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.
2.
LOW COMPLEXITY MACHINE LEARNING-BASED CHANNEL STATE INFORMATION COMPRESSION
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for low-complexity ML-based CSI compression (270a). A UE (102) receives (240), from a network entity (104), a CSI-RS for a channel estimation (250). The UE (102) sends (285), to the network entity (104), a CSI report including a first PMI and a second PMI. The first PMI indicates a wideband preceder (355) associated with the channel estimation (250) and the second PMI indicates a compressed subband eigenvecter associated with the wideband preceder (355).
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
H04B 7/0456 - Sélection de matrices de pré-codage ou de livres de codes, p. ex. utilisant des matrices pour pondérer des antennes
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
3.
Semiconductor Packaging Technique To Reduce Die Edge Stress
Systems, methods, and methods of manufacture are described for implementing a device package for an integrated circuit or packaging feature for an integrated circuit. The package includes multiple layers and a semiconductor die (102) among the multiple layers, where the semiconductor die includes multiple perpendicular comers. The package also includes a mold portion (212) along the multiple corners and the packaging feature includes a semiconductor stress reduction element (302). The mold portion encapsulates the semiconductor die and the multiple layers within the package for the integrated circuit. The semiconductor stress reduction element is: i) encompassed within the mold portion and ii) adjacent at least one corner of the multiple comers of the semiconductor die.
Provided is a parameter-efficient transfer learning method that gains both parameter and inference efficiency. These approaches can in some cases be referred to as conditional adapter (CODA) and example models adapted in the proposed manner can be referred to as CODA models.
Tap-constrained convolutional cross-component model (CCCM) prediction enables hardware coder implementations of CCCM prediction by limiting the number of taps used to predict chroma samples while maintaining accuracy in the prediction. During encoding, a current luma sample of a block is identified. A number of taps to use for predicting a chroma sample associated with the current luma sample is determined based on a size of the block and/or whether the block is downs ampled. The chroma sample is predicted using a prediction model limited to the number of taps and then encoded to an encoded bitstream. During decoding, a current luma sample of a block and a number of taps for predicting a chroma sample associated with the current luma sample are decoded from an encoded bitstream. The chroma sample is predicted using a prediction model limited to the number of taps and then output within an output video stream.
H04N 19/117 - Filtres, p. ex. pour le pré-traitement ou le post-traitement
H04N 19/132 - Échantillonnage, masquage ou troncature d’unités de codage, p. ex. ré-échantillonnage adaptatif, saut de trames, interpolation de trames ou masquage de coefficients haute fréquence de transformée
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 19/186 - 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 couleur ou une composante de chrominance
H04N 19/80 - Détails des opérations de filtrage spécialement adaptées à la compression vidéo, p. ex. pour l'interpolation de pixels
6.
CROSS-DEVICE DATA SYNCHRONIZATION BASED ON SIMULTANEOUS HOTWORD TRIGGERS
Techniques are described herein for cross-device data synchronization based on simultaneous hotword triggers. A method includes: executing a first instance of an automated assistant in an inactive state at least in part on a first computing device operated by a user; while in the inactive state, receiving, via one or more microphones of the first computing device, audio data that captures a spoken utterance of the user; processing the audio data using a machine learning model to generate a predicted output that indicates a probability of one or more hotwords being present in the audio data; determining that the predicted output satisfies a threshold that is indicative of the one or more hotwords being present in the audio data; in response to determining that the predicted output satisfies the threshold, performing arbitration with at least one other computing device that is executing at least in part at least one other instance of the automated assistant; and in response to performing arbitration with the at least one other computing device, initiating synchronization of user data or configuration data between the first instance of the automated assistant on the first computing device and the at least one other instance of the automated assistant on the at least one other computing device, the user data comprising data that is based on one or more interactions with the user at the first computing device, the one or more interactions occurring prior to the receiving of the audio data.
G10L 15/197 - Grammaires probabilistes, p. ex. n-grammes de mots
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet
A method includes executing a process of refining auto-exposure settings for an image sensor. The method also includes determining an autofocus confidence level based on at least one image captured by the image sensor while executing the process of refining the auto-exposure settings for the image sensor. The method further includes, based on the autofocus confidence level, interrupting the process of refining the auto-exposure settings to capture an image with greater exposure than the at least one image. The method additionally includes determining autofocus settings based on the image captured with greater exposure than the at least one image. The method further also includes configuring the image sensor based on the autofocus settings.
H04N 23/67 - Commande de la mise au point basée sur les signaux électroniques du capteur d'image
H04N 23/63 - Commande des caméras ou des modules de caméras en utilisant des viseurs électroniques
H04N 23/72 - Combinaison de plusieurs commandes de compensation
H04N 23/73 - Circuits de compensation de la variation de luminosité dans la scène en influençant le temps d'exposition
H04N 23/741 - Circuits de compensation de la variation de luminosité dans la scène en augmentant la plage dynamique de l'image par rapport à la plage dynamique des capteurs d'image électroniques
H04N 23/75 - Circuits de compensation de la variation de luminosité dans la scène en agissant sur la partie optique de la caméra
A computing device is described that receives a first indication of user input that corresponds to a capture action to capture content being outputted for display at a display device. The computing device may, in response to the content being captured, output, for display at the display device, a graphical user interface element that includes indications of a plurality of actions, wherein the plurality of actions include one or more of an edit action, a cross-device sharing action, a cross-application sharing action, or one or more recommended actions. The computing device may receive a second indication of user input that corresponds to selection of an action of the plurality of actions indicated by the GUI element. The computing device may, in response to receiving the second indication of user input that corresponds to selection of the action, perform the action.
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
G06F 40/166 - Édition, p. ex. insertion ou suppression
9.
GENERATING HUMAN-READABLE SYNTHETIC TEXT FOR TRAINING GENERATIVE NEURAL NETWORKS
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating human-readable synthetic text for training generate neural networks thereon.
The technology generally relates to spatial audio communication between devices. For example, a first device and a second device may be connected via a communication link. The first device may capture audio signals in an environment through two or more microphones. The first device may encode the captured audio with direction information. The first device may transmit the encoded audio via the communication link to the second device. The second device may decode the encoded audio to be output by one or more speakers of the second device. The second device may output the decoded audio to recreate positions of the captured audio signals.
In general, techniques are described by which to enable secure virtualization for third party graphics drivers. A computing device comprising a memory and processing circuitry may be configured to perform the techniques. The memory may store a host operating system. The processing circuitry may execute the host operating system, which is configured to initiate execution of a main process that emulates a guest operating system to support execution of an application native to the guest operating system. The main process may initiate execution of a graphics rendering process. The main process may next, responsive to initiating execution of the graphics rendering process, perform a memory validation to obtain memory mapping information that identifies memory that is available for mapping between the main process and the graphics rendering process. The main process may configure the main process to support the mapping of the memory.
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
12.
FACILITATING MODEL OUTPUT MODIFICATIONS VIA PHYSICAL GESTURE DIRECTED TO PORTION OF GENERATIVE OUTPUT
Implementations set forth herein relate to modifying a generative output of an application according to an input gesture that is performed without necessarily interacting with a GUI element that is rendered separate from a generative output (e.g., a GUI element separate from a natural language output generated using an LLM or an image generated using an image diffusion model). Various different input gestures can be performed by a user to refine a generative output to be simpler, more complex, to include an image, to modify a generated image, and/or otherwise modify the generative output. In some implementations, an input gesture can be processed as one or more predetermined gestures, and/or an input gesture can be interpreted per case using an available model for interpreting such gestures. In this way, models for interpreting gestures and/or refining generative output can be enhanced through further training of such models.
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 3/14 - Sortie numérique vers un dispositif de visualisation
A curved folded color-corrected rotationally symmetric collimator (200) includes a first portion (204) that collimates display light generated by a micro-display (202) for coupling to a second portion that includes a curved lightguide (206) via a turning prism (220) and a folded, reflective incoupler (208). The curved collimator further incorporates a diffractive optical element (218) for color correction. The curved collimator and the curved lightguide together output a fully collimated pupil at or near the middle of an eyewear display lens to interact with a one-dimensional pupil expander (300) within the curved lightguide.
