A vehicle data streaming service provides a curated catalog of vehicle attributes and allows a vehicle data stream source to register to the vehicle data streaming system and associate its data stream to a vehicle attribute of the attribute catalog. The vehicle data streaming service also allows vehicle data stream destinations to subscribe to the vehicle attribute in the vehicle catalog, receives streamed vehicle data from the data stream source, and sends streamed vehicle data conforming to registration requirements to the data stream destinations. Additionally, the vehicle data streaming service may allow management of the vehicle attribute catalog and may further manage the registration one or more sources and the subscriptions of one or more destinations.
G07C 5/00 - Enregistrement ou indication du fonctionnement de véhicules
G06F 16/21 - Conception, administration ou maintenance des bases de données
G06F 16/28 - Bases de données caractérisées par leurs modèles, p. ex. des modèles relationnels ou objet
G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p. ex. véhicules ou piétonsReconnaissance des objets de la circulation, p. ex. signalisation routière, feux de signalisation ou routes
Some embodiments provide an IC for implementing a machine-trained network with multiple layers. The IC includes a set of circuits to compute a dot product of (i) a first number of input values computed by other circuits of the IC and (ii) a set of predefined weight values, several of which are zero, with a weight value for each of the input values. The set of circuits includes (i) a dot product computation circuit to compute the dot product based on a second number of inputs and (ii) for each input value, at least two sets of wires for providing the input value to at least two of the dot product computation circuit inputs. The second number is less than the first number. Each input value with a corresponding weight value that is not equal to zero is provided to a different one of the dot product computation circuit inputs.
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
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
Provided are systems and methods for a storage adapter device for communicating with network storage. In some implementations, the storage adapter device comprises a host interface. In these implementations, the host interface may be configured to communicate with a host device using a local bus protocol. In some implementations, the storage adapter device also includes a network interface. In these implementations, the network interface may communicate with a network using a network protocol. In some implementations, the storage adapter device may be configured to communicate with a remote storage device. In some implementations, the storage adapter device may also be configured to translate a request from the host interface from the local bus protocol to the network protocol. The storage adapter device may further be configured to transmit the translated request to the remote storage device.
G06F 15/173 - Communication entre processeurs utilisant un réseau d'interconnexion, p. ex. matriciel, de réarrangement, pyramidal, en étoile ou ramifié
G06F 13/42 - Protocole de transfert pour bus, p. ex. liaisonSynchronisation
H04L 67/1097 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau pour le stockage distribué de données dans des réseaux, p. ex. dispositions de transport pour le système de fichiers réseau [NFS], réseaux de stockage [SAN] ou stockage en réseau [NAS]
H04L 69/16 - Implémentation ou adaptation du protocole Internet [IP], du protocole de contrôle de transmission [TCP] ou du protocole datagramme utilisateur [UDP]
4.
Automatic failure diagnosis and correction in machine learning models
Automatic failure diagnosis and correction may be performed on trained machine learning models. Input data that causes a trained machine learning model may be identified in order to determine different model failures. The model failures may be clustered in order to determine failure scenarios for the trained machine learning model. Examples of the failure scenarios may be generated and truth labels for the example scenarios obtained. The examples and truth labels may then be used to retrain the machine learning model to generate a corrected version of the machine learning model.
Techniques for dynamic database redaction using protected encryption secret material are described. A masking policy is defined that includes a reference to a secret material stored by a secrets manager service. The masking policy further identifies a pseudonymous redaction function that utilizes a cryptographic function requiring such a secret material. The secrets manager service is configured to grant access to the secret material by an entity of the database service that executes queries, such as a leader node of a cluster. For a particular query, the cluster obtains the secret material from the secrets manager service in a secure manner, uses the secret material for applying the cryptographic function to values for redaction purposes, and deletes any copies of secret material thereafter.
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
Described herein is a computer-implemented method for techniques relating to anomalous content marking and determination. A content marking request of anomalous content can be received by a computer system. A content marking count associated with the content can be determined for the content. A content marking ratio can be determined based on the content marking count. A parameter indicative of the anomalous status of the content can be determined based on the content marking count and/or content marking ratio, and the parameter can be compared to a threshold parameter. Alerts of anomalous content can be delivered at the user device based on the content marking count, the content marking ratio, the parameter or the comparison result between the parameter and the threshold parameter.
Systems, methods, and computer-readable media are disclosed for systems and methods multimodal indexing of video using machine learning. An example method may include deceiving, by a video encoder of an audio-video transformer neural network comprising one or more computer processors coupled to memory, a first frame and a second frame associated with a first segment of a video. The example method may also include receiving, by an audio encoder of the audio-video transformer neural network, an audio spectrogram comprising first audio data associated with the first segment of the video. generating, by the video encoder, a first video embedding. The example method may also include generating, by the audio encoder, a first audio embedding. The example method may also include determining a fusion of the first video embedding and the first audio embedding using a multimodal bottleneck token. The example method may also include determining an output including the first video embedding and the first audio embedding. The example method may also include determining a classification of the first portion of the video based on the output.
G06F 16/71 - IndexationStructures de données à cet effetStructures de stockage
G06F 16/783 - Recherche de données caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
A clock signal in a clock distribution network is transmitted using network packets with the clock signal embedded within a bit of the network packets. The clock signal is adjusted to account for propagation delay in transmitting the clock signal throughout a clock distribution network. The propagation delay is computed using a round-trip packet. When a previous clock signal is received, a timer is set to a local clock's estimate of when the next clock signal will occur minus the propagation delay to the downstream device. When this timer expires, the clock signal is sent to the downstream device, which will allow it to arrive at the downstream device at the same time as the next clock signal is received on the local device.
H04L 7/00 - Dispositions pour synchroniser le récepteur avec l'émetteur
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]
9.
System for interference mitigation in a satellite communication system
A radio network with many endpoints, such as many user terminals (UTs) accessing a constellation of many satellites, may experience self-interference due to reduced apparent angular separation between endpoints. For example, many UTs that are covered by a satellite's antenna gain pattern of an uplink may interfere with one another if those UTs use the same frequencies at the same time. The UTs may be geographically separated, but due to relative position between them and the satellite, they appear within the same gain pattern. Resource mapping is performed to allocate link resources to mitigate self-interference. A conflict graph is determined and used to allocate link resources, such as particular combinations of timeslot and frequency, to reduce or eliminate self-interference. The conflict graph may be determined using one or more analytical or heuristic techniques. Resource allocation may be performed for links such as satellite uplinks, satellite downlinks, or both.
Disclosed herein is a method for determining tumor margins relative to non-tumorous tissue using a deep-learning platform. Also disclosed herein are methods for removing a tumor from a tumor biopsy.
Systems and techniques are disclosed for determining and provisioning and data resources for use in migrations data processing systems. Source data processing system metadata may be used to estimate data storage requirements for a migration and subtask parameters may be used to determine processing requirements. Migration resources may be determined based on these requirements and provisioned to perform migration operations. A migration may be monitored for resource utilization and resources allocated to the migration may be adjusted to increase migration efficiency and performance.
G06F 16/00 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet
G06F 11/14 - Détection ou correction d'erreur dans les données par redondance dans les opérations, p. ex. en utilisant différentes séquences d'opérations aboutissant au même résultat
G06F 11/16 - Détection ou correction d'erreur dans une donnée par redondance dans le matériel
G06F 16/21 - Conception, administration ou maintenance des bases de données
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
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
Techniques for reducing interference graph generation time may include obtaining a data flow graph representing a computational flow. For each memory object in the data flow graph, a memory object live interval can be added to a vector of intervals. The memory object live interval indicates a last-use of the memory object and a first-definition of the memory object. The vector of intervals can be converted into a binary tree of interval nodes. For each interval node in the binary tree, an earliest-first-definition value is determined for the sub-tree rooted at the interval node, and is associated with the interval node. The binary tree can be queried for interferences of a memory object, and memory allocation can be performed for the computational flow based on the interferences.
Techniques for improving image processing related to item deliveries are described. In an example, a computer system receives an image showing a drop-off of an item, the item associated with a delivery to a delivery location. The computer system inputs the image to a first artificial intelligence (AI) model. The computer system receives first data comprising an indication of whether the drop-off is correct from the first AI model. The computer system causes a presentation of the indication at a device associated with the delivery of the item to the delivery location.
G06V 20/56 - Contexte ou environnement de l’image à l’extérieur d’un véhicule à partir de capteurs embarqués
G06F 18/2135 - Extraction de caractéristiques, p. ex. en transformant l'espace des caractéristiquesSynthétisationsMappages, p. ex. procédés de sous-espace basée sur des critères d'approximation, p. ex. analyse en composantes principales
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
Some embodiments provide a method for training a machine-trained network that includes multiple parameters. The method propagates a batch of input training items through the network to generate output values and compute values of a loss function for each of the input training items. The method computes a weight for each input training item based on the computed loss function values for each of the input training items. The method selects input training items with larger weights more often than input training items with smaller weights for subsequent batches of input training items.
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
G06V 10/776 - ValidationÉvaluation des performances
Multiple video files that are captured by calibrated imaging devices may be annotated based on a single annotation of an image frame of one of the video files. An operator may enter an annotation to an image frame via a user interface, and the annotation may be replicated from the image frame to other image frames that were captured at the same time and are included in other video files. Annotations may be updated by the operator and/or tracked in subsequent image frames. Predicted locations of the annotations in subsequent image frames within each of the video files may be determined, e.g., by a tracker, and a confidence level associated with any of the annotations may be calculated. Where the confidence level falls below a predetermined threshold, the operator may be prompted to delete or update the annotation, or the annotation may be deleted.
