To mine and visualize related topics in a knowledge base, where a knowledge base is mined for related topics to create a knowledge graph that is output as a visualization display of automatically suggested related topics. To mine the knowledge base an approach has been developed which incorporates user personalized results in addition to semantic context. The results are displayed in a visualization display for user interaction. While interacting with a suggested topic the user can view and select related topic information which enables users to discover other similar or related topics they would be interested in gaining additional context about. Thus, the related topics and visualization display according to aspects described herein may serve the purpose of more effective utilization and exploration of the knowledge base.
A system and method for using a semi-polling model to monitor a Non-Volatile Memory Express (NVMe) completion queue (CQ). The method, implemented at an interrupt thread, includes receiving an input/output (I/O) request from an application and submitting a submission queue (SQ) entry into an NVMe SQ in response to the I/O request. The method further includes registering for notifications from a polling thread after submitting the SQ entry and receiving, from the polling thread, a notification of the presence of a CQ entry within an NVMe CQ. Additionally, the method involves removing the CQ entry from the NVMe CQ and notifying the application of completion of the I/O request.
G06F 13/26 - Gestion de demandes d'interconnexion ou de transfert pour l'accès au bus d'entrée/sortie utilisant l'interruption avec commande prioritaire
G06F 13/22 - Gestion de demandes d'interconnexion ou de transfert pour l'accès au bus d'entrée/sortie utilisant le balayage successif, p. ex. l'appel sélectif
3.
USER MODE DIRECT DATA ACCESS TO NON-VOLATILE MEMORY EXPRESS DEVICE VIA KERNEL-MANAGED QUEUE PAIR
Systems and methods are disclosed for implementing a Non-Volatile Memory Express (NVMe) driver in a computer system. The method involves mapping a memory buffer into a user mode address space to facilitate data transfer with an NVMe device via direct memory access (DMA). Additionally, a first NVMe queue pair, including a submission queue (SQ) and a completion queue (CQ), is mapped into the user mode address space, allowing a user mode component to submit commands to the NVMe device. The method further enables the user mode component to ring a doorbell at the NVMe device. Finally, an NVMe command is processed in kernel mode using a second NVMe queue pair comprising a second SQ and a second CQ.
Systems and methods are provided for facilitating the discovery and presentations of skills and data processed by the skills within blocks of a canvas displayed to a user within a user interface. The systems also identify schemas and data output formats of the relevant skills, and a desired presentation schema based on the user contexts and the application contexts. The systems further identify modules for normalizing the schemas of the relevant skills to the desired presentation schema, and uses the identified modules to normalize data obtained from the relevant skills into the desired presentation schema for presentation within the blocks of the canvas.
Embodiments of the present disclosure include countermeasure circuit techniques for cyberattacks. In one embodiment, portions of combinational logic receive shared input bit groups and produce shared output bit groups. Shared output bit groups may be coupled between series configured combinational logic portions using control gates. Clock signals are delayed to activate the control gates after the outputs are stable. In some embodiments, a first combinational logic group and second combinational logic group operate on a clock and inverse clock.
Innovations in use of chroma quantization parameter (“QP”) offsets when determining a control parameter for deblock filtering. For example, as part of encoding, an encoder sets a picture-level chroma QP offset and slice-level chroma QP offset for encoding of a slice of a picture. The encoder also performs deblock filtering of at least part of the slice, where derivation of a control parameter considers only the picture-level chroma QP offset. The encoder outputs at least part of a bitstream including the encoded content. As part of decoding, a corresponding decoder sets a picture-level chroma QP offset and a slice-level chroma QP offset for decoding of a slice of a picture, but derivation of a control parameter for deblock filtering considers only the picture-level chroma QP offset.
H04N 19/117 - Filtres, p. ex. pour le pré-traitement ou le post-traitement
H04N 19/126 - Détails des fonctions de normalisation ou de pondération, p. ex. matrices de normalisation ou quantificateurs uniformes variables
H04N 19/15 - Débit ou quantité de données codées à la sortie du codeur par contrôle de la taille réelle des données compressées au niveau de la mémoire avant de décider du stockage dans la mémoire tampon de transmission
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 19/174 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant une tranche, p. ex. une ligne de blocs ou un groupe de blocs
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 19/184 - 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 des bits, p. ex. de flux vidéo compressé
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 19/70 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques caractérisés par des aspects de syntaxe liés au codage vidéo, p. ex. liés aux standards de compression
H04N 19/86 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le pré-traitement ou le post-traitement spécialement adaptés pour la compression vidéo mettant en œuvre la diminution des artéfacts de codage, p. ex. d'artéfacts de blocs
7.
INTEGRATED OPERATING SYSTEM SEARCH USING SCOPE OPTIONS
Methods and systems are provided for narrowing a scope of a search on a computing device to provide relevant search results to the user. Prior to receiving a search query from the user in a search box of a desktop taskbar, scope options are communicated for presentation from which the user can select. These scope options enable the user to select from web-based searches and local searches. A selection is received form the user of one of the scope options. Suggested search results are generated based on this scope option selection. At least one suggested search results is communicated for presentation on a display of the computing device.
A data collection and reporting system for a multinational corporation (MNC) identifies the regions with which the MNC is associated and generates region data collecting jobs to be executed in each of these regions. The jobs are executed during off-peak hours for each region. Each region data collecting job results in a region aggregate to be computed based on the data collected from the region. Once all region aggregates have been computed and stored, a global aggregate is computed from the region aggregates. The global aggregate is then processed to generate reporting data which is used to generate one or more data reports for the MNC.
A computer device instantiates a first Transport Layer Security (TLS) endpoint having access to a trusted execution environment (TEE) of the processor; generates in the TEE in an endpoint-specific public-private key pair bound to the first TLS endpoint; generates of attestation data verifying that the endpoint-specific public-private key pair was generated in the TEE and is bound to the first TLS endpoint; and signs the attestation data in the TEE using a TEE private key securely embedded in the processor. The device generates a TEE signature using an endpoint-specific private key of an endpoint-specific public-private key pair; and indicates of the attestation data, an endpoint-specific public key of the endpoint-specific public public-private key pair and the TEE signature to a second TLS endpoint within a TLS handshake message exchange between the first TLS endpoint and the second TLS endpoint.
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
Methods, systems, and apparatuses include receiving text input via a user interface for an online system. An embedding is generated based on the text input. Supplemental text is generated using the embedding and a vector store including a standardized content items, the supplemental text having a standardized format. The standardized content items are generated by applying a large language model to a plurality of content items. A prompt is formulated including the supplemental text. A generative language model is applied to the prompt. A recommendation is output by the generative language model based on the prompt. The recommendation is provided to the user interface based on at least the text input.
A computer system and method are disclosed for replicating logs in a distributed environment. The method includes identifying a write input/output (I/O) operation and identifying a log to be replicated based on the write I/O operation. The log is then persisted to local non-volatile memory in the computer system. Subsequently, the log is replicated to multiple remote hosts, where each remote host of the plurality of remote hosts stores the log in its corresponding local non-volatile memory without de-staging the log to a backing store. The write I/O operation is committed once the log is replicated to at least a subset of the remote hosts that forms a quorum. Finally, the log is de-staged to the backing store after the write I/O operation is successfully committed.
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
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
12.
System and Method for Proactively Reducing Hallucinations in Generative Artificial Intelligence (AI) Model Responses
A method, computer program product, and computing system for processing a prompt for a target generative AI model and a corresponding response generated by the target generative AI model for the prompt. The prompt and the corresponding response from the generative AI model are compared to a plurality of predefined verified prompt-response pairs. In response to determining at least a threshold similarity between the prompt and the corresponding response and a predefined verified prompt-response pair, the corresponding response from the target generative AI model is provided to a source of the prompt.
Methods, systems, and computer storage media for providing attack path discovery management using an attack path discovery engine of a security management system. Attack path discovery management supports automatic attack path discovery that involves identifying and mapping potential pathways that attackers could use to infiltrate computing environments. In operation, an attack path discovery computation model comprising an entry point element, an advancement step element, and a target element, is accessed. A computing environment graph comprising computing components of a computing environment is accessed. Based on the entry point element, an entry point is identified in the computing graph; based on the advancement step element, an advancement step is identified in the computing environment graph; and based on the target element, a target is identified in the computing environment graph. An attack path is generated based on the entry point, the advancement steps, and the target. The attack path is communicated.
A method for generating storyboards is described. An extraction prompt is provided to a first generative neural network model. The extraction prompt is a text-based prompt that instructs the first generative neural network model how to identify timestamps of segments having related content within transcripts according to dialog within the transcripts. A transcript of a meeting is provided as an input to the first generative neural network model. Segment timestamps for identified segments within the meeting are received from the first generative neural network model based on the extraction prompt and the transcript. Segment images for the identified segments are generated using a second generative neural network model, wherein each of the segment images represents segment content within a corresponding identified segment.
Innovations in syntax and semantics of coded picture buffer removal delay (“CPBRD”) values potentially simplify splicing operations. For example, a video encoder sets a CPBRD value for a current picture that indicates an increment value relative to a nominal coded picture buffer removal time of a preceding picture in decoding order, regardless of whether the preceding picture has a buffering period SEI message. The encoder can signal the CPBRD value according to a single-value approach in which a flag indicates how to interpret the CPBRD value, according to a two-value approach in which another CPBRD value (having a different interpretation) is also signaled, or according to a two-value approach that uses a flag and a delta value. A corresponding video decoder receives and parses the CPBRD value for the current picture. A splicing tool can perform simple concatenation operations to splice bitstreams using CPBRD value for the current picture.
