Database systems and methods are provided for managing usage of large language models (LLMs). One method involves determining a numerical representation of a conversational input to a user interface, identifying a semantically similar subset of prior conversational inputs based at least in part on the numerical representation of the conversational input, and determining numerical representations of respective conversational responses generated by a language model responsive to the respective prior conversational input of the semantically similar subset. When the numerical representations of the respective conversational responses satisfy a semantic similarity threshold, the method automatically generates an automated response to the conversational input based at least in part on one or more prior conversational responses and automatically provides the automated response to the user interface responsive to the conversational input.
A user device (e.g., a computing device, a smart device, a mobile device, a laptop, a tablet, a set-top box, a display device, etc.) may generate, based on a request for conversational history information, a timeline for a user interface comprising an interactive timeline indicator indicating a conversation that occurred during a time window. The user device may display, based on an interaction with the interactive timeline indicator, a graphical representation of the time window that indicates a circular order of occurrence for the conversation and other conversations that occurred during the time window. The user device may display the conversation based on an interaction with an interactive element that indicates when within the circular order of occurrence the conversation occurred.
Techniques are disclosed relating to implementing multiple index scans. A computer system may store a database table comprising fields and indexes corresponding to those fields. The computer system may receive a request to access records based on a Boolean expression that affects a selection of records from the database table and that comprises clauses, at least two of which are joined by an AND operation. The computer system may access the requested records. The accessing may include, for a given one of the at least two clauses, performing an index scan on an index that corresponds to the given clause to identify records that satisfy that clause. The computer system may update a hash table based on the identified records and then identify the requested records based on the hash table. The Boolean expression may include clauses joined by an OR operation that are processed using multiple index scans.
A computing device receives a user query regarding a dataset that includes variability. The computer device obtains the dataset that includes one or more data fields and data corresponding to the one or more data fields and determines data uncertainty corresponding to the data. The device generates a multi-modal data representation of the data and the data uncertainty, including rendering a data visualization that represents the data and the data uncertainty; generating, according to statistics of the dataset, text content describing the data and the data uncertainty; translating the text content into a speech synthesis markup language to generate an audio narrative of the text content; and synchronizing the data visualization, the text content, and the audio narrative according to a timestamp of the audio narrative. The computing device causes the multi-modal data representation to be presented at a user interface of an electronic device.
G06F 16/638 - Présentation des résultats des requêtes
G06F 16/68 - Recherche de données caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
G06F 16/683 - Recherche de données caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06F 16/783 - Recherche de données caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
Examples include a system and computer-implemented method to receive a notification from an application programming interface (API) of creation of a just in time (JIT) grant, the JIT grant defining a request for a user to be authorized to access a cluster according to a JIT policy; determine if access to the cluster by the user is authorized according to the JIT policy; grant access to the user to the cluster when access is authorized according to the JIT policy; and send a notification to the API that access by the user to the cluster is granted.
G06F 21/62 - Protection de l’accès à des données via une plate-forme, p. ex. par clés ou règles de contrôle de l’accès
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
G06F 16/13 - Structures d’accès aux fichiers, p. ex. indices distribués
G06F 21/10 - Protection de programmes ou contenus distribués, p. ex. vente ou concession de licence de matériel soumis à droit de reproduction
6.
UPDATING COMMUNICATIONS WITH MACHINE LEARNING AND PLATFORM CONTEXT
Techniques for generating modified messages via a communication platform are discussed herein. For example, one or more machine-learning models associated with a communication platform may be configured to receive, as input and from a user of the communication platform, characteristics of one or more previously modified messages shared to the communication platform. The machine-learning model may generate one or more modified messages containing at least one characteristic of the previously modified messages and allowing the user to share the modified message to the communication platform.
A text interaction record is received at a database system. The text interaction record may include interaction text from one or more messages between a client machine and a service provider. An input database record creation prompt that includes natural language instructions to generate database record field text based on the text interaction record may be determined. The input database record creation prompt may include some or all of the interaction text. The input database record creation prompt may be transmitted to a large language model for completion. A completed database record creation prompt may be received from the large language model. The completed database record creation prompt may include a text element created by the large language model based on the input database record creation prompt. A database record including a database field storing the text element may be generated in the database system.
Embodiments described herein provide a large language model (LLM) based AI agent that adopts Monte-Carlo Tree Search (MCTS) to execute a task. The LLM is prompted with a task description and it responds with its first attempted list of actions. Based on the success or failure of the first attempt, the LLM is prompted with an updated prompt which includes feedback from the first attempt based on a determined reward. The prompt may include a relative “score” for each action taken at each step. A numeric score may be mapped to a set of pre-defined text labels, such as “high” or “low” value putting the score in a form more suited for an LLM prompt. In this way, the LLM is iteratively given prompts which are updated with the scores from each action taken at each previous iterations so that it traverses different paths on the tree in each iteration.
Techniques are disclosed relating to controlling the ordering of a set of processes executing as part of a distributed computing system on different execution nodes of a computer system. A computer system may track status information relating to execution of the set of processes. The computer system receives a call from a process indicating that the process has reached a code breakpoint. The computer system updates the status information to indicate that the process is in a blocked state and is waiting to be scheduled by the control server. The computer system deterministically selects, from a group of the set of processes that are in the blocked state, a particular process executing on a particular execution node to be executed next in a serial execution order. The computer system responds to a call from the particular process by indicating that the particular process is to unblock and resume execution.
A system receives a natural language query directed to a data source, and in response generates insights associated with predefined questions for the data source based on predefined types of analyses. In accordance with a determination that the natural language query semantically matches a respective predefined question, the computer system selects the respective predefined question and associated respective generated insights. In accordance with a determination that the natural language query semantically matches a respective generated insight, selecting the respective generated insight and associated respective predefined question. The system generates instructions for displaying on a display communicatively connected to the system the selected representative predefined question and associated respective generated insights and/or the selected representative generated insight and associated predefined question.
