Systems and methods are provided to perform multiply-accumulate operations of reduced precision numbers in a systolic array. Each row of the systolic array can receive reduced inputs from a respective reducer. The reducer can receive a particular input and generate multiple reduced inputs from the input. The reduced inputs can include reduced input data elements and/or a reduced weights. The systolic array may lack support for inputs with a first bit-length and the reducers may reduce the bit-length of a given input from the first bit-length to a second shorter bit-length and provide multiple reduced inputs with second shorter bit-length to the array. The systolic array may perform multiply-accumulate operations on each unique combination of the multiple reduced input data elements and the reduced weights to generate multiple partial outputs. The systolic array may sum the partial outputs to generate the output.
G06F 7/544 - Méthodes ou dispositions pour effectuer des calculs en utilisant exclusivement une représentation numérique codée, p. ex. en utilisant une représentation binaire, ternaire, décimale utilisant des dispositifs n'établissant pas de contact, p. ex. tube, dispositif à l'état solideMéthodes ou dispositions pour effectuer des calculs en utilisant exclusivement une représentation numérique codée, p. ex. en utilisant une représentation binaire, ternaire, décimale utilisant des dispositifs non spécifiés pour l'évaluation de fonctions par calcul
G06F 7/499 - Maniement de valeur ou d'exception, p. ex. arrondi ou dépassement
G06F 15/80 - Architectures de calculateurs universels à programmes enregistrés comprenant un ensemble d'unités de traitement à commande commune, p. ex. plusieurs processeurs de données à instruction unique
2.
TECHNIQUES FOR ACCESSING LOCAL NETWORKS VIA A VIRTUALIZED GATEWAY
Disclosed are various embodiments for receiving, via a network, a request from a client to establish a network tunnel over the network. Various embodiments can create a virtual network comprising a virtual network gateway in response to receiving a service call. Various embodiments can further allocate an available computing resource to the virtual network gateway to augment a first computing resource. Allocating the available computing resource can be performed in response to a usage of the first computing resource assigned to the virtual network gateway.
H04L 41/04 - Architectures ou dispositions de gestion de réseau
H04L 41/0896 - Gestion de la bande passante ou de la capacité des réseaux, c.-à-d. augmentation ou diminution automatique des capacités
H04L 41/0897 - Capacité à monter en charge au moyen de ressources horizontales ou verticales, ou au moyen d’entités de migration, p. ex. au moyen de ressources ou d’entités virtuelles
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 67/02 - Protocoles basés sur la technologie du Web, p. ex. protocole de transfert hypertexte [HTTP]
H04L 67/08 - Protocoles spécialement adaptés à l'émulation du terminal, p. ex. Telnet
H04L 67/10 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau
H04L 67/1008 - Sélection du serveur pour la répartition de charge basée sur les paramètres des serveurs, p. ex. la mémoire disponible ou la charge de travail
H04L 67/141 - Configuration des sessions d'application
H04L 69/24 - Négociation des capacités de communication
H04L 69/329 - Protocoles de communication intra-couche entre entités paires ou définitions d'unité de données de protocole [PDU] dans la couche application [couche OSI 7]
A quantum computing service provides a quantum algorithm development kit that enables a customer to define a quantum task, a quantum algorithm, or a quantum circuit using an intermediate representation. The quantum computing service is then configured to automatically translate the quantum task, quantum algorithm, or quantum circuit into a specific representation specific to a particular quantum computing technology selected by the customer to be used to execute the customer's quantum task, quantum algorithm, or quantum circuit.
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
G06N 10/20 - Modèles d’informatique quantique, p. ex. circuits quantiques ou ordinateurs quantiques universels
G06N 10/60 - Algorithmes quantiques, p. ex. fondés sur l'optimisation quantique ou les transformées quantiques de Fourier ou de Hadamard
G06N 10/80 - Programmation quantique, p. ex. interfaces, langages ou boîtes à outils de développement logiciel pour la création ou la manipulation de programmes capables de fonctionner sur des ordinateurs quantiquesPlate-formes pour la simulation ou l’accès aux ordinateurs quantiques, p. ex. informatique quantique en nuage
A system can operate a speech-controlled device to perform user engagement detection (UED) processing to detect when speech represented in audio data is directed to the device. For example, the device may extract audio features from the audio data and process these audio features using a classifier to estimate an orientation of the user's head, which may be used as a proxy for user engagement. Thus, if the head orientation is within an engagement zone (which varies based on distance to the user), the device may determine that the user is engaged with the device and perform language processing on input speech. In contrast, if the head orientation is outside of the engagement zone, the device may determine that the user is not engaged and ignore the input speech. To enable additional functionality, the classifier may optionally output a coarse estimate of the head orientation along with the UED determination.
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
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
Training a machine learning model for application to an audio enhancement system for a target speaker may be performed. When at least one clean audio speech sample of a target speaker is captured, the machine learning model may then be trained using noisy audio speech samples in which the voice of the target speaker is present in addition to the voices of other speakers and/or background noise. Once the machine learning model is sufficiently trained, it may be deployed for use in audio enhancement and voice processing for an audio transmission service.
G10L 17/04 - Entraînement, enrôlement ou construction de modèle
G10L 17/02 - Opérations de prétraitement, p. ex. sélection de segmentReprésentation ou modélisation de motifs, p. ex. fondée sur l’analyse linéaire discriminante [LDA] ou les composantes principalesSélection ou extraction des caractéristiques
G10L 17/06 - Techniques de prise de décisionStratégies d’alignement de motifs
6.
Artificial intelligence model supporting tasks of different program codes
Techniques are described herein for using an artificial intelligence (AI) model to support tasks for different program codes. For example, a computer system can determine a request of an application that is executing on a device. The application can include program codes that each provide content functions and present content at a user interface for the application. The request may be associated with contextual data and user data and may indicate content entities for a first program code. The computer system can input the content entities, the contextual data, and the user data into an AI model that is trained on historical data associated with the content functions. The AI model can output a relevance score or an embedding vector for the content entities that can be used in performing the first content function and presenting the content entities on the user interface.
Techniques for displaying a graphical user interface of a software development system are described. An initial description of an initial structure of a software system is obtained, the initial description including an identification of interconnections amongst provider network resources that form the initial structure. A modified description of a modified structure of the software system is obtained, the modified structure based on at least a change to the initial structure of the software system described in the initial description. A difference between the initial description and the modified description is determined. An electronic device is caused to display a graphical user interface including a visual depiction of the initial structure of the software system and an indication of the difference, the visual depiction based at least in part on the initial description.
An acoustic echo cancellation (AEC) system that dynamically controls an adaptation speed of an adaptive filter, enabling the adaptive filter to converge quickly while protecting near-end speech. For example, a device may control the adaptation speed of the adaptive filter to adapt quickly when near-end speech is not present and adapt slowly when near-end speech is present. The device controls the adaptation speed by dynamically determining step-size values and/or performing error normalization to limit the rate of adaptation. In some examples, the device determines the variable step-size parameter based on a relative strength of a microphone signal and a reference signal over time. For example, the device can compare current energy levels of the microphone signal and the reference signal to a range of energy levels to determine a microphone step-size value, a reference step-size value, and an AEC step-size value.
G10K 11/178 - Procédés ou dispositifs de protection contre le bruit ou les autres ondes acoustiques ou pour amortir ceux-ci, en général utilisant des effets d'interférenceMasquage du son par régénération électro-acoustique en opposition de phase des ondes acoustiques originales
H04M 9/08 - Systèmes téléphoniques à haut-parleur à double sens comportant des moyens pour conditionner le signal, p. ex. pour supprimer les échos dans l'une ou les deux directions du trafic
This disclosure describes, in part, systems and techniques for determining compensation models to adjust load cell data and ensure accuracy of the load cells on a mobile apparatus through changing environments and conditions. This disclosure relates, specifically, to generating a compensation model by gathering sensor data for known weights of items over a range of weights, locations, temperatures, and humidity values and building a compensation model to infer compensation error to apply to the estimated weight data from the load cells. The compensation model can be used by a fleet of carts to infer weights of items in a manner accurate enough for sale-by-weight of items.
G01G 19/415 - 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 combinés à des moyens d'enregistrement
G06Q 30/0283 - Estimation ou détermination de prix
Context-based control inputs for a device are described herein. In an example, a device presents a first user interface (UI). The device determines, while the first UI is presented, a first user interaction corresponding to a first instance of a user input with the device. The device determines a first context associated with the first user interaction and a first control input based on the first user interaction and the first context. The device causes execution of a first action based on the first control input. The device determines a second user interaction with the device that corresponds to a second instance of the user input. The device determines a second context associated with the second user interaction and determines a second control input based on the second user interaction and the second context. The device causes execution of a second action based on the second control input.
A watchdog timer may be implemented to detect whether a software-based activity monitor is alive. A software-based activity monitor may manage throttling performed by a hardware-based activity monitor. The software-based activity monitor may reset the watchdog timer in the hardware-based activity monitor within a period of time to indicate that the software-based activity monitor is alive. If the watchdog timer is not reset within a period of time, the hardware-based activity monitor begins throttling using watchdog throttling criteria.
G06F 9/48 - Lancement de programmes Commutation de programmes, p. ex. par interruption
G06F 15/80 - Architectures de calculateurs universels à programmes enregistrés comprenant un ensemble d'unités de traitement à commande commune, p. ex. plusieurs processeurs de données à instruction unique
A computing system assembly can include a drawer defining an interior volume sized for holding a computing appliance. The drawer can be slidable between a deployed state and a stowed state relative to a rack-mountable chassis slidably receivable in a rack. A drawer latch coupled with the drawer's exterior can be actuatable to release the drawer from being retained in the chassis and to release the drawer for movement from the stowed state toward the deployed state. An appliance latch also coupled with the drawer's exterior can be actuatable in response to movement of the drawer from the stowed state toward the deployed state to move from a secure state in which the appliance latch at least partially blocks access out of the interior volume of the drawer and to an accessible state in which the appliance latch permits access out of the interior volume of the drawer.
Systems and methods are disclosed for containers for use in collaborative work environments. In one embodiment, an example container may include a first container wall configured to rotate toward a center of the container, a second container wall coupled to the first container wall and configured to rotate toward the center of the container, the second container wall having a first rotatable flap configured to rotate outwards with respect to the second container wall, a third container wall coupled to the first container wall and configured to rotate toward the center of the container, the third container wall having a second rotatable flap configured to rotate outwards with respect to the third container wall, a fourth container wall coupled to the second container wall and the third container wall, the fourth container wall configured to rotate toward the center of the container, and a base.
