Systems and methods to generate tailored content of a user are provided. The system may receive a set of attributes associated with a user. The set of attributes may be indicative of user interests based on content consumption of the user. The system may generate, via a machine learning model, tailored content including a visual representation of a set of content characteristics determined based on the set of attributes. The machine learning model may utilize training data including content items of the set of content characteristics. The system may display, by a user interface, the tailored content tailored to the user.
A computer-implemented method for virtual assistants performing actions during virtual reality meetings may include (i) identifying a meeting in a virtual reality environment that includes a plurality of participants, (ii) monitoring, by an artificial intelligence (AI) agent, the meeting in the virtual reality environment, (iii) detecting, by the AI agent while monitoring the meeting, a trigger behavior by at least one of the participants that correlates to an action within capabilities of the AI agent, and (iv) altering, by the AI agent, the virtual reality environment by performing the action correlated to the trigger behavior. Various other methods, systems, and computer-readable media are also disclosed.
In some embodiments, a system includes a processor; and a memory in communication with the processor for storing instructions, which when executed by the processor causes the device to receive a frontal frame component image of a frontal frame of a pair of eyeglasses; receive a temple component image of a temple component of the pair of eyeglasses; and use the frontal frame component image and the temple component image to generate a three-dimensional (3D) model of the pair of eyeglasses.
A system and method to generate artificial intelligence (AI) images are provided. The system may analyze a first image including visual content indicating a user(s). The system may implement a machine learning (ML) model including training data trained on images associated with genres, categories, styles and/or themes. The system may generate, by implementing the ML model, a first AI image(s) of the first user(s), including an AI style, associated with the first image. The system may generate a first user interface including the first AI image(s) of the first user(s). The first user interface may include indicia to enable a second user(s) to mimic the AI style of the first AI image(s) to apply to a second image including visual data indicating the second user(s) to generate a second AI image(s) associated with the second user(s).
A system and method for typeahead image generation are provided. The method may include receiving, via a user interface during a prompting session, a text prompt describing an image. The method also may include generating, via a trained diffusion model, the image representative of the text prompt. The method further may include determining, via the trained diffusion model, a reconciled risk score based on a determined risk score of the text prompt and a determined risk score of the generated image. The method even further may include causing, via the trained diffusion model in response to the determined reconciled risk score, to (i) approve the generated image in an instance in which the determined reconciled risk score meets or exceeds a predetermined threshold, or (ii) deny the generated image in an instance in which the determined reconciled risk score fails to meet the predetermined threshold.
Methods, systems, and apparatuses may assist with implementing hands free sharing of files between head-mounted displays or other devices. The devices in proximity may be identified and then eye-gaze tracking information or electromyogram information may be used to select and share files.
In one embodiment, a method includes receiving training utterances associated with a domain, receiving ontology labels for the domain, wherein the ontology labels comprise one or more of an intent or a slot, generating an inventory for the domain, wherein the inventory comprises at least a respective index and respective span for each intent or slot, wherein the respective span comprises a respective descriptive label associated with the intent or slot, and wherein the respective descriptive label comprises a natural-language description of the intent or slot, generating frames for training utterances based on the training utterances and the inventory by a natural-language understanding (NLU) model, wherein each frame comprises a structural representation of the respective training utterance, wherein the structural representation is generated based on a comparison between the corresponding training utterance and the inventory, and updating the NLU model based on the frames.
A system and method for providing recommended or suggested format(s) of content are provided. The system may analyze one or more items of content associated with a user being input or captured by a user interface. The system may also implement a machine learning model including training data pre-trained, or trained in real-time, on one or more content items having one or more content formats. The system may also automatically determine at least one suggested content format applied to the one or more items of content responsive to determining that at least a subset of the one or more items of content are similar to corresponding content items of a same or similar type associated with, or within, the training data. The system may also present, by a user interface or a display device, the at least one suggested content format applied to the one or more items of content.
A system and method for generating summaries of a resource(s) are provided. The system may analyze a resource(s), associated with a user, being input/captured by a user interface. The resource(s) may be sharable among users of a group. The system may implement a machine learning model including training data pre-trained, or trained in real-time, on summaries of resources as a same or similar type as the resource(s), one or more content items associated with content of the resource(s), or user interaction historical data. The system may automatically determine a suggested summary, of the resource(s), tailored to the user in response to determining interests or focuses of the user based in part on analyzing the user interaction historical data. The system may present, by a user interface or a display device, the suggested summary of the resource(s).
A system for network optimization using geo-data may access a set of geotagged data samples associated with a cell. The set of geotagged data samples may be obtained from an application associated with one or more user equipment associated with the cell and the cell includes at least one antenna. The system may also determine a metric associated with the cell based on the set of geotagged data samples, generate one or more antenna adjustments based on the metric, and predict, based on the one or more antenna adjustments, a performance improvement associated with the cell.
Systems, apparatuses and methods provide technology that identifies a first color for an area, adjusts a first color space of the first color to a second color space when a predetermined condition is met, identifies a second color of text that is to overlay the area and adjusts one or more of the first color or the second color so that a contrast ratio between the first and second colors meets a threshold contrast value. The technology further modifies an original image to include the text overlaid on the area to generate an adjusted image, where the one or more of the text and the area in the adjusted image includes the adjusted one or more of the first color or the second color, and transmits the adjusted image to a user device to be displayed.
G06T 7/90 - Détermination de caractéristiques de couleur
G06T 5/94 - Modification de la plage dynamique d'images ou de parties d'images basée sur les propriétés locales des images, p. ex. pour l'amélioration locale du contraste
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06V 10/56 - Extraction de caractéristiques d’images ou de vidéos relative à la couleur
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
13.
SYSTEMS AND METHODS FOR DIGITALLY VERIFIED GRAPHICAL ELEMENTS
The disclosed systems and methods may include (1) digitally verifying that a user of a social media application has attained an achievement, (2) in response to digitally verifying that the user has attained the achievement, providing the user with an option to digitally claim a graphical element corresponding to the achievement, where the option to digitally claim the graphical element is only provided to users who have been digitally verified as having attained the achievement, and (3) in response to receiving user input digitally claiming the graphical element, posting the graphical element to a digital user footprint corresponding to the user within a page of the social media application. Various other methods, systems, and computer-readable media are also disclosed.
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
H04W 4/021 - Services concernant des domaines particuliers, p. ex. services de points d’intérêt, services sur place ou géorepères
14.
TECHNIQUES FOR EDITING THREE-DIMENSIONAL SCENES AND RELATED SYSTEMS AND METHODS
The present disclosure is generally directed to techniques for editing a portion of a 3D scene represented by a neural field model. Embodiments of the present disclosure may erase an object from a 3D scene by identifying the object in one or more images of the scene and generating mask regions around (e.g., covering) the object in these images. A neural field model that represents the scene without the object in it may be trained by relying on an image generative model configured for inpainting. When trained, this ‘background’ neural field model can be used to render the implicit background of light rays that pass through the region of 3D space represented by the mask regions, thereby producing different views of the scene with the object effectively erased from the scene.
Systems, methods, and non-transitory computer readable media are configured to perform operations comprising determining a ranking of users participating in a video call; determining a selected number of videos to be included in a grid of videos associated with the video call based on the ranking; and generating the grid of videos based on a combination of the selected number of videos.
H04N 21/4402 - Traitement de flux élémentaires vidéo, p. ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène du flux vidéo codé impliquant des opérations de reformatage de signaux vidéo pour la redistribution domestique, le stockage ou l'affichage en temps réel
H04N 21/4788 - Services additionnels, p. ex. affichage de l'identification d'un appelant téléphonique ou application d'achat communication avec d'autres utilisateurs, p. ex. discussion en ligne
16.
