09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Recorded and pre-installed computer software using
artificial intelligence (AI) for the production and
processing of speech, text, images, video, sound, and code
sold as a component of mobile phones; recorded and
pre-installed software using artificial intelligence (AI)
for simulating conversations and answering queries; recorded
software using artificial intelligence (AI) for tracking and
assisting users with tasks sold as a component of mobile
phones; recorded and pre-installed software using artificial
intelligence (AI) for use as an intelligent personal digital
assistant; recorded software using artificial intelligence
(AI) for providing personalized recommendations, task
management advice, scheduling, and reminders sold as a
component of mobile phones; recorded and pre-installed
software using artificial intelligence (AI) for processing,
compiling, and organizing personal information across
various applications sold as a component of mobile phones;
downloadable computer software using artificial intelligence
(AI) for the production and processing of speech, text,
images, video, sound, and code; downloadable software using
artificial intelligence (AI) for simulating conversations
and answering queries; downloadable software using
artificial intelligence (AI) for tracking and assisting
users with tasks; downloadable software using artificial
intelligence (AI) for use as an intelligent personal digital
assistant; downloadable software using artificial
intelligence (AI) for providing personalized
recommendations, task management advice, scheduling, and
reminders; downloadable software using artificial
intelligence (AI) for processing, compiling, and organizing
personal information across various applications. Providing online non-downloadable software using artificial
intelligence (AI) for the production and processing of
speech, text, images, video, sound, and code; providing
online non-downloadable software using artificial
intelligence (AI) for simulating conversations and answering
queries; providing online non-downloadable software using
artificial intelligence (AI) for tracking and assisting
users with tasks; providing online non-downloadable software
using artificial intelligence (AI) for use as an intelligent
personal digital assistant; providing online
non-downloadable software using artificial intelligence (AI)
for providing personalized recommendations, task management
advice, scheduling, and reminders; providing online
non-downloadable software using artificial intelligence (AI)
for processing, compiling, and organizing personal
information across various applications; providing search
engines for obtaining data via the internet and other
electronic communications networks; creating indexes of
online information, sites and other resources available on
the Internet and other electronic communications networks.
2.
Self-Destructive Code Device for a Rechargeable Battery Device
This document describes systems and techniques for a self-destructive code device for a rechargeable battery device. For example, a system comprises a rechargeable battery device certified for use with an electronic device. An authentication code is associated with the rechargeable battery device to validate that the rechargeable battery device is authenticated for use with the electronic device. A self-destructive code device is attachable to the rechargeable battery device, the self-destructive code device being configured to present the authentication code and cause the authentication code to become unusable after that rechargeable battery device is deployed for use with the electronic device.
Implementations set forth herein relate to off-loading, or temporarily ceasing such off-loading, computational tasks to a separate computing device based on a network metric(s) that is not limited to signal strength. Rather, a network metric for determining whether to continue relying on a network connection with a server computing device for certain computational tasks can be based on a current, or recent, interaction with the server computing device. In this way, an application executing at a computing device having a powerful antenna—but an otherwise limited network velocity, can determine to temporarily rely exclusively on local processing. For instance, an automated assistant can temporarily cease communicating audio data to a remote server computing device, during a dialog session, in response to determining a network metric fails to satisfy a threshold—even though there may appear to be adequate signal strength to effectively transmit the audio data.
Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for processing a network input using a neural network to generate a network output for the network input. That is, by using a neural network that includes a sequence of layer blocks that, for each layer block, processes a block input for the particular layer block through a learned non-linear transformation to generate an initial block output for the particular layer block and combines the initial block output for the particular layer block with at least the block input in accordance with one or more learned parameters to generate the block output for the particular layer block, the described techniques maximize the neural network performance for a given neural network footprint.
A self-monitoring system for a micro-LED display panel can track a health status of the micro-LED emitters over the life cycle of the display. The self-monitoring system can include, for example, light sensors and a coverglass treated with an anti-reflective coating that directs light emitted by the micro-LED array toward the light sensors. Light captured by the light sensors can then be analyzed to determine the current value of light attributes such as color, polarization, and intensity, and to compare the current values of the light attributes with their previous values to monitor changes over time.
H10H 29/24 - Ensembles de plusieurs dispositifs comprenant au moins un composant émetteur de lumière à semi-conducteurs couvert par le groupe comprenant plusieurs dispositifs émetteurs de lumière à semi-conducteurs
H01L 25/16 - Ensembles consistant en une pluralité de dispositifs à semi-conducteurs ou d'autres dispositifs à l'état solide les dispositifs étant de types couverts par plusieurs des sous-classes , , , , ou , p. ex. circuit hybrides
H10F 55/25 - Dispositifs à semi-conducteurs sensibles au rayonnement couverts par les groupes , ou structurellement associés à des sources lumineuses électriques et électriquement ou optiquement couplés avec lesdites sources dans lesquels la source lumineuse électrique commande les dispositifs à semi-conducteurs sensibles au rayonnement, p. ex. optocoupleurs dans lesquels les dispositifs sensibles au rayonnement et la source lumineuse électrique sont tous des dispositifs à semi-conducteurs
A method includes receiving, by a processing device of a security analytics platform, data associated with a computing resource and assigning a first subset of a set of security rules to a first node of the security analytics platform and a second subset of the set of security rules to a second node of the security analytics platform. The first node applies, to the data, the first subset of security rules to generate first analytics data and the second node applies, to the data, the second subset of security rules to generate second analytics data. The first analytics data and the second analytics data are sent to a system associated with the computing resource.
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 21/54 - Contrôle des utilisateurs, des programmes ou des 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 ajout de routines ou d’objets de sécurité aux programmes
G06F 21/55 - Détection d’intrusion locale ou mise en œuvre de contre-mesures
7.
SYSTEMS AND METHODS FOR PREVENTING SPLITS OF RELATED DATA IN A DISTRIBUTED DATABASE
A method includes receiving, by a security analytics platform, first data associated with a computing resource, storing the first data in a first database table associated with the computing resource, and generating a first set of indicators associated with the first database table. Each indicator of the first set of indicators identifies a corresponding horizontal partition associated with the first database table. The method further includes receiving second data associated with the computing resource, storing the second data in a second database table associated with the first database table, and generating a second set of indicators associated with the second database table. The method further includes storing, based on the first and second set of indicators, a first partition of the first database table and a corresponding partition of the second database table, on a same database node.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
A method includes receiving training data comprising a plurality of pairs of images. Each pair comprises a noisy image and a denoised version of the noisy image. The method also includes training a multi-task diffusion model to perform a plurality of image-to-image translation tasks, wherein the training comprises iteratively generating a forward diffusion process by predicting, at each iteration in a sequence of iterations and based on a current noisy estimate of the denoised version of the noisy image, noise data for a next noisy estimate of the denoised version of the noisy image, updating, at each iteration, the current noisy estimate to the next noisy estimate by combining the current noisy estimate with the predicted noise data, and determining a reverse diffusion process by inverting the forward diffusion process to predict the denoised version of the noisy image. The method additionally includes providing the trained diffusion model.
G06V 10/80 - Fusion, c.-à-d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
A computing device is configured to obtain information for an application. The computing device is further configured to generate, using a machine learning model and based on the usage information, at least one intent score. The computing device is further configured to determine, based on the at least one intent score, one or more navigation settings for the application, wherein the one or more navigation settings indicate a particular page that the application should open upon launching of the application. The computing device is further configured to cause, upon launching of the application, the application to open the particular page.
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
10.
CLOUD OBTAINABILITY OPTIMIZATION AND STOCKOUT EXPERIENCE
A method includes determining a first access control band based on a first historical computing usage of a distributed computing system by one or more workloads. The method also includes determining a second access control band based on a second historical computing usage of the distributed computing system. The method also includes determining a third access control band based on an amount of computing resources of the distributed computing system not defined by the first access control band or the second access control band. The method also includes receiving a request for a particular amount of computing resources and determining one or more access control bands from the first access control band, the second access control band, and the third access control band. The method also includes allocating to the one or more workloads at least a portion of the requested particular amount of computing resources.