B62B 3/02 - Voitures à bras ayant plus d'un essieu portant les roues servant au déplacementDispositifs de direction à cet effetAppareillage à cet effet comportant des parties réglables, rabattables, attachables, détachables ou transformables
B07C 3/00 - Tri du courrier ou des documents selon la destination
B62B 5/06 - Appareillage pour la propulsion à main, p. ex. guidons
A device includes a housing, a first cover coupled to the housing, a second cover coupled to the housing, and a microphone assembly. The first cover includes a first channel. The microphone assembly includes a printed circuit board (PCB) having a microphone and a second channel, and a seal. The seal has a third channel configured to align with the first channel and the second channel, and a cavity. The seal is configured to transition between an unfolded state in which the PCB is insertable into the cavity, and a folded state in which the PCB is at least partially enclosed within the cavity and the microphone is enclosed within the cavity.
A system configured to improve SSL processing and/or target goal detection by fusing SSL data with object information to generate a combined target likelihood estimate that takes into account what the device knows about the surrounding environment. For example, the device may generate object information by performing object detection, floorplan estimation, distance measurements, and/or the like. Using this object information, the device may calculate a likelihood estimate value for each direction around the device, with known objects (e.g., walls) corresponding to low likelihood values. In response to an acoustic event (e.g., wakeword detection), the device may fuse the target likelihood estimates generated using SSL data and/or object information to generate the combined target likelihood estimate. Thus, the combined target likelihood estimate enables the device to accurately associate the acoustic event with a corresponding SSL track (e.g., direct sound) and ignore reflections caused by objects in the environment.
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 15/06 - Création de gabarits de référenceEntraînement des systèmes de reconnaissance de la parole, p. ex. adaptation aux caractéristiques de la voix du locuteur
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
G10L 25/78 - Détection de la présence ou de l’absence de signaux de voix
G10L 25/87 - Détection de points discrets dans un signal de voix
16.
Data flow analysis of cloud-based software applications
Techniques for data flow analysis of cloud-based software applications are described. A first portion of source code of a software application is determined to obtain data from a data source identified by a first resource identifier of a cloud provider network, the determination based on a mapping of a first statement in the first portion of the source code to an application programming interface (API) call. A trace of a data flow from the first portion of the source code to a second portion of the source code is obtained. A data sink identified by a second resource identifier of the cloud provider network is determined based on a mapping of a second statement in the second portion of the source code to another API call. A result that includes an identification of a data flow from the data source identified by the first resource identifier to the data sink identified by the second resource identifier is generated.
A technique for arranging matrix multiplications for concurrent execution in an integrated circuit device may include obtaining a representation of a data dependency graph of a neural network model. The data dependency graph may include having an accumulation group (AG) pack of accumulation groups (AGs), in which each of the AGs has one or more matrix multipartition instructions. A representation of a memory location base partition constraint graph of the AG pack can be generated, and an AG row group constraint graph can be generated based on the memory location base partition constraint graph. The AGs of the AG pack can then be assigned to tiles in an integrated circuit device based on the AG row group constraint graph.
A system and techniques for configuring a trained model to dedicate a portion of its output data to include signature data that may be used to identify information about the model. The model may be configured so that some portion of its least-significant output bits may represent the signature data. The signature data may be a unique code that corresponds to the particular model. The signature data may also include encoded data that may represent information such as a model version, model author, or the like. A recipient of the model output data may thus use the signature data to determine information about the particular model, even if the model itself is inaccessible.
A system configured to improve audio processing by performing dereverberation, noise reduction, and residual echo suppression during a communication session. The system may include a deep neural network (DNN) configured to jointly mitigate additive noise, reverberation, and residual echo. The DNN may be a convolutional recurrent network with dense connectivity (CRN-DC) and may be configured to process complex-valued spectrograms corresponding to the isolated audio data and/or estimated echo data generated by during echo cancellation. The DNN may generate a speech mask and/or an ambient noise mask, enabling the device to generate output audio data representing target speech and a variable amount of ambient noise. For example, the device may separately reconstruct the target speech using the speech mask and the background noise using the ambient noise mask, which enables the device to control the amount of ambient noise represented in the output audio data.
G10K 11/175 - Procédés ou dispositifs de protection contre le bruit ou les autres ondes acoustiques ou pour amortir ceux-ci, en général utilisant des effets d'interférenceMasquage du son
A system may receive sound information and generate an inference embedding using the sound information. The system may additionally receive a set of speaker embeddings, which may represent voice information for a set of speakers. The system may compare the inference embedding to the set of speaker embeddings to generate a result. The system may determine, based on the result, a speaker identity match rating for each speaker embedding in the set of speaker embeddings. The system may identify a speaker associated with a speaker embedding of the set of speaker embeddings having the highest speaker identity match rating as an active speaker.
H04S 7/00 - Dispositions pour l'indicationDispositions pour la commande, p. ex. pour la commande de l'équilibrage
H04R 1/40 - Dispositions pour obtenir la fréquence désirée ou les caractéristiques directionnelles pour obtenir la caractéristique directionnelle désirée uniquement en combinant plusieurs transducteurs identiques
H04R 3/12 - Circuits pour transducteurs pour distribuer des signaux à plusieurs haut-parleurs
21.
Automatic speech recognition using language model-generated context
Techniques for ASR processing using language model (LM)-generated context are described. A LM is prompted to generate words that are relevant for/may be included in a future user input. The prompt to the LM can include words from user interaction history, dialog history, dialog topic, user preferences, etc. The information included in the prompt may focus on rare or unique words rather than words that the ASR model is already confident in recognizing. The techniques can be plugged into an existing/pretrained ASR model and can be used with any existing/pretrained LM, thus saving resources needed to implement and maintain the components.
G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
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
An alert generation system is disclosed. The alert generation system uses rules to identify which of many network events are threats. The rules include a first set of rules as pre-selection rules to identify a large number of network events as not threatening, and therefore not needing to be tested against a second set of rules as detection rules, which are used to identify the threats.
Techniques are provided herein for detecting bot activity. Tasks associated with one or more delivery sub-tasks can be provided to various user devices. The task requests received from the user devices can be processed according to a variety of factors to determine a likelihood that the task requests were initiated by a bot. In some embodiments, transient tasks (e.g., task that are deleted within a relatively short period of time and for which a user device cannot be assigned) may be utilized at any suitable time. User devices that request assignment of such transient tasks may be identified as using a bot, or at least the probability identified for those user devices can be increased, indicating a heightened likelihood that the user device is using a bot. A number of remedial actions can be executed when the likelihood that the user device is using a bot exceeds a threshold.
A future workload may be predicted for a database system to make a scaling determination based on a performance objective. A performance budget for a database system may be determined and used to select different scaling decisions that may reconfigure a current processing cluster of the database system or add a new processing cluster to the database system to provide further access to a database.
Systems, methods, and computer-readable media are disclosed for multi-level storage systems to facilitate movement of shuttles across a multi-level storage structure for retrieving, loading, unloading, and moving packages. The multi-level storage system may include the multi-level storage structure for storing packages, which may be placed into containers and positioned on a container tray. Shuttles may navigate the multi-level storage system to load and unload containers. Each shuttle may hold one or more container and may include one or more actuator systems to load and unload the containers. The actuator systems may include a motor connected to a tether fixed to one or more protrusions which may enter a channel under a container tray to move the container tray onto the shuttle or may unload the container off the shuttle.
B65G 1/06 - Dispositifs d'emmagasinage mécaniques avec des moyens pour que les objets se présentent à l'enlèvement dans des positions ou à des niveaux prédéterminés
26.
Concentric suction cup tools with integrated air chambers
Systems and methods are disclosed for concentric suction cup tools with integrated air chambers. In one embodiment, an example picking assembly may include a vacuum manifold having a first integrated air pressure path and a second integrated air pressure path, and a first piston subassembly that includes a first air pipe, a first vacuum pipe, a first bushing disposed between the first air pipe and the first vacuum pipe, and a first suction cup, where the first piston subassembly is configured to independently actuate from a retracted position to an extended position. The picking assembly may include a second piston subassembly having a second air pipe, a second vacuum pipe, a second bushing disposed between the second air pipe and the second vacuum pipe, and a second suction cup, where the second piston subassembly is configured to independently actuate from the retracted position to the extended position.
This disclosure describes techniques and architecture for enriching dataset metadata of datasets arranged in tabular form comprising rows and columns, wherein each column has a name. The dataset metadata is enriched with business semantics for natural language question answering. The techniques include one or more of generating one or more synonyms for each name; ranking the names with respect to a likelihood that a column includes possible data to be returned to a user in response to a received NLQ from the user; predicting a date granularity for each column; and predicting a semantic type to describe values in the columns.
Techniques are described herein for generating an autocomplete query. An example method includes a system receiving, via graphical user interface (GUI) at a first device, a first prefix. The system can generate, via a first language model, a first autocomplete query associated with an item based at least in part on the first prefix. The system can determine a non-user based input for a reward model. The system can generate a reward for the first language model based at least in part on the non-user based input. The system can cause a change of weights of the first language model based at least in part on the reward. The system can receive, via a second GUI at a second device, the first autocomplete prefix. The system can generate a second autocomplete query based at least in part on the first autocomplete prefix and the change of weights.
Systems and methods are provided for managing illumination associated with image capture by devices by adjusting illumination in one or more sub-portions of an area during image capture. In order to adjust illumination, configuration data may be obtained relating to image data generated by an image capture device. The image capture device may be associated with a controllable illumination device configured to provide independently controllable illumination for at least two sub portions of an area. The controllable illumination device may cause the generation of illumination based on independent illumination attributes associated with the at least two sub-portions. The illumination device can include independently controllable illumination units can include a light emitting diode (LED) array in which individual LEDs in the LED array are independently controllable.
H04N 23/00 - Caméras ou modules de caméras comprenant des capteurs d'images électroniquesLeur commande
H04N 23/56 - Caméras ou modules de caméras comprenant des capteurs d'images électroniquesLeur commande munis de moyens d'éclairage
H04N 23/71 - Circuits d'évaluation de la variation de luminosité
H04N 23/72 - Combinaison de plusieurs commandes de compensation
H04N 23/74 - Circuits de compensation de la variation de luminosité dans la scène en influençant la luminosité de la scène à l'aide de moyens d'éclairage
Waveguide combiners with multiple image planes are described herein. In an example, an apparatus includes a first optical element configured to receive light, a substrate having an input surface and an output surface and configured to propagate the light received by the first optical element along a propagation path within the substrate, and a second optical element configured to output the light propagated along the propagation path. The input surface of the substrate is coupled to the first optical element. The second optical element includes a first diffraction grating coupled to the output surface and characterized by a first focal distance and a second diffraction grating coupled to the first diffraction grating in a stacked arrangement and characterized by a second focal distance.
