Embodiments of the present disclosure include systems and methods for sparsifying narrow data formats for neural networks. A plurality of activation values in a neural network are provided to a muxing unit. A set of sparsification operations are performed on a plurality of weight values to generate a subset of the plurality of weight values and mask values associated with the plurality of weight values. The subset of the plurality of weight values are provided to a matrix multiplication unit. The muxing unit generates a subset of the plurality of activation values based on the mask values and provides the subset of the plurality of activation values to the matrix multiplication unit. The matrix multiplication unit performs a set of matrix multiplication operations on the subset of the plurality of weight values and the subset of the plurality of activation values to generate a set of outputs.
Aspects of the disclosure include a recommendation service that leverages conformal inference for adaptive model switching. A method includes receiving, by a second pass ranker (SPR) of the recommendation service, a ranking request for the top K candidate items of a plurality of candidate items. The method includes generating, by a default model of the SPR, a score for each candidate items and dynamically assigning the ranking request to the default model or a backup model using conformal inference based on a prediction uncertainty of the scores generated by the default model. The backup model has at least a greater number of parameters or a greater number of layers than the default model. The method includes generating, by the assigned one of the default model and the backup model, the top K candidate items and returning, responsive to receiving the ranking request, a response comprising the top K candidate items.
Methods for automatic language detection for handwritten text are performed by systems and devices. Such automatic language detection is performed prior to sending representations of the handwritten text to a language recognition engine. Handwritten inputs including one or more writing strokes are received from an input interface, and are associated with coordinates of the inputs and times that the inputs are made. The handwritten inputs are grouped into words based on the coordinates and times. Writing strokes are normalized, and then the words are individually transformed to generate language vectors, such as through a recurrent neural network. The language vectors are used to determine language probabilities for the handwritten inputs. Based on the language probabilities, the handwritten inputs are provided to a specific language recognition engine to determine the language thereof prior to translation or transcription.
G06F 3/0354 - Dispositifs de pointage déplacés ou positionnés par l'utilisateurLeurs accessoires avec détection des mouvements relatifs en deux dimensions [2D] entre le dispositif de pointage ou une partie agissante dudit dispositif, et un plan ou une surface, p. ex. souris 2D, boules traçantes, crayons ou palets
G06N 5/04 - Modèles d’inférence ou de raisonnement
G06N 5/046 - Inférence en avantSystèmes de production
G06V 30/142 - Acquisition d’images en utilisant des instruments déplacés manuellementDétails structurels des instruments
G06V 30/246 - Division des suites de caractères en groupes avant la reconnaissanceSélection des dictionnaires utilisant des propriétés linguistiques, p. ex. spécifiques à la langue anglaise ou à la langue allemande
4.
CONTAINER MODE MANAGEMENT ENGINE IN A SECURITY MANAGEMENT SYSTEM
Methods, systems, and computer storage media for providing container secure computing modes using a container mode management engine of a security management system. A container secure computing mode can include a secure state in which a container operates to prioritize security measures and practices. A container secure computing mode can be assigned to a container instance and enforced via a container security agent. In operation, a container instance is initialized, the container instance is associated with a container security agent having a secure compute mode transition control for the container instance. Based on the secure compute mode transition control, the container instance is transitioned into a secure state. A container operation of the container instance is accessed. The execution of the container operation is restricted based on the secure state of the container instance. The secure state is associated with a secure state configuration that supports restricting the container operation.
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
A method for controlling user access to a touch-screen system comprises: (a) receiving an uplink signal from a key device and extracting corresponding uplink data from the uplink signal; (b) providing challenge data in response to the uplink data and transmitting a corresponding challenge signal to the key device, where the challenge signal is transmitted by a touch-sensor transmitter also configured to transmit a synchronization signal to an active pen; (c) receiving a downlink signal from the key device and extracting corresponding downlink data from the downlink signal, where the uplink and downlink signals are received by a touch-sensor receiver also configured to receive sensory signal from the active pen; and (d) forbidding a user from accessing the touch-screen system unless the downlink data authenticates the user and signal of pre-determined signal strength continues to be received from the key device.
A data processing system implements receiving user selection information, from a first client device of a first user, indicating a plurality of user selections of content from multiple locations of one or more files; analyzing the user selection information to extract data and formatting information associated with each selection of the plurality of user selections; formatting the data extracted into data structures representing the data and formatting information; receiving a content sharing command from the first client device to share content with one or more second client devices participating in an online meeting via an online meeting platform; and in response to receiving the content sharing command: generating a graphical representation of the plurality of user selections based on the data structures; and providing the graphical representation to the first client device and one or more second client devices participating in the online meeting via the online meeting platform.
This disclosure describes configuring and deploying virtual desktop environments based on preconfigured, customized resource container environments (called stamps). In particular, this disclosure describes a cloud stamp system that facilitates quickly and efficiently developing and implementing virtualization at a large scale while allowing for personalization. In various implementations, the cloud stamp system streamlines the engineering process for creating, deploying, and maintaining virtual desktop infrastructure at scale by utilizing preconfigured cloud resources customized at deployment based on configuration settings. Among the benefits of improved computing efficiency, the cloud stamp system provides significant security improvements by enforcing security measures and policies at every level.
A data processing system implements an image generation system configured to operate in a search-assisted mode in response to receiving a natural language prompt to generate image content. The image generation system conducts a search for example image content from one or more image sources external to the image asset repository to obtain example images of a subject matter of the natural language prompt. The image generation system selects one or more candidate images from the search results and analyzes the candidate images to obtain a description of the elements of the subject matter of the one or more candidate images and positional information and scale information for these elements. The image generation system identifies image assets in an image asset repository associated with these elements by evaluating the description of the elements of the subject matter and generates the requested image content using the identified image assets.
Methods and apparatuses for increasing performance and reducing power consumption in computing systems that utilize cache coherence mechanisms to observe memory accesses and to perform memory management for cache memories are described. A cache coherence device may observe memory accesses over time by selectively controlling a set of cache lines to be sampled within a cache memory and then observing access notifications to the set of cache lines. In some cases, the cache coherence device controls a cache line to be sampled by obtaining the cache line in a coherence state other than an invalid state. By obtaining a cache line in the coherence state, the cache coherence device will be notified of attempted accesses to that cache line from processing units of the computing system.
A processor system may include multiple circuits that are each powered by their own power rails. Incomplete cycling of system power may fail to discharge memory circuits, leaving that data vulnerable to unauthorized access upon reboot. A processor system includes a boot detection circuit that, upon detecting a threshold voltage on a first power rail in the bootup sequence, captures voltage measurements on each of the remaining power rails in multiple time periods, wherein the captured voltage measurements may be analyzed to determine whether the voltages on the power rails fully cycled to a sufficiently low voltage and remained there for a sufficient time to ensure that memory circuits adequately discharged. The power system may proceed to generate an indication of complete reset or incomplete reset depending on the voltage measurements stored in the memory. In this way, the confidentiality of data previously stored in the memory circuits is protected.
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
G01R 19/165 - Indication de ce qu'un courant ou une tension est, soit supérieur ou inférieur à une valeur prédéterminée, soit à l'intérieur ou à l'extérieur d'une plage de valeurs prédéterminée
G01R 19/25 - Dispositions pour procéder aux mesures de courant ou de tension ou pour en indiquer l'existence ou le signe utilisant une méthode de mesure numérique
11.
LARGE LANGUAGE MODEL INTERFACE FOR COMPLEX DATABASES
This disclosure introduces a novel method and system for using a large language model (LLM) to create a convenient interface for a complex database. The system includes a custom prompt generator that creates custom prompts from natural language queries. The custom prompts are used to control how the LLM interacts with a database look-up tool. The database look-up tool provides queries to the database in a format understandable by the database and receives responses from the database. This system is useful for obtaining information that is not in a natural language, and thus, is poorly suited for being processed as an embedding by the LLM. Information obtained from the database is included in an answer produced by the LLM.
A data processing system implements extracting a second caller ID for presentation on a callee device from routing information component of a call notification message received during a call signaling session; assigning the extracted second caller ID as a value to a caller identification parameter, the value being used to compare against a first caller ID associated with the caller device to determine whether there is a mismatch; generating a feedback integrity value including the extracted second caller ID and a random string and assigning the feedback integrity value to a feedback integrity parameter, the feedback integrity value being tracked to determine a change to the value of the caller identification parameter made on a call path; generating a response to the call notification message that includes the caller identification parameter, the feedback integrity parameter, and their values; and forwarding the response to the caller device.
A disclosed method provides for distributing portions of a database query among different processors of a same host device. The method includes determining query fragments collectively executable to implement the database query; identifying one or more subplans for executing the query fragments; accessing capabilities of a hardware coprocessor of the host device; determining, based on the capabilities stored for the hardware coprocessor, whether a select subplan of the one or more subplans is viable for implementation by the hardware coprocessor; and in response to determining that the select subplan is viable for implementation by the hardware coprocessor, selecting and executing a first distributed execution plan that includes the select subplan and that delegates execution of the select subplan to the hardware coprocessor.
A method for servicing a component within a filter chamber assembly of a liquid-to-air Heat Rejection Unit (HRU) includes mating a receptacle on a fluid containment apparatus with a drain valve component on a filter chamber assembly to open a seal between the drain valve component and the filter chamber assembly. While the receptacle is mated to the drain valve component, a plunger of the fluid containment apparatus is actuated to draw coolant from the filter chamber assembly into a vial of the fluid containment apparatus. While the coolant from the filter chamber assembly is contained within the vial, the component within the filter chamber assembly is serviced and, following servicing, the plunger of the fluid containment apparatus is actuated toward the vial to reinject the coolant into the filter chamber assembly.
B01D 35/12 - Dispositifs pour mettre hors service une ou plusieurs unités dans des filtres à unités multiples, p. ex. pour la régénération
F28F 19/01 - Prévention de la formation de dépôts ou de la corrosion, p. ex. en utilisant des filtres en utilisant des moyens pour séparer les éléments solides du fluide échangeur de chaleur, p. ex. des filtres
15.
