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Date
Nouveautés (dernières 4 semaines) 73
2023 février (MACJ) 19
2023 janvier 54
2022 décembre 69
2022 novembre 59
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Classe IPC
G06F 17/30 - Recherche documentaire; Structures de bases de données à cet effet 366
G06N 3/04 - Architecture, p.ex. topologie d'interconnexion 307
G06N 3/08 - Méthodes d'apprentissage 257
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur 213
H04L 29/06 - Commande de la communication; Traitement de la communication caractérisés par un protocole 200
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1.

APPLICATION COMPATIBILITY ON A COMPUTING DEVICE

      
Numéro d'application US2022074192
Numéro de publication 2023/010040
Statut Délivré - en vigueur
Date de dépôt 2022-07-27
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Nathwani, Sanjay
  • Mccanny, Ben
  • Takise, Kazuki

Abrégé

According to an aspect, a method includes installing an application on a computing device, determining whether to activate a compatibility mode for the application, in response to activating the compatibility mode, determining a restriction to a change to an application window size or shape of a user interface of the application, and rendering a user interface object defining a plurality of predefined sizing options for the user interface of the application.

Classes IPC  ?

  • G06F 8/61 - Installation
  • G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
  • G06F 8/38 - Création ou génération de code source pour la mise en œuvre d'interfaces utilisateur

2.

SMART ALGORITHM FOR SEAMLESS TRANSITION WITH UNDER DISPLAY FINGERPRINT SENSORS

      
Numéro d'application US2021043860
Numéro de publication 2023/009136
Statut Délivré - en vigueur
Date de dépôt 2021-07-30
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Wen, Chien-Hui
  • Chen, Hsin-Yu

Abrégé

An example method includes determining, for a device having a display component configured to operate at multiple brightness levels and for a range of DBVs, a gamma value offset to a default gamma value at the second brightness level. The method includes determining, for a tap point representative of the range, a brightness value offset to a default brightness value at the second brightness level. The method includes storing the gamma value offset and the brightness value offset. Subsequent to the storing, the device is configured to transition, in response to a fingerprint authentication triggering event, the display component from a first brightness level to a second brightness level by: overriding a default gamma value based on the gamma value offset, and displaying a portion of the display component by applying a value offset to a default brightness value at the second brightness level based on the brightness value offset.

Classes IPC  ?

  • G09G 5/10 - Circuits d'intensité
  • G06F 21/32 - Authentification de l’utilisateur par données biométriques, p.ex. empreintes digitales, balayages de l’iris ou empreintes vocales

3.

DETERMINING AVAILABLE MEMORY ON A MOBILE PLATFORM

      
Numéro d'application US2022072375
Numéro de publication 2023/009905
Statut Délivré - en vigueur
Date de dépôt 2022-05-17
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Carbon-Ogden, Scott James
  • Blackler, James Andrew

Abrégé

An application from a plurality of applications executing at one or more processors of a computing device may determine a plurality of memory metrics of the computing device. The application may determine information indicative of a predicted safe amount of memory available for allocation by an application from the plurality of applications based at least in part on the plurality of memory metrics. The application may adjust, based at least in part on the information indicative of the predicted safe amount of memory available for allocation by the application, one or more characteristics of the application executing at the one or more processors to adjust an amount of memory allocated by the application.

Classes IPC  ?

  • G06F 12/02 - Adressage ou affectation; Réadressage
  • G06F 9/50 - Allocation de ressources, p.ex. de l'unité centrale de traitement [UCT]
  • G06N 3/063 - Réalisation physique, c. à d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
  • G06N 20/00 - Apprentissage automatique

4.

GENERATION OF MACHINE LEARNING PREDICTIONS USING MULTIPLE DOMAIN DATA SETS

      
Numéro d'application US2021043134
Numéro de publication 2023/009101
Statut Délivré - en vigueur
Date de dépôt 2021-07-26
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s) Kanazawa, Noritsugu

Abrégé

A method includes obtaining an input matrix (402) and determining (406) a domain index matrix (408) that includes, for each respective input value of the input matrix, a corresponding domain index value that indicates a corresponding training data distribution of a plurality of training data distributions. The method also includes providing the input matrix and the domain index matrix to a machine learning model (404) that has been trained using the plurality of training data distributions, where each respective training data distribution is associated with a different attribute. The method further includes generating, by the machine learning model and based on the input and the domain index matrices (402,408), an output matrix (410) that includes, for each respective input value, a corresponding output value generated based on (i) the respective input value and (ii) the corresponding domain index value such that the corresponding output value exhibits the attribute of the corresponding training data distribution.

Classes IPC  ?

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

5.

AUGMENTED REALITY DEPTH DETECTION THROUGH OBJECT RECOGNITION

      
Numéro d'application US2022074004
Numéro de publication 2023/009965
Statut Délivré - en vigueur
Date de dépôt 2022-07-21
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Faaborg, Alexander, James
  • Wu, Shengzhi

Abrégé

A computer-implemented method includes receiving a two-dimensional image of a scene captured by a camera, recognizing one or more objects in the scene depicted in the two-dimensional image, and determining whether the one or more recognized objects have known real-world dimensions. The computer-implemented method further includes determining a depth of at least one recognized object having known real-world dimensions from the camera, and overlaying three-dimensional (3-D) augmented reality content over a display the 2-D image of the scene considering the depth of the at least one recognized object from the camera.

Classes IPC  ?

  • G06V 20/20 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans les scènes de réalité augmentée
  • G06V 20/64 - Objets tridimensionnels
  • G06T 7/50 - Récupération de la profondeur ou de la forme

6.

SELECTIVE BLACK LEVEL CONTROL IN ACTIVE MATRIX DISPLAYS

      
Numéro d'application US2021043960
Numéro de publication 2023/009141
Statut Délivré - en vigueur
Date de dépôt 2021-07-30
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Choi, Sangmoo
  • Solomon, Daniel

Abrégé

A method includes: (a) receiving initial image frame data to display an image frame on a display panel, a luminance of each pixel of the display corresponding to a gray level; (b) identifying dark pixels at or below a first threshold gray level; (c) identifying pixels to be modified as a subset of the dark pixels neighbored by at least one bright pixel exceeding a second threshold gray level; (d) increasing by an incremental amount the gray level of the pixels to be modified, providing modified image frame data composed of: (i) the dark pixels that are neighbored by at least one bright pixel having gray levels that have been increased by the incremental gray level amount, and (ii) other pixels that have gray levels from the initial image frame data; and (e) displaying the image frame using the modified image frame data.

Classes IPC  ?

  • G09G 3/3225 - Dispositions ou circuits de commande présentant un intérêt uniquement pour l'affichage utilisant des moyens de visualisation autres que les tubes à rayons cathodiques pour la présentation d'un ensemble de plusieurs caractères, p.ex. d'une page, en composant l'ensemble par combinaison d'éléments individuels disposés en matrice utilisant des sources lumineuses commandées utilisant des panneaux électroluminescents semi-conducteurs, p.ex. utilisant des diodes électroluminescentes [LED] organiques, p.ex. utilisant des diodes électroluminescentes organiques [OLED] utilisant une matrice active

7.

CONTEXTUAL TRIGGERING OF ASSISTANCE FUNCTIONS

      
Numéro d'application US2022036170
Numéro de publication 2023/009281
Statut Délivré - en vigueur
Date de dépôt 2022-07-06
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Gray, Kristin A.
  • Wantland, Tim
  • Stokes, Matthew
  • Bingying, Xia
  • Vertierra, Karen
  • Barnhart, Melissa
  • Winkleman, Gus

Abrégé

A method (500) includes, while a user device (110) is using a first presentation mode (234) to present content to a user (10), obtaining a current state (212) of the user of the user device. The method also includes, based on the current state of the user, providing, as output from a user interface (400) of the user device, a user-selectable option (402) that when selected causes the user device to use a second presentation mode to present the content to the user. The method further includes, in response to receiving a user input indication (14) indicating selection of the user-selectable option, initiating presentation of the content using the second presentation mode.

Classes IPC  ?

  • G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
  • G06F 3/16 - Entrée acoustique; Sortie acoustique

8.

TACTILE COPRESENCE

      
Numéro d'application US2021043694
Numéro de publication 2023/009124
Statut Délivré - en vigueur
Date de dépôt 2021-07-29
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Barnett, Donald, Allen
  • Hinz, Carsten
  • Cunningham, Corbin, Alexander
  • Rickerby, George, Joseph
  • Pawle, Benjamin Guy, Alexander
  • Colville, Michael
  • Douwes, Scott

Abrégé

The technology provides tactile copresence for participants working in a real-time remote collaborative arrangement. This enhanced user experience enables different users to work remotely through their own local physical media such as whiteboards, large screen displays or poster boards (208, 228, 262, 272). Participants are able to view a common workspace in the same way, and are able to experience tactile copresence via silhouette representations (106, 312, 600) of the other people. A method includes receiving depth map information of a participant at a first location, the depth map information being derived from a raw image captured at the first location (904). A presence shadow for the participant is generated by a computing device associated with a different location, in which the presence shadow reprojects aspects of the participant according to the depth map information where the aspects are blurred according to a proximity of each aspect to a physical medium at the first location (906).

Classes IPC  ?

9.

EVALUATING OUTPUT SEQUENCES USING AN AUTO-REGRESSIVE LANGUAGE MODEL NEURAL NETWORK

      
Numéro d'application US2022038742
Numéro de publication 2023/009766
Statut Délivré - en vigueur
Date de dépôt 2022-07-28
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • De Freitas Adiwardana, Daniel
  • Shazeer, Noam M.

Abrégé

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for evaluating candidate output sequences using language model neural networks. In particular, an auto-regressive language model neural network is used to generate a candidate output sequence. The same auto-regressive language model neural network is used to evaluate the candidate output sequence to determine rating scores for each of one or more criteria. The rating score(s) are then used to determine whether to provide the candidate output sequence.

Classes IPC  ?

  • G06N 3/08 - Méthodes d'apprentissage
  • G06N 3/04 - Architecture, p.ex. topologie d'interconnexion
  • G06F 40/284 - Analyse lexicale, p.ex. segmentation en unités ou cooccurrence

10.

GENERATING AUDIOVISUAL CONTENT BASED ON VIDEO CLIPS

      
Numéro d'application US2021043276
Numéro de publication 2023/009104
Statut Délivré - en vigueur
Date de dépôt 2021-07-27
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Clark, Nicholas, James
  • Murphy, Glen
  • Cornwell, Jason, Briggs
  • O'Sullivan, Conor, Patrick
  • Burk, Philip, Loyd
  • Park, Eunyoung
  • Rowe, Philip, Francis
  • Turner, Donald, Peter
  • Möllerstedt, Karl, David
  • Ericson, Finn, Ake, Axel
  • Stadler, Svante, Sten, Johan
  • Claesson, Johan, Philip
  • Josefsson, Ola, Fredrik

Abrégé

A method includes capturing, by a content generation component of a computing device, initial content comprising video, and audio associated with the video; identifying one or more audio clips in the audio associated with the video based on one or more transient points in the audio; extracting, for each audio clip, a corresponding video clip from the video of the initial content; providing a control interface to enable a user-generated sequence of audio clips, wherein each audio clip in the sequence of audio clips is selected from the one or more identified audio clips; generating new audiovisual content comprising a sequence of video clips to correspond to the user-generated sequence of audio clips, wherein each video clip in the sequence of video clips is the extracted corresponding video clip for each audio clip in the user- generated sequence of audio clips; and providing, by the control interface, the new audiovisual content.

Classes IPC  ?

  • G11B 27/031 - Montage électronique de signaux d'information analogiques numérisés, p.ex. de signaux audio, vidéo
  • G11B 27/34 - Aménagements indicateurs
  • G11B 27/28 - Indexation; Adressage; Minutage ou synchronisation; Mesure de l'avancement d'une bande en utilisant une information détectable sur le support d'enregistrement en utilisant des signaux d'information enregistrés par le même procédé que pour l'enregistrement principal
  • G10H 1/00 - INSTRUMENTS DE MUSIQUE ÉLECTROPHONIQUES; INSTRUMENTS DANS LESQUELS LES SONS SONT PRODUITS PAR DES MOYENS ÉLECTROMÉCANIQUES OU DES GÉNÉRATEURS ÉLECTRONIQUES, OU DANS LESQUELS LES SONS SONT SYNTHÉTISÉS À PARTIR D'UNE MÉMOIRE DE DONNÉES Éléments d'instruments de musique électrophoniques

11.

DYNAMIC ADAPTATION OF GRAPHICAL USER INTERFACE ELEMENTS BY AN AUTOMATED ASSISTANT AS A USER ITERATIVELY PROVIDES A SPOKEN UTTERANCE, OR SEQUENCE OF SPOKEN UTTERANCES

      
Numéro d'application US2021061000
Numéro de publication 2023/009157
Statut Délivré - en vigueur
Date de dépôt 2021-11-29
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Barros, Brett
  • Jang, Joanne J.
  • Schoneweis, Andrew

Abrégé

Implementations described herein relate to an automated assistant that iteratively renders various GUI elements as a user iteratively provides a spoken utterance, or sequence of spoken utterances, corresponding to a request directed to the automated assistant. These various GUI elements can be dynamically adapted as the user iteratively provides the spoken utterance to assist the user with efficiently completing the request. In some implementations, a generic container graphical element associated with candidate intent(s) can be initially rendered at a display interface of a computing device and dynamically adapted with tailored container graphical elements as a particular intent is determined while the user iteratively provides the spoken utterance. In additional or alternative implementations, the tailored container graphical elements can include a current status of one or more settings associated with the computing device or additional computing device(s) such that the user can view the current status while completing the spoken utterance.

Classes IPC  ?

  • G06F 3/16 - Entrée acoustique; Sortie acoustique

12.

CONTRASTIVE LEARNING AND MASKED MODELING FOR END-TO-END SELF-SUPERVISED PRE-TRAINING

      
Numéro d'application US2022038699
Numéro de publication 2023/009740
Statut Délivré - en vigueur
Date de dépôt 2022-07-28
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Zhang, Yu
  • Chung, Yu-An
  • Han, Wei
  • Chiu, Chung-Cheng
  • Qin, Weikeng
  • Pang, Ruoming
  • Wu, Yonghui

Abrégé

Provided are improved end-to-end self-supervised pre-training frameworks that leverage a combination of contrastive and masked modeling loss terms. In particular, the present disclosure provides framework that combines contrastive learning and masked modeling, where the former trains the model to discretize input data (e.g., continuous signals such as continuous speech signals) into a finite set of discriminative tokens, and the latter trains the model to learn contextualized representations via solving a masked prediction task consuming the discretized tokens. In contrast to certain existing masked modeling-based pre-training frameworks which rely on an iterative re-clustering and re-training process or other existing frameworks which concatenate two separately trained modules, the proposed framework can enable a model to be optimized in an end-to-end fashion by solving the two self-supervised tasks (the contrastive task and masked modeling) simultaneously.

Classes IPC  ?

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

13.

COMPUTATIONAL PHOTOGRAPHY UNDER LOW-LIGHT CONDITIONS

      
Numéro d'application US2021043767
Numéro de publication 2023/009128
Statut Délivré - en vigueur
Date de dépôt 2021-07-29
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Gao, Jinglun
  • Velarde, Ruben Manuel
  • Hung, Szepo Robert

Abrégé

This document describes techniques and apparatuses for computational photography under low-light conditions for an image-capture device on a mobile computing device. In aspects, described are techniques and apparatuses for an image-capture device to utilize sensor data in determining whether to enable flash photography or capture multiple images of the scene without use of a flash under low-light conditions. In other aspects, an image-capture device may utilize device data in determining whether to enable flash photography or capture multiple images of the scene without use of a flash under low-light conditions. The disclosed techniques and apparatuses may provide improved computational photography under low-light conditions for an image-capture device on a mobile computing device.