H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c.-à-d. systèmes dans lesquels le signal vidéo n'est pas diffusé
H04N 19/573 - Compensation de mouvement avec prédiction multi-trame utilisant plusieurs trames de référence dans une direction de prédiction donnée
H04N 19/58 - Compensation de mouvement par prédiction à long terme, c.-à-d. que la trame de référence pour une trame courante n’est pas la plus proche temporellement
H04N 19/70 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques caractérisés par des aspects de syntaxe liés au codage vidéo, p. ex. liés aux standards de compression
H04N 21/234 - Traitement de flux vidéo élémentaires, p. ex. raccordement de flux vidéo ou transformation de graphes de scènes du flux vidéo codé
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
A hash-based cryptographic system includes hash input registers and hash generators coupled in parallel with each other. Each hash generator of the hash generators is coupled to receive a corresponding hash input from one of the input registers. A controller is coupled to the input registers and configured to provide the corresponding hash input to the hash registers to cause the parallel hash generators to generate a private key for corresponding leaf nodes of a hash-based signature tree structure.
H04L 9/06 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité l'appareil de chiffrement utilisant des registres à décalage ou des mémoires pour le codage par blocs, p. ex. système DES
Disclosed in some examples are methods, systems, and machine-readable mediums to provide for automated ad-hoc meeting room management. The disclosed systems may leverage the communication application executing on the participant computing devices of participants of the communication session to report information about connected peripheral devices to a management service, such as a communication service. Peripheral devices may then be assigned, either automatically or via manual assignment to a particular conference room. Various management actions may then be performed on those devices.
H04L 41/22 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets comprenant des interfaces utilisateur graphiques spécialement adaptées [GUI]
H04L 41/12 - Découverte ou gestion des topologies de réseau
The present disclosure provides methods, apparatuses and computer program products for hand-drawing shape-based document retrieval. An input hand-drawing shape may be obtained. A hand-drawing shape feature of the hand-drawing shape may be extracted through a feature extracting model. At least one target document may be retrieved by using the hand-drawing shape feature and a feature index library associated with a plurality of candidate documents, at least one document page in the target document locally matching the hand-drawing shape.
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
G06F 16/901 - IndexationStructures de données à cet effetStructures de stockage
G06V 10/32 - Normalisation des dimensions de la forme
G06V 10/46 - Descripteurs pour la forme, descripteurs liés au contour ou aux points, p. ex. transformation de caractéristiques visuelles invariante à l’échelle [SIFT] ou sacs de mots [BoW]Caractéristiques régionales saillantes
G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
G06V 10/75 - Organisation de procédés de l’appariement, p. ex. comparaisons simultanées ou séquentielles des caractéristiques d’images ou de vidéosApproches-approximative-fine, p. ex. approches multi-échellesAppariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexteSélection des dictionnaires
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 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 30/418 - Appariement de documents, p. ex. d’images de documents
19.
USE OF CHROMA QUANTIZATION PARAMETER OFFSETS IN DEBLOCKING
Innovations in use of chroma quantization parameter (“QP”) offsets when determining a control parameter for deblock filtering. For example, as part of encoding, an encoder sets a picture-level chroma QP offset and slice-level chroma QP offset for encoding of a slice of a picture. The encoder also performs deblock filtering of at least part of the slice, where derivation of a control parameter considers only the picture-level chroma QP offset. The encoder outputs at least part of a bitstream including the encoded content. As part of decoding, a corresponding decoder sets a picture-level chroma QP offset and a slice-level chroma QP offset for decoding of a slice of a picture, but derivation of a control parameter for deblock filtering considers only the picture-level chroma QP offset.
H04N 19/117 - Filtres, p. ex. pour le pré-traitement ou le post-traitement
H04N 19/126 - Détails des fonctions de normalisation ou de pondération, p. ex. matrices de normalisation ou quantificateurs uniformes variables
H04N 19/15 - Débit ou quantité de données codées à la sortie du codeur par contrôle de la taille réelle des données compressées au niveau de la mémoire avant de décider du stockage dans la mémoire tampon de transmission
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 19/174 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant une tranche, p. ex. une ligne de blocs ou un groupe de blocs
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 19/184 - 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 des bits, p. ex. de flux vidéo compressé
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 19/70 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques caractérisés par des aspects de syntaxe liés au codage vidéo, p. ex. liés aux standards de compression
H04N 19/86 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le pré-traitement ou le post-traitement spécialement adaptés pour la compression vidéo mettant en œuvre la diminution des artéfacts de codage, p. ex. d'artéfacts de blocs
20.
ON DEMAND CONTEXTUAL SUPPORT AGENT WITH SPATIAL AWARENESS
Techniques for toggling a visibility of tagged spatial data and for correlating the tagged spatial data with output of an LLM are disclosed. Scene data describing a real-world scene in which a 3D object is located is accessed. A digital file that models the 3D object is accessed. The digital file includes tagged spatial data associated with the 3D object. User input is received. The user input is directed to the 3D object. The digital file and the user input are provided as input to the LLM. The LLM correlates the user input with the tagged spatial data and generates a response. While the LLM's response is being provided to the user, a display of a hologram is toggled, where this hologram overlays at least a portion of the 3D object.
G06F 16/909 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des informations géographiques ou spatiales, p. ex. la localisation
Techniques for selectively transforming one or more coding units when coding video content are described herein. The techniques may include determining whether or not to transform a particular coding unit. The determination may be based on a difference in pixel values of the particular coding unit and/or one or more predefined rate-distortion constraints. When it is determined to not perform a transform, the particular coding unit may be coded without transforming the particular coding unit.
H04N 19/645 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant un codage par transformée utilisant une transformée en sous-bandes, p. ex. ondelettes caractérisé par l’ordonnancement des coefficients ou des bits à transmettre par regroupement de coefficients en blocs après la transformée
H04N 19/12 - Sélection parmi plusieurs transformées ou standards, p. ex. sélection entre une transformée en cosinus discrète [TCD] et une transformée en sous-bandes ou sélection entre H.263 et H.264
H04N 19/13 - Codage entropique adaptatif, p. ex. codage adaptatif à longueur variable [CALV] ou codage arithmétique binaire adaptatif en fonction du contexte [CABAC]
H04N 19/14 - Complexité de l’unité de codage, p. ex. activité ou estimation de présence de contours
H04N 19/147 - Débit ou quantité de données codées à la sortie du codeur selon des critères de débit-distorsion
H04N 19/172 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant une image, une trame ou un champ
H04N 19/176 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant un bloc, p. ex. un macrobloc
H04N 21/845 - Structuration du contenu, p. ex. décomposition du contenu en segments temporels
22.
USE OF CHROMA QUANTIZATION PARAMETER OFFSETS IN DEBLOCKING
Innovations in use of chroma quantization parameter (“QP”) offsets when determining a control parameter for deblock filtering. For example, as part of encoding, an encoder sets a picture-level chroma QP offset and slice-level chroma QP offset for encoding of a slice of a picture. The encoder also performs deblock filtering of at least part of the slice, where derivation of a control parameter considers only the picture-level chroma QP offset. The encoder outputs at least part of a bitstream including the encoded content. As part of decoding, a corresponding decoder sets a picture-level chroma QP offset and a slice-level chroma QP offset for decoding of a slice of a picture, but derivation of a control parameter for deblock filtering considers only the picture-level chroma QP offset.
H04N 19/117 - Filtres, p. ex. pour le pré-traitement ou le post-traitement
H04N 19/126 - Détails des fonctions de normalisation ou de pondération, p. ex. matrices de normalisation ou quantificateurs uniformes variables
H04N 19/15 - Débit ou quantité de données codées à la sortie du codeur par contrôle de la taille réelle des données compressées au niveau de la mémoire avant de décider du stockage dans la mémoire tampon de transmission
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 19/174 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant une tranche, p. ex. une ligne de blocs ou un groupe de blocs
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 19/184 - 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 des bits, p. ex. de flux vidéo compressé
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 19/70 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques caractérisés par des aspects de syntaxe liés au codage vidéo, p. ex. liés aux standards de compression
H04N 19/86 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le pré-traitement ou le post-traitement spécialement adaptés pour la compression vidéo mettant en œuvre la diminution des artéfacts de codage, p. ex. d'artéfacts de blocs
23.
CONTEXT-BASED PROMPT GENERATION FOR AUTOMATED TRANSLATIONS BETWEEN NATURAL LANGUAGE AND QUERY LANGUAGE
A disclosed method facilitates translation of natural language queries into query language statements usable to retrieve data from or write data to a particular database. The method includes obtaining a pool of shots. Each shot in the pool includes a natural language query component and a corresponding database translation component. The method further provides for vectorizing the natural language query component for each of the shots into a common vector space; receiving a natural language query from a user interface; vectorizing the natural language query within the common vector space; identifying a subset of vectorized natural language query components that satisfy a similarity metric when compared to the vectorized natural language query; and generating an LLM prompt that includes shots from the pool corresponding to the subset of the vectorized natural language query.