EMAIL MANAGEMENT ENGINE IN AN ELECTRONIC MAIL SYSTEM
Methods, systems, and computer storage media for providing context-aware out-of-office (OOO) assistance using an email management engine. Context-aware OOO assistance supports email management operations that provide OOO functionality with artificial intelligence and context-awareness for email management during periods of unavailability. In operation, an email message is accessed. An email message category is determined for the email message. The email message category can be a scheduling meeting category, a requesting information category, or a task delegation category. Based on the email message category, a plurality of OOO operations associated with the email message category are executed. Based on executing the plurality of OOO operations associated with the email message category, a response for the email message is generated. The response is associated with one of the following: scheduling meeting data, requesting information data, or task delegation data. The response is communicated to cause display of the response on an email interface.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
G06F 40/40 - Traitement ou traduction du langage naturel
G06Q 10/107 - Gestion informatisée du courrier électronique
G06Q 10/109 - Gestion du temps, p. ex. agendas, rappels, réunions ou décompte de temps
16.
DOMAIN-INTEGRATED CONTEXTUAL RESPONSE ENGINE IN AN ARTIFICIAL INTELLIGENCE SYSTEM
Methods, systems, and computer storage media for providing domain-integrated contextual response management using a domain-integrated contextual response engine in an artificial intelligence (AI) system are described. Domain-integrated contextual response management is a systematic approach that combines specific industry knowledge with contextual understanding to generate accurate, relevant, and specific industry-tailored responses to user queries. Domain-integrated contextual response management further includes fine-tuning models for Retrieval-Augmented Generation (RAG) tasks using customer-specific data based on a two-fold approach involving skill distillation and knowledge distillation (i.e., skill distillation from a more powerful model like Large Language Model “LLM” and knowledge distillation from domain-specific data). Domain-integrated contextual response management also includes creating a synthetic dataset that enables smaller models (e.g., domain-integrated contextual response models) to effectively manage RAG tasks while incorporating domain-specific knowledge. Domain-integrated contextual response management further ensures that the domain-integrated contextual response models can retrieve relevant information, support citations, and decline out-of-domain (OOD) questions.
A hollow core fiber launch tail for use in Optical Time Domain Reflectometry (OTDR) or Optical Frequency Domain Reflectometry (OFDR) measurements on hollow core fiber is described. The hollow core fiber launch tail comprises a length of hollow core fiber, having a first end and a second end; and coupling optics coupled to the first end of the length of hollow core fiber, wherein voids in the length of hollow core fiber are filled with a known gas.
A self-centering plug for transferring fluid comprises an elongated body comprising an annular surface extending from an outer surface. A washer is located between the annular surface and an annular wall of a housing, with the housing enclosing a portion of the elongated body. A float spring encircles the elongated body and comprises a distal spring end abutting an annular shoulder of the elongated body and a proximal spring end retained in the housing, wherein the float spring urges the annular surface of the elongated body into contact with the washer.
F16L 3/205 - Supports pour tuyaux, pour câbles ou pour conduits de protection, p. ex. potences, pattes de fixation, attaches, brides, colliers avec disposition particulière permettant au tuyau de bouger permettant un mouvement latéral avec des ressorts supportant les tuyaux
F16L 37/60 - Accouplements du type à action rapide avec un connecteur mâle et une prise fixée sur une paroi
This document relates to generative machine learning. Users can provide input relating to a content generation task. A generative machine learning model can be prompted to suggest concept tags based on the received user input. Then, users can select a concept tag and a value for that concept tag. A content item can be generated for the user using the generative machine learning model, where the content generation is based on the value for the selected concept tag. Over time, additional content tags can be updated with user-designated values while updating the generated content accordingly. In this manner, user generation of content with a generative machine learning model can be guided via received user inputs.
G06F 40/166 - Édition, p. ex. insertion ou suppression
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
The present disclosure proposes a method, apparatus and computer program product for digital asset transaction. A transaction order for a transaction of digital asset may be received, the transaction order containing a set of initial transaction parameters. The transaction may be divided into multiple sub-transactions based on the set of initial transaction parameters. For each sub-transaction in the multiple sub-transactions, the sub-transaction may be executed, to obtain an execution result of the sub-transaction. A proof for the sub-transaction may be generated based on the execution result. The transaction may be verified with multiple execution results and multiple proofs corresponding to the multiple sub-transactions. Furthermore, the present disclosure also proposes a digital asset transaction system comprising multiple rollup networks on layer 2 and a blockchain network on layer 1.
G06Q 20/06 - Circuits privés de paiement, p. ex. impliquant de la monnaie électronique utilisée uniquement entre les participants à un programme commun de paiement
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
A data processing system implements an image generation system configured to operate in a search-assisted mode in response to receiving a natural language prompt to generate image content. The image generation system conducts a search for example image content from one or more image sources external to the image asset repository to obtain example images of a subject matter of the natural language prompt. The image generation system selects one or more candidate images from the search results and analyzes the candidate images to obtain a description of the elements of the subject matter of the one or more candidate images and positional information and scale information for these elements. The image generation system identifies image assets in an image asset repository associated with these elements by evaluating the description of the elements of the subject matter and generates the requested image content using the identified image assets.
G06F 16/58 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
According to implementations of the disclosure, a solution for image generation is provided. According to the solution, in a plurality of iterative rounds of a machine learning model, an input image for a current round is obtained; a plurality of image blocks in the input image are classified into a first category and a second category to obtain at least one image block of the first category and at least one image block of the second category; a first noise corresponding to the at least one image block of the first category is determined; a second noise for the at least one image block of the second category in the previous round is obtained; and the first noise and the second noise are removed respectively from the plurality of image blocks, to obtain an input image of a next round; and a target image is determined.
This disclosure describes utilizing a generative document system to create generative search results documents using generative artificial intelligence (AI) models and dynamically determining which one of the generative search results documents to provide in response to a search query. For example, in response to receiving a search query, the generative document system obtains search link results (e.g., website links and corresponding grounding information) for the search query and utilizes this information with multiple generative AI models to generate various types of generative search results documents. Additionally, the generative document system generates and utilizes a generative document arbitration model to determine, based on the search link results, which of the generative search results documents to provide in response to the search query.
This document relates to secure storage techniques that can be employed in a multi-tenant environment. In the disclosed implementations, a host CPU can perform data at rest encryption on data that is written to an SSD. By offloading the data at rest encryption from the SSD to the CPU, the complexity and power consumption of the SSD can be reduced. Furthermore, redundant cryptographic operations can be mitigated, because the SSD and CPU do not necessarily perform link encryption on data payloads themselves. Rather, link encryption can be limited to associated command data used to configure transfers of encrypted data payloads.
G06F 12/14 - Protection contre l'utilisation non autorisée de mémoire
G06F 21/78 - Protection de composants spécifiques internes ou périphériques, où la protection d'un composant mène à la protection de tout le calculateur pour assurer la sécurité du stockage de données
This document relates to secure storage techniques that can be employed in a multi-tenant environment. In the disclosed implementations, a host CPU can perform data at rest encryption on data that is written to an SSD via an interposer. By offloading the data at rest encryption from the interposer to the CPU, the complexity and power consumption of the interposer can be reduced. Furthermore, redundant cryptographic operations can be mitigated, because the interposer and CPU do not necessarily perform link encryption on data payloads themselves. Rather, link encryption can be limited to associated command data used to configure transfers of encrypted data payloads.
G06F 12/14 - Protection contre l'utilisation non autorisée de mémoire
G06F 21/78 - Protection de composants spécifiques internes ou périphériques, où la protection d'un composant mène à la protection de tout le calculateur pour assurer la sécurité du stockage de données
The present disclosure generally relates to systems and methods for adaptively retransmitting data packets utilizing a biased moving average retransmission time. Systems and methods described herein avoid the risks of retransmission flooding and congestion collapse common to existing out-of-order packet transmission systems by generating, updating, and utilizing a biased moving average retransmission time that adapts to a specific latency of a data flow. By tailoring fast and regular timeout events to this biased moving average retransmission time, the systems and methods described herein can dynamically adapt to in-the-moment network conditions to ensure that data packets reach their intended endpoints.
MICRO-LIGHT EMITTING DIODE (MICRO-LED) SYSTEMS WITH BRIDGE CIRCUITS SCALING VOLTAGES AND CURRENTS TO APPROPIATE LEVELS TO INTERFACE WITH MEASUREMENT CIRCUITS
Microscopic light emitting diodes (micro-LEDs) systems are described. An example micro-LED system includes a set of micro-LEDs having a respective voltage terminal configured to receive a first positive voltage supply and a common cathode terminal configured to receive a negative voltage supply. The micro-LED system includes sensing circuits to sense voltages and currents received from a selected subset of the set of micro-LEDs, where each of the sensing circuits is configured to receive a second positive voltage supply, different from the first positive voltage supply. The micro-LED system includes bridge circuits to: (1) during sensing of any voltages received from a selected subset of the set of micro-LEDs, scale voltages to an appropriate level for measurement by the sensing circuits, and (2) during sensing of any current received from a selected subset of the set of micro-LEDs, limit current flowing through a respective sensing circuit.
G09G 3/32 - Dispositions ou circuits de commande présentant un intérêt uniquement pour l'affichage utilisant des moyens de visualisation autres que les tubes à rayons cathodiques pour la présentation d'un ensemble de plusieurs caractères, p. ex. d'une page, en composant l'ensemble par combinaison d'éléments individuels disposés en matrice utilisant des sources lumineuses commandées utilisant des panneaux électroluminescents semi-conducteurs, p. ex. utilisant des diodes électroluminescentes [LED]
28.
NETWORK TRAFFIC ARBITRATION BASED ON PACKET PRIORITY
A method for network traffic arbitration includes, at a network router, receiving two or more network packets over two or more input ports. During an observation window, traffic parameters for the two or more network packets are stored in a traffic history table, the traffic parameters including a Quality-of-Service (QoS) priority value for a network packet of the two or more network packets. Based at least in part on the traffic parameters recorded in the traffic history table, including the QoS priority value, arbitration weights are calculated for each of the two or more input ports for a weighted round robin arbitration process.
H04L 47/6275 - Ordonnancement des files d’attente caractérisé par des critères d’ordonnancement pour des créneaux de service ou des commandes de service basé sur la priorité
H04L 47/62 - Ordonnancement des files d’attente caractérisé par des critères d’ordonnancement
A mounting assembly for a fluid-transfer plug comprises a mounting receptacle comprising proximal left and right retention surfaces and distal left and right retention surfaces. A support surface includes an alignment aperture, and a locking member is located between the proximal and distal left retention surfaces. A housing for the plug is removably secured in the mounting receptacle. The housing comprises a housing body and left and right locking slots. An alignment pin extends from a bottom surface into the alignment aperture of the mounting receptacle. The locking member of the mounting receptacle extends into the left locking slot to removable secure the housing and plug in the mounting receptacle.