Various implementations described herein relate to displaying images with different dynamic ranges on display units with different capabilities. In some implementations, a computer-implemented method includes obtaining an initial image comprising an image having a first dynamic range and metadata that includes a gain map with information about a relationship between the image and a version of the image having another dynamic range; receiving input indicative of a control value corresponding to a target dynamic range; generating an updated image for display on a display unit of a client device by using the metadata to modify the image based on the control value and a boost value associated with the display unit; and displaying the updated image on the display unit. The control value may be received from a slider control element, allowing a user to adjust an amount of additional dynamic range incorporated into the image.
Techniques and devices for sharing intelligence-derived information by a hub in a home network are described in which the hub exposes a virtual device including one or more clusters on the home network and receives from a partner device a request to subscribe to a cluster of the one or more clusters. The hub receives state information from an intelligence service, stores the received state information as an attribute of the cluster, and publishes the attribute of the cluster to the partner device, the publishing being effective to direct the partner device to determine whether to perform a local action based on the attribute. Alternatively, the hub receives, from a partner device, an advertisement of a custom cluster installed at the partner device and provides state information by sending a command to the custom cluster at the partner device, the command being indicative of the received state information.
H04L 12/28 - Réseaux de données à commutation caractérisés par la configuration des liaisons, p. ex. réseaux locaux [LAN Local Area Networks] ou réseaux étendus [WAN Wide Area Networks]
16.
WAVEGUIDE GRATING DEPTH AND FILLING FACTOR DUAL MODULATION
Inverse aspect-ratio dependent etching (ARDE) effects are utilized to produce grating structures with modulation of etch depth and/or grating filling factor. A series of grating trenches (201-209) is defined for etching in an optical substrate of a waveguide (210), each grating trench having a depth and a width. The series of grating trenches is formed in the optical substrate by removing a portion of the optical substrate from each of the grating trenches, such that forming the series includes modulating a respective etch depth of each grating trench of the series of grating trenches along a first dimension (250).
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining whether to perform a translation lookaside buffer (TLB) invalidation procedure. In one aspect, a system comprises a computing device configured to receive a request to perform the TLB invalidation procedure, where the request specifies an application space identifier (ASID) for multiple virtual addresses. The computing device is configured to maintain multiple Bloom filters, where each Bloom filter corresponds to a respective hash function, and the computing device provides the ASID as input to each hash function to obtain multiple hash values. The computing device is configured to determine whether one of the hash values is not represented in one of the Bloom filters, and, in response, the computing device bypasses performing the TLB invalidation procedure.
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]
18.
IMPROVING CONTRASTIVE LOSS FOR DUAL ENCODER RETRIEVAL MODELS WITH SAME TOWER NEGATIVES
The technology provides neural network models having dual encoder architectures. For instance, a token embedder layer section of a dual encoder is associated with a first input and a second input and generates token embeddings (2002). An encoder layer section receives the token embeddings from the token embedder layer and generates encodings based on the token embeddings (2004). A projection layer receives the encodings from the encoder section and generates a set of projections (2006). The projection layer is shared by the dual encoders (FIG. 4C). An embedding space generates, based on the set of projections, a question embedding and a response embedding (2008). These question and response embeddings are used in identifying a set of candidate responses to an input question (2008). The embedding space is configured during training according to a contrastive loss that applies same tower negatives to generate the question and response embeddings (2010).
G06F 11/10 - Détection ou correction d'erreur par introduction de redondance dans la représentation des données, p. ex. en utilisant des codes de contrôle en ajoutant des chiffres binaires ou des symboles particuliers aux données exprimées suivant un code, p. ex. contrôle de parité, exclusion des 9 ou des 11
A method includes obtaining proximity information for each of a plurality of assistant-enabled devices within an environment of a user device. Each assistant-enabled device is controllable by an assistant application to perform a respective set of available actions associated with the assistant-enabled device. For each assistant-enabled device, the method also includes determining a proximity score based on the proximity information indicating a proximity estimation of the corresponding assistant-enabled device relative to the user device. The method further includes generating, using the proximity scores determined for the assistant-enabled devices, a ranked list of candidate assistant-enabled devices, and for each corresponding assistant-enabled device in the ranked list, displaying, in a graphical user interface (GUI), a respective set of controls for performing the respective set of actions associated with the corresponding assistant- enabled device. 49 75276250.1
This disclosure describes CSI reports based on ML techniques. A UE receives one or more CSI-RSs from a network entity for generating one or more CSI reports. When at least one CSI report is based on an ML model, the UE may compress the at least one CSI report with a selected wideband precoder using one or more subband eigenvectors for one or more precoded estimated subband channels. The UE may also omit at least a portion of the at least one CSI report when a total payload size of the one or more CSI reports exceeds a threshold. The UE may be configured for parallel processing of a plurality of CSI reports that are based on the ML model and/or parallel processing of a first CSI report based on the ML model and a second CSI report that is not based on the ML model.
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
H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
21.
USING MACHINE LEARNING MODELS FOR INFORMATION RETRIEVAL DURING NON-TERRESTRIAL NETWORK COMMUNICATION
An example computing device connected with an accessible non-terrestrial network receives data indicative of a user input and determines whether an amount of the data exceeds a maximum amount of data that can be transmitted via the connection within a predetermined time frame. Responsive to determining the amount of the data exceeds the maximum amount of data, the computing device generates modified data by applying transformations to the data indicative of the user input. Responsive to determining an amount of the modified data does not exceed the maximum amount of data, the computing device transmits, to a computing system via the connection, the modified data and a prompt including context information for the computing device. The computing device receives, from the computing system, output generated by the computing system based on the modified data and the prompt. The computing device generates, for display, a user interface including the output.
This document describes systems and techniques directed at a large language model (LLM) augmented user emergency interface. A user device receives, via a user emergency interface, a free-form input associated with an emergency event. An LLM, at least partially deployed on the user device, extracts one or more relevant details of the emergency event from the free-form input. The free-form input can be a text input, a voice input, or a natural language input. The LLM generates an emergency message based on the one or more relevant details. The user device provides, for an emergency messaging session, the emergency message. The LLM can further be configured to compress the emergency message prior to the providing of the emergency message over a bandwidth-constrained network or a latency-constrained network, such as a satellite communication network.
Examples of the present disclosure are directed to a method for operating a quantum computing system (QCS) to load classical data. The method includes configuring a quantum circuit to implement a target classical function that is defined over a string of input bits. The target classical function is decomposed into a set of parity sub-functions. A set of quantum registers of the QCS is initialized with a superposition of input states corresponding to the string of input bits. A sequence of Quantum Read-Only Memory (QROM) operations is executed on the set of quantum registers to generate a set of outputs. Each QROM operation of the sequence of QROM operations encodes a separate parity sub-function of the set of parity sub-functions. Each output of the set of outputs is routed to a separate output register of a set of output registers of the QCS.
G06N 10/20 - Modèles d’informatique quantique, p. ex. circuits quantiques ou ordinateurs quantiques universels
G06N 10/40 - Réalisations ou architectures physiques de processeurs ou de composants quantiques pour la manipulation de qubits, p. ex. couplage ou commande de qubit
24.
Methods and Systems for High Bit Depth and High Dynamic Range Image Compression
An example method includes receiving, in a frame buffer of a computing device, an image captured by an image capturing device of the computing device. The image comprises a first number of bits. The method includes applying, based on the first number of bits, a linear transform to the received image. The linear transform comprises: (i) a color transform, and (ii) a discrete cosine transform (DCT), to generate a frequency spectrum comprising one or more frequency components. The method includes quantizing respective amplitudes of the one or more frequency components. A number of quantization steps to be applied is configured to reduce error in the one or more frequency components. The method includes generating, by an image processor and based on the quantizing, a compressed image comprising a second number of bits. The second number of bits is larger than a bit capacity for compression associated with the image processor.