Systems and methods for generating autocomplete text using a language model are disclosed. An image and text-prefix may be entered at an input field of a search application. The image is processed to generate an image description. The image description and the text-prefix signals may be used as input at a language model to generate an autocomplete text by the language model. A contextual history may also be included as input to the language model. The autocomplete text is an output by the language model based on the input at the language model. The auto-complete text may be a next-word ghosting.
G06F 16/583 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
The description relates to executing an inference query relative to a database management system, such as a relational database management system. In one example a trained machine learning model can be stored within the database management system. An inference query can be received that applies the trained machine learning model on data local to the database management system. Analysis can be performed on the inference query and the trained machine learning model to generate a unified intermediate representation of the inference query and the trained model. Cross optimization can be performed on the unified intermediate representation. Based upon the cross-optimization, a first portion of the unified intermediate representation to be executed by a database engine of the database management system can be determined, and, a second portion of the unified intermediate representation to be executed by a machine learning runtime can be determined.
Caching write input/output (I/O) operations in a replica-based storage system. A write I/O operation is received from a consumer, and a corresponding replica list is identified. A first replica set is selected from the replica list for caching the I/O operation, and a first log corresponding to the I/O operation is added to a primary ring buffer of the first replica set. When the first log cannot be replicated to a secondary ring buffer of the first replica set, a second replica set is selected from the replica list for caching the I/O operation. A second log corresponding to the I/O operation is added to a primary ring buffer of the second replica set. Once the second log has been replicated to a secondary ring buffer of the second replica set, the I/O operation is acknowledged to the consumer, and the second log is de-staged to a backing store.
Described herein are technologies related to analyzing behavioral data of an entity in a cloud computing environment and determining suitability of providing the behavioral data to a computer-executable model that is configured to identify anomalous behavior of the entity. The technologies described herein improve performance of computer-executable models that are configured to detect anomalous behavior in a cloud computing environment.
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
Techniques are described herein in which a programmable logic device (PLD) is integrated into a baseboard management controller (BMC). A programming-enhanced BMC is powered on by a PLD that is integrated into the programming-enhanced BMC and that is coupled to an internal bus of the programming-enhanced BMC. A configuration file is provided from immutable BMC hardware in the BMC to the PLD based at least on the programming-enhanced BMC being powered on. The configuration file specifies a configuration to be programmatically applied to programmable hardware of the PLD. The programmable hardware of the PLD is programmed by loading the configuration file, which causes the programmable hardware to render a peripheral interface that is defined by the configuration file natively on the internal bus of the programming-enhanced BMC.
G06F 30/34 - Conception de circuits pour circuits reconfigurables, p. ex. réseaux de portes programmables [FPGA] ou circuits logiques programmables [PLD]
29.
GENERATIVE AI-DRIVEN MULTI-SOURCE DATA QUERY SYSTEM
Embodiments of the disclosed technologies include, in response to receiving a query, matching the query to metadata from a plurality of heterogeneous data sources, and selecting one or more data sources from the plurality of heterogeneous data sources for answering the query, by sending the query and embeddings of the matched metadata to a generative artificial intelligence (GAI), and prompting the GAI to select matching data sources. Based on the data from the GAI, generating one or more custom queries targeted to the matching data sources selected by the GAI, the custom queries formatted to be sent to the selected data sources, executing the one or more custom queries across the selected data sources, and summarizing results from the executing and providing a response to the query.
A computing system including a quantum computing device. The quantum computing device includes a Majorana island at which Majorana zero modes (MZMs) are instantiated. The quantum computing device further includes a quantum dot electrically connectable to an MZM, a capacitance sensor capacitively coupled to the quantum dot, and a controller. The controller is configured to set a Majorana island gate voltage of the Majorana island and a quantum dot gate voltage of the quantum dot to a candidate resonance Majorana island voltage and a candidate resonance quantum dot voltage. The controller is further configured to receive a capacitance measurement of the quantum dot and the Majorana island and determine whether resonance occurs based on the capacitance measurement. The controller is further configured to reset the gate voltages. The controller is further configured to output a quasiparticle poisoning value indicated by the one or more determinations of whether resonance occurs.
31.
SUB-KELVIN TEMPERATURE GRADIENT SYSTEM FOR SCALABLE QUANTUM CONTROL
Examples described in this disclosure relate to sub-kelvin control systems and methods for scalable quantum control. An example system includes a first cooling sub-system operable to maintain an operating temperature for a first device within a first sub-kelvin temperature range. The system further includes a second cooling sub-system, separate from the first cooling sub-system, operable to maintain an operating temperature for a second device, different from the first device, within a second sub-kelvin temperature range. The first sub-kelvin range may comprise a range between 50 milli-kelvin (mK) to 999 mK and the second sub-kelvin range may comprise a range between 1 mK to 299 mK. The combination of the first cooling sub-system and the second cooling sub-system is configured to maintain a temperature gradient between the first device and the second device despite the first device and the second device being in close proximity to each other.
32.
CURSOR PROMPT INTERFACES FOR FACILITATING MULTIPLE FUNCTIONALITY
Systems and methods are provided for utilizing cursor prompt interfaces to facilitate multiple functionalities and techniques for interacting with content selected by corresponding cursor prompts. Selection of displayed content causes the cursor prompt interfaces to be generated and displayed with interactive components for generating new content corresponding to the selected content and that is based on a context of the application instance and/or a user context. In some instances, the cursor prompt interfaces also interact with remote machine-learning models to obtain the new content.
G06F 3/04812 - Techniques d’interaction fondées sur l’aspect ou le comportement du curseur, p. ex. sous l’influence de la présence des objets affichés
G06F 3/04817 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p. ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comportement ou d’aspect utilisant des icônes
G06F 3/04842 - Sélection des objets affichés ou des éléments de texte affichés
33.
PERSONALIZED DATA PROCESSING EXPERIENCE AND CANVAS INTERFACES
Systems and methods are provided for facilitating the discovery and presentations of skills and data within blocks of a canvas displayed to a user within a user interface. The data is presented to provide personalized and contextually relevant experiences as the user interacts with the data and interfaces.
Example solutions read compressed data from a file in persistent storage, directly into an in-memory store without decompression. Some examples transcode the compression scheme used by the file into the compression scheme used by the in-memory store whereas, in other examples, the in-memory store is compatible with the compression scheme used by the file. In some examples that use transcoding, radix clustering is used to speed up compression dictionary transcoding. The radix clustering minimizes cache misses, thereby increasing the efficiency of memory access. These approaches significantly improve cold start times when responding to queries.
Embodiments of the disclosed technologies include, in response to receiving a query, matching the query to metadata from a plurality of heterogeneous data sources, and selecting one or more data sources from the plurality of heterogeneous data sources for answering the query, by sending the query and embeddings of the matched metadata to a generative artificial intelligence (GAI), and prompting the GAI to select matching data sources. Based on the data from the GAI, generating one or more custom queries targeted to the matching data sources selected by the GAI, the custom queries formatted to be sent to the selected data sources, executing the one or more custom queries across the selected data sources, and summarizing results from the executing and providing a response to the query.
Systems and methods are provided for managing the utilization and switching of modes for a multi-modal cursor. A user interface with selectable content is displayed, along with a cursor prompt for a multi-modal cursor. A traditional cursor mode icon and an artificial intelligence (AI) cursor mode icon are also displayed with user interface. The systems determine which mode of the multi-modal cursor to activate and utilize based on user input selecting either the traditional cursor mode icon or the AI cursor mode icon. User selection of the traditional cursor mode icon activates the first cursor mode and user selection of the AI cursor mode icon activates the second cursor mode.
In an example embodiment, a generator model such as a large language model (LLM) is leveraged to generate embeddings for both pieces of content and users. The embeddings map the pieces of content and the users into the same latent n-dimensional space. The embeddings are then fine-tuned using a two-tower deep neural network, with one of the towers representing users and the other tower representing content. The two-tower deep neural network is trained to optimize the embeddings over some shared goal, such as user engagement with content, and uses information such as user interactions with content in that process. A clustering technique, such as K-nearest neighbor (kNN) can then be used to identify a grouping of top user/content pairs based on similarity between users and content, as reflected in the embeddings. For a given piece of content, therefore, the top users from that cluster can then be recommended as an audience for the content.
A method for artificial intelligence (AI) inferencing workload allocation includes, at a computing device of a distributed AI inferencing platform, receiving an estimated prompt load and an estimated generation load of an AI inferencing workload to be fulfilled by a processing unit of a computing node of the distributed AI inferencing platform. Based at least in part on the estimated prompt load and the estimated generation load, an inference unit (IU) processing load is estimated, the IU processing load to be applied to the processing unit while fulfilling the AI inferencing workload. Fractional processing capacity of the processing unit is allocated for fulfilling the AI inferencing workload based at least in part on the IU processing load.
A system for providing enhanced search functionality is provided. A system may receive a prefix from a user device and identify that a user's intent is to do an exploratory query. The system may further identify a topic based on the prefix and generate one or more associated queries relating to the topic. The system may further generate one or more headings relating to the topic. The system may then provide one or more of the one or more headings and the one or more associated queries relating to the topic to a user device.
