09 - Appareils et instruments scientifiques et électriques
14 - Métaux précieux et leurs alliages; bijouterie; horlogerie
Produits et services
Pedometers; personal electronic devices used to track
fitness goals and statistics; wearable activity trackers;
wearable electronic devices, namely, smart-bracelets and
smart-wristbands comprised primarily of recorded software
for activity and fitness tracking; wearable activity tracker
straps; multifunctional electronic devices for measuring,
receiving, processing, transmitting, tracking, and uploading
to the Internet information including time, date, steps
taken, calories burned, distance travelled, active time,
speed, pace, hours slept, quality of sleep, silent wake
alarm, heart rate, blood oxygen levels, navigational
information, and routes, including by means of artificial
intelligence (AI); downloadable and recorded computer
software for receiving, processing, transmitting, tracking,
and displaying information relating to fitness, body fat,
body mass index, sleep activity, blood oxygen levels, and
heart rate, including by means of artificial intelligence
(AI); downloadable and recorded computer software for
providing personalized health, fitness, and recovery
recommendations based on wearable sensor data. Watches; wrist watches; sports watches; watch bands; watch
straps; watch straps made of metal, leather, plastic,
silicone, or rubber.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable software for editing, uploading, capturing,
showing, playing, streaming, transmitting, viewing,
previewing, displaying, tagging, distributing, publishing,
reproducing, linking, commenting on, finding, generating,
processing, sharing, storing, and revising videos,
electronic media, multimedia content, movies, pictures,
images, text, photos, user-generated content, audio content,
and information, and also for adding special effects to
videos, electronic media, multimedia content, movies,
pictures, images, text, photos, user-generated content, and
audio content; downloadable software using AI for the
production, display, streaming, enhancing, and editing of
video, images, music, sound, audio, speech and text;
downloadable software for facilitating and processing
multi-modal natural language, speech, text, image, video,
and sound input and queries; downloadable software for use
in the fields of artificial intelligence, machine learning,
natural language generation, statistical learning,
mathematical learning, supervised learning, and unsupervised
learning; downloadable computer software for social
networking and interacting with online communities;
downloadable software for building, managing, updating,
developing, training, evaluating, and monitoring generative
user experiences powered by machine learning, deep learning,
and artificial intelligence; downloadable software for
developing AI applications and features to enhance user
experience and ecosystem. Providing online non-downloadable software for editing,
uploading, capturing, showing, playing, streaming,
transmitting, viewing, previewing, displaying, tagging,
distributing, publishing, reproducing, linking, commenting
on, finding, generating, processing, sharing, storing, and
revising videos, electronic media, multimedia content,
movies, pictures, images, text, photos, user-generated
content, audio content, and information, and also for adding
special effects to videos, electronic media, multimedia
content, movies, pictures, images, text, photos,
user-generated content, and audio content; software as a
service (SAAS) services featuring software for editing,
uploading, capturing, showing, playing, streaming,
transmitting, viewing, previewing, displaying, tagging,
distributing, publishing, reproducing, linking, commenting
on, finding, generating, processing, sharing, storing, and
revising videos, electronic media, multimedia content,
movies, pictures, images, text, photos, user-generated
content, audio content, and information, and also for adding
special effects to videos, electronic media, multimedia
content, movies, pictures, images, text, photos,
user-generated content, and audio content; providing online
non-downloadable software using AI for the production,
display, streaming, enhancing, and editing of video, images,
music, sound, audio, speech and text; providing online
non-downloadable software for facilitating and processing
multi-modal natural language, speech, text, image, video,
music, audio, and sound input and queries; providing online
non-downloadable software for use in the fields of
artificial intelligence, machine learning, natural language
generation, statistical learning, mathematical learning,
supervised learning, and unsupervised learning; providing
information from searchable indexes and databases of
information, including text, music, sound, audio, images,
videos, software algorithms, mathematical equations,
electronic documents, and databases, by means of
non-downloadable chatbot software (term considered too vague
by the International Bureau pursuant to Rule 13 (2) (b) of
the Regulations); software as a service (saas) services for
building, managing, updating, developing, training,
evaluating, and monitoring generative user experiences
powered by machine learning, deep learning, and artificial
intelligence; providing online non-downloadable computer
software for social networking and interacting with online
communities; providing online non-downloadable software for
developing AI applications and features to enhance user
experience and ecosystem; research, design, and development
services in the field of artificial intelligence and machine
learning; research, design, and development of AI software
features to enhance user experience.
3.
ENABLING USER GESTURES AT PORTIONS OF GENERATIVE CONTENT FOR INTELLIGENT SELECTION OF GENERATIVE CONTENT BASED ON SUCH PORTIONS
Implementations set forth herein relate to an application that can receive gestures at portions of generative content so that generative content can be intelligently selected and/or supplemental generative content can be rendered without requiring another detailed prompt from the user. Each portion of generative content can be identified based on semantic relationship between terms that would comprise the portion. These portions of the generative content can be predetermined or determined in response to receiving input from the user (e.g., a gesture). A particular portion of generative content can be assigned one or more GUI features that can indicate portion(s) of the generative content is available for receiving one or more gestures. When the user directs a gesture at the particular portion of the generative content, one or more models can be utilized to select portion(s) of the generative content and/or to provide supplemental generative content for the user.
G06F 3/04842 - Sélection des objets affichés ou des éléments de texte affichés
G06F 3/0481 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p. ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comportement ou d’aspect
G06F 3/0488 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p. ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p. ex. des gestes en fonction de la pression exercée enregistrée par une tablette numérique utilisant un écran tactile ou une tablette numérique, p. ex. entrée de commandes par des tracés gestuels
A method includes obtaining an image and, based thereon, generating a bilateral grid representing transform coefficients configured to map pixel values of the input image to filter guide values. Each respective transform coefficient may be based on a corresponding class of an image feature represented by a corresponding pixel of the image. The corresponding class may be one of multiple image feature classes represented by the image. The method may also include generating, based on the bilateral grid, a transform coefficient corresponding to a selected pixel of the image, and applying the transform coefficient to a pixel value of the selected pixel to generate a filter guide value. The method may further include generating an output image by applying an operator to the pixel value based on the filter guide value. A parameter of the operator may differ across the multiple classes as a function of the filter guide value.
Methods, systems, and apparatus, for aligning clock edges of a plurality of on-chip clock controllers. One of the methods includes receiving, by an on-chip edge alignment controller, a scan enable signal. A counter is incremented using a fastest clock among a plurality of on-chip clock controllers. When the counter reaches a value that is based on a least-common multiple between the fastest clock and a slowest clock, a trigger output value is generated on a trigger output port.
The technology is generally directed to a coherent optical system that performs phase folding for one-dimensional coherent optical signals using a first optical coupler configured to output a first signal portion and a second signal portion, a second optical coupler to receive the first signal portion through a first branch and the second signal portion through a parallel second branch, and a phase-folding time delay unit including at least a first time delay component positioned on the first branch and a second time delay component positioned on the second branch. An output of the second optical coupler is a phase-folded version of the one-dimensional modulated optical signal that is capable of being demodulated by a one-dimensional vector receiver.
A superconducting structure is provided. In one example, the superconducting layer includes a substrate, a patterned seed layer on the substrate and a superconducting layer on the patterned seed layer and the substrate. The superconducting layer includes a first portion with a first resistivity directly on the substrate and a second portion with a second resistivity directly on the patterned seed layer.
A method includes receiving a natural language query specifying an action for an assistant interface to perform and selecting one or more business large language models (LLMs) for the assistant interface to interact with to fulfill performance of the action. For each business LLM, method also includes accessing an adapter module to structure the natural language query into a respective prompt specifically formulated for the corresponding business LLM, issuing, for input to the corresponding business LLM, the respective prompt, and receiving corresponding response content from the corresponding business LLM that conveys details regarding performance of a corresponding portion of the action. The method also includes presenting, for output from the user device, presentation content based on the corresponding response content received from each corresponding business LLM.
G10L 25/48 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier
G10L 15/183 - Classement ou recherche de la parole utilisant une modélisation du langage naturel selon les contextes, p. ex. modèles de langage
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
9.
Handling a One-to-Multiple Handshake with Asynchronous Completion
Techniques and apparatuses are described for handling a one-to-multiple handshake with asynchronous completion. In example aspects, a system-on-chip is implemented with at least one handler, at least one second entity, and at least one subsystem having multiple first entities and at least one scheduler. To facilitate communications between the multiple first entities of the subsystem and the second entity, the handler performs a request-and-acknowledgement handshake with the scheduler. Additionally, the handler performs multiple request-and-acknowledgement handshakes with the second entity. The handler completes the request-and-acknowledgement handshake with the scheduler based on the completion of the multiple request-and-acknowledgement handshakes with the second entity. This enables the handler to address a situation in which the multiple request-and acknowledgement handshakes with the second entity complete in an asynchronous manner.
