A data processing system implements a framework that utilizes in-situ user interactions as a source of feedback for improving the training of LLMs to generate outputs that align with user preferences. The framework includes a user preference evaluation pipeline analyzes in-situ user interactions with the LLMs and generates preference information that can be used to improve the training of the LLM to improve the alignment of the LLM with user preferences. The user preference evaluation pipeline includes a feedback signal identification unit that identifies explicit and/or implicit feedback provided by the users in response to content output by the LLM in response to a user prompt. The feedback signal identification unit estimates user satisfaction with a set of satisfaction rubrics and user dissatisfaction with a set of user dissatisfaction rubrics to generate user preference data that can be used to align an LLM with these user preferences.
A computerized method reduces contraction loss in a network using dynamically generated rules. Topology and traffic matrix data of a network are obtained, wherein the obtained data includes data associated with a network link of the network. A first traffic scenario is simulated to determine a down loss value of the network link, and a second traffic scenario is simulated to determine a contraction loss value of the network link. A capacity aware local repair (CALR) rule of the network link is generated using the determined down loss value and the determined contraction loss value, wherein the CALR rule includes a contracted bandwidth threshold. The generated CALR rule is provided to a network device associated with the network link, wherein the network device is enabled to set the network link to inactive based on bandwidth of the network link falling below the contracted bandwidth threshold of the generated CALR rule.
A method, computer program product, and computing system for generating signed data for training an artificial intelligence (AI) model by processing data stored on a ledger using a signing authority. Signed firmware is generated for training the AI model by processing data stored on the ledger using the signing authority. The AI model is trained with signed data and the signed firmware from the ledger using a data processing unit in response to determining that the signed data and the signed firmware are signed by the signing authority.
Examples are disclosed that relate to forming embeddings comprising vector representations of chemical structures, and performing chemical similarity searches. One example provides a method of forming a vector database using a pre-trained neural network, the method comprising inputting labeled training data into the pre-trained neural network configured to form embeddings of chemical structures, the labeled training data comprising structural information and a value of a property for each chemical object in a first set of chemical objects. The method further comprises fine-tuning the pre-trained neural network and forming the vector database by inputting a reference dataset into the fine-tuned neural network to generate embeddings of chemical structures, the reference dataset comprising structural information for each chemical object in a second set of chemical objects. Each embedding stored in the vector database comprises a vector representation of a chemical structure and embedded information for the property for the chemical structure.
Systems and methods for providing efficient shot transition detection for shot segmentation of a video. A traditional shot transition detector and a neural network shot transition detector are used in multiple stages to identify transitions between shots in the video. Further, dynamic thresholds are determined based on visual attributes of the video that are used to detect cut transitions and gradual transitions.
H04N 5/14 - Circuits de signal d'image pour le domaine des fréquences vidéo
G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
G06V 20/40 - ScènesÉléments spécifiques à la scène dans le contenu vidéo
The disclosure herein describes training an encoder network to inpaint images with masked portions. A primary encoding process is used to encode a visible portion of a masked input image into encoded token data. The encoded token data is then decoded into both pixel regression output and feature prediction output, wherein both outputs include inpainted image data associated with the masked portion of the masked input image. A pixel regression loss is determined using the pixel regression output and pixel data of an unmasked version of the masked input image. A feature prediction loss is determined using the feature prediction output and ground truth encoding output of the unmasked version of the masked input image. The primary encoding process is then trained using the pixel regression loss and the feature prediction loss, whereby the primary encoding process is trained to encode structural features of input images into encoded token data.
G06V 10/40 - Extraction de caractéristiques d’images ou de vidéos
G06V 10/766 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la régression, p. ex. en projetant les caractéristiques sur des hyperplans
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”
7.
GENERATING REAL-TIME AUDIO DUBBING FOR A VIDEO USING A CONCURRENT BATCH FRAMEWORK
This disclosure describes a framework for generating real-time audio translations of videos on a client device. Specifically, this disclosure describes a video dubbing system that utilizes a concurrent batch-processing architecture to provide real-time audio translations of videos on a client device. Additionally, in one or more implementations, the video dubbing system utilizes time-aware segmentation to prevent audio misalignment of the translated audio. As described below, the video dubbing system efficiently provides high-quality audio translations of videos that accurately align with the video content for the entire video, regardless of the video's length.
A method, computer program product, and computing system for processing a plurality of compatibility constraints for a cloud computing environment. A plurality of prevalence metrics for the cloud computing environment are processed. A graph structure mapping compatibility relationships between cloud computing resources is generated based upon, at least in part, the plurality of compatibility constraints. A constrained optimization process is defined for the graph structure. A testing schedule for the cloud computing resources is generated using the graph structure and the constrained optimization process.
Aspects of the disclosure include methods and systems for meta learning, and specifically to hybrid meta learning for agnostic recommender platforms. A method includes receiving, by a global block ranker of a hybrid meta learning recommendation service, a request corresponding to an entity in a network. A meta block encoder generates, at a first cadence decoupled from the request, a meta embedding of an entity-specific meta feature of the entity. The meta embedding is aggregated with one or more non-meta features at a second cadence responsive to the request and the aggregated data is input to the global block ranker. A prediction score is generated for each candidate of one or more candidates corresponding to the request and a response including a candidate is returned using the prediction score.
Systems and methods for resource-efficient retrieval of information using a generative AI model are disclosed. An input query requesting information from a set of documents is used in a prompt for a generative AI model to generate a search query to identify the documents relevant to the input query and their respective relevancy scores. The input query is used as an input another model to determine a depth score indicating a predicted number of documents needed to retrieve the information. Based on the depth score and the relevancy scores of the relevant documents, the system extracts grounding data from the identified relevant documents to generate an answer synthesis prompt for the generative AI model. The generative AI model processes the second to produce a response to the input query including the requested information.
G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
A computer-implemented method includes receiving input data items, each input data item comprising: first attributes representative of characteristics of the input data item, a treatment variable associated with the data item and an outcome variable representative of an outcome associated with the input data item. Second attributes of the input data items are generated from the first attributes, the second plurality of attributes having smaller dimensions than the first attributes. A first input data item having a first value for the treatment variable is selected; and a matching second input data item is selected based on a distance along a manifold between the first input data item and the second input data item, the second input data item having a second value for the treatment variable. The method provides a means of estimating the treatment effect of the treatment.
G06F 18/22 - Critères d'appariement, p. ex. mesures de proximité
G16H 10/60 - TIC spécialement adaptées au maniement ou au traitement des données médicales ou de soins de santé relatives aux patients pour des données spécifiques de patients, p. ex. pour des dossiers électroniques de patients
G16H 50/70 - 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 extraire des données médicales, p. ex. pour analyser les cas antérieurs d’autres patients
Systems and methods for generating virtual assistant proactive queries improving virtual assistant-user interaction. Initial user prompts, including trigger conditions, corresponding to one or more initial user sessions are received. The initial user prompts, and trigger conditions, are stored in a query log database. Pattern recognition is performed on the stored initial user prompts, and the corresponding trigger conditions, to determine a proactive prompt for a subsequent user session. A proactive response is generated from the proactive prompt. Prior to receiving a subsequent user prompt, the proactive response is provided during the subsequent user session upon detection of one or more trigger conditions corresponding to the proactive prompt.
Methods and systems for generating completely automated public Turing test (CAPTCHA) images are provided. In some examples, a method includes generating a plurality of images using a generative imaging model, providing the plurality of images to a user with a description that corresponds to one of a similarity or difference between the plurality of images, receiving a selection of an image of the plurality of images, determining if the selection is correct based on the provided description, and outputting an indication of whether the selection is correct.
Systems and methods are provided for implementing a cluster-wide root secret (“CWRS”) key for distributed node clusters. In a multi-node cluster, a leader node has a leader node security system that generates the CWRS key, which is a common secret key for all workloads (e.g., containers or VMs) in the multi-node cluster. The leader node security system encrypts the generated CWRS key using a public key and/or a bootstrap key received from a non-leader node that requests the CWRS key. In examples, the leader node security system signs the encrypted CWRS key using its private key for subsequent verification, by the requesting non-leader node, that the CWRS key was generated by the leader node security system. The CWRS thus encrypted can be securely sent to the requesting non-leader node for subsequent encryption or decryption of secret data by the security system of the non-leader node.
H04L 9/14 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité utilisant plusieurs clés ou algorithmes
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
15.
SYSTEM AND METHOD FOR TRANSLATING AND TRANSCRIBING
A computer-implemented method, computer program product and computing system for: receiving speech in a source language to define source language speech; performing a first token-based translation of the source language speech into text of an intermediate language to define intermediate language text; and performing a second token-based translation from the intermediate language text into text of a target language to define target language text.
G06F 40/58 - Utilisation de traduction automatisée, p. ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel
16.
SECURE CROSS-CLOUD RESOURCE ACCESS WITH SINGLE USER IDENTITY
Systems and methods are provided for a secure cross-cloud resource access based on user identity. In particular, the system includes a plurality of clouds where a first cloud enforces more restrictive access than a second cloud. In particular, an end user of the second cloud also uses user identity stored in the less restrictive first cloud. The system includes authenticating and authorizing tokens associated with an administrator of the first tenant in the first cloud and the second tenant in the second cloud. The onboarding establishes a two-way trust between the two tenants across the first and second clouds. Once established, operating an application service and accessing data resources in the second cloud is accomplished by logging into the first cloud and leverage the two-way trust to remotely launch application services in the second cloud using a tenant graph and a location service in the first cloud.
A data processing system implements obtaining user prompts s that include instructions to an AI application to perform one or more tasks; storing the user prompts in a prompts datastore in a secure computing environment; analyzing the user prompts using an LLM operating within the secure computing environment to generate normalized prompts based on the user prompts; extracting first n-grams from the normalized prompts using differentially private n-gram extraction that preserves user-level privacy; generating masked normalized prompts by comparing the normalized prompts with the first n-grams and replacing, with a placeholder n-gram, n-grams of the normalized prompts that do not match an n-gram of the first n-grams; extracting second n-grams from the masked normalized prompts using the differentially private n-gram extraction that preserves user-level privacy; outputting the second n-grams from the secure computing environment; and storing the second n-grams in an anonymized prompts datastore outside of the secure computing environment.