Systems and methods for semantic temporal segmentation based on topic recognition are disclosed. Text classification and segmentation may be used to index media content for subsequent searches. The method may include using a text classification model to semantically analyze sentences in a text file (such as a transcript) to determine topics with which sentences are associated. The output of the text classification model may be provided to a text segmentation model to enable more-accurate identification of text segments (e.g., paragraphs) within the text file. In some examples, the output of a text segmentation model is provided to a text classification model to enable the text classification model to perform more-accurate classification based on text segments rather than (or in addition to) performing classification on single sentences. The classification of the text segments may be used to assign labels to the text segments to enable subsequent searches based on the labels.
Methods and systems for transitioning write input/output (I/O) requests from a write-caching mode to a pass-through mode. The method includes determining when a condition has been met for transitioning the write I/O requests and de-staging one or more logs from a write cache to a backing store, where each log corresponds to a pending write I/O operation by a consumer. The method further includes determining when no log remains in the write cache for de-staging to the backing store and initiating the pass-through mode by routing new write I/O requests directly to the backing store instead of the write cache.
G06F 12/0888 - 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 utilisant la mémorisation cache sélective, p. ex. la purge du cache
42.
REMOTE DIRECT MEMORY ACCESS DATA REPLICATION MODEL
A computer-implemented method for replicating a log to a remote computer system is disclosed. The method involves identifying a log comprising a data portion and a metadata portion for replication. The data portion is sent to the remote computer system using a Remote Direct Memory Access (RDMA) write operation, while the metadata portion is sent using a first RDMA send operation after the data portion has been sent. The method further includes identifying a second RDMA send operation received from the remote computer system, which indicates the completion of the first RDMA send operation. Based on identifying the second RDMA send operation, the method determines the completion of log replication to the remote computer system. This method enables efficient and reliable replication of logs in a computer system.
H04L 67/1097 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau pour le stockage distribué de données dans des réseaux, p. ex. dispositions de transport pour le système de fichiers réseau [NFS], réseaux de stockage [SAN] ou stockage en réseau [NAS]
G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p. ex. des interruptions ou des opérations d'entrée–sortie
43.
SYNTAX AND SEMANTICS FOR BUFFERING INFORMATION TO SIMPLIFY VIDEO SPLICING
Innovations in syntax and semantics of coded picture buffer removal delay (“CPBRD”) values potentially simplify splicing operations. For example, a video encoder sets a CPBRD value for a current picture that indicates an increment value relative to a nominal coded picture buffer removal time of a preceding picture in decoding order, regardless of whether the preceding picture has a buffering period SEI message. The encoder can signal the CPBRD value according to a single-value approach in which a flag indicates how to interpret the CPBRD value, according to a two-value approach in which another CPBRD value (having a different interpretation) is also signaled, or according to a two-value approach that uses a flag and a delta value. A corresponding video decoder receives and parses the CPBRD value for the current picture. A splicing tool can perform simple concatenation operations to splice bitstreams using CPBRD value for the current picture.
H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c.-à-d. systèmes dans lesquels le signal vidéo n'est pas diffusé
H04N 19/573 - Compensation de mouvement avec prédiction multi-trame utilisant plusieurs trames de référence dans une direction de prédiction donnée
H04N 19/58 - Compensation de mouvement par prédiction à long terme, c.-à-d. que la trame de référence pour une trame courante n’est pas la plus proche temporellement
H04N 19/70 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques caractérisés par des aspects de syntaxe liés au codage vidéo, p. ex. liés aux standards de compression
H04N 21/234 - Traitement de flux vidéo élémentaires, p. ex. raccordement de flux vidéo ou transformation de graphes de scènes du flux vidéo codé
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
44.
Using a Search to Determine What a Group of People are Working On
Examples of the present disclosure describe systems and methods for determining relationships between content items to create a visualization associated with the various content items. The visualization may provide information regarding what various individuals in a group, team, or organization have been working on (e.g., content, documents, projects).
This disclosure relates to utilizing a threat detection system to detect anomalous actions provided by a compromised large generative language model (LLM). For instance, the threat detection system utilizes a detection-based large generative model to process select communication between an application system and the LLM and determine when the LLM may have been potentially compromised. In various implementations, utilizing the detection-based large generative model, the threat detection system determines when an LLM is improperly instructing an application system to invoke tools to perform unapproved actions. Furthermore, when an LLM becomes compromised, the threat detection system intelligently safeguards the detection-based large generative model against similar threats that seek to evade detection or compromise the detection-based large generative model.
Examples of the present disclosure describe systems and methods for configuring and executing per-service TLS settings in a forward proxy. In examples, a proxy device receives a connection request from a client device to access a service. The proxy device identifies service connection information included in the connection request and selects a connection scheme for connecting to the service. The service connection information is compared to a static mapping of connection data in the connection scheme. If the service connection information matches the static mapping of connection data, a TLS type is determined for the connection request. If the service connection information does not match the static mapping of connection information, the service connection information is compared to a dynamic mapping of session information. Based on the comparison of the service connection information to the dynamic mapping of session information, a TLS type is determined for the connection request.
A method for network data communication includes, at an initiator subsystem, generating a data stream including a series of n transaction requests for delivery to two or more target subsystems via a network fabric. The series of n transaction requests are transmitted to the two or more target subsystems. An initiator aggregation controller transmits (n−1) preliminary request responses to the initiator subsystem for a first (n−1) transaction requests of the series of n transaction requests. The initiator aggregation controller receives target-specific aggregated responses to the data stream corresponding to each of the two or more target subsystems. Upon receiving the target-specific aggregated responses corresponding to each of the two or more target subsystems, the initiator aggregator controller transmits an aggregated stream response to the initiator subsystem.
Techniques for selectively transforming one or more coding units when coding video content are described herein. The techniques may include determining whether or not to transform a particular coding unit. The determination may be based on a difference in pixel values of the particular coding unit and/or one or more predefined rate-distortion constraints. When it is determined to not perform a transform, the particular coding unit may be coded without transforming the particular coding unit.
H04N 19/645 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant un codage par transformée utilisant une transformée en sous-bandes, p. ex. ondelettes caractérisé par l’ordonnancement des coefficients ou des bits à transmettre par regroupement de coefficients en blocs après la transformée
H04N 19/12 - Sélection parmi plusieurs transformées ou standards, p. ex. sélection entre une transformée en cosinus discrète [TCD] et une transformée en sous-bandes ou sélection entre H.263 et H.264
H04N 19/13 - Codage entropique adaptatif, p. ex. codage adaptatif à longueur variable [CALV] ou codage arithmétique binaire adaptatif en fonction du contexte [CABAC]
H04N 19/14 - Complexité de l’unité de codage, p. ex. activité ou estimation de présence de contours
H04N 19/147 - Débit ou quantité de données codées à la sortie du codeur selon des critères de débit-distorsion
H04N 19/172 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant une image, une trame ou un champ
H04N 19/176 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant un bloc, p. ex. un macrobloc
H04N 21/845 - Structuration du contenu, p. ex. décomposition du contenu en segments temporels
Mixed reality instructional technology is enhanced. Comparison of a guide mixed reality data stream to a candidate mixed reality data stream detects a deviation, e.g., different hand movement, different tool placement, or different sensor telemetry. Experiencing a feedback stream of output that is based on the deviation facilitates device control and helps candidates improve their skills. Some feedback output emphasizes deviation size, e.g., by varying colors, sounds, or haptic output proportionally to the deviation size. Some feedback output renders a translucent or skeletal guide overlaid on a live video of current candidate activity. Some embodiments support searches whose results show, e.g., how a certain expert performed a task differently than the candidate, or examples of a certain task such as widget replacement in the field. Stream optimization, summarization, synchronization, and other stream derivation functionality is provided.
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06F 3/0487 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p. ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p. ex. des gestes en fonction de la pression exercée enregistrée par une tablette numérique
Techniques for selectively transforming one or more coding units when coding video content are described herein. The techniques may include determining whether or not to transform a particular coding unit. The determination may be based on a difference in pixel values of the particular coding unit and/or one or more predefined rate-distortion constraints. When it is determined to not perform a transform, the particular coding unit may be coded without transforming the particular coding unit.
H04N 19/645 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant un codage par transformée utilisant une transformée en sous-bandes, p. ex. ondelettes caractérisé par l’ordonnancement des coefficients ou des bits à transmettre par regroupement de coefficients en blocs après la transformée
H04N 19/12 - Sélection parmi plusieurs transformées ou standards, p. ex. sélection entre une transformée en cosinus discrète [TCD] et une transformée en sous-bandes ou sélection entre H.263 et H.264
H04N 19/13 - Codage entropique adaptatif, p. ex. codage adaptatif à longueur variable [CALV] ou codage arithmétique binaire adaptatif en fonction du contexte [CABAC]
H04N 19/14 - Complexité de l’unité de codage, p. ex. activité ou estimation de présence de contours
H04N 19/147 - Débit ou quantité de données codées à la sortie du codeur selon des critères de débit-distorsion
H04N 19/172 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant une image, une trame ou un champ
H04N 19/176 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant un bloc, p. ex. un macrobloc
H04N 21/845 - Structuration du contenu, p. ex. décomposition du contenu en segments temporels
The present disclosure relates to utilizing resources provided by large language models (LLMs) to generate models to be used in model-based testing of a variety of protocols. In particular, systems described herein utilize a vasty body of protocol knowledge defined in RFCs and standards, networking forums, blogs, and other online resources and documents to extract this knowledge in generating models that can be used for testing one or more components of a variety of protocols. The features and functionalities described herein provide a framework for utilizing LLMs to generate a protocol model while providing constraints for a harness (e.g., a symbolic harness) that will guide a symbolic execution engine in generating any number of protocol tests that may be used in determining whether a given application, hardware, and/or software implementation will perform as designed for a given protocol.