Classes IPC  ?

  • H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance
  • H04N 5/225 - Caméras de télévision
  • H04N 5/235 - Circuits pour la compensation des variations de la luminance de l'objet
  • G06T 5/50 - Amélioration ou restauration d'image en utilisant plusieurs images, p.ex. moyenne, soustraction
  • G06N 20/00 - Apprentissage automatique
  • 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

14.

MANAGING UE MEASUREMENTS IN AN IDLE OR INACTIVE STATE

      
Numéro d'application US2022038621
Numéro de publication 2023/009691
Statut Délivré - en vigueur
Date de dépôt 2022-07-28
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s) Wu, Chih-Hsiang

Abrégé

A user equipment (UE) can perform a method for managing inter-frequency measurements. The method includes: determining (902) one or more carrier frequencies for inter-frequency measurements while a radio connection between the UE and the RAN is not active; determining (904) that data is available for early data communication between the UE and a core network (CN); and determining (906) whether to suspend (i) inter-frequency measurements on at least one of the carrier frequencies or (ii) the early data communication.

15.

PROVIDING CERTAIN REASONING WITH RESPECT TO FULFILLMENT OF AN ASSISTANT COMMAND

      
Numéro d'application US2021060986
Numéro de publication 2023/009156
Statut Délivré - en vigueur
Date de dépôt 2021-11-29
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Weissenberger, Felix
  • Frömmgen, Alexander
  • Prisacari, Bogdan

Abrégé

Implementations described herein relate to causing certain reasoning with respect to why an automated assistant performed (or did not perform) certain fulfillment and/or alternate fulfillment of an assistant command. For example, implementations can receive user input that includes the assistant command, process the user input to determine data to be utilized in performance of the certain fulfillment or the alternate fulfillment of the assistant command, and cause the automated assistant to utilize the data to perform the certain fulfillment or the alternate fulfillment of the assistant command. In some implementations, output that includes the certain reasoning can be provided for presentation to a user in response to additional user input that requests the certain reasoning. In some implementations, a selectable element can be visually rendered and, when selected by the user, the output that includes the certain reasoning can be provided for presentation to the user.

Classes IPC  ?

  • G06N 5/04 - Modèles d’inférence ou de raisonnement
  • 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
  • G06F 3/16 - Entrée acoustique; Sortie acoustique
  • G10L 13/00 - Synthèse de la parole; Systèmes de synthèse de la parole à partir de texte
  • G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
  • G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
  • G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p.ex. des menus
  • G06F 40/30 - Analyse sémantique
  • G06F 40/56 - Génération de langage naturel
  • G06F 21/12 - Protection des logiciels exécutables
  • H04W 12/08 - Sécurité d'accès

16.

AUTOMATIC WHITE-BALANCE (AWB) FOR A CAMERA SYSTEM

      
Numéro d'application US2021054630
Numéro de publication 2023/009155
Statut Délivré - en vigueur
Date de dépôt 2021-10-12
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Liang, Liang
  • Chatterjee, Anirban
  • Masharani, Nisha
  • Penner, Eric Scott
  • Reynolds, Isaac William

Abrégé

This document describes techniques and apparatuses for automatic white-balance for a camera system. The techniques and apparatuses utilize a precursor image to detect one or more detected faces and determine a tone. The camera system retrieves tonal data based on a group of images determined to contain a same face as the detected face. Based on this tonal data, a difference in white balance is determined based on the difference in tone of the detected face within the precursor image and the associated tonal data. Camera settings are adjusted based on the difference in white balance to enable capture of an image having an improved tone.

Classes IPC  ?

  • H04N 9/73 - Circuits pour l'équilibrage des couleurs, p.ex. circuits pour équilibrer le blanc ou commande de la température de couleur
  • H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance

17.

MANAGING RADIO FUNCTIONS IN THE INACTIVE STATE

      
Numéro d'application US2022038789
Numéro de publication 2023/009781
Statut Délivré - en vigueur
Date de dépôt 2022-07-29
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s) Wu, Chih-Hsiang

Abrégé

A central unit (CU) of a distributed base station, the distributed base station including the CU and a distributed unit (DU), can implement a method for managing a radio function for communicating with a UE. The method may include determining (1202) to transmit a CU-to-DU message, related to control of data communication with the UE, to the DU, and determining (1204) whether the data communication requires the DU to perform the radio function. The method further includes, based on whether the data communication requires the DU to perform the radio function, determining (1206) whether to include an indication in the CU-to-DU message to enable the radio function at the DU. The method also includes transmitting (1208) the CU-to-DU message to the DU.

Classes IPC  ?

  • H04W 76/10 - Gestion de la connexion Établissement de la connexion
  • H04W 76/27 - Transitions entre états de commande de ressources radio [RRC]
  • H04W 88/08 - Dispositifs formant point d'accès

18.

DEVICES FOR MEDIA HANDOFF

      
Numéro d'application US2021043883
Numéro de publication 2023/009139
Statut Délivré - en vigueur
Date de dépôt 2021-07-30
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Wang, Jian
  • Jindal, Nihar
  • Chung, Meng-Hsuan
  • Yee, Dennis
  • Feldman, Arnold

Abrégé

Example embodiments relate to devices for media handoff. An example device includes a first transceiver configured to transmit and receive signals to communicate with a second transceiver of a handoff device. The signals are indicative of an orientation and a position of the first transceiver relative to the second transceiver. The device also includes an inertial measurement unit configured to measure changes in angular orientation or position of the device. Additionally, the device includes a memory. The memory stores a first set of instructions. Further, the device includes a processor communicatively coupled to the first transceiver, the inertial measurement unit, and the memory. The processor is configured to execute the first of instructions to cause the handoff device to output a piece of media.

Classes IPC  ?

  • H04W 4/02 - Services utilisant des informations de localisation
  • H04N 21/40 - Dispositifs clients spécialement adaptés à la réception de contenu ou à l'interaction avec le contenu, p.ex. boîtier décodeur [STB]; Leurs opérations
  • H04W 4/80 - Services utilisant la communication de courte portée, p.ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
  • H04W 76/14 - Gestion de la connexion Établissement de la connexion Établissement de la connexion en mode direct

19.

DETERMINING WATCH TIME LOSS REGIONS IN MEDIA CONTENT ITEMS

      
Numéro d'application US2021043487
Numéro de publication 2023/009114
Statut Délivré - en vigueur
Date de dépôt 2021-07-28
Date de publication 2023-02-02
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Zhang, Wenbo
  • Gupta, Kartikey

Abrégé

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining watch time loss regions in media content items. In one aspect, features for a video are input into a trained model that is trained to output watch time loss regions. The trained model is trained using labels corresponding to known watch time loss regions in training videos and features of training videos that correspond to the known watch time loss regions. A watch time loss region defines a time window of a video during which a likelihood of a user stopping playback of the video is more than a threshold likelihood. In response to inputting the feature for the first video into the trained model, data regarding watch time loss regions for the video is obtained from the model and provided to an entity involved in providing the video to a user.

Classes IPC  ?

  • H04N 21/25 - Opérations de gestion réalisées par le serveur pour faciliter la distribution de contenu ou administrer des données liées aux utilisateurs finaux ou aux dispositifs clients, p.ex. authentification des utilisateurs finaux ou des dispositifs clients ou
  • G06V 20/40 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans le contenu vidéo
  • H04N 21/442 - Surveillance de procédés ou de ressources, p.ex. détection de la défaillance d'un dispositif d'enregistrement, surveillance de la bande passante sur la voie descendante, du nombre de visualisations d'un film, de l'espace de stockage disponible dans l
  • H04N 21/658 - Transmission du client vers le serveur

20.

GUIDED CONTEXTUAL ATTENTION MAP FOR INPAINTING TASKS

      
Numéro d'application US2021042150
Numéro de publication 2023/003528
Statut Délivré - en vigueur
Date de dépôt 2021-07-19
Date de publication 2023-01-26
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Kanazawa, Noritsugu
  • Aberman, Kfir
  • Knaan, Yael, Pritch
  • Wadhwa, Neal

Abrégé

Systems and methods for augmenting data can leverage one or more machine-learned models and contextual attention data to provide more realistic and efficient data augmentation. For example, systems and methods for inpainting can leverage a machine-learned model to generate predicted contextual attention data and blend the predicted contextual attention data with obtained contextual attention data to determine replacement data for augmenting an image to replace one or more occlusions. The obtained contextual attention data can include user-guided contextual attention.

Classes IPC  ?

  • G06T 5/00 - Amélioration ou restauration d'image

21.

PAUSING OR HANDING OFF A NAVIGATION SESSION TO SAVE POWER

      
Numéro d'application US2021042177
Numéro de publication 2023/003530
Statut Délivré - en vigueur
Date de dépôt 2021-07-19
Date de publication 2023-01-26
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Sharifi, Matthew
  • Carbune, Victor

Abrégé

Techniques for saving mobile device power during turn by turn navigation sessions are provided. An example method includes receiving an indication of an origin location and a destination location via a navigation application operating on a mobile computing device; generating a navigation route based on the origin location and the destination location using the navigation application,, including indications of one or more maneuvers required to be performed by a user; comparing a remaining battery power level associated with the mobile computing device to a predicted processing power required for the navigation application to perform one or more functions related to the navigation route; and causing the navigation application to operate in a power-saving mode based on the comparison. Operating the navigation application in the power-saving mode may include temporarily suspending at least one of the one or more functions of related to the navigation route.

Classes IPC  ?

  • G01C 21/26 - Navigation; Instruments de navigation non prévus dans les groupes spécialement adaptés pour la navigation dans un réseau routier
  • H04M 1/73 - Dispositions pour économiser la batterie
  • G06F 1/3212 - Surveillance du niveau de charge de la batterie, p.ex. un mode d’économie d’énergie étant activé lorsque la tension de la batterie descend sous un certain niveau

22.

BIT VECTOR-BASED CONTENT MATCHING FOR THIRD-PARTY DIGITAL ASSISTANT ACTIONS

      
Numéro d'application US2021042231
Numéro de publication 2023/003537
Statut Délivré - en vigueur
Date de dépôt 2021-07-19
Date de publication 2023-01-26
Propriétaire GOOGLE LLC (USA)
Inventeur(s) Muppalla, Dharmadeep

Abrégé

Matching content to third-party digital assistant actions using a bit vector is provided. A system receives an application with voice-assistant compatible actions. The system identifies the actions in the application. The system identifies content items provided by third-party computing devices. The system generates, via a machine learning model and performance data for the content items, bit vectors or the actions. The system selects, responsive to a request for content from a client device that executes an action of the application, a content item based on the bit vector that corresponds to the action.

Classes IPC  ?

23.

AUTOMATED GENERATION OF IMMERSIVE INTERFACES

      
Numéro d'application US2021042701
Numéro de publication 2023/003555
Statut Délivré - en vigueur
Date de dépôt 2021-07-22
Date de publication 2023-01-26
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Anuar, Ahmad Nizam
  • Karunia, Sandy
  • Dian, Zahng
  • Kale, Sumeet

Abrégé

The present disclosure provides systems, methods, and computer program products for performing automated generation of immersive interfaces. For example, a computing device may perform automated generation of immersive interfaces by analyzing a web-based resource comprising textual content, extracting a plurality of textual content segments from the web-based resource, obtaining visual content and audio content related to each respective textual content segment from the plurality of textual content segments, generating target content for an audio-visual display of the web-based resource based on combining at least a portion of each respective textual content segment with the visual content and the audio content related to the respective textual content segment, and providing data descriptive of the generated target content to a computing device for presentation of the audio-visual display of the web-based resource.

Classes IPC  ?

  • G06F 16/957 - Optimisation de la navigation, p.ex. mise en cache ou distillation de contenus
  • G06F 16/34 - Navigation; Visualisation à cet effet

24.

TRANSFERRING DIALOG DATA FROM AN INITIALLY INVOKED AUTOMATED ASSISTANT TO A SUBSEQUENTLY INVOKED AUTOMATED ASSISTANT

      
Numéro d'application US2021063753
Numéro de publication 2023/003585
Statut Délivré - en vigueur
Date de dépôt 2021-12-16
Date de publication 2023-01-26
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Sharifi, Matthew
  • Carbune, Victor

Abrégé

Systems and methods for providing dialog data, from an initially invoked automated assistant to a subsequently invoked automated assistant. A first automated assistant may be invoked by a user utterance, followed by a dialog with the user that is processed by the first automated assistant. During the dialog, a request to transfer dialog data to a second automated assistant is received. The request may originate with the user, by the first automated assistant, and/or by the second automated assistant. Once authorized, the first automated assistant provides the previous dialog data to the second automated assistant. The second automated assistant performs one or more actions based on the dialog data.

Classes IPC  ?

  • G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
  • G06F 3/16 - Entrée acoustique; Sortie acoustique
  • G06F 40/35 - Représentation du discours ou du dialogue

25.

BIOMETRIC DETECTION USING PHOTODETECTOR ARRAY

      
Numéro d'application US2022073715
Numéro de publication 2023/004250
Statut Délivré - en vigueur
Date de dépôt 2022-07-14
Date de publication 2023-01-26
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Shin, Dongeek
  • Silberschatz, Paul Joseph

Abrégé

A computing device, such as a wearable device, may include a light source and a photodetector array. The photodetector array may be used to determine a touch event of a user that occurs during a time interval. A subset of the plurality of photodetectors associated with the touch event may provide detection signals at each of a plurality of times within the time interval, which may be aggregated to obtain a time series of aggregated detection signals. Biometric data of the user may be generated, based on the time series of aggregated detection signals.

Classes IPC  ?

  • G06V 40/13 - Capteurs à cet effet
  • G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
  • G06V 40/10 - Corps d’êtres humains ou d’animaux, p.ex. occupants de véhicules automobiles ou piétons; Parties du corps, p.ex. mains
  • G06V 40/12 - Empreintes digitales ou palmaires
  • A61B 5/00 - Mesure servant à établir un diagnostic ; Identification des individus
  • G04G 21/02 - Détecteurs de valeurs physiques externes, p.ex. de température
  • G06V 40/70 - Biométrique multimodale, p.ex. combinaison d’informations de modalités biométriques distinctes

26.

LOW-LATENCY BRIDGE TO SUPPORT OUT-OF-ORDER EXECUTION

      
Numéro d'application US2021042186
Numéro de publication 2023/003533
Statut Délivré - en vigueur
Date de dépôt 2021-07-19
Date de publication 2023-01-26
Propriétaire GOOGLE LLC (USA)
Inventeur(s) Kashyap, Raj Shekhar

Abrégé

This document describes systems and techniques for a low-latency bridge to support out-of-order execution. The described systems and techniques can facilitate out-of-order execution by a memory controller of in-order transaction requests. When it receives transaction responses associated with in-order transaction requests, the bridge can send a first transaction response without storing it in a reorder buffer. Similarly, the bridge can determine whether a next transaction response is available to send to the respective client. The bridge introduces latency to a larger system (e.g., an SoC) only when a secondary response (e.g., not first) of in-order transaction responses is received first. In this way, the memory controller can process transaction requests from one or more clients with minimal latency introduced by the bridge and a smaller reorder buffer.

Classes IPC  ?

  • G06F 13/16 - Gestion de demandes d'interconnexion ou de transfert pour l'accès au bus de mémoire

27.