A user computing device includes one or more first sensors that output one or more first signals based on a magnetic field measured via the one or more first sensors and one or more processors configured to execute instructions to perform operations. The operations include detecting whether the user computing device is receiving a charge from a charging device. When the user computing device is not receiving the charge, the operations include determining, based on the one or more first signals output by the one or more first sensors, whether the user computing device is proximate to the charging device, and when the user computing device is determined, based on the one or more first signals output by the one or more first sensors, to be proximate to the charging device, providing an output indicating the user computing device is not receiving the charge.
A system and related method for determining a temperature of an object via a mobile device having a camera that generates image data across a first field of view, and a temperature sensor that generates temperature data across a second field of view overlapping the first field of view. A computing system receives the image data, each of the plurality of images of the image data being associated with a different respective position of the mobile device. The computing system also receives the temperature data, each of the plurality of average temperatures of the temperature data corresponding to a respective one of the plurality of images. The computing system determines, based at least in part on the plurality of images and the plurality of average temperatures, a temperature of a desired object at least partially within the first and second fields of view.
A system and method for exploring security rule chains in a security platform. The method includes displaying a first plurality of graphical elements of a graphical user interface (GUI), each graphical element of the first plurality of graphical elements referencing a respective chained outcome of a plurality of chained outcomes of a respective chained rule, The respective chained rule includes two or more security rules that are linked based on their respective security outcomes, receiving, via the GUI, a selection of a first graphical element of the first plurality of graphical elements, the first graphical element corresponding to a first chained outcome of the plurality of chained outcomes, and displaying a second plurality of graphical elements in a visual association with the first element, each element of the second plurality of elements referencing a respective security outcome of the two or more security rules that are serially linked.
H04L 41/22 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets comprenant des interfaces utilisateur graphiques spécialement adaptées [GUI]
14.
Task-Specific Prompt Recycling for Machine-Learned Models that Perform Multiple Tasks
Systems and methods of the present disclosure are directed to a computer-implemented method for recycling of task-specific prompts for machine-learned models. The method includes obtaining a task-specific prompt for a first machine-learned model, wherein the task-specific prompt is indicative of a task of a plurality of tasks the first machine-learned model is configured to perform. includes determining a difference between the first machine-learned model and a second machine-learned model different than the first machine-learned model. The method includes, based at least in part on the difference, modifying the task-specific prompt to obtain an updated task-specific prompt that corresponds to the second machine-learned model.
Methods, a system, and a device are provided to allow co-watch devices to coordinate text interpretation services while co-watching a video or live event. A server receives an indication that a first co-watch device and a second co-watch device are preparing to co-watch a video or a live event while displaying a text interpretation of a speech component of the video or live event. An indication is sent to a first device of the first and second co-watch devices to operate as a text-processing device, generating the text interpretation, and transmitting the text interpretation to a second device of the first and second co-watch devices. The first device receives a portion of a video, processes a speech component of the portion of the video to generate a text interpretation, and sends the text interpretation to a second device.
H04N 21/488 - Services de données, p. ex. téléscripteur d'actualités
H04N 21/41 - Structure de clientStructure de périphérique de client
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
A system to broadcast an audio signal includes user equipment (UE) configured to concurrently broadcast the audio signal to a plurality of receiving devices based on a broadcast template. As the UE broadcasts the audio signal, the UE receives reception data from the plurality of receiving devices indicating the quality of the reception of the audio signal. Based on one or more event triggers occurring, the UE adjusts one or more broadcast parameters of the broadcast template based on the reception data received from the plurality of receiving devices. The UE then continues to broadcast the audio signal based on the adjusted broadcast parameters of the broadcast template.
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
Implementations set forth herein relate to a system that employs an automated assistant to further interactions between a user and another application, which can provide the automated assistant with permission to initialize relevant application actions simultaneous to the user interacting with the other application. Furthermore, the system can allow the automated assistant to initialize actions of different applications, despite being actively operating a particular application. Available actions can be gleaned by the automated assistant using various application-specific schemas, which can be compared with incoming requests from a user to the automated assistant. Additional data, such as context and historical interactions, can also be used to rank and identify a suitable application action to be initialized via the automated assistant.
Systems and techniques for a reverberation cancellation framework include receiving a far-field audio signal from a far-field microphone array and a near-field audio signal from a near-field microphone array, where the far-field microphone array is a greater distance from an audio source than the near-field microphone array. The far-field audio signal and the near-field audio signal are synchronized. The far-field audio signal and the near-field audio signal are encoded to remove noise artifacts from the far-field audio signal and the near-field audio signal. The far-field audio signal and the near-field audio signal are decoded to output an output audio signal with the noise artifacts removed.
G10L 21/10 - Transformation en information visible
G10L 25/18 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par le type de paramètres extraits les paramètres extraits étant l’information spectrale de chaque sous-bande
G10L 25/30 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par la technique d’analyse utilisant des réseaux neuronaux
A system to broadcast an audio signal includes user equipment (UE) configured to concurrently broadcast the audio signal to a plurality of receiving devices based on a broadcast template. As the UE broadcasts the audio signal, the UE receives reception data from the plurality of receiving devices indicating the quality of the reception of the audio signal. Based on one or more event triggers occurring, the UE adjusts one or more broadcast parameters of the broadcast template based on the reception data received from the plurality of receiving devices. The UE then continues to broadcast the audio signal based on the adjusted broadcast parameters of the broadcast template.
H04W 4/06 - Répartition sélective de services de diffusion, p. ex. service de diffusion/multidiffusion multimédiaServices à des groupes d’utilisateursServices d’appel sélectif unidirectionnel
H04L 1/18 - Systèmes de répétition automatique, p. ex. systèmes Van Duuren
H04L 65/611 - Diffusion en flux de paquets multimédias pour la prise en charge des services de diffusion par flux unidirectionnel, p. ex. radio sur Internet pour la multidiffusion ou la diffusion
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
One example method includes receiving, by an artificial intelligence (AI) system, a query; generating, by the AI system and based on the query, a plurality of candidate digital components using a machine learning model; obtaining, by the AI system, evaluation results associated with the plurality of candidate digital components, each evaluation result indicating whether a corresponding candidate digital component comprises restricted content; obtaining, by the AI system, performance data indicating an acceptance level of each candidate digital component of the plurality of candidate digital components; identifying, by the AI system and based on the evaluation results and the performance data, a candidate digital component of the plurality of candidate digital components; generating, by the AI system and based on the candidate digital component, training data; and refining, by the AI system and using the training data, the machine learning model.
21.
SPEECH SIGNAL REPAIR AND ENHANCEMENT USING AN INTEGRATED NETWORK BASED ON PROGRESSIVE LEARNING
Techniques are provided for speech signal repair and enhancement using an integrated network based on progressive learning. For example, a degraded speech signal is received on a speech channel and processed through an integrated repairer enhancer network (IREN) to generate a clean speech signal. Embodiments initially train a repairer network to ameliorate one or more of a first type of degradations (disrepair-related degradations). Embodiments then use transfer learning from the repairer network to train the IREN to ameliorate one or more of a second type of degradations (de-enhancement-related degradations). The resulting IREN is a single integrated machine learning network that ameliorates both types of degradations.