Techniques are described herein in which a programmable logic device (PLD) is integrated into a baseboard management controller (BMC). A programming-enhanced BMC is powered on by a PLD that is integrated into the programming-enhanced BMC and that is coupled to an internal bus of the programming-enhanced BMC. A configuration file is provided from immutable BMC hardware in the BMC to the PLD based at least on the programming-enhanced BMC being powered on. The configuration file specifies a configuration to be programmatically applied to programmable hardware of the PLD. The programmable hardware of the PLD is programmed by loading the configuration file, which causes the programmable hardware to render a peripheral interface that is defined by the configuration file natively on the internal bus of the programming-enhanced BMC.
Relational database systems are disclosed that are enabled to operate with versioned metadata. The relational database system includes a lock manager, a transaction manager and a version aware metadata storage and cache configured to store to store and manage versions of metadata, to determine which of such versions should be visible at any given point in time, and to enable creation of the proper versions of metadata. In an aspect, the transaction manager manages transaction identifiers and their associated start times, abort times and/or commit times. Such data enables determination of transaction visibility, and consequently the metadata version visibility, for any point in time. In an aspect, such metadata versioning support enables snapshot isolation of metadata transactions.
Examples are disclosed that relate to a generative model for generating inorganic material candidates, such as crystalline structures. One example provides a method, comprising training an unconditional generative model using a dataset of stable periodic material structures, the unconditional generative model comprising a diffusion model. The training comprises learning the diffusion model to iteratively noise the stable periodic material structures of the dataset towards a random periodic structure by noising atom types of atoms in the periodic material structure, noising fractional coordinates of the atoms in the periodic material structure, and noising a lattice of the periodic material structure. The method further comprises using the trained unconditional generative model to generate a material structure by iteratively denoising an initial structure sampled from a random distribution.
A method, computer program product, and computing system for processing a first data access request from a first computing device for accessing a cloud storage portion of a plurality of cloud storage portions associated with a cloud-based storage resource. A current ownership sequence identifier associated with the cloud storage portion is obtained. The current ownership sequence identifier from the cloud storage portion is compared to a previously obtained ownership sequence identifier for the cloud storage portion on the first computing device. The first data access request is effectuated on the cloud storage portion when the current ownership sequence identifier is identical to the previously obtained ownership sequence identifier.
Systems and methods are provided for generating a pseudo-labeled training dataset by at least one of: (1) extracting a set of intermediate outputs from an automatic speech recognition model based on applying the automatic speech recognition model to the set of unlabeled speech data, clustering the set of intermediate outputs into different clusters, and generating a first set of pseudo-labels comprising cluster assignments associated with the different clusters and which correspond to the unlabeled speech data, or (2) generating a set of decoded word sequences for the unlabeled speech data by applying the automatic speech recognition model to the set of unlabeled speech data, and generating a second set of pseudo-labels associated with the unlabeled speech data by applying the automatic speech recognition model to both (i) the set of decoded word sequences and (ii) the set of unlabeled speech data.
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
57.
CONTROL AND USE OF CHROMA QUANTIZATION PARAMETER VALUES
Innovations in control and use of chroma quantization parameter (“QP”) values that depend on luma QP values. More generally, the innovations relate to control and use of QP values for a secondary color component that depend on QP values for a primary color component. For example, during encoding, an encoder determines a QP index from a primary component QP and secondary component QP offset. The encoder maps the QP index to a secondary component QP, which has an extended range. The encoder outputs at least part of a bitstream including the encoded content. A corresponding decoder receives at least part of a bitstream including encoded content. During decoding, the decoder determines a QP index from a primary component QP and secondary component QP offset, then maps the QP index to a secondary component QP, which has an extended range.
H04N 19/126 - Détails des fonctions de normalisation ou de pondération, p. ex. matrices de normalisation ou quantificateurs uniformes variables
H04N 19/146 - Débit ou quantité de données codées à la sortie du codeur
H04N 19/174 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant une tranche, p. ex. une ligne de blocs ou un groupe de blocs
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 19/44 - Décodeurs spécialement adaptés à cet effet, p. ex. décodeurs vidéo asymétriques par rapport à l’encodeur
H04N 19/70 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques caractérisés par des aspects de syntaxe liés au codage vidéo, p. ex. liés aux standards de compression
58.
DYNAMIC DATA PROCESSING OF STRUCTURED DATA AND ADAPTIVE CONTEXTUAL MINING OF UNSTRUCTURED DATA DURING SKILL RUNTIME
Systems and methods are provided for facilitating the discovery and presentations of skills and data processed by the skills within blocks of a canvas displayed to a user within a user interface. The systems selectively process structured and unstructured data based on user context for facilitating presentation of contextually relevant data to a user.
G06F 16/683 - Recherche de données caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
59.
USE OF CHROMA QUANTIZATION PARAMETER OFFSETS IN DEBLOCKING
Innovations in use of chroma quantization parameter (“QP”) offsets when determining a control parameter for deblock filtering. For example, as part of encoding, an encoder sets a picture-level chroma QP offset and slice-level chroma QP offset for encoding of a slice of a picture. The encoder also performs deblock filtering of at least part of the slice, where derivation of a control parameter considers only the picture-level chroma QP offset. The encoder outputs at least part of a bitstream including the encoded content. As part of decoding, a corresponding decoder sets a picture-level chroma QP offset and a slice-level chroma QP offset for decoding of a slice of a picture, but derivation of a control parameter for deblock filtering considers only the picture-level chroma QP offset.
H04N 19/117 - Filtres, p. ex. pour le pré-traitement ou le post-traitement
H04N 19/126 - Détails des fonctions de normalisation ou de pondération, p. ex. matrices de normalisation ou quantificateurs uniformes variables
H04N 19/15 - Débit ou quantité de données codées à la sortie du codeur par contrôle de la taille réelle des données compressées au niveau de la mémoire avant de décider du stockage dans la mémoire tampon de transmission
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 19/174 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage adaptatif caractérisés par l’unité de codage, c.-à-d. la partie structurelle ou sémantique du signal vidéo étant l’objet ou le sujet du codage adaptatif l’unité étant une zone de l'image, p. ex. un objet la zone étant une tranche, p. ex. une ligne de blocs ou un groupe de blocs
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 19/184 - 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 des bits, p. ex. de flux vidéo compressé
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 19/70 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques caractérisés par des aspects de syntaxe liés au codage vidéo, p. ex. liés aux standards de compression
H04N 19/86 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le pré-traitement ou le post-traitement spécialement adaptés pour la compression vidéo mettant en œuvre la diminution des artéfacts de codage, p. ex. d'artéfacts de blocs
60.
SYNTAX AND SEMANTICS FOR BUFFERING INFORMATION TO SIMPLIFY VIDEO SPLICING
Innovations in syntax and semantics of coded picture buffer removal delay (“CPBRD”) values potentially simplify splicing operations. For example, a video encoder sets a CPBRD value for a current picture that indicates an increment value relative to a nominal coded picture buffer removal time of a preceding picture in decoding order, regardless of whether the preceding picture has a buffering period SEI message. The encoder can signal the CPBRD value according to a single-value approach in which a flag indicates how to interpret the CPBRD value, according to a two-value approach in which another CPBRD value (having a different interpretation) is also signaled, or according to a two-value approach that uses a flag and a delta value. A corresponding video decoder receives and parses the CPBRD value for the current picture. A splicing tool can perform simple concatenation operations to splice bitstreams using CPBRD value for the current picture.
H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c.-à-d. systèmes dans lesquels le signal vidéo n'est pas diffusé
H04N 19/573 - Compensation de mouvement avec prédiction multi-trame utilisant plusieurs trames de référence dans une direction de prédiction donnée
H04N 19/58 - Compensation de mouvement par prédiction à long terme, c.-à-d. que la trame de référence pour une trame courante n’est pas la plus proche temporellement
H04N 19/70 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques caractérisés par des aspects de syntaxe liés au codage vidéo, p. ex. liés aux standards de compression
H04N 21/234 - Traitement de flux vidéo élémentaires, p. ex. raccordement de flux vidéo ou transformation de graphes de scènes du flux vidéo codé
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
Methods and apparatuses for handling raw DNS queries or DNS queries that generate responsive DNS records from a DNS server that are not recognized or decodable by a DNS client are described. The DNS client may receive a DNS query from an application and provide a numerical IP address and one or more DNS records for a given domain name to the application. In some cases, a DNS client provides an interface that enables the application to send and receive DNS queries and responses to DNS queries that use DNS functionalities and customizations that are not recognized by the DNS client. This capability allows the application to craft its own DNS queries to send and receive resulting response bytes or packets through the DNS client, while allowing the DNS queries to pass through the DNS client to make use of host system cache, policies, and settings.
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]
H04L 61/58 - Mise en antémémoire d'adresses ou de noms
62.