G02F 1/295 - 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 la position ou de la direction des rayons lumineux, c.-à-d. déflexion dans une structure de guide d'ondes optique
A docking station includes a housing for accommodating an aerial vehicle, a charging contact for transferring electrical power to the aerial vehicle within the housing, and a sensor trigger for initiating or terminating the charging of the aerial vehicle. The sensor trigger includes an insertable element having an upper platform biased into contact with a surface of the housing and a blade-like extension that descends below the surface, as well as a sensor, such as a photointerrupter module, for determining positions of the blade-like extension. When an aerial vehicle is docked within the charging station, the aerial vehicle depresses the insertable element into the housing and causes a change in state of the sensor trigger, thereby energizing the charging contact, and transferring electrical power to the aerial vehicle. When the aerial vehicle departs the docking station, the state of the sensor trigger is restored, and the charging contact is deenergized.
B64U 80/25 - Transport ou stockage spécialement adaptés aux véhicules aériens sans pilote avec des dispositions pour assurer le service du véhicule aérien sans pilote pour la recharge de batteriesTransport ou stockage spécialement adaptés aux véhicules aériens sans pilote avec des dispositions pour assurer le service du véhicule aérien sans pilote pour le ravitaillement en combustible
B64U 70/97 - Moyens de guidage du véhicule aérien sans pilote vers un emplacement spécifique sur la plate-forme, p. ex. structures de plate-forme empêchant un atterrissage hors-piste
B64U 10/14 - Plates-formes volantes comportant quatre axes distincts de rotors, p. ex. quadcoptères
32.
Ruggedized, shielded, floating connectors for printed circuit board assemblies
Floating connectors described herein may comprise a frame, a flexible cable, and a soft mounted connector within the frame. A first portion of the frame may be configured to couple to a printed circuit board assembly, and a second portion of the frame may receive the soft mounted connector via a membrane. The flexible cable may extend between the first portion of the frame and the second portion of the frame, and may operatively couple at first and second ends with the printed circuit board assembly and the soft mounted connector, respectively. Further, the floating connectors may include a housing to provide additional electrical shielding, and/or a lock mechanism to provide positive retention between the floating connector and an external connector and/or associated housing.
H01R 12/91 - Dispositifs de couplage autorisant un mouvement relatif entre les pièces de couplage, p. ex. un flottement ou un auto-alignement
H01R 13/426 - Fixation de manière démontable par un dispositif de retenue indépendant et élastique porté par le socle ou par le boîtier, p. ex. par un collier
H01R 13/518 - Moyens pour maintenir ou envelopper un corps isolant, p. ex. boîtier pour maintenir ou envelopper plusieurs pièces de couplage, p. ex. châssis
H01R 13/66 - Association structurelle avec des composants électriques incorporés
H05K 3/32 - Connexions électriques des composants électriques ou des fils à des circuits imprimés
H05K 1/18 - Circuits imprimés associés structurellement à des composants électriques non imprimés
Systems and methods for test account management are disclosed. Data may be stored for a plurality of test accounts in at least one memory. Each test account is associated with a test account pool. Data indicative of a plurality of rules associated with the plurality of test accounts may be received. At least one rule indicates an expiration date. The expiration date for the plurality of test accounts may be set in the at least one memory based on the at least one rule indicating the expiration date. The plurality of test accounts may be automatically suspending upon determining that the expiration date is expired. The at least one memory may be updated to indicate that the plurality of test accounts are suspended.
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
Approaches for predicting manufacturability of a peptide are provided. A request for information related to manufacturability of a peptide can be received. A determination as to whether the peptide is predicted to be synthesizable can be made, such as by using a machine learning model. The machine learning model can be trained on data including manufacturer specifications and descriptions associated with a peptide and features for peptides. A second determination can be made as to whether the peptide is predicted to be soluble, using the same or different machine learning model trained with solubility data for peptides. If the peptide is predicted to be soluble and synthesizable, a manufacturability score for the peptide can be determined. The manufacturability score can correspond to or be indicative of a chance of successfully manufacturing the peptide.
G16B 40/00 - TIC spécialement adaptées aux biostatistiquesTIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p. ex. extraction de connaissances ou détection de motifs
35.
GENERATING LONG-TERM MEMORY FOR ORCHESTRATION AGENT SESSIONS
Long-term memory data objects may be generated for orchestrations agents. When a session completes or ends, a long-term memory data object may be generated according to a specified long-term memory type based on turn inputs during the session. When a new session is started, the long-term memory data object may be used as part of inputs to a generative machine learning model to perform or respond to turn inputs of the new session.
G06N 3/0442 - Réseaux récurrents, p. ex. réseaux de Hopfield caractérisés par la présence de mémoire ou de portes, p. ex. mémoire longue à court terme [LSTM] ou unités récurrentes à porte [GRU]
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
36.
GENERATING LONG-TERM MEMORY FOR ORCHESTRATION AGENT SESSIONS
Long-term memory data objects may be generated for orchestrations agents. When a session completes or ends, a long-term memory data object may be generated according to a specified long-term memory type based on turn inputs during the session. When a new session is started, the long-term memory data object may be used as part of inputs to a generative machine learning model to perform or respond to turn inputs of the new session.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
Multifunctional electronic devices for voice recognition, natural language processing, appointment and task reminders, and researching and summarizing information; personal digital assistants; sound recording and sound reproducing apparatus; downloadable computer software for speech recognition; downloadable computer software for voice recognition, natural language processing, appointment and task reminders, and researching and summarizing information; downloadable computer software for summarizing speech; downloadable artificial intelligence personal assistant software for performing tasks or services on behalf of a user that is activated by user input, location awareness, and online information; electronic personal assistant device incorporating advanced processing capabilities and microphone system designed to provide continuous AI presence and assistance; downloadable computer programs and downloadable computer software for machine-learning based language and speech processing software; downloadable computer software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices via wireless networks; operating software for machine-learning based language and speech processing software; operating software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices via wireless networks; downloadable or embedded intent recognition and user intent fulfillment software for deep learning, machine learning, pattern recognition, and predictive analytics. Providing temporary use of non-downloadable software for voice command and recognition software, speech-to-text conversion software, and voice-enabled software applications for personal information management; offering software as a service (SaaS) featuring computer software for use as an application programming interface (API) for the development, testing, and integration of AI-driven personal assistant applications; providing temporary use of non-downloadable software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices, home climate devices, and lighting products via wireless networks; providing temporary use of non-downloadable software for use in controlling, integrating, operating, connecting, and managing voice-controlled information devices, namely, cloud-connected and voice-controlled smart consumer electronic devices and electronic personal assistant devices; providing temporary use of non-downloadable software for managing and facilitating payment transactions, including electronic funds transfer and currency conversion; offering software as a service (SaaS) featuring software for sending and receiving electronic messages, notifications, and alerts; offering software as a service (SaaS) featuring computer software for accessing, browsing, and searching online databases, audio, video, and multimedia content; application service provider (ASP) services featuring software for controlling, integrating, operating, connecting, and managing voice controlled information devices, namely, cloud-connected and voice-controlled smart consumer electronic devices and electronic personal assistant devices; software as a service (SaaS) services featuring software using artificial intelligence for indexing, integrating, and retrieving data for both internal applications and external communication; providing subscription-based temporary use of online non-downloadable software for the provision of virtual personal assistant services; providing subscription-based temporary use of online non-downloadable intelligent personal assistant software for providing information in response to user-generated inquiries; providing subscription-based temporary use of online non-downloadable computer software for simulating conversations; platform as a service (PaaS) featuring computer software platforms for voice command and recognition software, intent recognition and user intent fulfillment software for deep learning, machine learning, pattern recognition, and predictive analytics; artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence for voice command and recognition, user intent fulfillment, managing and operating internet of things devices, facilitating e-commerce transactions, and database management. Personal concierge services for others comprising making requested personal arrangements and reservations and providing customer-specific information to meet individual needs; subscription services, namely, provision of subscription-based access to personal concierge services; security services for the protection of property and individuals, namely, home security services for the protection of people and tangible property, and personal security services for the protection of people and tangible property.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
Multifunctional electronic devices for voice recognition, natural language processing, appointment and task reminders, and researching and summarizing information; personal digital assistants; sound recording and sound reproducing apparatus; downloadable computer software for speech recognition; downloadable computer software for voice recognition, natural language processing, appointment and task reminders, and researching and summarizing information; downloadable computer software for summarizing speech; downloadable artificial intelligence personal assistant software for performing tasks or services on behalf of a user that is activated by user input, location awareness, and online information; electronic personal assistant device incorporating advanced processing capabilities and microphone system designed to provide continuous AI presence and assistance; downloadable computer programs and downloadable computer software for machine-learning based language and speech processing software; downloadable computer software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices via wireless networks; operating software for machine-learning based language and speech processing software; operating software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices via wireless networks; downloadable or embedded intent recognition and user intent fulfillment software for deep learning, machine learning, pattern recognition, and predictive analytics. Providing temporary use of non-downloadable software for voice command and recognition software, speech-to-text conversion software, and voice-enabled software applications for personal information management; offering software as a service (SaaS) featuring computer software for use as an application programming interface (API) for the development, testing, and integration of AI-driven personal assistant applications; providing temporary use of non-downloadable software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices, home climate devices, and lighting products via wireless networks; providing temporary use of non-downloadable software for use in controlling, integrating, operating, connecting, and managing voice-controlled information devices, namely, cloud-connected and voice-controlled smart consumer electronic devices and electronic personal assistant devices; providing temporary use of non-downloadable software for managing and facilitating payment transactions, including electronic funds transfer and currency conversion; offering software as a service (SaaS) featuring software for sending and receiving electronic messages, notifications, and alerts; offering software as a service (SaaS) featuring computer software for accessing, browsing, and searching online databases, audio, video, and multimedia content; application service provider (ASP) services featuring software for controlling, integrating, operating, connecting, and managing voice controlled information devices, namely, cloud-connected and voice-controlled smart consumer electronic devices and electronic personal assistant devices; software as a service (SaaS) services featuring software using artificial intelligence for indexing, integrating, and retrieving data for both internal applications and external communication; providing subscription-based temporary use of online non-downloadable software for the provision of virtual personal assistant services; providing subscription-based temporary use of online non-downloadable intelligent personal assistant software for providing information in response to user-generated inquiries; providing subscription-based temporary use of online non-downloadable computer software for simulating conversations; platform as a service (PaaS) featuring computer software platforms for voice command and recognition software, intent recognition and user intent fulfillment software for deep learning, machine learning, pattern recognition, and predictive analytics; artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence for voice command and recognition, user intent fulfillment, managing and operating internet of things devices, facilitating e-commerce transactions, and database management. Personal concierge services for others comprising making requested personal arrangements and reservations and providing customer-specific information to meet individual needs; subscription services, namely, provision of subscription-based access to personal concierge services; security services for the protection of property and individuals, namely, home security services for the protection of people and tangible property, and personal security services for the protection of people and tangible property.