G10L 21/02 - Amélioration de l'intelligibilité de la parole, p. ex. réduction de bruit ou annulation d'écho
G10L 25/30 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par la technique d’analyse utilisant des réseaux neuronaux
A computing device may determine a current location, obtain user-context, and generate location-specific insights by applying an artificial intelligence model to the current location and user-context associated with the computing device. For instance, the computing device may, while operating in a locked mode, output a graphical indication of a location-specific insight to an always-on-display device and detect a user input at a location of the always-on-display device associated with the graphical indication. In response to detecting the user input, the computing device may execute an application associated with the at least one location-specific insight and output to a display device while operating in the unlocked mode, additional information to a graphical user interface of the application associated with the at least one location-specific insight.
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06F 3/0481 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p. ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comportement ou d’aspect
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
G06F 3/04883 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p. ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p. ex. des gestes en fonction de la pression exercée enregistrée par une tablette numérique utilisant un écran tactile ou une tablette numérique, p. ex. entrée de commandes par des tracés gestuels pour l’entrée de données par calligraphie, p. ex. sous forme de gestes ou de texte
G06F 3/04886 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p. ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p. ex. des gestes en fonction de la pression exercée enregistrée par une tablette numérique utilisant un écran tactile ou une tablette numérique, p. ex. entrée de commandes par des tracés gestuels par partition en zones à commande indépendante de la surface d’affichage de l’écran tactile ou de la tablette numérique, p. ex. claviers virtuels ou menus
23.
NEURAL NETWORKS WITH NESTED MIXTURE-OF-EXPERTS LAYERS
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using a neural network. In particular, the neural network includes one or more nested mixture of experts (MoE) layers that each include a routing layer and a respective set of nested expert layer blocks.
A method includes determining a viewpoint modification of a first viewpoint from which an input image represents a scene, and determining, based on the input image and the viewpoint modification, a reoriented image that (i) represents the scene from a second viewpoint that differs from the first viewpoint and (ii) includes a visual distortion of the scene. The visual distortion may be associated with the viewpoint modification. The method also includes processing the input image and the reoriented image using an image correction model configured to remove visual distortions associated with viewpoint modifications, and generating, using the image correction model and based on processing the input image and the reoriented image, an output image that includes a correction of at least part of the visual distortion in the reoriented image. The output image may represent the scene from the second viewpoint. The method additionally includes outputting the output image.
To allow user equipment (UE) to place a call on a cellular network, the UE first determines whether an operating mode associated with a fallback network is disabled. Based on the operating mode associated with the fallback network being disabled, the UE then transmits a call registration request that omits call function data to the cellular network. After receiving an acknowledgement from the cellular network, the UE removes data from the acknowledgement indicating which call functions are supported by the cellular network. Based on the modified acknowledgement, the UE then initiates a call on the cellular network using a predetermined call function.
A computing device is configured to determine that there is insufficient available memory in a memory of a secure element of the computing device for a first application. The computing device is further configured to identify a second application to evict from the memory of the secure element. The computing device is further configured to evict at least a portion of the second application from the memory of the secure element, where evicting the second application includes encrypting the second application in the memory of the secure element. The computing device is further configured to install the first application in the memory of the secure element. The computing device is further configured to execute the first application in the memory of the secure element.
G06F 21/79 - Protection de composants spécifiques internes ou périphériques, où la protection d'un composant mène à la protection de tout le calculateur pour assurer la sécurité du stockage de données dans les supports de stockage à semi-conducteurs, p. ex. les mémoires adressables directement
27.
GENERATING ALIGNED IMAGES USING A DENOISING NEURAL NETWORK
Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for generating aligned output images. In particular, the described techniques include processing, for each target image of the output images and over a plurality of reverse diffusion steps, a respective first denoising input using a feature updating layer. The denoising input includes an input feature representation that in turn includes the feature representations of the target image and reference images. By processing the input feature representations of the target image and each of the reference images simultaneously using the feature updating layer, the system can ensure generation of style aligned output images.
Methods and devices are provided to allow for the transfer of a display of a visual representation between a head mounted device and a computing device during the display of a video. A video is displayed on a computing device display of a computing device, a visual representation of a speech for an audio component of the video is received, the visual representation is displayed on the computing device display, and the display of the visual representation is transferred to the head mounted device to display on a head mounted device display upon determining that a head mounted device is in use.
Aspects of the disclosed technology include techniques and mechanisms for restoring program states using microarchitectural scratchpads. A processor is configured to store, in a buffer, program state data which indicates a current state of microarchitectural registers therein during execution of a current program. Based on receiving a command to terminate execution of the current program and restore execution of a different program, the processor retrieves, from the buffer, the program state data associated with the program to be executed and restores the retrieved data.
A data storage management method includes detecting a data change to data in a data repository, identifying metadata of the data change, and storing the metadata in a virtual file, the virtual file being in a data storage format that is compatible with one or more data analysis tools. In response to a subsequent user request to access metadata of the data in the data repository, the method may transmit one or more virtual files containing metadata identified in the user request.
Implementations relate to processing multi-turn dialogs each showing (1) dialog turns that correspond to user input(s) providing user intent(s) and associated parameter(s), and (2) dialog turns that correspond to input(s) from a virtual assistant (or a human agent/responder) that are responsive to the user input(s). A multi-turn dialog (e.g., a pre-processed variation thereof) can be processed, using a generative model, to generate one or more one-shot queries summarizing the user input(s) of the multi-turn dialog. Whether the generated one-shot queries accurately reflect the user intent(s) and/or the associated parameters can be verified, and only verified one-shot queries are selected to form part of a dataset. The dataset can be used, for example, for training machine learning model(s) for handling a single, complex user query and/or for validating machine learning model(s).
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting a most accurate data source to be used to respond to a query and responding with a result using data from the selected data source. In one aspect, a method includes receiving, from a user, an input query related to user interactions with a platform for one or more users of the platform. The input query is processed to select a data source to be used for responding to the input query. The output includes a likelihood that a first result corresponding to the input query obtained using a first data source has a higher accuracy than each of one or more second results corresponding to the input query obtained using one or more second data sources. A result corresponding to the input query is obtained using the selected data source and the result is provided.
A method can include receiving an image including a label identifying inclusion of at least one opacity artifact is received, generating a transformed semantic latent space based on the image using a linear transformation model. generating a noisy image based on the image, generating a first estimated image based on the transformed semantic latent space using a diffusion model, generating a second estimated image based on the transformed semantic latent space and the noisy image using the diffusion model, and training the linear transformation model based on the first estimated image, the second estimated image, and a loss that enforces a linear change in the linear transformation model.
Access to an electronic document is provided to a first client device associated with a first user via a first application, with one or more entries of an approval data structure. When the first user engages with first GUI elements associated with the entries, indicating an approval request for a second user to approve portions of the content, the approval data structure is updated accordingly. The second client device is provided with access, via a second application, to the relevant content and approval data. If the second user engages with second GUI elements associated with the entries, providing a response to the approval request, a notification indicating this response is sent to the first client device.
According to at least one implementation, a method includes identifying a screen state associated with a first device and determining whether the screen state associated with the first device satisfies at least one criterion. In response to determining that the screen state associated with the first device satisfies the at least one criterion, the method further includes identifying touch input for a second device at the first device. In response to determining that the screen state associated with the first device fails to satisfy the at least one criterion, the method further includes identifying touch input for the first device at the first device.
A user equipment (UE) (102) in a mobile cellular network (100) implements one or more techniques to relax radio resource management mobility (RRM) actions. For example, the UE selects, based on a stationary confidence rank (214) indicating a confidence level in an assessment that the UE is in a stationary state, a set of conditions (216) for one or more stationary modes (138). The UE implements a stationary mode of the one or more stationary modes or a non-stationary mode based on the selected set of conditions. Responsive to whether the stationary mode or the non-stationary mode is implemented at the UE, the UE selectively performs one or more RRM relaxation actions (218) to relax or reduce RRM actions at the UE.