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/98 - Détection ou correction d’erreurs, p. ex. en effectuant une deuxième exploration du motif ou par intervention humaineÉvaluation de la qualité des motifs acquis
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 20/52 - Activités de surveillance ou de suivi, p. ex. pour la reconnaissance d’objets suspects
G06V 30/194 - Références réglables par une méthode adaptative, p. ex. par apprentissage
H04N 5/77 - Circuits d'interface entre un appareil d'enregistrement et un autre appareil entre un appareil d'enregistrement et une caméra de télévision
H04N 5/91 - Traitement du signal de télévision pour l'enregistrement
H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c.-à-d. systèmes dans lesquels le signal vidéo n'est pas diffusé
H04N 23/45 - Caméras ou modules de caméras comprenant des capteurs d'images électroniquesLeur commande pour générer des signaux d'image à partir de plusieurs capteurs d'image de type différent ou fonctionnant dans des modes différents, p. ex. avec un capteur CMOS pour les images en mouvement en combinaison avec un dispositif à couplage de charge [CCD] pour les images fixes
H04N 23/62 - Commande des paramètres via des interfaces utilisateur
H04N 23/63 - Commande des caméras ou des modules de caméras en utilisant des viseurs électroniques
H04N 23/661 - Transmission des signaux de commande de la caméra par le biais de réseaux, p. ex. la commande via Internet
17.
Obstacle detection and localization of aerial vehicles using active or passive sonar
An aerial vehicle configured for operating within indoor or outdoor spaces is equipped with acoustic sensors for detecting reflections of sound, or echoes, from objects. Distances and bearings to such objects may be calculated based on such echoes. The echoes may be reflections of sound actively emitted by the aerial vehicle, such as by a speaker, or sound radiating from operating components aboard the aerial vehicle, such as rotating motors or propellers. The echoes may be captured by multiple sensors such as microphones provided around the aerial vehicle and used to calculate distances or bearings to the objects, such as by trilateration, triangulation, or in any other manner. Such distances or bearings may also be utilized along with distances or bearings determined from cameras, range sensors, or other systems, and used to generate a navigation map of the space, or compared to a navigation map generated for that space.
B64U 40/10 - Dispositions mécaniques embarquées pour régler les surfaces de commande ou les rotorsDispositions mécaniques embarquées pour régler en vol la configuration de base pour régler les surfaces de commande ou les rotors
B64C 27/57 - Mécanismes pour la commande du réglage ou du mouvement de la pale par rapport à la tête du rotor, p. ex. mouvement de traînée caractérisés par les dispositifs de déclenchement de la commande, p. ex. à commande manuelle automatiques ou sensibles à certains facteurs, p. ex. sensibles à la vitesse du rotor, au couple ou à la poussée
B64U 20/83 - Composants électroniques structurellement intégrés à des éléments de l’aéronef, p. ex. circuits imprimés portant des charges
18.
Speaker disambiguation and transcription from multiple audio feeds
Participants may use one or more devices for engaging in a meeting, such as phones, conferencing devices, and/or computers. The devices include microphones that capture speech for determining the presence of distinct participants. Speech signals originating from different participants, or microphones, may be determined and associated with the participants. For example, microphones may be directional and more sensitive to sound coming from one or more specific directions than sound coming from other directions. By associating an individual with a microphone, or set of microphones, overlapping voices may be disambiguated to provide clear voice streams that aid in producing a clear transcript indicating the speech of the participants, respectively. An identity of the participants may be determined using voiceprint and/or voice recognition techniques.
Computer-implemented techniques for verifying translated access controls for application modernization include an application modernization service of a provider network obtaining a source access control. The service translates the source access control to a target access control. The service compiles the source access control and the target access control into respective automated reasoning solver encodings. The service uses the automated reasoning solver encoding to query an automated reasoning solver such as a Satisfiability Modulo Theories (SMT) solver to determine whether the source access control is less or more permissive than the target access control representing a security issue or an availability issue with the target access control, respectively.
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]
20.
Enhanced streaming video advertisement integration
Devices, systems, and methods are provided for enhanced streaming video advertisement integration. A method may include receiving, by a first device, from a second device, a request for advertisement opportunities for a streaming video title; identifying, by the first device, a first advertisement bid for a first advertisement; identifying, by the first device, a second advertisement bid for a second advertisement; sending, by the first device, in response to the request for advertisement opportunities, the first advertisement bid and the second advertisement bid to the second device; sending, by the second device, a request for advertisements to an advertisement server, including the first advertisement bid and the second advertisement bid; receiving, by the second device, a first group of advertisements for a first advertisement opportunity and a second group of advertisements for a second advertisement opportunity of the advertisement opportunities.
Techniques for managed services of cloud systems to perform inventory discovery of computing instances with particular configurations across user accounts in an organization, and across regions in the cloud systems. Cloud systems offer managed services that automate the management of configurations of computing instances on behalf organizations. To perform cross-account discovery of inventory information for these computing instances, the managed services often harness other internal cloud services, such as internal data-integration services and query services. However, some of these internal cloud services are not available in all the geographic regions of the cloud system, and due to these dependencies, the managed services are in turn not available in all regions. Techniques and architectures are described herein for managed services to perform the cross-account inventory discovery such that these managed services can be made available across all regions, and for launches of new regions in the cloud system.
41 - Éducation, divertissements, activités sportives et culturelles
Produits et services
Entertainment in the nature of an ongoing television dramatic series; entertainment services, namely, an ongoing dramatic series provided through television, cable, the Internet and wireless communications networks
Devices and techniques are generally described for audio-based entity resolution. In various examples, first audio data representing speech comprising a mention of a first entity may be received. In some examples, first embedding data representing the first audio data may be received. Second embedding data representing the first entity may be determined. A first modified embedding may be generated using a first attention mechanism to compare the first embedding data to the second embedding data. In some examples, a determination may be made that the first audio data includes a mention of the first entity.
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 15/06 - Création de gabarits de référenceEntraînement des systèmes de reconnaissance de la parole, p. ex. adaptation aux caractéristiques de la voix du locuteur
G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
24.
CONVERSATIONAL LANGUAGE MODEL BASED CONTENT RETRIEVAL
Devices and techniques are generally described for LM-based content retrieval. First query data including a first request related to first content may be received. First action data associated with the first query data may be determined. First prompt data including a representation of the first query data and data representing the first action data may be generated. The first prompt data may instructs a first LM to recognize entities in the first query data relevant to the first action data. The first LM may determine a first recognized entity from the first request. The first recognized entity may be associated with the first content. A request to resolve the first recognized entity may be generated. A first resolved entity for the first recognized entity may be determined. The first LM may generate first instructions to perform the first action data using the first resolved entity.
An emulated hardware security device is configured for a compute instance. A state descriptor of the compute instance comprising software identification metadata prepared using the emulated hardware security device is provided to a resource verifier. The metadata identifies a program to be executed at the compute instance. In response to a response received from the resource verifier, a decision is made as to whether to execute the software program at the compute instance.
G06F 21/53 - 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 exécution dans un environnement restreint, p. ex. "boîte à sable" ou machine virtuelle sécurisée
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
A provider network implements a machine learning deployment service for generating and deploying packages to implement machine learning at connected devices. The service may receive from a client an indication of an inference application, a machine learning framework to be used by the inference application, a machine learning model to be used by the inference application, and an edge device to run the inference application. The service may then generate a package based on the inference application, the machine learning framework, the machine learning model, and a hardware platform of the edge device. To generate the package, the service may optimize the model based on the hardware platform of the edge device and/or the machine learning framework. The service may then deploy the package to the edge device. The edge device then installs the inference application and performs actions based on inference data generated by the machine learning model.
Devices, systems, and methods are provided for a front light for use with reflective displays. A display device (such as an e-reader, for example) may include a light source and a light guide able to receive first light from the light source. The light guide includes a plurality of extraction features (900) that control optimal movement of light emitted by the light source through a display stack of the display device. The extraction feature is provided in a wedge shape at an angle (906) such that when light refracts from the extraction feature, it is directed towards the reflective LCD display at an incidence angle close to the display normal for the reflected light to exhibit similar properties as the reflected light from the EPD panel.