A server system is communicatively connected to a plurality of computing devices and one or more databases. The server system receives one or more queries from a computing device. The one or more queries specify a data source. The server system determines a level of security applicable to a user of the computing device. The server system translates the one or more queries into one or more logical queries according to semantics of the data source, and transmits the logical queries to a query pipeline of the server system. The server system executes the one or more queries against a first database of the one or more databases to retrieve query results from the data source. The server system applies the determined level of security to the query results to obtain one or more data sets, and returns the one or more data sets to the computing device.
Disclosed herein are system, method, and device embodiments for providing a mobile interface powered by artificial intelligence. A user remains on a single user-interface page, conducting interactions with a customer relationship management tool using natural language. The technique leverages a large language model as an intermediary middle-layer, allowing a user to engage core functions. The technique builds an appropriate prompt including the natural language and uses the large language model to build an execution plan that references tools and tasks performable in the customer relationship management tool. By chaining prompts, the technique incorporates prior interactions into subsequent prompts. Mobile-specific information such as location, images, and scanned barcodes may be included in a prompts. Running the large language model on the client device allows the user to perform CRM functions while operating in an offline mode, a mode that secures user data and enhances privacy.
Applied artificial intelligence technology for adaptively classifying sentences based on the concepts they express to improve natural language understanding
Disclosed herein is computer technology that provides adaptive mechanisms for learning concepts that are expressed by natural language sentences, and then applies this learning to appropriately classify new natural language sentences with the relevant concept that they express.
Techniques for determining and/or displaying summarized messages are discussed herein. A user may post a message to a virtual space of a communication platform. That is, a user may send a message to one or more users via a virtual space. In such instances, the communication platform may identify the intended recipients of the message and input the message and the intended recipients into a machine-learning model to receive, as output from the machine-learning model, data indicative of a summarized representation of the message. The summary may include information that is relevant and/or important for the intended recipient user(s). Further, the summary may include a summary of the message, action items included in the message, recommendations, and/or any other information. In some examples, the communication platform may cause the summary to be displayed via a user interface of the recipient user.
A request to execute a database query against a database system may be received. The request may identify a database context of a plurality of database contexts in which to execute the database query. Usage statistic values for the database system may be determined. The usage statistic values may include a contextual usage statistic value that is specific to the database context. A database query execution plan may be determined based at least in part on the contextual usage statistic value. The database query execution plan may include a plurality of operations to perform to execute the database query. A database query result may be determined by performing the plurality of operations within the database system.
Embodiments provide an attention mechanism that computes attention weights for an input sequence by employing a set of multi-head learnable vectors (referred to as “binder vectors”) to attend to the input sequence.
Techniques for generating and displaying a summary of multiple virtual spaces are discussed herein. A communication platform may determine whether to generate a summary of the content posted across a set of virtual spaces. For instance, the communication platform can identify a set of virtual spaces that the user is a member of. For a pre-determined period, the communication platform can determine a first number of content items posted to the set of virtual spaces. The communication platform may also determine, over the same period, a second number indicating the number of the content items the user has yet to view. Based on the second number meeting or exceeding a threshold, the communication platform may generate a summary for the user. Accordingly, the communication platform may generate a summary of the content posted to the set of virtual spaces and display the summary via a user interface of the user.
G06F 3/14 - Sortie numérique vers un dispositif de visualisation
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06F 3/0481 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p. ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comportement ou d’aspect
21.
SYSTEMS AND METHODS FOR EDITING A LARGE LANGUAGE MODEL
Systems and methods for editing a large language model are provided. The large language model generates a sequence of tokens, a first probability of a pre-edit output based on the sequence of tokens, and a second probability of a target output based on the sequence of tokens. A loss function is provided based on the first probability and the second probability. A plurality of gradients of the large language model with respect to the loss function is computed. An edit location of the large language model is determined based on the plurality of gradients. The large language model is edited by editing weights at the edit location of the large language model, such that the updated large language model generates the target output for an input including the sequence of words.
A large language model (LLM) may receive a query via a user interface of a client device. The LLM may generate one or more data queries from the query to query one or more data sets. The LLM may then transmit the one or more data queries to the one or more data sets. The LLM may then receive information associated with the query and a source for the information from the one or more data sets. The source may be indicative of a location within the one or more data sets from where the information was obtained. Following, the LLM may generate a response to the query that includes the information associated with the query and the source for the information and transmit the response to the user interface of the client device for display.
Systems and methods are provided for replacing a first server of a first node a computer network by adding a second server configured with a database system as a second node to the computer network. A snapshot of a first database of database files that is communicatively coupled to the first node may be taken. The snapshot may be applied to the second server by attaching the snapshot of the first database as a second database to the second server and configuring the second server to match the first server. A failover of the computer network to the second node with the second server having the second database attached may be forced. Voting may be switched to the second server of the second node of the computer network and the first node of the computer network may be removed when the forced failover is successful.
G06F 11/20 - Détection ou correction d'erreur dans une donnée par redondance dans le matériel en utilisant un masquage actif du défaut, p. ex. en déconnectant les éléments défaillants ou en insérant des éléments de rechange
G06F 11/14 - Détection ou correction d'erreur dans les données par redondance dans les opérations, p. ex. en utilisant différentes séquences d'opérations aboutissant au même résultat
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet
Methods, apparatuses, and computer-program products are disclosed. The method may include deriving a first intermediate LLM based on a broad spectrum LLM. The first intermediate LLM may be associated with a first level of a hierarchy and deriving the first intermediate LLM may include training the first intermediate LLM on first training data associated with the first level of the hierarchy. The method may include deriving a user-level LLM based on the first intermediate LLM, where the user-level LLM is associated with a user associated with a second level of the hierarchy included in the first level of the hierarchy. Deriving the user-level LLM may include training the user-level LLM on second training data associated with the user, where deriving the user-level LLM may further include inheriting one or more first characteristics from the first intermediate LLM.