F16L 37/60 - Accouplements du type à action rapide avec un connecteur mâle et une prise fixée sur une paroi
F16L 3/08 - Supports pour tuyaux, pour câbles ou pour conduits de protection, p. ex. potences, pattes de fixation, attaches, brides, colliers entourant pratiquement le tuyau, le câble ou le conduit de protection
30.
MULTI-STAGE ACTION DETERMINATION FOR CONTENT DELIVERY
Methods, systems, and apparatuses include receiving feature data for a user of an online system and a first content delivery action for content delivery on the online system. The feature data is sent to a trained propensity machine learning model. Propensity data is received from the trained propensity model. A first content delivery decision is determined for the user and the first content delivery action using the propensity data. Second feature data is received for the user and a second content delivery action for the content deliver. The second feature data is sent to the trained propensity model. Second propensity data is received from the trained propensity model. A second content delivery decision is determined for the user and the second content delivery action using the second propensity data. The content is delivered to the user on the online system based on the first and the second content delivery decisions.
Methods, systems, and computer storage media for providing speech-text alignment management using a speech processing engine in an artificial intelligence system are described. The speech processing engine includes a speech-text alignment engine that operates as intermediary between raw spoken language and its corresponding textual representation, ensuring real-time processing and translation tasks. The speech processing engine utilizes a speech encoder to convert spoken language into digital signals that represent its acoustic features. The data then passes through the speech-text alignment engine, which includes a Connectionist Temporal Classification (CTC) layer for temporal and sequence alignment and a modified q-former layer for fine-tuning the speech embeddings to closely match the latent space of text embeddings used by Large Language Models (LLMs). This alignment process enables translating or transcribing spoken content into multiple languages without delay. The speech processing engine also incorporates token swapping to support adapting to various speech patterns and accents effectively.
Methods, systems, and computer storage media for providing security graph analysis using a security graph analysis engine of a security management system are described. Security graph analysis is the evaluation of a computing environment’s security graph by generating an adjacency matrix that represents connections between assets, users, and permissions as nodes and edges. The security graph analysis engine leverages the adjacency matrix of a security graph to uncover various security insights by performing advanced matrix operations. The security graph analysis engine efficiently processes large and complex graphs, particularly those that are sparse and organized into blocks, by utilizing optimized algorithms and advanced computational techniques and processor (e.g., GPU) acceleration. The security graph analysis engine calculates metrics like connectivity scores to assess the overall security posture of the organization. By providing real-time analysis and actionable insights, the security graph analysis engine helps organizations quickly identify vulnerabilities and improve their security measures.
A zero-knowledge CBDC (zkCBDC) banking system uses zero-knowledge proofs to prove that commercial banks are processing transactions correctly. Commercial banks are the provers of the zero-knowledge proof system. The banks generate a zero-knowledge proof for one or more transactions processed by the bank. The banks also generate a hash of the total amount of the transaction(s). The zero-knowledge proof and associated transaction hash are submitted to a blockchain. The blockchain stores a bank state commitment value for each commercial bank in the system and includes a smart contract with code for verifying the zero-knowledge proofs. Once a proof is verified, the bank's state commitment value is updated and the transaction hash is added to the verifier's state.
G06Q 40/02 - Opérations bancaires, p. ex. calcul d'intérêts ou tenue de compte
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
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
A method for processing an image of a hollow core fiber, HCF, is described. For an edge of a tube of the HCF, brightness of the image is used to detect points corresponding to the edge. The method further includes fitting an edge model function to detected edge points, and identifying an outlier point of the detected edge points that is above a threshold distance from the fitted model. The outlier point is removed and the model is refitted to the remaining points. The method comprises iteratively identifying and removing subsequent outlier points and refitting the model to remaining points until all remaining points are inlier points below a final distance threshold from the model. Remaining inlier points are fitted to a final model. The final model is used to determine a geometric parameter of the HCF for use during quality control, splicing and/or fiber drawing.
G06V 10/60 - Extraction de caractéristiques d’images ou de vidéos relative aux propriétés luminescentes, p. ex. utilisant un modèle de réflectance ou d’éclairage
Methods and apparatuses for improving the yield and performance of integrated circuit structures by utilizing rotatable chiplets are described. During manufacturing of an integrated circuit structure that includes multiple chiplets arranged within a plurality of integration layers, each integration layer may be dynamically rotated or oriented prior to being bonded based on chiplet characteristics of the chiplets within the integration layers. Each integration layer comprises one or more chiplets. An automated manufacturing system determines the degree of rotation of a first integration layer relative to a second integration layer to which the first integration layer is to be directly or indirectly attached based on chiplet performance, capacity, and/or thermal characteristics of the chiplets within the integration layers.
One example provides a computing device comprising a regular expression (regex) hardware accelerator including a deterministic finite automaton (DFA) engine configured to execute an object file, and a compiler. The compiler is executable to generate the object file based at least upon a target DFA graph by receiving a predicate including one or more of an integer condition or a floating-point condition, transforming the predicate to form a rewritten predicate with an equivalent expression, and building the target DFA graph based at least upon the rewritten predicate.
This disclosure describes utilizing a generative document system to dynamically build and provide generative text documents using one or more generative artificial intelligence (AI) models. For example, the generative document system efficiently utilizes various systems and one or more generative AI models to determine intents and topics, curate topic sections, and generate a generative text document that includes a directed answer along with select curated topic sections for search queries. In various implementations, the generative document system performs additional actions that enhance the efficiency and accuracy of operations used to produce generative text documents. Additionally, in many cases, these generative text documents provide a foundation for providing an interactive, intuitive, wide-ranging, and flexible curation of answers to users that address the corresponding search queries.
G06F 16/2457 - Traitement des requêtes avec adaptation aux besoins de l’utilisateur
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
G06F 16/9538 - Présentation des résultats des requêtes
38.
REDUCING SCREEN FLICKER USING A CONCEALED ROLLING PATTERN
Methods, systems and computer program products are provided for reducing screen flicker using a concealed rolling pattern. Field application of a concealable rolling pattern (e.g., alternating electric field), causing liquid crystal module pixels to display alternating patterns, may increase mobile ion diffusion (e.g., release accumulated charges), thereby reducing screen flickering and image burn-in. A rolling pattern may be applied in a low power mode (e.g., standby, sleep, off), with backlight off, and with ambient lighting below a threshold or a concealed display (e.g., lid closed). A concealed rolling pattern may be stopped based on a detected transition to a full power mode, rolling pattern timer expiration, ambient light above the threshold while the display is viewable, removal of a power supply and/or battery power below a threshold. A transition to full power may delay powering on the backlight, e.g., to allow multiple frames of black video for the display panel.
G09G 3/20 - Dispositions ou circuits de commande présentant un intérêt uniquement pour l'affichage utilisant des moyens de visualisation autres que les tubes à rayons cathodiques pour la présentation d'un ensemble de plusieurs caractères, p. ex. d'une page, en composant l'ensemble par combinaison d'éléments individuels disposés en matrice
G09G 3/34 - Dispositions ou circuits de commande présentant un intérêt uniquement pour l'affichage utilisant des moyens de visualisation autres que les tubes à rayons cathodiques pour la présentation d'un ensemble de plusieurs caractères, p. ex. d'une page, en composant l'ensemble par combinaison d'éléments individuels disposés en matrice en commandant la lumière provenant d'une source indépendante
G09G 3/36 - Dispositions ou circuits de commande présentant un intérêt uniquement pour l'affichage utilisant des moyens de visualisation autres que les tubes à rayons cathodiques pour la présentation d'un ensemble de plusieurs caractères, p. ex. d'une page, en composant l'ensemble par combinaison d'éléments individuels disposés en matrice en commandant la lumière provenant d'une source indépendante utilisant des cristaux liquides
Some embodiments address technical challenges arising from efforts to identify and mitigate security risks, in particular but not only, risks that sensitive data will be exfiltrated. Some embodiments provide or utilize an anomaly detector which is configured to detect a security anomaly in data based on at least a distribution of sensitive information type documents in a collection of documents and classifications of documents by trainable classifiers based on machine learning. Some embodiments provide or utilize a security policy generator which is configured to proactively and automatically generate security policy recommendations, rank at least two of the security policy recommendations, and present at least one top-ranked generated security policy recommendation in a user interface. Some embodiments generate a security policy in a managed computing system based on at least an anomaly score, and then configure the managed computing system according to the generated security policy.
A method of fabricating a hollow core optical fiber. The method comprising: providing a preform comprising a transverse cross-sectional structure comprising a hollow core surrounded by a plurality of capillaries defining a plurality of voids encased by a tubular cladding. The hollow core and the plurality of voids extend longitudinally along a length of the preform. The method further comprising purging the preform by flowing gas comprising a noble gas through the hollow core and the plurality of voids, and subsequent to purging the preform, drawing a hollow core optical fiber by passing the purges preform through a draw furnace heated to a temperature suitable for softening a material of the preform.
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
G02B 6/032 - Fibres optiques avec revêtement le noyau ou le revêtement n'étant pas un solide
A system for providing a content holding layer includes a processing system and memory storing instructions that, when executed by the processing system, cause the system to display a content holding element on a desktop of an operating system, receive an input for adding a content item to the content holding element, obtain content item data of the content item, add the content item data to a database associated with the content holding element, display a content holding application on the desktop, display a representation of the content item in the content holding application, determine one or more recommended actions applicable to the content item based at least in part of the content item data, and display, one or more selectable elements for executing the one or more recommended actions.
Various technologies pertaining to digital rights management (DRM) for artificial intelligence (AI) models are provided. In an example, a client computing device comprises a first processor and one or more second processors. Subsequent to transmitting a request for access to a computer-implemented AI model, the client computing device receives an encryption key, AI model certification information, and an AI model payload, wherein the AI model payload comprises an encrypted AI model. The client computing device validates the AI model payload based upon the AI model certification information and decrypts the AI model payload. The client computing device stores the decrypted AI model payload in a second memory where a first memory stores a DRM application. Responsive to a request to execute the AI model stored in the second memory, the computing device causes execution of the AI model by the one or more second processors.