A multi-layer printed circuit board (PCB) and a method of manufacturing thereof are provided. The PCB includes a core layer, a first stack of alternative layers including metal layers and dielectric layers stacked alternatively over a top surface of the core layer, a second stack of alternative layers including metal layers and dielectric layers stacked alternatively over a bottom surface of the core layer, and an embedded via structure through the core layer and connecting a first metal layer of the first stack of alternative layers to a second metal layer of the second stack of alternative layers. The embedded via structure includes a first signal path layer through the core layer, a first ground layer through the core layer and enclosing the first signal path layer, and a first dielectric layer through the core layer and between the first signal path layer and the first ground layer.
This document describes techniques and apparatuses for automatic white-balance for a camera system. The techniques and apparatuses utilize a precursor image to detect one or more detected faces and determine a tone. The camera system retrieves tonal data based on a group of images determined to contain a same face as the detected face. Based on this tonal data, a difference in white balance is determined based on the difference in tone of the detected face within the precursor image and the associated tonal data. Camera settings are adjusted based on the difference in white balance to enable capture of an image having an improved tone.
H04N 23/88 - Chaînes de traitement de la caméraLeurs composants pour le traitement de signaux de couleur pour l'équilibrage des couleurs, p. ex. circuits pour équilibrer le blanc ou commande de la température de couleur
H04N 23/12 - Caméras ou modules de caméras comprenant des capteurs d'images électroniquesLeur commande pour générer des signaux d'image à partir de différentes longueurs d'onde avec un seul capteur
H04N 23/57 - Détails mécaniques ou électriques de caméras ou de modules de caméras spécialement adaptés pour être intégrés dans d'autres dispositifs
H04N 23/611 - Commande des caméras ou des modules de caméras en fonction des objets reconnus les objets reconnus comprenant des parties du corps humain
H04N 23/63 - Commande des caméras ou des modules de caméras en utilisant des viseurs électroniques
12.
TEXT-CONTENT SELECTION FOR DIGITAL CONTENT COLLECTIONS
A method for text asset selection includes determining preferred text-content pairs based at least in part on predicted performance outcomes for a plurality of text-content pairs. The method also includes obtaining a plurality of content items from a digital content collection; obtaining a plurality of text assets; generating a plurality of sample text-content pairs; determining performance outcomes for the plurality of sample text-content pairs; generating a text asset performance model based on the performance outcomes for the plurality of sample text-content pairs; applying the text asset performance model to each target text-content pair of a plurality of target text content pairs to generate predicted performance outcomes for the plurality target text-content pairs; determining one or more preferred text-content pairs based at least in part on the predicted performance outcomes for the plurality of target text-content pairs; and providing the one or more preferred text-content pairs to one or more devices.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for using cryptographic protocols to generate network measurements in privacy preserving ways are described. In one aspect, a method includes sending, by a network measurement system and to a client device, data indicating a size for a partial identifier for an application of the client device. The network measurement system receives, from the client device, (i) a partial masked identifier generated by masking a complete identifier for the client device or a user of the client device and removing a portion of a resulting complete masked identifier based on the size and (ii) a first encrypted identifier generated by encrypting the complete masked identifier using an encryption key of the client device.
H04L 9/14 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité utilisant plusieurs clés ou algorithmes
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
Techniques for an artificial intelligence (AI) function in spreadsheet documents is provided herein. A user is provided with access to a spreadsheet document via a user interface (UI). A user query pertaining to an operation associated with an AI model is received based on a user interaction with one or more cells of the spreadsheet document. The UI is updated to include the received user query in the cell(s) and one or more UI elements that enable the user to initiate the operation associated with the AI model. Responsive to a detection of a user interaction with the UI element(s), a prompt associated with the user query is provided as an input to the AI model. One or more outputs of the AI model are obtained, the output(s) including response data pertaining to the user query. The UI is updated to include the response data in the cell(s).
Provided are improved machine learning-based text editing models. Specifically, example implementations include a flexible semi-auto-regressive text-editing approach for generation, designed to derive the maximum benefit from non-auto-regressive text-editing and autoregressive decoding. In contrast to conventional sequence-to-sequence (seq2seq) models, the proposed approach is fast at inference time, while being capable of modeling flexible input-output transformations.
To perform inter-frequency measurements, a UE in an NTN cell receives a reference location of the NTN cell and one or more pairs of (i) a TN frequency, and (ii) a bitmap indication identifying one or more of a plurality of segments of the NTN cell with which the TN frequency is associated, the plurality of segments being determined by dividing the NTN cell in N sectors around the reference location, when the bitmap indication includes N bits. The UE searches for a TN cell on the TN frequency only if a current location of the UE is within the one or more of the plurality of segments with which the TN frequency is associated according to the bitmap indication.
A computer-implemented method for transcribing an utterance includes receiving, at a computing system, speech data that characterizes an utterance of a user. A first set of candidate transcriptions of the utterance can be generated using a static class-based language model that includes a plurality of classes that are each populated with class-based terms selected independently of the utterance or the user. The computing system can then determine whether the first set of candidate transcriptions includes class-based terms. Based on whether the first set of candidate transcriptions includes class-based terms, the computing system can determine whether to generate a dynamic class-based language model that includes at least one class that is populated with class-based terms selected based on a context associated with at least one of the utterance and the user.
G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
G10L 15/183 - Classement ou recherche de la parole utilisant une modélisation du langage naturel selon les contextes, p. ex. modèles de langage
G10L 15/197 - Grammaires probabilistes, p. ex. n-grammes de mots
G10L 15/30 - Reconnaissance distribuée, p. ex. dans les systèmes client-serveur, pour les applications en téléphonie mobile ou réseaux
G10L 15/06 - Création de gabarits de référenceEntraînement des systèmes de reconnaissance de la parole, p. ex. adaptation aux caractéristiques de la voix du locuteur
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task. In particular, the machine learning task is performed by augmenting a trained neural network with residual memorization.
G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels
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
19.
USING MASKED STREAM IDENTIFIERS FOR A TRANSLATION LOOKASIDE BUFFER
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a translation lookaside buffer to use stream id masking. A system includes a translation lookaside buffer unit configured to map an input translation request to a physical address, in which each input translation request includes a virtual address and stream id. The system further includes stream id masking logic that is configured to mask the stream id of related input translation requests, generating a masked translation request. Each masked translation request includes a virtual address and masked stream id, in which the translation lookaside buffer services the input translation request using the masked translation request.
G06F 12/1027 - Traduction d'adresses utilisant des moyens de traduction d’adresse associatifs ou pseudo-associatifs, p. ex. un répertoire de pages actives [TLB]
Provided are methods, systems, devices, and tangible non-transitory computer readable media for providing data including vehicle map service data. The disclosed technology can perform operations including accessing vehicle map service data including information associated with a geographic area and sensor observations of a vehicle. The vehicle map service data is based on a vehicle map service protocol specifying layers associated with portions of the vehicle map service data to which each client system is subscribed. Further, the layers to which the client systems are subscribed can be determined and access to the plurality of layers to which each of the vehicle client systems is subscribed can be provided to each of the client systems. Access to the layers can include authorization to send or receive one or more portions of the vehicle map service data associated with a corresponding layer.
H04W 4/44 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p. ex. communication véhicule-piétons pour la communication entre véhicules et infrastructures, p. ex. véhicule à nuage ou véhicule à domicile
G01C 21/00 - NavigationInstruments de navigation non prévus dans les groupes
G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués
H04W 4/38 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour la collecte d’informations de capteurs
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
21.
WAVEFRONT PARALLEL PROCESSING WITH PROBABILITY UPDATES
Multiple wavefronts and multiple wavefront groups are configured for a tile of a current frame. Each wavefront group of the multiple wavefront groups comprises a set of consecutive coding unit rows. Each wavefront of the multiple wavefronts comprises coding unit rows selected at intervals across the tile such that an nth row of each of the multiple wavefront groups belongs to an nth wavefront. For each wavefront of at least some of the multiple wavefronts, a probability model is initialized for a first row of the wavefront. The probability model is updated during coding of subsequent rows of the wavefront using finishing probability values from a previous row of the wavefront.
H04N 19/196 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par le procédé d’adaptation, l’outil d’adaptation ou le type d’adaptation utilisés pour le codage adaptatif étant spécialement adaptés au calcul de paramètres de codage, p. ex. en faisant la moyenne de paramètres de codage calculés antérieurement
H04N 19/14 - Complexité de l’unité de codage, p. ex. activité ou estimation de présence de contours
H04N 19/174 - 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 tranche, p. ex. une ligne de blocs ou un groupe de blocs
H04N 19/436 - 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 utilisant des dispositions de calcul parallélisées
22.
Integrated Circuit Design with Logic Cells Associated with Dependent Operating Elements Arranged in a Widening Structure
This document describes systems and techniques for designing an integrated circuit with logic cells associated with dependent operating elements arranged in a widening structure. For example, a method includes associating logic cells into a plurality of groups associated with one of a plurality of dependent operating elements that each depend on a base operating element. The plurality of dependent operating elements are arranged in a widening structure at which the base operating element is at a proximal end of the widening structure and one or more levels of dependent operating elements are hierarchically arranged from the base operating element at the proximal end to a distal level of one or more operating elements at a distal end. Each of the plurality of groups of logic cells are clustered around each of the plurality of dependent operating elements with which the logic cells are associated.