A method of data processing is described. The method may include receiving an indication of one or more operational constraints for an unstructured compute process to be executed by the data processing system. The method may further include transmitting a request to decompose the unstructured compute process into a set of structured sub-processes and allocate the set of structured sub-processes to a set of compute resources of the data processing system in accordance with the one or more operational constraints. The method may further include receiving a response that contains information associated with an allocation scheme for the set of structured sub-processes of the unstructured compute process. The method may further include allocating the set of structured sub-processes to the set of compute resources and executing the set of structured sub-processes in accordance with the allocation scheme and the one or more operational constraints.
A computing device receives, via a user interface, a drawing input directed to a dataset of time series data. The computing device converts the drawing input into a set of search terms and executes a query against a search index for the dataset using the set of search terms to retrieve a plurality of labeled trend events. Each of the labeled trend events corresponds to a respective portion of a respective line chart of a set of line charts representing the time series data and has a respective chart identifier. The computing device generates a first subset of line charts according to the retrieved plurality of labeled trend events. The computing device displays, on the user interface, one or more line charts of the first subset of line charts.
A method that includes receiving a first configuration and a second configuration that define a set of rules for matching and merging a set of source data objects that are associated with a tenant and that are received from a plurality of data sources. The method may further include generating a set of merged data objects from the set of source data objects based on an identification of matching values from fields of the set of source data objects and selecting a value for each field of each merged data object having multiple values. The method may further include generating a mapping between primary keys associated with each merged data object and corresponding primary keys of the source data objects. The method may further include storing the merged data objects and the mappings in a first datastore and a second datastore that is different from the first datastore.
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
Embodiments described herein provide a method for mitigating toxic content in text generation by a neural network based framework. The method includes the following operations. A text input of a sequence of tokens is received via a communication interface. A first output probability for a next token generating is generated by a first neural network model that is trained to generate tokens belonging to a prioritized category of vocabulary, in response to the text input. A second output probability of the next token is generated by a second neural network model that is trained to generate tokens belonging to an indiscriminate vocabulary, in response to the text input. The next token for a text output based on a combined output probability computed based on a correction item reflective of the first output probability and the second output probability is generated in response to the text input.
In view of the need to improve text generation technology, embodiments described herein provide a neural network model that generates a text output with constraints to achieve desired output behavior, such as reduced toxicity or hallucinations, and inclusion of certain keywords.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for automated data modification reconstruction and attribution. One of the methods includes determining two or more net modifications between a first backup and a second backup; attributing, for each of the two or more net modifications, the net modification to an entity from a plurality of entities; determining, for an event of interest and using first data that indicates the net modifications, a likelihood that a modification during the event of interest is attributable to a first entity; determining whether the likelihood satisfies a likelihood criterion; and performing, in response to determining that the likelihood satisfies the likelihood criterion, an action for the event of interest using second information for the first entity and the modification.
G06F 11/14 - Détection ou correction d'erreur dans les données par redondance dans les opérations, p. ex. en utilisant différentes séquences d'opérations aboutissant au même résultat
31.
SYSTEMS AND METHODS FOR QUESTION ANSWERING WITH DIVERSE KNOWLEDGE SOURCES
Embodiments described herein provide systems and methods for retrieval augmented generation. A neural network based language model may be provided a question as a user input. Based on the user input, semantically diverse queries may be generated for retrieval from diverse data sources. For example, a structured data source (e.g., database or knowledge base) and unstructured data (e.g., text articles) may be used to retrieve information relevant to the user input. The retrieve information may be ranked so that the most relevant information is used by the language model in generating an answer to the question in the user input. A non-retrieval based answer generated by the language model may be utilized in some embodiments in generating the final answer.
The embodiments are directed to generating source code for a program from a problem description. One or more pre-trained code large language models (LLMs) generate sub-modules from a problem description in a natural language. The sub-modules are filtered based on testing criteria and encoded into sub-module encodings in an embedding space. The sub-module encodings are clustered into multiple clusters. A subset of sub-modules encoding that are close to the centroids of the clusters are selected. The sub-set of sub-modules is decoded into representative sub-modules. The problem description is augmented with the representative sub-modules and fed into one or more pre-trained code LLMs and new sub-modules are generated. The iterations continue until a program is generated from the representative sub-modules.
System and method for fine granularity control of data access and usage for across multi-tenant systems. A user makes a request to access a particular set of data from a particular remote data source for a specific purpose. The system authorizes the user to validate whether the user is qualified to make the request. The data source is checked to see if the particular data has been granted access for that particular purpose. A cloud neutral token is created and converted into a cloud specific token upon reaching the remote data source. The cloud specific token is used to create a temporary IAM role and IAM policy with a predetermined time to live. After the time to live expires, the IAM role and IAM policy are deleted.
System and method for fine granularity control of data access and usage for across multi-tenant systems. A user makes a request to access a particular set of data from a particular remote data source for a specific purpose. The system authorizes the user to validate whether the user is qualified to make the request. The data source is checked to see if the particular data has been granted access for that particular purpose. A cloud neutral token is created and converted into a cloud specific token upon reaching the remote data source. The cloud specific token is used to create a temporary IAM role and IAM policy with a predetermined time to live. After the time to live expires, the IAM role and IAM policy are deleted.
A computer-implemented method for automatically performing routine location-based operations. The method includes receiving, at a server, information related to a current location, a current time, and a current status from a client device having a client application. The method also includes responsive to the information related to the current location, the current time, and the current status, configuring, by the server, the client device to automatically perform an operational routine. The operational routine includes at least one of: (i) performing, by the client device, a local action at the current location, (ii) performing, by the client device, a backend action, (iii) prompting, by the client device, a user to perform, using the client application, a first predefined action, and (iv) preventing, by the client device, the user from performing, using the client application, a second predefined action. The method further includes logging and reporting a completion of the operational routine.