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
Cross-correlating identifiers are created and stored in a security asset database for corresponding security assets. The security asset database is used with the cross-correlating identifiers to perform functions for the underlying security assets without exposing the security assets. One function is a scan of network resources for potential exposure of the security assets. A key generation algorithm used to generate the cross-correlating identifiers is applied to any identified potential security data to generate a corresponding reference identifier. A determination is made whether a security asset comprises a security risk that exceeds a predetermined threshold based on at least a determination of whether the cross-correlating identifier for the security asset matches any reference identifiers generated for the potential security data, as well as based on other data stored in the security asset database for the security asset.
G06F 21/30 - Authentification, c.-à-d. détermination de l’identité ou de l’habilitation des responsables de la sécurité
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
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
An off-axis lighting system for optical storage media comprises a Gaussian laser illumination source and illumination optics including collimating and cylindrical lenses. The system generates an illumination beam that propagates through the optical storage medium at angles different from the optical axis of a line scanning camera system. A collimating lens shapes the beam, adjusting characteristics such as beam waist diameter and location. A cylindrical lens further shapes the beam, creating an elliptical two-dimensional profile across its cross-section. The combination of off-axis propagation and elliptical profile restricts illumination to the voxels in the field of view of the line scanning camera at a specific layer in the optical storage medium. The off-axis lighting system enhances an ability of an optical read system to read voxels by improving the signal-to-noise ratio (SNR) of the voxel images captured by the line scanning camera used to decode symbols stored by the voxels.
G11B 7/00 - Enregistrement ou reproduction par des moyens optiques, p. ex. enregistrement utilisant un faisceau thermique de rayonnement optique, reproduction utilisant un faisceau optique à puissance réduiteSupports d'enregistrement correspondants
G11B 7/09 - Dispositions ou montage des têtes ou des sources lumineuses par rapport aux supports d'enregistrement comportant des dispositions pour déplacer le rayon lumineux ou son plan focal dans le but de maintenir l'alignement relatif du rayon lumineux et du support d'enregistrement pendant l'opération de transduction, p. ex. pour compenser les irrégularités de surface ou pour suivre les pistes du support
09 - Appareils et instruments scientifiques et électriques
Produits et services
(1) Downloadable computer software; downloadable computer software development tools; downloadable speech recognition software; downloadable computer software using artificial intelligence (AI) to convert text-to-speech and speech-to-text; downloadable computer software using artificial intelligence (AI) for recognizing, capturing, analyzing, processing, editing, generating, transmitting, and sharing text, human speech, voice data, audio, and information; downloadable computer software using artificial intelligence (AI) for long-form automatic speech recognition, long-form multi-speaker text-to-speech, speaker diarization and audio timestamping, voice transcription, live dictation, and generating structured transcripts; downloadable computer software for using Large Language Models (LLMs) to process and generate conversational audio and text; downloadable computer software using artificial intelligence (AI) for voice-based and text-based content creation and natural language processing.
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Downloadable software in the nature of a mobile application;
downloadable computer software that enables users to access
and interact with information and databases; downloadable
computer software for collecting, editing, organizing,
modifying, bookmarking, storing, sharing and publishing data
and information; downloadable computer software for
uploading, managing, tracking, and sharing customized
content; downloadable computer software for searching,
accessing, displaying, sharing and reviewing newsletters,
research reports, blogs, and articles; downloadable computer
software featuring multimedia content; downloadable computer
software for enabling transmission of images and audiovisual
and video content; downloadable computer software for use in
creating, downloading, uploading, designing, modifying,
reproducing, transmitting, and sharing images, graphics,
fonts, photographs, text, videos, and data; downloadable
recreational game software; downloadable mobile applications
for interactive and recreational games; downloadable
software for games and social networking; downloadable
logic, word, trivia, and puzzle game software via a global
computer network and wireless devices; downloadable
electronic publications in the nature of newsletters,
research reports, articles and white papers on topics of
professional interest; downloadable computer software
development tools; downloadable computer software that
provides web-based access to applications and services
through a web-operating system or portal interface;
downloadable computer software for use in business analytics
and database management; downloadable computer software for
social media, marketing, merchandising, customer service,
website performance, search engine optimization, technology,
consumer goods, retail, and manufacturing; downloadable
computer software for tracking and analyzing user
interaction with customized content; downloadable education
software; downloadable computer software for providing
online courses, seminars, interactive classes, educational
instruction, and course materials; downloadable computer
software for providing access to internet search engines
featuring information for obtaining job listings, resume
postings, and other job searches; downloadable job
searching, sourcing and recruiting software using artificial
intelligence (ai) for users on a social networking,
employment, and business networking communication platform;
downloadable chatbot software using artificial intelligence
(ai) for users on a social networking, employment, and
business networking communication platform; downloadable
writing and communication software using artificial
intelligence (ai) for assisting platform users with
employment, job sourcing and recruiting, lead generation,
and business-related inquiries; content creation software
using artificial intelligence for users 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 providing online
courses, seminars, interactive classes, educational
instruction, and course materials; downloadable podcasts in
the field of employment, recruitment of personnel, careers,
job resources and listings, and professional networking and
wide field of topics; downloadable computer software using
artificial intelligence (ai) for providing online courses,
seminars, interactive classes, educational instruction, and
course materials. Providing online employment information and employment
services; providing online business networking services;
providing online career networking services; recruitment and
placement services; providing online employment counseling,
career placement services, and personnel recruitment;
providing an online searchable databases and interactive
databases featuring employment and career opportunities
(Term considered too vague by the International Bureau
pursuant to Rule 13 (2) (b) of the Regulations); providing
online information in the fields of employment, recruitment
of personnel, careers, job resources and listings, career
development, professional networking, and employment
advertising; providing an online interactive computer
database featuring recruitment and employment information,
employment advertising, job listings, career information and
resources via a global computer network (Term considered too
vague by the International Bureau pursuant to Rule 13 (2)
(b) of the Regulations); providing an online artificial
intelligence (ai) enhanced searchable database featuring
employment and career opportunities and business, employment
and professional queries and answers (Term considered too
vague by the International Bureau pursuant to Rule 13 (2)
(b) of the Regulations); business research and survey
services utilizing artificial intelligence; providing
artificial intelligence (ai) enhanced online computer
databases and online searchable databases in the fields of
marketing, lead generation, sourcing, recruiting, and
business and professional networking (Term considered too
vague by the International Bureau pursuant to Rule 13 (2)
(b) of the Regulations); online business networking services
featuring artificial intelligence (ai) solutions;
advertising services; marketing services; marketing
consulting services; advertising, marketing, and promotion
services for businesses; providing advertising and
advertisement services; providing marketing and advertising
solutions for marketing campaigns across a wide range of
industries; providing resources for creating advertising and
marketing campaigns that meet business specific business and
b2b needs; creating, placing, displaying, targeting and
disseminating online advertisements for others; providing a
web site which features advertisements for the goods and
services of others on a global computer network (Term
considered too vague by the International Bureau pursuant to
Rule 13 (2) (b) of the Regulations); providing advertising
and marketing services via an online platform featuring
sponsored ad content, sponsored ad messaging, text ads,
dynamic ads, and ad placements; providing online advertising
on a computer network; providing business and business
networking information; advertising and marketing services
rendered using artificial intelligence (ai); lead generation
activities and services; advertising and marketing services
in the nature of accessing, extracting, and organizing
information from the internet and other sources regarding
people, companies, products, marketing, industries and other
categories; lead generation services rendered using
artificial intelligence; employment recruiting services;
professional, staff, personnel and talent recruiting
services; providing an online searchable database featuring
employment and career opportunities and business information
(Term considered too vague by the International Bureau
pursuant to Rule 13 (2) (b) of the Regulations); providing
an online searchable database featuring business, employment
and professional queries and answers (Term considered too
vague by the International Bureau pursuant to Rule 13 (2)
(b) of the Regulations); providing information online
regarding recruiting and talent solutions; providing an
online searchable database featuring professional queries
and answers concerning staffing and hiring information (Term
considered too vague by the International Bureau pursuant to
Rule 13 (2) (b) of the Regulations); charitable services,
namely, promoting public awareness about charitable,
philanthropic, community service, humanitarian activities
and volunteer activities; providing online career networking
services and information in the fields of employment,
recruitment, job resources, job listings and career path
suggestions; providing business information; providing a web
site featuring business information in the form of audio,
video, transcripts, and other educational materials (Term
considered too vague by the International Bureau pursuant to
Rule 13 (2) (b) of the Regulations); providing information,
news and commentary in the field of business; promotion
services for businesses. Providing temporary use of on-line non-downloadable
software; 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); application service
provider (asp) services; providing an online software
platform; providing temporary use of on-line
non-downloadable software that enables users to access and
interact with information and databases; providing
customized web pages featuring user-defined information,
audio, text, video, and images; hosting an interactive
website featuring technology that allows users to create,
download, upload, design, modify, reproduce, transmit, and
share images, graphics, fonts, photographs, text, videos,
and data; providing temporary use of on-line
non-downloadable software for collecting, editing,
organizing, modifying, bookmarking, storing, sharing and
publishing data and information; providing temporary use of
on-line non-downloadable software for uploading, managing,
tracking, and sharing customized content; providing
temporary use of on-line non-downloadable software for
searching, accessing, displaying, sharing and reviewing
newsletters, research reports, blogs, and articles;
providing general and customized information in a wide
variety of fields, namely, business, social networking,
employment, careers and recruiting via a website (Term
considered too vague by the International Bureau pursuant to
Rule 13 (2) (b) of the Regulations); providing general and
customized information relating to business, current events,
education, entertainment, technology, culture,
entrepreneurship, leadership, management, marketing,
recruiting, career, and professional development via a
website (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 software
featuring multimedia content; providing temporary use of
on-line non-downloadable software for enabling transmission
of images and audiovisual and video content; providing
temporary use of on-line non-downloadable software for use
in creating, downloading, uploading, designing, modifying,
reproducing, transmitting, and sharing images, graphics,
fonts, photographs, text, videos, and data; providing
temporary use of online non-downloadable recreational game
software; providing temporary use of online non-downloadable
software for interactive games and recreational game playing
purposes; providing temporary use of online non-downloadable
software for games and social networking; providing
temporary use of online non-downloadable logic, word,
trivia, and puzzle game software; 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 software development tools;
providing temporary use of on-line non-downloadable software
that provides web-based access to applications and services
through a web-operating system or portal interface;
providing temporary use of on-line non-downloadable software
for use in business analytics and database management;
providing temporary use of on-line non-downloadable software
for social media, marketing, merchandising, customer
service, website performance, search engine optimization,
technology, consumer goods, retail, and manufacturing;
providing temporary use of on-line non-downloadable software
for tracking and analyzing user interaction with customized
content; providing an online education software platform;
providing temporary use of on-line non-downloadable software
for providing online courses, seminars, interactive classes,
educational instruction, and course materials; providing an
online software platform for employee training and
professional development; providing temporary use of on-line
non-downloadable software for providing access to internet
search engines featuring information for obtaining job
listings, resume postings, and other job searches; providing
an online software platform for employee training and
professional development that allows users to upload,
manage, and share customized content, access online courses
and content, receive data analytics and insights on learning
and skills development, host online web facilities, links,
webcasts and podcasts for managing and sharing online
content; providing non-downloadable job searching, sourcing
and recruiting software using artificial intelligence (ai)
for users on a social networking, employment, and business
networking communication platform; providing
non-downloadable chatbot using artificial intelligence (ai)
for users on a social networking, employment, and business
networking communication platform; providing
non-downloadable writing and communication online software
using artificial intelligence (AI) for assisting platform
users with writing, communicating, and with employment, job,
recruiting, lead generation, and business-related inquiries;
providing non-downloadable content creation online software
using artificial intelligence for users on a social
networking, employment, and business networking
communication platform; providing non-downloadable software
using artificial intelligence (ai) for employee training and
professional development; providing non-downloadable
software platform tools for creating, placing, displaying,
controlling and tracking advertising and marketing content;
providing non-downloadable software platform tools for use
in customer relationship management (crm), lead generation
activities and services, and tracking, accessing, extracting
and organizing sales information; hosting digital content on
internet; testing, analysis and evaluation of the knowledge,
skills and abilities of others for job and employment skills
in the field of business, social networking, employment,
careers and recruiting utilizing artificial intelligence
(AI) (Term considered too vague by the International Bureau
pursuant to Rule 13 (2) (b) of the Regulations); providing
temporary use of a non-downloadable computer software for
providing certification of job skill assessments online.