Systems and methods for improving channel estimation for 5G-NR PUSCH
A disclosed computer-implemented method may include (1) receiving a channel estimation signal comprising a plurality of frequency domain signals, each frequency domain signal corresponding to an antenna in a plurality of antennas, a port in a plurality of ports, and a demodulation reference signal sequence, (2) for each frequency domain signal, (A) converting the frequency domain signal to a time domain signal, and (B) determining a power level of the time domain signal, (3) determining, for each port in the plurality of ports (a) a sum of the power levels of the time domain signals corresponding to the port, each antenna in the plurality of antennas, and the DMRS sequence, and (b) based on the determined sum corresponding to the port, measuring (i) a pre-equalization signal-to-interference-plus noise ratio, and (ii) a power delay profile (PDP). Various other systems and methods are also disclosed.
A system and method to generate personalized user interfaces are provided. The system may determine one or more items of demographic content associated with at least one user to obtain one or more user attributes. The system may determine one or more signals associated with a communication device associated with the at least one user or one or more user activities associated with the communication device or associated with at least one application associated with the communication device. The system may generate, based on the determined user attributes and the determined one or more signals, a first user interface, including content items and one or more elements, that is tailored or personalized to the at least one user.
A network can operate a WiFi access point with credentials. An unconfigured device can (i) support a Device Provisioning Protocol (DPP), (ii) record responder bootstrap public and private keys, and (iii) be marked with a tag. The network can record initiator bootstrap public and private keys, as well as derived initiator ephemeral public and private keys. An initiator can (i) operate a DPP application, (ii) read the tag, (iii) establish a secure and mutually authenticated connection with the network, and (iv) send the network data within the tag. The network can record the responder bootstrap public key and derive an encryption key with the (i) recorded responder bootstrap public key and (ii) derived initiator ephemeral private key. The network can encrypt credentials using the derived encryption key and send the encrypted credentials to the initiator, which can forward the encrypted credentials to the device, thereby supporting a device configuration.
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
G06K 7/14 - Méthodes ou dispositions pour la lecture de supports d'enregistrement par radiation électromagnétique, p. ex. lecture optiqueMéthodes ou dispositions pour la lecture de supports d'enregistrement par radiation corpusculaire utilisant la lumière sans sélection des longueurs d'onde, p. ex. lecture de la lumière blanche réfléchie
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
H04L 9/14 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité utilisant plusieurs clés ou algorithmes
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
H04W 4/60 - Services basés sur un abonnement qui utilisent des serveurs d’applications ou de supports d’enregistrement, p. ex. boîtes à outils d’application SIM
H04W 4/70 - Services pour la communication de machine à machine ou la communication de type machine
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 12/04 - Gestion des clés, p. ex. par architecture d’amorçage générique [GBA]
Systems, apparatuses and methods provide technology that receives a first operator prompt associated with a character in a computing application, and generates, with a first machine learning model during an offline process, first responses based on the first prompt, where the first responses are dialog of the character. The technology stores the first responses into a dialog tree during the offline process, receives a second prompt associated with an end user of the computing application, generates, with the first machine learning model during the offline process, second responses to the second prompt and based on the first responses stored in the dialog tree, where the second responses are dialog of the end user, and adds the second responses to the dialog tree during the offline process.
A63F 13/60 - Création ou modification du contenu du jeu avant ou pendant l’exécution du programme de jeu, p. ex. au moyen d’outils spécialement adaptés au développement du jeu ou d’un éditeur de niveau intégré au jeu
A clock synchronization method is disclosed. The method comprises receiving master timing synchronization data from a master clock device, wherein the master timing synchronization data is based at least in part on quantum data obtained from a first quantum device of the master clock device. The method comprises determining local timing synchronization data that is based at least in part on local quantum data obtained from a second local quantum device, wherein the second local quantum device is entangled with the first quantum device such that the local quantum data and the quantum data obtained from the first quantum device are identical at a same time. The method comprises using the determined local timing synchronization data and the received master timing synchronization data to determine a timing correction factor. The method comprises correcting a local system time using the timing correction factor.
G06N 10/00 - Informatique quantique, c.-à-d. traitement de l’information fondé sur des phénomènes de mécanique quantique
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
H04L 7/00 - Dispositions pour synchroniser le récepteur avec l'émetteur
H04L 7/04 - Commande de vitesse ou de phase au moyen de signaux de synchronisation
Methods, systems, and storage media to determine an available number of hardware decoders that can be used to process a video feed. The system can receive a select group of participants from a plurality of participants associated with the video call. The system can include identify a video feed for each participant of the select group of participants. The system can send a request for the video feed of each participant of the select group of participants. The system can receive a video feed associated with each of the select group of participants in the video call. The system can allocate a hardware decoder to each participant of the select group based on the request for each video feed. The system can determine operational parameters of at least one of the hardware decoders. The system can include switching the video feed from the hardware decoder to the software decoder.
H04N 19/127 - Établissement des priorités des ressources en matériel ou en calcul
H04N 19/42 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques caractérisés par les détails de mise en œuvre ou le matériel spécialement adapté à la compression ou à la décompression vidéo, p. ex. la mise en œuvre de logiciels spécialisés
Methods and systems are described for audio pipelines between a user and a machine learning (ML) model. The method involves obtaining digital audio data associated with an audio signal from a user's equipment. This data is then analyzed using a trained ML model, which includes one or more neural networks. The ML model determines an indication related to the digital audio data, which can be a prediction of the ending of one or more portions of the data, an intention behind the data, a key term within the data, an action to take based on the data, or the context associated with the data. Based on the determined indication, a response is generated and subsequently transmitted back to the user's equipment.
G10L 13/08 - Analyse de texte ou génération de paramètres pour la synthèse de la parole à partir de texte, p. ex. conversion graphème-phonème, génération de prosodie ou détermination de l'intonation ou de l'accent tonique
G10L 15/02 - Extraction de caractéristiques pour la reconnaissance de la paroleSélection d'unités de reconnaissance
G10L 15/06 - Création de gabarits de référenceEntraînement des systèmes de reconnaissance de la parole, p. ex. adaptation aux caractéristiques de la voix du locuteur
29.
Display screen or portion thereof with an animated graphical user interface
In one embodiment, a method includes detecting a change in context of a computing system to a current context, changing a first registration status of a first set of actions with an assistant system associated with the computing system based on the change in context to the current context, where the first application is in a foreground of a user interface of the computing system, changing a second registration status of a second set of actions with the assistant system based on the change in context to the current context, where the second application is in a background of the user interface of the computing system, updating a list of registered actions for the assistant system based on the changed first and second registration statuses, and storing the updated list of registered actions on the computing system.
G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
H04L 67/133 - Protocoles pour les appels de procédure à distance [RPC]
32.
METHODS, APPARATUSES AND COMPUTER PROGRAM PRODUCTS FOR GENERATING PORTIONS OF CODE AND PROVIDING A RISK AND COMPLIANCE ARTIFICIAL INTELLIGENCE VIRTUAL ASSISTANT
Systems, and methods are described for a ML model generating sections of code based on inputs. The systems of generating code may receive inputs. The first input may be a description of problems for solving and a specification of an output. The second input may be variables. The third input may be data categories associated with the variables and the output. The fourth input may be responses to prompts. The prompts may be generated by a ML model based on association between the first, second, and third inputs. Boilerplate sections of code may be determined by the ML model. Missing portions of code are determined. Missing portions of code may be associated with core sections of code. The ML model may utilize the inputs to generate code to fill in missing portions of code. Generated code including missing portions and sections of generated code are provided to a user interface.