G06F 30/398 - Vérification ou optimisation de la conception, p. ex. par vérification des règles de conception [DRC], vérification de correspondance entre géométrie et schéma [LVS] ou par les méthodes à éléments finis [MEF]
The methods and systems described herein provide for depth-aware image editing and interactive features. In particular, a computer application may provide image-related features that utilize a combination of a (a) the depth map, and (b) segmentation data to process one or more images, and generate an edited version of the one or more images.
The present disclosure provides a diffractive element between a light engine and a waveguide and a corresponding incoupler architecture for eyewear displays. The eyewear display includes a light engine to project light associated with displaying an image; a waveguide including a plurality of incouplers; and a diffractive element between the light engine and the waveguide to receive the light projected by the light engine, split the light into a plurality of diffraction orders of light, and direct one diffraction order of light of the plurality of diffraction orders of light to one incoupler of the plurality of incouplers.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for cryptographically implemented data restrictions. One of the methods includes receiving a data access request prompted by user interaction on an application executing on the client device; transmitting an attestation nonce request to a secure processor of the client device; generating a cryptographic key pair and an attestation nonce; transmitting data comprising (i) the public key from the cryptographic key pair and (ii) the generated attestation nonce; validating an attestation packet received from the server in response to the transmission of the data; in response to validating the attestation packet, transmitting data restrictions specified by the user of the client device; generating a signed policy including the data restrictions; and transmitting the signed policy to the server specifying the data restrictions for the server to abide by.
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for modulating an output of a deployed machine learning system. In one aspect a method comprises maintaining a set of one or more signatures that each represent a respective set of inputs to a base machine learning model that have been determined to degrade a performance of the base machine learning model, receiving a new input to the base machine learning model, for each of the set of one or more signatures, processing the new input and the signature using an auxiliary machine learning model to generate a respective auxiliary output that indicates whether the new input belongs to the respective set of inputs represented by the signature, and modulating an output of the base machine learning model for the new input based on the respective auxiliary outputs for the set of one or more signatures.
A method efficiently tests campaign configuration modifications with reduced risk of performance degradation. The method includes obtaining content sponsor data for content sponsor(s) associated with respective campaign(s), generating a prompt based at least in part on the content sponsor data, and generating candidate modifications at least in part by applying the prompt to a generative artificial intelligence (Al) model. The method also includes generating test configuration(s) that differ from the configuration(s) of the respective campaign(s) in accordance with a first candidate modification, and applying the test configuration(s) to a virtual test environment that is isolated from a production environment. The method also includes determining performance metric(s) associated with applying the test configuration(s) to the virtual test environment.
Systems, methods, and apparatuses for wireless communication include techniques for dynamic selection of carriers. A network entity (120) can configure a user equipment (130) with carriers and carrier selection criteria. The UE selects carriers for uplink and downlink communication based on the carrier selection criteria or based on an indication of a carrier received from a network entity. The UE can select carriers for performing a random access procedure using multiple carriers. The UE can select between a first carrier and a second carrier to transmit or receive transport blocks or portions of transport blocks on multiple carriers. The UE can monitor the performance of a carrier and report a performance failure or a carrier to the network entity.
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for CSI accuracy enhancement in wireless communication systems. A UE (102) receives (304) a configuration for a CSI report including a CMR and a target BLER, the CSI report being based on at least one of: a first factor corresponding to an association of a SINR and a BLER, a second factor corresponding to a number of repetitions of a PDSCH, or a third factor corresponding to whether the CSI report is for a PDCCH, a PDSCH, or both the PDCCH and the PDSCH. The UE (102) receives (308), from the network entity (104), a CSI-RS on the CMR. The UE (102) transmits (310), to the network entity (104), the CSI report including CSI calculated based on the CSI-RS and the at least one of: the first factor, the second factor, or the third factor.
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for reporting functionality combination information. A user equipment, UE (102) transmits (405), to a network entity (104), functionality combination information indicating at least two functionalities for simultaneous performance by the UE (102). The UE (102) communicates (415), with the network entity (104), based on the functionality combination information.
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for a data collection configuration scheme in a wireless communication system. A UE receives (306), from a network entity (104), a data collection configuration (450) for performing data collection at the UE (102). The data collection configuration including at least one of: data collection identity information (452), data collection object information (454), or data collection type information (456). The UE (102) transmits (316), to the network entity (104), a report based on the data collection configuration.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing network inputs using recurrent interface networks.
Methods, systems, and apparatus for reducing latency of configuration of image sensor and image signal processor. A computing system can include a machine learning (ML) processing engine that can process denoising diffusion ML models for execution on statically compiled ML accelerators. The system can determine that a partitioned graph representation, which includes connected subgraphs that each represent at least one layer of the neural network of the ML model, forms a directed acyclic graph. The system can insert cache nodes in the graph representation, where each cache node corresponds to a respective subgraph and is configured to cache output of the respective subgraph. The system can generate an execution dataflow graph including a plurality of iterations of the partitioned graph representation, where, at one or more iterations during model inference operations, the execution dataflow graph uses inputs from cache nodes and excludes execution of subgraphs corresponding to the cache nodes.
Generally, the present disclosure is directed to methods and systems for automatically generating data that encodes natural language conversations between at least two parties. The conversational data may be automatically generated by one or more language generative models. As such, the automatically generated conversational data may be referred to as synthetic conversational data. The synthetic conversational data may simulate the speech patterns (e.g., prompts, responses to prompts, and combinations thereof) of one or more hypothetical or real humans (e.g., users) participating a conversation. In various applications, the synthetic conversational data is employed to train, pre-train, fine-tune, and/or evaluate the performance of at least one of the generative language models employed to generate the synthetic conversational data and/or other generative language models. Such other generative language models may be employed in various interactive recommendation systems, chat-bots, or any other application that interacts with one
A docking system may include a dock that stores a dock identifier and an electronic device that docks with the dock. The electronic device can receive the dock identifier, obtain wireless network information, and create a hash value based on the dock identifier and additional information, such as wireless network information. The hash value may be analyzed using a stored hash to identify a match. In response to the match, the system may permit access to a restricted feature while the electronic device remains docked.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
Techniques are described herein for providing smart suggestions for image zoom regions. A method includes: receiving a search query; performing a search using the search query to identify search results that include image search results including a plurality of images that are responsive to the search query; for a given image of the plurality of images included in the image search results, determining at least one zoom region in the given image; and providing the search results including the image search results, including providing the given image and an indication of the at least one zoom region in the given image.
Methods, systems, and apparatus, including computer-readable media, are described for a hardware circuit configured to implement a neural network. The circuit includes multiple super tiles. Each super tile includes a unified memory for storing inputs to a neural network layer and weights for the layer. Each super tile includes multiple compute tiles. Each compute tile executes a compute thread that is used to perform the computations to generate an output for the neural network layer. Each super tile includes arbitration logic coupled to the unified memory and each compute tile. The arbitration logic is configured to: pass inputs stored in the unified memory to the compute tiles; pass weights stored in the unified memory to the compute tiles; and pass, to the unified memory, the output generated for the layer based on computations performed at the compute tiles using the inputs and the weights for the layer.
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 3/04 - Architecture, p. ex. topologie d'interconnexion
38.
ENCODING AND DECODING USING INDEPENDENT RECTANGULAR SUB-SECTIONS
Video coding using tiling may include encoding a current frame by identifying a tile-width for encoding a current tile of the current frame, the tile-width indicating a cardinality of horizontally adjacent blocks in the current tile, identifying a tile-height for encoding the current tile of the current frame, the tile-height indicating a cardinality of vertically adjacent block in the current tile, and generating an encoded tile by encoding the current tile, such that a row of the current tile includes tile-width horizontally adjacent blocks from the plurality of blocks, and a column of the current tile includes tile-height vertically adjacent blocks from the plurality of blocks. Encoding the current frame may include outputting the encoded tile, wherein outputting the encoded tile includes including an encoded-tile size in an output bitstream, the encoded-tile size indicating a cardinality of bytes for including the encoded tile in the output bitstream.
H04N 19/152 - Débit ou quantité de données codées à la sortie du codeur par mesure de l’état de remplissage de la mémoire tampon de transmission
H04N 19/119 - Aspects de subdivision adaptative, p. ex. subdivision d’une image en blocs de codage rectangulaires ou non
H04N 19/174 - 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 tranche, p. ex. une ligne de blocs ou un groupe de blocs
H04N 19/436 - 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 utilisant des dispositions de calcul parallélisées
39.
Learning to Rank via Univariate Ordinal Regression
A relaxed solution of ordinal regression, which constrains the solution to a grid of thresholds learned over training example data is disclosed. A single shift parameter is predicted for each example, shifting that grid to produce a distribution of label predictions. Ranking losses for both pairwise on pairs of examples, and listwise on lists of several examples are disclosed. The use of distillation for both pointwise and ranking losses are described. Use of relaxed ordinal regression with a pre-set grid of thresholds are described, and. an additional temperature parameter is introduced to the different losses and settings as a. method that allows for more flexibility on the solution's modality, which can help in providing unimodality when possible or necessary, but allowing for multimodality when necessary.