An entropy-based technique is used to select a large language model capable of generating fluent natural language text. An entropy model, trained on fluent natural language samples, is used to determine the entropy of a large language model based on an output text generated by the large language model. The entropy of a machine-generated natural language text is used to quantify the amount of information that the large language model holds with respect to the tokens and context of an input text segment. The entropy score of a model is then used to select a large language model capable of generating fluent text or to select the most fluent machine-generated output text produced by a set of large language models.
A system comprises a first computer system and a second computer system. The first computer system includes a processor and a computer-readable medium storing instructions executable to determine that a primary host in a replica set is unavailable, the replica set comprising a primary ring buffer and one or more secondary ring buffers stored across a plurality of hosts. The first computer system is further configured to choose a secondary host as a de-stage primary for the replica set and to communicate an election to the secondary host. The second computer system includes a processor and a computer-readable medium storing instructions executable to receive the election as the de-stage primary for the replica set, identify a ring buffer stored in persistent memory comprising logs replicated from the primary ring buffer, and de-stage the logs from the ring buffer to a backing store.
G06F 3/06 - Entrée numérique à partir de, ou sortie numérique vers des supports d'enregistrement
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
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 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
20.
CENTRALIZED CONTROL OF BASEBOARD MANAGEMENT CONTROLLERS FOR A FLEET OF NETWORK SERVERS
Disclosed herein is a system for implementing centralized control of a fleet of hardware devices (e.g., network servers) via baseboard management controllers (BMCs) configured on printed circuit boards of the hardware devices in the fleet. The system enables a user (e.g., a fleet “admin”) to efficiently view information for the different types of BMCs configured across the fleet of hardware devices, identify a set of BMCs in the fleet that is associated with a shared characteristic, and implement a control action across the set of BMCs that is associated with the shared characteristic. Consequently, one of the technical benefits of the disclosed subject matter relates to efficient and effective scaling of BMC-related control actions across a set of hardware devices that respectively correspond to the set of BMCs that is associated with the shared characteristic.
Methods, systems, and computer storage media for providing security incident prioritization management using an adaptive incident prioritization engine of a security management system are described. The adaptive incident prioritization engine provides security incident prioritization based on an adaptive incident prioritization (AIP) framework built using a ranking algorithm. In particular, the adaptive incident prioritization framework employs a Best Matching 25 (BM25) algorithm and strategically and programmatically adapts the algorithm (e.g., an adaptive incident prioritization model) to rank security incidents based on a local security incident relevance metric (an adaptation of Term Frequency—TF—in BM25) and a global security incident rarity metric (an adaptation of Inverse Document Frequency—IDF—in BM25) associated with security incidents. A prioritization score for a security incident is calculated based on aggregating weighted frequencies of security incident ranking components (e.g., security incident metadata) within a security incident to determine an overall significance of the security incident.
A system for secure dynamic loading and execution of web workers and WebAssembly modules in web-based communication applications employs a reusable generic worker approach. The system includes a main application executing in its own environment subject to a first content security policy, which loads a first web worker from a resource indicated by this policy. The first web worker executes in its own environment, specifies a second content security policy, and dynamically loads and executes additional web workers or compiles WebAssembly modules upon request from the main application. This approach improves flexibility, efficiency, and security by offloading resource-intensive tasks, simplifying security implementations, enhancing scalability, and boosting performance for real-time audio and video processing in communication applications.
A computer-implemented method involves identifying a wireless band or channel linked to a wireless module's operation at a computer system. The method further includes detecting that the data bus is in a first data bus operation mode that causes radio frequency (RF) interference at the wireless band or channel and identifying a second data bus operation mode that mitigates RF interference at the wireless band or channel. Subsequently, the method configures the data bus to operate in the second data bus operation mode, thereby reducing RF interference and enhancing the computer system's overall performance and power usage.
This disclosure describes a framework for generating real-time audio translations of videos on a client device. Specifically, this disclosure describes a video dubbing system that utilizes a concurrent batch-processing architecture to provide real-time audio translations of videos on a client device. Additionally, in one or more implementations, the video dubbing system utilizes time-aware segmentation to prevent audio misalignment of the translated audio. As described below, the video dubbing system efficiently provides high-quality audio translations of videos that accurately align with the video content for the entire video, regardless of the video's length.
G10L 13/00 - Synthèse de la paroleSystèmes de synthèse de la parole à partir de texte
G06F 40/58 - Utilisation de traduction automatisée, p. ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel
G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
G10L 15/04 - SegmentationDétection des limites de mots
A method for fabricating a sensor comprises: (a) providing a wafer comprising a substrate, a dielectric layer on the substrate, and a semiconductor adlayer on the dielectric layer; (b) enacting microlithographic processing on the semiconductor adlayer to define: (i) a curved and elongate semiconductor waveguide confined to the dielectric layer, (ii) an in-coupling window arranged at a first end of the waveguide and configured to couple optically to an optical source, and (iii) an out-coupling window arranged at a second end of the waveguide and configured to couple optically to an optical detector; and (c) selectively etching the dielectric layer to define: (iv) a first dielectric region that supports and encloses the first end of the waveguide; (v) a second dielectric region that supports and encloses the second end of the waveguide; and (vi) a third dielectric region that supports but does not enclose a middle segment of the waveguide.
G01N 21/77 - Systèmes dans lesquels le matériau est soumis à une réaction chimique, le progrès ou le résultat de la réaction étant analysé en observant l'effet sur un réactif chimique
G02B 6/125 - Courbures, branchements ou intersections
G02B 6/136 - Circuits optiques intégrés caractérisés par le procédé de fabrication par gravure
26.
UPDATING COMPUTATIONAL WORKFLOW USING TRACE FEEDBACK
A computing system including one or more processing devices configured to receive context data. The one or more processing devices obtain a workflow graph of a computational workflow. The one or more processing devices process a workflow input at the computational workflow to obtain a workflow output. The one or more processing devices select an adjustable parameter included in the computational workflow. The one or more processing devices compute a trace feedback including an execution trace of the processing of the workflow input starting at a selected workflow node that includes the selected adjustable parameter. The trace feedback further includes an output feedback received in response to the workflow output. The one or more processing devices compute a parameter update to the selected adjustable parameter based at least in part on the context data and the trace feedback and apply the parameter update to the selected adjustable parameter.
Methods, systems, and computer storage media for providing a sensitive data scanning in a sensitive data leak-detection engine of a security management system. Sensitive data scanning—for example confidential information scanning or credential scanning—provides sensitive data leak-detection via a software development environment during a software development process. In operation, a request—to execute a sensitive data scanning operation on an instance of in-development code—is accessed. The sensitive data scanning operation executable via a sensitive data leak-detection engine that provides code security management services in a software development environment. A code scanning package is accessed. The code scanning package comprises software development environment code scanning parameters. Based on the software development environment code scanning parameters, the in-development code is scanned for sensitive data. A notification comprising a sensitive data scan result associated with the in-development code is generated. The notification is communicated to cause the notification to be displayed.
Techniques are described herein that are capable of performing cross-environment execution of a file in a hybrid runtime environment. A first virtual machine (VM) corresponding to a first managed runtime environment (MRE) is caused to load bridging logic by loading the first VM. The bridging logic loads a second VM corresponding to a second MRE. The first VM converts a source code file, which is created from metadata associated with a file in the second MRE, into a compiled file, which corresponds to the first MRE, and exposes the compiled file as an HTTP endpoint. The first VM passes an argument, which is included in a call from the HTTP endpoint, to the second VM via the bridging logic, which causes the second VM to generate a result by executing the file using the argument. The first virtual machine provides the result to the HTTP endpoint.
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
A woven panel for a computing device includes a woven pattern having a repeat size of greater than 25 cm by 25 cm. The woven pattern has a thread density of at least 25 threads by 25 threads per square cm. The woven panel is integrated to a computing device and is tailored to the specifics of the computing device, thereby improving the visual aesthetic and the tactile feel of the computing device.
D03D 3/08 - Tissus cintrés, ondulés, ou tissus similaires
D03D 13/00 - Tissus caractérisés par la disposition particulière des fils de chaîne ou de trame, p. ex. avec fils de trame incurvés, avec fils de chaîne discontinus, avec fils de chaîne ou de trame en diagonale
H01H 13/705 - Interrupteurs ayant un organe moteur à mouvement rectiligne ou des organes adaptés pour pousser ou tirer dans une seule direction, p. ex. interrupteur à bouton-poussoir ayant une pluralité d'éléments moteurs associés à différents jeux de contacts, p. ex. claviers avec des contacts portés par ou formés à partir de couches dans une structure multicouche, p. ex. interrupteurs à membrane caractérisés par la structure, le montage ou l'agencement des organes d'actionnement, p. ex. des boutons-poussoirs ou des touches
30.
DATA PACKET TRAFFIC USING SPECULATIVE UNIFIED FLOW
A method is presented for processing directional data packet traffic. A main data packet, characterized by a plurality of main data packet characteristics including at least a first IP address representing a first source, and a second IP address representing a first destination, is received at a rule processing engine. Based on the main data packet characteristics, a set of rules for processing the main data packet is retrieved. A main direction unified flow for the main data packet, and a speculative reverse direction unified flow for a reverse direction data packet are generated based on the main data packet characteristics and the retrieved set of rules. The main data packet is processed based on the main direction unified flow. Responsive to receiving the reverse direction data packet, the reverse direction data packet is processed based on the speculative reverse direction unified flow.