47.
ANOMALY DETECTION USING ACCUMULATED DATA USAGE COMPARED TO PROJECTED DATA USAGE IN A CLOUD COMPUTING ENVIRONMENT
A method for detecting an anomaly in resource utilization observed within a cloud computing platform includes observing an actual resource utilization for a customer of the cloud computing platform; determining a historical utilization distribution for the customer that defines values of a resource utilization metric across repeated instances of a time interval and sub-intervals therein; identifying a temporal location of an anomaly detection period within the time interval; filtering the historical utilization distribution to construct a distribution of relevant values of the resource utilization metric; and computing, based on the distribution of relevant values, a resource utilization projection for the customer. An anomaly risk metric in calculated and fit to an anomaly classification to determine the presence of a data usage anomaly. The data usage anomaly is confirmed the using a language model trained on prior data usage anomalies over prior time intervals tagged with actual unauthorized access.
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
48.
Reducing Computational Burden in a Generative Model through Target Vocabulary Constraints
A technique generates a response based on a query using a generative model. The technique includes encoding the query into a shortlist embedding and a sequence of response-part embeddings. The technique uses the shortlist embedding to identify a reduced-size vocabulary that is relevant to the query, selected from a larger target vocabulary of tokens. The technique then identifies at least one group of ranked tokens associated with a corresponding response-part embedding. The group of ranked tokens is selected from the reduced-size vocabulary. The technique then constructs a part of a response in a manner that is constrained by the group of ranked tokens. Such constraint helps reduce computational burden and latency. Some implementations construct the response non-autoregressively in a single pass, while others perform this operation autoregressively in plural passes. Some implementations of the target vocabulary include plural-word tokens, each including two or more words.
This document relates to generative machine learning. Users can provide input relating to a content generation task. A generative machine learning model can be prompted to suggest concept tags based on the received user input. Then, users can select a concept tag and a value for that concept tag. A content item can be generated for the user using the generative machine learning model, where the content generation is based on the value for the selected concept tag. Over time, additional content tags can be updated with user-designated values while updating the generated content accordingly. In this manner, user generation of content with a generative machine learning model can be guided via received user inputs.
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
G06F 3/04845 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs pour la transformation d’images, p. ex. glissement, rotation, agrandissement ou changement de couleur
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
50.
DIGITAL RIGHTS MANAGEMENT ARCHITECTURE FOR ARTIFICIAL INTELLIGENCE MODELS
Various technologies pertaining to digital rights management (DRM) for artificial intelligence (AI) models are provided. In an example, a client computing device comprises a first processor and one or more second processors. Subsequent to transmitting a request for access to a computer-implemented AI model, the client computing device receives an encryption key, AI model certification information, and an AI model payload, wherein the AI model payload comprises an encrypted AI model. The client computing device validates the AI model payload based upon the AI model certification information and decrypts the AI model payload. The client computing device stores the decrypted AI model payload in a second memory where a first memory stores a DRM application. Responsive to a request to execute the AI model stored in the second memory, the computing device causes execution of the AI model by the one or more second processors.
Cross-correlating identifiers are created and stored in a security asset database for corresponding security assets. The security asset database is used with the cross-correlating identifiers to perform functions for the underlying security assets without exposing the security assets. One function is a scan of network resources for potential exposure of the security assets. A key generation algorithm used to generate the cross-correlating identifiers is applied to any identified potential security data to generate a corresponding reference identifier. A determination is made whether a security asset comprises a security risk that exceeds a predetermined threshold based on at least a determination of whether the cross-correlating identifier for the security asset matches any reference identifiers generated for the potential security data, as well as based on other data stored in the security asset database for the security asset.
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
Disclosed herein is the compound CCC═C(F)CF. The compound can be provided as a pure isomer of either the E-isomer or Z-isomer, or as a diastereomeric mixture of both isomers (i.e., an E/Z mixture). The boiling point and dielectric constant of the compound enable effective use of the compound as a coolant in a two-phase immersion cooling system. A two-phase immersion cooling system incorporating the compound as coolant can include: an immersion tank configured to contain the coolant and to contain a component capable of generating heat; and a condenser configured to receive vaporized coolant and to condense the vaporized coolant back to liquid form.
C09K 5/04 - Substances qui subissent un changement d'état physique lors de leur utilisation le changement d'état se faisant par passage de l'état liquide à l'état vapeur ou vice versa
H05K 7/20 - Modifications en vue de faciliter la réfrigération, l'aération ou le chauffage
53.
MODEL CHECKPOINT SAVING BASED ON MULTI-TIER STORAGE
The present disclosure proposes a method, apparatus, and computer-readable medium for model checkpoint saving based on multi-tier storage. During a training of a machine learning model performed through a Graphics Processing Unit (GPU) in a target node, a checkpoint to be saved of the machine learning model may be identified from a GPU memory that directly exchanges data with the GPU. The checkpoint may be saved from the GPU memory to a central processing unit (CPU) memory that directly exchange data with a CPU in the target node. The checkpoint may be saved from the CPU memory to a non-transitory memory, the non-transitory memory including at least one of: a local non-transitory memory in the target node, a neighbor non-transitory memory in a neighbor node of the target node, and a remote non-transitory memory located remotely from the target node.
Systems and methods for service incident detection in a cloud computing platform. According to an example implementation, the incident detection system retrieves a single user metric corresponding to a service call to a resource on which the service is dependent and uses unsupervised anomaly detection to detect anomalies indicative of a service incident. Detected anomalies include an anomaly score indicating a level of anomality. Additionally, a supervised learning classifier is trained and used to filter/classify the anomaly detection results based on features corresponding to the anomaly score. The features are learned based on characteristic dimensions, distribution, and statistics of anomaly scores of the user metric at different resolution/aggregation levels. Anomaly detection results are classified as an incident or not an incident. A report is generated for a determined incident.
H04L 41/5061 - Gestion des services réseau, p. ex. en assurant une bonne réalisation du service conformément aux accords caractérisée par l’interaction entre les fournisseurs de services et leurs clients réseau, p. ex. la gestion de la relation client
H04L 41/5074 - Traitement des plaintes des utilisateurs ou des tickets d’incident
55.
SYSTEMS AND METHODS FOR ACCELERATING THE COMPUTATION OF THE EXPONENTIAL FUNCTION
Aspects of embodiments of the present disclosure relate to a field programmable gate array (FPGA) configured to implement an exponential function data path including: an input scaling stage including constant shifters and integer adders to scale a mantissa portion of an input floating-point value by approximately log2 e to compute a scaled mantissa value, where e is Euler's number; and an exponential stage including barrel shifters and an exponential lookup table to: extract an integer portion and a fractional portion from the scaled mantissa value based on the exponent portion of the input floating-point value; apply a bias shift to the integer portion to compute a result exponent portion of a result floating-point value; lookup a result mantissa portion of the result floating-point value in the exponential lookup table based on the fractional portion; and combine the result exponent portion and the result mantissa portion to generate the result floating-point value.
G06F 7/556 - Méthodes ou dispositions pour effectuer des calculs en utilisant exclusivement une représentation numérique codée, p. ex. en utilisant une représentation binaire, ternaire, décimale utilisant des dispositifs n'établissant pas de contact, p. ex. tube, dispositif à l'état solideMéthodes ou dispositions pour effectuer des calculs en utilisant exclusivement une représentation numérique codée, p. ex. en utilisant une représentation binaire, ternaire, décimale utilisant des dispositifs non spécifiés pour l'évaluation de fonctions par calcul de fonctions logarithmiques ou exponentielles
G06F 7/483 - Calculs avec des nombres représentés par une combinaison non linéaire de nombres codés, p. ex. nombres rationnels, système de numération logarithmique ou nombres à virgule flottante
56.
DYNAMICALLY SUBSTITUTING A MODIFIED QUERY BASED ON PERFORMANCE ANALYSIS
The disclosure herein describes analyzing queries and dynamically modifying those queries based on the analysis. An indication that a query is to be executed by a first process is detected. It is determined that an analysis results data store does not include an active analysis result for the query using a query identifier of the query and, as a result, a modified instance of the query is generated using a modification pattern. The query and the modified instance of the query are analyzed based on a performance metric using a second process that is independent of the first process. An active analysis result of the query is recorded based on the analysis, wherein the analysis result indicates whether future executions of the query should be modified using the modification pattern. Further, in some examples, analysis results expire, such that associated queries are reanalyzed to generate active analysis results periodically.