One aspect provides a machine-learned video prediction model configured to receive and process one or more previous video frames to generate one or more predicted subsequent video frames, wherein the machine-learned video prediction model comprises a convolutional variational auto encoder, and wherein the convolutional variational auto encoder comprises an encoder portion comprising one or more encoding cells and a decoder portion comprising one or more decoding cells.
H04N 19/59 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage prédictif mettant en œuvre un sous-échantillonnage spatial ou une interpolation spatiale, p. ex. modification de la taille de l’image ou de la résolution
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
H04N 19/117 - Filtres, p. ex. pour le pré-traitement ou le post-traitement
H04N 19/176 - 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 un bloc, p. ex. un macrobloc
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
A computing device may receive an indication of a handwriting input that includes a sequence of handwritten characters, where the sequence of handwritten characters includes a sequence of word abbreviations. The computing device may determine, using a language model, a sequence of candidate words that correspond to the sequence of word abbreviations. The computing device may output, for display at a display device, a user interface that presents tire sequence of candidate words.
G06F 3/023 - Dispositions pour convertir sous une forme codée des éléments d'information discrets, p. ex. dispositions pour interpréter des codes générés par le clavier comme codes alphanumériques, comme codes d'opérande ou comme codes d'instruction
G06F 3/04883 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p. ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p. ex. des gestes en fonction de la pression exercée enregistrée par une tablette numérique utilisant un écran tactile ou une tablette numérique, p. ex. entrée de commandes par des tracés gestuels pour l’entrée de données par calligraphie, p. ex. sous forme de gestes ou de texte
G06F 40/274 - Conversion de symboles en motsAnticipation des mots à partir des lettres déjà entrées
42.
On-Head Detection and/or Hearable-Adjustment Detection using Active Acoustic Sensing
Techniques and apparatuses are described for performing on-head detection and/or hearable-adjustment detection using active acoustic sensing. A hearable, such as an earbud, is capable of performing audioplethysmography. Audioplethysmography is an active acoustic method capable of sensing subtle physiologically-related changes observable at a user's outer and middle ear. The hearable forms at least a partial seal in or around the user's outer ear, which enables formation of an acoustic circuit involving the seal, the hearable, an ear canal, and an ear drum. By transmitting and receiving acoustic signals, the hearable can recognize changes in the acoustic circuit to perform aspects of on-head detection and/or hearable-adjustment detection. The size, cost, and power usage of the hearable can help make these features accessible to a larger group of people and improve the user experience with hearables.
Implementations are directed to providing a voice wrapper to an existing third-party text-based chatbot to enable the existing third-party text-based chatbot to engage in corresponding voice-based conversations. The voice wrapper can include a plurality of components. For instance, the voice wrapper can include a plurality of input components for utilization in responding to a spoken utterance, and in lieu of the existing third-party text-based chatbot, and/or to modify input to be provided to the existing third-party text-based chatbot in responding to the spoken utterance. Also, for instance, the voice wrapper can include a plurality of output components for utilization in responding to the spoken utterance, to reduce perceived latency of the existing third-party text-based chatbot, and/or to modify output generated by the existing third-party text-based chatbot in responding to the spoken utterance.
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
G10L 13/08 - Analyse de texte ou génération de paramètres pour la synthèse de la parole à partir de texte, p. ex. conversion graphème-phonème, génération de prosodie ou détermination de l'intonation ou de l'accent tonique
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
A method includes receiving training data including a corpus of multilingual unspoken textual utterances, a corpus of multilingual un-transcribed non-synthetic speech utterances, and a corpus of multilingual transcribed non-synthetic speech utterances. For each un-transcribed non-synthetic speech utterance, the method includes generating a target quantized vector token and a target token index, generating contrastive context vectors from corresponding masked audio features, and deriving a contrastive loss term. The method also includes generating an alignment output, generating a first probability distribution over possible speech recognition hypotheses for the alignment output, and determining an alignment output loss term. The method also includes generating a second probability distribution over possible speech recognition hypotheses and determining a non-synthetic speech loss term. The method also includes pre-training an audio encoder based on the contrastive loss term, the alignment output loss term, and the non-synthetic speech loss term.
G10L 15/06 - Création de gabarits de référenceEntraînement des systèmes de reconnaissance de la parole, p. ex. adaptation aux caractéristiques de la voix du locuteur
G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
45.
UCI MULTIPLEXING ON MULTI-CODEWORD AND MULTI-BEAM PUSCH
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for multiplexing UCI (508) for multiple codewords on one or more beams. A UE (102) multiplexes, on PUSCH resources (202), UCI (508) for a plurality of codewords to generate a multiplexed UCI. The plurality of codewords is associated with the one or more beams. The UE (102) transmits (316, 716), to a network entity (104), the multiplexed UCI (508) on the PUSCH resources (202).
H04W 72/21 - Canaux de commande ou signalisation pour la gestion des ressources dans le sens ascendant de la liaison sans fil, c.-à-d. en direction du réseau
H04L 1/00 - Dispositions pour détecter ou empêcher les erreurs dans l'information reçue
46.
Joint Engagement Prediction, Distillation, and Inference with Label Context
Provided are systems and methods that enable computerized systems to optimize predictions of joint label probabilities of lists of items returned in response to a query. The proposed approaches take into account the effects of relational interactions among items included in the same list and can improve predictions of a deployed model for either predicting engagement rates, ranking, and/or distillation. Unlike methods that combine label engagement loss with a ranking loss, the proposed methods directly model the joint probability of the vector of labels in a list of examples included in a training dataset (e.g., by modeling conditional probabilities) instead of modeling the marginal and conditional projections of the joint probability and empirically balancing between them to infer some approximation of the joint probability. The approach gives models the ability to refine their label predictions based on the contexts of co-recommended items.
A device includes a plurality of cores having a plurality of configurable self-repair pipelines, wherein each core of the plurality of cores comprises a plurality of pipeline flops for routing self-repair data to the plurality of cores in parallel, wherein a series of connected pipeline flops forms one of the plurality of configurable self-repair pipelines.
Waveguides for displays constructed from a combination of flat and curved surfaces using plural incouplers include additional incouplers incorporated into a waveguide spaced from one another at precise angles, e.g., that match or correspond to grating angles associated with the waveguides, allowing the injection of light in multiple locations to the same grating structure while still maintaining k-space closure and thus preventing unintended refraction or distortion. Regions immediately surrounding incouplers and outcouplers in the waveguide include flat surfaces, while regions between the incouplers and outcouplers-include one or more curved surfaces.
Decoding using no-show keyframes includes obtaining encoded no-show keyframe data from an encoded bitstream, wherein the encoded no-show keyframe data includes data indicating that the encoded no-show keyframe data is for a keyframe and data indicating that the encoded no-show keyframe data is for a no-show frame. Decoding using no-show keyframes includes obtaining reconstructed no-show keyframe data by decoding the encoded no-show keyframe data using intra prediction, subsequent to obtaining the encoded no-show keyframe data, obtaining encoded overlay frame data from the encoded bitstream, subsequent to obtaining the reconstructed no-show keyframe data, obtaining reconstructed overlay frame data by decoding the encoded overlay frame data using inter prediction using the reconstructed no-show keyframe data as available reference frame data, including the reconstructed overlay frame data in output data for display, excluding the reconstructed no-show keyframe data from the output data, and outputting the output data.
H04N 19/107 - Sélection du mode de codage ou du mode de prédiction entre codage prédictif spatial et temporel, p. ex. rafraîchissement d’image
H04N 19/117 - Filtres, p. ex. pour le pré-traitement ou le post-traitement
H04N 19/159 - Type de prédiction, p. ex. prédiction intra-trame, inter-trame ou de trame bidirectionnelle
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 19/61 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant un codage par transformée combiné avec un codage prédictif
An interposer chip includes a substrate and a plurality of through-vias extending from a first side of the substrate to a second side of the substrate. Each through-via includes an electrical conductor extending between the first and second sides. A plurality of first electrically conducting pads is disposed on the first side, where each first electrically conducting pad is coupled to a respective electrical conductor and has a first size. A plurality of second electrically conducting pads is disposed on the second side, where each second electrically conducting pad is coupled to a respective electrical conductor and has a second size. The second size of the second electrically conducting pads is greater than the first size of the first electrically conducting pads. The interposer chip facilitates electrical connectivity between components while suppressing microwave crosstalk and parasitic capacitance in applications such as quantum computing systems.
The present disclosure is directed to a wearable computing device that includes a housing defining an upper surface and a lower surface for placement adjacent to a wrist of a user. The wearable computing device includes a plurality of biometric measurement devices arranged on the lower surface of the housing. The plurality of biometric measurement devices is configured for measuring, at least, a localized impedance at the wrist of the user. The wearable computing device also includes a processor configured to execute instructions stored in a memory device. The instructions includes receiving continuous passive measurements from the plurality of biometric measurement devices relating to the localized impedance at the wrist of the user and detecting impedance changes in a tissue composition of the user using the passive measurements from the plurality of biometric measurement devices relating to the localized impedance at the wrist of the user longitudinally.