H04W 64/00 - Localisation d'utilisateurs ou de terminaux pour la gestion du réseau, p. ex. gestion de la mobilité
G01S 5/00 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de positionLocalisation par coordination de plusieurs déterminations de distance
A computing device for generating, managing, or editing a document includes one or more memories to store instructions and one or more processors to execute the instructions to perform operations, the operations including: obtaining a prompt indicating an intended meaning of a document, receiving a plurality of inputs editing a first version of the document to produce a second version of the document, determining whether specified editing criteria associated with editing the document are satisfied, in response to the specified editing criteria being satisfied, implementing one or more machine-learned models to determine whether specified drift criteria associated with the intended meaning of the document is satisfied, based on the prompt, the edits to the first version of the document, and the second version of the document, and providing an output based on whether the specified drift criteria associated with the intended meaning of the document is satisfied.
A system and related method for determining a temperature of an object via a mobile device having a camera that generates image data across a first field of view, and a temperature sensor that generates temperature data across a second field of view overlapping the first field of view. A computing system receives the image data, each of the plurality of images of the image data being associated with a different respective position of the mobile device. The computing system also receives the temperature data, each of the plurality of average temperatures of the temperature data corresponding to a respective one of the plurality of images. The computing system determines, based at least in part on the plurality of images and the plurality of average temperatures, a temperature of a desired object at least partially within the first and second fields of view.
G01J 5/07 - Dispositions pour ajuster l’angle solide des radiations captées, p. ex. ajustement ou orientation du champ de vue, suivi de la position ou encodage de la position angulaire
.. One of the methods includes receiving new inputs at an invocation by a fully convolutional network deployed for processing inputs with a fixed size. For each of the received new inputs, a group of fixed-size input tiles are determined, and each of the groups of fixed-size input tiles are provided to a hardware accelerator for generating respective fixed-size outputs of the invocation using the deployed fully convolutional network. From the respective fixed-size outputs, a respective final output of the invocation is generated for the output layer that is equivalent to an output that would be generated from the output layer by processing the new inputs at the invocation using the fully convolutional network deployed for processing the corresponding inputs with the respective sizes.
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
Methods and systems, including computer-readable media, are described for high-throughput data decoding at an integrated circuit. A system receives addresses that specify memory locations storing encoded data at a memory device. Each of the addresses are provided by a respective decoder of the circuit. For each address: the system generates a request for an encoded block of an encoded data stream that includes multiple encoded blocks. Over multiple clock cycles, the system processes each of the requests corresponding to the addresses based on available buffer space for storing encoded data in at least one of the decoders. In response to processing each request, the system retrieves multiple encoded blocks from non-contiguous memory locations of the memory and decodes the encoded blocks based on an interleaved configuration used to generate a corresponding encoded data stream. A portion of the encoded blocks are decoded in parallel across two or more decoders.
Described techniques capture a first image of a scene using a first device and cause a second device to capture a second image of the scene. A distance between the first device and the second device may be determined, and a spatial image of the scene may be generated using the first image, the second image, and the distance.
H04N 13/344 - Affichage pour le visionnement à l’aide de lunettes spéciales ou de visiocasques avec des visiocasques portant des affichages gauche et droit
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer software for use in processing and generating natural language queries; Downloadable computer software using artificial intelligence (AI) for the production of speech, text, images, video, sound, and code; Downloadable computer software for multi-modal machine-learning based language, text, speech, image, video, code, and sound processing software; Downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of science, engineering, mathematics, computing, art, music, language, entertainment, and general interest; Downloadable computer software for facilitating multi-modal natural language, speech, text, images, video, code and sound input; Downloadable chatbot software for simulating conversations, analyzing images, sound and video, summarizing text, creating content, generating code, brainstorming, trip planning, and answering queries; Downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; Downloadable chatbot software for providing information from searchable indexes and databases of information, including text, music, images, videos, software algorithms, mathematical equations, electronic documents, and databases Providing online non-downloadable software for use in large language models and artificial intelligence; providing online non-downloadable software using artificial intelligence for the production of human speech and text; providing online non-downloadable software for natural language processing, generation, understanding and analysis; providing online non-downloadable software for artificial intelligence and machine-learning based language and speech processing software; providing online non-downloadable software for creating generative models; providing online non-downloadable software for processing speech, text, sound, code, videos, images, and sound input; providing online non-downloadable software for generating speech, text, sound, code, videos, images, and sound output; research and development services in the field of artificial intelligence; research, development and evaluation of large language models and data sets; research, design and development of computer programs and software; providing online non-downloadable software for managing data sets and performing safety checks in the field of artificial intelligence; providing online non-downloadable software for multi-modal artificial intelligence and machine-learning based language, text, sound, code, video, image, speech, and sound processing software; providing temporary use of online non-downloadable software for facilitating multi-modal natural language, speech, text, sound, code, videos, images, and sound input; research and development services in the field of multi-modal computer natural language processing, artificial intelligence, and machine learning; providing temporary use of online non-downloadable software for an integrated development environment for large language models; providing online non-downloadable software for use in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; providing online non-downloadable software for accessing information from searchable indexes and databases of information, including text, music, images, videos, software algorithms, mathematical equations, electronic documents, and databases; application service provider featuring application programming interface (API) software
Techniques for tuning an image editing operator for reducing a distractor in raw image data are presented herein. The image editing operator can access the raw image data and a mask. The mask can indicate a region of interest associated with the raw image data. The image editing operator can process the raw image data and the mask to generate processed image data. Additionally, a trained saliency model can process at least the processed image data within the region of interest to generate a saliency map that provides saliency values. Moreover, a saliency loss function can compare the saliency values provided by the saliency map for the processed image data within the region of interest to one or more target saliency values. Subsequently, the one or more parameter values of the image editing operator can be modified based at least in part on the saliency loss function.
A method includes receiving an audio input that represents an utterance of a voice command that is preceded by a predefined hotword. The first computing device is configured to process voice commands that are preceded by the predefined hotword and is in proximity of a second computing device that is also configured to process voice commands that are preceded by the same, predefined hotword. The method also includes receiving a local area wireless signal from the second computing device. Based on receiving the local area wireless signal from the second computing device, the method also includes placing the first computing device into a sleep mode, bypassing further processing of the voice command, and bypassing outputting a visual indication that the first computing device is processing the voice command.
G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
G10L 15/30 - Reconnaissance distribuée, p. ex. dans les systèmes client-serveur, pour les applications en téléphonie mobile ou réseaux
G10L 25/03 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par le type de paramètres extraits
G10L 25/78 - Détection de la présence ou de l’absence de signaux de voix
G10L 25/87 - Détection de points discrets dans un signal de voix
A method for detecting malware by modifying executable code includes identifying executable code that includes branch instructions. The method includes determining whether any of the branch instructions of the executable code mask maliciousness of the executable code. The determining includes modifying first one or more of the branch instructions of the executable code, causing execution of the executable code with the modified first one or more branch instructions in a first testing environment, and evaluating a result of the execution of the executable code with the modified first one or more branch instructions. The result can indicate whether the executable code is malicious. The method includes, responsive to determining that the branch instructions of the executable code mask the maliciousness of the executable code, performing one or more preventative actions with respect to the executable code.
A method for participation, in a virtual meeting, of an absent invited virtual meeting user includes receiving input of a first user that has been invited to participate in the virtual meeting. The input of the first user indicates an inability to attend the virtual meeting and provides first data to be discussed during the virtual meeting. The method includes causing a virtual meeting UI to be presented during the virtual meeting between multiple participants. The UI includes a UI element associated with the first data provided by the first user that is not present during the virtual meeting. The method includes generating a summary of the virtual meeting. The summary covers presentation of at least a portion of the first data during the virtual meeting. The method includes causing the summary to be accessible by a client device of the first user.