09 - Appareils et instruments scientifiques et électriques
41 - Éducation, divertissements, activités sportives et culturelles
Produits et services
Pre-recorded video recordings featuring dramatic entertainment programs and music; pre-recorded and downloadable audio and visual recordings featuring dramatic entertainment programs and music; motion picture films featuring dramatic entertainment programs and music; pre-recorded audio and visual recordings in optical discs, DVD and CD format featuring dramatic entertainment programs and music. Entertainment in the nature of an ongoing television dramatic series; entertainment services, namely, an ongoing dramatic series provided through television, cable, the Internet and wireless communications networks.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer software using artificial intelligence (AI) for developing and running intelligent agents; Downloadable computer software using artificial intelligence (AI) for user interface (UI) automation, secure code execution and file management; Downloadable computer software using artificial intelligence (AI) for intelligent memory systems to enable agents to retain context across interactions and adjust behavior; Downloadable computer software using artificial intelligence (AI) for a secure, serverless runtime capability to deploy and scale intelligent agents and tools across various frameworks, protocols, and models; Downloadable computer software using artificial intelligence (AI) for building personalized intelligent agent experiences with fully-managed memory infrastructure and the ability to customize memory; Downloadable computer software using artificial intelligence (AI) to manage the digital identities of intelligent agents and control their access to resources; Downloadable computer software using artificial intelligence (AI) for developing and running intelligent agents using virtual machine (VM)-level isolation, identity controls, virtual private cloud (VPC) integration, and flexible network modes; Downloadable computer software using artificial intelligence (AI) for securely writing and executing code to perform complex calculations, validate reasoning, process data, and generate visualizations; Downloadable computer software using artificial intelligence (AI) to enable intelligent agents to navigate websites, complete multi-step forms, and perform complex web-based tasks within a fully managed, secure sandbox environment with low latency; Downloadable computer software using artificial intelligence (AI) to help developers trace, debug, and monitor intelligent agent performance in production environments Providing on-line non-downloadable software using artificial intelligence (AI) for developing and running intelligent agents; Providing on-line non-downloadable software using artificial intelligence (AI) for user interface (UI) automation, secure code execution and file management; Providing on-line non-downloadable software using artificial intelligence (AI) for intelligent memory systems to enable agents to retain context across interactions and adjust behavior; Providing on-line non-downloadable software using artificial intelligence (AI) for a secure, serverless runtime capability to deploy and scale intelligent agents and tools across various frameworks, protocols, and models; Providing on-line non-downloadable software using artificial intelligence (AI) for building personalized intelligent agent experiences with fully-managed memory infrastructure and the ability to customize memory; Providing on-line non-downloadable software using artificial intelligence (AI) to manage the digital identities of intelligent agents and control their access to resources; Providing on-line non-downloadable software using artificial intelligence (AI) for developing and running intelligent agents using virtual machine (VM)-level isolation, identity controls, virtual private cloud (VPC) integration, and flexible network modes; Providing on-line non-downloadable software using artificial intelligence (AI) for securely writing and executing code to perform complex calculations, validate reasoning, process data, and generate visualizations; Providing on-line non-downloadable software using artificial intelligence (AI) to enable intelligent agents to navigate websites, complete multi-step forms, and perform complex web-based tasks within a fully managed, secure sandbox environment with low latency; Providing on-line non-downloadable software using artificial intelligence (AI) to help developers trace, debug, and monitor intelligent agent performance in production environments
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer software using artificial intelligence (AI) for developing and running intelligent agents; Downloadable computer software using artificial intelligence (AI) for user interface (UI) automation, secure code execution and file management; Downloadable computer software using artificial intelligence (AI) for intelligent memory systems to enable agents to retain context across interactions and adjust behavior; Downloadable computer software using artificial intelligence (AI) for a secure, serverless runtime capability to deploy and scale intelligent agents and tools across various frameworks, protocols, and models; Downloadable computer software using artificial intelligence (AI) for building personalized intelligent agent experiences with fully-managed memory infrastructure and the ability to customize memory; Downloadable computer software using artificial intelligence (AI) to manage the digital identities of intelligent agents and control their access to resources; Downloadable computer software using artificial intelligence (AI) for developing and running intelligent agents using virtual machine (VM)-level isolation, identity controls, virtual private cloud (VPC) integration, and flexible network modes; Downloadable computer software using artificial intelligence (AI) for securely writing and executing code to perform complex calculations, validate reasoning, process data, and generate visualizations; Downloadable computer software using artificial intelligence (AI) to enable intelligent agents to navigate websites, complete multi-step forms, and perform complex web-based tasks within a fully managed, secure sandbox environment with low latency; Downloadable computer software using artificial intelligence (AI) to help developers trace, debug, and monitor intelligent agent performance in production environments Providing on-line non-downloadable software using artificial intelligence (AI) for developing and running intelligent agents; Providing on-line non-downloadable software using artificial intelligence (AI) for user interface (UI) automation, secure code execution and file management; Providing on-line non-downloadable software using artificial intelligence (AI) for intelligent memory systems to enable agents to retain context across interactions and adjust behavior; Providing on-line non-downloadable software using artificial intelligence (AI) for a secure, serverless runtime capability to deploy and scale intelligent agents and tools across various frameworks, protocols, and models; Providing on-line non-downloadable software using artificial intelligence (AI) for building personalized intelligent agent experiences with fully-managed memory infrastructure and the ability to customize memory; Providing on-line non-downloadable software using artificial intelligence (AI) to manage the digital identities of intelligent agents and control their access to resources; Providing on-line non-downloadable software using artificial intelligence (AI) for developing and running intelligent agents using virtual machine (VM)-level isolation, identity controls, virtual private cloud (VPC) integration, and flexible network modes; Providing on-line non-downloadable software using artificial intelligence (AI) for securely writing and executing code to perform complex calculations, validate reasoning, process data, and generate visualizations; Providing on-line non-downloadable software using artificial intelligence (AI) to enable intelligent agents to navigate websites, complete multi-step forms, and perform complex web-based tasks within a fully managed, secure sandbox environment with low latency; Providing on-line non-downloadable software using artificial intelligence (AI) to help developers trace, debug, and monitor intelligent agent performance in production environments
Techniques for a multi-check in-line container inspection system are provided herein. In an example, a computer system determines, during movement of a container in a scanning tunnel, first sensor data generated by a first sensor attached to a frame that forms the scanning tunnel. The movement is caused by material handling equipment. The computer system determines, during the movement of the container in the scanning tunnel, second sensor data generated by a second sensor attached to the frame. The computer system performs a first container integrity check based on the first sensor data and a second container integrity check based on the second sensor data. The computer system causes a corrective action to be initiated based on at least one of the first container integrity check or the second container integrity check indicating a container defect.
A telescopic boom system provides an extensible boom that may be used in spacecraft applications including supporting photovoltaic panels, communication antennas, instrumentation, and so forth. A stowed configuration is volumetrically compact, including the boom and actuators such as a motor. During deployment, threaded nuts for each nested section of the boom are self-aligning with respect to a leadscrew driven by the motor. Sections are staged for extension in staged sequence by a flexure arm engaging a ramp feature on a portion of the nested section. Extension failure mitigation is enhanced by allowing partial retraction of some sections during extension. Once fully extended, tension of the boom may be later adjusted, modifying the structural fundamental frequency. A ratchet may be engaged with extension of a final nested section to prevent retraction of the extended boom.
Systems and methods are described for reducing performance variance of code executions on a serverless code execution system. A serverless code execution system can operate to obtain requests to invoke code and handle such requests by generating an execution environment for the code on a host computing device and executing the code within the environment. In some cases, an execution environment is poorly placed, resulting in underperformance of code executions on that environment and variance in overall performance of the code executions. The present disclosure enables a serverless code execution system to identify underperforming execution environments and to replace such environments with new environments, reducing variation in performance across execution of the code. New environments may be placed on host computing devices asynchronously, using a placement algorithm that includes additional processing relative to an algorithm that operates synchronously to code invocation.
G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
A system generates a recommendation that includes at least one action to address at least one root cause anomaly that causes other anomalies occurred within a distributed system. The at least one root cause anomaly is determined by at least using a graph that represents the distributed system and metrics that are associated with the distributed system.
Systems and techniques are described for tracking and providing access reports for individual pieces of data managed by a data storage service. A service may generate and store a record of operations performed on a piece of data, such that may be classified as containing sensitive or important data, in a data store. The record may link representations of users and the operations performed by those users to instances of the piece of data, as it is found in one or more data objects within the data store. The data store may link other instances of the piece of data and other operations performed on the piece of data to the first instance of the piece of data. The service may access the data store to produce a history record of the various instances of the piece of data and operations performed on those instances of the piece of data.
A system and method for project-based uniform data analytics in a provider network. The system and method provide data projects. A data project is a secure container that brings people, data, and tools together to enable easy collaboration and access management for data analytic projects. A data project enables a group of users to collaborate on a particular business use case for producing and consuming data. A data project and its content are subject to their own access controls so that only authorized individuals, groups, and roles can access the projects and data that project has subscribed to, and only use tools permitted by project permissions.
Systems and methods are described for automatic action item detection and generation. In some aspects, textual data, such as may be generated based on an interaction between at least two entities, may be received. At least one issue may be identified in the text using a first machine learning model. At least one action item, corresponding to the issue, may similarly be identified using a second machine learning model, with the action item including an action to be performed to resolve the at least one issue. The action item may be assigned to a queue of a plurality of queues based on attributes of the action item, with the queue corresponding to an action that is specified in the action item. In some aspects, a notification of the action item may also be provided, such as in real-time or near-real-time with the occurrence of the interaction between the two entities.
G06N 5/04 - Modèles d’inférence ou de raisonnement
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
H04M 3/51 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur
H04M 3/523 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur avec répartition ou mise en file d'attente des appels
G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
38.
Artificial intelligence models for verification of packaging removed deliveries
Artificial intelligence (AI) models for verifying packing removed deliveries are described herein. In an example, a computer system receives image data corresponding to a portion of a delivery location. The computer system determines an indication of at least one delivery object in the portion. The computer system inputs the indication into a first AI model trained for detecting entity-associated packaging associated with the at least one delivery object. The computer system receives, from the first AI model, an output of whether the at least one delivery object includes the entity-associated packaging. The computer system causes a first presentation about the output to be provided at a device.
Technologies directed to a radio frequency (RF) boundary choke between modules in phased array antennas. An antenna module may include a circuit board having one or more conducting layers and one or more electrically insulating layers. The antenna module may include an antenna disposed on a first surface of the circuit board. The antenna module may further include radio frequency front end (RFFE) circuitry disposed on a second surface of the circuit board. The antenna module further includes a first set of vias extending between the antenna and the RFFE circuitry and a second set of vias disposed within the circuit board. Each of the second set of vias is positioned along a first axis parallel to and a first distance from a first edge of the antenna module.
Techniques are disclosed for digitally signing uniform resource locators (URLs) to prevent manipulation of search result rankings. A computer system of a service provider can receive a first request to navigate to a network page provided by the service provider and corresponding to items associated with the first request. The computer system can generate the network page by generating a URL for an additional network page linked from the network page. The computer system can use the URL to generate a signed URL that includes a digital signature. The computer system can include the signed URL in the network page and cause the network page to be presented at a user device.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
G06F 16/9538 - Présentation des résultats des requêtes
G06F 16/955 - Recherche dans le Web utilisant des identifiants d’information, p. ex. des localisateurs uniformisés de ressources [uniform resource locators - URL]
41.
Device configuration by natural language processing system
Systems and methods for device control by a natural language processing system are disclosed. A user may desire to utilize a voice-enabled device to associate an accessory device with a hub device without having to utilize third-party software associated with the accessory device and/or the hub device. The user may provide a user utterance to associate the accessory device with the hub device. Audio data corresponding to the user utterance may be analyzed and utilized to generate and send directive data to a third-party remote system to transition the hub device to a join mode. Upon association completion, audio may be output confirming that the association has been established successfully.