39.
Pre-configured rule flows for game feature integration
An event notification may be received, by a game feature integration service, from a first video game feature component. The game feature integration service may integrate a plurality of video game feature components that each have a respective set of one or more events for which event notifications are sent by a corresponding video game feature component to the game feature integration service and a respective set of one or more actions that are called on the corresponding video game feature component by the game feature integration service. A pre-configured or custom rule flow may trigger the game feature integration service to call an action on the second video game feature component based on the event notification. The game feature integration service, based at least in part on the rule flow, may call the action on the second video game feature component in response to the event notification.
A63F 13/79 - Aspects de sécurité ou de gestion du jeu incluant des données sur les joueurs, p. ex. leurs identités, leurs comptes, leurs préférences ou leurs historiques de jeu
G06F 13/00 - Interconnexion ou transfert d'information ou d'autres signaux entre mémoires, dispositifs d'entrée/sortie ou unités de traitement
G06F 17/00 - Équipement ou méthodes de traitement de données ou de calcul numérique, spécialement adaptés à des fonctions spécifiques
Systems and methods are disclosed for automated unloading of packages from trailers. In one embodiment, an example package unloading system may include a suction cup support having a first suction cup assembly and a second suction cup assembly, a moveable frame coupled to the suction cup support, a telescoping conveyor configured to convey packages downstream, an angled conveyor configured to convey the packages to the telescoping conveyor, and a controller configured to cause the moveable frame to move the suction cup support to a first vertical position, and cause the first suction cup assembly to engage a first package.
Described apparatus, systems, and methods for audibly presenting multiple options to a user and using user signals received from a user device worn by or otherwise coupled to the user, that are generated without any need for physical motion-based interaction from the user, to determine a presented option that is to be selected and performed on behalf of the user. In addition, the disclosed implementations may also be utilized to authenticate a user based on authentication signals received from the user device that are generated without any need for physical motion-based interaction from the user.
Techniques for leveraging a large language model (LLM) in software development are described. A description of an error message associated with a software system hosted by the multi-tenant provider network is received. An LLM is prompted with an analysis prompt to generate an analysis of the error message, the analysis prompt including the description of the error message, and the analysis of the error message is received from the LLM. The LLM is prompted with a suggested resolution prompt to generate a suggested resolution to a cause of the error message, the suggested resolution prompt including the description of the error message and the analysis of the error message, and the suggested resolution is received from the LLM. A change to resolve the cause of the error message is sent to an originator of the received description, the change based at least in part on the suggested resolution.
Techniques to reduce direct memory access (DMA) overhead may include retrieving an address translation descriptor from a descriptor queue of a DMA engine, and updating an address translation table in the DMA engine with address translation information obtained from the location indicated by the address translation descriptor. A set of memory descriptors is then obtained from the descriptor queue. The set of memory descriptors can be processed by determining that the addresses in the set of memory descriptors are to be translated using the address translation table, and performing memory access operations by using the address translation table to translate the addresses in the set of memory descriptors.
G06F 13/28 - Gestion de demandes d'interconnexion ou de transfert pour l'accès au bus d'entrée/sortie utilisant le transfert par rafale, p. ex. acces direct à la mémoire, vol de cycle
G06F 12/1009 - Traduction d'adresses avec tables de pages, p. ex. structures de table de page
44.
System and method for vision-based event detection
This disclosure describes systems and techniques for identifying events that occur within an environment using image data captured at the environment. For example, one or more cameras may generate image data representative of a user interacting with an item on the shelf. This image data may be used to generate feature data associated with the user and the item, which may be analyzed by one or more classifiers for identifying an interaction between the user and the item. The systems and techniques may then generate interaction data, which in turn may be analyzed by one or more additional classifiers for identifying an event, such as the user picking a particular item from the shelf within the environment. Event data indicative of the event may then be used to update a virtual cart of the user.
A prompt refinement service can be used to enhance output from a generative model. Entity input that indicates a request for a response can be received. A set of prompt refiners can be received. A prompt refinement service can identify a particular prompt refiner among the set of prompt refiners having a highest similarity to the entity input compared with other prompt refiners. The prompt refinement service can aggregate the particular prompt refiner with the entity input to generate an aggregated input. The prompt refinement service can transmit the aggregated input to the generative model to cause the generative model to return an output in response to the entity input.
This disclosure describes techniques for determining an appropriate cost to charge a user for an item in an environment that relies on sensor data rather than traditional checkout methods. For instance, a physical store may include a weight sensor on a shelf to identify a customer picking an item from the shelf Upon the user picking the item, the techniques may determine a first cost of the item. When a camera is used to observe the user exiting the store, triggering automatic payment for the item, the techniques may determine whether the price of the item has changed. If so, then the techniques may charge the user for the lesser of the two prices.
Systems and methods for product data inconsistency detection are provided. For instance, a method may include: retrieve a set of product listing datasets based on a product identification, wherein the product listing datasets include one or more product listings, and wherein each product listing includes at least one image and at least one text segment; compare new product data including at least one new image or at least one text segment, the at least one image, and the at least one text segment to determine whether any are inconsistent; transmit a result message to the client device, wherein the result message indicates the at least one new image or the at least one text segment is inconsistent.
G06V 10/77 - Traitement 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
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
48.
Enhanced automatic speech recognition to avoid misrecognition of voice utterances
A method for detecting misrecognized voice utterances for which automated responses are presented may include inputting, to a post-speech recognition device, possible misrecognized utterances for respective utterances; inputting, to the post-speech recognition device, an interpretation of an utterance of a user, the utterance including a command; determining, using the post-speech recognition device, based on the possible misrecognized utterances, that the interpretation of the utterance is a misrecognition of the utterance; identifying, using the post-speech recognition device, based on the possible misrecognized utterances, a corrected utterance for the interpretation of the utterance, wherein the corrected utterance is different than the interpretation of the utterance; selecting, using the ASR model, the corrected utterance to replace the interpretation of the utterance; and sending the corrected utterance to a device associated with generating a response to the command to present to the user.
G10L 15/01 - Estimation ou évaluation des systèmes de reconnaissance de la parole
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 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
49.
Lightweight orthogonal frequency division multiple access (OFDMA) scheduler for low latency applications
An OFDMA scheduling device receives a data stream from a network router and can identify audio packets based on metadata. The OFDMA scheduling device is aware of the number of the wireless devices connected to it, and can determine a resource unit (RU) allocation for each of the wireless devices based on the payload of the wireless devices. The OFDMA scheduling device can also determine a modulation and coding scheme (MCS) for each of the wireless devices based on the received signal strength indicator (RSSI) value and any feedback data received from each of the wireless devices. Based on the RU allocation and MCS determination, the OFDMA scheduling device can send a downlink OFDMA frame to the wireless devices, notifying them of their respective RU allocations and the MCS to be used while receiving audio packets from the OFDMA scheduling device.
H04L 5/00 - Dispositions destinées à permettre l'usage multiple de la voie de transmission
H04L 1/00 - Dispositions pour détecter ou empêcher les erreurs dans l'information reçue
H04R 1/40 - Dispositions pour obtenir la fréquence désirée ou les caractéristiques directionnelles pour obtenir la caractéristique directionnelle désirée uniquement en combinant plusieurs transducteurs identiques
H04R 3/12 - Circuits pour transducteurs pour distribuer des signaux à plusieurs haut-parleurs
H04W 84/12 - Réseaux locaux sans fil [WLAN Wireless Local Area Network]
50.
Versioned policy collection management for certificate issuance
A public certificate authority (CA) manages versioned sets of a collection of individual policies that serve as a basis for how a certificate issuance workflow processes certificate requests, and tracks the particular set of policies applied by the issuance workflow process to produce a particular certificate. For example, the public CA responds to a certificate request by identifying a current policy collection version, and performing a certificate issuance workflow in accordance with the set of individual policy versions specified by the current policy collection version. If the requested certificate is correctly produced, the public CA publishes the certificate and records, to a tracking data store, an identifier of the certificate and the policy collection version used in performance of the issuance workflow. The records may be used to respond to audit requests, matching certificates to the policy collection version used in performance of the issuance workflow for that certificate.
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
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
Techniques for a service provider network to determine a subset of available metric data associated with an entity in a cloud computing environment are discussed herein. A system can receive metrics associated with a service and determine a subset of the metrics that represents service performance. In some examples, the system can identify a frequency of unique application activity over time in a dynamic data stream, and report metric data to a client device based on the frequency.
This disclosure describes, in part, techniques for sharing content associated with network applications. For instance, a user may want to share content for a network application, such as a game stream for a gaming application. As such, system(s) may launch a broadcasting session on a first virtual server and launch the network application on a second virtual server. The first virtual server may then receive content data representing states of the network application from the second virtual server. Additionally, the first virtual server may receive video data representing the user and/or audio data representing user speech from a user device. The first virtual server may then generate broadcasting data using the content data, the video data, and the audio data. After generating the broadcasting data, the system(s) may send the broadcasting data to one or more computing devices associated with a user account.
Systems, devices, and methods are provided for context aware graphical subtitles. Techniques described herein may involve determining a representation for a graphical subtitle. The graphical subtitles may have various customizable properties that allow for creative expression. Representations of graphical subtitles may be determined using lower-level information extracted from detectors, such as audio detectors and/or visual detectors, as well as higher-level information determined by encoder-decoders.