FLEXIBLE NAVIGATION AND ROUTE GENERATION

      
Numéro d'application US2021042304
Numéro de publication 2023/003540
Statut Délivré - en vigueur
Date de dépôt 2021-07-20
Date de publication 2023-01-26
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Sharifi, Matthew
  • Carbune, Victor

Abrégé

Methods, systems, devices, and tangible non-transitory computer readable media for navigation are provided. The disclosed technology can include accessing navigation data that includes information associated with a navigation request from a user. Based on the navigation data, a determination of whether the navigation request indicates a specific location or a deferred travel time can be made. Based on the navigation request, one or more locations and one or more travel times associated with fulfilling the navigation request can be determined. The one or more locations can be based on whether the navigation request indicates a specific location. The one or more travel times can be based on whether the navigation request indicates a deferred travel time. Furthermore, output including a time window of the one or more travel times for the user to travel to at least one of the one or more locations can be generated.

Classes IPC  ?

  • G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués

28.

JOINT VIDEO STABILIZATION AND MOTION DEBLURRING

      
Numéro d'application US2021042751
Numéro de publication 2023/003556
Statut Délivré - en vigueur
Date de dépôt 2021-07-22
Date de publication 2023-01-26
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Shi, Fuhao
  • Delbracio, Mauricio
  • Liang, Chia-Kai
  • Kelly, Damien, Martin
  • Milanfar, Peyman

Abrégé

Systems and methods for real-time image deblur and stabilization can utilize sensor data for estimating motion blur without the high computational cost of image analysis techniques. The estimated motion blur can then be utilized to generate a motion blur kernel for image correction. The systems and methods can further refine the correction by processing the motion blur kernel with a polynomial filter to generate a sharpening kernel. The systems and methods can provide for real-time correction even with minimal to no stabilization masking.

Classes IPC  ?

  • H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance

29.

UNDER-DISPLAY FINGERPRINT SENSOR TIMING CONTROL

      
Numéro d'application US2021042931
Numéro de publication 2023/003568
Statut Délivré - en vigueur
Date de dépôt 2021-07-23
Date de publication 2023-01-26
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Choi, Sangmoo
  • Mienko, Marek

Abrégé

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for under-display fingerprint sensor timing control are disclosed. A method includes receiving, by fingerprint sensor control circuitry, an indication to activate a fingerprint sensor that is located under a display panel of a computing device, the fingerprint sensor attached with respect to the display panel such that the fingerprint sensor is exposed to light produced by the display panel and reflected off a finger placed over the display panel at a location of the fingerprint sensor; outputting, for receipt by the fingerprint sensor, a start¬ sensing trigger signal at a start time synchronized with a display panel timing signal that is provided to the display panel to control emission of the display panel; and outputting, for receipt by the fingerprint sensor, a stop-sensing trigger signal at a stop time synchronized with the display panel timing signal.

Classes IPC  ?

30.

USING SIMPLE MASKS FOR ONLINE EXPRESSION

      
Numéro d'application US2021047326
Numéro de publication 2023/003575
Statut Délivré - en vigueur
Date de dépôt 2021-08-24
Date de publication 2023-01-26
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Marchant, Robert
  • Butler, Tríona Éidín
  • Holland, Henry John
  • Jones, David Matthew
  • Pawle, Benjamin Guy Alexander
  • Colville, Michael
  • Rickerby, George Joseph

Abrégé

The technology provides enhanced co-presence of interactive media participants without high quality video or other photo-realistic representations of the participants. A low-resolution graphical representation (318) of a participant provides real-time dynamic co-presence. Face detection captures a maximum amount of facial expression with minimum detail in order to construct the low-resolution graphical representation. A set of facial mesh data (304) is generated by the face detection to include a minimal amount of information about the participant's face per frame. The mesh data, such as facial key points, is provided to one or more user devices so that the graphical representation of the participant can be rendered in a shared app at the other device(s) (308, 804). The rendering can include generating a hull (314, 806) that delineates a perimeter of the user mask and generating a set of facial features (316, 808), in which the graphical representation is assembled by combining the hull and the set of facial features (318, 810).

31.

INVERTED PROJECTION FOR ROBUST SPEECH TRANSLATION

      
Numéro d'application US2022036469
Numéro de publication 2023/003701
Statut Délivré - en vigueur
Date de dépôt 2022-07-08
Date de publication 2023-01-26
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Padfield, Dirk, Ryan
  • Cherry, Colin, Andrew

Abrégé

The technology provides an approach to train translation models (608) that are robust to transcription errors and punctuation errors. The approach includes introducing errors from actual automatic speech recognition and automatic punctuation systems into the source side of the machine translation training data. A method for training a machine translation model includes performing automatic speech recognition on input source audio to generate a system transcript (802). The method aligns a human transcript of the source audio to the system transcript, including projecting system segmentation onto the human transcript (804). Then the method performs segment robustness training of a machine translation model according to the aligned human and system transcripts (806), and performs system robustness training of the machine translation model, e.g., by injecting token errors into training data (808).

Classes IPC  ?

  • G06F 40/44 - Méthodes statistiques, p.ex. modèles probabilistes

32.

ITERATIVE OLIGONUCLEOTIDE BARCODE EXPANSION FOR LABELING AND LOCALIZING MANY BIOMOLECULES

      
Numéro d'application US2022037673
Numéro de publication 2023/003931
Statut Délivré - en vigueur
Date de dépôt 2022-07-20
Date de publication 2023-01-26
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Bashir, Ali
  • Berndl, Marc
  • Pawlosky, Annalisa
  • Kim, Jun
  • Ahadi, Sara
  • Tran, Alexander

Abrégé

Contemporary gene sequencing techniques, including "Next. Generation Sequencing" techniques, can include sequencing a plurality of fragments of a target polynucleotide. However, the limitations of existing sequencing techniques means that it can be difficult and/or expensive to align the generated read fragments. Methods provided herein include inserting dual polynucleotide 'barcodes' into a target polynucleotide that remain mechanically connected via a Tinker.' Tire barcodes can then be 'grown' via. a. pool-split-pool process such that polynucleotide fragments that are linked by linkers exhibit the same complete barcode sequence that is different, from the complete barcode sequence exhibited by non-linked polynucleotide fragments. The joined fragments can then be separated and sequenced. Each read sequence thus begins with a regionally-specific barcode that can be used to associate fragments from the region together, allowing for increased accuracy and reduced computational cost in aligning the read fragments and/or performing other sequencing processes on the read fragments.

Classes IPC  ?

  • C12N 15/10 - Procédés pour l'isolement, la préparation ou la purification d'ADN ou d'ARN
  • C40B 40/08 - Bibliothèques comprenant de l'ARN ou de l'ADN codant des protéines, p.ex. bibliothèques de gènes
  • C40B 50/06 - Procédés biochimiques, p.ex. utilisant des enzymes ou des micro-organismes viables entiers
  • C12Q 1/6806 - Préparation d’acides nucléiques pour analyse, p.ex. pour test de réaction en chaîne par polymérase [PCR]

33.

REMOTE ATTESTATION TRANSPORT LAYER SECURITY AND SPLIT TRUST ENCRYPTION

      
Numéro d'application US2022073768
Numéro de publication 2023/004261
Statut Délivré - en vigueur
Date de dépôt 2022-07-15
Date de publication 2023-01-26
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Moyer, Keith
  • Moore, Benjamin Seth
  • Medvinksy, Ari
  • Yap, Kevin
  • Petrov, Ivan
  • Santoro, Tiziano
  • Feldman, Ariel Joseph
  • Rosu, Marcel Catalin

Abrégé

A method (400) for remote attestation includes establishing, using a cryptographic protocol (20), a communication session (22) between a first computing device (10a) and a second computing device (10b). The communication session includes communications encrypted by an ephemeral session key (24). The method includes receiving, at the first communication device via the communication session, from the second computing device, an attestation request (172) requesting the first computing device to provide an attestation report (162). The method includes generating, by the first computing device, the attestation report based on the ephemeral session key and sending, using the communication session, the attestation report to the second computing device.

Classes IPC  ?

  • H04L 9/40 - Protocoles réseaux de sécurité
  • G06F 21/53 - Contrôle des usagers, programmes ou dispositifs de préservation de l’intégrité des plates-formes, p.ex. des processeurs, des micrologiciels ou des systèmes d’exploitation au stade de l’exécution du programme, p.ex. intégrité de la pile, débordement de tampon ou prévention d'effacement involontaire de données par exécution dans un environnement restreint, p.ex. "boîte à sable" ou machine virtuelle sécurisée
  • 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
  • H04L 9/00 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité
  • H04L 9/08 - Répartition de clés

34.

DISPLAY SYSTEM WITH VARIABLE BEAM EXPANSION FOR MULTIPLE LASERS

      
Numéro d'application US2022036445
Numéro de publication 2023/287647
Statut Délivré - en vigueur
Date de dépôt 2022-07-08
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Adema, Daniel
  • Andrews, Ian

Abrégé

Display systems (100) may include a laser projection system (200) having an optical engine (202) and an optical scanner (204). Light output by the optical engine may be directed into a first scan mirror (206) as two angularly separated laser light beams (1002, 1102). The angularly separated laser light beams typically have different angles of incidence (1010, 1110) on a second scan mirror (208) of the optical scanner. Respectively different levels of magnification are applied to the beam diameter of each of the angularly separated laser light beams in a first dimension, such that the angularly separated laser light beams have respectively different beam diameters (1012, 1112) upon incidence at the second scan mirror. The different beam diameters of the angularly separated laser light beams can result in regions of incidence of each of the angularly separated laser light beams on the second scan mirror being equal or substantially similar.

Classes IPC  ?

  • G02B 26/10 - Systèmes de balayage
  • G02B 27/01 - Dispositifs d'affichage "tête haute"
  • G02B 27/18 - Systèmes ou appareils optiques non prévus dans aucun des groupes , pour projection optique, p.ex. combinaison de miroir, de condensateur et d'objectif
  • G02B 27/00 - Systèmes ou appareils optiques non prévus dans aucun des groupes ,

35.

ENABLING PAGING SUBGROUPING FOR A USER DEVICE

      
Numéro d'application US2022036900
Numéro de publication 2023/287846
Statut Délivré - en vigueur
Date de dépôt 2022-07-13
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s) Wu, Chih-Hsiang

Abrégé

A radio access network (RAN), a core network (CN), and a user equipment (UE) can implement a method for managing paging subgrouping for the UE when the UE operates in an inactive or idle state. The method includes: transmitting and/or receiving a configuration for a paging subgroup, determining whether to use the configuration in paging and monitoring procedures, and monitoring or paging the UE in accordance with the determination. The method may further include transitioning between base stations for the UE and pausing any monitoring using the configuration for the paging subgroup during an emergency PDU session.

36.

ERROR CHECKING FOR SYSTOLIC ARRAY COMPUTATION

      
Numéro d'application US2022036946
Numéro de publication 2023/287871
Statut Délivré - en vigueur
Date de dépôt 2022-07-13
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Yoon, Doe, Hyun
  • Jouppi, Norman, Paul

Abrégé

Aspects of the disclosure are directed to a computation unit implementing a systolic array and configured for detecting errors while processing data on the systolic array. Checksum circuit in communication with a systolic array is configured to compute checksums and perform error detection while the systolic array processes input data. Instead of pre-generating checksums in input matrices, input matrices can be directly fed into the systolic array through the checksum circuit. On the output side, the checksum circuit can generate and compare checksums with checksums in an output matrix generated by the systolic array. Error checking the operations to generate the output matrix can be performed without delaying the operations of the systolic array, and without preprocessing the input matrices.

Classes IPC  ?

  • G06F 15/80 - Architectures de calculateurs universels à programmes enregistrés comprenant un ensemble d'unités de traitement à commande commune, p.ex. plusieurs processeurs de données à instruction unique

37.

MANAGING AN EARLY DATA COMMUNICATION CONFIGURATION

      
Numéro d'application US2022036948
Numéro de publication 2023/287873
Statut Délivré - en vigueur
Date de dépôt 2022-07-13
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s) Wu, Chih-Hsiang

Abrégé

A central unit (CU) of a distributed base station, the distributed base station including the CU and a distributed unit (DU), can implement a method for managing early data communication with a user equipment (UE). The method may include: determining (2202) whether the DU supports early data communication with a UE; determining (2204) whether to include a configuration for performing early data communication in a message to the UE based at least in part on whether the DU supports early data communication; and transmitting (2206) the message to the UE.

Classes IPC  ?

  • H04W 76/27 - Transitions entre états de commande de ressources radio [RRC]
  • H04W 74/08 - Accès non planifié, p.ex. accès aléatoire, ALOHA ou accès multiple par détection de porteuse [CSMA Carrier Sense Multiple Access]
  • H04W 88/08 - Dispositifs formant point d'accès

38.

BACKPLANE AND METHOD FOR PULSE WIDTH MODULATION

      
Numéro d'application US2022037046
Numéro de publication 2023/287936
Statut Délivré - en vigueur
Date de dépôt 2022-07-14
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Li, Jeffrey
  • Lo, Robert

Abrégé

A backplane for driving a display includes a two-dimensional array of pixel drive circuits, organized as a plurality of rows and a plurality of columns. The backplane has at least one shift register addressing assembly that includes a shift register chain formed of a plurality of controlling shift registers serially connected with, and separated by, equal sized groups of non-controlling shift registers. Each controlling shift register controls a different one of a plurality of word lines that each connect with pixel drive circuits of one row. The backplane also includes a plurality of bit lines that each connect with pixel drive circuits of one column. A shift register data sequence is input to a first one of the plurality of controlling shift registers and propagates through the shift register chain to control the plurality of word lines to load display values from the bit lines into the pixel drive circuits.

Classes IPC  ?

  • G09G 5/395 - Dispositions spécialement adaptées pour le transfert du contenu de la mémoire à mappage binaire vers l'écran
  • G09G 5/393 - Dispositions pour la mise à jour du contenu de la mémoire à mappage binaire

39.

ROBOT APPENDAGE ACTUATION

      
Numéro d'application US2021041288
Numéro de publication 2023/287393
Statut Délivré - en vigueur
Date de dépôt 2021-07-12
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Kew, J. Chase
  • Lubin, James
  • Caluwaerts, Ken
  • Saliceti, Stefano
  • Kinman, Brandon
  • David, Byron
  • Fantacci, Claudio

Abrégé

In various implementations a removable appendage of a robot can allow for stable pitch and yaw, while mitigating interference with other movements of the robot. A neck of the robot can include at least two linear actuators, each coupled to a rod that is driven to move linearly from the linear actuators. An appendage of the robot can be coupled to the neck. The appendage can include a at least two tracks, where each track receives an end of the rods to slidably engage the rod.

Classes IPC  ?

  • B25J 17/02 - Joints articulés
  • B25J 9/14 - Manipulateurs à commande programmée caractérisés par des moyens pour régler la position des éléments manipulateurs à fluide

40.

WARPED MOTION COMPENSATION WITH EXPLICITLY SIGNALED EXTENDED ROTATIONS

      
Numéro d'application US2021041824
Numéro de publication 2023/287417
Statut Délivré - en vigueur
Date de dépôt 2021-07-15
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Chen, Yue
  • Wang, Yu
  • Su, Hui
  • Mukherjee, Debargha
  • Wang, Yunqing

Abrégé

Decoding a current block includes decoding, from a compressed bitstream, motion parameters for predicting the current block; decoding, from the compressed bitstream, a rotation angle; obtaining a warping matrix using the motion parameters and the rotation angle; and obtaining a prediction block by projecting the current block to a quadrilateral in a reference frame. A computing device for decoding a current block includes a processor that is configured to determine a prediction model of the current block; and obtain a prediction block by projecting the current block to a quadrilateral in a reference frame. To determine the prediction model includes to determine whether to predict the current block using a motion vector, a local warping model, or a global motion model; obtain motion parameters of the prediction model; decode, from a compressed bitstream, a rotation angle; obtain a warping matrix using the motion parameters and the rotation angle.