Methods, systems, and apparatuses for combining pressure measurement with a dynamic system model to estimate the air mass within an actuator, and then estimate the applied contact force which may be based on a quasi-static deformation model.
A computer-implemented method for efficiently encoding video may include (i) determining, by a computing device, that a video file is a candidate for vertical-slice-based region-of-interest compression, (ii) identifying a video encoder that supports horizontal-slice-based compression but does not support vertical-slice-based compression, (iii) rotating each frame of the video file ninety degrees, and (iv) performing, by the video encoder, horizontal-slice-based region-of-interest compression on the rotated video file. Various other methods, systems, and computer-readable media are also disclosed.
H04N 19/167 - Position dans une image vidéo, p. ex. région d'intérêt [ROI]
H04N 19/119 - Aspects de subdivision adaptative, p. ex. subdivision d’une image en blocs de codage rectangulaires ou non
H04N 19/136 - Caractéristiques ou propriétés du signal vidéo entrant
H04N 19/164 - Retour d’information en provenance du récepteur ou du canal de transmission
H04N 19/174 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant une tranche, p. ex. une ligne de blocs ou un groupe de blocs
36.
Integrating Applications with Dynamic Virtual Assistant Avatars
In one embodiment, a method includes rendering a first output image of an XR assistant avatar within a first environment associated with a first XR application for displays of a first extended-reality (XR) display device, wherein the XR assistant avatar has a first form according to a first rendering specification associated with the first XR application and the XR assistant avatar is interactable by a first user to access an assistant system, receiving an indication that the first user is switching from the first XR application to a second XR application, accessing a second rendering specification associated with the second XR application, and rendering a second output image of the XR assistant avatar within a second environment associated with the second XR application for displays of a second XR display device, wherein the XR assistant avatar is rendered to have a second form according to the second rendering specification.
A video to be encoded using a codec is received Pre-filter encoding of a specific frame of the video is performed. In a single-pass processing of the specific frame, frame-level filter parameters determined using a previous frame are used to apply an in-loop filter on blocks of the specific frame during encoding of the specific frame. Statistics for the specific frame are gathered for determining frame-level filter parameters to be used for a future frame of the video. Post-filter encoding of the specific frame of the video is performed.
H04N 19/00 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques
H04N 19/117 - Filtres, p. ex. pour le pré-traitement ou le post-traitement
H04N 19/13 - Codage entropique adaptatif, p. ex. codage adaptatif à longueur variable [CALV] ou codage arithmétique binaire adaptatif en fonction du contexte [CABAC]
H04N 19/137 - Mouvement dans une unité de codage, p. ex. différence moyenne de champs, de trames ou de blocs
H04N 19/154 - Qualité visuelle après décodage mesurée ou estimée de façon subjective, p. ex. mesure de la distorsion
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 19/182 - 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 un pixel
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 19/463 - Inclusion d’information supplémentaire dans le signal vidéo pendant le processus de compression par compression des paramètres d’encodage avant la transmission
H04N 19/82 - Détails des opérations de filtrage spécialement adaptées à la compression vidéo, p. ex. pour l'interpolation de pixels mettant en œuvre le filtrage dans une boucle de prédiction
H04N 19/85 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le pré-traitement ou le post-traitement spécialement adaptés pour la compression vidéo
In one embodiment, one or more computing systems may generate a knowledge graph representing relationships between a global model and a number of local models for updating the global model. Each local model may have access to a local dataset for machine-learning training. The systems may access, from the knowledge graph, a sharing policy associated with the local dataset of a first local model. The systems may determine, based on the knowledge graph and one or more pre-determined criteria, that the sharing policy permits the local dataset of the first local model to be shared with a second local model. The system may cause the second local model to be trained using at least the local dataset of the first local model and the local dataset of the second local model. The system may update the global model using the trained second local model.
Systems and methods for social audio streaming may include (1) configuring, for a user participating in a media call with a group of additional users, a virtual visual surface that includes (i) an on-screen media call interface, presented within a viewable area of a display element of a device of the user, with a first set of user tiles, and (ii) an off-screen media call interface, positioned outside of the viewable area in a lateral direction relative to the viewable area, with a second set of user tiles and (2) spatializing an audio stream of an additional user, from the group of additional users, based on a position of the additional user's user tile within the virtual visual surface. Various other methods, systems, and computer-readable media are also disclosed.
H04S 7/00 - Dispositions pour l'indicationDispositions pour la commande, p. ex. pour la commande de l'équilibrage
G06F 3/0488 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p. ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p. ex. des gestes en fonction de la pression exercée enregistrée par une tablette numérique utilisant un écran tactile ou une tablette numérique, p. ex. entrée de commandes par des tracés gestuels
A product packaging system for a consumer product, and a product packaging for such a product packaging system. The product packaging is formed from a material blank operable for movement between an unfolded state/open structural configuration and a folded state/closed structural configuration forming the product packaging. In the folded state/closed structural configuration, the product packaging defines a cavity that is operable to receive the consumer product. The product packaging includes a releasable locking system that is selectively moveable between a disengaged state and an engaged state which locks and maintains the product packaging in the folded state/closed structural configuration.
B65D 5/20 - Réceptacles de section transversale polygonale rigides ou semi-rigides, p. ex. boîtes, cartons ou plateaux, formés en pliant ou montant un ou plusieurs flans de papier en repliant les parties reliées à un panneau central sur chaque côté pour former le corps du réceptacle, p. ex. en forme de plateau
B65D 5/44 - Parties insérées dans le réceptacle, qui en font partie intégrante ou qui lui sont fixées pour former des garnitures intérieures ou extérieures
41.
METHODS, APPARATUSES AND COMPUTER PROGRAM PRODUCTS FOR FACILITATING ACTIONS BASED ON TEXT CAPTURED BY HEAD MOUNTED DEVICES
A system and method for determining interesting text to trigger actions of devices are provided. The system may include one or more head-mounted devices associated with a network. A head mounted device(s) may capture an image(s) and/or a video(s) corresponding to an environment detected in a field of view of a camera(s). The image(s) and/or the video(s) may include one or more text items associated with the environment. The head mounted device may determine whether a text item(s) of the one or more text items is interesting. The head mounted device may extract the text item(s) determined as being interesting and may superimpose the text item(s) at a position in the image(s) and/or the video(s). The head mounted device may trigger, based on the text item(s) determined as being interesting, one or more actions capable of being performed by the head mounted device.
The disclosed computer-implemented method may include capturing, by a computing device, a media clip. The method may also include dividing, by the computing device, the media clip into a set of frames, wherein each frame may include an audio portion of the media clip of a predetermined length of time. Additionally, the method may include performing, by the computing device, a noise suppression process on each frame of the set of frames using a trained neural network model, wherein the trained neural network model is quantized to use input tensors. Finally, the method may include creating, by the computing device, a clean media clip based on the noise suppression process. Various other methods, systems, and computer-readable media are also disclosed.
G10L 21/0232 - Traitement dans le domaine fréquentiel
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
A disclosed computer-implemented method may include presenting, within a video creation interface, a list of video styles, each video style within the list of video styles representative of a trained model, the trained model trained to apply different aesthetic attributes to video content in accordance with a different video style. The method may also include receiving, via user input to the video creation interface, a selection of a particular video style from the list of video styles and, in response to receiving the selection, applying, to video content received via the video creation interface, a video editing technique corresponding to the particular video style. Various other methods, systems, and devices are also disclosed.