H04N 21/488 - Services de données, p. ex. téléscripteur d'actualités
H04N 21/431 - Génération d'interfaces visuellesRendu de contenu ou données additionnelles
H04N 21/44 - Traitement de flux élémentaires vidéo, p. ex. raccordement d'un clip vidéo récupéré d'un stockage local avec un flux vidéo en entrée ou rendu de scènes selon des graphes de scène du flux vidéo codé
09 - Appareils et instruments scientifiques et électriques
38 - Services de télécommunications
42 - Services scientifiques, technologiques et industriels, recherche et conception
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
(1) Multifunctional electronic devices for voice recognition, natural language processing, appointment and task reminders, and researching and summarizing information; personal digital assistants; sound recording and sound reproducing apparatus; downloadable computer software for speech recognition; downloadable computer software for voice recognition, natural language processing, appointment and task reminders, and researching and summarizing information; downloadable computer software for summarizing speech; downloadable artificial intelligence personal assistant software for performing tasks or services on behalf of a user that is activated by user input, location awareness, and online information; electronic personal assistant device incorporating advanced processing capabilities and microphone system designed to provide continuous AI presence and assistance; downloadable computer programs and downloadable computer software for machine-learning based language and speech processing software; downloadable computer software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices via wireless networks; operating software for machine-learning based language and speech processing software; operating software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices via wireless networks; downloadable or embedded intent recognition and user intent fulfillment software for deep learning, machine learning, pattern recognition, and predictive analytics. (1) Providing temporary use of non-downloadable software for voice command and recognition software, speech-to-text conversion software, and voice-enabled software applications for personal information management; offering software as a service (SaaS) featuring computer software for use as an application programming interface (API) for the development, testing, and integration of AI-driven personal assistant applications; providing temporary use of non-downloadable software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices, home climate devices, and lighting products via wireless networks; providing temporary use of non-downloadable software for use in controlling, integrating, operating, connecting, and managing voice-controlled information devices, namely, cloud-connected and voice-controlled smart consumer electronic devices and electronic personal assistant devices; providing temporary use of non-downloadable software for managing and facilitating payment transactions, including electronic funds transfer and currency conversion; offering software as a service (SaaS) featuring software for sending and receiving electronic messages, notifications, and alerts; offering software as a service (SaaS) featuring computer software for accessing, browsing, and searching online databases, audio, video, and multimedia content; application service provider (ASP) services featuring software for controlling, integrating, operating, connecting, and managing voice controlled information devices, namely, cloud-connected and voice-controlled smart consumer electronic devices and electronic personal assistant devices; software as a service (SAAS) services featuring software using artificial intelligence for indexing, integrating, and retrieving data for both internal applications and external communication; providing subscription-based temporary use of online non-downloadable software for the provision of virtual personal assistant services; providing subscription-based temporary use of online non-downloadable intelligent personal assistant software for providing information in response to user-generated inquiries; providing subscription-based temporary use of online non-downloadable computer software for simulating conversations; platform as a service (PaaS) featuring computer software platforms for voice command and recognition software, intent recognition and user intent fulfillment software for deep learning, machine learning, pattern recognition, and predictive analytics; artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence (AI) for voice command and recognition, user intent fulfillment, managing and operating internet of things (IoT) devices, facilitating e-commerce transactions, and database management.
(2) Personal concierge services for others comprising making requested personal arrangements and reservations and providing customer-specific information to meet individual needs; subscription services, namely, provision of subscription-based access to personal concierge services; security services for the protection of property and individuals, namely, home security services for the protection of people and tangible property, and personal security services for the protection of people and tangible property.
09 - Appareils et instruments scientifiques et électriques
38 - Services de télécommunications
42 - Services scientifiques, technologiques et industriels, recherche et conception
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
(1) Multifunctional electronic devices for voice recognition, natural language processing, appointment and task reminders, and researching and summarizing information; personal digital assistants; sound recording and sound reproducing apparatus; downloadable computer software for speech recognition; downloadable computer software for voice recognition, natural language processing, appointment and task reminders, and researching and summarizing information; downloadable computer software for summarizing speech; downloadable artificial intelligence personal assistant software for performing tasks or services on behalf of a user that is activated by user input, location awareness, and online information; electronic personal assistant device incorporating advanced processing capabilities and microphone system designed to provide continuous AI presence and assistance; downloadable computer programs and downloadable computer software for machine-learning based language and speech processing software; downloadable computer software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices via wireless networks; operating software for machine-learning based language and speech processing software; operating software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices via wireless networks; downloadable or embedded intent recognition and user intent fulfillment software for deep learning, machine learning, pattern recognition, and predictive analytics. (1) Providing temporary use of non-downloadable software for voice command and recognition software, speech-to-text conversion software, and voice-enabled software applications for personal information management; offering software as a service (SaaS) featuring computer software for use as an application programming interface (API) for the development, testing, and integration of AI-driven personal assistant applications; providing temporary use of non-downloadable software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices, home climate devices, and lighting products via wireless networks; providing temporary use of non-downloadable software for use in controlling, integrating, operating, connecting, and managing voice-controlled information devices, namely, cloud-connected and voice-controlled smart consumer electronic devices and electronic personal assistant devices; providing temporary use of non-downloadable software for managing and facilitating payment transactions, including electronic funds transfer and currency conversion; offering software as a service (SaaS) featuring software for sending and receiving electronic messages, notifications, and alerts; offering software as a service (SaaS) featuring computer software for accessing, browsing, and searching online databases, audio, video, and multimedia content; application service provider (ASP) services featuring software for controlling, integrating, operating, connecting, and managing voice controlled information devices, namely, cloud-connected and voice-controlled smart consumer electronic devices and electronic personal assistant devices; software as a service (SAAS) services featuring software using artificial intelligence for indexing, integrating, and retrieving data for both internal applications and external communication; providing subscription-based temporary use of online non-downloadable software for the provision of virtual personal assistant services; providing subscription-based temporary use of online non-downloadable intelligent personal assistant software for providing information in response to user-generated inquiries; providing subscription-based temporary use of online non-downloadable computer software for simulating conversations; platform as a service (PaaS) featuring computer software platforms for voice command and recognition software, intent recognition and user intent fulfillment software for deep learning, machine learning, pattern recognition, and predictive analytics; artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence (AI) for voice command and recognition, user intent fulfillment, managing and operating internet of things (IoT) devices, facilitating e-commerce transactions, and database management.
(2) Personal concierge services for others comprising making requested personal arrangements and reservations and providing customer-specific information to meet individual needs; subscription services, namely, provision of subscription-based access to personal concierge services; security services for the protection of property and individuals, namely, home security services for the protection of people and tangible property, and personal security services for the protection of people and tangible property.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
Multifunctional electronic devices for voice recognition, natural language processing, appointment and task reminders, and researching and summarizing information; personal digital assistants; sound recording and sound reproducing apparatus; downloadable computer software for speech recognition; downloadable computer software for voice recognition, natural language processing, appointment and task reminders, and researching and summarizing information; downloadable computer software for summarizing speech; downloadable artificial intelligence personal assistant software for performing tasks or services on behalf of a user that is activated by user input, location awareness, and online information; electronic personal assistant device incorporating advanced processing capabilities and microphone system designed to provide continuous AI presence and assistance; downloadable computer programs and downloadable computer software for machine-learning based language and speech processing software; downloadable computer software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices via wireless networks; operating software for machine-learning based language and speech processing software; operating software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices via wireless networks; downloadable or embedded intent recognition and user intent fulfillment software for deep learning, machine learning, pattern recognition, and predictive analytics. Providing temporary use of non-downloadable software for voice command and recognition software, speech-to-text conversion software, and voice-enabled software applications for personal information management; offering software as a service (SaaS) featuring computer software for use as an application programming interface (API) for the development, testing, and integration of AI-driven personal assistant applications; providing temporary use of non-downloadable software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices, home climate devices, and lighting products via wireless networks; providing temporary use of non-downloadable software for use in controlling, integrating, operating, connecting, and managing voice-controlled information devices, namely, cloud-connected and voice-controlled smart consumer electronic devices and electronic personal assistant devices; providing temporary use of non-downloadable software for managing and facilitating payment transactions, including electronic funds transfer and currency conversion; offering software as a service (SaaS) featuring software for sending and receiving electronic messages, notifications, and alerts; offering software as a service (SaaS) featuring computer software for accessing, browsing, and searching online databases, audio, video, and multimedia content; application service provider (ASP) services featuring software for controlling, integrating, operating, connecting, and managing voice controlled information devices, namely, cloud-connected and voice-controlled smart consumer electronic devices and electronic personal assistant devices; software as a service (SaaS) services featuring software using artificial intelligence for indexing, integrating, and retrieving data for both internal applications and external communication; providing subscription-based temporary use of online non-downloadable software for the provision of virtual personal assistant services; providing subscription-based temporary use of online non-downloadable intelligent personal assistant software for providing information in response to user-generated inquiries; providing subscription-based temporary use of online non-downloadable computer software for simulating conversations; platform as a service (PaaS) featuring computer software platforms for voice command and recognition software, intent recognition and user intent fulfillment software for deep learning, machine learning, pattern recognition, and predictive analytics; artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence (AI) for voice command and recognition, user intent fulfillment, managing and operating internet of things (IoT) devices, facilitating e-commerce transactions, and database management. Personal concierge services for others comprising making requested personal arrangements and reservations and providing customer-specific information to meet individual needs; subscription services, namely, provision of subscription-based access to personal concierge services; security services for the protection of property and individuals, namely, home security services for the protection of people and tangible property, and personal security services for the protection of people and tangible property.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
45 - Services juridiques; services de sécurité; services personnels pour individus
Produits et services
Multifunctional electronic devices for voice recognition, natural language processing, appointment and task reminders, and researching and summarizing information; personal digital assistants; sound recording and sound reproducing apparatus; downloadable computer software for speech recognition; downloadable computer software for voice recognition, natural language processing, appointment and task reminders, and researching and summarizing information; downloadable computer software for summarizing speech; downloadable artificial intelligence personal assistant software for performing tasks or services on behalf of a user that is activated by user input, location awareness, and online information; electronic personal assistant device incorporating advanced processing capabilities and microphone system designed to provide continuous AI presence and assistance; downloadable computer programs and downloadable computer software for machine-learning based language and speech processing software; downloadable computer software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices via wireless networks; operating software for machine-learning based language and speech processing software; operating software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices via wireless networks; downloadable or embedded intent recognition and user intent fulfillment software for deep learning, machine learning, pattern recognition, and predictive analytics. Providing temporary use of non-downloadable software for voice command and recognition software, speech-to-text conversion software, and voice-enabled software applications for personal information management; offering software as a service (SaaS) featuring computer software for use as an application programming interface (API) for the development, testing, and integration of AI-driven personal assistant applications; providing temporary use of non-downloadable software for connecting, operating, integrating, controlling, and managing networked consumer electronic devices, home climate devices, and lighting products via wireless networks; providing temporary use of non-downloadable software for use in controlling, integrating, operating, connecting, and managing voice-controlled information devices, namely, cloud-connected and voice-controlled smart consumer electronic devices and electronic personal assistant devices; providing temporary use of non-downloadable software for managing and facilitating payment transactions, including electronic funds transfer and currency conversion; offering software as a service (SaaS) featuring software for sending and receiving electronic messages, notifications, and alerts; offering software as a service (SaaS) featuring computer software for accessing, browsing, and searching online databases, audio, video, and multimedia content; application service provider (ASP) services featuring software for controlling, integrating, operating, connecting, and managing voice controlled information devices, namely, cloud-connected and voice-controlled smart consumer electronic devices and electronic personal assistant devices; software as a service (SaaS) services featuring software using artificial intelligence for indexing, integrating, and retrieving data for both internal applications and external communication; providing subscription-based temporary use of online non-downloadable software for the provision of virtual personal assistant services; providing subscription-based temporary use of online non-downloadable intelligent personal assistant software for providing information in response to user-generated inquiries; providing subscription-based temporary use of online non-downloadable computer software for simulating conversations; platform as a service (PaaS) featuring computer software platforms for voice command and recognition software, intent recognition and user intent fulfillment software for deep learning, machine learning, pattern recognition, and predictive analytics; artificial intelligence as a service (AIAAS) services featuring software using artificial intelligence (AI) for voice command and recognition, user intent fulfillment, managing and operating internet of things (IoT) devices, facilitating e-commerce transactions, and database management. Personal concierge services for others comprising making requested personal arrangements and reservations and providing customer-specific information to meet individual needs; subscription services, namely, provision of subscription-based access to personal concierge services; security services for the protection of property and individuals, namely, home security services for the protection of people and tangible property, and personal security services for the protection of people and tangible property.