System and method for fine granularity control of data access and usage for across multi-tenant systems. A user makes a request to access a particular set of data from a particular remote data source for a specific purpose. The system authorizes the user to validate whether the user is qualified to make the request. The data source is checked to see if the particular data has been granted access for that particular purpose. A cloud neutral token is created and converted into a cloud specific token upon reaching the remote data source. The cloud specific token is used to create a temporary IAM role and IAM policy with a predetermined time to live. After the time to live expires, the IAM role and IAM policy are deleted.
System and method for fine granularity control of data access and usage for across multi-tenant systems. A user makes a request to access a particular set of data from a particular remote data source for a specific purpose. The system authorizes the user to validate whether the user is qualified to make the request. The data source is checked to see if the particular data has been granted access for that particular purpose. A cloud neutral token is created and converted into a cloud specific token upon reaching the remote data source. The cloud specific token is used to create a temporary IAM role and IAM policy with a predetermined time to live. After the time to live expires, the IAM role and IAM policy are deleted.
A method and system for generating a digital access token consumable by a piggy-back service system has been developed. A request for a digital access token for a client is received. The digital access token is associated with a tenant. At least one standard claim associated with a client attribute of the client is generated. The digital access token includes a header, a payload, and a signature. The payload includes the at least one standard claim associated with the client attribute. The digital access token enables the piggy-back service system to independently authorize access by the client to at least one service at the piggy-back service system based on the at least one standard claim. The digital access token is transmitted to a first device associated with the client.
Disclosed herein are system, method, and computer program product embodiments for providing asynchronous backend processing of complex, time consuming, and/or expensive jobs. A command line interface (CLI) provides a command interface between a user and a backend processing server. The CLI sends a job request to the backend server, which immediately replies to the request with a confirmation message that includes a job identifier. This response is sufficient to allow the CLI to end its wait for the job response. Meanwhile, the backend server carries out the job by parsing the job into component tasks according to a declaration file and assigns those different tasks to different work servers. The backend server functions as a master server, tracking the statuses of the different tasks and assigning out new tasks until the job is complete. An overall job status is provided to the CLI upon request by referencing the job identifier.
Described herein is a system and method for capturing data associated with actions attempted by an automated agent. The system described herein captures data associated with the actions attempted by an automated agent during the messaging session between an automated agent and the user and present a summary of the actions in a messaging platform. In an embodiment, the automated agent uploads data associated with actions attempted during the messaging session to a server. The server captures the data associated with the actions and generates a description of each action that was attempted. The server generates a summary including the description of each action. The summary of the actions are rendered in the messaging platform.
H04M 3/42 - Systèmes fournissant des fonctions ou des services particuliers aux abonnés
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
H04L 51/046 - Interopérabilité avec d'autres applications ou services réseau
H04L 51/234 - Surveillance ou traitement des messages pour le suivi des messages
H04M 3/523 - Dispositions centralisées de réponse aux appels demandant l'intervention d'un opérateur avec répartition ou mise en file d'attente des appels
H04M 3/53 - Dispositions centralisées pour enregistrer des messages entrants
44.
AVOIDING INFORMATION DISCLOSURE ABOUT USER ACTIONS ON CONFIGURATION DATA SUBMISSIONS IN MULTI-TENANT NETWORK MANAGEMENT INTERFACES
Techniques are provided for thwarting attackers in a computing system which uses network management interfaces (NMIs). Before submitting NMI form data, a user computing device queries a server using a user id to obtain a signature which defines a shuffling map and random data such as a random key. The NMI form data is divided into portions and the random data is appended to each portion to provide respective data units, or buckets of data. The data units are then shuffled according to the shuffling map before being transmitted to a server, with the signature or an identifier of the signature included in a header. At the server, the data units are unshuffled to recover the data units, and the random data is removed to recover the form data portions. The instructions of the form data can then be executed.
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 are systems, methods, apparatus and computer program products for facilitating voice-based interaction by a mobile device with an enterprise database. In some implementations, a command and a type of enterprise work record are identified in a first one or more voice signals obtained at the mobile device. One or more fields of the identified record type required by the enterprise database to be populated to carry out the identified command is determined. Field data can be identified in a second one or more voice signals obtained at the mobile device. Structured text data comprising the identified command, the identified record type and the identified field data can be sent to a server.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet
G06F 16/21 - Conception, administration ou maintenance des bases de données
Disclosed are some implementations of systems, apparatus, methods and computer program products for implementing a bi-level subscription process. A server computing device subscribes to a global topic. The server computing device receives a discovery message published to the global topic, where the discovery message specifies an instance name, a data center, and an instance URL. The server computing device subscribes to a regional topic having the instance name. The server computing device receives a metadata message published to the regional topic having the instance name, where the metadata message includes a tenant identifier, source information pertaining to a source from which events are to be obtained, and destination information pertaining to a destination via which the events are to be transmitted. The server computing device stores the source information and destination information in association with the tenant identifier, obtains events from the source and transmits the events to the destination.
A computing device, in response to a user query regarding a dataset that includes variability, obtains a multimodal data representation of the dataset. The device displays an interactive media playback element in a first region of a user interface. In response to receiving a user input via the interactive media playback element, the device causes playback of the multimodal data representation on the user interface, including presenting audio content describing data in the multimodal representation; and while presenting the audio content, simultaneously presenting visual content via a visualization in a second region of the user interface. The visual content is time-synchronized with the audio content. The device detects a user interaction with the interactive media playback element. The device, in response to detecting the user interaction, modifies a playback portion of the visual content and the audio content that is time-synchronized with the visual content.