H04N 19/132 - Échantillonnage, masquage ou troncature d’unités de codage, p. ex. ré-échantillonnage adaptatif, saut de trames, interpolation de trames ou masquage de coefficients haute fréquence de transformée
H04N 19/136 - Caractéristiques ou propriétés du signal vidéo entrant
H04N 19/184 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant des bits, p. ex. de flux vidéo compressé
H04N 19/186 - 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 couleur ou une composante de chrominance
H04N 19/625 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant un codage par transformée utilisant une transformée en cosinus discrète
25.
RADAR SENSING USING ADAPTIVE PHASE-CHANGING DEVICES (APDS)
This disclosure provides devices, methods, and systems for radar sensing using adaptive phase-changing devices (APDs). An example device (e.g., a base station, a user equipment, a satellite network entity, etc.) first transmits (340) initial radar signals in multiple directions to identify a blocking object. Based on the identified position of the blocking object, the device may employ (366) an APD (e.g., based on its registered position) that helps routing signals around the at least one blocking object (such as an APD positioned to reflect the radio waves from the device to reach behind the blocking object). The device has the APD vary (377) phase vectors to change reflection directions of APD radar sensing signals to scan for a blocked object blocked by the blocking object. When the device finds a blocked object, the device determines (386) a set of APD configurations for a signal path specific to the blocked object.
Implementations are directed to generating voice-based chatbot policy override(s) and/or utilizing voice-based chatbot policy override(s) in conjunction with existing voice-based chatbot(s). The voice-based chatbot policy override(s) can correspond to, for example, machine learning (ML) model(s) that supplement functionality of the existing voice-based chatbot(s). Notably, the voice-based chatbot policy override(s) are associated with rule(s) (e.g., by virtue of training the ML model(s) that correspond to the voice-based chatbot policy override(s)) for when the voice-based chatbot policy override(s) should be utilized in lieu of the existing voice-based chatbot(s) in responding to spoken utterance(s) of human user(s) engaged in corresponding conversation(s) with the voice-based chatbot policy override(s). Nonetheless, from a perspective of the human user(s), it appears as if they are still engaging in the corresponding conversations with the existing voice-based chatbot(s). Thus, the functionality of the existing voice-based chatbot(s) can be supplemented without having to re-train the existing voice-based chatbot(s).
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
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
27.
COMPUTER CODE MODIFICATION USING LANGUAGE MODEL NEURAL NETWORKS
Systems and methods for performing software development tasks using a language model neural network. The software development tasks can include any of a variety of tasks that require modifying computer program code in one or more programming languages. For example, the language model neural network can be used to predict code edits that will be made to a piece of computer code, to predict comments that will be left on a piece of computer code by a code reviewer, to resolve comments on a piece of computer code, to predict edits that will repair a given code build, or to predict build errors that will result from compiling a piece of computer code. To perform these tasks, the system represents code development actions as tokens in a domain-specific language and processes these tokens using the language model neural network.
Systems and methods for iterative non-autoregressive image synthesis using a first generative model and an independent second token-critic model. In some examples, an image may be synthesized using one or more passes in which the generative model predicts a first plurality of tokens representing a first vector-quantized image, the token-critic model generates a first plurality of scores based on the first plurality of tokens, the processing system selects a first set of one or more tokens of the first plurality of tokens to be preserved based on the first plurality of scores, and the generative model then predicts a second plurality of tokens based on the first set of tokens, the second plurality of tokens including the first set of tokens. In some examples, the generative model may be configured to predict probability distributions, which may be sampled to generate the first and second pluralities of tokens.
Visual search in an operating system of a computing device can process and provide additional information on the content being provided for display. The computing device can include an operating system that includes a visual search interface that obtains and processes display data associated with content currently being provided for display. The visual search interface can generate display data based on the current content provided for display, process the display data with one or more on-device machine-learned models, and provide additional information to the user. The visual search interface may transmit data associated with the display data to perform additional data processing tasks. Application suggestions may be determined and provided based on the visual search data.
A first computing device is configured to receive an indication of subscription request to at least one data of a plurality of data fields of a media bridging protocol from a second computing device. The first computing device receives the indication of subscription request as specifying one or more types of messages that correspond to at least one of the plurality of data fields and that are to be sent via the bridging protocol to the second computing device. The first computing device is further configured to generate messages for the plurality of data fields and determine a subset of the generated messages that are associated with the subscribed data fields to be provided to the second computing device. The first computing device is further configured to provide the subset of messages to the second computing device via the bridging protocol.
H04L 67/60 - Ordonnancement ou organisation du service des demandes d'application, p. ex. demandes de transmission de données d'application en utilisant l'analyse et l'optimisation des ressources réseau requises
H04L 47/24 - Trafic caractérisé par des attributs spécifiques, p. ex. la priorité ou QoS
31.
DETERMINING WHETHER AND/OR HOW TO IMPLEMENT REQUEST TO PREVENT PROVISION OF SEARCH RESULT(S)
Implementations relate to determining whether and/or how to implement a user request to prevent a particular search result from being provided in response to a search query. Some of those implementations grant or deny the request based on processing of the particular search result, the search query, and/or account information for a user submitting the user request. For example, some implementations process such information utilizing a classifier in determining whether to automatically deny the request, automatically approve the request, or to provide the request for manual review. Some additional or alternative implementations at least selectively automatically expand (or suggest for automatic expansion) an approval of a request to search result(s) and/or to one or more search queries that are not specified in the request.
This disclosure provides methods and apparatuses for network entities to exchange sensing configuration information in a wireless communications system that supports integrated sensing and communication (ISAC). A network entity (120A) exchanges sensing configuration information (121A, 121B) with a second network entity (120B). The sensing configuration information includes one or more parameters that may be used when performing sensing operations. A network entity can trigger another network entity to perform sensing operations and to report measurements obtained or derived from the sensing operations. The network entity can forward the sensing measurement report to other network entities or to a sensing function (110) of a core network (150).
H04W 24/10 - Planification des comptes-rendus de mesures
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
Examples of the present disclosure are directed to a method for operating a quantum computing system (QCS) to load classical data. The method includes configuring a quantum circuit to implement a target classical function that is defined over a string of input bits. The target classical function is decomposed into a set of parity sub-functions. A set of quantum registers of the QCS is initialized with a superposition of input states corresponding to the string of input bits. A sequence of Quantum Read-Only Memory (QROM) operations is executed on the set of quantum registers to generate a set of outputs. Each QROM operation of the sequence of QROM operations encodes a separate parity sub-function of the set of parity sub-functions. Each output of the set of outputs is routed to a separate output register of a set of output registers of the QCS.
G06N 10/20 - Modèles d’informatique quantique, p. ex. circuits quantiques ou ordinateurs quantiques universels
G06N 10/80 - Programmation quantique, p. ex. interfaces, langages ou boîtes à outils de développement logiciel pour la création ou la manipulation de programmes capables de fonctionner sur des ordinateurs quantiquesPlate-formes pour la simulation ou l’accès aux ordinateurs quantiques, p. ex. informatique quantique en nuage
34.
TECHNIQUES FOR DETERMINING CROSS PLATFORM USER JOURNEY AND ATTRIBUTION USING A DYNAMIC BANNER
Systems and methods for determining cross platform attribution. The system receives, from a user device of a user, user interaction data associated with an interaction of the user with a website associated with an entity. The user interaction data can be generated by a banner that is embedded in the website. In response to receiving the user interaction data, the system can cause a mobile application associated with the entity to be launched on the user device. Additionally, the system can receive, from the mobile application, application event data associated with an action performed in the mobile application. Moreover, the system can process the application event data to generate attribution data. Furthermore, the system can store the attribution data in an attribution database.