A vehicle software bill of materials management service generates or updates a composite software bill of materials (composite SBOM) for a vehicle using one or more received software bill of materials (SBOMs). The vehicle software bill of materials management service allows a customer to define software artifact instances of the composite SBOM, including granularity of specificity and identity attributes. The vehicle software bill of materials management service maintains the composite SBOM and resolves conflict between different SBOMs. The vehicle software bill of materials management service resolves anomalies encountered during software artifact instance generation and enriches software artifact instances of the composite SBOM with additional metadata.
Techniques implemented by container services to provide users with utilization metrics indicating which processes running inside containers of distributed container systems are driving computing resource consumption. The container service may deploy agents in VMs alongside the containers that are supporting applications, and the agents may include profilers that inject eBPF programs into the kernels of each VM in which containers are running. The eBPF programs collect stack traces from the kernels that represent which processes were being executed when the stack traces were sampled. The profilers may use the stack traces to determine resource utilization for each process, and group the stack trace results based on the container in which the processes are executing. The utilization metrics may be converted into easily digestible visualizations and provided to a user to determine which processes are driving utilization in the containers, which in turn helps the users improve their application code.
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
60.
Systems and methods for automated analysis of one or more tables
Systems and methods for automated analysis of one or more tables are provided. Particularly, a large language model is provided that analyzes a table (or multiple tables) and produces output information about the table without requiring the contents of the table to be provided as an input. For example, the large language model can output a natural language description of the table, description of the table contents, applications of the data in the table, privacy and security concerns, and/or any other types of relevant information. Additionally, a mechanism is provided that evaluates the outputs of the large language model for inaccurate or nonsensical natural language and provides an indication of the quality of the output to a user. The mechanism, for example, may include a combination of a hallucination filter and one or more metrics. The mechanism may also be used to filter the outputs of the large language model.
Technology is described for an object cache layer for a rules engine. The object cache layer may store derived objects. The object cache layer may take advantage of machine learning for incoming objects that have variable attributes. A trainable hash function may use a machine learning model to predict the incoming event schema and signature of derived objects from the incoming objects or queries. The trainable hash function may determine an incoming event schema and signature of a derived object using the machine learning model and a set of attributes of an incoming object. A cache manager of the object cache layer may use a hash value determined by the trainable hash function using the signature of the incoming object to determine whether to access the derived object in the cache. The trainable hash function may be trained at runtime using training signatures from the rules engine on cache misses.
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
G06F 12/0802 - Adressage d’un niveau de mémoire dans lequel l’accès aux données ou aux blocs de données désirés nécessite des moyens d’adressage associatif, p. ex. mémoires cache
G06N 3/042 - Réseaux neuronaux fondés sur la connaissanceReprésentations logiques de réseaux neuronaux
A vehicle resource management system provides vehicles with vehicle agents that enable sharing of resources, via peer-to-peer connections between vehicles or other resource providers and resource consumers. For example, a first vehicle may be allocated computing capacity, storage capacity, or networking capacity of a second vehicle for use by the first vehicle over an ephemeral connection between the vehicles. The first vehicle may broadcast a request for leased resources to a plurality of potential resource providers and may select a given resource provider based on attributes of the task to be performed, policies of the vehicle, and information indicated in responses received in response to the broadcast. Upon acceptance a cryptographic handshake may be performed to establish an ephemeral connection between the vehicle and resource provider to enable sharing of resources over a direct peer-to-peer connection.
G06Q 10/0631 - Planification, affectation, distribution ou ordonnancement de ressources d’entreprises ou d’organisations
G06Q 30/0645 - Transactions de locationTransactions de crédit-bail
G06Q 50/40 - Procédés d’affaires s’appliquant à l’industrie du transport
G08G 1/00 - Systèmes de commande du trafic pour véhicules routiers
G08G 1/09 - Dispositions pour donner des instructions variables pour le trafic
H04H 20/61 - Dispositions spécialement adaptées à des applications spécifiques, p. ex. aux informations sur le trafic ou aux récepteurs mobiles à la radiodiffusion locale, p. ex. la radiodiffusion en interne
63.
Maintaining accurate cart-state using auto-generated sensor data
Described are techniques for determining whether a transaction may be finalized for a user. It may first be determined whether the inventory management is to resolve any events prior to finalizing the transaction. In some instances, the inventory management system may refrain from finalizing a transaction if the user is associated with a low-confidence result/event, if the user remains a candidate user for an unresolved event, or if a global-blocking event is in place at the time of the user's exit. In some instances, the transaction may be finalized upon the user's exit of the facility if the user is associated with high-confidence events/results, is not associated with any low-confidence events/results, is not a candidate user for an unresolved event, and if no global-blocking event is in place at the time of exit.
An electronic filter includes a ground plane and a top conductor overlying the ground plane. The top conductor includes an input and an output for receiving and outputting signals, respectively. The filter further includes a plurality of unit cells arranged in series along the top conductor. Each of the plurality of unit cells includes a planar structure disposed between the top conductor and the ground plane. Each of the plurality of unit cells further includes a pair of vias connecting the planar structure to the ground plane.
H01R 13/7197 - Association structurelle avec des composants électriques incorporés spécialement adaptée à la haute fréquence, p. ex. avec des filtres avec des filtres solidaires des contacts ou montés sur ces derniers, p. ex. filtres tubulaires
H03H 1/00 - Détails de réalisation des réseaux d'impédances dont le mode de fonctionnement électrique n'est pas spécifié ou est applicable à plus d'un type de réseau
H01P 1/212 - Sélecteurs de fréquence, p. ex. filtres supprimant ou atténuant les fréquences harmoniques
Systems and methods described herein provide for novel security policy features network devices. In embodiments, a first domain name system (DNS) request for a resource may be obtained by a first computer system associated with a firewall. The first DNS request may be between a second computer system and a DNS server and include a domain name for the resource. A determination may be made that the domain name included in the first DNS request is included in an access control list maintained by the first computer system. A first DNS response from the DNS server may be received by the first computer system and include an internet protocol (IP) address for the resource. The access control list may be updated to include the IP address for the domain name for the resource. The IP address may be transmitted to the second computer system by the first computer system.
H04L 61/4511 - Répertoires de réseauCorrespondance nom-adresse en utilisant des répertoires normalisésRépertoires de réseauCorrespondance nom-adresse en utilisant des protocoles normalisés d'accès aux répertoires en utilisant le système de noms de domaine [DNS]
A first network namespace and second network namespace are created in a computing instance of a computer system, with the second network namespace being accessible to the first network namespace via an interface. A service is executed in the first namespace and an encoder is executed in the second namespace, with the encoder transforming media from one format to another format. Communication from the encoder to the service is regulated via the interface.
H04N 21/236 - Assemblage d'un flux multiplexé, p. ex. flux de transport, en combinant un flux vidéo avec d'autres contenus ou données additionnelles, p. ex. insertion d'une adresse universelle [URL] dans un flux vidéo, multiplexage de données de logiciel dans un flux vidéoRemultiplexage de flux multiplexésInsertion de bits de remplissage dans le flux multiplexé, p. ex. pour obtenir un débit constantAssemblage d'un flux élémentaire mis en paquets
H04N 21/2387 - Traitement de flux en réponse à une requête de reproduction par un utilisateur final, p. ex. pour la lecture à vitesse variable ("trick play")
H04N 21/266 - Gestion de canal ou de contenu, p. ex. génération et gestion de clés et de messages de titres d'accès dans un système d'accès conditionnel, fusion d'un canal de monodiffusion de VOD dans un canal multidiffusion
H04N 21/433 - Opération de stockage de contenu, p. ex. opération de stockage en réponse à une requête de pause ou opérations de cache
H04N 21/6334 - Signaux de commande issus du serveur dirigés vers des éléments du réseau ou du client vers le client pour l’autorisation, p. ex. en transmettant une clé
Face masks and related methods with improved filtering, wear-ability, quality of seal against a wearer's face, filtration efficiency, lifetime, and/or durability are provided. A face mask includes a filter assembly and a retention mechanism. The filter assembly is configured to conform to a face of a user and cover a nose and mouth of the user. The filter assembly can include a tessellated filter layer shaped to extend above and below a reference medial surface of the tessellated filter layer so that the tessellated filter layer has an air-filtering area greater than an area of the reference medial surface.
The following description is directed to a logic repository service. In one example, a method of a logic repository service can include receiving a first request to generate configuration data for configurable hardware using a specification for application logic of the configurable hardware. The method can include generating the configuration data for the configurable hardware. The configuration data can include data for implementing the application logic. The method can include encrypting the configuration data to generate encrypted configuration data. The method can include signing the encrypted configuration data using a private key. The method can include transmitting the signed encrypted configuration data in response to the request.
G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
G06F 15/78 - Architectures de calculateurs universels à programmes enregistrés comprenant une seule unité centrale
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
69.
PERFORMANCE OF ENTANGLING GATES BETWEEN FLUXONIUM QUBITS COUPLED TOGETHER USING A RESONATOR COUPLER
Techniques and apparatus for performing entangling gates between two fluxonium qubits, coupled together using a resonator coupler, are disclosed. A microwave pulse is emitted such that the resonator coupler is driven from the ground state to the first excited state and back down when the two fluxonium qubits are in their first excited states, and such that the resonator coupler is maintained in the ground state when one or both of the fluxonium qubits are in energy states other than their first excited states. High quality materials, such as Tantalum, are used to fabricate the given system in order to suppress potential incoherent errors. In addition, by tuning circuit parameters of the given system, such entangling gates are designed to be microwave-activated, and trend towards maximal entanglement fidelity of the two fluxonium qubits for a duration of a given gate.