A wearable device comprises an exteriorly positioned first electrode and a reporting capacitor. The first electrode forms a first side of the reporting capacitor, and a second side of the reporting capacitor is formed by skin of a user when the wearable device is worn. An oscillator is configured to output a signal to drive the first electrode at a first frequency. The oscillator is configured such that changes in capacitance at the reporting capacitor adjust the signal output by the oscillator from the first frequency to a second frequency. A frequency-to-voltage converter is configured to generate a voltage representation of the second frequency. A controller determines a change between the first frequency and the second frequency based on the voltage representation and indicates an amount of movement of skin of the user relative to the first electrode based on the determined frequency change.
The virtually divided input trackpad disclosed herein is designed to provide enhanced ergonomic user interaction with digital interfaces. The virtually divided input trackpad comprises two virtually separated functional areas, each of which may be dedicated to distinct functionalities such as object movement and object rotation. This arrangement allows for simultaneous two-handed operation, offering users an intuitive, efficient, and accessible way of controlling digital environments, which is especially beneficial for users with specific accessibility needs.
Various security mechanisms are considered for detecting and mitigating potential security risks posed by generative artificial intelligence. In one example, a generative model prompt is separated into a meta prompt part and an input prompt part, which in turn are separately encoded. Based on the resulting meta prompt embedding vector and input prompt embedding vector, the prompt is identified as anomalous, which in turn triggers an appropriate security action. In one example implementation, the meta prompt embedding vector is used to classify the prompt (e.g. by application type or application flow type), and the input prompt embedding vector is used for context-aware anomaly detection, using a class assigned to the prompt based on its meta prompt embedding vector. In another example implementation, the prompt is identified as anomalous based on distance between the meta prompt and input prompt embedding vector.
60.
EMBEDDING DYNAMIC CONTENT IN VIDEO DATA ALLOWING REAL-TIME INTERACTION VIA CONFIGURATION CHANGES DURING RENDERING
A method for embedding dynamic content into video data. The method comprises detecting, by a video editor, an indication that a piece of dynamic content is intended be rendered in a dynamic video and determining at least one dynamic variable associated with the piece of dynamic content. The method further comprises detecting a selection of a selected dynamic variable of the at least one dynamic variable and determining a dynamic interaction associated with the selected dynamic variable. The method further comprises generating the dynamic video to comprise interactive dynamic content based on the piece of dynamic content and a static video, and labeling, in metadata of a dynamic video using a backwards-compatible video format, that the dynamic interaction is configured to be performed on the interactive dynamic content to modify the selected dynamic variable during playback of the dynamic video.
A delay optimizer includes circuits for detecting delay between the received data and the received clock, such as in an integrated circuit having separate dies coupled to communication via an interconnect that includes a clock channel and a data channel. The electrical characteristics of the clock channel and the data channel (including on-chip buffers) may introduce significant differences in the delay between the received clock and the received data coupled with the effects of clock jitter, inter-symbol interference, and duty-cycle distortion that may introduce significant sampling errors in the sampled data. The delay optimizer operates to detect the delay and optimize a sampling clock in real-time and on a continuous basis using the difference between the data edge of the received data and the received clock edge to determine an optimal delay value to sample the received data to produce the sampled data with the desired very-low bit-error-rate (BER).
Techniques are provided for improving chat response generation using knowledge graph-based data retrieval. A chat system receives a user message and identifies relevant entities by performing a web search. A first generative language model receives a prompt containing the chat history, identified entities, and a knowledge graph schema defining entity types and relationships. The first model generates a structured query targeting specific entity attributes in the knowledge graph. After executing the query to retrieve targeted entity data, a second generative language model receives the retrieved data and user message to generate a contextually relevant response. The system enables precise control over grounding data by using the knowledge graph schema to specify exactly which entity attributes to retrieve, avoiding excessive or irrelevant information while maintaining comprehensive responses. This approach improves upon conventional database solutions by allowing flexible, relationship-aware queries that retrieve diverse yet focused entity information based on conversational context.
Disclosed herein is the compound CCC=C(F)CF. The compound can be provided as a pure isomer of either the E-isomer or Z-isomer, or as a diastereomeric mixture of both isomers (i.e., an E/Z mixture). The boiling point and dielectric constant of the compound enable effective use of the compound as a coolant in a two-phase immersion cooling system. A two-phase immersion cooling system incorporating the compound as coolant can include: an immersion tank configured to contain the coolant and to contain a component capable of generating heat; and a condenser configured to receive vaporized coolant and to condense the vaporized coolant back to liquid form.
C09K 5/04 - Substances qui subissent un changement d'état physique lors de leur utilisation le changement d'état se faisant par passage de l'état liquide à l'état vapeur ou vice versa
H05K 7/20 - Modifications en vue de faciliter la réfrigération, l'aération ou le chauffage
C07C 21/18 - Composés acycliques non saturés contenant des atomes d'halogène contenant des liaisons doubles carbone-carbone contenant du fluor
09 - Appareils et instruments scientifiques et électriques
Produits et services
Downloadable computer software; downloadable computer software development tools; downloadable speech recognition software; downloadable computer software using artificial intelligence (AI) to convert text-to-speech and speech-to-text; downloadable computer software using artificial intelligence (AI) for recognizing, capturing, analyzing, processing, editing, generating, transmitting, and sharing text, human speech, voice data, audio, and information; downloadable computer software using artificial intelligence (AI) for long-form automatic speech recognition, long-form multi-speaker text-to-speech, speaker diarization and audio timestamping, voice transcription, live dictation, and generating structured transcripts; downloadable computer software for using Large Language Models (LLMs) to process and generate conversational audio and text; downloadable computer software using artificial intelligence (AI) for voice-based and text-based content creation and natural language processing.
65.
DUAL-CONSTRAINED NEURAL NETWORK COMPRESSION WITH DYNAMIC WEIGHT RESTORATION
Techniques for dynamically compressing neural network models enable efficient deployment on resource-constrained devices. A compression manager monitors device metrics and application requirements to determine when compression is needed. Model-specific compression instructions guide sequential operations including quantization, layer fusion, pruning, and aggressive pruning, with each operation's parameters specified per layer. Compression metadata preserves information needed for potential restoration. When resources become constrained, the system progressively applies compression while maintaining critical model capabilities. As resources become available, compressed components can be selectively restored using stored metadata. The compression level adapts automatically based on real-time conditions, optimizing the balance between model size and performance.
A system for providing a content holding layer includes a processing system and memory storing instructions that, when executed by the processing system, cause the system to display a content holding element on a desktop of an operating system, receive an input for adding a content item to the content holding element, obtain content item data of the content item, add the content item data to a database associated with the content holding element, display a content holding application on the desktop, display a representation of the content item in the content holding application, determine one or more recommended actions applicable to the content item based at least in part of the content item data, and display, one or more selectable elements for executing the one or more recommended actions.
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
67.
PERFORMING SPATIAL REASONING USING GENERATIVE LANGUAGE MODEL
Examples are disclosed that relate to performing reasoning processes using generative language models. One disclosed example provides a method of performing a spatial reasoning task. The method comprises, iteratively, at a reasoner agent, receiving query results from a retriever agent, and based upon the query results, generating a reasoner prompt. The method further comprises inputting the reasoner prompt into a reasoner language model, receiving a reasoner output from the reasoner language model, and sending a query to a retriever agent. The method further comprises, at the retriever agent, receiving the query from the reasoner agent, generating a retriever prompt, and inputting the retriever prompt into a retriever language model. The method further comprises receiving an output from the retriever language model, querying scene data, and receiving one or more results of the query, and sending the one or more results of the query to the reasoner agent.
This document relates to secure storage techniques that can be employed in a multi-tenant environment. In the disclosed implementations, a host CPU can perform data at rest encryption on data that is written to an SSD via an interposer. By offloading the data at rest encryption from the interposer to the CPU, the complexity and power consumption of the interposer can be reduced. Furthermore, redundant cryptographic operations can be mitigated, because the interposer and CPU do not necessarily perform link encryption on data payloads themselves. Rather, link encryption can be limited to associated command data used to configure transfers of encrypted data payloads.
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
A structured query language (SQL) query including an SQL intrinsic function is processed using native code including single instruction multiple data (SIMD) or single instruction multiple thread (SIMT) processor instructions for execution on a processor having native parallelism. The native code is compiled from an implementation of the SQL intrinsic function in a platform-independent source code. The compiling comprises compiling the source code to generate a platform-independent intermediate representation (IR) of the source code. The IR is optimized for improved performance through parallelization. The optimized IR is lowered to generate the native code.
This disclosure describes utilizing a generative document system to create generative search results documents using generative artificial intelligence (AI) models and dynamically determining which one of the generative search results documents to provide in response to a search query. For example, in response to receiving a search query, the generative document system obtains search link results (e.g., website links and corresponding grounding information) for the search query and utilizes this information with multiple generative AI models to generate various types of generative search results documents. Additionally, the generative document system generates and utilizes a generative document arbitration model to determine, based on the search link results, which of the generative search results documents to provide in response to the search query.
This document relates to secure storage techniques that can be employed in a multi-tenant environment. In the disclosed implementations, a host CPU can perform data at rest encryption on data that is written to an SSD. By offloading the data at rest encryption from the SSD to the CPU, the complexity and power consumption of the SSD can be reduced. Furthermore, redundant cryptographic operations can be mitigated, because the SSD and CPU do not necessarily perform link encryption on data payloads themselves. Rather, link encryption can be limited to associated command data used to configure transfers of encrypted data payloads.
Embodiments of the present disclosure include techniques for moving data between electronic system components using buffers. A digital processor stores data generated in response to a series of commands in a first buffer. Commands are received with a reference to a second buffer. The digital processor tracks the last location that a data result was stored in the first buffer. When a data result fills the buffer, the remaining data is automatically stored in the second buffer. Downstream devices may empty full buffers. A client may receive an indication that a buffer is empty and subsequently send commands with a reference to empty buffer.