09 - Appareils et instruments scientifiques et électriques
Produits et services
(1) Integrated circuit chips for mobile computing sold as a component of mobile phones, smartphones, and electronic devices; Integrated circuit chips for mobile computing sold as a component of mobile phones, smartphones, and electronic devices specifically designed for artificial intelligence (AI), namely, integrated circuit chips that power AI; computer hardware, namely, computers, mobile phones, mobile devices, tablets, glasses, smartwatches, televisions, gaming consoles, smart home devices, and computer systems for vehicles, powered by AI and machine learning; AI supercomputers; computer hardware for AI, machine learning, deep learning, natural language generation, statistical learning, supervised learning, un-supervised learning, inferencing, cognitive computing, and computer visions; computer hardware for AI and systems for autonomous navigation and power management of motor vehicles; computer hardware for collecting, analyzing, storing, managing, transmitting, and receiving information and data within machine-based cognitive architectures and neural networks.
38.
Managing Cell Group Configurations for Conditional Secondary Node Procedures
A network node, operating as a candidate secondary node (C-SN), can implement a method for managing a conditional procedure that involves a user equipment (UE), the C-SN, and a network node operating as an MN. The method includes generating (902) an information element for conveying a list of one or more candidate cells of the C-SN for one of (i) addition or modification or (ii) release of cell configurations. The list includes, for each candidate cell of the one or more candidate cells, a respective conditional configuration associated with a condition to be satisfied for the UE to connect to the candidate cell.
A method can include measuring, at one or more mid-cycle states of a quantum error correction code executing on a quantum computing system, one or more mid-cycle gauge operators and one or more mid-cycle stabilizers. In the method, the one or more mid-cycle gauge operators comprise one or more single-qubit mid-cycle gauge operators. The method can include performing, based at least in part on a result of the measuring, a quantum error correction operation.
Decoding using compound warp inter-intra prediction includes obtaining reconstructed block data for a current block. Obtaining the reconstructed block data includes accessing, from an encoded bitstream, compound warp inter-intra mode data indicating that the current block is coded using compound warp inter-intra mode, accessing, from the encoded bitstream, intra prediction mode data indicating an intra prediction mode for the current block, generating intra prediction block data for the current block in accordance with the intra prediction mode data, generating warp inter prediction block data for the current block, obtaining predicted block data for the current block by combining the warp inter prediction block data and the intra prediction block data, obtaining decoded block data by decoding encoded block data accessed from the encoded bitstream, and including, in the reconstructed block data, a sum of the decoded block data and the predicted block data.
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 19/46 - Inclusion d’information supplémentaire dans le signal vidéo pendant le processus de compression
Generally, the present disclosure is directed to systems and methods that provide a simple, scalable, yet effective strategy to perform transfer learning with a mixture of experts (MoE). In particular, the transfer of pre-trained representations can improve sample efficiency and reduce computational requirements for new tasks. However, representations used for transfer are usually generic, and are not tailored to a particular distribution of downstream tasks. In contrast, example systems and methods of the present disclosure use expert representations for transfer with a simple, yet effective, strategy.
A cyber-security analysis method uses machine learning (ML) technology to classify cyber-threat indicators, for example, as malicious or benign, by generating a threat score. The method includes receiving, at a compute device, a cyber-threat indicator (IUE) and associated verdicts from a set of sources. Augmenting the verdicts associated with the IUE with verdicts associated with at least one related indicator having a defined relationship with the TUE. The relationship between the IUE and the at least one related indicator can be operational, e.g., based on an administrative domain, or functional, e.g., based on a protocol specification. The cyber-threat score is generated for the TUE based on the ML model and the combined verdicts of the TUE and the at least one related indicator.
H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
H04L 41/22 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets comprenant des interfaces utilisateur graphiques spécialement adaptées [GUI]
43.
Generating Radar-Based Gesture Detection Events in an Ambient Compute Environment
Techniques and apparatuses are described that generate radar-based gesture detection events in an ambient compute environment. Compared to other smart devices that rely on a physical user interface, a smart device with a radar system can support ambient computing by providing an eye-free interaction and less cognitively demanding gesture-based user interface. The radar system uses an ambient-computing machine-learned module to quickly recognize gestures performed by a user up to at least two meters away. To improve the false positive rate, a gesture debouncer evaluates class probabilities generated by the ambient-computing machine-learned module. In particular, the gesture debouncer recognizes a gesture if a probability of a gesture class is greater than a first threshold for one or more consecutive frames. The first threshold is determined to balance recall and false positive performance of the radar system.
This document describes a secure machine learning platform. In some aspects, a method includes transmitting by the application to the machine learning platform, a set of data including a user profile, one or more characteristics of a digital component, contextual signals, model identifier, and data indicating a type of event. The application receives a request generated based on the computer-readable instructions to upload a user profile of a user of the client device to a machine learning platform. The computer-readable instructions initiate the request in response to detecting an occurrence of the event with the digital component. In response to the request, the application can obtain the user profile request data element that includes a model identifier for a machine learning model and one or more characteristics of at least one of the digital component or the first content page.
A media application receives, from a server, an identification of a first composition type from a set of compositions to apply to an initial image captured with a user device. Responsive to one or more people being detected in the initial image, the media application generates a modified image, where the one or more people are removed from the initial image to obtain the modified image. The media application scores at least one candidate position within the modified image based on corresponding composition rules for the first composition type. The media application provides a graphical guide on a viewfinder of the user device to guide a user to capture a final image, wherein the graphical guide indicates a recommended position for the one or more people in the final image.
A method of providing emotive text-to-speech includes obtaining input text characterizing a natural language response generated by an assistant LLM to a query input by a user during a conversation between the user and the assistant LLM, and processing, using the assistant LLM, the input text conditioned on an emotion detection task prompt to predict, as output from the assistant LLM, an emotional state of the natural language response. The method also includes determining, based on the emotional state of the natural language response predicted as output from the assistant LLM, an emotional embedding for the input text and instructing a TTS model to process the input text and the emotional embedding to generate a synthesized speech representation of the natural language response conveying the emotional state of the natural language response as specified by the emotional embedding.
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 13/10 - Règles de prosodie dérivées du texteIntonation ou accent tonique
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
G10L 25/63 - 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 pour estimer un état émotionnel
A method involves receiving a perceptual representation including a plurality of feature vectors, and initializing a plurality of slot vectors represented by a neural network memory unit. Each respective slot vector is configured to represent a corresponding entity in the perceptual representation. The method also involves determining an attention matrix based on a product of the plurality of feature vectors transformed by a key function and the plurality of slot vectors transformed by a query function. Each respective value of a plurality of values along each respective dimension of the attention matrix is normalized with respect to the plurality of values. The method additionally involves determining an update matrix based on the plurality of feature vectors transformed by a value function and the attention matrix, and updating the plurality of slot vectors based on the update matrix by way of the neural network memory unit.
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
An update parameter modifier for a probability model parameter that controls an update function for a probability model for entropy coding the sequence of syntax elements is determined. The probability model parameter is modified using the update parameter modifier to obtain a modified probability model parameter. A symbol for a syntax element of the sequence is entropy coded using the probability model. The probability model is then updated using the update function with the modified probability model parameter.
H04N 19/13 - Codage entropique adaptatif, p. ex. codage adaptatif à longueur variable [CALV] ou codage arithmétique binaire adaptatif en fonction du contexte [CABAC]
The technology is generally directed identifying content responsive to a search query having a format corresponding to a determined query intent. The format may be, for example, images, videos, text, audio, or a combination of these formats. The query intent may indicate a given format for the responsive content. The query intent may correspond to an intent index value, which indicates the likelihood that the search query is for content having a given format. The intent index value may be determined using an algorithm, such as a ratio or an artificial intelligence model. The intent index value may be used to identify the content responsive to the search query.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a large language model (LLM) to predict generalizations of public queries. The generalizations are then used to generate retrieval tokens for annotating the public queries.
The technology is generally directed to protecting data privacy in query results. In response to a query, data responsive to the query results is identified. The responsive data can include rows of data. The rows of data are aggregated based on an aggregation function. A function or hash of the aggregated values may be used to generate a signature associated with the rows of data within the query results. The signatures of the current query may be compared to other signatures associated with the current query results and/or historical query result signatures, e.g., signatures associated with results of previously submitted queries. If the similarity of the signature associated with the current query result is above a threshold similarity to another signature, the query and its associated results are identified as a privacy attack. Preventative actions may be enabled in response to determining that the query is a privacy attack.