Provided are systems and methods for tracking device engagement The system includes a viewing device (e.g., television, etc.) and an eye-tracking device (e.g., a camera, etc.). The eye-tracking device is configured to capture data about the gaze of a viewer to determine if the viewer is watching the television at any given point in time. This information may be used for a variety of purposes, such as tracking user engagement with advertisement content. As another example, the information may be used for device energy saving purposes. For example, a screen of the device can be dimmed or turned off if the gaze of the viewer has not been directed towards the television for a given period of time. A notification may also be presented to the viewer prompting the viewer to indicate if they are still viewing the content being presented on the device.
H04N 21/442 - Surveillance de procédés ou de ressources, p. ex. détection de la défaillance d'un dispositif d'enregistrement, surveillance de la bande passante sur la voie descendante, du nombre de visualisations d'un film, de l'espace de stockage disponible dans le disque dur interne
H04N 21/41 - Structure de clientStructure de périphérique de client
Techniques for performing a machine learning (ML) cinematic (e.g., movie) question answering are described. According to some examples, a computer-implemented method includes receiving a request from a viewer device at a content delivery service to play a video; sending the video from the content delivery service to the viewer device; receiving, by the content delivery service, a question from the viewer device during playing of the video; generating, by a script context retrieval machine learning model of the content delivery service, a proper subset of a script of the video based on an input of the question; generating, by a cinematic question answering machine learning model of the content delivery service, an answer based on an input of the proper subset of the script of the video; and sending, by the content delivery service, the answer to the viewer device.
G06F 16/783 - Recherche de données caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06F 40/40 - Traitement ou traduction du langage naturel
44.
Management of SCTE and contents in ad breaks for compatibility
Methods and apparatus are described for delivering streams of media content in ways that maintain compatibility among different streaming protocols for inserting secondary content into the streams of media content. This is accomplished by encoding media content the same for each streaming protocol but generating different output groups based on each streaming protocol.
A rack may include a frame having vertical uprights and transverse members coupled together so as to form boundaries of an internal volume of the rack. The boundaries may include a first lateral face, a second lateral face, a front face, and a back face. A cartridge can be installed forward or rearward of the front face and laterally inward from the first lateral face or the second lateral face. Cables may be connected between the cartridge and a plurality of appliances. The cables may include a first cable extending between the cartridge and a first appliance supported by the rack. The cables may further include a second cable extending between a second appliance and the cartridge so as to establish a signal path between the first appliance and the second appliance through the first cable, the cartridge, and the second cable.
A host server computer with an uncorrectable memory error can be repaired without a reboot operation. While initially booting a hypervisor, a special software Application Programming Interface (API) can be loaded between a BIOS System Management Mode (SMM) code and the hypervisor. Once the host server computer is booted and a number of virtual machines are executing, a memory error (e.g., uncorrectable error correction code (UECC)) can occur. In response, the hypervisor calls into the special software API identifying the defective memory rows that the BIOS needs to repair. The BIOS starts a soft Post Package Repair (PPR) process on those rows and gives back control to the hypervisor. When the repair is completed, the hypervisor loads a scrubbing virtual machine and validates that the memory is corrected. After the repair is validated, the hypervisor allows the available partition to take a new customer instance.
Techniques for a service provider network to communicatively couple services and/or applications in a serverless computing environment. A pipe component can configure a pipe to integrate two services by transmitting data between services and/or applications using the pipe. The pipe may also be configured to transform how a service processes an event, control timing of event transmissions using the pipe, define an event structure for an event, and/or batch events. Pipes enable an application or service to exchange data with a variety of services provided by the service provider network while controlling what type of data is generated, stored, or transmitted.
Edge functions at an edge location of a content delivery network (CDN) may use APIs of a datastore engine in order to read/write or create/delete local tables at the edge location. Data may be accumulated in the local tables and the new data may be used to enhance decision at the edge. Some of the local tables may be initially populated from a back-end database. This allows the functions to modify the data from the back-end database, without affecting the actual source data at the back-end database (modifications to local tables remain local to the edge location).
A certificate renewal service may receive an indication to request renewal of a certificate in a test mode that allows testing of certificate characteristic property change effects. The certificate renewal service may select, based on the renewal of the certificate being requested in the test mode, a renewal time for renewing of the certificate. The certificate renewal service may change, based on the renewal of the certificate being requested in the test mode, one or more properties of one or more certificate characteristics of the certificate in a certificate renewal request. The certificate renewal service may request renewal of the certificate based on the renewal time with one or more changes to the one or more properties of the one or more certificate characteristics.
G06F 21/33 - Authentification de l’utilisateur par certificats
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
52.
Extending cover properties in formal verification to generate failure traces that reach end-of-test
Cover properties are extended in formal verification to reach an effective end-of-test stage for a design under test. A formal verification task for a design under test may be received at a verification system. A cover property asserted in the formal verification task may be identified. An additional condition may be implemented for the identified cover property to extend the identified cover property to cause performance of the formal verification task to generate a trace to reach an effective end-of-test stage for the design under test in the event of a failure of the cover property.
G06F 30/398 - Vérification ou optimisation de la conception, p. ex. par vérification des règles de conception [DRC], vérification de correspondance entre géométrie et schéma [LVS] ou par les méthodes à éléments finis [MEF]
Systems and methods are provided for classifying images associated with an item, and generating an image set for that item which includes image classifications determined to be helpful for the item type of the item. To classify images, an image classification model is generated and trained using two phases. The first phase uses intermediate model with text and visual processing to teach the model to recognize patterns created by text without requiring OCR at inference. The second phase uses visual processing to refine the model for use at inference. To generate an image set, image classifications helpful to an item type are identified, items are associated with item types, images are obtained for an item, the images are classified using the image classification model, missing image classifications set out in the preferred image set are identified, and a request or requests is generated for the missing image classifications.
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/778 - Apprentissage de profils actif, p. ex. apprentissage en ligne des caractéristiques d’images ou de vidéos
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos
G06V 30/19 - Reconnaissance utilisant des moyens électroniques
Systems and methods for device state reversion are disclosed. For example, a requested and/or scheduled device state change may occur, and prior to the device state change, devices may be queried for their device states. This prior device state data may be saved. A user may provide an undo request and the prior device state data may be utilized along with current device state data to select a device to revert device state on, as well as the device state to revert to. In more complex situations and/or when prior state data is unavailable, machine learning techniques may be utilized to select the target device and device state.
Devices and techniques are generally described for wake word suppression using variable step size of an acoustic echo cancellation (AEC) unit. A reference signal representing an audio stream may be sent to an acoustic echo cancellation (AEC) unit. A microphone may receive an input audio signal and send the input audio signal to the AEC unit. The AEC unit may determine a first set of variable step size (Vss) values over the first time period. Vss values may define a rate at which the AEC unit determines a transfer function between the reference signal and the first input audio signal. A wake-word may be detected during the first time period. A determination may be made that the wake-word is part of the audio output by the loudspeaker based at least in part on the first set of Vss values.
Techniques for more precise access control policy findings, use a conditional injection of policy constraints into a findings analysis. The injection of a policy constraint of a policy being analyzed into the findings analysis is conditioned on the policy itself. In particular, the injection is conditioned on whether the policy constraint is trusted in the context of the policy (e.g., unlikely to be spoofed or manipulated in the policy context). As a result, where a policy constraint can be trusted in the context of a given policy, a more precise (e.g., more specific) findings analysis of the policy based on the policy constraint can be conducted than if the policy constraint were not included in the policy finding analysis (e.g., because the policy constraint is not trusted in other policy contexts).
A network service incorporating service groups to increase fault tolerance and data isolation for integrated network services and client data is provided. The network service provider can process network requests utilizing individual service groups that correspond to a set of integrated services and client data (e.g., cells). The service groups can be associated according to customer identifier. Computing resources within a service group are isolated from computing resources utilized in other service groups and resources that host/provide the service or the integrated data can be independently scaled by the service provider.
H04L 67/51 - Découverte ou gestion de ceux-ci, p. ex. protocole de localisation de service [SLP] ou services du Web
H04L 12/28 - Réseaux de données à commutation caractérisés par la configuration des liaisons, p. ex. réseaux locaux [LAN Local Area Networks] ou réseaux étendus [WAN Wide Area Networks]
H04L 67/1095 - Réplication ou mise en miroir des données, p. ex. l’ordonnancement ou le transport pour la synchronisation des données entre les nœuds du réseau
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer software using artificial intelligence (AI) and large language models (LLMs) for integrated development software; downloadable computer software using artificial intelligence (AI) and large language models (LLMs) for use in an integrated development environment (IDE); downloadable computer software using artificial intelligence (AI) and large language models (LLMs) for software development tools for use in connection with automating test-driven development (TDD), code reviews, and documentation generation; downloadable computer software using artificial intelligence (AI) and large language models (LLMs) for software development productivity tools Providing on-line non-downloadable software using artificial intelligence (AI) and large language models (LLMs) for integrated development software; providing on-line non-downloadable software using artificial intelligence (AI) and large language models (LLMs) for use in an integrated development environment (IDE); providing on-line non-downloadable software using artificial intelligence (AI) and large language models (LLMs) for software development tools for use in connection with automating test-driven development (TDD), code reviews, and documentation generation; providing on-line non-downloadable software using artificial intelligence (AI) and large language models (LLMs) for software development productivity tools
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable computer software using artificial intelligence (AI) and large language models (LLMs) for integrated development software; downloadable computer software using artificial intelligence (AI) and large language models (LLMs) for use in an integrated development environment (IDE); downloadable computer software using artificial intelligence (AI) and large language models (LLMs) for software development tools for use in connection with automating test-driven development (TDD), code reviews, and documentation generation; downloadable computer software using artificial intelligence (AI) and large language models (LLMs) for software development productivity tools Providing on-line non-downloadable software using artificial intelligence (AI) and large language models (LLMs) for integrated development software; providing on-line non-downloadable software using artificial intelligence (AI) and large language models (LLMs) for use in an integrated development environment (IDE); providing on-line non-downloadable software using artificial intelligence (AI) and large language models (LLMs) for software development tools for use in connection with automating test-driven development (TDD), code reviews, and documentation generation; providing on-line non-downloadable software using artificial intelligence (AI) and large language models (LLMs) for software development productivity tools
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Business data analysis; business management services, namely, supply chain logistics, reverse logistics, and management of shipments and returned shipments; logistics management in the field of shipping, returning, and exchanging consumer and wholesale products; freight management services in the nature of shipment processing, facilitating the exchange of shipping documents and invoices, and tracking documents, packages and freight over computer networks, intranets and the internet for business purposes; computerized tracking of packages in transit; transportation logistics services, namely, planning and scheduling shipments for users of transportation services; monitoring package shipments and deliveries for business purposes; computerized tracking and tracing of packages in transit to ensure on-time delivery for business purposes; facilitating shipping disputes between senders and carriers. Providing temporary use of non-downloadable computer software for shipment processing; software as a service (SaaS) services featuring online non-downloadable computer software for shipment processing; providing temporary use of non-downloadable computer software for analyzing and reporting data relating to shipments; software as a service (SaaS) services featuring online non-downloadable computer software for analyzing and reporting data relating to shipments; providing temporary use of non-downloadable computer software for estimating and facilitating payment of shipping costs, coordinating shipment booking and pickup between senders and carriers, tracking shipments, facilitating communications and dispute resolution between senders and carriers, resolving shipping incidents, customs documentation management, and facilitating shipment payments and returns between senders and carriers; software as a service (SaaS) services featuring online non-downloadable computer software for estimating and facilitating payment of shipping costs, coordinating shipment booking and pickup between senders and carriers, tracking shipments, facilitating communications and dispute resolution between senders and carriers, resolving shipping incidents customs documentation management, and facilitating shipment payments and returns between senders and carriers.