A system and method for integrating external content sharing into scheduled network-based meetings is disclosed. The system comprises a meeting service that receives a request from a client device for future-scheduled meetings, presents meeting options, and receives a selection of a meeting and content reference. The meeting service updates the meeting record with the content reference, performs validation checks, and enables seamless content presentation during the meeting. The method includes receiving meeting data requests, presenting future meetings, receiving meeting and content selections, updating meeting records, validating content sharing applications, and facilitating content presentation during meetings. This solution streamlines content sharing workflows, enhances meeting preparation, and improves collaboration efficiency in network-based meeting environments.
This disclosure provides redox cyclable molecules for energy storage. These molecules belong to either the 4H-pyran-4-ylidene family or include a six-membered aromatic ring with one nitrogen atom at position 1 (pyridinium family) or two nitrogen atoms at positions 1 and 4 (pyrazinium family) or at positions 1 and 3 (pyrimidinium family). Molecules in these families are used as analytes in redox flow batteries.
H01M 8/18 - Éléments à combustible à régénération, p. ex. batteries à flux REDOX ou éléments à combustible secondaires
C07D 241/12 - Composés hétérocycliques contenant des cycles diazine-1,4 ou diazine-1,4 hydrogéné non condensés avec d'autres cycles comportant trois liaisons doubles entre chaînons cycliques ou entre chaînons cycliques et chaînons non cycliques avec uniquement des atomes d'hydrogène, des radicaux hydrocarbonés ou des radicaux hydrocarbonés substitués, liés directement aux atomes de carbone du cycle
C07D 335/02 - Composés hétérocycliques contenant des cycles à six chaînons comportant un atome de soufre comme unique hétéro-atome du cycle non condensés avec d'autres cycles
33.
SYSTEM AND METHOD FOR TRANSLATING AND TRANSCRIBING
A computer-implemented method, computer program product and computing system for: receiving speech in a source language to define source language speech; performing a first token-based transcription of the source language speech into text of the source language using a first look-ahead encoder to define source language text; and performing a first token-based translation of the source language speech into text of a target language using a second look-ahead encoder to define target language text, wherein the first look-ahead encoder is smaller than the second look-ahead encoder.
G06F 40/58 - Utilisation de traduction automatisée, p. ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel
G06F 40/284 - Analyse lexicale, p. ex. segmentation en unités ou cooccurrence
G10L 13/08 - Analyse de texte ou génération de paramètres pour la synthèse de la parole à partir de texte, p. ex. conversion graphème-phonème, génération de prosodie ou détermination de l'intonation ou de l'accent tonique
G10L 15/16 - Classement ou recherche de la parole utilisant des réseaux neuronaux artificiels
G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
34.
COMPUTING PHYSICAL REPRESENTATION MATRIX OF LOGICAL CLIFFORD OPERATION
A computing system is provided, including one or more processing devices configured to receive an extended stabilizer form of a quantum error correction code. The one or more processing devices are further configured to receive a logical Clifford operation specification of a logical Clifford operation. Based at least in part on the extended stabilizer form, the one or more processing devices are further configured to compute a stabilizer tableau of the quantum error correction code. Based at least in part on the stabilizer tableau and the logical Clifford operation specification, the one or more processing devices are further configured to compute a physical representation matrix of the logical Clifford operation. The one or more processing devices are further configured to output the physical representation matrix.
A method of AI content evaluation includes receiving, from a generative artificial intelligence (AI) model, a set of AI-generated instructions that identifies steps for performing a task within an application, and selecting checkpoint interactions from an interaction index that define a plurality of interactions with a user interface. Each of the checkpoint interactions satisfies a similarity metric with a corresponding step in the set of AI-generated instructions. The method further includes determining, based on detected user interactions with the user interface, a subset of the checkpoint interactions completed by a user within an observation period, and evaluating a metric that to compute a quality score that quantifies user success with respect to performing the task associated with the AI-generated instructions. The metric depending at least in part on the subset of the checkpoint interactions completed by the user within the observation period. In response to determining that the quality score satisfies low-quality criteria, a remedial action is performed.
A method of forcefully terminating a flow includes identifying, by a hardware accelerator, a flow entry in a flow table corresponding to a flow determined to satisfy predefined criteria for flow termination and transmitting, by the hardware accelerator, a flow termination request to a software-based reset packet generator. The flow termination request includes flow match characteristics included in the flow entry of the flow table. The method further includes generating, at the software-based reset packet generator, a first transport control protocol (TCP) reset packet with header information matching the flow match characteristics and transmitting the first TCP reset packet to the hardware accelerator. The method still further provides for transforming the first TCP reset packet, at the hardware accelerator, according to a first transformation defined in the flow entry. The transformed TCP reset packet is transmitted to the destination device and the flow entry is deleted from the flow table.
H04L 45/76 - Routage dans des topologies définies par logiciel, p. ex. l’acheminement entre des machines virtuelles
H04L 45/00 - Routage ou recherche de routes de paquets dans les réseaux de commutation de données
H04L 47/193 - Commande de fluxCommande de la congestion au niveau des couches au-dessus de la couche réseau au niveau de la couche de transport, p. ex. liée à TCP
H04L 47/26 - Commande de fluxCommande de la congestion utilisant un retour explicite à la source, p. ex. paquets de signalisation de congestion
A computing system including one or more processing devices configured to extract ontology elements from conversational turns. The ontology elements are extracted at least in part by executing a generative language model. The one or more processing devices assign a respective ontology element type to each ontology element and store the ontology elements in an ontology index. The one or more processing devices receive a user input, and, at the generative language model, compute a structured retrieval-augmented generation (RAG) query. The one or more processing devices execute the structured RAG query over the ontology index to obtain one or more retrieved ontology elements. At the generative language model, the one or more processing devices compute and output a generative language model output based at least in part on the user input and the one or more retrieved ontology elements.
A method for estimating an origin of an audio impulse in a real-world environment performed by computing system is disclosed. Audio signals are received from a microphone array. The audio signals characterize an audio impulse generated in the real-world environment. Locations in the real-world environment of the plurality of microphones are received. A device related transfer function (DRTF) of the microphone array is recognized. A geometric computer model of the real-world environment is recognized. The geometric computer model includes virtual structures that model real-world structures in the real-world environment. The virtual structures include virtual surfaces that are assigned acoustic parameters. An impulse response path of the audio impulse throughout the geometric computer model of the real-world environment is simulated. Simulation data including an estimated origin of the audio impulse in the real-world environment is output based at least on the simulated impulse response path of the audio impulse.
A data processing system implements a framework that utilizes in-situ user interactions as a source of feedback for improving the training of LLMs to generate outputs that align with user preferences. The framework includes a user preference evaluation pipeline analyzes in-situ user interactions with the LLMs and generates preference information that can be used to improve the training of the LLM to improve the alignment of the LLM with user preferences. The user preference evaluation pipeline includes a feedback signal identification unit that identifies explicit and/or implicit feedback provided by the users in response to content output by the LLM in response to a user prompt. The feedback signal identification unit estimates user satisfaction with a set of satisfaction rubrics and user dissatisfaction with a set of user dissatisfaction rubrics to generate user preference data that can be used to align an LLM with these user preferences.
A system for secure dynamic loading and execution of web workers and WebAssembly modules in web-based communication applications employs a reusable generic worker approach. The system includes a main application executing in its own environment subject to a first content security policy, which loads a first web worker from a resource indicated by this policy. The first web worker executes in its own environment, specifies a second content security policy, and dynamically loads and executes additional web workers or compiles WebAssembly modules upon request from the main application. This approach improves flexibility, efficiency, and security by offloading resource-intensive tasks, simplifying security implementations, enhancing scalability, and boosting performance for real-time audio and video processing in communication applications.
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
This disclosure provides redox cyclable molecules for energy storage. These molecules belong to either the 4H-pyran-4-ylidene family or include a six-membered aromatic ring with one nitrogen atom at position 1 (pyridinium family) or two nitrogen atoms at positions 1 and 4 (pyrazinium family) or at positions 1 and 3 (pyrimidinium family). Molecules in these families are used as analytes in redox flow batteries.
A computing system (1) including one or more processing devices (10) configured to extract ontology elements (24) from conversational turns (20). The ontology elements are extracted at least in part by executing a generative language model (22). The one or more processing devices assign a respective ontology element type (32) to each ontology element and store the ontology elements in an ontology index (30). The one or more processing devices receive a user input (20A), and, at the generative language model, compute a structured retrieval-augmented generation (RAG) query (50). The one or more processing devices execute the structured RAG query over the ontology index to obtain one or more retrieved ontology elements. At the generative language model, the one or more processing devices compute and output a generative language model output (58) based at least in part on the user input and the one or more retrieved ontology elements.
In a computing network implementing an adaptive load balancing scheme, an indication of a link failure in the computing network is received. In response to receiving the indication, a temporary freeze mode is implemented that prevents the adaptive load balancing scheme from attempting further path exploration. A subset of routing options is used that is known to having been recently acknowledged to be valid.
In a computing network implementing an adaptive load balancing scheme using entropy values (EVs) to select network paths, the next expected packet sequence numbers (PSNs) sent along different paths are tracked. A generation number is increased to obtain a new EV and a last probe packet is sent to clear an old EV. If a starting PSN is divisible by a number k, an entropy slot is derived for each PSN using a modulo function based on k.
Systems and methods are provided for detecting memory hazards in memory, such as in massively parallel and distributed systems. In examples, a memory hazard logic detects access of a memory by a device communicatively coupled to the memory. The memory hazard logic identifies a memory access type and a memory address range being accessed, based on information obtained when monitoring a memory access transaction between the device and the memory. The memory hazard logic determines whether the memory access type on the memory address range is subject to memory access rules contained in a rules table. If so, the memory hazard logic determines whether the memory access type matches a memory access rule associated with each memory address in the memory address range. For each such memory address, the memory hazard logic notifies the device regarding a memory hazard triggered by the memory access transaction.