Classes IPC  ?

  • H04N 19/527 - Estimation de vecteurs de mouvement globaux
  • H04N 19/53 - Estimation de mouvement multi-résolution; Estimation de mouvement hiérarchique

41.

REFERENCE MOTION VECTOR CANDIDATE BANK

      
Numéro d'application US2021041831
Numéro de publication 2023/287418
Statut Délivré - en vigueur
Date de dépôt 2021-07-15
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Su, Hui
  • Mukherjee, Debargha

Abrégé

A method for inter-prediction includes coding a first block of a current frame using a first motion vector (MV) and a reference frame type; storing, in at least one MV buffer, the first MV and the reference frame type; identifying MV candidates for coding a current block using the reference frame type; responsive to a determination that a cardinality of the MV candidates is less than a maximum number of MV candidates identifying the first motion vector in the at least one MV buffer, and responsive to a determination that the first MV is not included in the MV candidates, adding the first MV as an MV candidate; and selecting one of the MV candidates for coding the current block.

Classes IPC  ?

  • H04N 19/00 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques

42.

POWER SEQUENCER FOR POWER STATE MANAGEMENT

      
Numéro d'application US2021042077
Numéro de publication 2023/287436
Statut Délivré - en vigueur
Date de dépôt 2021-07-16
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Carlson, Alex Robert
  • Patel, Ronak Subhas
  • Tuohy, William James
  • Vijay Kumar, Vinu

Abrégé

Methods, systems, and apparatus, for handling applications in an ambient computing system. One of the apparatus includes multiple devices arranged in multiple power blocks, wherein each device of the multiple devices belongs to one of the multiple power blocks; and multiple local power managers, each local power manager being programmable to execute respective sets of instruction sequences for a respective power block in order to effectuate power state transitions for one or more devices in the respective power block.

Classes IPC  ?

  • G06F 1/18 - Installation ou distribution d'énergie
  • G06F 1/26 - Alimentation en énergie électrique, p.ex. régulation à cet effet
  • G06F 1/3203 - Gestion de l’alimentation, c. à d. passage en mode d’économie d’énergie amorcé par événements
  • G06F 1/3234 - Gestion de l’alimentation, c. à d. passage en mode d’économie d’énergie amorcé par événements Économie d’énergie caractérisée par l'action entreprise
  • G06F 1/3287 - Gestion de l’alimentation, c. à d. passage en mode d’économie d’énergie amorcé par événements Économie d’énergie caractérisée par l'action entreprise par la mise hors tension d’une unité fonctionnelle individuelle dans un ordinateur
  • G06F 1/3296 - Gestion de l’alimentation, c. à d. passage en mode d’économie d’énergie amorcé par événements Économie d’énergie caractérisée par l'action entreprise par diminution de la tension d’alimentation ou de la tension de fonctionnement

43.

METHOD FOR IDENTIFYING NEW AUDIENCES FOR CONTENT OF A CONTENT PROVIDER

      
Numéro d'application US2021062472
Numéro de publication 2023/287444
Statut Délivré - en vigueur
Date de dépôt 2021-12-08
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Zink, Daniel
  • Huang, Jane
  • Chen, Hao
  • Porteous, Ian
  • Maheshwari, Surbhi

Abrégé

A method is disclosed for providing, for display to a content provider, a user interface comprising an option to view new audiences to be added to a plurality of users currently designated to receive content of the content provider, receiving a user selection of the option, and causing display of information identifying the new audiences, the information identifying the new audiences comprises, for each new audience, an audience identifier, an indication of an estimated number of user actions related to the content of the content provider, and an option to request that a corresponding audience be added to the plurality of users currently designated to receive the content of the content provider.

Classes IPC  ?

  • H04N 21/466 - Procédé d'apprentissage pour la gestion intelligente, p.ex. apprentissage des préférences d'utilisateurs pour recommander des films
  • H04H 60/40 - Dispositions d'identification ou de reconnaissance de caractéristiques en liaison directe avec les informations radiodiffusées ou le créneau spatio-temporel de radiodiffusion, p.ex. pour identifier les stations de radiodiffusion ou pour identifier le pour identifier le temps ou l'espace de radiodiffusion pour identifier le temps de radiodiffusion
  • 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
  • H04N 21/81 - Composants mono média du contenu
  • H04H 60/33 - Dispositions de contrôle du comportement ou des opinions des utilisateurs
  • G06Q 30/02 - Marketing; Estimation ou détermination des prix; Collecte de fonds

44.

ROBUST DIRECT SPEECH-TO-SPEECH TRANSLATION

      
Numéro d'application US2021063429
Numéro de publication 2023/287446
Statut Délivré - en vigueur
Date de dépôt 2021-12-15
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Jia, Ye
  • Ramanovich, Michelle Tadmor
  • Remez, Tal
  • Pomerantz, Roi

Abrégé

A direct speech-to-speech translation (S2ST) model (200 includes an encoder (210) configured to receive an input speech representation (102) that to an utterance (108) spoken by a source speaker (104) in a first language and encode the input speech representation into a hidden feature representation (215). The S2ST model also includes an attention module (220) configured to generate a context vector (225) that attends to the hidden representation encoded. The S2ST model also includes a decoder (230) configured to receive the context vector generated by the attention module and predict a phoneme representation (235) that corresponds to a translation of the utterance in a second different language. The S2ST model also includes a synthesizer (300) configured to receive the context vector and the phoneme representation and generate a translated synthesized speech representation (355) that corresponds to a translation of the utterance spoken in the different second language.

Classes IPC  ?

  • G10L 13/00 - Synthèse de la parole; Systèmes de synthèse de la parole à partir de texte
  • G06F 40/40 - Traitement ou traduction du langage naturel
  • G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels
  • G10L 15/26 - Systèmes de synthèse de texte à partir de la parole

45.

SYSTEMS AND METHODS FOR FEDERATED LEARNING OF MACHINE-LEARNED MODELS WITH SAMPLED SOFTMAX

      
Numéro d'application US2021041225
Numéro de publication 2023/287392
Statut Délivré - en vigueur
Date de dépôt 2021-07-12
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Qi, Hang
  • Waghmare, Sagar Manohar
  • Meron, Tomer

Abrégé

Example aspects of the present disclosure provide a novel, resource-efficient approach for learning image representation with federated learning, which can be referred to as federated sampled SoftMax. According to example aspects of the present disclosure, the federated learning clients sample a set of negative classes and optimize only the corresponding model parameters with respect to a sampled SoftMax objective that approximates the global full SoftMax objective. This approach significantly reduces the number of parameters transferred to and optimized by the client devices, while performing on par with the standard full SoftMax method. This creates a possibility for efficiently learning image representations on decentralized data with a large number of classes in a privacy preserving way.

Classes IPC  ?

  • G06N 3/04 - Architecture, p.ex. topologie d'interconnexion
  • G06N 3/08 - Méthodes d'apprentissage
  • G06N 20/20 - Techniques d’ensemble en apprentissage automatique

46.

MULTIPLIER AND ADDER IN SYSTOLIC ARRAY

      
Numéro d'application US2022035660
Numéro de publication 2023/287589
Statut Délivré - en vigueur
Date de dépôt 2022-06-30
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Yoon, Doe, Hyun
  • Nai, Lifeng

Abrégé

The subject matter described herein provides systems and techniques for the design and use of multiply-and- accumulate (MAC) units to perform matrix multiplication by systolic arrays, such as those used in accelerators for deep neural networks (DNNs). These MAC units may take advantage of the particular way in which matrix multiplication is performed within a systolic array. For example, when a matrix A is multiplied with a matrix B, the scalar value, a, of the matrix A is reused many times, the scalar value, b, of the matrix B may be streamed into the systolic array and forwarded to a series of MAC units in the systolic array, and only the final values and not the intermediate values of the dot products, computed for the matrix multiplication, may be correct. MAC unit hardware that is particularized to take advantage of these observations is described herein.

Classes IPC  ?

  • G06F 7/544 - Méthodes ou dispositions pour effectuer des calculs en utilisant exclusivement une représentation numérique codée, p.ex. en utilisant une représentation binaire, ternaire, décimale utilisant des dispositifs non spécifiés pour l'évaluation de fonctions par calcul

47.

AUTOMATIC SPEECH RECOGNITION WITH SOFT HOTWORDS

      
Numéro d'application US2022073345
Numéro de publication 2023/288168
Statut Délivré - en vigueur
Date de dépôt 2022-07-01
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Barros, Brett Aladdin
  • Flynn, James
  • Goguely, Theo

Abrégé

A method (300) for a soft acceptance of a hotword (24) receives audio data (14) characterizing a soft hotword event (202) detected by a hotword detector (200) in streaming audio (12) captured by a user device (110). The method also processes the audio data to determine that the audio data corresponds to a query (22) specifying an action (148) to perform on the user device. Without triggering performance of the action on the user device, the method provides a notification (204) for output from the user device where the notification prompts a user (10) associated with the user device to provide an affirmative input indication (16) in order to trigger performance of the action on the user device and, when the user fails to provide the affirmative input indication, instructs the user device to not perform the action specified by the query.

Classes IPC  ?

  • G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
  • G10L 15/08 - Classement ou recherche de la parole

48.

TWO-LEVEL TEXT-TO-SPEECH SYSTEMS USING SYNTHETIC TRAINING DATA

      
Numéro d'application US2022073390
Numéro de publication 2023/288169
Statut Délivré - en vigueur
Date de dépôt 2022-07-01
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Finkelstein, Lev
  • Chan, Chun-An
  • Chun, Byungha
  • Casagrande, Norman
  • Zhang, Yu
  • Clark, Robert Andrew James
  • Wan, Vincent

Abrégé

A method (600) includes obtaining training data (10) including a plurality of training audio signals (102) and corresponding transcripts (106). Each training audio signal is spoken by a target speaker in a first accent/dialect. For each training audio signal, the method includes generating a training synthesized speech representation (202) spoken by the target speaker in a second accent/dialect and training a text-to-speech (TTS) system (300) based on the corresponding transcript and the training synthesized speech representation. The method also includes receiving an input text utterance (320) to be synthesized into speech in the second accent/dialect. The method also includes obtaining a speaker embedding (108) and an accent/dialect identifier (109) that identifies the second accent/dialect. The method also includes generating an output audio waveform (152) corresponding to a synthesized speech representation of the input text sequence that clones the voice of the target speaker in the second accent/dialect.

Classes IPC  ?

  • G10L 13/033 - Procédés d'élaboration de parole synthétique; Synthétiseurs de parole Édition de voix, p.ex. transformation de la voix du synthétiseur

49.

IMAGE-BASED FITTING OF A WEARABLE COMPUTING DEVICE

      
Numéro d'application US2022073642
Numéro de publication 2023/288221
Statut Délivré - en vigueur
Date de dépôt 2022-07-12
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Aleem, Idris Syed
  • Simmons, Rees Anwyl Samuel
  • Gawish, Ahmed

Abrégé

A system and method are provided for sizing and fitting a head mounted wearable computing device for a user based on image data of the head of the user, including a known reference device having a known scale. The system and method may include capturing image data including a face of the user to be fitted for the head mounted wearable computing device. The known reference device having the known scale is compared to features detected in the image data to determine a scaling factor. The scaling factor is used to size, or assign measures to facial features detected in the image data. A three-dimensional model of the head of the user may be generated from the captured image data.

Classes IPC  ?

50.

ADAPTIVE EXPONENTIAL MOVING AVERAGE FILTER

      
Numéro d'application US2022073743
Numéro de publication 2023/288280
Statut Délivré - en vigueur
Date de dépôt 2022-07-14
Date de publication 2023-01-19
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Horowitz, Michael
  • Eliasson, Philip

Abrégé

A method (400) includes establishing communication between a first user device (10a) and a second user device (10b) using a first codec (180) and filtering an input signal (162) indicating an estimated unfiltered available bandwidth for the communications by applying a first filter (220a) when the estimated unfiltered available bandwidth is less than a first threshold value (310a) or greater than a second threshold value (310b) or a second filter (220b) when the estimated unfiltered available bandwidth is between and including the first and second threshold values. The method includes adaptively switching the current filter as a function of the filtered input signal (210) and the first and second threshold values. When the filtered input signal satisfies a channel bandwidth threshold, the method includes switching from using the first codec to using a second codec for the communication between the first and second user devices.

Classes IPC  ?

  • G10L 19/22 - Décision en matière de mode, c. à d. fondée sur le contenu du signal audio plutôt que sur des paramètres externes
  • H04N 19/146 - Débit ou quantité de données codées à la sortie du codeur
  • H04N 19/164 - Retour d’information en provenance du récepteur ou du canal de transmission
  • H04L 47/38 - Commande de flux; Commande de la congestion en adaptant le codage ou le taux de compression
  • H04L 47/263 - Modification du taux à la source après avoir reçu des retours

51.

REAL-TIME MICRO-PROFILE GENERATION USING A DYNAMIC TREE STRUCTURE

      
Numéro d'application US2021040553
Numéro de publication 2023/282890
Statut Délivré - en vigueur
Date de dépôt 2021-07-06
Date de publication 2023-01-12
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Davies, Ruxandra Georgiana
  • Davies, Scott Tadashi

Abrégé

Real-time micro-profile generating using a dynamic tree structure is provided. A system receives a first voice query. The system generates, from historical searches related to the first voice query, a first pivot point in a tree structure for the first voice query having child nodes. The system outputs an audio prompt to request selection of one of the child nodes. The system receives, responsive to the audio prompt, a voice input with a selection of a first child node. The system generates, from historical searches related to the first child node, a second pivot point in the tree structure including grandchild nodes. The system determines, based on a resource reduction policy, to generate a checkpoint to reduce additional child node generation. The system builds, based on a response to the checkpoint, a micro-profile for the electronic account identifier with the tree structure.

Classes IPC  ?

  • G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur

52.

DATASET REFINING WITH MACHINE TRANSLATION QUALITY PREDICTION

      
Numéro d'application US2021040492
Numéro de publication 2023/282887
Statut Délivré - en vigueur
Date de dépôt 2021-07-06
Date de publication 2023-01-12
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Zhou, Junpei
  • Li, Yuezhang
  • Chelba, Ciprian
  • Feng, Fangxiaoyu
  • Liang, Bowen
  • Wang, Pidong

Abrégé

Aspects of the technology employ a machine translation quality prediction (MTQP) model to refine datasets that are used in training machine translation systems. This includes receiving, by a machine translation quality prediction model, a sentence pair of a source sentence and a translated output (802). Then performing feature extraction on the sentence pair using a set of two or more feature extractors, where each feature extractor generates a corresponding feature vector (804). The corresponding feature vectors from the set of feature extractors are concatenated together (806). And the concatenated feature vectors are applied to a feedforward neural network, in which the feedforward neural network generates a machine translation quality prediction score for the translated output (808).

Classes IPC  ?

  • G06F 40/51 - Traitement ou traduction du langage naturel Évaluation de la traduction
  • G06F 40/211 - Parsage syntaxique, p.ex. basé sur une grammaire hors contexte ou sur des grammaires d’unification
  • G06F 40/44 - Méthodes statistiques, p.ex. modèles probabilistes

53.