G06F 16/638 - Présentation des résultats des requêtes
G06F 16/68 - Recherche de données caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
G06F 16/683 - Recherche de données caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06F 16/783 - Recherche de données caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
A machine learning model hosted on a cloud platform may be used to proactively predict if a maintenance procedure should be performed for a vehicle. In some examples, to support the prediction, the machine learning model may be connected to a different cloud platform that includes a customer relationship management (CRM) system and receives data from sensors of the vehicle. As such, the cloud platform with the CRM data may transmit the CRM data and the sensor data of the vehicle to the cloud platform hosting the machine learning model to aid in generating the maintenance procedure predictions. Further, the maintenance procedure predictions may also include the generation of a prediction score associated with a maintenance procedure. In some examples, the prediction score may satisfy a prediction score threshold, thus a notification may be transmitted to a computing device that indicates the maintenance procedure to be performed for the vehicle.
G07C 5/00 - Enregistrement ou indication du fonctionnement de véhicules
B60R 16/023 - Circuits électriques ou circuits de fluides spécialement adaptés aux véhicules et non prévus ailleursAgencement des éléments des circuits électriques ou des circuits de fluides spécialement adapté aux véhicules et non prévu ailleurs électriques pour la transmission de signaux entre des parties ou des sous-systèmes du véhicule
A cloud platform may include a model interface that receives from a client and at an interface for accessing a large language model, a prompt for a response from the large language model, and the client is associated with a set of configuration parameters via a cloud platform that supports the interface. The cloud platform may modify, in accordance with the set of configuration parameters, the prompt that results in a modified prompt and transmit, to the large language model, the modified prompt. The cloud platform may receive the response generated by the large language model and provide the response to a model that determines one or more probabilities that the response contains content from one or more content categories. The cloud platform may transmit the response or the one or more probabilities to the client.
Methods, apparatuses, systems, and computer-program products are disclosed. For example, a system may receive, via a cloud-based platform, first user input including a request for generation of the output data object. The system may generate a prompt based on the first user input and a prompt appendix that defines a response format for a plurality of responses to the prompt that are to be generated by a large language model (LLM). The system may transmit the prompt to the LLM and may receive, from the LLM, the plurality of responses formatted in the response format. The system may generate the output data object that comprises the plurality of responses.
G06F 40/58 - Utilisation de traduction automatisée, p. ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel
Methods, systems, apparatuses, devices, and computer program products are described. A flow generation service may receive a natural language input that indicates instructions for automating a task according to a first process flow. Using a large language model (LLM), the flow generation service may decompose the natural language input into a set of elements (e.g., logical actions) and connectors, where the LLM may be trained on first metadata corresponding to a second process flow that is created manually by a user. In addition, using the LLM, the flow generation service may generate second metadata corresponding to each of the set of elements based on decomposing the natural language input. The flow generation service may sequence and merge the set of elements to generate the first process flow. In some examples, the flow generation service may send, for display to a user interface of a user device, the first process flow.
A method of data processing is described. The method includes converting a plain-text query into a vector-based object by using a text embedding function to process one or more tokens in the plain-text query. The method further includes retrieving a set of passages from a first datastore of the data processing system based on using one or more search indexes stored in a second datastore of the data processing system to compare the vector-based object and the one or more tokens in the plain-text query to vector-based objects and token-based objects associated with the set of passages. The method further includes generating a prompt that includes tokens from the plain-text query, tokens from one or more of the set of passages retrieved from the first datastore, and instructions for creating a response to the plain-text query. The method further includes transmitting the prompt to a large language model (LLM).
Methods, systems, apparatuses, devices, and computer program products are described. A system may support a machine learning model for legal clause extraction. The machine learning model may receive, as an input, at least a portion of a document and may output an indication of one or more legal clauses included in the document. To train the model, the system may receive a document and an indication of ground truths (e.g., legal clauses) for the document. The system may determine one-to-one mappings between the legal clauses indicated by the ground truths and the legal clauses indicated by the output of the machine learning model. The system may perform a longest common substring analysis on the one-to-one mappings to determine an accuracy of the machine learning model and may iteratively update the model based on the analysis.
A plurality of data fields are obtained from a selected data source. A first subset of the plurality of data fields corresponds to a plurality of measures and a second subset of the plurality of data fields corresponds to a plurality of dimensions. A machine learning model is prompted to generate a plurality of suggested metric objects. In response to prompting the machine learning model, a respective metric definition is generated for each measure in the plurality of measures. Each generated metric definition includes a plurality of data fields, including: (i) a name; (ii) a measure; (iii) a time dimension; and (iv) an aggregation type.
Techniques for generating a structured data container configured to receive and present data associated with third-party applications are described herein. The structured data container may be shared and embedded within multiple surfaces associated with the communication platform, such as collaborative documents, virtual spaces, channels, etc. In some examples, data presented in association with the structured data container may be updated based at least in part on a triggering event such that data presented in association with the structured data container is accurate and up to date. Additionally, techniques for generating templates with placeholders that can be utilized in a workflow are described herein.
Techniques for displaying answers to questions via a user interface of a virtual space are discussed herein. A communication platform may receive text from a virtual space. In such examples, a user profile may be inputting the text to a compose pane such that the user profile may post the text to the virtual space. Based on receiving the text, the communication platform may input the text into a machine-learning model trained to output key words and/or phrases included in the text. The communication platform may use the output from the machine-learning model to identify a question-answer pair contained in a canvas of the virtual space. In such instances, the communication platform may display the question-answer pair via the user interface of the user device associated with the user profile. Based on the user profile selecting the question-answer pair, the communication platform may display the canvas including the question-answer pair.
Disclosed herein are system, method, and computer program product embodiments for implementing global rate limiting of an API cluster capable of dynamically implementing updates without a restart of any instantiation within the API cluster. A local service includes an envoy and a customer resource definition. When an update is received, the customer resource definition identifies changes to be made to a global rate limiting service and dynamically injects those changes into the global rate limiting service. The changes can be instance-specific, with multiple different versions stored for the various instantiations within the cluster. The envoy also extracts and converts header information from a received request into one or more descriptor keys. The global rate limiting service determine global rate limiting based on a set of rules applied to the descriptor keys.