70.
Probabilistic data structures embedded in database indexes
Embedded probabilistic data structures may be maintained in database indexes. Different data pages linked in a database index structure may have different respective probabilistic data structures stored within the index. When an access request for the database is received, the probabilistic data structure for a particular database page that is identified as possibly storing an item for the access request may be evaluated to determine whether or not to obtain the data page. If the probabilistic data structure indicates the possible presence of the item, then the data page is obtained to perform the request. If the probabilistic data structure indicates that the item is not present, then the data page is not obtained.
G06F 16/901 - IndexationStructures de données à cet effetStructures de stockage
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 16/22 - 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
71.
Generative artificial intelligence model streaming
An artificial intelligence system includes one or more generative artificial intelligence models (“generative model”) and a retrieval system. The artificial intelligence (“AI”) system receives a natural language input query. The retrieval system retrieves data objects associated with the input query. A generative model streams content in response to input query. The AI system formats output from the generative model and the data objects into formatted data. A user interface outputs the formatted data.
Systems and methods provide for analysis, identification, and automated repair of portions associated with configuration files or templates. The portions may be analyzed using different rules-based approaches to identify various vulnerabilities and their locations. The vulnerability and location information may be provided to a fixing service to implement different remediation workflows to identify specific areas within the portions associated with the vulnerabilities and to apply one or more patches to address the vulnerabilities. The patched files may then be validated and released for use.
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
73.
Address decoding by neural network inference circuit read controller
Some embodiments provide a neural network inference circuit (NNIC) for executing a neural network (NN) that includes computation nodes at multiple layers. The NNIC includes a set of processing circuits for executing the computation nodes of the NN, a set of memories for storing data used by the processing circuits to execute the NN layers, and a read controller for retrieving the data from the memories for use by the processing circuits. The data is stored in the memories as multiple varying-size blocks. The read controller receives read instructions for a requested block of data to be used by the processing circuits for one or more computation nodes. The read instructions include a base memory address for multiple blocks of data, a size of the requested block of data, and a location of the requested block of data within the multiple blocks of data.
The shape of an article of footwear is initially represented by first mesh data. Second mesh data having fewer faces is determined by using an occlusion algorithm to determine occluded faces of the first mesh, using a first function to determine values based on the non-occluded faces, and a second function to determine values based on occluded and non-occluded faces. Using these values, the second mesh is spaced a minimum offset distance to enclose the first mesh without intersection. Proxy data representing the shape of a foot is aligned with the inner surface of the second mesh, then deformed to place the shape adjacent to the inner surface. The mesh representing the footwear may be deformed to place portions of the representation of footwear adjacent to the shape of the foot. Locations of keypoints in the shape of the foot are used to add keypoints to the mesh.
G06V 10/24 - Alignement, centrage, détection de l’orientation ou correction de l’image
G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
Devices and techniques are generally described for virtual avatar generation using a user-submitted face image. In various examples, first image data including an image of a user face and hair may be received. A first machine learning model may predict a skin tone of skin of the user face in the first image data. Modified first image data may be generated by modifying pixels representing the user face using the predicted skin tone. First segmented image data representing the user face and at least a portion of the hair may be generated. Second image data representing a virtual avatar with a pre-defined head may be generated. First scaled image data may be generated by scaling the modified first segmented image data based at least in part on the virtual avatar. Third image data may be generate by rendering the first scaled image data on a body of the virtual avatar.
G06T 17/00 - Modélisation tridimensionnelle [3D] pour infographie
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 40/16 - Visages humains, p. ex. parties du visage, croquis ou expressions
76.
Searchability and discoverability of contextually relevant frames within digital content
Systems, devices, and methods are provided for searchability and discoverability of contextually relevant frames within digital content. Digital content, such as videos, may be segmented to identify a plurality of shots. Discoverability may be performed by identifying key frames of the digital content and using a contrastive language-image pre-training (CLIP) model to determine contextual relevance of a frame or shot to textual information associated with the digital content. Searchability may be performed by receiving search parameters and applying various filters to digital content to identify frames or shots that satisfy a user's search query.
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
H04N 19/172 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant une image, une trame ou un champ
Devices and techniques are generally described for semantic clustering and fixing of like-failed processing inputs. In some examples, first embedding data representing a first input may be generated. A first cluster of embeddings including the first embedding data may be generated. Second embedding data of the first cluster may be determined. First metadata for the first input may be determined. The first metadata may be associated with processing of the first input by the processing system. Second metadata for the second input may be determined. A first remedial action may be determined for processing the first input based at least in part on the first metadata and the second metadata.
G10L 15/01 - Estimation ou évaluation des systèmes de reconnaissance de la parole
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 15/183 - Classement ou recherche de la parole utilisant une modélisation du langage naturel selon les contextes, p. ex. modèles de langage
78.
Frequency multiplying using variable inductors and capacitors
Technologies directed to a frequency multiplier circuit of a customer terminal (CT) for doubling and tripling a frequency of a local oscillator are described. One communication device includes a local oscillator (LO) to generate a first differential signal having a first frequency and a frequency multiplier circuit to generate a second differential signal having a second frequency that is double the first frequency or a third differential signal having a third frequency that is triple the first frequency. The frequency multiplier circuit includes a variable capacitance circuit that is programmable to one of multiple capacitances and a variable inductance circuit that is programmed to a first inductance for the second differential signal and to a second inductance for the third differential signal.
H01Q 3/28 - Dispositifs pour changer ou faire varier l'orientation ou la forme du diagramme de directivité des ondes rayonnées par une antenne ou un système d'antenne faisant varier la phase relative ou l’amplitude relative et l’énergie d’excitation entre plusieurs éléments rayonnants actifsDispositifs pour changer ou faire varier l'orientation ou la forme du diagramme de directivité des ondes rayonnées par une antenne ou un système d'antenne faisant varier la distribution de l’énergie à travers une ouverture rayonnante faisant varier l'amplitude
H01Q 3/36 - Dispositifs pour changer ou faire varier l'orientation ou la forme du diagramme de directivité des ondes rayonnées par une antenne ou un système d'antenne faisant varier la phase relative ou l’amplitude relative et l’énergie d’excitation entre plusieurs éléments rayonnants actifsDispositifs pour changer ou faire varier l'orientation ou la forme du diagramme de directivité des ondes rayonnées par une antenne ou un système d'antenne faisant varier la distribution de l’énergie à travers une ouverture rayonnante faisant varier la phase par des moyens électriques avec des déphaseurs variables
H03B 5/12 - Éléments déterminant la fréquence comportant des inductances ou des capacités localisées l'élément actif de l'amplificateur étant un dispositif à semi-conducteurs
H03D 7/00 - Transfert de modulation d'une porteuse à une autre, p. ex. changement de fréquence
Communications, such as system event log messages, can be authenticated using a secondary message, as may be sent over a common channel. Each secondary message can be a copy of a corresponding primary message signed with a hash, where that hash can be generated using a secret, such as a secret key. In some embodiments, the secret can be stored in BIOS where the messages are generated, such that an operating system executing on a computing device cannot access the secret in order to send valid secondary messages that match content of the corresponding first messages.
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 13/20 - Gestion de demandes d'interconnexion ou de transfert pour l'accès au bus d'entrée/sortie
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
80.
Distributed configuration management for secure hardware
Approaches presented herein relate to the configuration of a secure hardware device, such as a hardware security module (HSM). A distributed set of independent services can be used to configure different settings for the HSM, without ensuring a specific ordering of operation. Each service can set the appropriate configuration, and if successful can indicate that the service is in a healthy state. Each indication can include a logical timestamp, such as a Lamport timestamp that is incremented from a last determined timestamp. A health service can monitor the state information reported by these various services to determine when all services have reported a healthy state. To ensure no settings were modified by another service since the reporting, the health service can set a high water timestamp, and can wait until all services report a healthy state with timestamps greater than the high water timestamp, before deploying the HSM.
Systems and methods are provided to round the numbers produced by a systolic array. A rounder can obtain a number from the systolic array and identify a data type conversion associated with the number. The data type conversion may indicate a first bit-length and a second bit-length of the number. The rounder can select a random number generator for rounding the number based on the data type conversion. The bit-length of the random number generator may be equal to a difference in bit-length between the first bit-length and the second bit-length. The rounder can perform a rounding operation using a random number generated by the random number generator.
G06F 7/499 - Maniement de valeur ou d'exception, p. ex. arrondi ou dépassement
G06F 7/58 - Générateurs de nombres aléatoires ou pseudo-aléatoires
G06F 15/80 - Architectures de calculateurs universels à programmes enregistrés comprenant un ensemble d'unités de traitement à commande commune, p. ex. plusieurs processeurs de données à instruction unique
Disclosed are systems and methods for adjusting the encoding/ordering of parts of a satisfiability modulo theories (“SMT”) problem indicated in an SMT configuration and/or optimizing the SMT solver configuration of an SMT solver to reduce the processing time needed to process the SMT problem using the SMT solver. For example, the order of parts of an SMT problem may be rearranged to reduce a processing time of the SMT problem. Likewise, the configuration options selected for an SMT solver may be adjusted to reduce the processing time required to process an SMT problem.
A data set may be replicated to another storage location according to a schema that projects a subset of the data set according to a replication technique. An additional replication technique, different from the first replication technique, that also projects a subset of the data set to a third storage location is performed. The correctness of the additional replication technique may be verified with regard to the data set to generate a performance report for the additional replication technique.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
A natural language processing system may implement dynamic entity catalog updates. An updated version of an entity catalog describing possible values for entities may be obtained. Respective natural language processing stage artifacts may be generated based on the updated version of the entity catalog for different processing stages of the natural language processing system. The natural language processing stage artifacts may be deployed to the different processing stages of the natural language processing system to replace a prior version of the entity catalog for processing subsequently received input text.
G06F 40/35 - Représentation du discours ou du dialogue
G06F 40/40 - Traitement ou traduction du langage naturel
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 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
In one example, a method comprises: performing backward propagation computations for a second layer of a neural network to generate second weight gradients; splitting the second weight gradients into a plurality of subsets each associated with a second exchange operation over a computer network, a number of the second weight gradients included in the each subset being based on at least one of first characteristics of the computer network or second characteristics of the neural network; performing backward propagation computations for a first layer of the neural network to generate first weight gradients in parallel with at least one of the second exchange operations; performing a first exchange operation to exchange the first weight gradients after the at least one of the second exchange operations completes; and after the first exchange operation completes, perform the remaining second exchange operations to exchange the remaining subsets of the second weight gradients.