09 - Appareils et instruments scientifiques et électriques
38 - Services de télécommunications
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable software in the nature of a mobile application
for business and social networking, employment, careers and
recruiting; downloadable computer software that enables
members to access, interact with, collect, edit, organize,
modify, bookmark, store, upload, manage, track, share, and
publish data, information, databases, and customized content
in the fields of business, social networking, employment,
careers, and recruiting; downloadable computer software for
searching, accessing, displaying, sharing, reviewing,
creating, downloading, uploading, designing, modifying,
reproducing, transmitting, and managing newsletters,
research reports, blogs, articles, images, graphics, fonts,
photographs, text, videos, audiovisual and multimedia
content, and data in the fields of business, social
networking, employment, careers, and recruiting;
downloadable job searching, sourcing and recruiting computer
software using artificial intelligence (AI) for members on a
social networking, employment, and business networking
communication platform; downloadable chatbot software using
artificial intelligence (AI) for members on a social
networking, employment, and business networking
communication platform; downloadable writing and
communication software using artificial intelligence (AI)
for assisting platform members with employment, job sourcing
and recruiting, lead generation, and business-related
inquiries; content creation software using artificial
intelligence for members on a social networking, employment,
and business networking communication platform; downloadable
computer software using artificial intelligence (AI) for
employee training and professional development; downloadable
computer software using artificial intelligence (AI) for
online courses. Electronic messaging services; providing online information,
forums, groups, and communities for transmission of messages
among members and users in the fields of employment,
staffing, recruiting, career development, professional
networking, and training, as well as concerning job
searching, and general business topics (term considered too
vague by the International Bureau pursuant to Rule 13 (2)
(b) of the Regulations); provision of online forums, groups,
and communities for transmission of messages among computer
users concerning job searching, professional networking, and
general business topics, as well as for employment,
staffing, recruiting, career development, professional
training, and educational course materials (term considered
too vague by the International Bureau pursuant to Rule 13
(2) (b) of the Regulations); providing access to computer,
electronic, and online databases in the field of business,
social networking, employment, careers and recruiting. Providing temporary use of on-line non-downloadable software
for business and social networking, employment, careers and
recruiting; providing a website featuring temporary use of
non-downloadable software for business and social
networking, employment, careers and recruiting (term
considered too vague by the International Bureau pursuant to
Rule 13 (2) (b) of the Regulations); providing an online
non-downloadable computer software platform for social
networking, employment, careers, recruiting and business;
providing customized web pages featuring member-defined
information, audio, text, video, and images; providing
temporary use of on-line non-downloadable software that
enables members to access, interact with, collect, edit,
organize, modify, bookmark, store, upload, manage, track,
share, and publish data, information, databases, and
customized content in the fields of business, social
networking, employment, careers, and recruiting; providing
temporary use of on-line non-downloadable software for
searching, accessing, displaying, sharing, reviewing,
creating, downloading, uploading, designing, modifying,
reproducing, transmitting, and managing newsletters,
research reports, blogs, articles, images, graphics, fonts,
photographs, text, videos, audiovisual and multimedia
content, and data in the fields of business, social
networking, employment, careers, and recruiting; providing a
website featuring temporary use of non-downloadable computer
software featuring electronic publications in the nature of
newsletters, research reports, articles and white papers on
topics of professional interest in the field of business,
social networking, employment, careers and recruiting (term
considered too vague by the International Bureau pursuant to
Rule 13 (2) (b) of the Regulations); providing temporary use
of on-line non downloadable computer software that provides
web-based access to applications and services through a
web-operating system and portal interface; providing
temporary use of on-line non-downloadable computer software
for use in business analytics and database management;
providing temporary use of on-line non-downloadable software
for tracking and analyzing user interaction with customized
content; providing temporary use of on-line non-downloadable
software for providing online courses, seminars, interactive
classes, educational instruction, and course materials;
providing temporary use of on-line non-downloadable software
for accessing internet search engines featuring information
for obtaining job listings, resume postings, and other job
searches; providing non-downloadable job searching, sourcing
and recruiting online software using artificial intelligence
(AI) for members on a social networking, employment, and
business networking communication platform; providing
non-downloadable chatbot online software using artificial
intelligence (AI) for members on a social networking,
employment, and business networking communication platform;
non-downloadable writing and communication online software
using artificial intelligence (AI) for assisting platform
members with writing, communicating, and with employment,
job, recruiting, lead generation, and business-related
inquiries (term considered too vague by the International
Bureau pursuant to Rule 13 (2) (b) of the Regulations);
non-downloadable content creation online software using
artificial intelligence (AI) for members on a social
networking, employment, and business networking
communication platform (term considered too vague by the
International Bureau pursuant to Rule 13 (2) (b) of the
Regulations); providing non-downloadable online software
using artificial intelligence (AI) for employee training and
professional development; providing non-downloadable online
computer software using artificial intelligence (AI) for
providing online courses, seminars, interactive classes,
educational instruction, and course materials.
47.
DYNAMIC MULTIMODAL PROMPT GENERATION FOR EFFICIENT CONTENT MODERATION
Aspects of the disclosure include methods and systems for content moderation, and specifically dynamic multimodal prompt generation for efficient content moderation. A method includes receiving, by a prompt generation system, a request for a decision corresponding to content. The method includes generating, by an encoder of the prompt generation system, an embedding of the content, and retrieving, by an embedding based retrieval (EBR) module of the prompt generation system, K retrieved chunks from a database, the K retrieved chunks having a Kth closest distance to the embedding in an embedding space. A dynamic prompt comprising a prompt template, multiple retrieved chunks of the K retrieved chunks, and the content is generated and input to a pre-trained large language model. The LLM generates the decision, which is returned responsive to the request.
Methods, systems, and computer storage media for providing a modular cybersecurity platform are described. The modular cybersecurity platform is implemented using a modular cybersecurity engine that operates based on an analytical framework for dynamic data analysis and data management in a data intelligence system. In particular, the analytical framework is based on complementary modular components that are designed to interoperate in the modular cybersecurity engine. The modular cybersecurity engine includes a modular distributed system, a credential detection system, and a credential semantic graph system. The modular cybersecurity engine supports cybersecurity and sensitive data management scenarios that can empower investigators in various investigations, and provide automated flows that are highly scalable and support different types of functionality (e.g., priority embedding pipeline, credential scanning, and credential semantic graph analysis). The utility of the modular cybersecurity engine is demonstrated by its wide-ranging application in addressing complex cybersecurity challenges and sensitive data management tasks.
A job scheduling technology computationally selects jobs from a set, excluding at least one job by applying at least one job filter, submits the selected jobs to a scheduler mechanism, receives in response a first phase schedule, and produces a second phase schedule updating the first phase schedule to include at least one excluded job. The update avoids computational costs of combinatorial explosion. In some scenarios, a location-agnostic job is excluded from the first phase schedule and then included in the updated schedule. In some scenarios, applying the filters sorts the jobs for submission to the scheduler mechanism.
Examples that relate to sensor modules removably attachable to wearable audio devices are disclosed. In one example, a removable sensor module comprises a light sensor, a fixed magnet, and a moveable shutter. The moveable shutter comprises a bistable magnet that repels the fixed magnet to urge the shutter into either a privacy position that blocks a field of view of the light sensor or an open position that does not block the field of view of the light sensor.
G01S 7/481 - Caractéristiques de structure, p. ex. agencements d'éléments optiques
G03B 9/26 - Obturateurs à lame ou disque tournant ou pivotant autour de la perpendiculaire à son plan comportant une seule ou plusieurs lames d'obturation
51.
HYBRID STORAGE OF REGISTER INFORMATION FOR AN INTEGRATED CIRCUIT
Embodiments of the present disclosure include a method of storing information about registers in an integrated circuit. In one embodiment, registers from an integrated circuit have corresponding structured and unstructured information describing the registers. In one embodiment, validation software stores the structured information in one database and stores the unstructured information in another database. The structured information may be stored in a high-speed local database, and the unstructured information may be stored in a cloud computing environment database with access to cloud software services. For each register, structured information in the local database may be linked to structured information in the cloud database using a unique ID.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet
52.
CONTENT TYPE DELIVERY MANAGEMENT FOR COMPUTATIONAL CAPACITY-CONSTRAINED RESOURCE SYSTEMS
This disclosure describes a content capacity system that provides a framework for solving computational capacity management problems for content providers that provide real-time content distribution. For example, the content capacity system generates and implements resource capacity policies that enable content providers to identify a target distribution location with available resources to provide optimal content types when applied to incoming content requests. Additionally, the content capacity system ensures that resources are efficiently used for the content type with the highest utility and avoids processing infeasible locations and content types, as well as exceeding computational capacities.
This document generally relates to matching power usage on follower devices to a level of user interaction. One example includes a processor configured to run an interactive application and to proactively identify active periods where a user interacts with a presentation of the interactive application and inactive periods where the user does not interact with the presentation. The example includes a communication component configured to send a first set of parameter values to a follower device for use during the identified active periods and to send a second set of parameter values to the follower device during the identified inactive periods that cause the follower device to use a relatively lower amount of power.
A63F 13/235 - Dispositions d'entrée pour les dispositifs de jeu vidéo pour l'interfaçage avec le dispositif de jeu, p. ex. des interfaces spécifiques entre la manette et la console de jeu utilisant une connexion sans fil, p. ex. infrarouge ou piconet
G06F 1/3206 - Surveillance d’événements, de dispositifs ou de paramètres initiant un changement de mode d’alimentation
G06F 1/3234 - Économie d’énergie caractérisée par l'action entreprise
54.
IDENTIFYING SOURCES OF PERFORMANCE VARIABILITY USING RELEVANT PERFORMANCE DATA
Methods, computer systems, and computer storage media are provided for identifying a source(s) of performance variability using relevant performance data. In embodiments, performance data indicating performance of a computing system is obtained. Such performance data is analyzed to identify relevant performance data including a representation of a differential graph that compares a first set of performance data associated with a first environment with a second set of performance data associated with second environment. Thereafter, a prompt is generated that includes the representation of the differential graph and a request for an identification of a source of performance variability associated with the relevant performance data. Based on the prompt, the source of performance variability associated with the relevant performance data may be identified via a large language model and provided for display.