An example method includes receiving a user query to search for a target in a live camera preview of a scene. The method includes detecting whether the target is present in a first frame of a plurality of frames of the live preview. The detecting comprises applying a detection algorithm on at least a larger context of the scene in the first frame. The method includes, responsive to detecting that the target is present in the first frame: pausing the detection algorithm on frames successive to the first frame, predicting a relative location of the detected target in the larger context based on a previously determined location of the detected target relative to the larger context, and initiating a tracking of the detected target in the plurality of frames based on the predicted relative location of the detected target. The method includes providing, in the live preview, the tracked target.
G06V 10/62 - Extraction de caractéristiques d’images ou de vidéos relative à une dimension temporelle, p. ex. extraction de caractéristiques axées sur le tempsSuivi de modèle
G06V 20/20 - ScènesÉléments spécifiques à la scène dans les scènes de réalité augmentée
G06V 20/62 - Texte, p. ex. plaques d’immatriculation, textes superposés ou légendes des images de télévision
53.
LOCALLY MAINTAINING GLOBAL NAVIGATION SATELLITE SYSTEM (GNSS) TIME BASED ON CELLULAR RADIO SIGNALS
An example method includes receiving, by a mobile computing device and from a cellular node, a plurality of radio frames; selectively maintaining, by the mobile computing device and based on information of the cellular node, a global navigation satellite system (GNSS) clock based on temporal spacing of the plurality of cellular radio frames; receiving, by a GNSS processor of the mobile computing device, a GNSS time via the GNSS clock; and acquiring, by the GNSS processor and based on the GNSS time received via the GNSS clock, a first fix for the mobile computing device.
G01S 19/25 - Acquisition ou poursuite des signaux émis par le système faisant intervenir des données d'assistance reçues en provenance d'un élément coopérant, p. ex. un GPS assisté
54.
SYSTEMS AND METHODS FOR INFERRING AND RESOLVING POTENTIAL ACCOUNT PROBLEMS
Systems and methods for generating personalized solution recommendations for a user problem. Such a method includes (i) receiving an error indication from a user associated with an account profile including user account data; (ii) retrieving, based on the error indication, an information resource of a plurality of information resources; (iii) inferring, based on the user account data, a problem associated with the information resource and the account profile; (iv) generating, using a trained machine learning model, an information resource summary for the information resource by using the information resource and the inferred problem as inputs to the trained machine learning model; (v) determining whether one or more metrics associated with the information resource summary meet one or more quality criteria; and (vi) training the trained machine learning model based on (a) whether the one or more metrics meet the one or more quality criteria and (b) the information resource summary.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating an output video conditioned on an input. In one aspect, a method comprises receiving the input; initializing a current intermediate representation; generating an output video by updating the current intermediate representation at each of a plurality of iterations, wherein the updating comprises, at each iteration: processing an intermediate input for the iteration comprising the current intermediate representation using a diffusion model that is configured to process the intermediate input to generate a noise output; and updating the current intermediate representation using the noise output for the iteration.
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 10/771 - Sélection de caractéristiques, p. ex. sélection des caractéristiques représentatives à partir d’un espace multidimensionnel de caractéristiques
A user equipment receives (509), from a radio access network (RAN) in a serving cell, a measurement configuration for a candidate cell. The UE receives (520), subsequently to the receiving of the measurement configuration and when the measurement configuration is deactivated, an indication of a non-active period in the serving cell. In response to the indication of the non-active period, the UE activates (535 A) the measurement configuration for the candidate cell.
Implementations relate to a drafting assistant that assists users in generating prompts for a language model that generates responses for text boxes for a web page. Implementations may receive a prompt from a user regarding an input for the text box, generate a modified prompt by incorporating contextual information identified from the web page, and provide the modified prompt to a generative language model, which generates a response for the modified prompt. The response is presented to the user and can be used as the input for the text box. Implementations dynamically engineer/enhance prompts based on the context of the web page, thereby facilitating more accurate and relevant responses from the generative language model.
A computer-implemented method for efficiently capturing statistics on long running queries includes obtaining a query and obtaining a linked list including a plurality of records, each record in the plurality of records including respective query execution statistics. The method includes executing the query and, during execution of the query, obtaining new query execution statistics associated with the executing query. The method includes creating a new record in the linked list, the new record including the new query execution statistics. The method includes determining that a query execution duration of the query satisfies a query execution threshold. Further, the method includes identifying each record in the linked list that corresponds to the query. The method includes storing, for each respective identified record in the linked list that corresponds to the query, the respective query execution statistics of the respective identified record in a statistics database.
Provided is a scalable and cost-efficient storage architecture for large-scale datasets, such as Internet-scale datasets that include very large numbers (e.g., billions) of data elements. More particularly, provided is a bifurcated storage architecture that includes a first data index stored by a first set of storage media and a second data index stored by a second set of storage media, where the first set of storage media has a lower latency than the second set of storage media.
A method is described for determining power grid density on an integrated circuit. The method includes determining an initial placement of power switches, logic circuits, the power grid, and the logic grid. The method creates tiles covering the entire integrated circuit and assigns a power grid density and a logic grid density for each tile. Power losses are simulated for the entire chip and chip timing function is simulated. Based on the simulated power losses and the simulated chip timing, the power grid density and the logic grid density are adjusted on a per tile basis. The assignment of power grid density and logic grid density and simulations are iterated until a cessation condition is met. After the cessation condition is met, a final chip simulation is performed and the final routing is determined.
H03K 17/56 - Commutation ou ouverture de porte électronique, c.-à-d. par d'autres moyens que la fermeture et l'ouverture de contacts caractérisée par l'utilisation de composants spécifiés par l'utilisation, comme éléments actifs, de dispositifs à semi-conducteurs
61.
INTERACTIVE GUI ELEMENTS FOR INDICATING OBJECTS TO SUPPLEMENT REQUESTS FOR GENERATIVE OUTPUT
Implementations set forth herein relate to a graphical user interface (GUI) element that can be manipulated at an interface to indicate a particular object and/or feature of interest to be considered when providing generative output for a separate user request. One or more GUI elements can be provided at a display interface, such as a touch display panel and/or virtual or augmented reality display interface, thereby allowing the GUI elements to be associated with rendered and/or tangible objects. When a user interacts with a GUI element, the GUI element can exhibit responsive behavior that is based on features of the interaction and/or other features of a particular object. When an object of interest is identified, processing can be performed to identify information about the object, and this information can then be utilized to facilitate provisioning of a generative output that is responsive to a separate user request.
G06F 3/04815 - Interaction s’effectuant dans un environnement basé sur des métaphores ou des objets avec un affichage tridimensionnel, p. ex. modification du point de vue de l’utilisateur par rapport à l’environnement ou l’objet
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
62.
Managing selective activation for continuous PSCell addition or change
A user equipment (UE) receives (308), from a first node of a radio access network (RAN), (i) a conditional secondary node (C-SN) configuration related to a plurality of candidate cells for connecting subject to a respective condition, (ii) an indication that the C-SN configuration is for a continuous primary secondary cell (PSCell) addition or change (CPAC) procedure; connects (316, 318) to a first one of the plurality of candidate cells associated with a second node in response to determining that a corresponding first condition is satisfied; communicates (336) with the RAN in dual connectivity (DC) in which the first node operates as a master node (MN), and the second node operates as a secondary node (SN); and connects (395) to a second one of the plurality of candidate cells in response to determining that a corresponding second condition is satisfied, using the received C-SN configuration.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling an agent interacting with an environment. In one aspect, a method comprises: receiving an observation image of an environment; receiving an input text sequence; generating an object localization input that includes the observation image; processing the object localization input using an object localization neural network to generate an object localization output that identifies respective locations of the one or more objects in the observation image; generating a policy input based on the observation image, the input text sequence, and the object localization output; processing the policy input using a policy neural network to generate a policy output that defines an action to be performed by the agent in response to the observation image; selecting an action to be performed by the agent using the policy output; and causing the agent to perform the selected action.