A computing services environment may include a database system, a generative language model interface, a communication interface, and a messaging interface. The database system may store database records reflecting interactions between tenants of the computer services environment and individuals interacting with the tenants, and may determine an input description of a communication campaign between a tenant of the plurality of tenants and a corresponding segment of the individuals. The generative language model interface may determine a textual description of one or more elements of the communication campaign by completing a campaign brief generation prompt via a generative language model. The communication interface may transmit to a client machine authenticated to a database system account linked to the tenant an instruction to generate a graphical user interface at the client machine. The messaging interface may transmit messages based on the input description and the refinement.
The described method may include receiving user input indicating a configuration identifying a large language model (LLM) and a subset of documents indicated in the configuration as being available to a tenant. The method may include generating one or more vectorizations of content of the subset of documents. The method may include receiving a request to generate a generative response. The method may include generating the generative artificial intelligence (AI) prompt using the content to ground the generative AI prompt. The subset of documents may be identified based on a comparison between a vectorization of the request and the one or more vectorizations and based at least in part on a determination that a user associated with the tenant is permitted to access the subset of documents. The method may include presenting a response to the generative AI prompt, the response generated by the LLM using the generative AI prompt.
A system obtains a bundle of insights generated based on insight templates and provides the bundle of insights as input to a machine learning model. The system then generates a summary of the bundle of insights using the machine learning model.
G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
Techniques for modifying data within a virtual space are discussed herein. A communication platform may receive a request from a user profile associated with a virtual space. The request may include instruction(s) for the communication platform to perform a modifying operation on data within a virtual space. Based on receiving the request, the communication platform may identify the data on which the operation is to be performed. The communication platform may input the data to one or more machine-learning models trained to output data that is a modified version of the data input to the machine-learning model (e.g., modified data). In such cases, the modified data may be modified consistent with the requested operation. The communication platform may receive the modified data from the machine-learning model and cause the modified data to be displayed via a user interface associated with the user profile.
Techniques for generating synthesized data within a virtual space are discussed herein. A communication platform may receive a request from a user profile of a communication platform. The request may include one or more instructions for the communication platform to send a survey to one or more user profiles to provide data (e.g., feedback) to the virtual space. Based on sending the request for feedback to the user profiles, the communication platform may receive feedback from the user profiles. Upon receiving such feedback, the communication platform may receive a request from a user profile synthesize the data. Upon receiving the request to synthesize the feedback, the communication platform may input the user feedback into a machine-learning model trained to output synthesized data. The communication platform may cause the synthesized data to be displayed via the virtual space.
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
H04L 51/52 - 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 pour la prise en charge des services des réseaux sociaux
63.
GENERATING VIRTUAL SPACE HEADERS UTILIZING MACHINE-LEARNED MODELS
Techniques for updating and/or maintaining a header of a virtual space are described herein. A communication platform may receive a request from a user profile associated with a virtual space. The request may include instructions for the communication platform perform various header updating operations. In some examples, upon receiving the request, the communication platform may identify the data (e.g., virtual space data (e.g., administrative data, user posts and/or responses, files, etc.), user data, etc.) on which to perform the header updating operation. Based on identifying the data, the communication platform may input such data to one or more machine-learning models trained to output the data consistent with the requested operation. In some examples, the communication platform may receive the data from the machine-learning model and cause the data to be displayed to the associating header of the canvas.
H04L 51/48 - Adressage des messages, p. ex. format des adresses ou messages anonymes, alias
H04L 51/52 - 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 pour la prise en charge des services des réseaux sociaux
Techniques for determining and/or utilizing various modular functions are discussed herein. In some examples, a communication platform may receive a request to generate or otherwise determine a workflow. Upon receiving the request, the communication platform may display modular functions which may be used to build the workflow. In some examples, the communication platform may include modular functions specific to list virtual spaces, modular functions specific to canvas virtual spaces, and/or modular functions specific to any other type of virtual space. In some examples, a user may build a workflow using one or more modular functions corresponding to one or more virtual spaces and/or machine-learning modular functions. Upon creating the workflow, a user of the communication platform may select or otherwise request that the communication platform performs the workflow. In such instances, performing the workflow may include executing the modular functions included in the workflow.
Methods, systems, apparatuses, and computer program products are described. A system may receive, via a cloud-based platform, user input comprising a request for generation of the integration flow. The system may generate a query based on the request and a query template including one or more example integration flows and a request to generate a natural language description of the integration flow. The system may transmit the query to the LLM and may receive, from the LLM, a response including the integration flow and the natural language description. The system may extract the integration flow and the natural language description from the response. The system may perform a validation process on the integration flow based at least in part on one or more integration flow validation rules.
Systems, devices, and techniques are disclosed for automation with composable asynchronous tasks. A prompt may be received at a computing device. Using a first large language model (LLM) composable asynchronous tasks may be determined from the prompt. One of the composable asynchronous tasks may use a second LLM. The composable asynchronous tasks may be performed. Performing one of the composable asynchronous tasks may include generating a first output with the second LLM based on the prompt and validating the first output of the LLM. Performing another of the composable asynchronous tasks may include generating a second output using the first output.
First and second dashboards that provide a visual representation of respective intelligence information for a firewall may be generated. An indicator of correspondence between a first data element of the respective intelligence information for the first dashboard and a second data element of the respective intelligence information for the second dashboard may be displayed as an overlay of the first and second dashboards. Additionally, a guidance indicator that indicates an order to access respective values of the first dashboard, the second dashboard, and a third dashboard may be displayed based on an identifier of the first data element mapped to an identifier of the second data element and an identifier of the second data element mapped to an identifier of a third data element for the third dashboard. A summary window that provides a summary of intelligence dashboards of a user interface may be displayed.
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
H04L 41/22 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets comprenant des interfaces utilisateur graphiques spécialement adaptées [GUI]
68.