ADAPTIVE VIDEO COMPRESSION USING GENERATIVE MACHINE LEARNING
Various embodiments of the technology described herein relate to compression of video data, including selecting a pivot image from a video including a plurality of images and causing a first machine learning model to generate a descriptor of the pivot image, where the descriptor includes a language description associated with the pivot image. In one example, the pivot image and the descriptor are provided to a decoder for reconstruction of the video. In an embodiment, the decoder includes a generative machine learning model that takes as an input the pivot image and the descriptor. The decoder uses the pivot image to generate an image based at least in part on the descriptor. The image is combined with other images generated by the generative machine learning model to reconstruct the video.
H04N 19/463 - Inclusion d’information supplémentaire dans le signal vidéo pendant le processus de compression par compression des paramètres d’encodage avant la transmission
H04N 19/503 - Procédés ou dispositions pour le codage, le décodage, la compression ou la décompression de signaux vidéo numériques utilisant le codage prédictif mettant en œuvre la prédiction temporelle
H04N 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
In an example embodiment, a generator model such as a large language model (LLM) is leveraged to generate embeddings for both pieces of content and users. The embeddings map the pieces of content and the users into the same latent n-dimensional space. The embeddings are then fine-tuned using a two-tower deep neural network, with one of the towers representing users and the other tower representing content. The two-tower deep neural network is trained to optimize the embeddings over some shared goal, such as user engagement with content, and uses information such as user interactions with content in that process. A clustering technique, such as K-nearest neighbor (kNN) can then be used to identify a grouping of top user/content pairs based on similarity between users and content, as reflected in the embeddings. For a given piece of content, therefore, the top users from that cluster can then be recommended as an audience for the content.
G06Q 30/0242 - Détermination de l’efficacité des publicités
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
Embodiments of the present disclosure include countermeasure circuit techniques for cyberattacks. In one embodiment, portions of combinational logic receive shared input bit groups and produce shared output bit groups. Shared output bit groups may be coupled between series configured combinational logic portions using control gates. Clock signals are delayed to activate the control gates after the outputs are stable. In some embodiments, a first combinational logic group and second combinational logic group operate on a clock and inverse clock.
G06F 21/75 - Protection de composants spécifiques internes ou périphériques, où la protection d'un composant mène à la protection de tout le calculateur pour assurer la sécurité du calcul ou du traitement de l’information par inhibition de l’analyse de circuit ou du fonctionnement, p. ex. pour empêcher l'ingénierie inverse
65.
GENERATING INFORMED PRIORS FOR HYPERPARAMETER SELECTION
A system iteratively evaluates the target machine learning model using evaluation hyperparameter values of the target machine learning model to measure performance of the target machine learning model for different combinations of the evaluation hyperparameter values. The system trains a surrogate machine learning model using the different combinations of the evaluation hyperparameter values as features and the performance of the target machine learning model based on a corresponding combination of the evaluation hyperparameter values as labels. The system generates a feature importance vector of the surrogate machine learning model based on the training of the surrogate machine learning model, generate informed priors based on the feature importance vector, and generates the target hyperparameter values of the target machine learning model based on the informed priors.
A computing system is provided, including a processor configured to receive a standardized stabilizer instrument specification including an input Clifford unitary, an output Clifford unitary, and a plurality of stabilizer instrument bit matrices. The processor is further configured to receive a logical instrument input error correction code and a logical instrument output error correction code. The processor is further configured to compute a logical instrument specification based at least in part on the standardized stabilizer instrument specification, the logical instrument input error correction code, and the logical instrument output error correction code. The logical instrument specification includes a logical input Clifford unitary, a logical output Clifford unitary, a plurality of logical instrument bit matrices, and a logical instrument relabeling matrix. The processor is further configured to store the logical instrument specification in memory.
67.
Long document topic summarization using large language models
A large language model predicts a topic summarization of a long document given a set of segments from the long document that pertain to a topic of interest. The set of segments from the long document are selected by searching for similar segments from other documents that have been labeled to indicate whether or not the segment pertains to a topic of interest. The search is based on an embedding of a segment from the long document closely matching embeddings of the labeled segments. Each segment of the long document is scored based on the labels of the closest-matching similar segments. The segments from the long document are ranked by their respective score and the highest-scored segments are included in a prompt to the large language model for the model to generate a topic summarization of the long document.
G06F 18/2415 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p. ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
68.
DATABASE MANAGEMENT ENGINE FOR A DATABASE MANAGEMENT SYSTEM
Methods, systems, and computer storage media provide a privacy compliance notification indicating a database's level of compliance with a privacy policy after restoring the database to the database's backup copy. The database is associated with a database management engine. The database supports privacy-based first-class data entities. The privacy-based first-class data entities are database entities having privacy system-level metadata properties associated with data operations in a database language syntax. The privacy compliance notification may be generated based on determining whether a privacy database operation associated with a database journal and a privacy journal has been executed on a database since the database was restored to a backup copy of the database. The database transaction journal includes a transaction log of database operations executed against the database, and the privacy journal includes the database operations logged as privacy database operations associated with the plurality of privacy-based first-class data entities.
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
G06F 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
In non-limiting examples of the present disclosure, systems and methods are described that relate to providing, in a browser environment, a sidebar search capability to users. Once in a primary content page, the user is able to select text for searching. In response, the system provides a context menu or keyboard shortcut that includes an option for conducting a sidebar search. In response to user selection, the system passes highlighted or selected text as a parameter to the search engine. The results are provided in an area alongside the currently displayed content page, such as in a sidebar search pane. The user is able to experience search results without leaving the context of their current search tab.
A device and method for robotic process automation (RPA) using speech recognition that receives a voice input; invokes, using the received voice input, an RPA workflow, the RPA workflow comprising a sequence of tasks; based at least on the invoked RPA workflow, retrieves an argument from a cloud device; modifies, with the retrieved argument, at least one task of the sequence of tasks; and executes the modified at least one task as part of the RPA workflow.
A contextual end-to-end automatic speech recognition (ASR) system includes: an audio encoder configured to process input audio signal to produce as output encoded audio signal; a bias encoder configured to produce as output at least one bias entry corresponding to a word to bias for recognition by the ASR system; a transcription token probability prediction network configured to produce as output a probability of a selected transcription token, based at least in part on the output of the bias encoder and the output of the audio encoder; a first attention mechanism configured to receive the at least one bias entry and determine whether the at least one bias entry is suitable to be transcribed at a specific moment of an ongoing transcription; and a second attention mechanism configured to produce prefix penalties for restricting the first attention mechanism to only entries fitting a current transcription context.
Electrode controlled hybridization is used to change local pH and selectively assemble oligonucleotide complexes on the surface of a microelectrode array. The oligonucleotide complexes have sticky ends that provide locations for subsequent oligonucleotide complexes to hybridize. The order in which specific oligonucleotide complexes are joined together encodes information. Controlled activation of individual electrodes in the microelectrode array creates negative voltages that reduces a buffer solution and raises the pH in proximity to the electrodes. At higher pH levels double-stranded oligonucleotides de-hybridize. Nicks between oligonucleotide complexes and oligonucleotides anchored to the microelectrode array are closed creating covalent attachments. De-hybridized single-stranded oligonucleotides are removed leaving only the oligonucleotides connected to microelectrode array. Thus, during a given round of synthesis, oligonucleotide complexes are added only to the locations on the microelectrode array where the electrodes are not activated.
A system for facilitating ray trace operations with shared traversal performs a pre-test operation that includes testing one or more volumes against an acceleration structure associated with a virtual environment to identify a set of candidate nodes of the acceleration structure. The virtual environment comprises one or more virtual objects defined by one or more object components. The system also performs a ray trace operation based upon the set of candidate nodes of the acceleration structure.
A phase-interpolator (PI) circuit generates an interpolated clock to capture data in a capture circuit at a target phase in a phase range between two reference clocks based on an interpolation code within a range of interpolation codes is described. A clamping circuit coupled to the PI circuit provides an interpolation code within a reduced range, where the integral non-linearity (INL) of the interpolated clocks is below a threshold, such that data capture based on the interpolated clock has a lower bit error rate (BER). As a result, the interpolated clock is generated within a reduced phase range corresponding to the reduced range of interpolation codes. When a target phase for an interpolated clock is outside the reduced phase range, the clamping circuit may adjust the target phase clock relative to a reference clock to adjust the target phase to be within the reduced phase range for improved BER.
H04L 7/02 - Commande de vitesse ou de phase au moyen des signaux de code reçus, les signaux ne contenant aucune information de synchronisation particulière
75.
ANNOTATING IMAGES FOR TRAINING COMPUTER VISION MODELS
A method for annotating images to create a corpus for training a multi-task computer vision machine learning model is presented. The method comprises receiving, at one or more annotation specialist models, a plurality of images to be annotated. Via operation of the one or more annotation specialist models, pre-filtered annotations are generated for the plurality of images. Via operation of a data filtering and enhancement module, the pre-filtered annotations are filtered in accordance with predefined noise criteria so as to output candidate annotations for the plurality of images. The method further comprises, for each of one or more candidate annotations, selectively (1) storing the candidate annotation into the corpus as a final annotation for its associated image, or (2) adding the candidate annotation to its associated image using the one or more annotation specialist models and the data filtering and enhancement module for subsequent iterative annotation and filtering.
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
G06F 40/284 - Analyse lexicale, p. ex. segmentation en unités ou cooccurrence
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
76.