VIDEO COMPRESSION USING OPTICAL FLOW

      
Numéro d'application US2022036111
Numéro de publication 2023/283184
Statut Délivré - en vigueur
Date de dépôt 2022-07-05
Date de publication 2023-01-12
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Toderici, George Dan
  • Agustsson, Eirikur Thor
  • Mentzer, Fabian Julius
  • Minnen, David Charles
  • Balle, Johannes
  • Johnston, Nicholas

Abrégé

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for compressing video data. In one aspect, a method comprises: receiving a video sequence of frames; generating, using a flow prediction network, an optical flow between two sequential frames, wherein the two sequential frames comprise a first frame and a second frame that is subsequent the first frame; generating from the optical flow, using a first autoencoder neural network: a predicted optical flow between the first frame and the second frame; and warping a reconstruction of the first frame according to the predicted optical flow and subsequently applying a blurring operation to obtain an initial predicted reconstruction of the second frame.

Classes IPC  ?

  • 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 19/182 - 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 19/537 - Estimation de mouvement autre que basée sur les blocs
  • G06N 3/04 - Architecture, p.ex. topologie d'interconnexion
  • H04N 19/503 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage prédictif mettant en œuvre la prédiction temporelle
  • H04N 19/513 - Traitement de vecteurs de mouvement
  • 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 19/82 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques - 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

54.

IN SITU SPARSE MATRIX EXPANSION

      
Numéro d'application US2022036258
Numéro de publication 2023/283267
Statut Délivré - en vigueur
Date de dépôt 2022-07-06
Date de publication 2023-01-12
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Young, Reginald Clifford
  • Gale, Trevor John

Abrégé

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for loading a matrix into a circuit having an array having M×N cells. One of the methods includes: receiving a plurality of non-zero input values from a first input matrix; receiving index metadata that indicates, for each non-zero input value in the plurality of input values, which cell of the M×N cells in the array the non-zero input value should be loaded into; sending the non-zero input values and the index metadata to the M×N cells; and at a particular cell of the M x N cells in the array: receiving a particular non-zero input value and corresponding index metadata; and determining from the corresponding index metadata for the particular non-zero input value whether to store the particular non-zero input value at the cell or to shift the particular non-zero input value to another cell.

Classes IPC  ?

55.

NFC ANTENNA STRUCTURE FOR RADIATION ENHANCEMENT

      
Numéro d'application US2022072757
Numéro de publication 2023/283509
Statut Délivré - en vigueur
Date de dépôt 2022-06-03
Date de publication 2023-01-12
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Yeh, Che-Ting
  • Wu, Wei-Yang
  • Chiu, Hung-Chi

Abrégé

A near-field communication (NFC) antenna structure for radiation enhancement of a computing device (102) that includes a ferrite sheet (208), separated into two sections. The NFC antenna structure is used to improve the magnetic field strength generated by an NFC antenna and inductive coupling to a receiving antenna of another computing device. A first ferrite section (210-1) is placed on a first side of the NFC antenna to at least partially overlap the NFC antenna, and a second ferrite section (210-2) is placed on a second side (opposite the first side) to at least partially overlap the NFC antenna. The first ferrite section (210-1) is positioned towards a top end that is often positioned closest to a receiving device, as held by a user when performing a contactless communication of the computing device (102), to increase the magnetic field strength and improve the inductive coupling at the top end.

Classes IPC  ?

  • H04B 5/00 - Systèmes de transmission à induction directe, p.ex. du type à boucle inductive
  • H01F 27/28 - Bobines; Enroulements; Connexions conductrices
  • H01F 38/14 - Couplages inductifs
  • H02J 50/10 - Circuits ou systèmes pour l'alimentation ou la distribution sans fil d'énergie électrique utilisant un couplage inductif

56.

ADVANCING THE USE OF TEXT AND SPEECH IN ASR PRETRAINING WITH CONSISTENCY AND CONTRASTIVE LOSSES

      
Numéro d'application US2022025139
Numéro de publication 2023/277993
Statut Délivré - en vigueur
Date de dépôt 2022-04-15
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Rosenberg, Andrew
  • Chen, Zhehuai
  • Ramabhadran, Bhuvana
  • Mengibar, Pedro J., Moreno
  • Wang, Gary
  • Zhang, Yu

Abrégé

A method (600) includes receiving training data that includes unspoken text utterances (320), un-transcribed non-synthetic speech utterances (306), and transcribed non-synthetic speech utterances (304). Each unspoken text utterance is not paired with any corresponding spoken utterance of non-synthetic speech. Each un-transcribed non- synthetic speech utterance is not paired with a corresponding transcription. Each transcribed non-synthetic speech utterance is paired with a corresponding transcription (302). The method also includes generating a corresponding synthetic speech representation (332) for each unspoken textual utterance of the received training da.ta using a text-to-speech model (330). The method also includes pre-training an audio encoder (210) on the synthetic speech representations generated for the unspoken textual utterances, the un-transcribed non-synthetic speech utterances, and the transcribed non- synthetic speech utterances to teach the audio encoder to jointly learn shared speech and text representations

Classes IPC  ?

  • G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur
  • G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels

57.

PERFORMING UNBIASED FERMIONIC QUANTUM MONTE CARLO CALCULATIONS USING QUANTUM COMPUTERS AND SHADOW TOMOGRAPHY

      
Numéro d'application US2022035334
Numéro de publication 2023/278462
Statut Délivré - en vigueur
Date de dépôt 2022-06-28
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Huggins, William
  • Lee, Joonho
  • Babbush, Ryan

Abrégé

Methods, systems, and apparatus for hybrid quantum-classical quantum Monte Carlo. In one aspect, a method includes receiving, by a classical computer, data generated by a quantum computer, the data representing results of measurements of a trial wavefunction, wherein the trial wavefunction approximates the target wavefunction and is prepared by the quantum computer; computing, by the classical computer, a classical shadow of the trial wavefunction using the data representing the results of the measurements of the trial wavefunction; and performing, by the classical computer, imaginary time propagation for a sequence of imaginary time steps of an initial wavefunction using a Hamiltonian that characterizes the fermionic quantum system, wherein: the imaginary time propagation is performed until predetermined convergence criteria are met; and performing each imaginary time step of the imaginary time propagation comprises updating the wavefunction for the previous imaginary time step using the classical shadow of the trial wavefunction to obtain a wavefunction for the current imaginary time step.

Classes IPC  ?

  • G06N 10/60 - Algorithmes quantiques, p.ex. fondés sur l'optimisation quantique ou les transformées quantiques de Fourier ou de Hadamard
  • G06N 5/00 - Agencements informatiques utilisant des modèles fondés sur la connaissance

58.

WAVEGUIDE WITH ANTI-REFLECTION PROPERTIES

      
Numéro d'application US2022035852
Numéro de publication 2023/278791
Statut Délivré - en vigueur
Date de dépôt 2022-06-30
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Bodiya, Timothy Paul
  • Potnis, Shreyas
  • Adema, Daniel
  • Andrews, Ian
  • Haque, Syed Moez

Abrégé

A head-mounted display system (100) includes a lens element (110) supported by a support structure (102). The lens element (110) includes a waveguide (212) to couple light from an image source. The waveguide (212) includes a waveguide surface (207) and a grating (250). The grating (250) is disposed onto the waveguide surface (207) and includes rows of three-dimensional, 3D, primitive structures (435), with a height of the 3D primitive structures being smaller than a wavelength of visible incident light at a surface of the sub-wavelength grating.

Classes IPC  ?

  • G02B 5/18 - Grilles de diffraction
  • G02B 27/00 - Systèmes ou appareils optiques non prévus dans aucun des groupes ,
  • G02B 27/01 - Dispositifs d'affichage "tête haute"

59.

ENCRYPTED INFORMATION RETRIEVAL

      
Numéro d'application US2022035965
Numéro de publication 2023/278848
Statut Délivré - en vigueur
Date de dépôt 2022-07-01
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Fox-Epstein, Eli Simon
  • Yeo, Kevin Wei Li

Abrégé

Methods, systems, and computer readable medium facilitating encrypted information retrieval. Methods can include receiving a batch of queries that includes queries to special buckets in each database shard. Query results responsive to the batch of queries are transmitted to the client device. The query results includes server-encrypted secret shares obtained from the special buckets. Client-encrypted versions of the secret shares are received. A full set of server-encrypted secret shares is transmitted to the client device, which is encrypted by the client device to create a full set of client-server-encrypted secret shares. The client device is classified based on how many of the secret shares are included in both of the client-encrypted secret shares received from the client device and the full set of client-server-encrypted secret shares received from the client device.

Classes IPC  ?

  • 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

60.

COMPRESSING AUDIO WAVEFORMS USING NEURAL NETWORKS AND VECTOR QUANTIZERS

      
Numéro d'application US2022036097
Numéro de publication 2023/278889
Statut Délivré - en vigueur
Date de dépôt 2022-07-05
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Zeghidour, Neil
  • Tagliasacchi, Marco
  • Roblek, Dominik

Abrégé

Methods, systems and apparatus, including computer programs encoded on computer storage media. One of the methods includes receiving an audio waveform that includes a respective audio sample for each of a plurality of time steps, processing the audio waveform using an encoder neural network to generate a plurality of feature vectors representing the audio waveform, generating a respective coded representation of each of the plurality of feature vectors using a plurality of vector quantizers that are each associated with a respective codebook of code vectors, wherein the respective coded representation of each feature vector identifies a plurality of code vectors, including a respective code vector from the codebook of each vector quantizer, that define a quantized representation of the feature vector, and generating a compressed representation of the audio waveform by compressing the respective coded representation of each of the plurality of feature vectors.

Classes IPC  ?

  • G10L 19/00 - Techniques d'analyse ou de synthèse de la parole ou des signaux audio pour la réduction de la redondance, p.ex. dans les vocodeurs; Codage ou décodage de la parole ou des signaux audio utilisant les modèles source-filtre ou l’analyse psychoacoustique
  • G10L 25/30 - Techniques d'analyses 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

61.

MULTI-SCALE TRANSFORMER FOR IMAGE ANALYSIS

      
Numéro d'application US2021040111
Numéro de publication 2023/277919
Statut Délivré - en vigueur
Date de dépôt 2021-07-01
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Ke, Junjie
  • Yang, Feng
  • Wang, Qifei
  • Wang, Yilin
  • Milanfar, Peyman

Abrégé

The technology employs a patch-based multi-scale Transformer (300) that is usable with various imaging applications. This avoids constraints on image fixed input size and predicts the quality effectively on a native resolution image. A native resolution image (304) is transformed into a multi- scale representation (302), enabling the Transformer's self-attention mechanism to capture information on both fine-grained detailed patches and coarse-grained global patches. Spatial embedding (316) is employed to map patch positions to a fixed grid, in which patch locations at each scale are hashed to the same grid. A separate scale embedding (318) is employed to distinguish patches coming from different scales in the multiscale representation. Self-attention (508) is performed to create a final image representation. In some instances, prior to performing self-attention, the system may prepend a learnable classification token (322) to the set of input tokens.

Classes IPC  ?

62.

VIRTUAL REMOTE CONTROL ON FIRST DEVICE TO CONTROL SECOND DEVICE, EG TV

      
Numéro d'application US2021040336
Numéro de publication 2023/277928
Statut Délivré - en vigueur
Date de dépôt 2021-07-02
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Wang, Bo
  • Ureno, Manuel Angel

Abrégé

Virtual remote control among digital assistant devices is provided. A first computing device detects a second computing device and determines a capability of the second computing device. The first computing device generates a prompt indicating that the first computing device is capable to control the second computing device. The first computing device receives, responsive to the prompt, an instruction to control the second computing device. The first computing device establishes a communication channel with the second computing device. The first computing device invokes a virtual controller on the first computing device. The virtual controller forwards queries received by the first computing device to the second computing device via the communication channel to control the second computing device.

Classes IPC  ?

  • H04L 69/18 - Gestionnaires multi-protocoles, p.ex. dispositifs uniques capables de gérer plusieurs protocoles
  • H04L 69/24 - Négociation des capacités de communication
  • 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]
  • H04N 21/422 - Périphériques d'entrée uniquement, p.ex. système de positionnement global [GPS]
  • H04N 21/436 - Interfaçage d'un réseau de distribution local, p.ex. communication avec un autre STB ou à l'intérieur de la maison
  • H04N 21/442 - Surveillance de procédés ou de ressources, p.ex. détection de la défaillance d'un dispositif d'enregistrement, surveillance de la bande passante sur la voie descendante, du nombre de visualisations d'un film, de l'espace de stockage disponible dans l
  • H04N 21/41 - Structure de client; Structure de périphérique de client
  • H04W 8/00 - Gestion de données relatives au réseau
  • H04W 12/06 - Authentification
  • G06F 3/16 - Entrée acoustique; Sortie acoustique

63.

SENSOR-BASED PRIVACY-EVENT DETECTION FOR A MOUNTED ELECTRONIC DEVICE

      
Numéro d'application US2021044197
Numéro de publication 2023/277933
Statut Délivré - en vigueur
Date de dépôt 2021-08-02
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Kraz, Mark Benjamin
  • Ghadiali, Aditya Shailesh
  • Cheng, Kok Yen
  • Senepin, Félix Ambroise Étienne
  • Lin, Chi-Ming

Abrégé

Sensor-based privacy-event detection for a mounted electronic device is described. In aspects, a security system (100) includes a head assembly (104) removably and magnetically coupled to a mounting device (602) having a magnet (902). The electronic device also includes a camera module and a sensor (500) disposed within the housing. The sensor detects a magnetic field associated with the magnet when the head assembly is coupled to the mounting device. When a user detaches the head assembly from the mounting device (e.g., to recharge the electronic device), the sensor no longer detects the magnetic field and determines the occurrence of a privacy event, which is used to deactivate the camera module to prevent unintentional recordings during the privacy event.

Classes IPC  ?

  • G08B 13/196 - Déclenchement influencé par la chaleur, la lumière, ou les radiations de longueur d'onde plus courte; Déclenchement par introduction de sources de chaleur, de lumière, ou de radiations de longueur d'onde plus courte utilisant des systèmes détecteurs de radiations passifs utilisant des systèmes de balayage et de comparaison d'image utilisant des caméras de télévision

64.

CONTROL INFORMATION-BASED INDEX MODULATION

      
Numéro d'application US2022073118
Numéro de publication 2023/278958
Statut Délivré - en vigueur
Date de dépôt 2022-06-23
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Wang, Jibing
  • Stauffer, Erik Richard

Abrégé

Techniques and apparatuses are described for control information-based index modulation. In aspects, a wireless transmitter (120, 110) modulates (1205) a first portion of data for a wireless receiver (110, 120) to provide modulation symbols that correspond to the first portion of the data. The wireless transmitter also selects (1215), based on a value of a second portion of the data, control information of a transmission configuration parameter by which to transmit the modulation symbols. The wireless transmitter then transmits (1230) the modulation symbols to the wireless receiver using the transmission configuration parameter to convey the first portion of the data and the second portion of the data to the wireless receiver. By so doing, the wireless transmitter conveys (1230) the second portion of the data through control information without using additional time-frequency resources of a communication channel, which can be useful when concurrently transmitting small amounts of data to many wireless receivers.

Classes IPC  ?

  • H04L 5/00 - Dispositions destinées à permettre l'usage multiple de la voie de transmission
  • H04L 27/20 - Circuits de modulation; Circuits émetteurs
  • H04L 27/22 - Circuits de démodulation; Circuits récepteurs
  • H04L 27/30 - Systèmes utilisant des codes à fréquences multiples dans lesquels chaque élément de code est représenté par une combinaison de fréquences

65.