METHOD, APPARATUS, AND COMPUTER PROGRAM PRODUCT FOR DIGITAL CONTENT AUDITING AND RETENTION IN A GROUP BASED COMMUNICATION REPOSITORY
Embodiments of the present disclosure provide methods, systems, apparatuses, and computer program products for digital content auditing in a group based communication repository, where the group based communication repository comprises a plurality of enterprise-based digital content objects organized among a plurality of group-based communication channels. In one embodiment, a computing entity or apparatus is configured to receive an enterprise audit request, where the enterprise audit request comprises an audit credential and digital content object retrieval parameters. The apparatus is further configured to determine if the audit credential satisfies an enterprise authentication protocol. In circumstances where the audit credential satisfies the enterprise authentication protocol, the apparatus is configured to retrieve and output digital content objects based on the digital content object retrieval parameters, receive a violating digital content object identifier, and replace a violating digital content object with a temporary digital content object based on the violating digital content object identifier.
System and method for transformer-based adversarial active learning system. A machine learning system includes a generator, a transformer encoder, a classifier, and a discriminator all working in combination to generate and select unlabeled data points for labeling. The system utilizes a generative adversarial network paired with an active learning framework to optimize text embedding and feature encoding according to distribution of training data.
A method and system for generating a privilege based segmented instruction prompt has been developed. Trusted instructions defining the trusted instructions as having a first privilege level, program instructions as having a second privilege level, and data instructions as having a third privilege level are received. The program instructions to implement tasks associated with the data instructions are received. The data instructions are received. The generated privilege based segmented instruction prompt includes the trusted instructions, the program instructions, and the data instructions. The privilege based segmented instruction prompt enables a generative LLM to determine whether the privilege based segmented instruction prompt is an instruction injection attack based on whether there is a conflict between the trusted instructions, the program instructions, and the data instructions in violation of the first, second, and third privilege levels.
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for backing up a data object in blocks. One of the methods includes determining, for a data object of a backup process, whether a size of the data object or an estimated backup time of the data object satisfies a criterion that, when satisfied, indicates that at least two blocks of the data object should be separately fetched from the source system by different workers; determining one or more markers for end points of the at least two blocks using data from a prior backup of the data object; and causing, at least partially concurrently for two or more blocks from the at least two blocks, a respective backup worker to fetch the respective block from a source system using at least one marker from the one or more markers that defines an end of the respective block.
G06F 11/14 - Détection ou correction d'erreur dans les données par redondance dans les opérations, p. ex. en utilisant différentes séquences d'opérations aboutissant au même résultat
72.
DYNAMIC ASSET MANAGEMENT SYSTEM AND METHODS FOR GENERATING INTERACTIVE SIMULATIONS REPRESENTING ASSETS BASED ON AUTOMATICALLY GENERATED ASSET RECORDS
Methods and systems are provided for generating an interactive simulation representing one or more assets based on one or more asset records. Based on information from asset records stored at a database system of a cloud-based computing system, an asset simulator module, executed at a cloud-based computing system, can generate one or more simulated representations of the assets. A simulator application executed at the cloud-based computing system can augment the simulated representations of the assets with (at least) additional information from the asset records stored in the database system, and generate a user interface that presents an interactive simulation of the assets. The user interface can include the simulated representations of the assets with the additional information from the asset records stored in the database system.
G06F 30/20 - Optimisation, vérification ou simulation de l’objet conçu
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
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
In some embodiments, a method provides first data for a first reporting object that is determined by performing a first operation using respective first values associated with first records to generate a second value for the first reporting object. The first records are determined from a relationship in a data model that specifies a defined set of fields to the first reporting object for a sustainability metric. The method retrieves second records that are tagged with a custom label not used in the defined set of fields. The second records is a different set of records than the first set of records. The method provides second data for a second reporting object that is determined by performing a second operation, using respective first values associated with the second records that are associated with the custom label, to generate a third value for the second reporting object for the sustainability metric.
A method by one or more electronic devices for creating an inference container on demand. The method includes receiving, over a network, a request to create the inferencing container, wherein the inferencing container is configured to provide inferencing functionality, creating the inferencing container responsive to receiving the request to create the inferencing container, and providing, over the network, a response to the request to create the inferencing container, wherein the response includes a uniform resource locator (URL) to use to submit inferencing requests to the inferencing container, wherein the URL includes a unique identifier (ID) of the inferencing container.
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
G06F 11/36 - Prévention d'erreurs par analyse, par débogage ou par test de logiciel
G06F 16/955 - Recherche dans le Web utilisant des identifiants d’information, p. ex. des localisateurs uniformisés de ressources [uniform resource locators - URL]
A computer system monitors for a change in a first data output generated by execution of a predefined dataflow. In accordance with a determination that the first data output has changed and the first data output meets triggering criteria, the computer system triggers execution of a predefined second dataflow distinct from the first dataflow. The execution of the second dataflow is dependent on the change in the first data output generated by the first dataflow. In accordance with a determination that the first data output has changed and the first data output does not meet triggering criteria, the computer system forgoes triggering execution of the second data flow. In accordance with a determination that the first data output has not changed, the computer system forgoes triggering execution of the second dataflow.
Embodiments described herein provide a diffusion-based framework that is trained on a dataset with limited text labels, to generate a distribution of data samples in the dataset given a specific text description label. Specifically, firstly, unlabeled data is used to train the diffusion model to generate a data distribution of data samples given a specific text description label. Then text-labeled data samples are used to finetune the diffusion model to generate data distribution given a specific text description label, thus enhancing controllability of training.
Techniques are disclosed relating to managing database queries. In some embodiments, a server system receives a query from a computer system and determines a set of aspects for the query, including at least a number of columns specified in the query and a computational cost of executing the query. The system generates a query vector based on the set of aspects determined for the query. The system then compares the query vector with a plurality of clusters, ones of the plurality of clusters comprising two or more previously generates query vectors generated based on aspects of queries previously received by the server system. Based on the comparing, specifically a distance between the query vector and the plurality of clusters of previously generated query vectors, the system classifies the query. Based on a classification of the query determined during the classifying, the system manages the query.