A software defined vehicle (SDV) operating system may include components for executing software packages that declare unit types (e.g., interfaces) and define service units that each implement a unit type. For each unit type, there may be several service units that each provide a different implementation of that unit type. The SDV operating system may manage a service discovery module that registers service units for each unit type in a centralized registry. While executing a software package that declares a unit type, the service discovery module may fetch, from the centralized registry, an implementation of the unit type by a service unit defined by a different software package. While still executing the software package (i.e., at runtime), the SDV operating system may load a service unit defined by the software package with the fetched implementation. The SDV operating system may then execute the service unit based on the fetched implementation.
Systems, methods, and apparatus for self-evolving decoding at inference. In an aspect, operations include processing, by a Large Language Model (LLM) of N layers, an input by an inference operation of the LLM; obtaining, from the LLM, logits of an evolution layer of the LLM, the evolution layer being subsequent to a first layer of the LLM; for a plurality of layers that occur before the evolution layer, processing the logits of the layer with the logits of the evolution layer to generate an approximated gradient; based on the approximated gradient and the logits of the evolution layer, generating adjusted logits for the evolution layer; and processing the adjusted logits for the evolution layer to generate an output for the LLM.
Systems, methods, and apparatus for generating binaural audio waveform from mono waveform data. In an aspect, operations include generating, based on a mono waveform data and positional data, left signal data and right signal data, wherein the left signal data and the right signal data are initial estimates of perceived signals of the mono waveform based on the positional data; processing the left signal data and right signal data, based on the positional data, to generate amplitude scaled left signal data and amplitude scaled right signal data; and separately processing the amplitude scaled left signal data and the amplitude scaled right signal data by a denoising vocoder to generate left output signal data and right output signal data that together define a binaural audio waveform based on the mono waveform data.
G10L 19/008 - Codage ou décodage du signal audio multi-canal utilisant la corrélation inter-canaux pour réduire la redondance, p. ex. stéréo combinée, codage d’intensité ou matriçage
G10L 25/30 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes caractérisées par la technique d’analyse utilisant des réseaux neuronaux
62.
Recursively-Cascading Diffusion Model for Image Interpolation
Despite recent progress, existing frame interpolation methods still struggle with extremely high resolution images and challenging cases such as repetitive textures, thin objects, and fast motion. To address these issues, provided is a cascaded diffusion frame interpolation approach that excels in these scenarios while achieving competitive performance on standard benchmarks.
G06T 3/4007 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement basé sur l’interpolation, p. ex. interpolation bilinéaire
G06T 3/4076 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement basé sur la super-résolution, c.-à-d. où la résolution de l’image obtenue est plus élevée que la résolution du capteur utilisant les images originales basse résolution pour corriger itérativement les images haute résolution
G06T 5/60 - Amélioration ou restauration d'image utilisant l’apprentissage automatique, p. ex. les réseaux neuronaux
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output temporal sequence of data elements conditioned on an input. In one aspect, a method comprises: obtaining the input, wherein the input comprises a noise input comprising a respective latent representation for each of a plurality of segments of the temporal sequence; updating, for each segment, the latent representation for the segment using a latent denoising neural network, the updating comprising, for each segment other than the first segment: obtaining a memory vector representing one or more hidden states generated by the latent denoising neural network when updating the latent representations for one or more preceding segments; updating the latent representation for the segment at each of a plurality of iterations; and generating the output temporal sequence of data elements by processing the latent representations for the plurality of segments.
The present disclosure describes systems and techniques directed to producing an all-in-focus image with a camera of a mobile device, in particular, cameras with shallow depth-of-field. User equipment includes a sensor for determining distance to an object in a camera's field-of-view. Based on a depth map of the field-of-view, a plurality of segments is inferred, each segment defining a unique focus area within the camera's field-of-view. An autofocus lens of the camera sweeps to a respective focal distance associated with each of the plurality of segments. The camera captures sample images at each focal distance swept by the autofocus lens. The user equipment produces an all-in-focus image by combining or merging portions of the captured sample images.
G06T 5/50 - Amélioration ou restauration d'image utilisant plusieurs images, p. ex. moyenne ou soustraction
G03B 13/36 - Systèmes de mise au point automatique
G06T 5/73 - Élimination des flousAccentuation de la netteté
H04N 23/67 - Commande de la mise au point basée sur les signaux électroniques du capteur d'image
H04N 23/80 - Chaînes de traitement de la caméraLeurs composants
H04N 23/959 - Systèmes de photographie numérique, p. ex. systèmes d'imagerie par champ lumineux pour l'imagerie à grande profondeur de champ en ajustant la profondeur de champ pendant la capture de l'image, p. ex. en maximisant ou en réglant la portée en fonction des caractéristiques de la scène
65.
INTEGRATION OF NTN-CELLULAR AND GNSS RECEIVE CHAINS
A device or receive-circuit has a first receive-chain (Rx-chain) configured to receive and process Non-Terrestrial-Network-cellular (NTN-cellular) signals received from one or more NTN-cellular access nodes, and a second Rx-chain configured to receive and process Global Navigation Satellite System (GNSS) signals wirelessly transmitted from one or more GNSS satellites, with the first Rx-chain and the second Rx-chain being at least partially integrated with each other, including sharing at least an antenna structure, a low-noise amplifier (LNA), and an Rx signal path through the antenna structure and the LNA. Further, the device or receive circuit may include a Radio Frequency Front End (RFFE) of which the LNA is a component, and the RFFE may switch between or split apart the first and second Rx-chains for downstream processing, or the Rx-chains may be split apart after a downstream analog-to- digital converter (ADC) to help avoid signal degradation from the splitting.
H04B 1/00 - Détails des systèmes de transmission, non couverts par l'un des groupes Détails des systèmes de transmission non caractérisés par le milieu utilisé pour la transmission
G01S 19/36 - Détails de construction ou détails de matériel ou de logiciel de la chaîne de traitement des signaux concernant l'étage d'entrée du récepteur
H04B 1/403 - Circuits utilisant le même oscillateur pour générer à la fois la fréquence de l’émetteur et la fréquence de l’oscillateur local du récepteur
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling the participation of generative models in a multi-agent system. One of the methods includes receiving an input for a task to be processed by a group of generative models to generate a final output for the task; and processing the input by the group of the generative models across a plurality of steps, including: for each intermediate step, obtaining context data at the intermediate step; generating based on the context data, control data for a target generative model in the group; providing the context data and the control data to the target generative model in the group; obtaining an output from the target generative model generated in response to the context data and the control data; and updating the context data for the task based on the output from the target generative model.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for upscaling segmentation masks for image processing. One of the methods includes generating, using a trained machine learning model, a semantic mask of a first image, wherein the semantic mask includes an image classification and a confidence value for pixels of the first image across N image classes; generating, using the image classification and confidence values for the pixels of the first image, a semantic mask subset that identifies a subset of classes for each of the pixels of the first image; generating an upscaled version of the semantic mask subset; generating a second semantic mask subset based on the upscaled version of the semantic mask subset; and processing an upscaled version of the first image using the second semantic mask subset to obtain a processed output image.
Techniques and apparatuses are described for performing howling prevention. In example aspects, a hearable (102) includes an acoustic circuit (116). The hearable (102) employs howling prevention (124) to monitor for one or more conditions that can lead to the unintentional generation of howling (122) via the acoustic circuit (116). Upon detecting a condition, the hearable (102) appropriately configures the acoustic circuit (116) to prevent howling (122) from occurring. Using various sensing techniques, the hearable (102) can quickly detect the condition and proactively adjust a gain of the acoustic circuit (116) to maintain stability of the acoustic circuit (116) and avoid howling (122). With howling prevention (124), an overall user experience with hearables (102) is improved while supporting features such as active noise cancellation and/or a transparency mode. Furthermore, some hearables (102) can be configured to perform howling prevention (124) without the need for additional hardware.