An electronic device hinge includes a first body, a second body, and a link. The link is rotatable relative to the first body around a first pivot point and rotatable relative to the second body around a second pivot point. The first pivot point has a first rotational resistance and the second pivot point has a second rotational resistance that is different from the first rotational resistance. The hinge further includes a third body that is selectively positionable relative to the first body in a first configuration. The third body limits a first rotational range of motion around the first pivot point when positioned in the first configuration.
A method, computer program product, and computing system for executing a plurality of requests to process data using a trained machine learning model. An anomalous pattern of requests including at least a threshold amount of out-of-domain data is identified from the plurality of requests. A potential model inversion attack is detected based upon, at least in part, identifying the anomalous pattern of requests.
Aspects of the technology disclosed herein related to a distributed architecture for securely delivering AI models and/or training data sets to client devices for local use. The distributed includes a licensing server that controls access to and decryption of the models. The licensing server controls the distribution of licensing packages for the different models delivered by the distribution server. The client device transmits a license request to the licensing server. The licensing request may include device-level details about the client device itself, and such details may be provided in a secure, trusted manner, such as through a hardware root of trust (HROT) of the client device. If the details in the license request satisfy the security requirements for the model, a license package for the model is delivered to the client device. The license package includes a license for the model and a decryption key for the model.
A set of geometric shapes to be applied by a machine learning model to objects identified in image data is defined. A learning rate of the machine learning model is updated in response to external events. The machine learning model is used to estimate spatial parameters for each of the objects identified in the image data. The spatial parameters are estimated by fitting the objects to the set of geometric shapes. Updates to the spatial parameters are temporally integrated. A spatial estimate of the objects identified in the image data is generated.
The automatic generation of synthetic training data that can be used to train a language model to generate code examples following a code language based on a natural language input. Thus, new language models may be created, or existing language models may be fine-tuned, to adapt to automatically generate code without having to manually generate bulk quantities of training data. Rather, a many-to-many grammar mapping is navigated to generate training data. Specifically, the many-to-many grammar mapping maps code grammar to natural grammar. Then, each training data is generated by navigating the many-to-many grammar mapping definition to generate a mapping of a respective code expression to a respective natural language expression.
Head-level key-value (KV) cache compression may be performed by allocating memory to individual heads of a multi-head attention model based on importance scores assigned to the individual heads of a multi-head attention model. The importance scores may be calculated based on both retrieval and reasoning attributes of the individual heads of the multi-head attention model. The retrieval attributes and reasoning attributes of the individual heads of the multi-head attention model may be determined based on an updated Needle-in-a-Haystack test that incorporates a Retrieval-Reasoning example. It should be appreciated that the reasoning attributes of the individual heads are different than attention scores associated with the multi-head attention model.
Disclosed solutions provide for interactive video editing and playback with three dimensional (3D) object manipulation. Examples enable video players to display both an underlying static video along with a 3D object as dynamic content. The video editor presents a settings editor that enables the creator of the video to specify the ability of viewers to interact with the 3D object. The video viewer exposes settings for the dynamic content to enable users to reconfigure the display of the 3D dynamic content, making the video rendering an interactive experience. Use of references (e.g., URLs) within the dynamic content enables videos distributed in the new format updateable and correctable, such that information that is subject to change may be kept current, and informational errors introduced at the time of the video production may be corrected – without requiring creation and distribution of a substitute video file.
G11B 27/031 - Montage électronique de signaux d'information analogiques numérisés, p. ex. de signaux audio, vidéo
G11B 27/32 - IndexationAdressageMinutage ou synchronisationMesure de l'avancement d'une bande en utilisant une information détectable sur le support d'enregistrement en utilisant des signaux d'information enregistrés par le même procédé que pour l'enregistrement principal sur des pistes auxiliaires séparées du même support d'enregistrement ou d'un support auxiliaire
Storage format solutions are disclosed for interactive video editing and playback that provide backwards compatibility for legacy players. Examples enable newer video players, that are able to extract dynamic content from the new video file format, to display both the underlying static video along with the dynamic content (according to a timeline within metadata stored in the new video file format), whereas legacy players display the static video. Some examples expose settings for the dynamic content to enable newer players to reconfigure the display of the dynamic content, making the video rendering an interactive experience. Use of references (e.g., URLs) within the dynamic content enables videos distributed in the new format updateable and correctable, such that information that is subject to change may be kept current, and informational errors introduced at the time of the video production may be corrected – without requiring creation and distribution of a substitute video file.
G11B 27/031 - Montage électronique de signaux d'information analogiques numérisés, p. ex. de signaux audio, vidéo
G11B 27/32 - IndexationAdressageMinutage ou synchronisationMesure de l'avancement d'une bande en utilisant une information détectable sur le support d'enregistrement en utilisant des signaux d'information enregistrés par le même procédé que pour l'enregistrement principal sur des pistes auxiliaires séparées du même support d'enregistrement ou d'un support auxiliaire
H04N 9/82 - Transformation du signal de télévision pour l'enregistrement, p. ex. modulation, changement de fréquenceTransformation inverse pour la reproduction les composantes individuelles des signaux d'image en couleurs n'étant enregistrées que simultanément
A technique is described for training a student model based on a larger teacher model. The training includes generating a loss measure having a contrastive combination of two parts. The first part is based on a forward measure of divergence between teacher-generated and student-generated probability distributions, which, in turn, are based on teacher-generated samples. The second part is based on a reverse measure of divergence between student-generated and teacher-generated probability distributions, which, in turn, are based on student-generated samples. The technique then updates parameters of the student model based on the loss. In some implementations, the first part of the loss is generated using forward Kullback-Leibler (KL) divergence, and the second part of the loss is generated using reverse KL divergence. The technique also involves dynamically updating hyper-parameters during training.
To avoid inconsistencies caused by clock skew across data storage tiers, a point-in-time restore service creates a restore account, restores a partition of the database to its state at the designated point-in-time, including data from that time. A restored value is read from the data and a list is retrieved of segments written to a disaggregated storage tier by the existing partition. The disaggregated storage tier includes a first segment with a first value and a second segment with a second value, the values indicating their creation order. A first comparison is made between the restored value and the first value, and a second comparison is made between the restored value and the second value. Based on these comparisons, the first segment but not the second segment is copied to the restore account.
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
83.
SPECIALIZED SUB-TASK MODELS USED TO PERFORM A TARGET TASK
Embodiments of the disclosed technologies are capable of deploying a sequence of sub-task models to perform a target task. A query is received that includes a digital content item with a criterion. A task responsive to the query is determined. A first sub-task and second sub-task are generated from the task. The first sub-task includes a classification task related to a user and the criterion. The second sub-task includes a content generation task related to the classification task. The first sub-task is performed by determining a classification for the user with respect to the criterion. The second sub-task is performed by determining a natural text explanation for the classification. The classification and the natural language text explanation are presented via a user interface.
A technique transforms an original model into an in-place mixture-of-experts model. To accomplish this, the technique first identifies at least one group of subnetworks that have different task-processing capabilities. The subnetworks are associated with respective groups of parameters. The technique then produces a router-supplemented model that includes a router that is capable of selecting a subset of the subnetworks to be used in processing a particular instance of input information. The router determines when a particular subnetwork should be selected based on a combination of two score parts. A first score part is based on token-related hidden state information, and a second score part is based on an assessed saliency of the particular subnetwork. The technique then fine-tunes the router-supplemented model, to produce the mixture-of-experts model. In inference, the mixture-of-experts model selects among the group of subnetworks using the router in a resource-efficient and low-latency manner.
A delay optimizer includes circuits for detecting delay between the received data and the received clock, such as in an integrated circuit having separate dies coupled to communication via an interconnect that includes a clock channel and a data channel. The electrical characteristics of the clock channel and the data channel (including on-chip buffers) may introduce significant differences in the delay between the received clock and the received data coupled with the effects of clock jitter, inter-symbol interference, and duty-cycle distortion that may introduce significant sampling errors in the sampled data. The delay optimizer operates to detect the delay and optimize a sampling clock in real-time and on a continuous basis using the difference between the data edge of the received data and the received clock edge to determine an optimal delay value to sample the received data to produce the sampled data with the desired very-low bit-error-rate (BER).
H03K 5/14 - Dispositions ayant une sortie unique et transformant les signaux d'entrée en impulsions délivrées à des intervalles de temps désirés par l'utilisation de lignes à retard
H03K 5/00 - Transformation d'impulsions non couvertes par l'un des autres groupes principaux de la présente sous-classe
H03K 5/135 - Dispositions ayant une sortie unique et transformant les signaux d'entrée en impulsions délivrées à des intervalles de temps désirés par l'utilisation de signaux de référence de temps, p. ex. des signaux d'horloge
86.
ASYNCHRONOUS SERVING ARCHITECTURE FOR CUSTOMIZED CONTENT ITEMS
Custom content generation techniques for connection networking are described. A method comprises receiving a first signal indicating an entity session associated with an entity identifier, retrieving a first content item associated with the entity identifier from a memory cache, presenting the first content item in a first content slot of a first section of a graphical user interface (GUI) in response to the first signal, wherein the first section is in a rendered section of the GUI, generating a second content item associated with the entity identifier using a generative artificial intelligence model in response to the first signal, determining whether the second content item is received, and assigning the second content item to a second content slot of a second section of the GUI when the second content item is received, wherein the second section is in a non-rendered section of the GUI.
A computing system for fragment-based quantum mechanical calculation of protein properties is provided. A processor implements a protein fragmentation module that separates a computer-readable polypeptide sequence into a plurality of data units. For each subsequence of three adjacent amino acids in the polypeptide sequence, a first amino acid, a second amino acid, and a third amino acid are identified, each amino acid having a respective main chain including an amino group, a carbon, and a carboxyl group, and a side chain attached to the alpha carbon. The protein fragmentation module generates a data unit representing a first alpha carbon, a first carboxyl group, a second amino group, a second alpha carbon, a second carboxyl group, a second side chain, a third amino group, and a third alpha carbon, and stores the generated data unit in the memory.
G16B 5/00 - TIC spécialement adaptées à la modélisation ou aux simulations dans la biologie des systèmes, p. ex. réseaux de régulation génétique, réseaux d’interaction entre protéines ou réseaux métaboliques
G06N 10/80 - Programmation quantique, p. ex. interfaces, langages ou boîtes à outils de développement logiciel pour la création ou la manipulation de programmes capables de fonctionner sur des ordinateurs quantiquesPlate-formes pour la simulation ou l’accès aux ordinateurs quantiques, p. ex. informatique quantique en nuage
G16B 40/00 - TIC spécialement adaptées aux biostatistiquesTIC spécialement adaptées à l’apprentissage automatique ou à l’exploration de données liées à la bio-informatique, p. ex. extraction de connaissances ou détection de motifs
88.