Methods and devices in a wireless network enable transmitting boosted phase tracking reference signals when using a subset of antenna ports. The network directs a user equipment to boost the power level of one or more antenna ports usable to transmit the phase tracking reference signals, and/or to use a precoder corresponding to a subset-codebook-coherency different yet compatible with a coherency type supported by user equipment's full set of antenna ports.
H04L 27/26 - Systèmes utilisant des codes à fréquences multiples
H04W 52/32 - TPC des canaux de radiodiffusion ou de commande
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
65.
METHODS FOR CHANNEL STATE INFORMATION REFERENCE SIGNAL OVERHEAD REDUCTION FOR CHANNEL CORRELATION REPORT
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for reducing overhead associated with configuring and measuring TRSs carried on CSI-RS resources to generate A channel correlation report. A UE (102) receives (904), from a network entity (104), a configuration for a channel correlation report. The configuration indicates a CMR that carries a TRS. The UE (102) receives (910), from the network entity (104), a triggering indication for the channel correlation report based on the CMR carrying the TRS. The UE (102) receives (912), from the network entity (104), the TRS. The TRS includes a periodic TRS, an aperiodic TRS, a semi-persistent TRS, or any combination thereof. The UE (102) transmits (916), to the network entity (104), the channel correlation report. The channel correlation report includes measurement information for a channel correlation associated with the TRS.
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for a UE sensing and reporting technique to assist a network entity (104) with generating an electromagnetic digital twin, EMDT, with increased accuracy. A UE (102) receives (206), from a network entity (104), a first resource grant for sensing an electromagnetic environment for electromagnetic propagations, a first sensing configuration for an EMDT sensing, a first sensing report configuration for reporting of the electromagnetic propagations within the electromagnetic environment of the UE (102), and a second resource grant for reporting sensing results of the sensing. The UE (102) transmits (210, 310), to the network entity (104) according to the second resource grant, an EMDT sensing report including the sensing results. The UE (102) obtains the sensing results according to the first resource grant, the first sensing configuration, and the first sensing report configuration.
This document describes systems and techniques for adaptive co-optimization of operating levels of subsystems of a computing device. For example, a system may include a co-optimization manager configured to use an operating profile for a plurality of subsystems of a computing device, the operating profile being configured to facilitate adaptive control of operating levels of each of the plurality of subsystems. A plurality of operating controllers is configured to generate operating limits for the plurality of subsystems based on operating targets and optimization parameters indicated by the operating profile and feedback signals received from the plurality of subsystems. A subsystem control unit is configured to set the operating levels for each of the plurality of subsystems, wherein the operating levels are based on the operating limits received from the plurality of operating controllers subject to adjustment according to the operating profile.
A broadcast framework for providing an audio stream to two or more receiver devices includes user equipment (UE) broadcasting packets of the audio stream on isochronous channels based on a broadcast channel mask. Concurrently with broadcasting these packets, the UE listens for advertisement packets each indicating a corresponding receiver channel mask and corresponding link quality data from the receiver devices receiving the audio stream. Using the link quality data from the advertisement packets, the UE weighs the receiver channel masks from the advertisement packets and modifies the broadcast channel mask based on the weighted receiver channel masks. The UE then broadcasts packets of the audio stream based on the modified broadcast channel mask.
H04L 65/611 - Diffusion en flux de paquets multimédias pour la prise en charge des services de diffusion par flux unidirectionnel, p. ex. radio sur Internet pour la multidiffusion ou la diffusion
H04L 12/18 - Dispositions pour la fourniture de services particuliers aux abonnés pour la diffusion ou les conférences
H04W 4/06 - Répartition sélective de services de diffusion, p. ex. service de diffusion/multidiffusion multimédiaServices à des groupes d’utilisateursServices d’appel sélectif unidirectionnel
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
H04W 76/40 - Gestion de la connexion pour la distribution ou la diffusion sélective
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for sensing-based mobility. A serving network entity (104S), upon detecting a sensing-triggering event (210) by the serving network entity (104S), obtains (216) sensing data from a sensor associated with at least one of: the serving network entity (104S), a neighbor network entity (104N), or a UE (102). The serving network entity (104S) transmits (230), to the UE (102) based on the sensing data, a command to perform a mobility procedure with the neighbor network entity (104N).
A data-driven intra-inter-prediction mode associated with a matrix is identified. An inter-prediction for a current block is generated. An input vector of features is extracted from source regions associated with the current block, including at least a first source region from the inter-prediction and at least a second source region from a neighboring block spatially adjacent to the current block. The input vector is multiplied by the matrix to obtain an output vector. The output vector is converted into an output matrix. The output matrix is resized to match dimensions of the current block to generate a prediction block for the current block.
H04N 19/103 - Sélection du mode de codage ou du mode de prédiction
H04N 19/136 - Caractéristiques ou propriétés du signal vidéo entrant
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 19/503 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage prédictif mettant en œuvre la prédiction temporelle
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
H04N 19/593 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage prédictif mettant en œuvre des techniques de prédiction spatiale
H04N 19/70 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques caractérisés par des aspects de syntaxe liés au codage vidéo, p. ex. liés aux standards de compression
71.
Surface tap and touch localization using thermal sensors
A method includes determining a pose of a portion of a user relative to a surface. The surface is associated with a virtual interface generated by a device worn by the user. The method further includes receiving thermal data indicating contact between the portion of the user and a portion of the surface. The method further includes determining a location of the contact relative to the virtual interface using the thermal data. The method further includes determining a type of interaction associated with the contact using the thermal data.
The subject matter of this specification can be implemented in, among other things, a computer-implemented user interface method including displaying on a touchscreen display a representation of a keyboard defining a top edge and a bottom edge, and a content area adjacent to the keyboard. The method further includes receiving a user dragging input having motion directed to the bottom edge of the keyboard. The method further includes removing the keyboard from the touchscreen display and expanding the content area to an area previously occupied by the keyboard.
G06F 3/04886 - 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 par partition en zones à commande indépendante de la surface d’affichage de l’écran tactile ou de la tablette numérique, p. ex. claviers virtuels ou menus
G06F 3/048 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI]
G06F 3/04842 - Sélection des objets affichés ou des éléments de texte affichés
G06F 16/951 - IndexationTechniques d’exploration du Web
G06F 16/957 - Optimisation de la navigation, p. ex. mise en cache ou distillation de contenus
The present disclosure relates to dynamically scheduling resource requests in a distributed system based on usage quotas. One example method includes identifying usage information for a distributed system including atoms, each atom representing a distinct item used by users of the distributed system; determining that a usage quota associated with the distributed system has been exceeded based on the usage information, the usage quota representing an upper limit for a particular type of usage of the distributed system; receiving a first request for a particular atom requiring invocation of the particular type of usage represented by the usage quota; determining that a second request for a different type of usage of the particular atom is waiting to be processed; and processing the second request for the particular atom before processing the first request.
H04L 47/762 - Contrôle d'admissionAllocation des ressources en utilisant l'allocation dynamique des ressources, p. ex. renégociation en cours d'appel sur requête de l'utilisateur ou sur requête du réseau en réponse à des changements dans les conditions du réseau déclenchée par le réseau
H04L 47/70 - Contrôle d'admissionAllocation des ressources
G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
Aspects of the disclosure are directed to entity context-based security threat detection by a security analytics platform. A plurality of heterogeneous data items associated with a specified enterprise computing environment can be received by one or more processing devices of a security analytics platform. An entity context data structure comprising a plurality of vertices connected by a plurality of edges can be generated based on the plurality of heterogeneous data items. Telemetry data associated with the specified enterprise computing environment can be received. Context data comprising a plurality of context data items associated with at least a subset of entities referenced by the telemetry data can be extracted from the entity context data structure. One or more security outcomes can be produced by applying a set of detection rules to the telemetry data and the context data.