62.
AUTOMATED VERIFICATION OF DOCUMENTS RELATED TO ACCOUNTS WITHIN A SERVICE PROVIDER NETWORK
This disclosure describes a verification service within a service provider network for automatically verifying and validating documents. A user may upload a document image to the verification service. A pre-processing service may pre-process the document image. The pre-processed document image may then be forwarded to a first machine learning ML model for similarity evaluation. Once the first ML model has completed its evaluation of the document image, the first ML model may forward the document image to a second ML model for symbol recognition, which may then forward the business license to an optical recognition (OCR) service for OCR validation. If the document image is validated, e.g., is an image of a purported document type, as will be discussed further herein, the publishing service may pre-populate, e.g., publish, information from the document image to an account template.
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
G06T 5/73 - Élimination des flousAccentuation de la netteté
G06V 10/40 - Extraction de caractéristiques d’images ou de vidéos
G06V 30/19 - Reconnaissance utilisant des moyens électroniques
Techniques for filtering the output of supplemental content are described. When a supplemental output system (e.g., a supplemental content system or notification system) receives supplemental content for output, the supplemental output system sends a user identifier (of the recipient user) and the supplemental content to separately implemented filtering component. The filtering component uses a machine learning (ML) model to determine a topic of the supplemental content. The filtering component determines whether the supplemental content should not be output based on the ML model-determined topic, one or more guardrail policies of the supplemental output system, and user frustration data regarding previously output supplemental content. Use of the ML model to determine the topic prevents a content publisher from surreptitiously associating supplemental content with a specific topic in an effort to bypass topic-based output guardrails.
Techniques are described for managing execution of programs. In some situations, program execution is managed for multiple users using excess program execution capacity of one or more computing systems. In some such situations, excess or otherwise unused program execution capacity may be made available to execute programs on a temporary basis, such that the programs executing using the excess program execution capacity may be terminated at any time if other preferred use for the excess program execution capacity arises. The excess program execution capacity may in some situations be provided in conjunction with other dedicated program execution capacity that is allocated to particular users, such as to use unused dedicated capacity of some users as excess capacity for other users. In some situations, the techniques are used in conjunction with a fee-based program execution service that executes multiple programs on behalf of multiple users of the service.
G06F 15/173 - Communication entre processeurs utilisant un réseau d'interconnexion, p. ex. matriciel, de réarrangement, pyramidal, en étoile ou ramifié
G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
65.
VIRTUAL VEHICLE DOMAIN CONTROL UNIT (DCU) SERVICE AND ORCHESTRATION ENVIRONMENTS
A system comprising one or more computers implements a virtual domain control unit/virtual electronic control unit service configured to deploy vehicle code packages to one or more of a plurality of supported virtual domain control unit/electronic control unit orchestration environments, which include both a local orchestration environment and one or more remote orchestration environments. In such orchestration environments, virtual domain control units and/or virtual electronic control units are implemented that execute code included in the vehicle code packages. In some embodiments, such virtual domain control units or virtual electronic control units allow computing capacity and/or data storage capacity of a vehicle to be augmented via remotely implemented virtual domain control units and/or remotely implemented virtual electronic control units.
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
H04L 41/5054 - Déploiement automatique des services déclenchés par le gestionnaire de service, p. ex. la mise en œuvre du service par configuration automatique des composants réseau
H04L 67/00 - Dispositions ou protocoles de réseau pour la prise en charge de services ou d'applications réseau
Devices, systems, and methods are provided for a front light for use with reflective displays. A display device (such as an e-reader, for example) may include a light source and a light guide able to receive first light from the light source. The light guide includes a plurality of extraction features that control optimal movement of light emitted by the light source through a display stack of the display device. The extraction feature is provided in a wedge shape at an angle such that when light refracts from the extraction feature, it is directed towards the reflective LCD display at an incidence angle close to the display normal for the reflected light to exhibit similar properties as the reflected light from the EPD panel.
F21V 8/00 - Utilisation de guides de lumière, p. ex. dispositifs à fibres optiques, dans les dispositifs ou systèmes d'éclairage
G02F 1/167 - Dispositifs ou dispositions pour la commande de l'intensité, de la couleur, de la phase, de la polarisation ou de la direction de la lumière arrivant d'une source lumineuse indépendante, p. ex. commutation, ouverture de porte ou modulationOptique non linéaire pour la commande de l'intensité, de la phase, de la polarisation ou de la couleur basés sur le mouvement de translation des particules dans un fluide sous l’influence de l’application d’un champ caractérisés par l’effet électro-optique ou magnéto-optique par électrophorèse
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Downloadable software development kits (SDK); downloadable computer software development tools; downloadable application programming interface (API) software; downloadable software for processing and generating text, voice, images, videos and computer code in response to natural language prompts, visual prompts, text, speech, images, and/or video; downloadable software for natural language recognition, processing, analysis, understanding, and generation; downloadable software for speech recognition, translation, transcription, and translating speech and text from one language to another; downloadable computer chatbot software for simulating conversations; downloadable software for using and creating generative AI models; downloadable software for use in machine learning and collecting, analyzing and organizing data in the field of deep learning; downloadable software for using artificial intelligence (AI) for use in software development, machine learning, and speech recognition; downloadable software for generating speech, voice, audio, text, conversations, computer code, graphics, images, and videos; downloadable software for creating digital content; downloadable software for recognizing, summarizing, searching, formatting, editing, extracting, and generating text, speech, images, videos, and/or computer code; downloadable software for the use of foundational AI models, namely, artificial intelligence models trained on a large quantity of data; downloadable software for developing and publishing computer programs and scripts; downloadable software for accessing, browsing, searching, processing, analyzing, monitoring, understanding, and taking actions in response to websites, user interfaces, and data; downloadable software for building, developing, testing, integrating, managing, updating, training, evaluating, monitoring, and publishing machine learning and artificial intelligence agents, algorithms, and/or programs; downloadable software for transmitting, receiving, accessing, viewing, uploading, downloading, sharing, integrating, encoding, decoding, displaying, formatting, organizing, storing, caching, transferring and streaming of data; downloadable software for recognizing, identifying, processing, analyzing, and understanding speech, voice, audio, text, conversations, computer code, graphics, images, and videos. (1) Providing on-line non-downloadable software for recognizing, identifying, processing, analyzing, and understanding speech, voice, audio, text, conversations, computer code, graphics, images, and videos; application service provider featuring application programming interface (API) software; providing on-line non-downloadable software for processing and generating text, voice, images, videos and computer code in response to natural language prompts, visual prompts, text, speech, images, and/or video; providing on-line non-downloadable software for natural language recognition, processing, analysis, understanding, and generation; providing on-line non-downloadable software for speech recognition, translation, transcription, and translating speech and text from one language to another; providing temporary use of online non-downloadable computer chatbot software for simulating conversations; providing on-line non-downloadable software for using and creating generative AI models; providing on-line non-downloadable software for use in machine learning and collecting, analyzing and organizing data in the field of deep learning; providing on-line non-downloadable software for using artificial intelligence (AI) for use in software development, machine learning, and speech recognition; providing on-line non-downloadable software for generating speech, voice, audio, text, conversations, computer code, graphics, images, and videos; providing on-line non-downloadable software for creating digital content; providing on-line non-downloadable software for recognizing, summarizing, searching, formatting, editing, extracting, and generating text, speech, images, videos, and/or computer code; providing on-line non-downloadable software for the use of foundational AI models, namely, artificial intelligence models trained on a large quantity of data; providing temporary use of online non-downloadable software development kits (SDKs); providing temporary use of on-line non-downloadable software development tools; providing on-line non-downloadable software for developing and publishing computer programs and scripts; providing on-line non-downloadable software for accessing, browsing, searching, processing, analyzing, monitoring, understanding, and taking actions in response to websites, user interfaces, and data; providing on-line non-downloadable software for building, developing, testing, integrating, managing, updating, training, evaluating, monitoring, and publishing machine learning and artificial intelligence agents, algorithms, and/or programs; providing on-line non-downloadable software for transmitting, receiving, accessing, viewing, uploading, downloading, sharing, integrating, encoding, decoding, displaying, formatting, organizing, storing, caching, transferring and streaming of data.