G11B 27/031 - Montage électronique de signaux d'information analogiques numérisés, p. ex. de signaux audio, vidéo
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
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
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
45.
Optimized video call grid for picture-in-picture mode
Systems, methods, and non-transitory computer readable media are configured to perform operations comprising determining a ranking of users participating in a video call; determining a selected number of videos to be included in a grid of videos associated with the video call based on the ranking; and generating the grid of videos based on a combination of the selected number of videos.
H04L 12/18 - Dispositions pour la fourniture de services particuliers aux abonnés pour la diffusion ou les conférences
H04N 21/4402 - Traitement de flux élémentaires vidéo, p. ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène du flux vidéo codé impliquant des opérations de reformatage de signaux vidéo pour la redistribution domestique, le stockage ou l'affichage en temps réel
H04N 21/4788 - Services additionnels, p. ex. affichage de l'identification d'un appelant téléphonique ou application d'achat communication avec d'autres utilisateurs, p. ex. discussion en ligne
Disclosed technology provides a solder ball including an outer layer having a first conductive material that is solid at an operating temperature of an electronic device, and an inner region having a second conductive material that flows at the operating temperature of the electronic device, wherein the inner region is surrounded by the outer layer. A method of manufacturing a solder ball includes forming an outer layer comprising a first conductive material that is solid at an operating temperature of an electronic device, wherein the outer layer surrounds an inner region, introducing a hole into the outer layer, injecting a second conductive material through the hole of the outer layer into the inner region, wherein the second conductive material flows at the operating temperature of the electronic device, and sealing the hole of the outer layer such that the second conductive material is retained within the inner region.
The present application is at least directed to a method including a step of receiving audio from a user. The method may further include a step of generating, via a trained encoder based upon the received audio, an audio embedding sequence. The method may even further include a step of receiving, via a trained large language model (LLM), the generated audio embedding sequence and a text embedding sequence. The text embedding sequence is arranged before or after the generated audio embedding sequence. The method may yet even further include a step of producing, via the trained LLM based upon text embedding sequence, a textual response associated with the audio received from the user. The method may still even further include a step of causing to display, via a user interface of the user, the produced textual response.
Methods and systems are described for an intelligent messaging platform between users to foster more an immersive and creative messaging environment. In various examples, systems and methods may receive, via a device associated with a user, a media input. Context associated with the media input may be determined. The creation of a media item may be based on the media input and context associated with the media input. The creation of a media item may be based on the use of a machine learning model. The media may be provided to a user or group of users via one or more user devices associated with the users.
Aspects of the present disclosure may include systems and methods generating visual content. The system may detect input of descriptive text associated with text content or audio content. The system may generate, based on the descriptive text, an initial latent representation by using a finetuned latent diffusion model. The system may apply a denoising process to the initial latent representation to produce a refined latent representation. The system may sample data points from a content distribution associated with prior timesteps and from a style distribution associated with subsequent timesteps, thereby generating a final image latent. The system may decode the final image latent to obtain a visually aligned image(s) corresponding to the descriptive text. The system may output the visually aligned image(s) on a user interface or a display.
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
G06F 3/04845 - 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 pour la transformation d’images, p. ex. glissement, rotation, agrandissement ou changement de couleur
A method for user input includes displaying a received message to a user on a user interface, displaying multiple characters as a one-dimensional (1D) list on the user interface, and receiving, from the user through the user interface, a first scrolling selection of a first character from the 1D list. The method further includes providing an input to a language model, the input including the received message and the first character, and responsive to providing the input, receiving a suggested output from the language model and displaying the suggested output on the user interface.
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06F 3/0485 - Défilement ou défilement panoramique
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 40/274 - Conversion de symboles en motsAnticipation des mots à partir des lettres déjà entrées
51.
Systems and methods for transitioning between asynchronous and synchronous interactions
A computer-implemented method for transitioning between asynchronous and synchronous interactions may include (i) detecting that a user is initiating a session within a digital media room via a computing device, (ii) enabling the user to interact with at least one digital media object in the digital media room in an asynchronous fashion, (iii) detecting that an additional user is initiating a new session within the digital media room, (iv) in response to detecting that the additional user is initiating the new session within the digital media room, creating a shared session within the digital media room for the user and the additional user, and (v) enabling the user and the additional user to interact with the at least one digital media object in the digital media room in a synchronous fashion via the shared session. Various other methods, systems, and computer-readable media are also disclosed.
In one embodiment, a method includes accessing visual signals comprising images portraying textual content in a real-world environment associated with a first user from a client system associated with the first user, recognizing the textual content based on machine-learning models and the visual signals, determining a context associated with the first user with respect to the real-world environment based on the visual signals, executing tasks determined based on the textual content and the determined context for the first user, and sending instructions for presenting execution results of the tasks to the first user to the client system.
In one embodiment, a method includes receiving, from a client system associated with a user, a first user request that includes a reference to a target object and one or more of an attribute or a relationship of the target object. Visual data including one or more images portraying the target object may then be accessed, and the reference may be resolved to the target object portrayed in the one or more images. Object information of the target object that corresponds to the referenced attribute or relationship of the first user request may be determined based on a visual analysis of the one or more images. Finally, responsive to receiving the first user request, the object information of the target object may be stored in a multimodal dialog state.
The present disclosure provides systems and methods for optimizing media content. One step of the method may include receiving, via a user interface, an indication of a selected post including media content associated with a user. Another step of the method may include evaluating, via a LLM model trained on training data, a quality of the media content of the selected post. A further step may include generating, via the trained LLM model and based upon the evaluated quality, a modified post including modified media content. A change in the modified post is of a first type when the evaluated quality is at or below a threshold or of a second type when the evaluated quality is above a threshold. Even a further step may include transmitting, via the user interface, the modified post for consideration by the user. Yet even a further step may include receiving, via the user interface, an indication of a rejection or an acceptance of the modified post.
In some embodiments, a computer-implemented method includes ascertaining a multitier topology representation of an edge cloud network; generating a pseudo node topology representation of the edge cloud network from the multitier topology representation; and utilizing the pseudo node topology representation of the edge cloud network to ascertain minimum-latency pseudo-node-based edge cloud clusters (ECCs), the minimum-latency pseudo-node-based ECCs being utilized to minimize a latency of user requests routed through the edge cloud network from a user of the edge cloud network. In some embodiments of the computer-implemented method, the minimum-latency pseudo-node-based ECCs are ascertained based upon a pseudo-node-based round-trip-times (RTTs) assessment from the user of the edge cloud network, the user requests being routed to the minimum-latency pseudo-node-based ECCs ascertained using the pseudo node topology representation.
H04L 41/122 - Découverte ou gestion des topologies de réseau des topologies virtualisées, p. ex. les réseaux définis par logiciel [SDN] ou la virtualisation de la fonction réseau [NFV]
Aspects of the present disclosure are directed to an artificial intelligence (“AI”) application running in conjunction with an artificial reality (“XR”) space. The AI Builder responds to user commands, verbal or gestural, to build or edit spaces or objects in space. If the requested object is of a type recognized by the AI Builder, then the AI Builder builds the object from one or more stored templates. The new object's location is determined by the objects that already exist in the user's XR environment and on commands or gestures from the user. If the AI Builder does not recognize the requested object, the user can show an image to the AI Builder, and the AI builds a 3D object in the XR space according to that image. To ease collaboration among users, the AI Builder may present its user interface as a non-player character within the XR world.