Example embodiments of the present disclosure provide for an example method including accessing data associated with a number of viewing sessions associated with client devices. The method can include making an API call to a session evaluator for the viewing sessions and receiving a score for each viewing session. The method can include comparing the scores for the viewing sessions to a threshold score to determine whether to provide a one-to-one or cohort viewing session to the respective client device. For instance, scores exceeding the threshold score can be provided one-to-one viewing sessions and scores below the threshold score can be provided with cohort viewing sessions.
H04N 21/234 - Traitement de flux vidéo élémentaires, p. ex. raccordement de flux vidéo ou transformation de graphes de scènes du flux vidéo codé
H04N 21/239 - Interfaçage de la voie montante du réseau de transmission, p. ex. établissement de priorité des requêtes de clients
H04N 21/24 - Surveillance de procédés ou de ressources, p. ex. surveillance de la charge du serveur, de la bande passante disponible ou des requêtes effectuées sur la voie montante
H04N 21/2668 - Création d'un canal pour un groupe dédié d'utilisateurs finaux, p. ex. en insérant des publicités ciblées dans un flux vidéo en fonction des profils des utilisateurs finaux
This disclosure provides systems, devices, apparatus, and methods, including computer readable media, for beam-specific common channel configuration. A UE (102) receives (1502) from a network entity (104) control signaling indicating a plurality of beam-specific configurations for a first set of parameters for a channel and a beam-common configuration for a second set of parameters for the channel. The plurality of beam-specific configurations are associated with a plurality of SSBs. The beam-common configuration is associated with all of the plurality of S SBs. The UE (102) receives (1508) from the network entity (104) one or more SSBs of the plurality of SSB. The UE (102) communicates (1510) with the network entity (104) in a transmission occasion associated with an SSB of the one or more SSBs. The transmission occasion is communicated on the channel based on the first set of parameters corresponding to the associated SSB and the second set of parameters.
H04W 74/0833 - Procédures d’accès aléatoire, p. ex. avec accès en 4 étapes
H04B 7/08 - Systèmes de diversitéSystèmes à plusieurs antennes, c.-à-d. émission ou réception utilisant plusieurs antennes utilisant plusieurs antennes indépendantes espacées à la station de réception
H04B 7/06 - Systèmes de diversitéSystèmes à plusieurs antennes, c.-à-d. émission ou réception utilisant plusieurs antennes utilisant plusieurs antennes indépendantes espacées à la station d'émission
This document describes reporting beam failure by a user equipment (110) to a base station (121) in a radio access network (140), in which the user equipment (110) receives a first uplink grant (502) and initiates a beam recovery procedure (504). Based on the beam recovery procedure determining that a beam has failed, the user equipment (110) transmits, using the first uplink grant, a first Media Access Control Protocol Data Unit including a first MAC Control Element that indicates a first Synchronization Signal Block and a second Synchronization Signal Block, the transmission being effective to cause the base station (121) to determine that, based on receiving the first Synchronization Signal Block, the beam failure was detected by the user equipment (110) on the first Synchronization Signal Block (506).
H04W 80/02 - Protocoles de couche liaison de données
H04B 7/06 - Systèmes de diversitéSystèmes à plusieurs antennes, c.-à-d. émission ou réception utilisant plusieurs antennes utilisant plusieurs antennes indépendantes espacées à la station d'émission
H04W 72/14 - Planification du trafic sans fil utilisant un canal d'autorisation
H04W 72/23 - Canaux de commande ou signalisation pour la gestion des ressources dans le sens descendant de la liaison sans fil, c.-à-d. en direction du terminal
H04W 76/11 - Attribution ou utilisation d'identifiants de connexion
09 - Appareils et instruments scientifiques et électriques
Produits et services
Computer hardware, namely, a single-board computer and system-on-module (SoM); electronic circuit boards; integrated circuits; semiconductor chips for use in integrated circuits; electronic development boards for use in the development of computer hardware and software; computer hardware for artificial intelligence and machine learning applications in the field of producing electronic devices
09 - Appareils et instruments scientifiques et électriques
41 - Éducation, divertissements, activités sportives et culturelles
42 - Services scientifiques, technologiques et industriels, recherche et conception
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et services
Downloadable computer software for managing information
regarding tracking, compliance, and motivation with a
health, fitness, and nutrition program; downloadable
software for providing personal training services, coaching,
workouts, nutrition, fitness, health, fertility, and sleep
assessments and feedback; downloadable software using
artificial intelligence (AI) for providing personal training
services, coaching, workouts, nutrition, fitness, health,
fertility, and sleep assessments and feedback; downloadable
software for creating personalized fitness training,
nutrition, sleep, and well-being programs. Providing information, counseling, and advice relating to
fitness, fitness training, and activity; physical fitness
conditioning classes; fitness boot camps; yoga classes;
providing online non-downloadable videos in the fields of
health, wellness, fitness, exercise, and nutrition;
providing and conducting classes, seminars and workshops in
the fields of health, wellness, fitness, exercise, and
nutrition; online journals, namely, blogs featuring
commentary, advice and information in the fields of health,
wellness, sleep, fitness and nutrition; providing coaching
services in the fields of diet, nutrition, wellness,
fitness, health, wellness, mental health, and disease and
condition management; personal coaching services in the
nature of offering calls or chats, notifications, the
ability to track activities, incentive management solutions
and wellness challenges; physical fitness consultation
services. Providing online non-downloadable computer software managing
information regarding tracking, compliance, and motivation
with a health, fitness, and nutrition program; providing
online non-downloadable software for providing personal
training services, coaching, workouts, nutrition, fitness,
health, fertility, and sleep assessments and feedback;
providing online non-downloadable software using artificial
intelligence (AI) for providing personal training services,
coaching, workouts, nutrition, fitness, health, fertility,
and sleep assessments and feedback; providing online
non-downloadable software for creating personalized fitness
training, nutrition, sleep, and well-being programs. Providing information, counseling, and advice relating to
health, nutrition, fertility, body fat, body mass index,
sleep, stress, blood oxygen levels, and heart rate;
healthcare services; healthcare services, namely, services
to enable effective management of one or more chronic
conditions; healthcare services, namely, wellness and
prevention programs, healthcare management programs, disease
management programs and medical condition management
programs, and chronic care management; providing wellness
services, namely, personal assessments, personalized
routines, maintenance schedules, and counseling; providing
healthcare and wellness services, namely, personal
assessments, personalized routines, maintenance schedules,
fitness evaluations, and counseling; providing personal
lifestyle wellness evaluation and consultation; providing
educational information about healthcare for wellness
program coordinators; consulting services in the field of
health.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for augmenting a machine learning model. The methods comprise obtaining a set of features including two or more features, generating an embedding of each feature in the set of features, aggregating the embedding of each feature in the set of features to create an aggregated embedding representing the set of features as a whole, and augmenting the machine learning model based on the aggregated embedding representing the set of features as a whole.
A device comprising: a camera housing; a first carrier; an image sensor carried by the first carrier; a second carrier, wherein: the first carrier is attached to the second carrier via a first plurality of objects that move within a first plurality of channels having first stroke axes that are not parallel to a plane of the image sensor, and the second carrier is attached to the camera housing via a second plurality of objects that move within a second plurality of channels having second stroke axes that are not parallel to both the plane of the image sensor and the first stroke axes; and one or more optical image stabilization (OIS) actuators configured to apply forces to move the first carrier along the first stroke axes and move the second carrier along the second stroke axes.
H04N 23/68 - Commande des caméras ou des modules de caméras pour une prise de vue stable de la scène, p. ex. en compensant les vibrations du boîtier de l'appareil photo
An example device includes a first lens; a first prism optically after the first lens; a second prism; a second lens optically between the first prism and the second prism; an image sensor optically after the second prism; a camera housing comprising a first plurality of slots; a first carrier comprising: a first plurality of shafts configured to translate within the first plurality of slots of the camera housing along a first axis; a second plurality of slots; and a second carrier comprising a second plurality of shafts configured to translate within the second plurality of slots along a second axis that is perpendicular to the first axis, wherein the image sensor is attached to and carried by the second carrier.
H04N 23/68 - Commande des caméras ou des modules de caméras pour une prise de vue stable de la scène, p. ex. en compensant les vibrations du boîtier de l'appareil photo
H04N 23/54 - Montage de tubes analyseurs, de capteurs d'images électroniques, de bobines de déviation ou de focalisation
H04N 23/67 - Commande de la mise au point basée sur les signaux électroniques du capteur d'image
66.
RADAR COORDINATION SET FOR JOINT RADAR SIGNAL PROCESSING
A system and method of forming a radar coordination set (RCS) for multi-static or bi-static radar signal processing is disclosed. The method includes the first base station (102A) transmitting (304), via an Xn interface, at least one message to one or more base stations (102B) requesting base station capability information of a corresponding base station. The method also includes the first base station receiving (306), via the Xn interface, at least one response. Each response of the at least one response comprises the base station capability information of the corresponding base station. The method further includes the first base station forming (308) an RCS (201) with at least one base station (102B) from the one or more base stations based on the base station capability information.