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
A software-defined vehicle (SDV) includes and connects to multiple independent SDV systems and independent external non-SDV systems that may interact with each other and provide execution environments for various services. The independent systems may be members of a leaderless group of trusted independent systems that provides a consistent record of service identities, service registrations, and runtime interfaces across the distributed system. Each member of the leaderless group of trusted independent systems may adhere to group membership protocols and may be a sole source of truth for the services it provides. The independent systems may be orchestrated using workflows specified by directed acyclic graphs of runtime interfaces and methods, and may be dynamically executed via service discovery. Communication between services may be secured based on a mapping between operating system process identities and SDV service identities that are created and stored using a network tap and packet filter system program.
H04L 67/12 - Protocoles spécialement adaptés aux environnements propriétaires ou de mise en réseau pour un usage spécial, p. ex. les réseaux médicaux, les réseaux de capteurs, les réseaux dans les véhicules ou les réseaux de mesure à distance
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
A display assembly includes a display cover defining an internal volume having a central portion and a peripheral portion. The display includes a first display area disposed within the central portion of the internal volume and having a first plurality of pixels, a second display area disposed at least partially within the peripheral portion of the internal volume and having a second plurality of pixels, the second display area extending around a periphery of the first display area; and a non-pixelated region disposed within the internal volume between the first and second display areas and around the periphery of the first display area. The display assembly also includes an antenna region having at least one antenna arranged within the non-pixelated region and an opaque coating arranged on an inner surface of the display cover so as to cover the antenna arranged within the non-pixelated region.
A user equipment (UE) communicating with a non-terrestrial network (NTN) node can implement a method for managing paging for the UE from a core network (CN). The method includes: (i) receiving, from the CN and at the UE, a downlink message including NTN time information associated with reachability; (ii) starting, at the UE, a timer with a time duration based on the NTN time information; and (iii) suspending, at the UE, monitoring paging for the UE while the timer is running.
A method for performing predictive store-to-load forwarding on a processor includes receiving a load instruction, performing a predictive store-to-load forwarding process including obtaining, from a prediction table, a predicted store queue id based on an address of the load instruction, looking up a value from a store queue based on the store queue id while performing a parallel verification process for the predicted store queue id, determining that the parallel verification process succeeded, and in response, reading the value for the load instruction from the store queue.
Techniques and apparatuses are described for performing interdependent human behavior detection and/or classification using active acoustic sensing. With active acoustic sensing, multiple human behaviors can be detected and/or classified during a given time period. Interdependent human behavior detection and/or classification involves using the detection and/or classification of a first human behavior to assist with the detection and/or classification of a second human behavior. With interdependent human behavior detection and/or classification, active acoustic sensing can increase the accuracy and/or reliability of human behavior detection and/or classification compared to other single-behavior-based techniques. Furthermore, interdependent human behavior detection and/or classification can be performed using a single type of sensing modality (e.g., active acoustic sensing) and using a single sensor in some implementations. By relying on active acoustic sensing instead of other sensing modalities, it can be cheaper and/or easier to implement interdependent human behavior and/or classification techniques within a hearable.
Light-based fiducials for camera calibration with respect to an optical component are described herein. In one implementation, an imaging system includes an image sensor configured for imaging a subject; an optical component positioned between the image sensor and the subject; a set of fiducials integrated with the optical component in a region associated with a field of view of the image sensor, and a processor. The fiducials may implement discontinuities in a refraction plane of the optical component. The processor may perform a process including receiving an image of the subject from the image sensor; detecting, using the image and based on anomalous light propagation through the respective discontinuities, an arrangement of the set of fiducials; and, based on the arrangement, performing a calibration operation to account for an effect of the optical component on light detected by the image sensor. Corresponding methods, systems, and media are also disclosed.
The present disclosure provides systems and methods that perform listwise learning to rank. The proposed training frameworks can be used to improve ranking performance in systems that benefit from better ranking of items that are recommended and shown together to a user in response to a query or request. In particular, the present disclosure provides new listwise loss functions and associated learning frameworks that can be applied to all items in a set of items potentially responsive to a request or query, which is a setting that occurs in recommendation systems, information retrieval systems, and other systems that provide items in response to a query. The proposed loss functions reduce bias in the resulting model as compared to alternative approaches, and can be used to tune between ranking objectives and pointwise prediction accuracy objectives. The proposed loss functions are also more computationally efficient than other alternatives such as an alternative pairwise approach.
A computing device includes one or more memories to store one or more instructions and one or more processors. The one or more processors execute the one or more instructions stored in the one or more memories to: obtain display screen information associated with a display screen of a biometric sensing computing device, obtain an image including the display screen, the image including biometric information associated with a user of the biometric sensing computing device, execute a digital display function based on the display screen information to determine biometric content information from the image corresponding to the biometric information, and store the biometric content information determined by executing the digital display function.
G16H 40/67 - TIC spécialement adaptées à la gestion ou à l’administration de ressources ou d’établissements de santéTIC spécialement adaptées à la gestion ou au fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement d’équipement ou de dispositifs médicaux pour le fonctionnement à distance
G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
An insect storage system is disclosed. The insect storage system includes a bottom section including multiple insect sections, each insect section including an insect compartment, each insect compartment including at least one live insect. The insect storage system further includes a lid that extends over and encloses each insect compartment, in which the lid is configured for individual access to each insect compartment.
A01K 1/08 - Dispositions pour libérer simultanément plusieurs animaux
A01K 1/03 - Logements pour animaux domestiques ou de laboratoire
A01K 67/31 - Dispositions pour leur libération dans l’environnement
B65D 75/36 - Objets ou matériaux enveloppés entre deux feuilles ou flans opposés à bords réunis, p. ex. par adhésifs à pression, pliage, thermosoudage ou soudage une ou les deux feuilles ou flans étant renfoncés pour épouser la forme du contenu une feuille ou un flan étant renfoncés et l'autre fait d'une feuille plate relativement rigide, p. ex. empaquetage pour ampoules
87.
DISTRIBUTED GENERATION OF DIFFERENTIAL PRIVACY NOISE
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating differential privacy noise and applying the noise to data. In one aspect, a method includes sending, to each noise generator or multiple noise generators, noise generation data including differential privacy parameters and a number of noise generators that are generating shares of differential privacy parameters. A share of differential privacy noise is received from two or more of the noise generators. Each share is generated based on the noise generation data. Each share of the differential privacy noise is generated by obtaining multiple subsamples from a negative binomial distribution using input parameters comprising a ratio between a scaling factor and the number of noise generators. Each share of noise received from the two or more noise generators is combined to obtain total noise that is applied to data of a dataset.
This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for configuring and managing sensing reference signals for ISAC. A sensing device (102) receives (306), from a network entity (104), a configuration for a sensing procedure, the configuration indicating time-frequency resources for the sensing procedure. The sensing device (102) generates a sensing reference signal based on the configuration. The sensing device (102) transmits (310) the sensing reference signal for the sensing procedure, the sensing reference signal being mapped to the time-frequency resources.
One example aspect of the present disclosure is directed to a method for operating a quantum computing system (QCS) that includes a set of qubits. The method includes generating a set of noisy data by repeatably measuring a noisy observable of a quantum circuit operating on the set of qubits. The set of noisy data and the noisy observable are subject to noise associated with the quantum circuit and each datapoint of the set of noisy data is associated with an eigenvalue of the noisy observable. The noisy data is modeled as a hybrid distribution, which includes a combination of a noiseless distribution and a noise-only distribution. Each datapoint of the set of noisy data is assumed to have a hidden label as coming from either the noiseless distribution or the noise-only distribution. This label is estimated for each datapoint, based on the eigenvalue associated with the datapoint, and a constructed model for any of the hybrid distribution, the noise-only distribution, and the noiseless distribution. This may require additional data to be taken from the QCS to model the noise-only distribution. A target quantity is determined based on either the model for the noiseless distribution or the datapoints combined with their attached labels.