Artificial intelligence (AI) techniques for connection networking are described. A method comprises generating a first training prompt based on a set of guidelines for a network service of a connection network system, the guidelines defining an objective for the network service, sending the first training prompt and a first set of training datapoints from a first training dataset to a first generative AI model, a training datapoint from the first set of training datapoints comprising a content item, receiving a second set of training datapoints for a second training dataset from the first generative AI model, wherein a training datapoint of the second training dataset comprises a first label for the content item generated by the first generative AI model based on the objective, and training a second generative AI model using the second set of training datapoints based on the objective. Other embodiments are described and claimed.
An adaptive artificial intelligence (AI) control system receives a prompt for an AI system from a user interface component of a software application on a mobile device. The adaptive AI control system determines a current battery level of the mobile device using a battery level monitoring component. The adaptive AI control system then selects a generative AI model of the AI system to use to generate a response for the prompt based on the current battery level using a model selection component. The generative AI model that is selected is one of a plurality of different generative AI models of the AI system which are capable of processing the prompt, each of the plurality of generative AI models having a different level of complexity.
A data processing system implements capturing, via a user interface of a client device, a photo; generating one or more photo design suggestion images using an artificial intelligence (AI) model based on metadata of the photo by inserting at least one first foreground object, extracting text from the metadata as a portion of a prompt, or a combination thereof, wherein the metadata includes a location, a time, and one or more image tags; and providing the one or more photo design suggestion images to display on the user interface of the client device.
In a computing network implementing an adaptive load balancing scheme, an indication of a link failure in the computing network is received. In response to receiving the indication, a temporary freeze mode is implemented that prevents the adaptive load balancing scheme from attempting further path exploration. A subset of routing options is used that is known to having been recently acknowledged to be valid.
A system for authenticating a principal comprises first and second authentication systems and an authentication artifact signing service. The first authentication system issues a request comprising an authentication artifact associated with the principal and a specification of one or more modifications to be made thereto, the authentication artifact being generated by a second authentication system, signed thereby using a key, and stored by the first authentication system. The signing service receives the request and, responsive thereto: applies the modification(s) to the authentication artifact to generate a modified authentication artifact, signs the modified authentication artifact using a key of the second authentication system, and returns the signed modified authentication artifact to the first authentication system for use in authenticating the principal. The first authentication system executes in a different security domain than the signing service and is unable to access the key used thereby.
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
60.
MIGRATION OF BARE-METAL INSTANCES USING SDN DEVICES
Systems and methods for migrating a bare-metal instances utilizing a software defined network device include invoking a migration of data from a source bare-metal instance to a destination bare-metal instance, generating, at a software defined networking (SDN) device of the destination bare-metal instance, a shadow mapping comprising a mapping to route data packets directed to a customer address of the source bare-metal instance to a provider address of the SDN device of the destination bare-metal instance, and forwarding, from the SDN device of the source bare-metal instance to the SDN device of the destination bare-metal instance, data packets received at the SDN device of the source bare-metal instance after the source bare-metal instance is disabled until completion of the migration of data.
H04L 47/762 - Contrôle d'admissionAllocation des ressources en utilisant l'allocation dynamique des ressources, p. ex. renégociation en cours d'appel sur requête de l'utilisateur ou sur requête du réseau en réponse à des changements dans les conditions du réseau déclenchée par le réseau
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
H04L 47/78 - Architectures d'allocation des ressources
H04L 61/5007 - Adresses de protocole Internet [IP]
61.
SYSTEMS AND METHODS FOR THERMAL MANAGEMENT OF ELECTRONIC DEVICES
A thermal management device includes a body, a fluid movement structure, and a movement mechanism. The body is configured to receive heat from a heat-generating component at a proximal surface, and the fluid movement structure is on a distal surface of the body that is distal to the proximal surface, wherein the fluid movement structure is configured to direct fluid flow of a working fluid and the body is configured to transfer heat to the working fluid. The movement mechanism is configured to move the fluid movement structure relative to the body.
According to implementations of the subject matter described herein, a solution for image rescaling is proposed. According to the solution, an input image of a first resolution is obtained. An output image of a second resolution and high-frequency information following a predetermined distribution are generated based on the input image by using a trained invertible neural network, where the first resolution exceeds the second resolution. Besides, a further input image of the second resolution is obtained. A further output image of the first resolution is generated based on the further input image and high-frequency information following the predetermined distribution by using an inverse network of the invertible neural network. This solution can downscale an original image into a visually-pleasing low-resolution image with the same semantics and also can reconstruct a high-resolution image of high quality from a low-resolution image.
G06T 3/4046 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement utilisant des réseaux neuronaux
G06T 3/4084 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement dans le domaine transformé, p. ex. changement d’échelle dans le domaine de la transformée de Fourier rapide
63.
PROVIDING EXTREMELY HIGH READ AVAILABILITY IN A TELECOMMUNICATION ENVIRONMENT
The present disclosure generally relates to providing extremely high read availability for data repositories within a telecommunication network. Systems described herein implement a novel architecture and backup scheme in connection with non-customized data servers to provide at least 99.99999% read availability within a telecommunication network. In one or more examples, the novel architecture includes a primary database and hot backup database that mirrors the primary database. Additionally, the novel architecture further includes a tepid backup database that stores asynchronous snapshot replications of the primary database that are sent to the tepid backup database out-of-band. In the event of a cascading failure that affects the primary database and the hot backup database, the best snapshot replication stored by the tepid backup database is initialized to service a read request that would otherwise have failed.
G06F 11/14 - Détection ou correction d'erreur dans les données par redondance dans les opérations, p. ex. en utilisant différentes séquences d'opérations aboutissant au même résultat
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet
64.
GENERATING NARRATIVE QUERY RESPONSES UTILIZING GENERATIVE LANGUAGE MODELS FROM SEARCH-BASED AUTOSUGGEST QUERIES
This disclosure describes a query gateway system that provides an efficient and flexible framework for providing context-retained autosuggest queries from an autosuggest query system (e.g., a search engine query experience) to a generative language model system (e.g., an AI chat experience). For instance, the query gateway system establishes a framework to leverage the features and services of the autosuggest query system and automatically provides context-retained queries to the generative language model system using separate user interfaces that do not disrupt user navigation or require manual duplicative user input. Additionally, the query gateway system incorporates additional enhancements, including an AI chat eligibility model and a query reformulation model, to improve the computational efficiency and accuracy of the AI chat system.
According to examples, an apparatus includes a processor that determines that an encryption operation has been requested or executed through a cloud control plane capability with respect to a cloud storage device. The processor also determines that the requested or executed encryption operation with respect to the cloud storage device is anomalous and, based on a determination that the requested or executed encryption operation with respect to the cloud storage device is anomalous, outputs an alert and/or performs a remedial action. By identifying anomalous encryption operation requests or executions on cloud storage devices, the processor is able to determine that ransomware attacks are or have occurred on the cloud storage devices. In some examples, the processor takes remedial actions to mitigate harm posed by or prevent the ransomware attacks.
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
67.
REDUCED POWER CONSUMPTION ON BATTERY OPERATED FOLLOWER DEVICES
This document generally relates to matching power usage on follower devices to a level of user interaction. One example includes a processor configured to run an interactive application and to proactively identify active periods where a user interacts with a presentation of the interactive application and inactive periods where the user does not interact with the presentation. The example includes a communication component configured to send a first set of parameter values to a follower device for use during the identified active periods and to send a second set of parameter values to the follower device during the identified inactive periods that cause the follower device to use a relatively lower amount of power.
A63F 13/235 - Dispositions d'entrée pour les dispositifs de jeu vidéo pour l'interfaçage avec le dispositif de jeu, p. ex. des interfaces spécifiques entre la manette et la console de jeu utilisant une connexion sans fil, p. ex. infrarouge ou piconet
A63F 13/24 - Parties constitutives, p. ex. manettes de jeu avec poignées amovibles
A63F 13/42 - Traitement des signaux de commande d’entrée des dispositifs de jeu vidéo, p. ex. les signaux générés par le joueur ou dérivés de l’environnement par mappage des signaux d’entrée en commandes de jeu, p. ex. mappage du déplacement d’un stylet sur un écran tactile en angle de braquage d’un véhicule virtuel
An apparatus comprising a processor and a memory storing instructions that, when executed by the processor, perform a method for estimating cleave quality of a cleaved hollow core optical fibre, is described. The method comprises receiving an image of an end face of the fibre, and analysing pixel data of the image to determine cleave quality of the fibre by: determining at least one feature of the image, the feature representing a characteristic of the end face of the fibre, and using a model to map the at least one feature to an indication of cleave quality. The method further comprises outputting the indication of cleave quality. A method for creating a model mapping at least one feature of an image of an end face of a cleaved hollow core optical fibre to an indication of cleave quality of the fibre is disclosed. The indication of cleave quality comprises an indication of at least one of cleave angle and cleave profile. The method comprises at least one of training a machine-learning model using training inputs, defining at least one rule mapping at least one feature of an end face image of a cleaved hollow core optical test fibre and defining a lookup table by associating at least one feature of an end face image of a cleaved hollow core optical test fibre.
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G02B 6/255 - Épissage des guides de lumière, p. ex. par fusion ou par liaison
Techniques are described for handling real-time data on a client delivered from a backend service via a transport mechanism such as a SignalR WebSocket connection. Once a communication channel between the backend service and the client is established via the transport mechanism, the backend service transmits summary data and current data to the client in a keyset-valueset format via the communication channel at respective intervals. The summary data includes data gathered by the backend service during equal-length summary data intervals, whereas the current data includes data gathered by the backend service since an end time of a most recent one of the summary data intervals. After activation of a UI element, the client transforms data associated with the element from the summary data packet and a most recent one of the current data packets into a desired format and displaying the transformed data via the UI.