Methods, systems, and apparatus, including computer-readable storage media for content group generation for a content delivery campaign. Content groups are generated from a resource identifier and a description. Digital content items are created for each content group, including digital content from the resource identifier and the description, as well as new digital content items. Candidate content groups are ranked according to request coverage gain and optionally one or more other ranking criteria. Request coverage gain is a measure of how much more request coverage is gained through keywords of one content group relative to the request coverage of one or more other content groups. By ranking according to request coverage gain, the selected candidate content groups are differentiated relative to one another, capturing potential content requests that would otherwise be missed by a campaign of content groups not selected based on request coverage gain.
G06F 16/955 - Recherche dans le Web utilisant des identifiants d’information, p. ex. des localisateurs uniformisés de ressources [uniform resource locators - URL]
G06F 16/958 - Organisation ou gestion de contenu de sites Web, p. ex. publication, conservation de pages ou liens automatiques
A user equipment (UE) selects (1101) a cell of a non-terrestrial network (NTN) that supports Internet-of-Things (loT) devices, transmits (1104), to a core network (CN) via the cell, a registration request message indicating that the UE requires an emergency messaging service (EMS), and establishes (1112), with the cell, a protocol data unit (PDU) session for the EMS.
H04L 67/04 - Protocoles spécialement adaptés aux terminaux ou aux réseaux à capacités limitéesProtocoles spécialement adaptés à la portabilité du terminal
H04L 67/141 - Configuration des sessions d'application
H04W 4/90 - Services pour gérer les situations d’urgence ou dangereuses, p. ex. systèmes d’alerte aux séismes et aux tsunamis
H04W 60/04 - Rattachement à un réseau, p. ex. enregistrementSuppression du rattachement à un réseau, p. ex. annulation de l'enregistrement utilisant des événements déclenchés
H04W 76/50 - Gestion de la connexion pour les connexions d'urgence
An image generation method is performed by one or more data processing apparatus, and comprises: obtaining an image showing an object; generating one or more additional images related to the object; fine-tuning a machine-learned text-to-image model using one or more of the additional images; providing, to the machine-learned text-to-image model, a prompt to generate an output image showing the object, and obtaining, from the machine-learned text-to-image generation model, the output image.
Methods, systems, and apparatus for classification. In one aspect, a method includes receiving an input and a request to classify the input into one of a plurality of classes, processing the input using a multimodal model to generate (i) a description of the input and (ii) a class prediction, processing the description of the input and the class prediction using a text encoder embedding neural network to generate a (i) text description feature embedding and (ii) a prediction feature embedding, generating, from at least the description feature embedding and the prediction feature embedding, a query feature embedding representing the input, and classifying the input into one of the plurality of classes using the query embedding.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for multi-vector retrieval via fixed dimensional encodings. In one aspect, a method includes: obtaining a set of embedding vectors of a query in an embedding vector space; obtaining an encoded dataset including, for each data item in a set of data items, a respective encoded vector of the data item in a target vector space; encoding the set of embedding vectors of the query in the embedding vector space into an encoded vector of the query in the target vector space; performing, with respect to the encoded vector of the query, a k-nearest neighbors search on the respective encoded vectors of the data items in the encoded dataset; and identifying, from the k-nearest neighbors search, a top-k subset of the set of data items.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for composing machine learning models to perform new tasks.
To monitor and report road quality, a server device is configured to receive, from a plurality of vehicles, respective reports, each of the reports indicating a geographic road location of a vehicle and a road quality indication for the geographic location; update, using the reports, a table correlating geographic road locations and road quality indications; determine average road quality indicia for a geographic road location, based on the road quality indications in the table; and in response to a query from a communication device, provide the communication device with an information update based on at least the average road quality indicia.
G07C 5/08 - Enregistrement ou indication de données de marche autres que le temps de circulation, de fonctionnement, d'arrêt ou d'attente, avec ou sans enregistrement des temps de circulation, de fonctionnement, d'arrêt ou d'attente
B60W 40/06 - Calcul ou estimation des paramètres de fonctionnement pour les systèmes d'aide à la conduite de véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier liés aux conditions ambiantes liés à l'état de la route
B60W 50/04 - Détails des systèmes d'aide à la conduite des véhicules routiers qui ne sont pas liés à la commande d'un sous-ensemble particulier pour surveiller le fonctionnement du système d'aide à la conduite
G07C 5/00 - Enregistrement ou indication du fonctionnement de véhicules
H04L 67/12 - Protocoles spécialement adaptés aux environnements propriétaires ou de mise en réseau pour un usage spécial, p. ex. les réseaux médicaux, les réseaux de capteurs, les réseaux dans les véhicules ou les réseaux de mesure à distance
76.
SELECTING A DEVICE TO RESPOND TO DEVICE-AGNOSTIC USER REQUESTS
Implementations relate to selecting a particular device, from an ecosystem of devices, to provide responses to a device-agnostic request of the user while a scenario is occurring. The user specifies a scenario and contextual features are identified from one or more devices of the ecosystem to generate scenario features indicative of the scenario occurring. The scenario features are stored with a correlation to a device that is specified by the user to handle responses while the scenario is occurring. When a subsequent device-agnostic request is received, current contextual features are identified and compared to the scenario features. Based on the comparison, the specified assistant device is selected to respond to the device-agnostic request.
A system, such as a voice assistant device, is disclosed which includes a base that houses at least one speaker and supports a display screen. The base is configured to hold the display screen at an angle relative to a surface, creating a predefined space between the screen's lower edge and the surface. To optimize sound, multiple speakers can be oriented in different directions, with one speaker potentially facing a front grille while another is aimed in another direction behind the display. The system may further integrate a camera and a radar transceiver within the bezel of the display screen.
G06F 21/83 - Protection des dispositifs de saisie, d’affichage de données ou d’interconnexion dispositifs de saisie de données, p. ex. claviers, souris ou commandes desdits claviers ou souris
G10L 15/28 - Détails de structure des systèmes de reconnaissance de la parole
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]
H04R 1/02 - BoîtiersMeublesMontages à l'intérieur de ceux-ci
H04R 1/34 - Dispositions pour obtenir la fréquence désirée ou les caractéristiques directionnelles pour obtenir la caractéristique directionnelle désirée uniquement en utilisant un seul transducteur avec des moyens réfléchissant, diffractant, dirigeant ou guidant des sons
78.
Cloud-Based Voice Interconnects for Contact Centers and Corporate Telephony
An example cloud-based voice interconnect system includes data processing hardware of a cloud-based computing platform, a network, and a public telecom carrier system. The data processing hardware is in communication with memory hardware storing instructions that, when executed on the data processing hardware, cause the data processing hardware to perform operations including providing a private virtualized computing environment, and implementing a private cloud-based session border controller (SBC) in the virtualized computing environment. The public telecom carrier system is connected to the private cloud-based SBC via the network, and is configured to provide telecom services between the private cloud-based SBC and customers of the public telecom carrier system.
H04M 7/00 - Dispositions d'interconnexion entre centres de commutation
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
H04L 65/1104 - Protocole d'initiation de session [SIP]
H04L 67/10 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau
A software-based extension of the instruction set of a processor includes instructions for the processor to prefetch virtual address translations and insert the prefetched translations into a translation lookaside buffer (TLB). A page walk may be performed to find a virtual address in a group of page tables and provide the address translation to the TLB. The TLB may be arranged in multiple levels and the instructions may specify a level for the prefetched entry to be inserted. The instruction may provide a hint to the processor for selecting candidate virtual address for prefetch based on a characteristic of an address such as a likelihood of reuse, a priority level of the data in the virtual address or other characteristic. A page walk can be performed asynchronously without affecting normal operations of a program. Instructions may specify between an instruction a data TLB for insertion of a new TLB entry.