INTRA-USER EQUIPMENT-COORDINATION SET COMMUNICATION

      
Numéro d'application US2022073143
Numéro de publication 2023/278960
Statut Délivré - en vigueur
Date de dépôt 2022-06-24
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Wang, Jibing
  • Stauffer, Erik Richard

Abrégé

Methods, devices, systems, and means for intra-UECS communication by a coordinating user equipment, UE, in a user equipment-coordination set, UECS, are described herein. The coordinating UE allocates first air interface resources to a second UE and second air interface resources to a third UE for intra-UECS communication (804). The coordinating UE receives, using the allocated first air interface resources, an Internet Protocol, IP, data packet from the second UE in the UECS (806). The coordinating UE determines that a destination address included in the IP data packet is an address of the third UE (808) and transmits, using the allocated second air interface resources, the IP data packet to the third UE (810).

Classes IPC  ?

  • H04W 76/14 - Gestion de la connexion Établissement de la connexion Établissement de la connexion en mode direct
  • H04B 7/026 - Diversité coopérative, p.ex. utilisant des stations fixes ou mobiles en tant que relais
  • H04W 88/04 - Dispositifs terminaux adapté à la retransmission à destination ou en provenance d'un autre terminal ou utilisateur
  • H04W 92/18 - Interfaces entre des dispositifs hiérarchiquement similaires entre des dispositifs terminaux

66.

DETECTING INACTIVE PROJECTS BASED ON USAGE SIGNALS AND MACHINE LEARNING

      
Numéro d'application US2022073316
Numéro de publication 2023/279066
Statut Délivré - en vigueur
Date de dépôt 2022-06-30
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s) Teng, Yung

Abrégé

A method (500) for detecting inactive projects based on usage signals and machine learning includes receiving a plurality of cloud computing projects (111) each associated with a client device (110) of a cloud computing environment (150). For each respective cloud computing project of the plurality of cloud computing projects associated with the client device of the cloud computing environment, the method also includes determining a similarity measurement (115A) between the respective cloud computing project and a reference cloud computing project (117), and generating a respective project usage score (115B) for the respective cloud computing project based on the similarity measurement determined between the respective cloud computing project and the reference cloud computing project. The method also includes communicating, to the client device of the cloud computing environment, one or more of the respective project usage scores generated for the plurality of cloud computing projects.

Classes IPC  ?

67.

EYE GAZE CLASSIFICATION

      
Numéro d'application US2022073337
Numéro de publication 2023/279076
Statut Délivré - en vigueur
Date de dépôt 2022-07-01
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Rodgers, Ivana Tosic
  • Fanello, Sean Ryan Francesco
  • Bouaziz, Sofien
  • Pandey, Rohit Kumar
  • Aboussouan, Eric
  • Kowdle, Adarsh Prakash Murthy

Abrégé

Techniques of tracking a user's gaze includes identifying a region of a display at which a gaze of a user is directed, the region including a plurality of pixels. By determining a region rather than a point, when the regions correspond to elements of a user interface, the improved technique enables a system to activate the element to which a determined region is selected. In some implementations, the system makes the determination using a classification engine including a convolutional neural network; such an engine takes as input images of the user's eye and outputs a list of probabilities that the gaze is directed to each of the regions.

Classes IPC  ?

  • G06V 40/18 - Caractéristiques de l’œil, p.ex. de l’iris
  • G06V 20/20 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans les scènes de réalité augmentée
  • 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

68.

MODEL FOR DETERMINING CONSISTENT DEPTH OF MOVING OBJECTS IN VIDEO

      
Numéro d'application US2021040307
Numéro de publication 2023/277925
Statut Délivré - en vigueur
Date de dépôt 2021-07-02
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Cole, Forrester
  • Zhang, Zhoutong
  • Dekel, Tali
  • Freeman, William, T.

Abrégé

A method includes determining, based on a first image, a first depth of a first pixel and, based on a second image, a second depth of a second pixel that corresponds to the first pixel. The method also includes determining a first 3D point based on the first depth and a second 3D point based on the second depth, and determining a scene flow between the first and second images. The method additionally includes determining an induced pixel position based on a post-flow 3D point representing the first 3D point displaced according to the scene flow, determining a flow loss value based on the induced pixel position and a position of the second pixel and a depth loss value based on the post-flow 3D point and the second 3D point, and adjusting the depth model or the scene flow model based on the flow and depth loss values.

Classes IPC  ?

  • G06T 7/593 - Récupération de la profondeur ou de la forme à partir de plusieurs images à partir d’images stéréo
  • G06T 7/55 - Récupération de la profondeur ou de la forme à partir de plusieurs images

69.

ON-DEVICE GENERATION AND PERSONALIZATION OF ZERO-PREFIX SUGGESTION(S) AND USE THEREOF

      
Numéro d'application US2021063108
Numéro de publication 2023/277942
Statut Délivré - en vigueur
Date de dépôt 2021-12-13
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Nainaparampil, Jeff J.
  • Chen, Chih-Wei
  • Patil, Umesh R.

Abrégé

Implementations described herein relate to generating, locally at a client device, corresponding subset(s) of zero-prefix suggestions, for a user of the client device, and for suggestion state(s) associated with the client device, and subsequently causing the client device to utilize the corresponding subset(s) of zero-prefix suggestions. The suggestion state(s) and a superset of candidate zero-prefix suggestions can be processed, using machine learning model(s), to generate a corresponding score for each of the candidate zero-prefix suggestions and with respect to the suggestion state(s). Further, zero-prefix suggestions can be selected for inclusion in the corresponding subset(s) of zero-prefix suggestions, and for the suggestion state(s), based on the corresponding scores. Accordingly, when a given suggestion state is subsequently detected at the client device, a given corresponding subset of zero-prefix suggestions that is stored in association with the given suggestion state can be obtained and provided for presentation to the user.

Classes IPC  ?

70.

LITHIUM ION BATTERY WITH COMPOSITE ELECTRODES

      
Numéro d'application US2021064225
Numéro de publication 2023/277945
Statut Délivré - en vigueur
Date de dépôt 2021-12-17
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Hwang, Taisup
  • Bhardwaj, Ramesh C.
  • Cao, Lei
  • Lee, Dookyoung
  • Luo, Qiang

Abrégé

Composite lithium ion batteries having an anode with an anode collector and a composite anode material, a cathode with a cathode collector and a composite cathode material, a separator positioned between the anode and the cathode, and an electrolyte in contact with the anode and the cathode. Advantages of the composite lithium ion batteries include lower DC impedance, faster charging times, more reserve capacity between 3.0V and 2.5V, and an increased volumetric energy density relative to lithium ion batteries that do not include the described composite material electrodes.

Classes IPC  ?

  • H01M 4/133 - PROCÉDÉS OU MOYENS POUR LA CONVERSION DIRECTE DE L'ÉNERGIE CHIMIQUE EN ÉNERGIE ÉLECTRIQUE, p.ex. BATTERIES Électrodes Électrodes composées d'un ou comprenant un matériau actif Électrodes pour accumulateurs à électrolyte non aqueux, p.ex. pour accumulateurs au lithium; Leurs procédés de fabrication Électrodes à base de matériau carboné, p.ex. composés d'intercalation du graphite ou CFx
  • H01M 4/131 - PROCÉDÉS OU MOYENS POUR LA CONVERSION DIRECTE DE L'ÉNERGIE CHIMIQUE EN ÉNERGIE ÉLECTRIQUE, p.ex. BATTERIES Électrodes Électrodes composées d'un ou comprenant un matériau actif Électrodes pour accumulateurs à électrolyte non aqueux, p.ex. pour accumulateurs au lithium; Leurs procédés de fabrication Électrodes à base d'oxydes ou d'hydroxydes mixtes, ou de mélanges d'oxydes ou d'hydroxydes, p.ex. LiCoOx
  • H01M 4/36 - Emploi de substances spécifiées comme matériaux actifs, masses actives, liquides actifs
  • H01M 4/525 - Emploi de substances spécifiées comme matériaux actifs, masses actives, liquides actifs d'oxydes ou d'hydroxydes inorganiques de nickel, de cobalt ou de fer d'oxydes ou d'hydroxydes mixtes contenant du fer, du cobalt ou du nickel pour insérer ou intercaler des métaux légers, p.ex. LiNiO2, LiCoO2 ou LiCoOxFy
  • H01M 4/48 - Emploi de substances spécifiées comme matériaux actifs, masses actives, liquides actifs d'oxydes ou d'hydroxydes inorganiques
  • H01M 4/587 - Matériau carboné, p.ex. composés au graphite d'intercalation ou CFx pour insérer ou intercaler des métaux légers
  • H01M 10/0525 - Batteries du type "rocking chair" ou "fauteuil à bascule", p.ex. batteries à insertion ou intercalation de lithium dans les deux électrodes; Batteries à l'ion lithium
  • H01M 10/0567 - Matériaux liquides caracterisés par les additifs
  • H01M 10/0569 - Matériaux liquides caracterisés par les solvants
  • H01M 4/02 - PROCÉDÉS OU MOYENS POUR LA CONVERSION DIRECTE DE L'ÉNERGIE CHIMIQUE EN ÉNERGIE ÉLECTRIQUE, p.ex. BATTERIES Électrodes Électrodes composées d'un ou comprenant un matériau actif

71.

SYSTEM AND METHOD FOR MOTION CAPTURE

      
Numéro d'application US2021070781
Numéro de publication 2023/277952
Statut Délivré - en vigueur
Date de dépôt 2021-06-28
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s) Shin, Dongeek

Abrégé

Ultra-wideband (UWB) tags can be used as part of a high-resolution motion capture system that may not require a cost or a complexity that is typically associated with visually based motion capture systems. The UWB based motion capture uses a bundle of UWB tags, which in a possible implementation, can be affixed to body parts of a user to sense motion of the body parts. The absolute positions of each UWB tag can then be determined by reconstructing a skeletal topology from a Euclidean distance matrix based on inter-tag ranging measurements using handshake signals of a UWB protocol.

Classes IPC  ?

  • G01S 13/76 - Systèmes utilisant la reradiation d'ondes radio, p.ex. du type radar secondaire; Systèmes analogues dans lesquels des signaux de type pulsé sont transmis

72.

EFFICIENT HARDWARE ACCELERATOR CONFIGURATION EXPLORATION

      
Numéro d'application US2022035740
Numéro de publication 2023/278712
Statut Délivré - en vigueur
Date de dépôt 2022-06-30
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Yazdanbakhsh, Amir
  • Levine, Sergey Vladimir
  • Kumar, Aviral

Abrégé

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a surrogate neural network configured to determine a predicted performance measure of a hardware accelerator having a target hardware configuration on a target application. The trained instance of the surrogate neural network can be used, in addition to or in place of hardware simulation, during a search process for determining hardware configurations for application-specific hardware accelerators, i.e., hardware accelerators on which one or more neural networks can be deployed to perform one or more target machine learning tasks.

Classes IPC  ?

  • G06N 3/08 - Méthodes d'apprentissage
  • G06N 3/063 - Réalisation physique, c. à d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
  • G06N 20/00 - Apprentissage automatique

73.

INJECTING TEXT IN SELF-SUPERVISED SPEECH PRE-TRAINING

      
Numéro d'application US2022073067
Numéro de publication 2023/278952
Statut Délivré - en vigueur
Date de dépôt 2022-06-21
Date de publication 2023-01-05
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Chen, Zhehuai
  • Ramabhadran, Bhuvana
  • Rosenberg, Andrew M.
  • Zhang, Yu
  • Mengibar, Pedro J. Moreno

Abrégé

A method (500) includes receiving training data that includes unspoken text utterances (320) and un-transcribed non-synthetic speech utterances (306). Each unspoken text utterance is not paired with any corresponding spoken utterance of non-synthetic speech. Each un-transcribed non-synthetic speech utterance is not paired with a corresponding transcription. The method also includes generating a corresponding synthetic speech representation (332) for each unspoken textual utterance of the received training data using a text-to-speech model (330). The method also includes pre-training an audio encoder (210) on the synthetic speech representations generated for the unspoken textual utterances and the un-transcribed non-synthetic speech utterances to teach the audio encoder to jointly learn shared speech and text representations.

Classes IPC  ?

  • G10L 15/06 - Création de gabarits de référence; Entraînement des systèmes de reconnaissance de la parole, p.ex. adaptation aux caractéristiques de la voix du locuteur
  • G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels

74.

FIXTURE AND METHOD FOR ATTACHING FIBERS TO V-GROOVES OF PHOTONIC INTEGRATED CIRCUIT

      
Numéro d'application US2022029545
Numéro de publication 2022/271337
Statut Délivré - en vigueur
Date de dépôt 2022-05-17
Date de publication 2022-12-29
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Wang, Daoyi
  • Urata, Ryohei
  • Verslegers, Lieven
  • Petykiewicz, Jan

Abrégé

A system for passive alignment of fibers to an interface of a photonic integrated circuit (PIC) includes an input frame, an actuator, and an output frame. The actuator arranged to apply force along a force axis to the input frame. The output frame including a tip for picking up a plate and transferring the force thereto, the output frame being connected to the input frame such that the output frame may tilt relative to the input frame and the output frame is elastically biased relative to the input frame into a position wherein the tip is aligned on the force axis.

Classes IPC  ?

75.

WIRELESS NETWORK EMPLOYING NEURAL NETWORKS FOR CHANNEL STATE FEEDBACK

      
Numéro d'application US2022034060
Numéro de publication 2022/271564
Statut Délivré - en vigueur
Date de dépôt 2022-06-17
Date de publication 2022-12-29
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Wang, Jibing
  • Stauffer, Erik Richard

Abrégé

A wireless system (100) employs neural networks (122, 128) to provide for CSI estimate feedback between a transmitting device (108) and a receiving device (110). A managing component (140) selects neural network architecture configurations (144) for implementation at the transmitting and receiving devices based on capability information (146, 148). The receiving device determines CSI estimate(s) (134) from CSI pilot signaling from the transmitting device. The CSI estimate(s) are processed by the neural network(s) at the receiving device to generate a CSF output (136), which can represent, for example, one or more predicted future CSI estimates and which is wirelessly transmitted to the transmitting device. The one or more neural networks at the transmitting device then process the received CSF output along to generate one or more recovered predicted future CSI estimates (138), which are then used to control one or more MIMO processes at the transmitting device.

Classes IPC  ?

  • H04B 7/06 - Systèmes de diversité; Systèmes à plusieurs antennes, c. à d. émission ou réception utilisant plusieurs antennes utilisant plusieurs antennes indépendantes espacées à la station d'émission
  • H04L 1/00 - Dispositions pour détecter ou empêcher les erreurs dans l'information reçue

76.

ESTABLISHING INTERNET PROTOCOL MULTIMEDIA SUBSYSTEM SERVICES IN A CELLULAR NETWORK

      
Numéro d'application US2022034444
Numéro de publication 2022/271772
Statut Délivré - en vigueur
Date de dépôt 2022-06-22
Date de publication 2022-12-29
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Wu, Chih-Hsiang
  • Chung, Chi-Wen
  • Chueh, Han-Jung

Abrégé

A user equipment (UE) device (102) of a wireless communication system (100) is configured to establish an Internet Protocol Multimedia Subsystem (IMS) service (706). The UE device (102) initiates an IMS service (706) with an IMS network (126). Responsive to initiating the IMS service (126), the UE device (102) sends a message to the IMS network (126) specifying one or more preconditions for establishing the IMS service (706). The UE device (102) selectively transmits one or more service packets (728) for the IMS service (706) over either a dedicated data radio bearer (730) configured for the IMS service (706) or a default data radio bearer (708) based 10 on whether at least one of the one or more preconditions has not been satisfied.