An example may provide at least one first generative machine learning model (GMLM) instruction and an intent to a GMLM. The at least one first GMLM instruction is to cause the GMLM to use the intent to generate first GMLM output. The first GMLM output includes GMLM-generated output sections. A device may provide the first GMLM output including the GMLM-generated output sections and at least one second GMLM instruction to the GMLM. The at least one second GMLM instruction is to cause the GMLM to use the intent, the GMLM-generated output sections, and a first data set to generate second GMLM output including at least one first digital element. A device may validate the second GMLM output by comparing the at least one first digital element to at least one second digital element. The at least one second digital element is accessible via a second data set.
Systems, methods, and computer readable storage mediums are disclosed herein for evaluating a health of a configuration distribution. In an example system, at least one health signal is obtained relating to the distribution of a configuration. In one implementation, a first health signal is obtained indicative of a transmission of the configuration to a target computing resource. A second health signal is obtained indicative of a consumption of the configuration. A third health signal is obtained indicative of a health of a computing platform that includes the target computing resource after the consumption. Based on the first health signal, the second health signal, and the third health signal, the health of the configuration distribution is determined. In an illustration, the health of the configuration distribution is determined to be in a healthy state, unhealthy state, or another state.
A system uses a single vision model to combine lower resolution images of a body and higher resolution images of a targeted body part to more efficiently identify a human action with respect to an object. The system receives images of a scene that include a body. For instance, the images may be sequential frames in a video captured by a camera. The system generates a body image by extracting a region from an image that includes a body. The system generates a target image by extracting a region from the image that includes a targeted body part interacting with an object. The system is configured to perform similar operations on the body image and the target image to ensure that a single vision model can process the target image at a more granular level compared to the body image.
G06V 40/20 - Mouvements ou comportement, p. ex. reconnaissance des gestes
G06V 10/25 - Détermination d’une région d’intérêt [ROI] ou d’un volume d’intérêt [VOI]
G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
G06V 10/32 - Normalisation des dimensions de la forme
G06V 10/80 - Fusion, c.-à-d. combinaison des données de diverses sources au niveau du capteur, du prétraitement, de l’extraction des caractéristiques ou de la classification
73.
DISRUPTOR VALIDATION ENGINE(S) FOR ASSESSING THE QUALITY OF DISRUPTORS
Systems and methods herein provide a disruptor validation engine and its related functions. In an aspect, a disruptor validation engine may determine disruption alerts issued by one or more disruptors, where a disruption alert indicates potential malicious activity within a tenant environment. Responsive to receiving the disruption alerts, the disruptor validation engine may generate a probability grade for a respective disruption alert. The probability grade may indicate a likelihood that the potential malicious activity triggering the disruption alert is actually malicious. The disruptor validation engine may then determine a disruption classification for the disruption alert based on a respective probability grade. Based on the disruption classification, the disruptor validation engine may generate a quality grade for the one or more disruptors indicating the validity and quality of the respective disruptor.
H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
Computing resources are managed in a computing environment comprising a computing service provider and an edge computing network. The edge computing network comprises computing and storage devices configured to extend computing resources of the computing service provider to remote users of the computing service provider. The edge computing network collects capacity and usage data for computing and network resources at the edge computing network. A predictive function is applied to the data to determine a predicted demand on the computing and network resources at a future time interval. Based on the predicted demand, a distribution of workloads is determined.
H04L 67/1008 - Sélection du serveur pour la répartition de charge basée sur les paramètres des serveurs, p. ex. la mémoire disponible ou la charge de travail
H04L 47/83 - Contrôle d'admissionAllocation des ressources basée sur la prédiction d'utilisation
75.
DETERMINING NETWORK TOPOLOGY INFORMATION FROM DOMAIN NAME SYSTEM (DNS) QUERIES
Systems, methods, apparatuses, and computer program products are disclosed for determining network topology information using domain name system (DNS) queries. Network connection and dependency information of elements in a compute cluster are determined from DNS requests. A network topology is generated based on the determined network connection and dependency information. A network policy is generated for the computing cluster based on the network topology.
H04L 41/12 - Découverte ou gestion des topologies de réseau
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 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]
76.
DYNAMICALLY ADJUSTING A DATA BUS CHARACTERISTIC BASED ON A WIRELESS CHANNEL
A computer-implemented method involves identifying a wireless band or channel linked to a wireless module's operation at a computer system. The method further includes detecting that the data bus is in a first data bus operation mode that causes radio frequency (RF) interference at the wireless band or channel and identifying a second data bus operation mode that mitigates RF interference at the wireless band or channel. Subsequently, the method configures the data bus to operate in the second data bus operation mode, thereby reducing RF interference and enhancing the computer system's overall performance and power usage.
In a computing network implementing an adaptive load balancing scheme, an indication of a link failure in the computing network is received. When forwarding a packet to the failed link, the entropy of the packet is incremented by a constant. A new output port is selected for the associated Equal-Cost Multi-Path (ECMP) group. The entropy in the packet is incremented by the constant if the re-hash leads to another failed link. In response to determining that the ECMP group leading to a destination has no working ports, a hash function is applied to select another working port.
H04L 41/0668 - Gestion des fautes, des événements, des alarmes ou des notifications en utilisant la reprise sur incident de réseau par sélection dynamique des éléments du réseau de récupération, p. ex. le remplacement par l’élément le plus approprié après une défaillance
H04L 41/0604 - Gestion des fautes, des événements, des alarmes ou des notifications en utilisant du filtrage, p. ex. la réduction de l’information en utilisant la priorité, les types d’éléments, la position ou le temps
H04L 41/0631 - Gestion des fautes, des événements, des alarmes ou des notifications en utilisant l’analyse des causes profondesGestion des fautes, des événements, des alarmes ou des notifications en utilisant l’analyse de la corrélation entre les notifications, les alarmes ou les événements en fonction de critères de décision, p. ex. la hiérarchie ou l’analyse temporelle ou arborescente
An optical fibre system comprises an optical fibre cable comprising at least one microstructured optical fibre within a jacket; and a barrier mechanism responsive to a breach of the optical fibre cable through which water from a surrounding environment of the optical fibre cable may enter voids of the microstructured optical fibre; wherein the barrier mechanism is responsive to the breach by operating to introduce a barrier across the voids of the microstructured optical fibre, the barrier configured to inhibit movement of water along the voids.
The described technology provides a method including generating a base snoop filter (SFT) entry for a coherence granule (cogran) in agent cache, the base SFT entry comprising a tracking_information field configured to track a plurality of agent IDs, each agent ID identifying an agent that holds a copy of the cogran, determining a number of agents that hold the copy of the cogran, comparing the and number of agents that store the copy of the cogran with number of the plurality of agent IDs tracked in the tracking_information field of the base SFT entry; and in response to determining that the number of agents that hold the copy of the cogran is greater than the number of the plurality of agent IDs tracked in the tracking_information field of the base SFT entry, selecting a second SFT entry as an extra SFT entry, wherein the extra SFT entry is configured to store a portion of tracking vector wherein each bit of the tracking vector indicates cache validity state of the cogran for a related agent.
A system for thermal management of a computing device includes an immersion chamber, a cooling fluid, a plurality of heat-generating components, and a means for removing vapor from a cooling volume of the cooling fluid. The cooling fluid is positioned in the immersion chamber and fills at least a portion of the immersion chamber. The plurality of heat-generating components is positioned in the cooling fluid and arranged in a series. The series defines the cooling volume of the cooling fluid contacting the plurality of heat-generating components to cool the plurality of heat-generating components.
This document relates to active speaker detection using distributed devices. For example, the disclosed implementations can employ personal devices of one or more users to detect when those users are speaking during a call with other users. Then, a camera on the personal device can be employed to obtain a front-facing view of the user, which can be provided to other call participants. In some cases, a microphone and/or camera on the user's device are employed to detect when the user is actively speaking.
This disclosure relates to a context enforcement system that efficiently and securely protects tenant context information that travels across microservices in a multi-tenant distributed cloud computing system and protects against data leaks that often occur in conventional microservice management systems. For example, the context enforcement system ensures secure external and internal communications and context isolation by providing various shared library functions to microservices of a multi-tenant distributed cloud computing system. Additionally, the shared library provided by the context enforcement system improves the efficiency of the multi-tenant distributed cloud computing system by allowing microservices to focus on target operations rather than also maintaining and performing additional redundant functions.
In some examples, a method of generating a security report is provided. The method includes receiving a user query and security data, and providing the user query and security data to a semantic model. The semantic model generates one or more first embeddings. The method further includes receiving, from a data model, one or more second embeddings. The data model is generated based on historical threat intelligence data. The model further includes generating an execution plan based on the one or more first embeddings and the one or more second embeddings, and returning a report that corresponds to the execution plan.
G06F 16/3329 - Formulation de requêtes en langage naturel
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
84.
ENFORCEMENT OF ATTESTATION OF READ-ONLY PROTECTED MEMORY DURING ATTESTATION VALIDITY PERIOD
Enforcing attestation of read-only protected memory during attestation validity period. A client computer system identifies a change in a read-only protected memory protection status for a software component loaded at the client computer system. The client computer system then determines that a validity time period of an attestation report is unexpired. The attestation report comprises one or more attested properties, including one or more read-only memory protection (ROMP) attested properties for the software component. The client computer system also determines that at least one ROMP attested property for the software component is no longer valid due to the change in the read-only protected memory protection status for a software component. Based on the at least one ROMP attested property for the software component being no longer valid, the client computer system initiates a remedial action to prevent interaction of the software component with a relying party computer system.
G06F 21/74 - 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 opérant en mode dual ou compartimenté, c.-à-d. avec au moins un mode sécurisé
85.