41 - Éducation, divertissements, activités sportives et culturelles
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
educational services, namely, conducting seminars, classes, workshops and conferences in the fields of technology, cloud computing, web services, software, artificial intelligence, software development, game development, databases, data processing and analytics, data storage, data warehousing, data archiving, data and information security, networking, mobile computing, and the internet of things (IoT); training in the use and operation of software development tools, languages, processes and methodologies; arranging of hackathons, challenges, contests, and educational meetups computer services, namely, creating an on-line community for registered users to get technical answers and guidance and to contribute to a technology related knowledge base so users can benefit from each other; computer services, namely, creating an on-line community for registered users to participate in discussions, get feedback from their peers, form virtual communities, and engage in social networking services in the field of technology, software development, artificial intelligence, and cloud computing; providing information in the field of web services, namely, web hosting, software, web application design, telecommunications and cloud computing services; hosting an online community website featuring shared communications between community members interested in technology, cloud computing, web services, software, artificial intelligence, software development, game development, databases, data processing and analytics, data storage, data warehousing, data archiving, data and information security, networking, mobile computing, and the internet of things (IoT); Providing online non-downloadable software for experimenting with software models and configurations; Providing online non-downloadable software for creating and generating software code using artificial intelligence; Providing temporary use of non-downloadable software using machine learning and artificial intelligence to process, understand, analyze, and generate text, images, speech, sound, and video.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable software development kits (SDK); downloadable computer software development tools; downloadable application programming interface (API) software; downloadable software for processing and generating text, voice, images, videos and computer code in response to natural language prompts, visual prompts, text, speech, images, and/or video; downloadable software for natural language recognition, processing, analysis, understanding, and generation; downloadable software for speech recognition, translation, transcription, and translating speech and text from one language to another; downloadable computer chatbot software for simulating conversations; downloadable software for using and creating generative AI models; downloadable software for use in machine learning and collecting, analyzing and organizing data in the field of deep learning; downloadable software for using artificial intelligence (AI) for use in software development, machine learning, and speech recognition; downloadable software for generating speech, voice, audio, text, conversations, computer code, graphics, images, and videos; downloadable software for creating digital content; downloadable software for recognizing, summarizing, searching, formatting, editing, extracting, and generating text, speech, images, videos, and/or computer code; downloadable software for the use of foundational AI models, namely, artificial intelligence models trained on a large quantity of data; downloadable software for developing and publishing computer programs and scripts; downloadable software for accessing, browsing, searching, processing, analyzing, monitoring, understanding, and taking actions in response to websites, user interfaces, and data; downloadable software for building, developing, testing, integrating, managing, updating, training, evaluating, monitoring, and publishing machine learning and artificial intelligence agents, algorithms, and/or programs; downloadable software for transmitting, receiving, accessing, viewing, uploading, downloading, sharing, integrating, encoding, decoding, displaying, formatting, organizing, storing, caching, transferring and streaming of data; downloadable software for recognizing, identifying, processing, analyzing, and understanding speech, voice, audio, text, conversations, computer code, graphics, images, and videos. Providing on-line non-downloadable software for recognizing, identifying, processing, analyzing, and understanding speech, voice, audio, text, conversations, computer code, graphics, images, and videos; application service provider featuring application programming interface (API) software; providing on-line non-downloadable software for processing and generating text, voice, images, videos and computer code in response to natural language prompts, visual prompts, text, speech, images, and/or video; providing on-line non-downloadable software for natural language recognition, processing, analysis, understanding, and generation; providing on-line non-downloadable software for speech recognition, translation, transcription, and translating speech and text from one language to another; providing temporary use of online non-downloadable computer chatbot software for simulating conversations; providing on-line non-downloadable software for using and creating generative AI models; providing on-line non-downloadable software for use in machine learning and collecting, analyzing and organizing data in the field of deep learning; providing on-line non-downloadable software for using artificial intelligence (AI) for use in software development, machine learning, and speech recognition; providing on-line non-downloadable software for generating speech, voice, audio, text, conversations, computer code, graphics, images, and videos; providing on-line non-downloadable software for creating digital content; providing on-line non-downloadable software for recognizing, summarizing, searching, formatting, editing, extracting, and generating text, speech, images, videos, and/or computer code; providing on-line non-downloadable software for the use of foundational AI models, namely, artificial intelligence models trained on a large quantity of data; providing temporary use of online non-downloadable software development kits (SDKs); providing temporary use of on-line non-downloadable software development tools; providing on-line non-downloadable software for developing and publishing computer programs and scripts; providing on-line non-downloadable software for accessing, browsing, searching, processing, analyzing, monitoring, understanding, and taking actions in response to websites, user interfaces, and data; providing on-line non-downloadable software for building, developing, testing, integrating, managing, updating, training, evaluating, monitoring, and publishing machine learning and artificial intelligence agents, algorithms, and/or programs; providing on-line non-downloadable software for transmitting, receiving, accessing, viewing, uploading, downloading, sharing, integrating, encoding, decoding, displaying, formatting, organizing, storing, caching, transferring and streaming of data.
70.
Video game session management on non-fixed computer hosting topologies
A request to create a game fleet may be received by a game session management service. The game session management service may manage a fixed host fleet type and a non-fixed host fleet type, wherein the fixed host fleet type allows game server execution only on a fixed host topology of a computing service provider affiliated with the game session management service, and wherein the non-fixed host fleet type allows game server execution on any host topology. The request may indicate that the first game fleet has the non-fixed host fleet type. A host registration request to register a host to the first game fleet may be received. A process registration request to register a game server process executing on the host may be received. The game session management service may communicate with the game server process executing on the host via a designated communications interface.
A63F 13/77 - Aspects de sécurité ou de gestion du jeu incluant les données relatives aux dispositifs ou aux serveurs de jeu, p. ex. données de configuration, version du logiciel ou quantité de mémoire
A63F 13/335 - Dispositions d’interconnexion entre des serveurs et des dispositifs de jeuDispositions d’interconnexion entre des dispositifs de jeuDispositions d’interconnexion entre des serveurs de jeu utilisant des connexions de réseau étendu [WAN] utilisant l’Internet
A63F 13/352 - Détails des serveurs de jeu comportant des dispositions particulières de serveurs de jeu, p. ex. des serveurs régionaux connectés à un serveur national ou à plusieurs serveurs gérant les partitions de jeu
Techniques and apparatus for adaptively controlling an end-effector of a robotic arm are provided. The end-effector includes at least one articulated finger having multiple facets arranged on a surface of the at least one articulated finger. The robotic arm is moved to engage an item using the at least one articulated finger. At least one of an amount of force or an amount of torque applied to the multiple facets on the surface of the at least one articulated finger is determined while the item is engaged using the at least one articulated finger. At least one of a position and orientation of the at least one articulated finger is adaptively controlled, based on at least one of the determined amount of force or the amount of torque.
Techniques for synchronizing device group data across different device control applications are described. A device group may include two or more smart home devices that may be controlled/supported by different device control applications. A user may set up device groups, including the same devices, in the different device control applications. A system may include a group synchronization service that synchronizes (e.g., merges) the multiple device groups across the different device control applications. The groups may be synchronized based on matching group names, matching devices included in the group, etc. After the device groups are synchronized, modifications made by the user to a device group in one device control application may be propagated to other device control applications.
An autonomous mobile device (AMD) may move around a physical space while performing tasks. The AMD may have sensors with a field of view (FOV) facing forward. As the AMD moves forward, a safe region is determined based on data from those forward-facing sensors. The safe region describes a geographical area that is clear of obstacles during recent travel. If the AMD moves outside of the FOV, such as moving backwards, a check is made as to whether the AMD remains within the safe region. If a portion of the AMD moves beyond the safe region, the AMD stops. This prevents the AMD from colliding with obstacles that are outside of the safe region and also outside of the FOV while moving.
G05D 1/00 - Commande de la position, du cap, de l'altitude ou de l'attitude des véhicules terrestres, aquatiques, aériens ou spatiaux, p. ex. utilisant des pilotes automatiques
B60W 60/00 - Systèmes d’aide à la conduite spécialement adaptés aux véhicules routiers autonomes
G01S 13/89 - Radar ou systèmes analogues, spécialement adaptés pour des applications spécifiques pour la cartographie ou la représentation
G01S 15/89 - Systèmes sonar, spécialement adaptés à des applications spécifiques pour la cartographie ou la représentation
G01S 17/894 - Imagerie 3D avec mesure simultanée du temps de vol sur une matrice 2D de pixels récepteurs, p. ex. caméras à temps de vol ou lidar flash
Data sets may be enhanced to create data stores. Request to create data stores may be received. As part of performing the request to create a data store, items stored in an extensible data format may be identified for machine learning enhancement. Machine learning models may be applied to generate additional data from data in the items. The additional data may be added to extend the items and store the extended items in a new data store.
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
75.
Volume durable log sequence number movement in a multi-volume database environment
Techniques for volume durable log sequence number movement in a multi-volume database environment are described. Blocks of database updates are persisted in an atomic and durable manner, where the blocks may include updates to a first volume, a second volume, or both. Backlinks from one block of updates to the previous block of updates may be set, in addition to backlinks between updates to the first volume and backlinks between updates to the second volume. Upon durably persisting a block, the block backlinks can be followed to help verify that all contiguous blocks—of changes to one or both volumes—have all been persisted. Thereafter, a volume durable log sequence number (VDL) pointer value can be updated to point to a known “last” durably persisted log update.
G06F 11/14 - Détection ou correction d'erreur dans les données par redondance dans les opérations, p. ex. en utilisant différentes séquences d'opérations aboutissant au même résultat
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
Techniques for generating a summary of text-based documents are described. A system may be configured to generate a summary based on context data. The system may receive different types of context data corresponding to a user input. The context data may be converted to a linearized representation so that it can be processed by a decoder along with a source document for which the summary is being generated.
