilization by supporting full-capacity buffer loads while accommodating fractional buffer consumption requirements through circular buffer organization.
A system is provided for predicting and optimizing resource utilization in artificial intelligence (AI) agent workflows. The system trains forecasting models using offline profiling data to predict future behavior patterns and resource utilization. During runtime execution, the system generates predictive metadata for incoming requests, including estimates of expected workflow execution patterns and anticipated resource utilization. This predictive metadata is then attached to the requests and sent to an inference server, which makes resource allocation decisions based on the predictive information.
An artificial intelligence (AI) agent system controls the processing of user requests by receiving function descriptions for various AI agent workflows, instructing a reasoning model to generate an execution plan based on the request and workflows, and then causing the workflows to process the request according to the generated plan.