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
A system and method for providing screen-aware adjustment visualizations associated with displays are provided. The system may facilitate training a large language model to generate a plurality of types of content to improve text content associated with at least one document. The system may further facilitate extracting and reconstructing items of content from a portable document format document. The system may further maintain a semantic order of elements associated with the content. The system may further dynamically organize the content within a display of a device.
A system for establishing trust of entities associated with a system based on social graphs is provided. The system may access a first social graph including at least a first node. The first node is associated with a first set of edges and a first set of neighboring nodes associated with the first set of edges. The system may access a first signature based on the first social graph. The system may receive a request from a second node to establish a trust relationship. The system may access a second signature determined based on a second social graph in response to receiving the request. The system may determine a similarity level between the first signature and the second signature. The system may generate an indication of approval or denial of the request based on the similarity level.
An embodiment includes generating, using a first set of parameters, a first quantization matrix. An embodiment includes encoding, using the first quantization matrix, a frame in an uncompressed video stream, the encoding generating a compressed video stream corresponding to the uncompressed video stream.
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 19/196 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par le procédé d’adaptation, l’outil d’adaptation ou le type d’adaptation utilisés pour le codage adaptatif étant spécialement adaptés au calcul de paramètres de codage, p. ex. en faisant la moyenne de paramètres de codage calculés antérieurement
H04N 19/463 - Inclusion d’information supplémentaire dans le signal vidéo pendant le processus de compression par compression des paramètres d’encodage avant la transmission
60.
METHODS, APPARATUSES AND COMPUTER PROGRAM PRODUCTS FOR GAZE-DRIVEN ADAPTIVE CONTENT GENERATION
Systems and methods are provided for generating adaptive content. The system may implement a machine learning model including training data pre-trained, or trained in real-time based on captured content or prestored content associated with gazes of users, pupil dilations, facial expressions, muscle movements, heart rates, or gaze dwell times of users determined previously or in real-time. The system may determine a gaze(s) of an eye of a user or facial features of a face associated with the user viewing, by a device, items of content in an environment. The system may include determining, based on the gaze(s) or facial features, a state(s) or interest(s) of the user. The system may determine, by implementing the machine learning model and based on the state(s) or interest(s) of the user, content to generate a modification of the items of content or to generate new content items associated with the items of content.
Systems, apparatuses and methods provide technology that receives different numbers of accesses to a data shard from different regions, where the data shard is a portion of a dataset. The technology identifies, with a machine learning model, access patterns based on the different numbers of accesses, and generates, with the machine learning model, values for the different regions based on the access patterns, where the values represent ranks of the different regions that correspond to a future number of predicted accesses to the data shard from the different regions. The technology determines a subset of the different regions to store the data shard based on the values and stores a replica of the data shard in each of the subset of the regions without scarifying latency.
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
G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p. ex. des interruptions ou des opérations d'entrée–sortie
62.
Display screen or portion thereof with a transitional graphical user interface
Aspects of the present disclosure are directed to applying voice controls to a target virtual object in an artificial reality environment. User controls in an artificial reality environment can take many forms. Some user controls, such as ray casting or gaze tracking, can incorporate selection mechanics to select the artificial reality environment element (e.g., virtual object) that the user is targeting for interaction. Other forms of user controls, such as voice controls, may not include such selection mechanics. Implementations disambiguate user voice input to select a target virtual object and control the target virtual object based on the voice input. For example, a disambiguation and control layer can select the virtual object the user intends to target with voice input, format input for the target virtual object using the voice input, and control the target virtual object via execution of one or more applications that manage the virtual object.
A system and method for determining content to recommend to a user interface are provided. The system may determine contexts of users within environments. The system may implement a machine learning model including training data pre-trained, or trained in real-time based on historical interactions of users with data, or determined interactions with content by the users in real time. The system may analyze an item(s) of context information associated with the contexts to determine content relevant to a user associated with the system capturing content items within an environment. The system may analyze the item(s) of context information or other items of context information to determine contextual variables, of the environments, determined as relevant to the system. The system may utilize the determined content relevant to the user and the contextual variables determined as relevant to the system to determine a recommendation(s) or action(s) to present to a user interface.
Methods and systems for a fiber optic assembly to propagate light into a waveguide associated with a photonic integrated circuit are provided. The system may include a fiber optic, and a fiber optic core for directing light into the fiber optic assembly. The fiber optic assembly may include at least one transparent layer, metal layer, electrode layer, or a liquid crystal layer. The fiber optic may be aligned in a photonic integrated circuit, where an active feedback loop may be configured to control regions of the fiber optic assembly individually based on the potential difference at both sides of the liquid crystal layer, via electrode layers. The molecules of the liquid crystal layer may be configured to move, change, or be reoriented to direct light into the waveguide based on the potential difference.
G02F 1/13 - Dispositifs ou dispositions pour la commande de l'intensité, de la couleur, de la phase, de la polarisation ou de la direction de la lumière arrivant d'une source lumineuse indépendante, p. ex. commutation, ouverture de porte ou modulationOptique non linéaire pour la commande de l'intensité, de la phase, de la polarisation ou de la couleur basés sur des cristaux liquides, p. ex. cellules d'affichage individuelles à cristaux liquides
66.
Generating Proactive Reminders for Assistant Systems
In one embodiment, a method includes receiving a user request from a first user to create a reminder at a client system, wherein the user request does not specify an activation-condition for the reminder, determining proactive activation-conditions for the reminder, determining whether the proactive activation-conditions for the reminder are satisfied based on user context associated with the first user, and presenting the reminder to the first user at the client system responsive to determining the proactive activation-conditions are satisfied.
In some embodiments, a metaverse optimization and prioritization enabled cloud-based controller includes a metaverse traffic classification unit; and a metaverse optimization and prioritization unit, wherein based upon the identification and classification of data packets as metaverse data packets, the metaverse optimization and prioritization unit optimizes and prioritizes a metaverse client device and metaverse traffic associated with a metaverse optimization and prioritization enabled network. In some embodiments, the metaverse optimization and prioritization unit optimizes the metaverse client device based upon a quality of experience associated with the metaverse client device. In some embodiments, the metaverse optimization and prioritization unit prioritizes the metaverse traffic based on quality of service management features ascertained utilizing the metaverse optimization and prioritization enabled cloud-based controller.
H04W 28/02 - Gestion du trafic, p. ex. régulation de flux ou d'encombrement
H04L 41/0631 - Gestion des fautes, des événements, des alarmes ou des notifications en utilisant l’analyse des causes profondesGestion des fautes, des événements, des alarmes ou des notifications en utilisant l’analyse de la corrélation entre les notifications, les alarmes ou les événements en fonction de critères de décision, p. ex. la hiérarchie ou l’analyse temporelle ou arborescente
H04L 41/5067 - Mesures de la qualité du service [QoS] centrées sur le client
H04W 84/12 - Réseaux locaux sans fil [WLAN Wireless Local Area Network]
68.
Display screen or portion thereof with a graphical user interface
Head-mounted displays may include a machine translation model designed to recognize text through optical character recognition or automatic speech recognition, and may translate the text from its original language to another language. The machine translation model may be trained to modify source text using various tasks, thus allowing the machine translation model to learn different versions of the source text in several different versions. The source text and a variation(s) derived from a task(s) may be mapped to a target text, representing the properly translated and formatted version of the source text. The machine translation model may provide a single model, to facilitate machine translation, implemented on the head-mounted display. Also, the machine translation model may include a bilingual machine translation model that may translate source text from one language to another language, and vice versa.