Methods, apparatuses, and non-transitory computer-readable medium for wireless communication. One method includes, in response to an internet protocol multimedia subsystem (IMS) associated with the UE being registered with a call service, monitoring an IMS call state of the UE at the UE, determining whether the IMS call state of the UE at the UE is unsynchronized with an IMS call state of the UE at a network (NW) with which the IMS associated with the UE is registered, determining whether the IMS call state of the UE at the UE is idle in response to the IMS call state of the UE at the UE being unsynchronized with the IMS call state for the UE at the NW, and performing an IMS recovery procedure in response to the IMS call state of the UE at the UE being idle.
H04L 65/1069 - Établissement ou terminaison d'une session
H04L 65/1104 - Protocole d'initiation de session [SIP]
H04L 69/40 - Dispositions, protocoles ou services de réseau indépendants de la charge utile de l'application et non couverts dans un des autres groupes de la présente sous-classe pour se remettre d'une défaillance d'une instance de protocole ou d'une entité, p. ex. protocoles de redondance de service, état de redondance de protocole ou redirection de service de protocole
91.
ESTIMATING THE FIDELITY OF QUANTUM LOGIC GATES AND QUANTUM CIRCUITS
Methods, systems and apparatus for estimating the fidelity of quantum logic gates. In one aspect, a method includes defining multiple sets of random quantum circuits; for each set of random quantum circuits: selecting an observable for each element in the set of random quantum circuits, wherein each selected observable corresponds to a respective element of the set of random quantum circuits and is dependent on the element to which it corresponds; estimating a value of a polarization parameter for the set of random quantum circuits, comprising performing a least mean squares minimization based on multiple expectation values, wherein each expectation value comprises an expectation value of a respective selected observable with respect to an output of an experimental implementation of a random quantum circuit corresponding to the respective selected observable; and processing the estimated polarization parameter values to obtain an estimate of the fidelity of the n-qubit quantum logic gate.
G06N 10/70 - Correction, détection ou prévention d’erreur quantique, p. ex. codes de surface ou distillation d’état magique
G06N 10/20 - Modèles d’informatique quantique, p. ex. circuits quantiques ou ordinateurs quantiques universels
G06N 10/40 - Réalisations ou architectures physiques de processeurs ou de composants quantiques pour la manipulation de qubits, p. ex. couplage ou commande de qubit
Methods, systems, devices, and non-transitory computer readable media for generating reformatted content are provided. The disclosed technology can include obtaining content data comprising content segments associated with audio-video content. Based on inputting the content data into machine-learned classification models, classes associated with the content segments can be determined. Based on inputting the content segments into content-specific machine-learned models, reformatted content data comprising reformatted content segments associated with text content or image content that is based on the features of the content segments can be generated. Each content segment can be inputted into a content-specific machine-learned model that is configured to generate a reformatted content segment based on the classes associated with the content segment. Query data comprising queries can be obtained and a reformatted content segment associated with the queries can be determined. Furthermore, reformatted content based on the reformatted content segment can be generated.
H04N 21/2343 - Traitement de flux vidéo élémentaires, p. ex. raccordement de flux vidéo ou transformation de graphes de scènes du flux vidéo codé impliquant des opérations de reformatage de signaux vidéo pour la distribution ou la mise en conformité avec les requêtes des utilisateurs finaux ou les exigences des dispositifs des utilisateurs finaux
An example computer-implemented method for image view synthesis is provided. The example method includes obtaining, by a computing system, a query associated with a target view of a scene; determining, by the computing system, a plurality of source poses associated with a plurality of source images of the scene; generating, by the computing system and based on the query, a plurality of pose-augmented queries, each respective pose-augmented query encoding pose information relative to a respective source pose associated with a respective source image of the plurality of source images; processing, by the computing system, the plurality of pose-augmented queries respectively with a plurality of attention streams of a machine-learned image view synthesis model; and generating, by the computing system and based on the plurality of attention streams, an output image of the scene associated with the target view.
Coding a current block using motion vector refinement is disclosed. A first initial motion vector and a first reference frame are obtained for the current block. A second initial motion vector and a second reference frame are obtained for the current block. An optimal motion vector refinement is identified for a sub-block of the current block. A first refined motion vector is obtained as a combination of the first initial motion vector and the optimal motion vector refinement. A first prediction block is obtained based on the first refined motion vector. A prediction block is obtained for the sub-block by combining the first prediction block and a second prediction block obtained using the second initial motion vector.
H04N 19/52 - Traitement de vecteurs de mouvement par encodage par encodage prédictif
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 19/109 - Sélection du mode de codage ou du mode de prédiction parmi plusieurs modes de codage prédictif temporel
H04N 19/119 - Aspects de subdivision adaptative, p. ex. subdivision d’une image en blocs de codage rectangulaires ou non
H04N 19/139 - Analyse des vecteurs de mouvement, p. ex. leur amplitude, leur direction, leur variance ou leur précision
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 19/176 - 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 19/70 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques caractérisés par des aspects de syntaxe liés au codage vidéo, p. ex. liés aux standards de compression
Provided are systems and methods that perform the following textual scene decomposition task: given a single image of a scene that may contain several concepts, the proposed techniques are able to extract a distinct text token for each concept, enabling fine-grained control over the generated scenes.
Systems and methods for effectively performing tasks using generative neural networks. In particular, data security policies are used to improve data security when performing tasks using the generative neural network(s).
According to an aspect, a method may include receiving, by a display device, ambient light measurement data from ambient light measurement devices. The method may include calculating distance measurements indicative of a measured distance between the ambient light measurement devices. The method may include generating a light measurement graph based on the ambient light measurement data and the distance measurements. The method may include generating a raw tone mapping function based on the light measurement graph, the generating using a spatially aware graph neural network. The method may include applying the raw tone mapping function to media content to generate a color corrected version of the media content. The method may include displaying on a display of the display device the color corrected version of the media content.
A method for providing proactive assistance includes obtaining, by a digital assistant, a contextual event associated with a user of a user device. The method includes determining, using a local large language model (LLM) executing on the user device, a remote LLM prompt confidence. The method includes determining that the remote LLM prompt confidence satisfies a threshold. Based on determining that the remote LLM prompt confidence satisfies the threshold, the method includes generating a remote LLM prompt for a remote LLM executing remote from the user device. The method includes transmitting, to the remote LLM, the remote LLM prompt. The method includes receiving, at the digital assistant, from the remote LLM, response content providing the proactive assistance associated with the contextual event. The method includes providing, for output from the user device, presentation content based on the response content received from the remote LLM.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for using cryptographic techniques to enhance data security and privacy and increase computational efficiency in selecting digital components are described. In one aspect, a method includes receiving, by an MPC computer of a group of MPC computers configured to perform computations of a secure MPC protocol to select digital components for distribution to client devices, a first secret share of location information indicating a location of a client device. The MPC computer generates, by performing the computations of the secure MPC protocol in collaboration with one or more second computers of the group of MPC computers, a first secret share of a selection result including data identifying a selected digital component that is selected from candidate digital components that are candidates based at least in part on the location of the client device.
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
The technology provides for a system for determining a gesture provided by a user. In this regard, one or more processors of the system may receive image data from one or more visual sensors of the system capturing a motion of the user, and may receive motion data from one or more wearable computing devices worn by the user. The one or more processors may recognize, based on the image data, a portion of the user’s body that corresponds to a gesture to perform a command. The one or more processors may also determine one or more correlations between the image data and the received motion data. Based on the recognized portion of the user’s body and the one or more correlations between the image data and the received motion data, the one or more processors may detect the gesture.
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
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 13/86 - Combinaisons de systèmes radar avec des systèmes autres que radar, p. ex. sonar, chercheur de direction
G06F 3/0346 - Dispositifs de pointage déplacés ou positionnés par l'utilisateurLeurs accessoires avec détection de l’orientation ou du mouvement libre du dispositif dans un espace en trois dimensions [3D], p. ex. souris 3D, dispositifs de pointage à six degrés de liberté [6-DOF] utilisant des capteurs gyroscopiques, accéléromètres ou d’inclinaison