CUSTOMIZING A CONTROL SYSTEM FOR DIFFERENT USE TYPES
A computing system adapts operation of a control system based on how the system is being used. The computing system obtains state information that describes a current state of operation of the computing system, including information indicative of interaction by a user with an application executed by the computing system. A machine-trained model processes the state information to determine use type information that identifies a use type associated with the user, the use type corresponding to a predefined category of computing behavior that characterizes a manner of using the computing system. The computing system modifies operation of the control system based on the use type information by automatically adjusting an operating parameter of the control system that affects allocation of computing resources to the application, thereby dynamically optimizing scheduling, throttling, bandwidth allocation, or other operating parameters for the identified use type.
G05B 13/02 - Systèmes de commande adaptatifs, c.-à-d. systèmes se réglant eux-mêmes automatiquement pour obtenir un rendement optimal suivant un critère prédéterminé électriques
86.
SYSTEM AND METHOD OF PROVIDING CONTEXT-AWARE AUTHORING ASSISTANCE
A method for automatically generating content for a user based on context results in personalized authoring assistance that can be provided by an already trained language model without the need for additional training. The method includes receiving a user query including a context, conducting a search of user data to generate first search results, applying one or more first models to the first search results to infer first patterns associated with the user and to generate a first set of content based on the first patterns, applying one or more second models to context data to infer second patterns associated with the context and to generate a second set of content based on the second patterns, and generating a pseudo-document. The method further includes transmitting the prompt to the language model to generate a response that is customized to the user and the context.
Montgomery multiplier architectures are provided. A circuit can include an initial processing element (PE) circuit configured to generate a first output including (i) a radix of a carry out and (ii) a radix of an intermediate result based on radixes of respective operands, a radix of an inverse of a modulus, and a radix of the modulus, middle PE circuits configured to generate a second output including (i) respective radixes of a Montgomery multiplication result and (ii) further respective radixes of a carry out on two consecutive clock cycles based on the first output, and a final PE circuit configured to generate further radixes of the Montgomery multiplication results on two consecutive, subsequent clock cycles based on the second output.
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
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
A cross-tenant authentication system is described. The system receives a user token from a client device that is registered with a first tenant of a service application of a server. The system receives a request, from the client device, to access a second feature of a second tenant of the service application. The second feature of the second tenant of the service application is separate from a first feature of the first tenant of the service application. The second feature is only accessible to devices registered with the second tenant of the service application. The system authenticates the request by validating the user token from the client device and determines a cross-tenant policy of the second tenant of the service application based on the user token. The system forms an identity object based on the cross-tenant policy.
High lattice thermal conductivity metallic materials for thermal management applications are disclosed. The metallic materials have a lattice thermal conductivity of above 100 W/mK. Examples of such metallic materials include tantalum phosphide (TaP) and manganese vanadium (MnV). These materials with high lattice thermal conductivity may be used in various applications to efficiently transfer heat. For example, they may be used in heat dissipation devices as well as in a thermally conductive unit of a device that also includes a heat generating unit. In an implementation, these materials may be used at interfaces between metallic and semiconductor or insulator materials.
A system for drawing a glass preform or input cane. The system has a feeding unit for the glass preform or input cane, a first furnace to soften a portion of the glass preform or input cane, a first pair of capstan belts to apply tension to the softened portion of the glass preform or input cane drawing the glass preform into a glass fiber or output cane, or to apply tension to the softened portion of the glass input cane drawing the glass input cane into the glass fiber, wherein each capstan belt comprises a belt surface comprising magnetic material such that only the belt surfaces contact the softened portion, and a first magnet positioned to remove magnetic particles from the glass fiber or output cane as it is drawn out by the first pair of capstan belts and without contacting the glass fiber.
C03B 37/025 - Fabrication de fibres ou de filaments de verre par étirage ou extrusion à partir de tubes, tiges, fibres ou filaments ramollis par chauffage
Systems and methods are provided for implementing data backup and recovery using cache-coherent interconnect node-based non-volatile memory. A cache-coherent interconnect node partitions a memory pool into a plurality of memory regions as well as a backup storage into a plurality of memory portions, and pre-allocates a memory region and a corresponding memory portion to each compute node. When a rack-level power loss occurs, and a battery-based power source is activated, a cache-coherent interconnect controller saves data from each memory region into the corresponding memory, portion, and subsequently saves an entry for each memoy portion in an index portion of the backup storage. Subsequently, the controller causes a power circuitry to shut down the backup power source. After rack-level power restoration and memory region initialization, the controller restores, for each memory region, the data saved in a corresponding memory portion into that memory region, based on information in a corresponding entry.
G06F 11/14 - Détection ou correction d'erreur dans les données par redondance dans les opérations, p. ex. en utilisant différentes séquences d'opérations aboutissant au même résultat
G06F 11/20 - Détection ou correction d'erreur dans une donnée par redondance dans le matériel en utilisant un masquage actif du défaut, p. ex. en déconnectant les éléments défaillants ou en insérant des éléments de rechange
92.
DRAWING GLASS WITH A SACRIFICIAL PROTECTIVE LAYER BACKGROUND
A system for drawing a glass preform or input cane with reduced glass contamination. The system has a first sacrificial layer made from a film free of loosely adhered particulate matter and having a first end and a second opposite end, wherein the first end is wound around a first supply bobbin and the second end is wound around a first take-up bobbin, and wherein a portion of the first sacrificial layer is configured to cover a surface of a first capstan belt, a second sacrificial layer made from a film free of loosely adhered particulate matter and having a first end and a second opposite end, wherein the first end is wound around the second supply bobbin, wherein the second end is wound around a second take-up bobbin, and wherein a portion of the second sacrificial layer covers a surface of a second capstan belt.
C03B 37/025 - Fabrication de fibres ou de filaments de verre par étirage ou extrusion à partir de tubes, tiges, fibres ou filaments ramollis par chauffage
09 - Appareils et instruments scientifiques et électriques
38 - Services de télécommunications
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Computer software which enables instant messaging and file sharing; communications software for enabling computer network, wireless network and Internet users to exchange data, audio transmissions, video images and graphics; computer programs for managing communications and data exchange between hand held computers and desktop computers; computer software for use as an application programming interface (API); computer software for collecting, managing, editing, organizing, modifying, transmitting, sharing, and storing of messages, text, images, data files, audio transmissions, videos and audio-visual content; computer software development tools; communications software for facilitating voice over internet protocol (VOIP) calls, phone calls, video calls, text messages, instant messages and online social networking services; computer software which enables multiple users to share files and memoranda and synchronize calendars; electronic communications devices, namely, telephones, handsets, headsets, speakers, microphones, earphones, webcams and video cameras; software using artificial intelligence for collecting, managing, summarizing, automating, editing, organizing, modifying, transmitting, sharing, searching, and storing of messages, text, images, data files, audio transmissions, videos and audio-visual content (1) Communication services, namely, electronic transmission of data and documents among users of computers; telecommunication services, namely, transmission of voice, data, graphics, images, audio and video by means of telecommunications networks, wireless communication networks, and the Internet; streaming of audio and video material over the Internet; information transmission via electronic communications networks; electronic transmission of mail and messages; electronic data transmission; electronic delivery of images and photographs via a global computer network; instant messaging services; voice over internet protocol (VOIP) services; audio and video broadcasting services over the Internet; providing on-line chat rooms for social networking; consulting in the field of transmission of voice, data and documents via telecommunications networks; providing access to databases; providing user access to global computer networks; telecommunications gateway services; telecommunications services, namely, personal communications services via wireless networks; software using artificial intelligence for communication and telecommunication services
(2) Providing temporary use of on-line non-downloadable software and applications for instant messaging and enabling and managing simultaneous, multiple modes of communication over local area networks and the Internet; platform as a service (PAAS) featuring computer software platforms which enables multiple users to share files, memoranda, and synchronize calendars; application service provider, namely, hosting, managing, developing, and maintaining applications, software and web sites of others in the fields of personal productivity, wireless communication, mobile information access, and remote data management for wireless delivery of content to handheld computers, laptops and mobile electronic devices; computer services, namely, creating an on-line community for registered users to create virtual communities, participate in discussions, get feedback from their peers, and engage in social, business and community networking; server and web site hosting for others to enable interactive discussions via communication networks; application service provider (ASP) featuring software to enable or facilitate the creating, editing, uploading, downloading, accessing, viewing, posting, displaying, tagging, blogging, streaming, linking, annotating, commenting on, embedding, transmitting, and sharing digital content or information via computer and communication networks; provision of Internet search engines; providing temporary use of non-downloadable software for enabling collaborative work, creating a virtual community, and transmission of audio, video, photographic images, text, graphics and data; file sharing services, namely, providing a website featuring technology enabling users to upload, modify and download electronic files, photographic images, text and graphics; software as a service (SAAS) services featuring software for sending and receiving electronic messages and for sending electronic message alerts; providing temporary use of on-line non-downloadable software using artificial intelligence for collecting, managing, summarizing, automating, editing, organizing, modifying, transmitting, sharing, searching, and storing of messages, text, images, data files, audio transmissions, videos and audio-visual content
09 - Appareils et instruments scientifiques et électriques
38 - Services de télécommunications
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Computer software which enables instant messaging and file sharing; communications software for enabling computer network, wireless network and Internet users to exchange data, audio transmissions, video images and graphics; computer programs for managing communications and data exchange between hand held computers and desktop computers; computer software for use as an application programming interface (API); computer software for collecting, managing, editing, organizing, modifying, transmitting, sharing, and storing of messages, text, images, data files, audio transmissions, videos and audio-visual content; computer software development tools; communications software for facilitating voice over internet protocol (VOIP) calls, phone calls, video calls, text messages, instant messages and online social networking services; computer software which enables multiple users to share files and memoranda and synchronize calendars; electronic communications devices, namely, telephones, handsets, headsets, speakers, microphones, earphones, webcams and video cameras; software using artificial intelligence for collecting, managing, summarizing, automating, editing, organizing, modifying, transmitting, sharing, searching, and storing of messages, text, images, data files, audio transmissions, videos and audio-visual content (1) Communication services, namely, electronic transmission of data and documents among users of computers; telecommunication services, namely, transmission of voice, data, graphics, images, audio and video by means of telecommunications networks, wireless communication networks, and the Internet; streaming of audio and video material over the Internet; information transmission via electronic communications networks; electronic transmission of mail and messages; electronic data transmission; electronic delivery of images and photographs via a global computer network; instant messaging services; voice over internet protocol (VOIP) services; audio and video broadcasting services over the Internet; providing on-line chat rooms for social networking; consulting in the field of transmission of voice, data and documents via telecommunications networks; providing access to databases; providing user access to global computer networks; telecommunications gateway services; telecommunications services, namely, personal communications services via wireless networks; software using artificial intelligence for communication and telecommunication services