G06F 40/58 - Utilisation de traduction automatisée, p. ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel
The application describes method of modifying an image. The method may include a step of receiving, via an user interface of a service, a reference image and an input including text associated with the reference image. The method may also include a step of determining, via a trained machine learning (ML) model, one or more features of the reference image. The method may further include a step of modifying, via one or more trained latent diffusion models (LDMs), the reference image based upon the determined features and the received input. Any one or more of a background of the reference image, an area of the reference image or a style of the reference image may be modified. The method may even further include a step of causing to display, via the user interface of the service, the modified image.
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
G06T 3/40 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement
G06T 5/50 - Amélioration ou restauration d'image utilisant plusieurs images, p. ex. moyenne ou soustraction
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
71.
CONTEXTUAL IMAGE GENERATION IN MESSAGING APPLICATIONS
A system or method for contextual image generation in messaging applications may include textual input being analyzed using natural language processing to determine an intent to generate an image without requiring an explicit command. An image may be generated based on the determined intent and conversation context, then displayed within the messaging application. The system supports collaborative image refinement, allowing multiple users in a group chat to modify and animate generated images through natural language interactions. A suggestion mechanism may be employed to maintain user control over image generation.
Various systems, methods, and devices are described for AI platform that may utilize a machine learning model configured to generate one or more overlays associated with a received input. In an example, systems and methods of generating one or more overlays may include receiving a media item and an input. The input may be natural language text or audio associated with a user. The machine learning model may be used to determine context associated with the input. Based on determining the context of the input, the machine learning model may generate one or more overlays. The user may select from the one or more overlays, indicating one or more overlays to user in conjunction to the media item. A combined media may be provided to the user where the selected overlays may be superimposed on the media item.
Technology herein provides a method, apparatus and computer readable storage medium for use in video encoding. The technology performs operations including pruning interframe candidate modes, based on one or more criteria, to provide a reduced set of candidate modes for encoding a video block, wherein a candidate mode includes an interframe mode type, a set of reference frame types, and one or more dynamic reference list (DRL) candidates, and wherein pruning interframe candidate modes comprises excluding one or more interframe mode types, determining a rate distortion (RD) cost for each of the candidate modes in the reduced set of candidate modes, selecting a candidate mode from the reduced set of candidate modes, based on the lowest RD cost, as a selected interframe mode, and encoding the video block using the selected interframe mode.
H04N 19/109 - Sélection du mode de codage ou du mode de prédiction parmi plusieurs modes de codage prédictif temporel
H04N 19/117 - Filtres, p. ex. pour le pré-traitement ou le post-traitement
H04N 19/139 - Analyse des vecteurs de mouvement, p. ex. leur amplitude, leur direction, leur variance ou leur précision
H04N 19/154 - Qualité visuelle après décodage mesurée ou estimée de façon subjective, p. ex. mesure de la distorsion
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 19/52 - Traitement de vecteurs de mouvement par encodage par encodage prédictif
74.
Smart Character Suggestion via XR Cubic Keyboard on Head-mounted Devices
In one embodiment, a method includes receiving a first user input from a user from a client system comprising a head-mounted extended-reality (XR) device, determining the user's intent to activate an XR cubic keyboard based on the first user input, rendering the XR cubic keyboard via XR displays of the head-mounted XR device, wherein the XR cubic keyboard comprises input areas representing respective characters in a three-dimensional (3D) space, and wherein the input areas are reachable by respective vectors from a centroid of the XR cubic keyboard in the 3D space, receiving a second user input comprising a hand movement of the user along a direction of a first vector from the centroid of the XR cubic keyboard in the 3D space, determining a first character that the user intended to input, and rendering an indication of the first character via the XR displays.
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 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06F 3/03 - Dispositions pour convertir sous forme codée la position ou le déplacement d'un élément
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
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
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
In one embodiment, a method includes receiving a first user input comprising a wake word associated with an assistant xbot from a first client system, setting the assistant xbot into a listening mode, wherein a continuous non-visual feedback is provided via the first client system while the assistant xbot is in the listening mode, receiving a second user input comprising a user utterance from the first client system while the assistant xbot is in the listening mode, determining the second user input has ended based on a completion of the user utterance, and setting the assistant xbot into an inactive mode, wherein the non-visual feedback is discontinued via the first client system while the assistant xbot is in the inactive mode.
In one embodiment, a method includes rendering a first output image of an XR assistant avatar within a first environment associated with a first XR application for displays of a first extended-reality (XR) display device, wherein the XR assistant avatar has a first form according to a first rendering specification associated with the first XR application and the XR assistant avatar is interactable by a first user to access an assistant system, receiving an indication that the first user is switching from the first XR application to a second XR application, accessing a second rendering specification associated with the second XR application, and rendering a second output image of the XR assistant avatar within a second environment associated with the second XR application for displays of a second XR display device, wherein the XR assistant avatar is rendered to have a second form according to the second rendering specification.
A method of protecting a risk threat is disclosed. A new entry to be stored in a database store is received, wherein the new entry identifies a risk threat. A plurality of disinformation entries is generated based on the new entry to be stored in the database store. Security signatures for the new entry and the plurality of disinformation entries are determined. An authorized user is allowed to use the security signatures to identify the new entry in the database store as a legitimate entry.
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
G06F 21/64 - Protection de l’intégrité des données, p. ex. par sommes de contrôle, certificats ou signatures
Methods, systems, and storage media for running unified simulations on clusters. Exemplary implementations may include: receiving simulation parameters for a simulation of a cluster; generating synthesized workload events based the simulation parameters of the cluster; determining a memory latency associated with the cluster; determining a reliability and availability of resources in the cluster for a predetermined duration of time; simulating events for jobs in the cluster based on the reliability and availability of resources in the cluster, each job associated with one or more synthesized workload events; and outputting simulation results based on the synthesized workload, the memory latency, and the events.
Systems and methods for extracting data from digital messages for digital group compositions may include (1) determining that a user of a social networking platform, which provides multiple digital services to its users, has accessed a digital service of the social networking platform less than a threshold amount and (2) in response to determining that the user has accessed the digital service less than the threshold amount, presenting a selectable tile, which serves as an entry point for the digital service, in a digital tray of entry point tiles provided via a social media feed configured for the user. Various other methods, systems, and computer-readable media are also disclosed.
G06F 16/9535 - Adaptation de la recherche basée sur les profils des utilisateurs et la personnalisation
G06F 16/951 - IndexationTechniques d’exploration du Web
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
A method for generating a realistic avatar of a user by an electronic device is presented. The method includes displaying, by at least one display, a sequence of frames to a user of the electronic device. The sequence of frames includes a first frame including a first color component, a second frame including a second color component, and third frame including a third color component. while displaying the sequence of images to the user, the method includes capturing, by one or more cameras, a plurality of images of the user. The method further includes determining, for each of the plurality of images, a visible wavelength for each of the first color component, the second color component, and the third color component, and generating a realistic avatar of the user based on the visible wavelength for each of the first color component, the second color component, and the third color component.
A61B 5/00 - Mesure servant à établir un diagnostic Identification des individus
A61B 5/0205 - Évaluation simultanée de l'état cardio-vasculaire et de l'état d'autres parties du corps, p. ex. de l'état cardiaque et respiratoire
A61B 5/1455 - Mesure des caractéristiques du sang in vivo, p. ex. de la concentration des gaz dans le sang ou de la valeur du pH du sang en utilisant des capteurs optiques, p. ex. des oxymètres à photométrie spectrale
G06T 7/90 - Détermination de caractéristiques de couleur
81.