G06F 12/1027 - Traduction d'adresses utilisant des moyens de traduction d’adresse associatifs ou pseudo-associatifs, p. ex. un répertoire de pages actives [TLB]
G06F 12/0811 - Systèmes de mémoire cache multi-utilisateurs, multiprocesseurs ou multitraitement avec hiérarchies de mémoires cache multi-niveaux
G06F 12/0862 - Adressage d’un niveau de mémoire dans lequel l’accès aux données ou aux blocs de données désirés nécessite des moyens d’adressage associatif, p. ex. mémoires cache avec pré-lecture
80.
HYBRID ANSWERS ON A HEAD-WEARABLE DISPLAY USING AN EDGE LARGE LANGUAGE MODEL AND EXTENDED LARGE LANGUAGE MODEL
To reduce the time needed to display an answer to a prompt received at a head-wearable device (HWD), the HWD includes an edge large-language (LLM) model implemented at the HWD. Based on the prompt, the HWD generates tokens and edge answers using the edge LLM. In response to one or more of the tokens being a delegation token and concurrently with displaying the edge answer, the HWD transmits token embeddings of the tokens to a server implementing an extended LLM. The HMD then displays a hybrid answer including the edge answer and the extended answer.
This document describes systems and techniques directed at semiconductor fault detection. In aspects, a semiconductor device includes a physical structure that facilitates detection and localization of defects. The physical structure includes at least one conductive interconnect that extends through two or more layers of a semiconductor device, enabling an electrical detection of faults. Such systems and techniques can help improve yield, accelerate failure analysis debugging, and improve reliability of semiconductor devices.
G01R 31/26 - Test de dispositifs individuels à semi-conducteurs
H01L 23/485 - Dispositions pour conduire le courant électrique vers le ou hors du corps à l'état solide pendant son fonctionnement, p. ex. fils de connexion ou bornes formées de couches conductrices inséparables du corps semi-conducteur sur lequel elles ont été déposées formées de structures en couches comprenant des couches conductrices et isolantes, p. ex. contacts planaires
H01L 23/528 - Configuration de la structure d'interconnexion
A method of semantic-based image copying includes generating a text prompt. Generating the text prompt includes by applying a source image to a first generative artificial intelligence (Al) model to generate a descriptive caption for the source image. The method also includes generating a visual embedding based on the source image, and generating a new image using a second generative Al model and based on the text prompt and the visual embedding.
A method efficiently restructures account data indicative a first plurality of keywords each mapped to a respective query space, a first plurality of campaigns, and associations therebetween. The method includes consolidating the first plurality of campaigns into a smaller, second plurality of campaigns, based on a degree of overlap between respective query spaces to which keywords associated with different campaigns are mapped. The method also includes generating a second plurality of keywords consisting of a subset of the first plurality of keywords, which includes, for each campaign in the second plurality of campaigns, determining whether to remove associations to particular keywords based on an incremental value added by the query spaces that are mapped to those keywords. The method also includes storing restructured account data indicative of the second plurality of keywords, the second plurality of campaigns, and new associations therebetween.
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for automated layout generation by an artificial intelligence system. Methods can include obtaining two or more discrete units of content. Based on the two or more discrete units of content a new layout is generated in a canvas. The layout generation can include: generating a bounding box as a presentation space for each given unit of content; generating positioning data specifying locations within the canvas at which each bounding box is located; assigning each bounding box to a corresponding user interface layer; and generating a compressed text representation of the new layout. The new layout can be rendered based on the text representation.
Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for training a student language model neural network for deployment in a cascade with a teacher neural network. That is, by training a student neural network using techniques that incorporate the difficulty of accurately predicting the target token for each output position of a target output of a training example for each training example for both the student and the teacher language model neural networks, the described techniques result in a student teacher cascade with higher overall task performance per unit of computational cost.
To reduce the time needed to display an answer to a prompt received at a head-wearable device (HWD) or for other reasons, the HWD includes an edge large-language (LLM) model implemented at the HWD. Based on the prompt, the HWD generates tokens and edge answers using the edge LLM. In response to one or more of the tokens being a delegation token and concurrently with displaying the edge answer, the HWD transmits token embeddings of the tokens to a server implementing an extended LLM. The HMD then displays a hybrid answer including the edge answer and the extended answer.
A method for optimizing file storage includes receiving columnar data to store at a columnar data store with columns ordered with an initial ordering. The method includes determining, based on historical access patterns for the columnar data store, an updated ordering for the columns. The method includes storing the columnar data at a first location of the columnar data store using the updated ordering. The method includes determining that the stored columnar data is to be compacted and compressing at least a portion of the columnar data using each of a plurality of compression techniques. The method includes, based on compressing the at least a portion of the columnar data, selecting one of the plurality of compression techniques. The method includes storing the columnar data at a second location of the columnar data store using the selected one of the plurality of compression techniques.
Implementations described herein relate to enabling natural language communications with an autonomous vehicle. In some implementations, processor(s) of a system can initiate and conduct a conversation with a remote communication participant that is located remotely from the autonomous vehicle whereas, in additional or alternative implementations, the processor(s) can answer an incoming electronic communication and conduct a conversation with a remote communication participant that is located remotely from the autonomous vehicle. In other additional or alternative implementations, the processor(s) can also conduct conversations with a local communication participant that is located proximate to the autonomous vehicle. Notably, the processor(s) can be implemented locally at the autonomous vehicle or remotely from the autonomous vehicle (e.g., at a remote server).
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
G10L 13/00 - Synthèse de la paroleSystèmes de synthèse de la parole à partir de texte
H04W 4/40 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p. ex. communication véhicule-piétons
41 - Éducation, divertissements, activités sportives et culturelles
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Providing computer security training and educational testing services in a simulated enterprise network environment, featuring virtualized hardware, software, and security elements, including simulated cloud services across multicloud environments and AI components like large language models (LLMs) and AI agents; Organizing computer security competitions in a simulated enterprise network environment, featuring virtualized hardware, software, and security elements, including simulated cloud services across multicloud environments and AI components like large language models (LLMs) and AI agents; Providing training in the field of computer network attack, defense, response and investigation. Providing computer security consulting services in the field of computer network attack, defense, response and investigation; Computer programming services for developing a simulated enterprise network environment, featuring virtualized hardware, software, and security elements, including simulated cloud services across multicloud environments and AI components like large language models (LLMs) and AI agents; Computer security threat detection and analysis for protecting data provided in a simulated enterprise network environment, featuring virtualized hardware, software, and security elements, including simulated cloud services across multicloud environments and AI components like large language models (LLMs) and AI agents.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Downloadable computer software for use in processing and generating natural language queries; downloadable computer software using artificial intelligence (AI) for the production of speech, text, images, video, sound, and code; downloadable computer software for multi-modal machine-learning based language, text, speech, image, video, code, and sound processing software; downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of science, engineering, mathematics, computing, art, music, language, entertainment, and general interest; downloadable computer software for facilitating multi-modal natural language, speech, text, images, video, code and sound input; downloadable chatbot software for simulating conversations, analyzing images, sound and video, summarizing text, creating content, generating code, brainstorming, trip planning, and answering queries; downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; downloadable chatbot software for providing information from searchable indexes and databases of information, including text, music, images, videos, software algorithms, mathematical equations, electronic documents, and databases. (1) Providing online non-downloadable software for use in large language models and artificial intelligence; providing online non-downloadable software using artificial intelligence for the production of human speech and text; providing online non-downloadable software for natural language processing, generation, understanding and analysis; providing online non-downloadable software for artificial intelligence and machine-learning based language and speech processing software; providing online non-downloadable software for creating generative models; providing online non-downloadable software for processing speech, text, sound, code, videos, images, and sound input; providing online non-downloadable software for generating speech, text, sound, code, videos, images, and sound output; research and development services in the field of artificial intelligence; research, development and evaluation of large language models and data sets; research, design and development of computer programs and software; providing online non-downloadable software for managing data sets and performing safety checks in the field of artificial intelligence; providing online non-downloadable software for multi-modal artificial intelligence and machine-learning based language, text, sound, code, video, image, speech, and sound processing software; providing temporary use of online non-downloadable software for facilitating multi-modal natural language, speech, text, sound, code, videos, images, and sound input; research and development services in the field of multi-modal computer natural language processing, artificial intelligence, and machine learning; providing temporary use of online non-downloadable software for an integrated development environment for large language models; providing online non-downloadable software for use in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; providing online non-downloadable software for accessing information from searchable indexes and databases of information, including text, music, images, videos, software algorithms, mathematical equations, electronic documents, and databases; application service provider featuring application programming interface (API) software.