(2) Providing temporary use of on-line non-downloadable software and applications for instant messaging and enabling and managing simultaneous, multiple modes of communication over local area networks and the Internet; platform as a service (PAAS) featuring computer software platforms which enables multiple users to share files, memoranda, and synchronize calendars; application service provider, namely, hosting, managing, developing, and maintaining applications, software and web sites of others in the fields of personal productivity, wireless communication, mobile information access, and remote data management for wireless delivery of content to handheld computers, laptops and mobile electronic devices; computer services, namely, creating an on-line community for registered users to create virtual communities, participate in discussions, get feedback from their peers, and engage in social, business and community networking; server and web site hosting for others to enable interactive discussions via communication networks; application service provider (ASP) featuring software to enable or facilitate the creating, editing, uploading, downloading, accessing, viewing, posting, displaying, tagging, blogging, streaming, linking, annotating, commenting on, embedding, transmitting, and sharing digital content or information via computer and communication networks; provision of Internet search engines; providing temporary use of non-downloadable software for enabling collaborative work, creating a virtual community, and transmission of audio, video, photographic images, text, graphics and data; file sharing services, namely, providing a website featuring technology enabling users to upload, modify and download electronic files, photographic images, text and graphics; software as a service (SAAS) services featuring software for sending and receiving electronic messages and for sending electronic message alerts; providing temporary use of on-line non-downloadable software using artificial intelligence for collecting, managing, summarizing, automating, editing, organizing, modifying, transmitting, sharing, searching, and storing of messages, text, images, data files, audio transmissions, videos and audio-visual content
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Computer software which enables instant messaging and file sharing; communications software for enabling computer network, wireless network and Internet users to exchange data, audio transmissions, video images and graphics; computer programs for managing communications and data exchange between hand held computers and desktop computers; computer software for use as an application programming interface (API); computer software for collecting, managing, editing, organizing, modifying, transmitting, sharing, and storing of messages, text, images, data files, audio transmissions, videos and audio-visual content; computer software development tools; communications software for facilitating voice over internet protocol (VOIP) calls, phone calls, video calls, text messages, instant messages and online social networking services; computer software which enables multiple users to share files and memoranda and synchronize calendars; electronic communications devices, namely, telephones, handsets, headsets, speakers, microphones, earphones, webcams and video cameras; software using artificial intelligence for collecting, managing, summarizing, automating, editing, organizing, modifying, transmitting, sharing, searching, and storing of messages, text, images, data files, audio transmissions, videos and audio-visual content. Providing temporary use of on-line non-downloadable software and applications for instant messaging and enabling and managing simultaneous, multiple modes of communication over local area networks and the Internet; platform as a service (PAAS) featuring computer software platforms which enables multiple users to share files, memoranda, and synchronize calendars; application service provider, namely, hosting, managing, developing, and maintaining applications, software and web sites of others in the fields of personal productivity, wireless communication, mobile information access, and remote data management for wireless delivery of content to handheld computers, laptops and mobile electronic devices; computer services, namely, creating an on-line community for registered users to create virtual communities, participate in discussions, get feedback from their peers, and engage in social, business and community networking; server and web site hosting for others to enable interactive discussions via communication networks; application service provider (ASP) featuring software to enable or facilitate the creating, editing, uploading, downloading, accessing, viewing, posting, displaying, tagging, blogging, streaming, linking, annotating, commenting on, embedding, transmitting, and sharing digital content or information via computer and communication networks; provision of Internet search engines; providing temporary use of non-downloadable software for enabling collaborative work, creating a virtual community, and transmission of audio, video, photographic images, text, graphics and data; file sharing services, namely, providing a website featuring technology enabling users to upload, modify and download electronic files, photographic images, text and graphics; software as a service (SAAS) services featuring software for sending and receiving electronic messages and for sending electronic message alerts; providing temporary use of on-line non-downloadable software using artificial intelligence for collecting, managing, summarizing, automating, editing, organizing, modifying, transmitting, sharing, searching, and storing of messages, text, images, data files, audio transmissions, videos and audio-visual content.
09 - Appareils et instruments scientifiques et électriques
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Computer software which enables instant messaging and file sharing; communications software for enabling computer network, wireless network and Internet users to exchange data, audio transmissions, video images and graphics; computer programs for managing communications and data exchange between hand held computers and desktop computers; computer software for use as an application programming interface (API); computer software for collecting, managing, editing, organizing, modifying, transmitting, sharing, and storing of messages, text, images, data files, audio transmissions, videos and audio-visual content; computer software development tools; communications software for facilitating voice over internet protocol (VOIP) calls, phone calls, video calls, text messages, instant messages and online social networking services; computer software which enables multiple users to share files and memoranda and synchronize calendars; electronic communications devices, namely, telephones, handsets, headsets, speakers, microphones, earphones, webcams and video cameras; software using artificial intelligence for collecting, managing, summarizing, automating, editing, organizing, modifying, transmitting, sharing, searching, and storing of messages, text, images, data files, audio transmissions, videos and audio-visual content. Providing temporary use of on-line non-downloadable software and applications for instant messaging and enabling and managing simultaneous, multiple modes of communication over local area networks and the Internet; platform as a service (PAAS) featuring computer software platforms which enables multiple users to share files, memoranda, and synchronize calendars; application service provider, namely, hosting, managing, developing, and maintaining applications, software and web sites of others in the fields of personal productivity, wireless communication, mobile information access, and remote data management for wireless delivery of content to handheld computers, laptops and mobile electronic devices; computer services, namely, creating an on-line community for registered users to create virtual communities, participate in discussions, get feedback from their peers, and engage in social, business and community networking; server and web site hosting for others to enable interactive discussions via communication networks; application service provider (ASP) featuring software to enable or facilitate the creating, editing, uploading, downloading, accessing, viewing, posting, displaying, tagging, blogging, streaming, linking, annotating, commenting on, embedding, transmitting, and sharing digital content or information via computer and communication networks; provision of Internet search engines; providing temporary use of non-downloadable software for enabling collaborative work, creating a virtual community, and transmission of audio, video, photographic images, text, graphics and data; file sharing services, namely, providing a website featuring technology enabling users to upload, modify and download electronic files, photographic images, text and graphics; software as a service (SAAS) services featuring software for sending and receiving electronic messages and for sending electronic message alerts; providing temporary use of on-line non-downloadable software using artificial intelligence for collecting, managing, summarizing, automating, editing, organizing, modifying, transmitting, sharing, searching, and storing of messages, text, images, data files, audio transmissions, videos and audio-visual content.
97.
Data backup and recovery using cache-coherent interconnect node-based non-volatile memory
Systems and methods are provided for implementing data backup and recovery using cache-coherent interconnect node-based non-volatile memory. A cache-coherent interconnect node partitions a memory pool into a plurality of memory regions as well as a backup storage into a plurality of memory portions, and pre-allocates a memory region and a corresponding memory portion to each compute node. When a rack-level power loss occurs, and a battery-based power source is activated, a cache-coherent interconnect controller saves data from each memory region into the corresponding memory portion, and subsequently saves an entry for each memory portion in an index portion of the backup storage. Subsequently, the controller causes a power circuitry to shut down the backup power source. After rack-level power restoration and memory region initialization, the controller restores, for each memory region, the data saved in a corresponding memory portion into that memory region, based on information in a corresponding entry.
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 1/26 - Alimentation en énergie électrique, p. ex. régulation à cet effet
G06F 1/30 - Moyens pour agir en cas de panne ou d'interruption d'alimentation
98.
Entity driven templates for customized user prompts
Solutions are disclosed that provide entity driven templates for customized user prompts. Examples surface suggested prompts to a user to guide a chat with a generative artificial intelligence (AI) model, so that the user is able to receive more accurate and more relevant responses than might be expected from a freeform query or a set of generic prompts that do not account for the user's particular organization or role within that organization. A user interface (UI) tooltip enables users to select available data fields associated with entities in a query prompt to receive more relevant, focused results. The entities are culled from data that is specific to the user's own organization, and in some examples prioritized and focused based on the user's particular role in the organization and/or permission to access various elements within the data.
Methods, systems, and apparatuses include receiving an event notification for an event associated with a node of a graph network. Event data including node state data and a timestamp is generated using the event notification. A node state change is generated for the node by applying a neural network to the node state data and the timestamp. An input sequence for a generative machine learning model is generated, the input sequence including the node state change and the node state data. Updated node state data is computed for the node by applying the generative machine learning model to the input sequence. A node encoding is generated for the node using the updated node state data. Input data for a trained machine learning model is generated using the node encoding. An output of the trained machine learning model is generated by applying the trained machine learning model to the input data.
Systems and methods evaluate computational reasoning performance of generative artificial intelligence (GAI) models. Both a factual prompt and a counterfactual prompt are submitted to both first and second GAI models, thereby generating first factual and counterfactual outputs for the first GAI model and second factual and counterfactual outputs for the second GAI model. Probability of necessity (PN) and probability of sufficiency (PS) values are computed for both the first and second GAI models based on their associated factual output and counterfactual output. The computational reasoning performance of the first GAI model relative to the second GAI model are compared based on the PN and PS values. One of the first or the second GAI models is selected based on the comparison and submitted a target prompt using the selected one of the first and second GAI model.