G01S 13/00 - Systèmes utilisant la réflexion ou la reradiation d'ondes radio, p. ex. systèmes radarSystèmes analogues utilisant la réflexion ou la reradiation d'ondes dont la nature ou la longueur d'onde sont sans importance ou non spécifiées
G01S 7/41 - Détails des systèmes correspondant aux groupes , , de systèmes selon le groupe utilisant l'analyse du signal d'écho pour la caractérisation de la cibleSignature de cibleSurface équivalente de cible
G01S 13/46 - Détermination indirecte des données relatives à la position
G01S 13/72 - Systèmes radar de poursuiteSystèmes analogues pour la poursuite en deux dimensions, p. ex. combinaison de la poursuite en angle et de celle en distance, radar de poursuite pendant l'exploration
67.
Text-to-Image Generation via Masked Generative Transformers
Provided are text-to-image Transformer models that achieve state-of-the-art image generation performance while being significantly more efficient than diffusion or autoregressive models. Some example models described herein can be trained on a masked modeling task in discrete token space. Given the text embedding extracted from a pre-trained large language model (LLM), example models can be trained to predict randomly masked image tokens. Compared to pixel-space diffusion models, such as Imagen and DALL-E 2, example models described herein are significantly more efficient due to the use of discrete tokens. Compared to autoregressive models, such as Parti, example models described herein are more efficient due to the use of parallel decoding. The use of a pre-trained LLM enables fine-grained language understanding, translating to high-fidelity image generation and the understanding of visual concepts such as objects, their spatial relationships, pose, cardinality etc.
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
G06F 40/284 - Analyse lexicale, p. ex. segmentation en unités ou cooccurrence
G06F 40/40 - Traitement ou traduction du langage naturel
G06T 3/4053 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement basé sur la super-résolution, c.-à-d. où la résolution de l’image obtenue est plus élevée que la résolution du capteur
Encoding and decoding a current block of a current frame is disclosed. A first prediction block for the current block is obtained based on motion information associated with the first prediction block. A second prediction block for at least a portion of the current block is obtained based on a motion information associated with a neighboring block. A prediction difference measure is obtained between the first prediction block and the second prediction block. The prediction difference measure is used to determine whether to combine the first prediction block and the second prediction block of the portion of the current block. The prediction difference measure can be a sum of absolute differences (SAD) between the first prediction block and the second prediction block. The prediction difference measure can be an absolute maximum of pair-wise differences between the first prediction block and the second prediction block.
H04N 19/137 - Mouvement dans une unité de codage, p. ex. différence moyenne de champs, de trames ou de blocs
H04N 19/105 - Sélection de l’unité de référence pour la prédiction dans un mode de codage ou de prédiction choisi, p. ex. choix adaptatif de la position et du nombre de pixels utilisés pour la prédiction
H04N 19/176 - 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 un bloc, p. ex. un macrobloc
69.
AUGMENTED REALITY DISPLAY WITH FREEFORM OPTICS AND INTEGRATED PRESCRIPTION LENS
A dual-component lightguide employs two freeform surfaces separated by a gap with prescription lens integration. A world-side component includes a spherical world-side surface and a freeform eye-side surface. An eye-side component includes a freeform world-side surface that conforms to the freeform eye-side surface of the world-side component and an eye-side surface that is shaped to provide corrective optics based on a desired prescription.
A display assembly for an electronic device is provided. The display assembly includes a display cover. The display cover defines an internal volume having a central portion and a peripheral portion. The display assembly further includes a display. The display includes a first display area having a first plurality of pixels. The first display area is disposed within the central portion of the internal volume. The display further includes a second display area having a second plurality of pixels. The second display area is disposed at least partially within the peripheral portion of the internal volume and extends around a periphery of the first display area. The display even further includes an arcuate connection portion disposed on the second display area. The arcuate connection portion defines a periphery of the second display area.
G09G 3/00 - Dispositions ou circuits de commande présentant un intérêt uniquement pour l'affichage utilisant des moyens de visualisation autres que les tubes à rayons cathodiques
G09G 3/3208 - Dispositions ou circuits de commande présentant un intérêt uniquement pour l'affichage utilisant des moyens de visualisation autres que les tubes à rayons cathodiques pour la présentation d'un ensemble de plusieurs caractères, p. ex. d'une page, en composant l'ensemble par combinaison d'éléments individuels disposés en matrice utilisant des sources lumineuses commandées utilisant des panneaux électroluminescents semi-conducteurs, p. ex. utilisant des diodes électroluminescentes [LED] organiques, p. ex. utilisant des diodes électroluminescentes organiques [OLED]
Aspects of the disclosed technology include techniques and mechanisms for performing clock synchronization at scale. A network device may gather, through repeated probe iterations to a swarm of peer network devices, a time indicated by each device. The network device may aggregate the gathered times to determine an offset and drift rate and may use one or more swarm consensus algorithms to determine a consensus time toward which the swarm may move. The swarm may synchronize to the consensus time. The network device may probe one or more time servers to retrieve a time signal indicated therein. The network device may propagate the retrieved time signal to the swarm. The swarm may move toward the time signal.
A video capture device and method for implementing a media, container are disclosed. An example method includes receiving a first video track in a first file format, wherein the first video track is compatible with a first group of data devices configured to play the first file format, generating at least one supplementary track in the first file format and supplementary' track metadata, wherein the at least one supplementary track is associated with the first video track, and generating a media container including the first video track and a nested container, wherein the nested container includes at least one supplementary track and the supplementary track metadata, and wherein the nested container is accessible to a second group of data devices configured to play one of the at least one supplementary' tracks according to the supplementary—track metadata.
An example method includes detecting, by a base computing device configured to transmit data over a Bluetooth communication link, a pause in a transmission of data payloads to a remote computing device over the Bluetooth communication link, wherein the Bluetooth communication link is associated with a payload transmission cadence, and wherein the remote computing device is configured to receive transmitted data from the base computing device over the Bluetooth communication link. The method also includes, subsequent to detecting the pause in the transmission of the data payloads, initiating transmission of null payloads at a modified transmission cadence based on a sniff delay sequence, wherein a number of null payloads transmitted during a time interval is smaller for the modified transmission cadence than for the payload transmission cadence.
H04W 4/80 - Services utilisant la communication de courte portée, p. ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
74.
Systems and Methods of Separating Audio Tracks and Matching to a Text Caption
A computer-implemented method is provided. The method includes receiving, by a computing device, an input audio waveform and an input textual description. The method further includes separating, by a neural network, the input audio waveform into a plurality of audio tracks. The method also includes determining, by the neural network, whether the input textual description describes an audio track of the plurality of separated audio tracks. The method additionally includes, upon a determination that the input textual description describes an audio track of the plurality of audio tracks, providing, by the computing device, the audio track corresponding to the input textual description to an interactive user interface.
G10L 25/51 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation
G06F 3/04842 - Sélection des objets affichés ou des éléments de texte affichés
G10L 25/30 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par la technique d’analyse utilisant des réseaux neuronaux
75.
DYNAMIC SPECTRUM SHARING WITH DYNAMIC RATE MATCHING
User equipment proximately located with two base stations operating using different radio access technologies determines whether to enable or disable rate matching of transmissions from its supporting base station. If rate matching is enabled, the user equipment receives transmissions from one of the base stations that avoids time-frequency resources used by the other base station to carry control signals. If rate matching is disabled, the user equipment receives transmissions from the one base station in the same time-frequency resources used by the other base station to carry control signals.
H04L 1/00 - Dispositions pour détecter ou empêcher les erreurs dans l'information reçue
H04W 72/0446 - Ressources du domaine temporel, p. ex. créneaux ou trames
H04W 72/0453 - Ressources du domaine fréquentiel, p. ex. porteuses dans des AMDF [FDMA]
H04W 72/231 - Canaux de commande ou signalisation pour la gestion des ressources dans le sens descendant de la liaison sans fil, c.-à-d. en direction du terminal les données de commande provenant des couches au-dessus de la couche physique, p. ex. signalisation RRC ou MAC-CE
H04W 72/541 - Critères d’affectation ou de planification des ressources sans fil sur la base de critères de qualité en utilisant le niveau d’interférence
Systems and methods for editing media data are disclosed herein. The method includes receiving a text file and media data, wherein the text file is a transcription of at least a portion of the media data and receiving a request to perform an edit to the media data. The method can also include identifying one or more portions of the text file for modification and determining at least one modification for at least one portion of the one or more portions of the text file. The method can further include performing the at least one modification to the at least one portion of the text file and performing an edit to the media data based on the at least one modification to the at least one portion of the text file.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an action selection policy neural network used to select an action to be performed by an agent interacting with an environment. In one aspect, a method includes: receiving a latent representation characterizing a current state of the environment; generating a trajectory of latent representations that starts with the received latent representation; for each latent representation in the trajectory: determining a predicted reward; and processing the state latent representation using a value neural network to generate a predicted state value; determining a corresponding target state value for each latent representation in the trajectory; determining, based on the target state values, an update to the current values of the policy neural network parameters; and determining an update to the current values of the value neural network parameters.