G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient
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
86.
Detecting shopping events based on contents of hands depicted within images
Images captured by cameras at a store or another facility are cropped to include portions of such images depicting hands. When an event of a type is determined to have occurred at a time and at a location within the facility, the cropped images are filtered based on the type, the location, and the time of the event to include only images that might depict one item within a hand of an actor. The cropped images, as filtered, are then processed to identify the item within the hand, such as by determining embeddings or other representations of the cropped images, and comparing such embeddings or other representations to embeddings or other representations of reference images of items that are available within the facility. Based on such comparisons, an item is identified as having been taken from the facility by the customer or deposited at the facility by the customer.
G06T 7/70 - Détermination de la position ou de l'orientation des objets ou des caméras
G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
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 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
G06V 40/10 - Corps d’êtres humains ou d’animaux, p. ex. occupants de véhicules automobiles ou piétonsParties du corps, p. ex. mains
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
87.
Computer-implemented multiscale multimodal transformer for multimodal action recognition
Techniques for implementing a multiscale multimodal transformer for multimodal action recognition with a computer are described. According to some examples, a computer-implemented method includes training a multiscale audio transformer (MAT) machine learning model to extract hierarchical audio representations; and generating an audio inference by the MAT machine learning model for an input audio file.
The present disclosure provide techniques for processing natural language inputs on a user device or a system. In some embodiments, a device includes a component configured to predict an output(s) responsive to a user input, where the component learns the output based on historic processing performed by a more robust natural language processing system. The component processes ASR and NLU data to determine: (1) a natural language output that is presented as an audio or a visual output, (2) an action to be performed by a skill, and/or (3) a skill to respond to the user input. The component is automatically updated for new intents, features, business logic, etc., based on learning from the natural language processing system.
This disclosure describes, in part, techniques for utilizing global models to generate local models for electronic devices in an environment, and techniques for utilizing the global models and/or the local models to provide notifications that are based on anomalies detected within the environment. For instance, a remote system may receive an identifier associated with an electronic device and identify a global model using the identifier. The remote system may then receive data indicating state changes of the electronic device and use the data and the global model to generate a local model associated with the electronic device. Using the global model and/or local model, the remote system can identify anomalies associated with the electronic device and, in response to identifying an anomaly, notify the user. The remote system can further cause the electronic device to change states after receiving a request from the user.
G06F 9/44 - Dispositions pour exécuter des programmes spécifiques
G06F 3/00 - Dispositions d'entrée pour le transfert de données destinées à être traitées sous une forme maniable par le calculateurDispositions de sortie pour le transfert de données de l'unité de traitement à l'unité de sortie, p. ex. dispositions d'interface
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
G10L 15/30 - Reconnaissance distribuée, p. ex. dans les systèmes client-serveur, pour les applications en téléphonie mobile ou réseaux
H04L 12/12 - Dispositions pour la connexion ou la déconnexion à distance de sous-stations ou de leur équipement
90.
Continuous video recording, storage, and on-demand event streaming
Systems and techniques are described for continuous video recording and event streaming. In various examples, first video data may be received from a camera device. The first video data may be stored as a first plurality of video segments. A first plurality of micro-events associated with the first video data may be determined. Each micro-event of the first plurality of micro-events may be associated with a respective time stamp. In some examples, a first video event may be determined based at least in part on a first subset of the first plurality of micro-events. In some cases, at least a first video segment of the first plurality of video segments corresponding to the first video event may be determined. A video file representing the first video event may be generated using at least the first video segment.
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
91.
Alpha block transforms for alpha channel compression
Systems and techniques are described for Alpha channel inter-frame and intra-frame compression. An example method includes receiving a first frame representative of a graphical user interface at a first time and a second frame representative of the graphical user interface at a second time. The example includes dividing the first frame and the second frame into respective blocks. The example includes searching the frames for at least one target block that share a common value with at least one other block in either the first frame and/or the second frame. The example includes applying at least one transform to the at least one target block that references either the common value or the at least one other block in either the first frame and/or the second frame. The example includes replacing the at least one target block with a transform block.
H04N 11/02 - Systèmes de télévision en couleurs avec réduction de la largeur de bande
H04N 19/119 - Aspects de subdivision adaptative, p. ex. subdivision d’une image en blocs de codage rectangulaires ou non
H04N 19/136 - Caractéristiques ou propriétés du signal vidéo entrant
H04N 19/172 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant une image, une trame ou un champ
H04N 19/176 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant un bloc, p. ex. un macrobloc
H04N 19/186 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une couleur ou une composante de chrominance
H04N 19/60 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant un codage par transformée
Techniques for predicting an action(s) to perform for a user and, optionally, delivering proactive experiences are described. A system receives data usable to determine a predicted action of a user and invokes a generative model to process the data and determine the predicted action. The system may thereafter determine a system-performable action corresponding to the predicted action and determine a task(s) for executing the system-performable action. The system may also invoke the or another generative model to determine a trigger event(s) for triggering performance of the task(s). The system may receive an event indicating the trigger event(s) has occurred and, based thereon, perform the task(s). Alternatively, a generative model may determine proactive content is to be output during a dialog with the user and, based thereon, the system may perform the task(s).
A system may be configured for cooperation between language model agents. An agent may be, for example, a computer system, or a software component executing on a computer system, that can accept text and/or natural language inputs, draw upon an LM to process the inputs and perform a function, and respond via text and/or natural language outputs. An agent may act as a mediator to interact with a user, identify a task requested by the user, and delegate one or more subtasks to another agent or other resource. An agent may act as a delegate to handle tasks or subtasks delegated by a mediator. Agents may communicate with each other using a combination of structured and unstructured language; for example, one or more parameters and a natural language message.
Techniques for speech-to-text are described. Examples of a speech-to-text services are described. In some examples, the service performs speech-to-text according to the request using the speech-to-text service to generate the transcript from the audio stream by: determining a compute instance to send the request to based, at least in part, on availability information maintained in a distributed routing cache for a plurality of compute instances and types of speech-to-text processing indicated by the request, sending the request to the determined compute instance, and processing the request using the determined compute instance to generate the transcript.
Disclosed are various embodiments for routing device traffic from one cloud to another cloud using connectors and account-based linking. In one embodiment, a serving cloud receives network traffic to be sent to a destination cloud different from the serving cloud. The network traffic is relative to a device managed in the destination cloud. A particular connector from a plurality of connectors that are capable of forwarding the network traffic from the serving cloud to the destination cloud is determined based at least in part on a connector selection rule set. The network traffic is forwarded via the particular connector to the destination cloud.
H04L 67/63 - 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 en acheminant une demande de service en fonction du contenu ou du contexte de la demande
H04L 67/12 - Protocoles spécialement adaptés aux environnements propriétaires ou de mise en réseau pour un usage spécial, p. ex. les réseaux médicaux, les réseaux de capteurs, les réseaux dans les véhicules ou les réseaux de mesure à distance
H04L 67/125 - Protocoles spécialement adaptés aux environnements propriétaires ou de mise en réseau pour un usage spécial, p. ex. les réseaux médicaux, les réseaux de capteurs, les réseaux dans les véhicules ou les réseaux de mesure à distance en impliquant la commande des applications des terminaux par un réseau
H04L 67/565 - Conversion ou adaptation du format ou du contenu d'applications
H04L 69/08 - Protocoles d’interopérabilitéConversion de protocole
96.
VECTOR LIFECYCLE MANAGEMENT IN DISTRIBUTED STORAGE SYSTEMS
Systems and methods are provided for receiving a vector to be inserted into a set of vectors, wherein the set of vectors is stored as a plurality of leaf objects of a distributed proximity-based graph across a plurality of storage devices of a distributed object storage system, identifying a subset of the plurality of leaf objects based at least partly on a similarity of the vector to one or more vectors stored in the subset of the plurality of leaf objects, storing the vector in an insert buffer associated with the subset of the plurality of leaf objects, and in response to a vector query, generating query results comprising the vector stored in the insert buffer, and at least one of the one or more vectors stored in the subset of the plurality of leaf objects.
G06F 16/56 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données d’images fixes en format vectoriel
G06F 16/901 - IndexationStructures de données à cet effetStructures de stockage
Techniques are generally described for Wi-Fi uplink and Bluetooth downlink optimization. An example method includes initiating a first counter corresponding to a first time period, executing first operation using a first radio during the first time period, and initiating a second counter corresponding to a second time period allotted for the first operation within the first time period. The example method also includes determining a second operation associated with a second radio, and in response to the determination, stopping execution of the first operation, pausing the second counter at a first counter value, and executing the second operation using the second radio during the first time period.
H04L 69/14 - Protocoles multicanaux ou multi-liaisons
H04W 4/80 - Services utilisant la communication de courte portée, p. ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
98.
RECORD REQUEST PERFORMANCE OF A RECORD-AWARE DISTRIBUTED STORAGE SYSTEM
Requests for records are performed at a record-aware distributed storage system. A request at a storage service engine for records received from a database access application has a time value identified corresponding to a state of a table and storage nodes that store the records. Requests are sent to the storage nodes to obtain the records. The storage nodes may identify rowblocks with respective record identifier ranges and time value ranges corresponding to the records and the time value for the access request. A result may be returned to the database access application based on the records received from the storage nodes.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet
A system may receive a request to generate an object. A system may generate, based on the request, a first portion of a prompt associated with a first layer of a hierarchy and a second portion of the prompt associated with a second layer of the hierarchy, where the second layer has a lower priority than the first layer. A system may generate the object based in part on providing the portions of the prompt as input to a machine learning model, where the object is formatted according to an object format and is internally consistent. To generate the object the machine learning model does not violate instructions associated with a layer of the hierarchy based on instructions associated with a layer of the hierarchy having a relatively lower priority.
Disclosed are systems and methods that offload the work traditionally performed by a thick software client operating on each container instance of a cloud provider network with a thin and generalized agent that can be instructed in a piece-wise manner to perform operations as instructed by a workload manager executing on a control plane that is separate from the container instance. The workload manager, executing on the control plane of a cloud provider network, may precompute a set of operations and order of execution of those operations in the control plane and present those operations in a controlled manner to the agent that executes each operation as instructed. The agent executing on the data plane, rather than polling an orchestrator for work and then establishing the runtime environment based on a received Application Specification, awaits an operation or task that is pushed to the agent from the control plane.