ARTIFICIAL REALITY DEVICES WITH LIGHT BLOCKING CAPABILITY AND PROJECTION OF VISUAL CONTENT OVER REGIONS OF BLOCKED LIGHT
A head-mounted display includes a filter that at least partially blocks light emitted from a light source. In some examples, the light source may be a liquid crystal display that emits polarized light, and the filter may be a polarizing component. Using the filter, the head-mounted display may filter out the light source directed to the head-mounted display. The filtered out region may appear as a grayscale region or a blacked out region mixed with a real-world environment. When the head-mounted display takes the form of an augmented reality device, the head-mounted display may use a display to project visual content onto the filtered out region, while permitting a view of a real-world environment. The visual content may include images, video(s), and/or text, with the images, video(s), and/or text being relevant to the user.
The present application describes systems, methods, devices, and computer program products for convolutional neural networks (CNN) applicable for image processing, image scaling, and computer vision-oriented operations. Various embodiments for image scaling may receive image data corresponding to a first resolution. The image data may have a channel size and a data size. A CNN may be applied to process the image data according to a set of kernels. A first kernel set and a second kernel set may be independently applied to the image data to generate a first output set and a second output set. An interleaved set may be generated from the first output set and the second output set. An output image having a second data size may be generated from the output sets.
The present application at least describes a method including a step of receiving, at a convolutional neural network (CNN), data over a network from a source. The CNN may include one or more blocks. Each block may include plural layers. The method may include a step of causing, via the CNN in a first layer of the first block, a representation of the received data as a first matrix having M rows and N columns. The M rows and N columns may be greater than or equal to 1. The method may also include a step of processing, via the CNN at the first layer of the first block, the first matrix via a predetermined kernel matrix. The kernel matrix may include M-X rows and N-Y columns. X and Y may be greater than or equal to 1. The method may also include a step of rendering, via the CNN based on the processed first matrix, a second matrix having M-2 rows and N-2 columns. The method may further include a step of causing, via the CNN in a second layer of the first block, a representation including a first buffer and the second matrix. The first buffer may include at least 2 columns of the first matrix. The method may include yet a further step of processing, via the CNN at the second layer of the first block, the second matrix via the predetermined kernel matrix. The method may include yet even a further step of rendering, via the CNN based on the processed second matrix, a third matrix having M-4 rows and N-4 columns.
In particular embodiments, a computing system may receive a video comprising a plurality of image frames. The system may generate, for each image frame in the video, an initial depth map using a machine-learning model. The system may compute a misalignment error indicating depth misalignments in initial depth maps using a reprojection technique. The system may generate, for each image frame in the video, an optimized camera pose and a flexible deformation spline associated with the image frame to minimize the misalignment error. The system may generate, for each image frame in the video, a refined depth map by adjusting the initial depth map associated with the frame using the flexible deformation spline associated with the image frame.
In one embodiment, a method includes receiving touch inputs from a user corresponding to an activation trigger for an assistant system executing on a head-mounted device at the head-mounted device, accessing signals from inertial measurement unit (IMU) sensors of the head-mounted device by the head-mounted device, determining that the user is either donning or doffing the head-mounted device by an on-device don/doff detection model and based only on the signals from the IMU sensors, and overriding the activation trigger to prevent an activation of the assistant system responsive to the received touch inputs.
In one embodiment, a method includes receiving at a head-mounted device a speech input from a user and a visual input captured by cameras of the head-mounted device, wherein the visual input comprises subjects and attributes associated with the subjects, and wherein the speech input comprises a co-reference to one or more of the subjects, resolving entities corresponding to the subjects associated with the co-reference based on the attributes and the co-reference, and presenting a communication content responsive to the speech input and the visual input at the head-mounted device, wherein the communication content comprises information associated with executing results of tasks corresponding to the resolved entities.
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
G06F 7/14 - Interclassement, c.-à-d. association d'au moins deux séries de supports d'enregistrement, chacun étant rangé dans le même ordre de succession, en vue de former une série unique rangée dans le même ordre de succession
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
G06F 16/176 - Support d’accès partagé aux fichiersSupport de partage de fichiers
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
G06F 16/951 - IndexationTechniques d’exploration du Web
G06F 16/9535 - Adaptation de la recherche basée sur les profils des utilisateurs et la personnalisation
G06F 18/2411 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur la proximité d’une surface de décision, p. ex. machines à vecteurs de support
G06F 40/40 - Traitement ou traduction du langage naturel
G06N 3/006 - Vie artificielle, c.-à-d. agencements informatiques simulant la vie fondés sur des formes de vie individuelles ou collectives simulées et virtuelles, p. ex. simulations sociales ou optimisation par essaims particulaires [PSO]
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/30 - ScènesÉléments spécifiques à la scène dans les albums, les collections ou les contenus partagés, p. ex. des photos ou des vidéos issus des réseaux sociaux
G06V 40/16 - Visages humains, p. ex. parties du visage, croquis ou expressions
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
G10L 13/00 - Synthèse de la paroleSystèmes de synthèse de la parole à partir de texte
G10L 13/04 - Détails des systèmes de synthèse de la parole, p. ex. structure du synthétiseur ou gestion de la mémoire
G10L 15/02 - Extraction de caractéristiques pour la reconnaissance de la paroleSélection d'unités de reconnaissance
G10L 15/06 - Création de gabarits de référenceEntraînement des systèmes de reconnaissance de la parole, p. ex. adaptation aux caractéristiques de la voix du locuteur
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]
H04L 41/00 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets
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]
H04L 43/0882 - Utilisation de la capacité de la liaison
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
H04L 51/046 - Interopérabilité avec d'autres applications ou services réseau
H04L 51/216 - Gestion de l'historique des conversations, p. ex. regroupement de messages dans des sessions ou des fils de conversation
H04L 67/10 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau
H04L 67/53 - Services réseau en utilisant des fournisseurs tiers de services
H04L 67/5651 - Conversion ou adaptation du format ou du contenu d'applications en réduisant la quantité ou la taille des données d'application échangées
H04L 67/75 - Services réseau en affichant sur l'écran de l'utilisateur les conditions du réseau ou d'utilisation
In one embodiment, a method includes accessing a first document, accessing a plurality of second documents, calculating a relevance score for each of the plurality of second documents indicating a degree of relevance of the second document to the first document using an encoder of a machine-learning model, selecting a subset of the second documents based on their corresponding relevance scores, generating a target document by using the machine-learning model to process the subset of second documents and their corresponding relevance scores, and updating parameters of the machine-learning model based on a comparison between the first document and the generated target document.
G06F 40/58 - Utilisation de traduction automatisée, p. ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel
G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
The disclosed systems for suspending cable (e.g., fiber optic cable) from an overhead powerline may include a payload subsystem for housing and dispensing a cable along an overhead powerline, a rotation subsystem for winging the cable from the payload subsystem around the powerline, an extension subsystem for moving the payload subsystem to avoid obstacles, an obstacle detection subsystem for automatically detecting obstacles encountered along the powerline, a drive subsystem for driving the system along the powerline, and at least one processor for controlling the payload subsystem, rotation subsystem, extension subsystem, obstacle detection subsystem, and drive subsystem in a manner that avoids obstacles as the system moves along the powerline. Various other related systems, devices, components, and methods are also disclosed.