SERVER-SIDE CONTROL OF POWER BASED ON SERVICE-LEVEL AGREEMENT
Various embodiments described herein dynamically control the distribution of power to individual components of a node in an overprovisioned rack, node, or accelerators of a data center based on service-level agreements (SLAs) defining priorities for workloads for certain user accounts. The SLA is used to determine a throttling order for throttling the accelerators. Controlling the distribution of power in the node includes throttling at an accelerator or coprocessor, based on the throttling order or SLA, until the power consumption is at or below a power policy limit. In this manner, various embodiments discussed herein provide (1) granular control over the execution of tasks in an overprovisioned rack and (2) a user experience consistent with a priority level defined by an SLA, while complying with power policy limit(s) to improve the lifespan and operation of hardware, as well as to reduce the wear and tear experienced by overprovisioned hardware.
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
G06T 1/20 - Architectures de processeursConfiguration de processeurs p. ex. configuration en pipeline
77.
QUERYING AND ANALYSIS OF CLINICAL TRIALS USING PROBABILISTIC GRAPHICAL MODELS
The present disclosure relates to methods and systems that provide querying and analysis of clinical trials using probabilistic graphical models. The methods and systems train a probabilistic graphical model using clinical trial data and use the probabilistic graphical model to perform inferences in response to queries for clinical trials. The methods and systems use the probabilistic graphical model to handle multimodal datatypes of the clinical trial data and predict multiple attributes of the clinical trial for an input query.
G16H 10/20 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des essais ou des questionnaires cliniques électroniques
G16H 50/20 - TIC spécialement adaptées au diagnostic médical, à la simulation médicale ou à l’extraction de données médicalesTIC spécialement adaptées à la détection, au suivi ou à la modélisation d’épidémies ou de pandémies pour le diagnostic assisté par ordinateur, p. ex. basé sur des systèmes experts médicaux
78.
APPARATUS AND METHODS FOR PRIME FIELD MODULAR REDUCTION
Apparatus and methods for prime field modular reduction are described. As an example, a custom modular reduction digital circuit for reducing an n-bit integer based on a modulus, where the modulus comprises a k-bit integer for use with a cryptographic algorithm, is described. The custom modular reduction digital circuit includes a first circuit to generate at least two partial results by processing: (1) k lower order significant bits of the n-bit integer and (2) at least a subset of bits for congruent representations corresponding to any n-k higher order bits of the n-bit integer that are higher in significance than the most significant bit of the k-bit integer. The custom modular reduction digital circuit further includes a second circuit to process the at least two partial results, output by the first circuit, to generate a reduced version of the n-bit integer for use with the cryptographic algorithm.
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
79.
ADMINISTRATIVE MANAGEMENT OF USER ACTIVITY DATA USING GENERATIVE ARTIFICIAL INTELLIGENCE
A device includes: a processor, and a memory storing executable instructions which, when executed by the processor, causes the processor, alone or in combination with other processors, to provide the following: a user interface comprising administrator access to a collaboration system, the user interface comprising a control to invoke an artificial intelligence (AI) assistant function; and an Application Programming Interface (API) to, in response to activation of the control, download user activity data for the collaboration system, generate a prompt for a Large Language Model (LLM) comprising the user activity data and instructing the LLM to generate a report based on the user activity data, and submit the prompt to the LLM and receive the report generated by the LLM. The user interface provides the report and controls for administrative actions suggested by the report.
Various embodiments of the technology described herein relate to compression of video data, including selecting a pivot image from a video including a plurality of images and causing a first machine learning model to generate a descriptor of the pivot image, where the descriptor includes a language description associated with the pivot image. In one example, the pivot image and the descriptor are provided to a decoder for reconstruction of the video. In an embodiment, the decoder includes a generative machine learning model that takes as an input the pivot image and the descriptor. The decoder uses the pivot image to generate an image based at least in part on the descriptor. The image is combined with other images generated by the generative machine learning model to reconstruct the video.
The techniques disclosed herein provide adaptable notifications for incoming messages. A system uses AI to recognize one or more categories for individual messages of a thread. The system can then generate a summary of specific categories of messages to provide contextually relevant notifications that summarize a specific set of interactions for a message thread. This approach is more efficient than systems that provide individual notifications for each message, as the disclosed techniques enable a system to generate a controlled number of notifications and/or more contextually accurate notifications for specific users. The disclosed techniques also improve the security of a system by generating notifications that can summarize the content of received messages and/or summarize specific interactions within a particular message thread.
H04L 51/224 - Surveillance ou traitement des messages en fournissant une notification sur les messages entrants, p. ex. des poussées de notifications des messages reçus
H04L 51/04 - Messagerie en temps réel ou quasi en temps réel, p. ex. messagerie instantanée [IM]
H04L 51/216 - Gestion de l'historique des conversations, p. ex. regroupement de messages dans des sessions ou des fils de conversation
Example solutions for processing LLM prompts include creating a first large language model (LLM) prompt based on an input LLM prompt. The first LLM prompt represents a first step toward generating a solution to the input LLM prompt. The first LLM prompt is submitted to an LLM as a first sub-query, thereby resulting in the generation of a first LLM output. A second LLM prompt is generated based on the input LLM prompt. The second LLM prompt represents a second step toward generating the solution. The second LLM prompt includes the first LLM output. The second LLM prompt is submitted to the LLM as a second sub-query, thereby resulting in the generation of a second LLM output. The second LLM output represents the solution to the input LLM prompt in response to the input LLM prompt.
Methods, systems, and computer storage media for providing generative artificial intelligence (AI) output validation using a generative AI output validation engine in an artificial intelligence system. The generative AI output validation engine assesses and determines the quality (e.g., quantified as an output validation score) of generative AI output (e.g., LLM output). In operation, a generative AI output comprising summary data is accessed. Raw data from which summary data is generated is accessed. A plurality of output validation operations associated with a generative AI output validation engine are executed. The generative AI output validation engine comprises multi-categorical analytical models that provide corresponding output validation operations for quantifying quality of generative AI outputs. Using the generative AI output validation engine, generating an output validation score for the summary data. Communicating the output validation score. A feedback loop is established to incorporate human feedback for fine-tuning the generative AI output validation engine models.
Examples are disclosed that relate to fans configured to automatically adjust for imbalances in mass. One example provides a self-balancing fan, comprising a hub comprising a plurality of blade interfaces, and a plurality of blade structures each attached to a corresponding blade interface of the hub, each blade interface comprising a tapered notch in the hub and being configured to increase a balancing force exerted by the hub against the blade structure as a function of increasing distance of the blade structure from the hub.
This document relates to communication by backscattering of satellite signals. One example includes a satellite backscatter transmitter having a first antenna configured to receive a radio frequency satellite signal, a modulator configured to modulate the radio frequency satellite signal to obtain a modulated radio frequency satellite signal, a digital logic circuit configured to selectively control the modulator to encode information according to a communication scheme, and a second antenna configured to passively retransmit the modulated radio frequency satellite signal to a receiver.
86.
SECURITY ENHANCEMENT FOR COMPUTING DEVICE STATE CHANGE
Systems and methods are disclosed herein for identifying a bypass of a computing device state change. In an example system, a determination is made that a computing component, such as an application executing on the computing device, is blocking a state change of the computing device. The state change includes various types of actions to protect the computing device, such as an automatic lock, logoff, standby mode change, or powering off change. An idle period of the computing device is detected. A proximity change of a user relative to the computing device is also detected. Based on the idle period and the proximity change, an action to remediate the blocking of the state change is performed, such as generating a notification associated with the blocking of the state change for providing to the user and/or automatically bypassing the blocking of the state change.
The techniques disclosed herein enable an autonomous agent to interpret an input dataset and orchestrate a suite of software modules to perform a computational task on a representation of a chemical material. The input dataset includes a prompt defining a computational task to be performed on a chemical material. Moreover, the input dataset includes data defining a chemical included in the chemical material, molecular descriptors describing the chemical and/or the chemical material, and an external variable. The agent analyzes the benefits and drawbacks of each model within the context of the computational task to determine a technique for performing the computational task. Accordingly, the agent formulates a chain of calls invoking the functionality of data processing tools and models to perform the computational task responsive to the prompt.
G16C 20/30 - Prévision des propriétés des composés, des compositions ou des mélanges chimiques
G16C 20/70 - Apprentissage automatique, exploration de données ou chimiométrie
G16C 60/00 - Science informatique des matériaux, c.-à-d. TIC spécialement adaptées à la recherche des propriétés physiques ou chimiques de matériaux ou de phénomènes associés à leur conception, synthèse, traitement, caractérisation ou utilisation
G06N 3/00 - Agencements informatiques fondés sur des modèles biologiques
Techniques for implementing an AI threat modeling tool are disclosed. A static analysis tool is used to extract a candidate code snippet from a code repository. The candidate code snippet is identified as potentially being a security relevant code element. The static analysis tool generates additional context associated with the candidate code snippet. An LLM prompt is generated. This prompt is structured to include the candidate code snippet, the context, and a directive to assign a classification to the candidate code snippet. The classification includes a source classification, a sink classification, a sanitizer classification, or a flow step classification. The LLM operates on the prompt to generate output comprising a specific classification for the candidate code snippet. The output is formatted into a data extension file that is consumable by the static analysis tool.