G06F 18/211 - Sélection du sous-ensemble de caractéristiques le plus significatif
G06F 18/213 - Extraction de caractéristiques, p. ex. en transformant l'espace des caractéristiquesSynthétisationsMappages, p. ex. procédés de sous-espace
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
In a general aspect, an electronic device includes a semiconductor structure including a doped surface, a silver-based (Ag-based) layer electrically contacting at least a portion of the doped surface and a passivation layer disposed on a portion of the semiconductor structure. A portion of the passivation layer is in physical contact with the Ag-based layer. The passivation layer is a material compound including a II-Nitride material.
H10H 20/857 - Interconnexions, p. ex. grilles de connexion, fils de connexion ou billes de soudure
H10H 20/819 - Corps caractérisés par leur forme particulière, p. ex. substrats incurvés ou tronqués
H10H 20/825 - Matériaux des régions électroluminescentes comprenant uniquement des matériaux du groupe III-V, p. ex. GaP contenant de l’azote, p. ex. GaN
A microelectronic system may include a substrate having a first surface, one or more interposers mounted to and electrically connected to the first surface, first and second application specific integrated circuits (ASICs) each at least partially overlying and electrically connected to one of the interposers, a plurality of high-bandwidth memory elements (HBMs) each at least partially overlying and electrically connected to one of the interposers, and an active silicon bridge mounted to and electrically connected to the first surface and providing an electrical connection between the first and second ASICs, the active silicon bridge having active microelectronic devices therein. The microelectronic system may be configured such that the first and second ASICs and the active silicon bridge each have a purely digital CMOS interface therein. A plurality of bumps providing the electrical connection between the ASICs and the active silicon bridge may be configured to receive serial data therethrough.
Provided is a system for generating responses to health-related queries based on multi-modal health features of a patient. The responses generated by the system are conditioned on multi-modal health features associated with the patient, which can include one or more features from each of a plurality of modalities.
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
G16H 50/30 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le calcul des indices de santéTIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour l’évaluation des risques pour la santé d’une personne
81.
GENERATING QUERY VARIANTS USING A TRAINED GENERATIVE MODEL
Systems, methods, and computer readable media related to generating query variants for a submitted query. In many implementations, the query variants are generated utilizing a generative model. A generative model is productive, in that it can be utilized to actively generate a variant of a query based on application of tokens of the query to the generative model, and optionally based on application of additional input features to the generative model.
A method for performing one or more tasks, wherein each of the one or more tasks includes predicting behavior of one or more agents in an environment, the method comprising: obtaining a three-dimensional (3D) input tensor representing behaviors of the one or more agents in the environment across a plurality of time steps; generating an encoded representation of the 3D input tensor by processing the 3D input tensor using an encoder neural network, wherein 3D input tensor comprises a plurality of observed cells and a plurality of masked cells; and processing the encoded representation of the 3D input tensor using a decoder neural network to generate a 4D output tensor.
The disclosure relates to power modules that include elevated inductors with capacitors disposed under the inductors. In one aspect, a power module includes a first circuit board having a first surface and a second surface opposite the first surface. One or more inductors are mounted on the first surface. Each of the one or more inductors includes a top surface and a bottom surface opposite the top surface and that faces the first surface of the first circuit board. Each inductor is elevated above the first surface of the first circuit board such that at least a portion of the bottom surface of the inductor does not contact the first surface of the first circuit board. The first circuit board includes capacitors arranged in an area below the portion of the bottom surface of the inductor that does not contact the first surface of the first circuit board.
Methods, systems, and apparatus for reducing power consumption in image processing applications. A computing system can include a system-on-chip (SoC), which in turn can include a central processing unit (CPU), an embedded processor, an image signal processor (ISP), and at least one hardware accelerator. The CPU and the embedded processor can run a first and a second image processing stack. An image processing application running on the CPU or other core in the SoC can call application programming interface (APIs) provided by the embedded processor to carry out image processing operations. This alleviates the need for the CPU and the image processing stack on the CPU to carry out image processing operations.
Methods of wireless communication at a network entity is provided. In one aspect, the network entity performs (707) a procedure to prepare a configuration of a radio resource for a UE. Based on a determination (709) that the procedure is for LTM, the network entity refrains (715) from starting a timer based on performing the procedure. In another aspect, the network entity performs (707) a procedure to prepare a configuration of a radio resource for a UE. The network entity starts (711A) a timer based on performing the procedure. The network entity detects (917) that the timer expires, and, based on a determination (709) that the procedure is for LTM, refrains (921) from performing a release or cancellation procedure for the configuration based on expiry of the timer.
A radio access network (RAN) node including a central unit (CU) can implement a method for managing a conditional primary- secondary cell addition or change procedure. The method includes: communicating with a user equipment (UE) via a distributed unit (DU) communicatively coupled to the CU; receiving, from the DU, a conditional master cell group (MCG) configuration for a conditional primary-secondary cell (PSCell) addition or change (CPAC) procedure to a candidate PSCell; and transmitting, to the DU, a message to release the conditional MCG configuration including a trigger to cancel the CPAC procedure.
A serving radio access network (RAN) node communicates with a user equipment (UE) according to a serving configuration. The RAN node configuring the UE with for a lower-layer triggered mobility (LTM) switch to a target cell associated with a target RAN node and determines, after the configuring but prior to initiating the LTM switch, that a change in the serving configuration is required. In response to the determining, the RAN node performing one of: (i) initiating a procedure to add or modify radio resources configured for the UE, or (ii) transmitting, to the UE, a command to initiate the LTM switch.
09 - Appareils et instruments scientifiques et électriques
41 - Éducation, divertissements, activités sportives et culturelles
42 - Services scientifiques, technologiques et industriels, recherche et conception
44 - Services médicaux, services vétérinaires, soins d'hygiène et de beauté; services d'agriculture, d'horticulture et de sylviculture.
Produits et services
Downloadable computer software for managing information
regarding tracking, compliance, and motivation with a
health, fitness, and nutrition program; downloadable
software for providing personal training services, coaching,
workouts, nutrition, fitness, health, fertility, and sleep
assessments and feedback; downloadable software using
artificial intelligence (AI) for providing personal training
services, coaching, workouts, nutrition, fitness, health,
fertility, and sleep assessments and feedback; downloadable
software for creating personalized fitness training,
nutrition, sleep, and well-being programs; downloadable
computer software for receiving, processing, transmitting,
tracking, and displaying information relating to health,
fitness, nutrition, fertility, activity, body fat, body mass
index, sleep, stress, blood oxygen levels, and heart rate;
downloadable computer software using artificial intelligence
(AI) for receiving, processing, transmitting, tracking, and
displaying information relating to health, fitness,
nutrition, fertility, activity, body fat, body mass index,
sleep, stress, blood oxygen levels, and heart rate. Providing information, counseling, and advice relating to
fitness, fitness training, and activity; physical fitness
conditioning classes; fitness boot camps; yoga classes;
providing online non-downloadable videos in the fields of
health, wellness, fitness, exercise, and nutrition;
providing and conducting classes, seminars and workshops in
the fields of health, wellness, fitness, exercise, and
nutrition; online journals, namely, blogs featuring
commentary, advice and information in the fields of health,
wellness, sleep, fitness and nutrition; providing coaching
services in the fields of diet, nutrition, wellness,
fitness, health, wellness, mental health, and disease and
condition management; personal coaching services in the
nature of offering calls or chats, notifications, the
ability to track activities, incentive management solutions
and wellness challenges; physical fitness consultation
services. Providing online non-downloadable software for providing
personal training services, coaching, workouts, nutrition,
fitness, health, fertility, and sleep assessments and
feedback; providing online non-downloadable software using
artificial intelligence (AI) for providing personal training
services, coaching, workouts, nutrition, fitness, health,
fertility, and sleep assessments and feedback; providing
online non-downloadable software for creating personalized
fitness training, nutrition, sleep, and well-being programs;
providing online non-downloadable computer software for
receiving, processing, transmitting, tracking, and
displaying information relating to health, fitness,
nutrition, fertility, activity, body fat, body mass index,
sleep, stress, blood oxygen levels, and heart rate;
providing online non-downloadable computer software using
artificial intelligence (AI) for receiving, processing,
transmitting, tracking, and displaying information relating
health, fitness, nutrition, fertility, activity, body fat,
body mass index, sleep, stress, blood oxygen levels, and
heart rate; providing online non-downloadable computer
software for managing information regarding tracking,
compliance, and motivation with a health, fitness, and
nutrition program. Providing information, counseling, and advice relating to
health, nutrition, fertility, body fat, body mass index,
sleep, stress, blood oxygen levels, and heart rate;
healthcare services; healthcare services, namely, services
to enable effective management of one or more chronic
conditions; healthcare services, namely, wellness and
prevention programs, healthcare management programs, disease
management programs and medical condition management
programs, and chronic care management; providing wellness
services, namely, personal assessments, personalized
routines, maintenance schedules, and counseling; providing
healthcare and wellness services, namely, personal
assessments, personalized routines, maintenance schedules,
fitness evaluations, and counseling; providing personal
lifestyle wellness evaluation and consultation; providing
educational information about healthcare for wellness
program coordinators; consulting services in the field of
health; corporate wellness services, namely, providing
assistance and consultation to corporate clients to help
their employees make health, fitness, wellness and
nutritional changes in their daily living to improve health
in the nature of wellness and health-related consulting
services.