G06T 7/521 - Récupération de la profondeur ou de la forme à partir de la télémétrie laser, p. ex. par interférométrieRécupération de la profondeur ou de la forme à partir de la projection de lumière structurée
G06T 7/593 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir d’images stéréo
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
H02G 1/04 - Méthodes ou appareils spécialement adaptés à l'installation, entretien, réparation, ou démontage des câbles ou lignes électriques pour lignes ou câbles aériens pour les monter ou les tendre
H02G 7/02 - Dispositifs pour ajustage ou maintien de la tension mécanique, p. ex. organe de compensation
H04N 13/239 - Générateurs de signaux d’images utilisant des caméras à images stéréoscopiques utilisant deux capteurs d’images 2D dont la position relative est égale ou en correspondance à l’intervalle oculaire
Methods, systems, and apparatuses for disguising or otherwise hiding big data access patterns and frequencies may include a replica being directly obtained from a key and a replica index, based on applying a pseudorandom permutation or pseudorandom function. Data object access frequencies may be quantized so that the ratios of quantized data object access frequencies are rational numbers of integers, which may allow for a uniform distribution of data object accesses. Fake accesses may be implemented by simulating an access schedule produced by a cryptographic primitive and by performing a greedy mapping between the real data object accesses and simulated data object accesses.
A method implemented by a computing device includes displaying on a display of the computing device an extended reality (XR) environment, and determining one or more virtual characteristics associated with a first virtual content and a second visual content viewable within the displayed XR environment, in which the second virtual content is at least partially occluded by the first virtual content. The method further includes generating, based on the one or more virtual characteristics, a plurality of user input interception layers to be associated with the first virtual content and the second visual content, and in response to determining a user intent to interact with the second virtual content, directing one or more user inputs to the second virtual content based on whether or not the one or more user inputs are intercepted by one or more of the plurality of user input interception layers.
According to examples, a system for implementing image modification functions via use of variable scanning orders is described. The system may include a processor and a memory storing instructions. The processor, when executing the instructions, may cause the system to partition an image into a plurality of image blocks, identify one or more image blocks of the plurality of image blocks associated with a region of interest (ROI), and scan the one or more image blocks in an image modification order. The processor, when executing the instructions, may then arrange the one or more image blocks according to the image modification order to form a modified image including the region of interest (ROI) and crop the region of interest (ROI) in the modified image to form a new image.
A method includes receiving, from a client system, one or more utterances comprising one or more first words in a first language and one or more second words in a second language. The method further includes generating, based on a single bilingual automatic-speech-recognition (ASR) model, a transcription of the one or more utterances, such that the transcription comprises one or more first text strings in the first language and one or more second text strings in the second language. The method further includes executing one or more tasks based on the one or more first text strings in the first language and the one or more second text strings in the second language, and sending, to the client system, instructions for presenting a response responsive to the one or more utterances, such that the response is based on both the first and second languages.
G06F 40/58 - Utilisation de traduction automatisée, p. ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel
G10L 15/183 - Classement ou recherche de la parole utilisant une modélisation du langage naturel selon les contextes, p. ex. modèles de langage
G10L 15/30 - Reconnaissance distribuée, p. ex. dans les systèmes client-serveur, pour les applications en téléphonie mobile ou réseaux
93.
Large Language Models for Voice-Driven NPC Interactions
In one embodiment, a method includes receiving, by a mixed reality (MR) display device, an audio input from a first user of the MR display device, where the MR display device is associated with an MR environment including several MR objects, processing, using a natural language understanding (NLU) model, the audio input to identify one or more intents and one or more slots associated with the audio input, identifying a first MR object from several MR objects that is in an active listening state, where the first MR object is associated with a first set of intents and a first set of slots, determining that either the first set of intents or the first set of slots do not include the one or more identified intents or the one or more identified slots associated with the audio input, and generating, using a large language model (LLM), an out-of-domain (OOD) response.
In one embodiment, a method includes accessing from a client system associated with a first user sensor signals captured by sensors of the client system, wherein the client system comprises a plurality of sensors, and wherein the sensors signals are accessed from the sensors based on cascading model policies, wherein each cascading model policy utilizes one or more of a respective cost or relevance associated with each sensor, detecting a change in a context of the first user associated with an activity of the first user based on machine-learning models and the sensor signals, wherein the change in the context of the first user satisfies a trigger condition associated with the activity, and responsive to the detected change in the context of the first user automatically capturing visual data by cameras of the client system.
In one embodiment, a method includes receiving a voice input having acoustic features from a first client system associated with a first user, determining emotions associated with the voice input based on one or more of the acoustic features by machine-learning models, determining facial features for a first extended-reality (XR) avatar representing the first user based on the emotions, and sending instructions for rendering the first XR avatar representing the first user to a second client system associated with a second user, wherein the first XR avatar is rendered with the determined facial features.
G06T 13/40 - Animation tridimensionnelle [3D] de personnages, p. ex. d’êtres humains, d’animaux ou d’êtres virtuels
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
Three-dimensional chip architecture is described herein. In one example aspect, an integrated circuit may include an interposer layer. The integrated circuit may further include a plurality of random access memory chiplets stacked atop the interposer layer, and a plurality of compute chiplets. The plurality of compute chiplets may be stacked atop a respective random access memory chip of the plurality of random access memory chiplets, such that the plurality of compute chiplets may be in electrical communication with the respective random access memory chip of the plurality of random access memory chiplets.
H10B 80/00 - Ensembles de plusieurs dispositifs comprenant au moins un dispositif de mémoire couvert par la présente sous-classe
H01L 25/16 - Ensembles consistant en une pluralité de dispositifs à semi-conducteurs ou d'autres dispositifs à l'état solide les dispositifs étant de types couverts par plusieurs des sous-classes , , , , ou , p. ex. circuit hybrides
98.
METHODS, APPARATUSES AND COMPUTER PROGRAM PRODUCTS FOR DETERMINING OPTIMIZED PERSONALIZED EXECUTION TIMES FOR DELIVERY OF OPTIMIZED CONTENT
A system for determining personalized execution times for delivery of optimized content is disclosed. The system may evaluate activities of a user, occurring within a network, during hours of days. The system may determine, based on the evaluated activities, optimal time periods during a time interval in which to schedule provision of content items to a communication device(s) associated with the user. The system may determine a best time period among the optimal time periods. The system may generate, in advance of the optimal time periods, content items tailored to the user. The system may determine, in advance of the best time period, a highest ranked generated content item, among the generated content items, for delivery to the communication device(s) associated with the user during the best time period. The system may enable provision, to the communication device(s), of the highest ranked generated content item during the best time period.
An online system receives explicit user data and explicit event data, and implicit user data and implicit event data from a third party system. The online system generates an implicit users/implicit events data feature, an explicit users/explicit events data feature, and an explicit users/implicit events data feature. The online system generates a prediction of the counterfactual rate based on the implicit users/implicit events data feature, the explicit users/explicit events data feature, and the explicit users/explicit events data feature, the counterfactual rate indicating the likelihood that target users matching certain characteristics caused an event to occur when the target are not been presented with content by the online system, the content configured to induce users to cause the event to occur. A combined prediction rate is presented to the third party system based on the counterfactual rate.
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
100.
Multimodal state tracking via scene graphs for assistant systems
In one embodiment, a method includes receiving, from a client system associated with a user, a first user request that includes a reference to a target object and one or more of an attribute or a relationship of the target object. Visual data including one or more images portraying the target object may then be accessed, and the reference may be resolved to the target object portrayed in the one or more images. Object information of the target object that corresponds to the referenced attribute or relationship of the first user request may be determined based on a visual analysis of the one or more images. Finally, responsive to receiving the first user request, the object information of the target object may be stored in a multimodal dialog state.
brevets