DIGITAL PHASE-LOCKED LOOPS (PLL) INCLUDING CLOSED-LOOP TIME-TO-DIGITAL CONVERTER (TDC) GAIN CALIBRATION CIRCUITS AND RELATED METHODS
In a calibrated digital phase-locked-loop (DPLL) circuit, during a normal operating mode, a control value provided to a digitally controlled oscillator (DCO) is updated by a feedback circuit to keep an output clock generated by the DCO synchronized with a reference clock. The feedback circuit includes a time-to-digital converter (TDC) circuit to measure a phase difference as a time interval. In a calibration operating mode of the calibrated DPLL circuit, calibration of a resolution of a time measurement of the time interval measured by the TDC is performed in the feedback circuit while the control value provided to the DCO is kept constant. Calibrating the TDCs in each of the DPLLs in an integrated circuit (IC) to a nominal resolution in this manner improves synchronization of the clock domains. In some examples, the TDC circuit is a Vernier type circuit and calibration sets a delay difference to a nominal resolution.
H03L 7/107 - Détails de la boucle verrouillée en phase pour assurer la synchronisation initiale ou pour élargir le domaine d'accrochage utilisant une fonction de transfert variable pour la boucle, p. ex. un filtre passe-bas ayant une largeur de bande variable
G04F 10/00 - Appareils pour mesurer des intervalles de temps inconnus par des moyens électriques
H03L 7/085 - Détails de la boucle verrouillée en phase concernant principalement l'agencement de détection de phase ou de fréquence, y compris le filtrage ou l'amplification de son signal de sortie
H03L 7/099 - Détails de la boucle verrouillée en phase concernant principalement l'oscillateur commandé de la boucle
89.
SYSTEMS AND METHODS FOR ISOLATING FAULTS IN DIE-TO-DIE INTERCONNECTS
Systems and methods for isolating faults in die-to-die interconnects are provided. A method includes providing a first transmission path, along a die-to-die interconnect, from a transmitter associated with a first die to an asynchronous buffer associated with a second die. The method further includes providing a second transmission path from voltage reference circuitry associated with the second die to the asynchronous buffer associated with the second die. The method further includes simultaneously enabling both the first transmission path and the second transmission path to allow the asynchronous buffer to receive inputs from both the transmitter associated with the first die and the voltage reference circuitry associated with the second die, such that the inputs received by the asynchronous buffer are indicative of: (1) no failure in the die-to-die interconnect, (2) an open failure in the die-to-die interconnect, or (3) a short failure in the die-to-die interconnect.
The disclosed concepts relate to contextualization of generative language models. In some implementations, a linked entity database is populated with entity resource identifiers of entities extracted from a search log by an entity linker. A contextualized prompt data structure is generated based on the linked entity database, e.g., by including linked entity context information in the contextualized prompt data structure. A response to the contextualized prompt data structure is received, where the response is conditioned on the linked entity context information.
Systems and methods are provided for handing off execution of an application from a local computing device to a cloud-based computing device. The disclosed technology is directed to determining whether and when to initiate handing off the execution of the application based on monitoring resource consumption of the local computing device. When the application is not previously installed on the cloud-based computing device, the local computing device transmits an application installer executable to the cloud-based computing device for enabling use of the same application on the cloud-based computing device.
A computing device (10) including a system-on-a-chip (SoC) (12). The SoC includes a plurality of logic circuit blocks, including synchronization blocks (22) and hardware accelerator blocks (24). A synchronization block is configured to receive a wait request (30) from a first hardware accelerator block. The wait request includes one or more semaphores (32) and one or more wait threshold values (34). The synchronization block is configured to store the wait request. The synchronization block is configured to receive, from a signal source block (42), a signal request (50) that indicates a semaphore included among the one or more semaphores in the wait request. In response to receiving the signal request, the synchronization block is configured to update the semaphore. The synchronization block is configured to determine that the updated value (52) of the semaphore has reached the wait threshold value and to transmit a wait completion response (54) to at least the first hardware accelerator block.
G06F 15/173 - Communication entre processeurs utilisant un réseau d'interconnexion, p. ex. matriciel, de réarrangement, pyramidal, en étoile ou ramifié
G06F 9/52 - Synchronisation de programmesExclusion mutuelle, p. ex. au moyen de sémaphores
Systems and methods to provide vulnerability detection and management in a cloud computing system according to examples. More specifically, a vulnerability detection system receives access logs of a package repository and records information that links packages downloaded from the package repository to the computing assets that downloaded the packages. The vulnerability detection system evaluates an inventory of the package repository against a report of identified vulnerabilities (e.g., Common Vulnerabilities and Exposures (CVEs)). When a vulnerable package is identified in the inventory, the vulnerability detection system removes the vulnerable package from the package repository. The vulnerability detection system further determines affected assets and contact information of corresponding users and provides notifications to the users. In some examples, a mitigation or remediation is determined and provided to the users.
G06F 21/55 - Détection d’intrusion locale ou mise en œuvre de contre-mesures
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
94.
ISOLATED PLATFORM FOR RESPONSIBLE ARTIFICIAL INTELLIGENCE
An isolated platform for responsible AI is described. In various examples, a method is performed by a computing device. Output is generated by executing at least part of an application in an isolated virtual machine with an input-output virtualized accelerator, where the application is an artificial intelligence application. The isolated virtual machine is used to check the output. In response to the check being successful, the output is returned. In response to the check being unsuccessful, an error message is returned.
G06F 21/53 - Contrôle des utilisateurs, des programmes ou des dispositifs de préservation de l’intégrité des plates-formes, p. ex. des processeurs, des micrologiciels ou des systèmes d’exploitation au stade de l’exécution du programme, p. ex. intégrité de la pile, débordement de tampon ou prévention d'effacement involontaire de données par exécution dans un environnement restreint, p. ex. "boîte à sable" ou machine virtuelle sécurisée
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
G06F 21/56 - Détection ou gestion de programmes malveillants, p. ex. dispositions anti-virus
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
95.
INFERENCE ACCELERATION OF A MODEL USING AN IN-PLACE MIXTURE-OF-EXPERTS
A technique transforms an original model into an in-place mixture-of-experts model. To accomplish this, the technique first identifies at least one group of subnetworks that have different task-processing capabilities. The subnetworks are associated with respective groups of parameters. The technique then produces a router-supplemented model that includes a router that is capable of selecting a subset of the subnetworks to be used in processing a particular instance of input information. The router determines when a particular subnetwork should be selected based on a combination of two score parts. A first score part is based on token-related hidden state information, and a second score part is based on an assessed saliency of the particular subnetwork. The technique then fine-tunes the router-supplemented model, to produce the mixture-of-experts model. In inference, the mixture-of-experts model selects among the group of subnetworks using the router in a resource-efficient and low-latency manner.
An example may, at a first device, input an entity embedding for an entity and an item embedding for a plurality of items to a scoring function. The entity embedding and the item embedding are pre-computed using a first language model. At the first device, a retrieval score for the entity and a first item of the plurality of items is computed. The retrieval score is used to identify an item subset of the plurality of items. A ranking prompt is input to a second language model. The second language model and the first language model have a common parameter value. The second language model generates a ranking score for the entity and a second item in response to the ranking prompt. An online system uses the ranking score to include or exclude the second item from a presentation of digital content to the entity via a device.
Artificial intelligence techniques for connection networking are described. A method comprises receiving a request for a set of content items for a content feed, generating a set of metrics for a first set of content items of a first type and a second set of candidate content items of a second type using a machine learning model, selecting a first content item of the first type from the first set of content items and a second content item of the second type from the second set of content items based on the set of metrics using a blending algorithm to form a blended set of content items, allocating the first content item and the second content item from the blended set of content items to multiple slots in the content feed, and presenting the blended set of content items within the content feed on a GUI of a device.
H04N 21/80 - Génération ou traitement de contenu ou de données additionnelles par un créateur de contenu, indépendamment du processus de distributionContenu en soi
A model output evaluator may, for each ground truth text of ground truth texts, link response entities of a language model output text and ground truth entities of the ground truth text to corresponding ontology entities of an ontology that includes the set of ontology entities and edges connecting the ontology entities. The evaluator may, for each ground truth text, determine a ground truth text score based on traversal distances within the ontology between each linked response entity and one or more linked ground truth entities of the ground truth text, wherein the traversal distances are calculated based on a number of edges traversed within the ontology between the linked response entity and the one or more linked ground truth entities. The evaluator may classify the output text of the language model based on at least one of the ground truth text scores satisfying a classification condition.
Disclosed solutions provide for interactive video editing and playback with three dimensional (3D) object manipulation. Examples enable video players to display both an underlying static video along with a 3D object as dynamic content. The video editor presents a settings editor that enables the creator of the video to specify the ability of viewers to interact with the 3D object. The video viewer exposes settings for the dynamic content to enable users to reconfigure the display of the 3D dynamic content, making the video rendering an interactive experience. Use of references (e.g., URLs) within the dynamic content enables videos distributed in the new format updateable and correctable, such that information that is subject to change may be kept current, and informational errors introduced at the time of the video production may be corrected—without requiring creation and distribution of a substitute video file.
G06F 3/04845 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs pour la transformation d’images, p. ex. glissement, rotation, agrandissement ou changement de couleur
G06F 3/04847 - Techniques d’interaction pour la commande des valeurs des paramètres, p. ex. interaction avec des règles ou des cadrans
Storage format solutions are disclosed for interactive video editing and playback that provide backwards compatibility for legacy players. Examples enable newer video players, that are able to extract dynamic content from the new video file format, to display both the underlying static video along with the dynamic content (according to a timeline within metadata stored in the new video file format), whereas legacy players display the static video. Some examples expose settings for the dynamic content to enable newer players to reconfigure the display of the dynamic content, making the video rendering an interactive experience. Use of references (e.g., URLs) within the dynamic content enables videos distributed in the new format updateable and correctable, such that information that is subject to change may be kept current, and informational errors introduced at the time of the video production may be corrected—without requiring creation and distribution of a substitute video file.