G06F 21/56 - Détection ou gestion de programmes malveillants, p. ex. dispositions anti-virus
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
Example solutions perform natural language query processing on hybrid utterances. A precise segment is identified, within the hybrid utterance, and processed with a symbolic AI interpreter configured to generate a first interpretation. The precise segment is replaced, within the hybrid utterance, with a placeholder term thereby resulting in a vague utterance. The vague utterance is processed with a statistical AI interpreter configured to generate a second interpretation. The first interpretation is merged with the second interpretation using the hybrid utterance as a template for the merger and using the placeholder term as the location for the first interpretation within the second interpretation. A complete interpretation is generated and transmitted to a query generator.
Detection of malicious direct memory access (DMA) device used for direct device assignment. A virtualization computer system assigns a peripheral device to an operating context within a virtualization environment. The peripheral device is DMA capable. The virtualization computer system monitors a signal source that is affected by DMA operations initiated by the peripheral device while the peripheral device is assigned to the operating context. Based on monitoring the signal source, the virtualization computer system identifies a signal pattern characterizing the DMA operations that are initiated by the peripheral device. Using the signal pattern, the virtualization computer system determines that the DMA operations initiated by the peripheral device are abnormal and the virtualization computer system identifies the peripheral device as malicious.
G06F 21/53 - Contrôle des utilisateurs, des programmes ou des dispositifs de préservation de l’intégrité des plates-formes, p. ex. des processeurs, des micrologiciels ou des systèmes d’exploitation au stade de l’exécution du programme, p. ex. intégrité de la pile, débordement de tampon ou prévention d'effacement involontaire de données par exécution dans un environnement restreint, p. ex. "boîte à sable" ou machine virtuelle sécurisée
G06F 21/56 - Détection ou gestion de programmes malveillants, p. ex. dispositions anti-virus
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
G06F 21/85 - Protection des dispositifs de saisie, d’affichage de données ou d’interconnexion dispositifs d’interconnexion, p. ex. les dispositifs connectés à un bus ou les dispositifs en ligne
91.
METHODS AND SYSTEMS FOR ENHANCING MULTIMODAL CAPABILITIES IN LARGE LANGUAGE MODELS
Systems and methods are provided for enhancing the speech modality in a large language model (LLM) and for retaining in-context learning capabilities without overfitting to trained tasks. Systems obtain a first set of training data comprising tuples of a sample of speech combined with synthetically generated pairings of speech comprehension test questions and answers that correspond to the sample of speech and obtain a second set of training data comprising pairings of automatic speech recognition data. Systems generate and align a first set of encodings of the first set of training data and a second set of encodings of the second set of training data. Systems train the LLM on a greater amount of the first set of training data than the second set of training data and use the trained LLM to perform a natural language processing task.
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 15/183 - Classement ou recherche de la parole utilisant une modélisation du langage naturel selon les contextes, p. ex. modèles de langage
G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
92.
APPARATUS AND METHODS FOR PRIME FIELD MODULAR REDUCTION
Apparatus and methods for prime field modular reduction are described. As an example, a custom modular reduction digital circuit for reducing an n-bit integer based on a modulus, where the modulus comprises a k-bit integer for use with a cryptographic algorithm, is described. The custom modular reduction digital circuit includes a first circuit to generate at least two partial results by processing: (1) k lower order significant bits of the n-bit integer and (2) at least a subset of bits for congruent representations corresponding to any n-k higher order bits of the n-bit integer that are higher in significance than the most significant bit of the k-bit integer. The custom modular reduction digital circuit further includes a second circuit to process the at least two partial results, output by the first circuit, to generate a reduced version of the n-bit integer for use with the cryptographic algorithm.
G06F 7/72 - Méthodes ou dispositions pour effectuer des calculs en utilisant une représentation numérique non codée, c.-à-d. une représentation de nombres sans baseDispositifs de calcul utilisant une combinaison de représentations de nombres codées et non codées utilisant l'arithmétique des résidus
Techniques for using a sensor to perform laser signal decoding are disclosed. The sensor may be a global shutter sensor or a rolling shutter sensor. The sensor generates a first set of images while operating in a first mode. In response to detecting a laser signal in the first set of images, the sensor is caused to operate in a second mode. The laser signal includes an embedded frequency signal component and repeats at a periodic rate. While the sensor is operating in the second mode, the sensor generates a second set of images, which capture an entire period of the laser signal. From the second set of images, the embedded frequency signal component is determined. A decoding operation is performed using the embedded frequency signal component.
A data processing system implements obtaining build logs that include information associated with a software build problem; analyzing the logs to generate a knowledge graph identifying the relationship between various entities in the logs; extracting a signature of a candidate root cause of the build problem from the knowledge graph representing a subset of nodes and edges of the knowledge graph; providing the signature of the candidate root cause to a graphical language model to obtain a prediction of a category of root cause failure selected from among a plurality of root cause failures; constructing a prompt for a language model to generate a root cause failure analysis that describes the root cause of the build problem, the prompt including the category of root cause; receiving the root cause failure analysis from the language model; and performing one or more actions in response to receiving the root cause failure analysis.
In a cloud computing environment, a cross-tenant access security measure monitors conditional access policies for changes or additions that hamper or threaten an authorized access from an assistant tenant user to a focus tenant. Some cross-tenant access security tracks role assignments to detect rogue roles, or detect hampering role changes. In some cases, focus tenant events and assistant tenant events are correlated in an audit. In some cases, the authorized access is a zero standing time bound access. In some cases, the authorized access is constrained to an IP address range, or constrained to login from a managed device, or both. In some cases, assets are excluded from managed response remediation actions. In some, managed response is modulated by product-specific Role Based Access Control. In some, repeated logins are avoided, to permit faster managed responses.
The disclosed concepts relate to contextualization of generative language models. In some implementations, a linked entity database is populated with entity resource identifiers of entities extracted from a search log by an entity linker. A contextualized prompt data structure is generated based on the linked entity database, e.g., by including linked entity context information in the contextualized prompt data structure. A response to the contextualized prompt data structure is received, where the response is conditioned on the linked entity context information.
A phase-interpolator, PI, circuit (700) generates an interpolated clock (PI_CLK) to capture data in a capture circuit at a target phase in a phase range between two reference clocks based on an interpolation code (S) within a range of interpolation codes is described. A clamping circuit (704) coupled to the PI circuit provides an interpolation code (S) within a reduced range, where the integral non-linearity, INL, of the interpolated clocks is below a threshold, such that data capture based on the interpolated clock has a lower bit error rate, BER. As a result, the interpolated clock is generated within a reduced phase range corresponding to the reduced range of interpolation codes. When a target phase for an interpolated clock is outside the reduced phase range, the clamping circuit may adjust the target phase clock (PHA_REF) relative to a reference clock to adjust the target phase to be within the reduced phase range for improved BER.
H03K 5/135 - Dispositions ayant une sortie unique et transformant les signaux d'entrée en impulsions délivrées à des intervalles de temps désirés par l'utilisation de signaux de référence de temps, p. ex. des signaux d'horloge
H03K 5/00 - Transformation d'impulsions non couvertes par l'un des autres groupes principaux de la présente sous-classe
98.
SYSTEMS AND METHODS FOR ZERO TRUST DNS BASED NETWORKING
Examples of the present disclosure describe systems and methods for zero trust domain name system (DNS) (ZTDNS) based networking. A computing device implementing ZTDNS based networking blocks any outbound connections that are not included in a list of trusted IP addresses. The list of trusted IP addresses is updated in response to the computing device receiving from a trusted DNS server an IP address corresponding to a DNS request. In some examples, the ZTDNS based networking intercepts and evaluates outbound communications for applications that implement a custom application DNS client. In other examples, the ZTDNS based networking intercepts and evaluates outbound communications for virtual environments. The outbound communications for both the custom application DNS client and the virtual environments are proxied through a local DNS client of the computing device.
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]
99.
AUTOMATICALLY ASSISTING CONVERSATIONS USING GRAPH DATABASE
Examples of the present disclosure describe systems and methods for automatically assisting conversations using a graph database. In order to minimize misunderstanding of words and phrases used by participants during a conversation, phrases from the conversation may be received by conversation assistance application as the conversation takes place. Entities may be extracted from the phrase based on natural language recognition according to a domain context of the participant being assisted. One or more tags may be looked up from a graph database, and may be provided to the participant as a list of hashtags related to the conversation. Links to documents may be extracted based on the tags for the participant for viewing during the conversation.
Systems and techniques for multi-phase cloud service node error prediction are described herein. A set of spatial metrics and a set of temporal metrics may be obtained for node devices in a cloud computing platform. The node devices may be evaluated using a spatial machine learning model and a temporal machine learning model to create a spatial output and a temporal output. One or more potentially faulty nodes may be determined based on an evaluation of the spatial output and the temporal output using a ranking model. The one or more potentially faulty nodes may be a subset of the node devices. One or more migration source nodes may be identified from one or more potentially faulty nodes. The one or more migration source nodes may be identified by minimization of a cost of false positive and false negative node detection.
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 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
G06F 9/48 - Lancement de programmes Commutation de programmes, p. ex. par interruption
G06F 9/50 - Allocation de ressources, p. ex. de l'unité centrale de traitement [UCT]
G06N 5/01 - Techniques de recherche dynamiqueHeuristiquesArbres dynamiquesSéparation et évaluation
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