89.
VARIABLE APERTURE DESIGN FOR DEVICE Z-HEIGHT REDUCTION
A example variable aperture for a camera of a mobile computing device includes a plurality of aperture blades, where a position of the plurality of aperture blades controls an amount of light that reaches a sensor of the camera. The variable aperture also includes a rotator configured to rotate the plurality of aperture blades along a rotational arc, where rotation of the plurality of aperture blades along the rotational arc adjusts the position of the plurality of aperture blades. The variable aperture also includes a base configured to house the rotator, the base defining a plurality of circumferentially distributed recesses, and where the rotator comprises a plurality of projections configured to move within the plurality of recesses.
G03B 9/06 - Plusieurs lames montées sur pivot et coopérant, p. ex. du type à iris
G03B 30/00 - Modules photographiques comprenant des objectifs et des unités d'imagerie intégrés, spécialement adaptés pour être intégrés dans d'autres dispositifs, p. ex. des téléphones mobiles ou des véhicules
90.
Hot Plugging Of Dynamic Random Access Memorys In Multi-Channel System On Chip Controllers
The technology is generally directed to a compute device having a multi-channel DRAM memory. Software within the device is executed by a processor to receive a request to enter a power save mode (PSM). The request may be provided by user via a user interface. The user may access user-configurable device settings, the PSM mode providing the user with an option to disable a selected number of the memory channels to save power. Upon initiating the request, data stored in the system dynamic random access memory (DRAM) is transferred to a secure persistent memory in the device. The memory controller and DRAM of the remaining active channels is reinitiated. Once reinitiated, the data saved in the persistent memory is swapped back into the remaining active DRAM. Power is isolated from the disabled memory channels to reduce power consumption. The reconfiguration of device DRAM is performed during runtime.
A system on a chip can include a memory, a clock generating a clock signal, and an MBIST controller that issues read/write commands, according to the clock signal, to test the memory. The disclosed system on a chip further includes a clock-decoupling circuit positioned between the clock and the MBIST controller. The clock-decoupling circuit can be configured to decouple the clock from the MBIST controller after a read/write command is issued and until an operation complete signal is received from a multi-cycle access control module. The clock-decoupling circuit allows the MBIST controller to adapt to the multi-cycle access protocol run by the multi-cycle access control module without having to be programmed with its details.
Disclosed implementations for incorporating source reliability and credibility information into the response generation process of search engine systems. In one example, a plurality of snippets responsive to a query are received. Each snippet of the plurality of snippets is associated with a credibility score. A response to the query is generated by processing the plurality of snippets and associated credibility scores through a generative model that includes a mask/attention layer configured to adjust attention weights associated with the plurality of snippets based on the respective credibility scores. The response to the query via the search system.
A lens includes a plurality of optical elements arranged along an optical axis. The plurality of optical elements includes, in order from an object side to an image side of the lens: a first refractive lens element, an aperture stop, a second refractive lens element, a third refractive lens element, a fourth refractive lens element, and a fifth refractive lens element. A diameter of the first refractive lens element is less than 50% of a total track length of the of lens.
G02B 13/06 - Objectifs panoramiquesLentilles dites "de ciel"
G02B 1/04 - Éléments optiques caractérisés par la substance dont ils sont faitsRevêtements optiques pour éléments optiques faits de substances organiques, p. ex. plastiques
G02B 9/62 - Objectifs optiques caractérisés à la fois par le nombre de leurs composants et la façon dont ceux-ci sont disposés selon leur signe, c.-à-d. + ou — ayant uniquement six composants
G02B 13/00 - Objectifs optiques spécialement conçus pour les emplois spécifiés ci-dessous
94.
Image Noise Reduction based on Human Vision Perception
A method includes applying a human perception model to determine a respective measure of noise perception for each tile of a. plurality of tiles of an image captured by an image capturing device. The method further includes determining, by a workload budget manager, a number of tiles of the image to process for noise reduction. The method additionally includes selecting at most the determined number of tiles from the plurality of tiles of the image based on the respective measure of noise perception determined for each tile of the plurality of tiles of the image. The method also includes applying one or more noise reduction processes to the selected tiles to produce a perception-optimized image. The method, additionally includes causing the image capturing device to display the perception-optimized image.
Provided is a new set-wise optimization task that accounts for the effects of total labels of other (or all) items in a co-recommendation set on the individual predictions of item engagement rates. As one example, a training system can train a model to perform the proposed optimization task along with an individual engagement task and/or a ranking task in a multi-task training arrangement.
According to an aspect, a method includes rendering, by a media aggregator application executable by a user device, a user interface that identifies a plurality of media content items hosted from a plurality of streaming platforms, connecting the media aggregator application to a network-enabled display device, rendering a user interface (UI) control on the user interface for streaming a media content item of the plurality of media content items on the network-enabled display device, and, in response to selection of the UI control, transmitting a cast request to the network-enabled display device over a network, where the cast request, when executed by the network-enabled display device, is configured to launch a native application on the network-enabled display device and cause the native application to stream the media content item on a display of the network-enabled display device.
H04N 21/41 - Structure de clientStructure de périphérique de client
H04N 21/414 - Plate-formes spécialisées de client, p. ex. récepteur au sein d'une voiture ou intégré dans un appareil mobile
H04N 21/436 - Interfaçage d'un réseau de distribution local, p. ex. communication avec un autre STB ou à l'intérieur de la maison
H04N 21/462 - Gestion de contenu ou de données additionnelles, p. ex. création d'un guide de programmes électronique maître à partir de données reçues par Internet et d'une tête de réseau ou contrôle de la complexité d'un flux vidéo en dimensionnant la résolution ou le débit en fonction des capacités du client
H04N 21/472 - Interface pour utilisateurs finaux pour la requête de contenu, de données additionnelles ou de servicesInterface pour utilisateurs finaux pour l'interaction avec le contenu, p. ex. pour la réservation de contenu ou la mise en place de rappels, pour la requête de notification d'événement ou pour la transformation de contenus affichés
H04N 21/482 - Interface pour utilisateurs finaux pour la sélection de programmes
H04N 21/485 - Interface pour utilisateurs finaux pour la configuration du client
H04N 21/858 - Création de liens entre données et contenu, p. ex. en liant une URL à un objet vidéo en créant une zone active ("hotspot")
97.
ENABLING A SELECTIVE ACTIVATION PROCEDURE FOR AN INTRA-DISTRIBUTED-UNIT SCENARIO
A central unit (CU) of a distributed base station equipped with a first distributed unit (DU) and a second distributed unit (DU) communicates with a user equipment (UE) in dual connectivity (DC), with the first DU operating as a master node (M-DU), and the second DU operating as a secondary node (Se-DU) (502). The CU transmits, to the Se-DU, a CU-to-DU message including a request for reference conditional DU (C-DU) configuration (590); receives, from the Se-DU, a DU-to-CU message including the reference C-DU configuration (590); and generates a conditional SN (C-SN) configuration for the UE based on the reference C-DU configuration (576).
A distributed unit (DU) of a distributed base station receives, from a central unit (CU) of the distributed base station, a CU-to-DU message including a request to prepare a low-lower layer mobility (LTM) DU configuration for a user equipment (UE), the request including a special cell (SpCell) identity (ID) and a secondary cell (SCell) ID (902). The DU generates the LTM DU configuration for an SpCell corresponding to the SPCell ID and an SCell corresponding to the SCell ID (904A) and transmits the LTM DU configuration to the CU (906A).
Systems and methods of the present disclosure are directed to a computing system. The computing system can obtain a message vector and video data comprising a plurality of video frames. The computing system can process the input video with a transformation portion of a machine-learned watermark encoding model to obtain a three-dimensional feature encoding of the input video. The computing system can process the three-dimensional feature encoding of the input video and the message vector with an embedding portion of the machine-learned watermark encoding model to obtain spatial-temporal watermark encoding data descriptive of the message vector. The computing system can generate encoded video data comprising a plurality of encoded video frames, wherein at least one of the plurality of encoded video frames includes the spatial-temporal watermark encoding data.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing tasks. One of the methods includes obtaining a sequence of input tokens, where each token is selected from a vocabulary of tokens that includes text tokens and audio tokens, and wherein the sequence of input tokens includes tokens that describe a task to be performed and data for performing the task; generating a sequence of embeddings by embedding each token in the sequence of input tokens in an embedding space; and processing the sequence of embeddings using a language model neural network to generate a sequence of output tokens for the task, where each token is selected from the vocabulary.
G10L 25/30 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par la technique d’analyse utilisant des réseaux neuronaux