Described herein is a computer-implemented method for registering selections of graphical elements on electronic displays using time-of-flight sensors. First sensor data that is output by a first time-of-flight sensor can be received by a computing device. The first time-of-flight sensor can be positioned in a first orientation with respect to an electronic display. A position of an object on a surface of the electronic display can be determined based at least in part on the first sensor data. A user selection of a graphical element displayed on the electronic display can be registered at the determined position.
Techniques for implementing and using a customizable framework for natural language processing explainability are described. A user of a machine learning (ML) service can generate an explainability configuration identifying a user-selected segmented or a user-selected granularity level, from multiple granularity levels supported by the ML service, at which to use for segmenting text during an ML explainability analysis. The ML service can execute a user-configurable explanation pipeline as part of the explainability analysis of at least an input text, including segmenting the input text using the user-selected segmented or a segmentation algorithm corresponding to the user-selected granularity level to yield candidate segments, generating a mask corresponding to the candidate segments, and executing an explanation algorithm based at least on use of the candidate segments and the mask to yield a result.
An adapter layer may be used to customize a machine learning component by transforming data flowing into, out of, and/or within the machine learning component. The adapter layer may include a number of neural network components, or “adapters,” configured to perform a transformation on input data. Neural network components may be configured into adapter groups. A router component can, based on the input data, select one or more neural network components for transforming the input data. The input layer may combine the results of any such transformations to yield adapted data. Different adapter groups can include adapters of different complexity (e.g., involving different amounts of computation and/or latency). Thus, the amount of computation or latency added by an adapter layer can be reduced for simpler transformations of the input data.
G10L 15/02 - Extraction de caractéristiques pour la reconnaissance de la paroleSélection d'unités de reconnaissance
G10L 15/06 - Création de gabarits de référenceEntraînement des systèmes de reconnaissance de la parole, p. ex. adaptation aux caractéristiques de la voix du locuteur
G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
80.
Overhang detection for use in three-dimensional object reconstruction
A method is disclosed to automatically detect overhangs from images with depth taken around an object during a scan of the object. An overhang detector can use an intersection of three filters based on these images and depth data associated with the images. The first filter looks for negative depth gradients along a 2D projection of a gravity vector, which is generally a vertical axis for images taken using a portrait orientation. The second filter selects the depth gradients that are oriented towards the projection of the gravity vector. The third filter is a salient object detection mask computed from the image. An intersection of the three filters can then be used to obtain overhangs. The method can be implemented in real time with a User Interface (UI) directing a user of a location of the overhang so that an image below the overhang can be taken.
Efficient voice synthesis using frame-based processing may be performed. An audio processing system converts an input speech waveform to an acoustic feature representation, which includes a sequence of frames at a lower resolution than the sampling resolution of the input waveform. The system propagates the acoustic feature representation through GRUs and fully-connected layers, while maintaining the lower resolution. At the end, the system performs a flattening operation on the frames of the final acoustic feature representation to generate an output waveform at a target sampling resolution.
G10L 13/08 - Analyse de texte ou génération de paramètres pour la synthèse de la parole à partir de texte, p. ex. conversion graphème-phonème, génération de prosodie ou détermination de l'intonation ou de l'accent tonique
G10L 13/04 - Détails des systèmes de synthèse de la parole, p. ex. structure du synthétiseur ou gestion de la mémoire
G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels
82.
Centralized feedback service for performance of virtual assistant
This disclosure describes a feedback service that collects feedback for skills, or capabilities, of a virtual assistant that interacts with users, and associates the feedback with the appropriate skills. Virtual assistants interact with users via voice-enabled devices that are backed by voice-processing systems that support various skills of the virtual assistants. Due to large numbers of skills, users are unable to determine which skill is invoked during interactions with virtual assistants, and are thus unable to provide feedback for the skill. The techniques described herein include continuing a speech dialogue with a user after completion of an interaction, and requesting feedback regarding the interaction. Additionally, the techniques may include collecting contextual data for the interaction (e.g., dropped packets, latency caused by jitter, etc.). The feedback service can associate the feedback and contextual data with the particular skill used in the interaction to improve the functioning of the virtual assistant.
Artificial intelligence agents generate media content for broadcasting to devices of listeners over one or more networks. An agent is trained based on episodes of previously aired media programs to identify topics of the episodes and media entities played during such episodes. Words or phrases spoken by human creators during such episodes are identified and modeled. The agent selects a topic of an episode of a media program and selects media entities that are consistent with those typically played during the media programs. The agent also identifies sets of words that are consistent with the topic, and transmits audio data including the selected media entities and sets of words spoken in voices having linguistic styles, a prosody or acoustic characteristics of the human creators to devices of listeners of the media program.
Techniques for secure cryptographic secret bootstrapping balance the need to quickly and conveniently restore cryptographic secrets to server computers in the event of an outage with the need for security. Before the outage, a server computer uses a trusted platform module of the server computer to seal an encryption key used to encrypt a secret stored at the server computer. In response to the outage, the server computer restores the secret by using the trusted platform module to unseal the encryption key and then using the unsealed encryption key to decrypt the encrypted secret. The techniques can be used to restore cryptographic secrets rapidly and securely to a cluster of server computers used for cryptographic operations in a provider network without the overhead of safe room procedures.
G06F 21/72 - 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 calcul ou du traitement de l’information dans les circuits de cryptographie
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
G06F 21/81 - Protection de composants spécifiques internes ou périphériques, où la protection d'un composant mène à la protection de tout le calculateur en agissant sur l’alimentation, p. ex. en branchant ou en débranchant l’alimentation, les fonctions de mise en veille ou de reprise
Techniques and apparatus for mechanical assembly of an imaging device are described. An example image sensor assembly disposed within an imaging device includes a circuit board, an image sensor disposed on the circuit board, and an optics holder. The optics holder includes a support location, a thermally conductive material, and a plurality of extended surfaces that extend outward in a direction away from the circuit board. Each of the plurality of extended surfaces includes the thermally conductive material. The image sensor assembly also includes an optical lens disposed in the support location of the optics holder. The circuit board is disposed between the optics holder and a housing of the imaging device.
H04N 23/52 - Éléments optimisant le fonctionnement du capteur d'images, p. ex. pour la protection contre les interférences électromagnétiques [EMI] ou la commande de la température par des éléments de transfert de chaleur ou de refroidissement
G02B 7/02 - Montures, moyens de réglage ou raccords étanches à la lumière pour éléments optiques pour lentilles
H04N 13/239 - Générateurs de signaux d’images utilisant des caméras à images stéréoscopiques utilisant deux capteurs d’images 2D dont la position relative est égale ou en correspondance à l’intervalle oculaire
H04N 23/45 - Caméras ou modules de caméras comprenant des capteurs d'images électroniquesLeur commande pour générer des signaux d'image à partir de plusieurs capteurs d'image de type différent ou fonctionnant dans des modes différents, p. ex. avec un capteur CMOS pour les images en mouvement en combinaison avec un dispositif à couplage de charge [CCD] pour les images fixes
A wearable audio output device (e.g., headphones) having an open design that allows ambient noise to pass to a listener without physically isolating the listener from a surrounding environment. The device may include an open earcup design that may partially or completely surround the listener's ear, and in some examples a portion of the listener's head may be uncovered by the open earcup. To improve comfort, the device includes a floating audio component configured to generate output audio in a direction of the listener's ear without contacting the listener's ear. To reduce audio leakage into the environment, the floating audio component may include two audio transducers and perform beamforming to target the output audio toward the listener's ear and cancel the output audio in other directions. For example, the beamforming may cause constructive interference in one direction and destructive interference in all other directions.
Systems and methods are disclosed for a shuttle sortation system having linear actuators for unloading items from shuttles and deposing the items into totes. For example, an item shuttle may transport items in an item holder which may contain an item and that is removable from a base of the item shuttle. The item holder may be constrained from moving with respect to the base and a depositing area may unlock the item holder from the base, permitting the item holder to separate from the base. An upper linear actuator may engage the item holder and may move the item over a tote receiving the area having a tote. The item holder may not have a bottom portion, such that once the item holder is separated from the based, the item is free to fall through to the tote below.
B65G 1/137 - Dispositifs d'emmagasinage mécaniques avec des aménagements ou des moyens de commande automatique pour choisir les objets qui doivent être enlevés
88.
Techniques for determining distances using passive infrared sensors
This disclosure describes, in part, techniques for using multiple passive infrared sensors to determine a distance to an object. For instance, an electronic device may include a first passive infrared sensor including a first FOV that extends a first distance from the electronic device and a second passive infrared sensor including a second FOV that extends a second distance from the electronic device further than the first distance. The electronic device may generate first sensor data using the first passive infrared sensor and second sensor data using the second passive infrared sensor. The electronic device may then determine a first amplitude associated with the first sensor data and a second amplitude associated with the second sensor data and determine a ratio between the amplitudes. After the ratio is determined, the electronic device may determine a distance associated with the ratio, generate image data based on the distance, and send the image data to computing device(s).
Technologies directed to using at least three antennas and Ultra-Wideband (UWB) protocols are described. One method includes sending a first signal to a second wireless device with data that cause it to send a second signal at a plurality of times. The method receives copies of the second signal at a first time via a first pair of antennas and copies of the second signal at a second time via a second pair of antennas. The method determines first and second angle-of-arrival (AoA) values from the copies of the second signals at the first and second times, respectively. The method determines location information of the second wireless device, the location information including an identifier corresponding to a first field of view (FOV) corresponding to the first pair or a second FOV corresponding to the second pair and at least one of the first AoA value or the second AoA value.
G01S 5/02 - Localisation par coordination de plusieurs déterminations de direction ou de ligne de positionLocalisation par coordination de plusieurs déterminations de distance utilisant les ondes radioélectriques
G01S 3/46 - Systèmes pour déterminer une direction ou une déviation par rapport à une direction prédéterminée en utilisant des antennes espacées et en mesurant la différence de phase ou de temps entre les signaux venant de ces antennes, c.-à-d. systèmes à différence de parcours
H04W 64/00 - Localisation d'utilisateurs ou de terminaux pour la gestion du réseau, p. ex. gestion de la mobilité
90.