Classes IPC  ?

  • H04L 65/1016 - Sous-système multimédia IP [IMS]
  • H04L 65/1069 - Gestion de session Établissement ou terminaison d'une session
  • H04L 65/80 - Dispositions, protocoles ou services dans les réseaux de communication de paquets de données pour prendre en charge les applications en temps réel en répondant à la qualité des services [QoS]
  • H04W 76/10 - Gestion de la connexion Établissement de la connexion
  • H04W 28/02 - Gestion du trafic, p.ex. régulation de flux ou d'encombrement

77.

PANEL AUDIO LOUDSPEAKERS INCLUDING MECHANICALLY GROUNDED MAGNETIC CIRCUIT

      
Numéro d'application US2022034975
Numéro de publication 2022/272117
Statut Délivré - en vigueur
Date de dépôt 2022-06-24
Date de publication 2022-12-29
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Phillis, Andrew
  • Gladwin, Timothy A.
  • Gomes, Rajiv Bernard
  • King, Anthony
  • Walker, Jason David
  • Harris, Neil John

Abrégé

A device includes: a panel and an electromagnetic actuator mechanically coupled to a rear side of the panel to form a panel audio loudspeaker, the electromagnetic actuator comprising a coil attached to the rear side of the panel and a magnet suspended with respect to the coil via one or more spring elements, the coil defining an axis, wherein during operation of the device an electric current through the coil varies a relative displacement of the magnet with respect to the coil along the axis. The device includes: a chassis supporting the panel, the chassis comprising a housing for the device, the housing comprising a rear panel on an opposite side of the device from the panel; and a grounding assembly positioned along the axis between the magnet and the rear panel of the device, wherein the grounding assembly mechanically grounds the magnet to the chassis.

Classes IPC  ?

  • H04R 7/04 - Membranes planes
  • H04R 9/02 - Transducteurs du type à bobine mobile, à lame mobile ou à fil mobile - Détails
  • H04R 9/06 - Haut-parleurs

78.

ZOOM AGNOSTIC WATERMARK EXTRACTION

      
Numéro d'application US2021038252
Numéro de publication 2022/271145
Statut Délivré - en vigueur
Date de dépôt 2021-06-21
Date de publication 2022-12-29
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • He, Dake
  • Zhang, Tianhao
  • Barshan Tashnizi, Elnaz
  • Luo, Xiyang
  • Chang, Huiwen
  • Yang, Feng
  • Haggarty, Ryan Matthew

Abrégé

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting and decoding a visually imperceptible or perceptible watermark. A watermark detection apparatus determines whether the particular image includes a visually imperceptible or perceptible watermark using detector a machine learning model. If the watermark detection apparatus detects a watermark, the particular image is routed to a watermark decoder. If the watermark detection apparatus cannot detect a watermark in the particular image, the particular image is filtered from further processing. The watermark decoder decodes the visually imperceptible or perceptible watermark detected in the particular image. After decoding, an item depicted in the particular image is validated based data extracted from the decoded visually imperceptible or perceptible watermark.

Classes IPC  ?

  • G06T 1/00 - Traitement de données d'image, d'application générale

79.

ZOOM AGNOSTIC WATERMARK EXTRACTION

      
Numéro d'application US2021038255
Numéro de publication 2022/271146
Statut Délivré - en vigueur
Date de dépôt 2021-06-21
Date de publication 2022-12-29
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • He, Dake
  • Zhang, Tianhao
  • Barshan Tashnizi, Elnaz
  • Luo, Xiyang
  • Chang, Huiwen
  • Yang, Feng
  • Haggarty, Ryan Matthew

Abrégé

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a visually imperceptible or a visually perceptible watermark and outputting a result based on the determination. A watermark decoder receives an input image. The watermark decoder applies a decoder machine learning model to decode a watermarks at different levels of zoom. The water mark decoder determines whether a watermark was decoded to obtain a decoded watermark. The watermark decoder outputs a result based on the determination whether the watermark was decoded through application of the decoder machine learning model to the input image that includes outputting a zoomed output decoded through application of the decoder machine learning model to the input image.

Classes IPC  ?

  • G06T 1/00 - Traitement de données d'image, d'application générale

80.

INDEPENDENT CLOCKING FOR CONFIGURATION AND STATUS REGISTERS

      
Numéro d'application US2021038378
Numéro de publication 2022/271154
Statut Délivré - en vigueur
Date de dépôt 2021-06-22
Date de publication 2022-12-29
Propriétaire GOOGLE LLC (USA)
Inventeur(s) Putti, Nagaraj Ashok

Abrégé

This document describes systems and techniques that enable independent clocking for configuration and status registers (CSRs). The described systems and techniques can provide a clock signal to a CSR set of an IP block with a derived clock rate an integer division slower than a clock rate of another clock signal that enables operation of the IP block, which may include communication between the IP block and an application processor. The derived clock rate is synchronous to but independent of the clock rate of the clock signal. In this way, the application processor and other entities can access the CSR set independent of clocking of the IP block. For example, the application processor can read from or write to the CSR set without waking the IP block from an Auto Clock Gated mode. By so doing, described aspects of independent clocking can reduce power dissipation associated with the CSR set.

Classes IPC  ?

  • G06F 1/3237 - Gestion de l’alimentation, c. à d. passage en mode d’économie d’énergie amorcé par événements Économie d’énergie caractérisée par l'action entreprise par désactivation de la génération ou de la distribution du signal d’horloge
  • G06F 1/04 - Génération ou distribution de signaux d'horloge ou de signaux dérivés directement de ceux-ci

81.

COMPLEMENTARY 2(N)-BIT REDUNDANCY FOR SINGLE EVENT UPSET PREVENTION

      
Numéro d'application US2021038250
Numéro de publication 2022/271144
Statut Délivré - en vigueur
Date de dépôt 2021-06-21
Date de publication 2022-12-29
Propriétaire GOOGLE LLC (USA)
Inventeur(s) Iqbal, Syed Shakir

Abrégé

The present disclosure describes various aspects of complementary 2(N)-bit redundancy for single event upset (SEU) prevention. In some aspects, an integrated circuit (104) includes a data storage element (206) to store a data value, another data storage element (202) to store a complementary data value, a multi-bit data storage element (e.g., a 2-bit storage element, (204)) to store both the data value and the complementary data value, and voting logic (124) that may enable a complementary data storage scheme with inter-circuit redundancy to prevent SEU. Additionally, the voting logic of the integrated circuit may enable detection and correction of data value errors and/or enable programming of voting logic criteria, which may be implemented dynamically based on a type of SEU failures that are detected or corrected.

Classes IPC  ?

82.

SUPPORTING MULTIPLE ROLES IN VOICE-ENABLED NAVIGATION

      
Numéro d'application US2021038608
Numéro de publication 2022/271162
Statut Délivré - en vigueur
Date de dépôt 2021-06-23
Date de publication 2022-12-29
Propriétaire GOOGLE LLC (USA)
Inventeur(s) Sharifi, Matthew

Abrégé

To determine whether a user is authorized to make a particular audio request during navigation, a client device receives a request for navigation directions from a starting location to a destination location. The client device provides a set of navigation directions for traversing to the destination location along a route. During a navigation session, an audio request related to the route is received from a user. The client device determines an authorization level of the user based on the audio request, and provides a response to the request based on the authorization level of the user.

Classes IPC  ?

  • G01C 21/36 - Dispositions d'entrée/sortie pour des calculateurs embarqués
  • B60R 16/037 - Circuits électriques ou circuits de fluides spécialement adaptés aux véhicules et non prévus ailleurs; Agencement des éléments des circuits électriques ou des circuits de fluides spécialement adapté aux véhicules et non prévu ailleurs électriques pour le confort des occupants
  • B60R 25/25 - Moyens pour enclencher ou arrêter le système antivol par biométrie

83.

DISPLAY SYSTEMS USING LIGHT EXTRACTION CONFIGURATIONS FOR MICRO LIGHT EMITTING DIODES

      
Numéro d'application US2022032125
Numéro de publication 2022/271430
Statut Délivré - en vigueur
Date de dépôt 2022-06-03
Date de publication 2022-12-29
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Klug, Michael Anthony
  • Sissom, Bradley Jay

Abrégé

A display system is disclosed including an emitter system assembly for providing a light output. The emitter system assembly includes a first emitter that provides a first emission spectrum, a cavity at least partially surrounding the first emitter, a first aperture configured for transmitting therethrough at least a portion of the first emission spectrum from the first emitter, and a shaping element in optical communication with the first aperture. The cavity includes reflectors that reflect the first emission spectrum within the cavity and toward the aperture.

Classes IPC  ?

  • G02B 27/00 - Systèmes ou appareils optiques non prévus dans aucun des groupes ,
  • G02B 6/00 - OPTIQUE ÉLÉMENTS, SYSTÈMES OU APPAREILS OPTIQUES - Détails de structure de dispositions comprenant des guides de lumière et d'autres éléments optiques, p.ex. des moyens de couplage
  • G02B 27/30 - Collimateurs
  • G06F 3/147 - Sortie numérique vers un dispositif de visualisation utilisant des panneaux de visualisation
  • H04N 13/332 - Affichage pour le visionnement à l’aide de lunettes spéciales ou de visiocasques

84.

EXPLAINABLE ARTIFICIAL INTELLIGENCE IN COMPUTING ENVIRONMENT

      
Numéro d'application US2022033822
Numéro de publication 2022/271528
Statut Délivré - en vigueur
Date de dépôt 2022-06-16
Date de publication 2022-12-29
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Cheng, Xi
  • Yin, Lisa
  • Liu, Jiashang
  • Hormati, Amir, Hossein
  • Deng, Mingge
  • Meyers, Christopher, Avery

Abrégé

The disclosure is directed to a query-driven machine learning platform for generating feature attributions and other data for interpreting the relationship between inputs and outputs of a machine learning model. The platform can receive query statements for selecting data, training a machine learning model, and generating model explanation data for the model. The platform can distribute processing for generating the model explanation data to scale in response to requests to process selected data, including multiple records with a variety of different feature values. The interface between a user device and the machine learning platform can streamline deployment of different model explainability approaches across a variety of different machine learning models.

Classes IPC  ?

  • G06N 5/00 - Agencements informatiques utilisant des modèles fondés sur la connaissance
  • G06N 20/20 - Techniques d’ensemble en apprentissage automatique
  • G06N 3/04 - Architecture, p.ex. topologie d'interconnexion

85.

PROTECTION ENVIRONMENT FOR ATTESTATION AND SEALING USING A DEVICE IDENTIFIER COMPOSITION ENGINE

      
Numéro d'application US2022034010
Numéro de publication 2022/271554
Statut Délivré - en vigueur
Date de dépôt 2022-06-17
Date de publication 2022-12-29
Propriétaire GOOGLE LLC (USA)
Inventeur(s) Krahn, Darren, David

Abrégé

Apparatus and methods related to receiving, by a secure component and from a client computing device via a secure communication channel, input data for a task associated with the client computing device, wherein the task is based on a device identifier composition engine (DICE) protocol, and wherein the secure component is to perform a cryptographic subtask of the task; receiving, by the secure component and from the client computing device via the secure communication channel, context data associated with the cryptographic subtask; executing, by the secure component, the cryptographic subtask based on the input data and the context data; and providing, by the secure component and to the client computing device via tiie secure communication channel, an output of the cryptographic subtask.

Classes IPC  ?

  • G06F 21/60 - Protection de données
  • 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 21/44 - Authentification de programme ou de dispositif
  • H04L 9/08 - Répartition de clés
  • H04L 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système

86.

MANAGING PAGING FOR A USER DEVICE

      
Numéro d'application US2022034438
Numéro de publication 2022/271768
Statut Délivré - en vigueur
Date de dépôt 2022-06-22
Date de publication 2022-12-29
Propriétaire GOOGLE LLC (USA)
Inventeur(s) Wu, Chih-Hsiang

Abrégé

A distributed unit, DU, of a distributed base station of a radio access network, RAN, the distributed base station including the DU and a central unit, CU, can implement a method for paging a user equipment, UE, when a radio connection between the distributed base station and the UE is not active. The method includes: receiving (1502), from the CU, a configuration for enhanced paging; and paging (1504) the UE using the configuration.

Classes IPC  ?

  • H04W 68/00 - Avertissement aux utilisateurs, p.ex. alerte ou messagerie, sur l'arrivée d'une communication, un changement de service ou similaires

87.

COMPUTATIONALLY EFFICIENT AND ROBUST EAR SADDLE POINT DETECTION

      
Numéro d'application US2022073017
Numéro de publication 2022/272230
Statut Délivré - en vigueur
Date de dépôt 2022-06-17
Date de publication 2022-12-29
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Bhargava, Mayank
  • Aleem, Idris Syed
  • Zhang, Yinda
  • Kulkarni, Sushant Umesh
  • Simmons, Rees Anwyl Samuel
  • Gawish, Ahmed

Abrégé

A computer-implemented method includes receiving a two-dimensional (2-D) side view face image of a person, identifying a bounded portion or area of the 2-D side view face image of the person as an ear region-of-interest (ROI) area showing at least a portion of an ear of the person, and processing the identified ear ROI area of the 2-D side view face image, pixel-by-pixel, through a trained fully convolutional neural network model (FCNN model) to predict a 2-D ear saddle point (ESP) location for the ear shown in the ear ROI area. The FCNN model has an image segmentation architecture.

Classes IPC  ?

  • G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
  • G06V 30/18 - Extraction d’éléments ou de caractéristiques de l’image
  • G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques

88.

OBJECT OBSERVATION TRACKING IN IMAGES USING ENCODER-DECODER MODELS

      
Numéro d'application US2022073092
Numéro de publication 2022/272267
Statut Délivré - en vigueur
Date de dépôt 2022-06-22
Date de publication 2022-12-29
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Guleryuz, Onur G.
  • Fanello, Sean Ryan Francesco

Abrégé

A method including, in a training phase, training a gaze prediction model including a first model and a second model, the first model and the second model being configured in conjunction to predict segmentation data based on training data, training a third model together with the first model and the second model, the third model being configured to predict a training characteristic using an output of the first model based on the training data, and in an operational phase, receiving operational data and predicting an operational characteristic using the trained first model and the trained third model.

Classes IPC  ?

  • G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
  • G06V 10/774 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p.ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]; Séparation aveugle de source méthodes de Bootstrap, p.ex. "bagging” ou “boosting”
  • G06V 40/19 - Capteurs à cet effet
  • G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos
  • G06V 20/20 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène dans les scènes de réalité augmentée

89.

ENCRYPTED INFORMATION RETRIEVAL

      
Numéro d'application US2022033393
Numéro de publication 2022/266071
Statut Délivré - en vigueur
Date de dépôt 2022-06-14
Date de publication 2022-12-22
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Fox-Epstein, Eli Simon
  • Yeo, Kevin Wei Li
  • Patel, Sarvar
  • Mirisola, Raimundo
  • Wright, Craig William

Abrégé

Encrypted information retrieval can include generating a database that is partitioned into shards each having a shard identifier, and database entries in each shard that are partitioned into buckets having a bucket identifier. A batch of client-encrypted queries are received. The batch of client-encrypted queries are processed using a set of server-encrypted data stored in a database. The processing includes grouping the client-encrypted queries according to shard identifiers of the client-encrypted queries, executing multiple queries in the group of client- encrypted queries for the shard together in a batch execution process, and generating multiple server-encrypted results to the multiple queries in the group of client-encrypted queries. The multiple server-encrypted results for each shard are transmitted to the client device.