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer software for use in processing and generating natural language queries; Downloadable computer software using artificial intelligence (AI) for the production of speech, text, images, video, sound, and code; Downloadable computer software for multi-modal machine-learning based language, text, speech, image, video, code, and sound processing software; Downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of science, engineering, mathematics, computing, art, music, language, entertainment, and general interest; Downloadable computer software for facilitating multi-modal natural language, speech, text, images, video, code and sound input; Downloadable chatbot software for simulating conversations, analyzing images, sound and video, summarizing text, creating content, generating code, brainstorming, trip planning, and answering queries; Downloadable computer software for facilitating interaction and communication between humans and artificial intelligence (AI) chatbots in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; Downloadable chatbot software for providing information from searchable indexes and databases of information, including text, music, images, videos, software algorithms, mathematical equations, electronic documents, and databases. Providing online non-downloadable software for use in large language models and artificial intelligence; providing online non-downloadable software using artificial intelligence for the production of human speech and text; providing online non-downloadable software for natural language processing, generation, understanding and analysis; providing online non-downloadable software for artificial intelligence and machine-learning based language and speech processing software; providing online non-downloadable software for creating generative models; providing online non-downloadable software for processing speech, text, sound, code, videos, images, and sound input; providing online non-downloadable software for generating speech, text, sound, code, videos, images, and sound output; research and development services in the field of artificial intelligence; research, development and evaluation of large language models and data sets; research, design and development of computer programs and software; providing online non-downloadable software for managing data sets and performing safety checks in the field of artificial intelligence; providing online non-downloadable software for multi-modal artificial intelligence and machine-learning based language, text, sound, code, video, image, speech, and sound processing software; providing temporary use of online non-downloadable software for facilitating multi-modal natural language, speech, text, sound, code, videos, images, and sound input; research and development services in the field of multi-modal computer natural language processing, artificial intelligence, and machine learning; providing temporary use of online non-downloadable software for an integrated development environment for large language models; providing online non-downloadable software for use in the fields of artificial intelligence, machine learning, natural language generation, statistical learning, mathematical learning, supervised learning, and unsupervised learning; providing online non-downloadable software for accessing information from searchable indexes and databases of information, including text, music, images, videos, software algorithms, mathematical equations, electronic documents, and databases; application service provider featuring application programming interface (API) software.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Integrated circuits; computer hardware; computer accelerator boards; integrated circuit cards and components; recorded software for accelerating the design and development of machine learning, data analysis, learning algorithms sold as a component of computer hardware and integrated circuits; recorded software for implementing a computer programming language sold as a component of computer hardware and integrated circuits; recorded and downloadable software development tools for facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning sold as a component of computer hardware and integrated circuits. Providing online non-downloadable software for implementing a computer programming language; providing online non-downloadable software for accelerating the design and development of machine learning, data analysis, learning algorithms; providing online non-downloadable software development tools for use in the fields of facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Integrated circuits; computer hardware; computer accelerator boards; integrated circuit cards and components; recorded software for accelerating the design and development of machine learning, data analysis, learning algorithms sold as a component of computer hardware and integrated circuits; recorded software for implementing a computer programming language sold as a component of computer hardware and integrated circuits; recorded and downloadable software development tools for facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning sold as a component of computer hardware and integrated circuits. Providing online non-downloadable software for implementing a computer programming language; providing online non-downloadable software for accelerating the design and development of machine learning, data analysis, learning algorithms; providing online non-downloadable software development tools for use in the fields of facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Integrated circuits; computer hardware; computer accelerator boards; integrated circuit cards and components; recorded software for accelerating the design and development of machine learning, data analysis, learning algorithms sold as a component of computer hardware and integrated circuits; recorded software for implementing a computer programming language sold as a component of computer hardware and integrated circuits; recorded and downloadable software development tools for facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning sold as a component of computer hardware and integrated circuits (1) Providing online non-downloadable software for implementing a computer programming language; providing online non-downloadable software for accelerating the design and development of machine learning, data analysis, learning algorithms; providing online non-downloadable software development tools for use in the fields of facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Integrated circuits; computer hardware; computer accelerator boards; integrated circuit cards and components; recorded software for accelerating the design and development of machine learning, data analysis, learning algorithms sold as a component of computer hardware and integrated circuits; recorded software for implementing a computer programming language sold as a component of computer hardware and integrated circuits; recorded and downloadable software development tools for facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning sold as a component of computer hardware and integrated circuits (1) Providing online non-downloadable software for implementing a computer programming language; providing online non-downloadable software for accelerating the design and development of machine learning, data analysis, learning algorithms; providing online non-downloadable software development tools for use in the fields of facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Integrated circuits; computer hardware; computer accelerator boards; integrated circuit cards and components; recorded software for accelerating the design and development of machine learning, data analysis, learning algorithms sold as a component of computer hardware and integrated circuits; recorded software for implementing a computer programming language sold as a component of computer hardware and integrated circuits; recorded and downloadable software development tools for facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning sold as a component of computer hardware and integrated circuits Providing online non-downloadable software for implementing a computer programming language; providing online non-downloadable software for accelerating the design and development of machine learning, data analysis, learning algorithms; providing online non-downloadable software development tools for use in the fields of facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Integrated circuits; computer hardware; computer accelerator boards; integrated circuit cards and components; recorded software for accelerating the design and development of machine learning, data analysis, learning algorithms sold as a component of computer hardware and integrated circuits; recorded software for implementing a computer programming language sold as a component of computer hardware and integrated circuits; recorded and downloadable software development tools for facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning sold as a component of computer hardware and integrated circuits Providing online non-downloadable software for implementing a computer programming language; providing online non-downloadable software for accelerating the design and development of machine learning, data analysis, learning algorithms; providing online non-downloadable software development tools for use in the fields of facilitating the deployment of artificial intelligence solutions, deep learning, high performance computing, and machine learning
99.
Diffusion Models for Generation of Audio Data Based on Descriptive Textual Prompts
A corpus of textual data is generated with a machine-learned text generation model. The corpus of textual data includes a plurality of sentences. Each sentence is descriptive of a type of audio. For each of a plurality of audio recordings, the audio recording is processed with a machine-learned audio classification model to obtain training data including the audio recording and one or more sentences of the plurality of sentences closest to the audio recording within a joint audio-text embedding space of the machine-learned audio classification model. The sentence(s) are processed with a machine-learned generation model to obtain an intermediate representation of the one or more sentences. The intermediate representation is processed with a machine-learned cascaded diffusion model to obtain audio data. The machine-learned cascaded diffusion model is trained based on a difference between the audio data and the audio recording.
Methods, systems, devices, and tangible non-transitory computer readable media for using incoming communications to generate suggestions for navigation. The disclosed technology can include accessing route data that includes information associated with navigation from a starting location to a destination. Based on the route data, one or more routes from the starting location to the destination can be determined. Message data including one or more messages to a user can be accessed. Based on the message data and one or more machine-learned models, at least one entity and objectives that are associated with the one or more messages can be determined. Based on the one or more routes, the at least one entity, and the objectives, suggestions associated with the one or more messages can be determined. Furthermore, output including indications associated with the suggestions directed to the user can be generated via a user interface.