Verification and citation for language model outputs
A user provides a question to be answered from detailed, dense or otherwise complex documents to a processing system that converts the question to a structured query language query and generates an embedding from the question, augmented by temporal data, synopses, themes, or other relevant information or data. The embedding is compared to embeddings generated from documents of a knowledge base to identify documents that are relevant to the question, and to rank such documents for their relevance. Highly ranking documents are combined with the query and provided to a language model that returns an answer to the question. A source for the answer is identified in at least one of the documents. The answer and the identified documents are presented to the user.
G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
Techniques for compensating signal data from first order high pass filters for exponential decay to extract and underlying step function associated with the signal data. The techniques include adjusting a time scale of the signal data using a charge amplifier. The techniques also include determining a dynamic neutral point for the signal data using an exponential decay function. The techniques further apply an inverse transfer function to remove decay from the signal data and generate a step function associated with the signal data.
G01G 19/414 - Appareils ou méthodes de pesée adaptés à des fins particulières non prévues dans les groupes avec dispositions pour indiquer, enregistrer ou calculer un prix ou d'autres quantités dépendant du poids utilisant des moyens de calcul électromécaniques ou électroniques utilisant uniquement des moyens de calcul électroniques
G01G 19/52 - Appareils de pesée combinés avec d'autres objets, p. ex. avec de l'ameublement
G01G 23/00 - Dispositifs accessoires pour appareils de pesée
A device comprises a display and a plurality of radars. For example, the radars may be arranged around a perimeter of the device, with their respective fields of view directed toward the display. Output from the radar is processed to generate input frames. The input frames are then processed to determine touch data indicative of a position with respect to the display. The input frames may be processed to determine the touch data using a trained machine learning module or a geometric estimation module. For example, the trained machine learning module may accept input frames comprising one or more of range fast-Fourier transform (FFT) data or doppler FFT data and provide as output the touch data.
A system configured to perform noise dependent volume control in order to increase a volume level of the playback audio in a noisy environment. For example, a device may adaptively increase gain based on an amount of ambient noise present in the environment. The device may determine a gain value based on a noise reference value and an estimated noise floor. The device may determine the estimated noise floor based on continuous noises, ignoring transient sounds and user speech. In addition, the device may include volume control logic to ensure that the volume level only increases when necessary and/or to control a rate at which the volume level increases. As part of selecting the volume level with which to generate the playback audio, the device may use a lookup table to convert between a device volume and a quantized volume used to perform noise dependent volume control.
H03G 3/32 - Commande automatique dans des amplificateurs comportant des dispositifs semi-conducteurs le réglage dépendant du niveau de bruit ambiant ou du niveau sonore ambiant
G10L 25/21 - 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 sur la puissance
G10L 25/69 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour l’évaluation de signaux de voix synthétiques ou décodés
G10L 25/84 - Détection de la présence ou de l’absence de signaux de voix pour différencier la parole du bruit
Techniques for machine learning (ML) model adaptation via segment replacement and student-teacher training are described. A model optimizer determines that a ML model sought to be deployed to an edge device includes a source segment that is not supported by the edge device. The model optimizer identifies a replacement segment that is equivalent to the source segment, constructs an adapted ML model by swapping in the replacement segment for the source segment, and trains the adapted ML model, optionally from the source ML model using a student-teacher training procedure. The trained adapted ML model is then deployed to the edge device, where it can be successfully run with minimal (if any) degradation of performance compared to the original source ML model.
G06N 20/20 - Techniques d’ensemble en apprentissage automatique
G06F 17/11 - Opérations mathématiques complexes pour la résolution d'équations
G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient
G06N 5/046 - Inférence en avantSystèmes de production
H04L 67/60 - Ordonnancement ou organisation du service des demandes d'application, p. ex. demandes de transmission de données d'application en utilisant l'analyse et l'optimisation des ressources réseau requises
95.
Image generation based on a multi-image set and pose data
Techniques for image generation based on a multi-image set and pose data is described herein. In an example, a computer system generates, by at least using a first machine learning (ML) model, a pose feature based on pose data that indicates a target pose. The computer system generates, by at least using a second ML model, a first appearance feature based on a first image and the pose data. The computer system generates, by at least using the second ML model, a second appearance feature based on a second image and the pose data. The computer system generates a combined appearance feature based on the first appearance feature and the second appearance feature. The computer system generates, by at least using a third ML model, a third image showing an appearance of an element in the target pose based on the pose feature and the combined appearance feature.
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
G06T 3/18 - Déformation d’images, p. ex. réarrangement de pixels individuellement
G06T 5/77 - RetoucheRestaurationSuppression des rayures
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
Techniques for generating a natural language response to a user input of a dialog are described. A system receives a natural language user input of a dialog and determines dialog history data including a previous natural language user input of the dialog. Based on the first natural language user input and the dialog history data, the system generates at least a first question associated with the natural language user input. Based on the first natural language user input and the dialog history data, the system generates at least a first answer to the at least first question. Using the dialog history data, the first natural language question, and the first natural language answer, the system generates an output responsive to the natural language user input.
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
G10L 15/06 - Création de gabarits de référenceEntraînement des systèmes de reconnaissance de la parole, p. ex. adaptation aux caractéristiques de la voix du locuteur
G10L 15/18 - Classement ou recherche de la parole utilisant une modélisation du langage naturel
G10L 15/30 - Reconnaissance distribuée, p. ex. dans les systèmes client-serveur, pour les applications en téléphonie mobile ou réseaux
Embodiments of methods, systems, and storage medium associated with providing user records associated with characteristics that may be used to identify the user are disclosed herein. In one instance, the method may include obtaining features of an individual, determining identifying characteristics associated with the obtained features, and initiating a search for a record associated with the individual based in part on the identifying characteristics associated with the obtained features, and, based on a result of the search, a verification of the record associated with the individual. The method may further include receiving at least a portion of the record associated with the individual, based at least in part on a result of the verification. The verification may be based in part on a ranking associated with the record. Other embodiments may be described and/or claimed.
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
G06V 40/16 - Visages humains, p. ex. parties du visage, croquis ou expressions
G10L 17/00 - Techniques d'identification ou de vérification du locuteur
G10L 17/06 - Techniques de prise de décisionStratégies d’alignement de motifs
G10L 25/54 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation pour la recherche
H04L 61/4594 - Carnets d'adresses, c.-à-d. répertoires contenant les coordonnées des correspondants
H04M 1/27453 - Répertoires permettant le stockage de données d'abonné supplémentaire, p. ex. des métadonnées
H04M 1/72457 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles avec des moyens permettant d’adapter la fonctionnalité du dispositif dans des circonstances spécifiques en s’appuyant sur la localisation géographique
Described herein is a system for encoding audio watermarks with time extensions to enable enhanced watermark detection. An extended audio watermark may include an existing audio watermark and a watermark extension, enabling backwards compatibility with existing watermark detection while also enabling enhanced watermark detection with increased accuracy. For example, embedding the extended acoustic watermark enables (i) limited devices to perform watermark detection to detect the existing audio watermark, and (ii) improved devices to perform enhanced watermark detection to detect the extended audio watermark. As the extended audio watermark has a longer time duration than the existing audio watermark, an accuracy of performing enhanced watermark detection is increased relative to detecting the existing audio watermark alone.
G10L 25/51 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
G10L 19/018 - Mise en place d’un filigrane audio, c.-à-d. insertion de données inaudibles dans le signal audio
G10L 25/06 - 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 des coefficients de corrélation
99.
Techniques for updating resource predictions and resource allocations
Techniques for allocating resources and generating resource allocation instructions are described herein. A model can generate a historical error for resources of a facility based on a first set of predicted resource imbalances and historical data for resources imbalances of the facility for a first set of previous time periods. The model can receive a second predicted resource imbalance for the resources of the facility associated with a first future time period. The model can receive real-time signals for states of the resources of the facility associated with a present time period. The model can generate an adjusted resource prediction for the resources of the facility based at least in part on the historical error, the second predicted resource imbalance, and the real-time resource signals. The adjusted resource prediction can be associated with the first future time period.
G06F 15/16 - Associations de plusieurs calculateurs numériques comportant chacun au moins une unité arithmétique, une unité programme et un registre, p. ex. pour le traitement simultané de plusieurs programmes
H04L 29/06 - Commande de la communication; Traitement de la communication caractérisés par un protocole
H04L 47/70 - Contrôle d'admissionAllocation des ressources
H04L 47/762 - Contrôle d'admissionAllocation des ressources en utilisant l'allocation dynamique des ressources, p. ex. renégociation en cours d'appel sur requête de l'utilisateur ou sur requête du réseau en réponse à des changements dans les conditions du réseau déclenchée par le réseau
H04L 47/80 - Actions liées au type d'utilisateur ou à la nature du flux
100.
Ephemeral authorization tokens from partner tokens
In a system that controls access to resources via tokens, a system includes an application that generates ephemeral authorization tokens from partner tokens, to increase confidentiality and security, in embodiments. Responsive to a request, received by an application provider, for a protected resource, a federated ID/authorization provider is caused to receive a request for access/ID tokens that the ID/authorization provider provides (in any of various ways) to the application. The application validates and stores the tokens, nests the access/ID tokens within an ephemeral token document having a unique ID and shortened expiration, encrypts the nested ephemeral token using at least resource-specific encryption and causes the encrypted nested token to be sent to the protected resource provider that decrypts and validates the ephemeral token, causes the Access/ID token(s) within the ephemeral token to be validated, and provides the protected resource for valid tokens.
H04L 9/30 - Clé publique, c.-à-d. l'algorithme de chiffrement étant impossible à inverser par ordinateur et les clés de chiffrement des utilisateurs n'exigeant pas le secret