Classes IPC  ?

  • 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 16/242 - Formulation des requêtes

90.

END-TO-END LEARNING-BASED, EG NEURAL NETWORK, PRE-PROCESSING AND POST-PROCESSING OPTIMIZATION FOR IMAGE AND VIDEO CODING

      
Numéro d'application US2021037593
Numéro de publication 2022/265627
Statut Délivré - en vigueur
Date de dépôt 2021-06-16
Date de publication 2022-12-22
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Guleryuz, Onur G.
  • Du, Ruofei
  • Hoppe, Hugues H.
  • Fanello, Sean Ryan Francesco
  • Chou, Philip Andrew
  • Tang, Danhang
  • Davidson, Philip

Abrégé

Nonlinear peri-codec optimization for image and video coding includes obtaining a source image including pixel values expressed in a first defined image sample space, generating a neuralized image representing the source image, the neuralized image including pixel values that are expressed as neural latent space values, encoding the input image wherein the neural latent space values are used as pixel values in a second defined image sample space and the input image is in an operative image format of the encoder, such that a decoder decodes the encoded image to obtain a reconstructed image in the second defined image sample space, wherein the reconstructed image is a reconstructed neuralized image including reconstructed neural latent space values, such that a deneuralized reconstructed image corresponding to the source image is obtained by a nonlinear post-codec image processor in the first defined image sample space.

Classes IPC  ?

  • H04N 19/117 - Filtres, p.ex. pour le pré-traitement ou le post-traitement
  • H04N 19/147 - Débit ou quantité de données codées à la sortie du codeur selon des critères de débit-distorsion
  • G06N 3/02 - Réseaux neuronaux
  • G06N 3/08 - Méthodes d'apprentissage
  • 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
  • G06N 20/00 - Apprentissage automatique
  • 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 19/59 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage prédictif mettant en œuvre un sous-échantillonnage spatial ou une interpolation spatiale, p.ex. modification de la taille de l’image ou de la résolution

91.

ADAPTABLE CELLULAR SESSION FOR LOW LATENCY AND POWER SAVING

      
Numéro d'application US2021037835
Numéro de publication 2022/265645
Statut Délivré - en vigueur
Date de dépôt 2021-06-17
Date de publication 2022-12-22
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Chuang, Po-Ying
  • Lo, Tai-Lun

Abrégé

A user equipment (UE) determines that a protocol data unit (PDU) session comprises an adaptable PDU session. The UE determines whether a current energy mode of the UE comprises an energy-saving mode. In response to determining that the current energy mode of the UE comprises the energy-saving mode, the UE establishes the adaptable PDU session in a non-always-on mode; and in response to determining that the current energy mode of the UE does not comprise the energy-saving mode, the UE establishes the adaptable PDU session in an always-on mode. Further, the UE can initially establish a mode of a plurality of adaptable PDU sessions as the non-always-on mode, when connecting to a certain type of network.

Classes IPC  ?

  • H04W 52/02 - Dispositions d'économie de puissance
  • H04L 67/145 - Interruption ou inactivation de sessions, p.ex. fin de session contrôlée par un événement en évitant la fin de session, p.ex. maintien en vie, battements de cœur, message de reprise ou réveil pour une session inactive ou interrompue
  • H04W 76/25 - Maintien de connexions établies

92.

PASSIVE DISAMBIGUATION OF ASSISTANT COMMANDS

      
Numéro d'application US2021062610
Numéro de publication 2022/265667
Statut Délivré - en vigueur
Date de dépôt 2021-12-09
Date de publication 2022-12-22
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Barros, Brett
  • Goguely, Theo

Abrégé

Implementations set forth herein relate to an automated assistant that can initialize execution of an assistant command associated with an interpretation that is predicted to be responsive to a user input, while simultaneously providing suggestions for alternative assistant command(s) associated with alternative interpretation(s) that is/are also predicted to be responsive to the user input. The alternative assistant command(s) that are suggested can be selectable such that, when selected, the automated assistant can pivot from executing the assistant command to initializing execution of the selected alternative assistant command(s). Further, the alternative assistant command(s) that are suggested can be partially fulfilled prior to any user selection thereof. Accordingly, implementations set forth herein can enable the automated assistant to quickly and efficiently pivot between assistant commands that are predicted to be responsive to the user input.

Classes IPC  ?

  • G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p.ex. dialogue homme-machine 
  • G06F 3/16 - Entrée acoustique; Sortie acoustique
  • G06F 16/332 - Formulation de requêtes
  • G06F 40/20 - Analyse du langage naturel

93.

MULTILINGUAL GRAMMATICAL ERROR CORRECTION

      
Numéro d'application US2022072965
Numéro de publication 2022/266642
Statut Délivré - en vigueur
Date de dépôt 2022-06-15
Date de publication 2022-12-22
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Krause, Sebastian
  • Rothe, Sascha
  • Mallinson, Jonathan
  • Malmi, Eric
  • Severyn, Aliaksei

Abrégé

A method (400) of training a text-generating model (122) for grammatical error correction (GEC) includes obtaining a multilingual set (210) of text samples (212). Each text sample includes a monolingual textual representation of a respective sentence. The operations also include, for each text sample of the multilingual set of text samples, generating a corrupted synthetic version (222) of the respective text sample, the corrupted synthetic version of the respective text sample including a grammatical change to the monolingual textual representation of the respective sentence associated with the respective text sample. The operations further include training the text-generating model using a training set (230) of sample pairs (232). Each sample pair includes one of the respective text samples of the multilingual set of text samples and the corresponding corrupted synthetic version of the one of the respective text samples of the multilingual set of text samples.

Classes IPC  ?

  • G06F 40/232 - Correction orthographique, p.ex. vérificateurs d’orthographe ou insertion des voyelles
  • G06F 40/253 - Analyse grammaticale; Corrigé du style

94.

SYSTEMS AND METHODS FOR MULTIDEVICE LEARNING AND INFERENCE IN AN AMBIENT COMPUTING ENVIRONMENT

      
Numéro d'application US2021037207
Numéro de publication 2022/265616
Statut Délivré - en vigueur
Date de dépôt 2021-06-14
Date de publication 2022-12-22
Propriétaire GOOGLE LLC (USA)
Inventeur(s) Sharifi, Matthew

Abrégé

Systems and methods for multi device learning and inference in an ambient computing environment. In some aspects, the present technology discloses systems and methods for performing cross-device learning in which new devices may be trained based on supervision signals from existing devices in the ambient computing environment. In some aspects, the present technology discloses systems and methods for performing multi-device inference across two or more devices in the ambient computing environment. Likewise, in some aspects, the present technology discloses systems and methods for training models that are robust to the addition or removal of one or more devices from an ambient computing environment.

Classes IPC  ?

  • G05B 13/04 - Systèmes de commande adaptatifs, c. à d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques impliquant l'usage de modèles ou de simulateurs
  • G05B 17/02 - Systèmes impliquant l'usage de modèles ou de simulateurs desdits systèmes électriques
  • G05B 13/02 - Systèmes de commande adaptatifs, c. à d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques
  • G05B 15/02 - Systèmes commandés par un calculateur électriques

95.

METHODS AND APPARATUS LOCALIZING OBJECT(S) IN VISION DATA

      
Numéro d'application US2021044970
Numéro de publication 2022/265661
Statut Délivré - en vigueur
Date de dépôt 2021-08-06
Date de publication 2022-12-22
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Kuo, Weicheng
  • Lin, Tsung-Yi
  • Angelova, Anelia
  • Kim, Dahun

Abrégé

e.g.e.g.e.g., a centerness score) as well as an intersection of union (IoU) score for one or more proposed object locations in the instance of vision data. Object(s) can be localized in the instance of vision data based on the objectness scores and the IoU scores.

Classes IPC  ?

  • G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
  • G06N 3/04 - Architecture, p.ex. topologie d'interconnexion
  • 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’intersections; Analyse de connectivité, p.ex. de composantes connectées
  • 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 20/00 - RECONNAISSANCE OU COMPRÉHENSION D’IMAGES OU DE VIDÉOS Éléments spécifiques à la scène

96.

DETERMINING AND UTILIZING SECONDARY LANGUAGE PROFICIENCY MEASURE

      
Numéro d'application US2021063640
Numéro de publication 2022/265671
Statut Délivré - en vigueur
Date de dépôt 2021-12-15
Date de publication 2022-12-22
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Kogan, David
  • Yuan, Wangqing
  • Wang, Guanglei
  • Lacey, Vincent
  • Chen, Wei
  • Post, Shaun

Abrégé

Implementations relate to determining a secondary language proficiency measure, for a user in a secondary language (i.e., a language other than a primary language specified for the user), where determining the secondary language proficiency measure is based on past interactions of the user that are related to the secondary language. Those implementations further relate to utilizing the determined secondary language proficiency measure to increase efficiency of user interaction(s), such as interaction(s) with a language learning application and/or an automated assistant. Some of those implementations utilize the secondary language proficiency measure in automatically setting value(s), biasing automatic speech recognition, and/or determining how to render natural language output.

Classes IPC  ?

97.

MANAGING EMISSIONS DEMAND RESPONSE EVENT GENERATION

      
Numéro d'application US2022032057
Numéro de publication 2022/265862
Statut Délivré - en vigueur
Date de dépôt 2022-06-03
Date de publication 2022-12-22
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Chang, Samuel Y.
  • Donhowe, Kristoffer J.
  • Bhagavatula, Ramya
  • Gleeson, Jeffrey
  • Chen, Kevin

Abrégé

Techniques for performing an emissions demand response event are described. In an example, a cloud-based HVAC control server system receives an emissions rate forecast for a predefined future time period. Using the emissions rate forecast, a plurality of emissions differential values are created for a plurality of points in time during the predefined future time period. The emissions differential values represent a change in predicted emissions over time. Based on the plurality of emissions differential values and a predefined maximum number of emissions demand response events, an emissions demand response event is generated during the predefined future time period. The cloud-based HVAC control server system then causes a thermostat to control an HVAC system in accordance with the generated emissions demand response event.

Classes IPC  ?

98.

MICRO LIGHT-EMITTING DIODE WITH IMPROVED LIGHT EXTRACTION

      
Numéro d'application US2022032986
Numéro de publication 2022/265926
Statut Délivré - en vigueur
Date de dépôt 2022-06-10
Date de publication 2022-12-22
Propriétaire GOOGLE LLC (USA)
Inventeur(s) David, Aurelien Jean Francois

Abrégé

A micro light-emitting diode, micro-LED, is configured to provide increased light-extraction. The micro-LED (300) according to the present disclosure has a lateral dimension less than 20 micrometres and comprises: a light-emitting layer (106), an output facet (308), an optical interface (via a mirror 114) parallel to the light-emitting layer, and a non-vertical sidewall (102) forming an angle with respect to a normal of the light-emitting layer. The layer (106) emits light (310) comprising a first light (312) in a first trajectory that escapes the micro-LED through the output facet (308) without reflecting off the optical interface and a second light (314) in a second trajectory that reflects off the optical interface and the non-vertical sidewall (102) and escapes through the output facet. The disclosed micro-LEDs may further include one or more of: cavity effects, light guiding, total internal reflection at low-index layers, mirrors, and micro-optics.

Classes IPC  ?

  • H01L 33/20 - DISPOSITIFS À SEMI-CONDUCTEURS NON COUVERTS PAR LA CLASSE - Détails caractérisés par les corps semi-conducteurs ayant une forme particulière, p.ex. substrat incurvé ou tronqué
  • H01L 27/15 - Dispositifs consistant en une pluralité de composants semi-conducteurs ou d'autres composants à l'état solide formés dans ou sur un substrat commun comprenant des composants semi-conducteurs avec au moins une barrière de potentiel ou une barrière de surface, spécialement adaptés pour l'émission de lumière
  • H01L 33/40 - Matériaux
  • H01L 33/44 - DISPOSITIFS À SEMI-CONDUCTEURS NON COUVERTS PAR LA CLASSE - Détails caractérisés par les revêtements, p.ex. couche de passivation ou revêtement antireflet
  • H01L 33/00 - DISPOSITIFS À SEMI-CONDUCTEURS NON COUVERTS PAR LA CLASSE - Détails
  • H01L 33/06 - DISPOSITIFS À SEMI-CONDUCTEURS NON COUVERTS PAR LA CLASSE - Détails caractérisés par les corps semi-conducteurs ayant une structure à effet quantique ou un superréseau, p.ex. jonction tunnel au sein de la région électroluminescente, p.ex. structure de confinement quantique ou barrière tunnel
  • H01L 33/22 - Surfaces irrégulières ou rugueuses, p.ex. à l'interface entre les couches épitaxiales
  • H01L 33/32 - Matériaux de la région électroluminescente contenant uniquement des éléments du groupe III et du groupe V de la classification périodique contenant de l'azote
  • H01L 33/46 - Revêtement réfléchissant, p.ex. réflecteur de Bragg en diélectriques
  • H01L 33/58 - DISPOSITIFS À SEMI-CONDUCTEURS NON COUVERTS PAR LA CLASSE - Détails caractérisés par les éléments du boîtier des corps semi-conducteurs Éléments de mise en forme du champ optique

99.

DIFFUSION MODELS HAVING IMPROVED ACCURACY AND REDUCED CONSUMPTION OF COMPUTATIONAL RESOURCES

      
Numéro d'application US2022033253
Numéro de publication 2022/265992
Statut Délivré - en vigueur
Date de dépôt 2022-06-13
Date de publication 2022-12-22
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Kingma, Diederik, Peter
  • Salimans, Tim

Abrégé

A computer-implemented method for use of a diffusion model having improved accuracy comprises obtaining input data, the input data comprising one or more channels; providing the input data to a machine-learned diffusion model, the machine-learned diffusion model comprising: a noising model comprising a plurality of noising stages, the noising model configured to introduce noise to receive the input data and produce intermediate data in response to receipt of the input data; and a denoising model configured to reconstruct output data from the intermediate data; and receiving, by the computing system, the output data from the machine-learned diffusion model. The diffusion model can include a learned noise schedule. Additionally and/or alternatively, input to the denoising model can include a set of Fourier features. Additionally and/or alternatively, the diffusion model can be trained based at least in part on a continuous-time loss for an evidence lower bound.

Classes IPC  ?

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

100.

HIERARCHICAL COMPILING AND EXECUTION IN A MACHINE LEARNING HARDWARE ACCELERATOR

      
Numéro d'application US2021036418
Numéro de publication 2022/260656
Statut Délivré - en vigueur
Date de dépôt 2021-06-08
Date de publication 2022-12-15
Propriétaire GOOGLE LLC (USA)
Inventeur(s)
  • Joseph, John Navil
  • Liu, Jack
  • Woo, Dong Hyuk
  • Pu, Jing

Abrégé

This disclosure describes a system and method for compiling and executing machine learning inferences in an array of multi-core computing devices. Each multi-core computing device can be an application specific integrated circuit (ASIC) or group of ASICs. In many applications, the array of computing devices changes from inference to inference, and can be adjusted based on the requirements of the inference. Additionally, each ASIC can have multiple processing cores, and multiple types of processing cores. Therefore, performing optimizations and scheduling at compile time, can dramatically increase the efficiency of the array in executing the inference. In some implementations, it is possible to select an amount of time or effort to be spent optimizing during compiling, giving the user flexibility in determining whether to spend time during compilation or during execution.

Classes IPC  ?

  • G06F 8/41 - Compilation
  • G06F 9/50 - Allocation de ressources, p.ex. de l'unité centrale de traitement [UCT]
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