A computing device displays, in a graphical user interface corresponding to a data visualization application, a dashboard having one or more data visualizations related to a data source. The device receives a user interaction with a first data visualization of the dashboard. The device compares the user interaction to a set of stored trigger actions and determines, based on the comparing, that the user interaction corresponds to a predefined trigger to initiate a workflow action to be executed by an external service, distinct from the data visualization application. In accordance with the determination, the device identifies parameters of a predefined action template corresponding to the workflow action. The device extracts a subset of data from the data source, corresponding to the parameters. The device maps the subset of data to the parameters of the action template and initiates execution of the external service.
An automated data extraction pipeline for large language model (LLM) training may include extracting a set of code segments from a set of natural language question-answer (Q&A) combinations that each include a provided input, a provided output, and a provided code segment formatted to transform the provided input into the provided output. The data extraction pipeline may then generate a predicted output from a question portion of a first natural language Q&A combination using a first LLM. A first extracted code segment from the extracted set of code segments may then be executed to generate a first actual output of the first extracted code segment. One or more data samples may then be generated for training a second LLM based on a comparison of the first actual output to the predicted output. The second LLM may then be trained using the one or more data samples.
Disclosed herein are system, method, and computer program product embodiments for implementing variable Sankey charting. The system receives a dataset for charting and various charting parameters. Using the information provided, the system identifies starting and ending categories that will be illustrated in the chart. For each of these categories, the system then calculates a starting and ending height, and corresponding positions in the chart. The heights may be based on a percentage of the total value of data that is included within a particular category. The system then calculates positions of the various bars on the chart based on information provided by the user, or automatically based on the analysis of the data corresponding to the different categories. One or more curves are then calculated for each of the different categories to illustrate the transition of the data from a starting point to an ending point on the chart.
Techniques are disclosed pertaining to layered filtering. A computer system may store records in a hierarchy of levels. The computer system may receive a request to perform a key range search to locate records that fall within a key range and satisfy selection criteria. The computer system may perform the key range search. As part of processing a particular level, the computer system may receive a first set of records associated with another level and select a second set of records from the particular level that fall within the key range and satisfy the selection criteria. The computer system may merge the first and second sets of records into a third set of records, which may include not inserting, into the third set, any record of the first set of records for which there is a newer version in the particular level that does not satisfy the selection criteria.
In accordance with embodiments, there are provided mechanisms and methods for managing business deals. The mechanisms and methods for managing business deals may enable embodiments to provide a dynamic and interactive user-interface including any combination of contacts, accounts, opportunities, allowing users to create tasks, events, leads (e.g., from Data.com), reports, dashboards, instant messenger, external deal spaces, email service (e.g., Outlook), a cloud-based productivity suite for businesses that allows work on any device (e.g., Google apps), mobile access, private messaging, lead management, mass email templates, social media monitoring (e.g., from Radian6), role-based sharing and security, and/or additional storage, for example. In an embodiment, the number of contacts may be unlimited.
Embodiments described herein provide a method for training a recommendation neural network model using multiple data sources. The method may include: receiving, via a data interface, time series data indicating a user-item interaction history; transforming the time series data into a user-item graph; encoding, by a neural network encoder, the user-item graph into user embeddings and item embeddings; generating a plurality of losses according to a plurality of training tasks performed based on the user embeddings and, item embeddings; training the recommendation neural network model by updating the user embeddings and the item embeddings via backpropagation based on a weighted sum of gradients of the plurality of losses; and generating, by a neural network decoder, one or more recommended items for a given user based on the updated user embeddings and the updated item embeddings.
Embodiments described herein provide a method of predicting an action by a plurality of language model augmented agents (LAAs). In at least one embodiment, a controller receives a task instruction to be performed using an environment. The controller receives an observation of a first state from the environment. The controller selects a LAA from the plurality of LAAs based on the task instruction and the observation. The controller obtains an output from the selected LAA generated using an input combining the task instruction, the observation, and an LAA-specific prompt template. The controller determines the action based on the output. The controller causes the action to be performed on the environment thereby causing the first state of the environment to change to a second state.
Embodiments described herein provide for optimizing a language model (LM) agent. In at least one embodiment, and LM agent comprises an “actor” LM and a “retrospective LM which provides reflections on attempts by the actor LM. The reflections are used to update subsequent prompts to the actor LM. Optimizing the LM agent comprises fine-tuning parameters of the retrospective LM while keeping parameters of the actor LM frozen. A gradient may be determined by a change in reward from the environment based on actions taken by the actor LM with and without a reflection of the retrospective LM. Using this gradient, parameters of the retrospective LM may be updated via backpropagation.
A method for configuring the operation of the software of a data as a service (DAAS) system during run time is described. The configuring includes receiving a match query from a customer relationship management system that transmitted the match query responsive to a user using an interface to trigger an update of records in the customer relationship management system that were previously imported from the DAAS system, querying for records in the dataset that match records in the customer relationship management system previously imported from the DAAS system, the querying configured at run time according to metadata that identifies, for records in the dataset, a field to match on and a match threshold, and producing a match query result that includes records in the dataset to be imported to update records that were previously imported from the DAAS system.
Disclosed are systems, apparatus, methods and computer-readable media for updating information stored in a database system over a network. In some implementations, first contact data is retrieved from a first virtual portion of a database system, where the first contact data provides first contact information associated with at least one entity. In some instances, the first contact data is compared with second contact data, where the second contact data provides second contact information associated with the at least one entity. In some instances, at least some of the second contact information is retrieved from a social networking system. In various implementations, at least one difference between the first contact data and the second contact data is identified, where the at least one difference is capable of being presented in a user interface displayed at a computer system. In some instances, a selection identifying contact data to store is received.
Disclosed herein are example embodiments that describe how a narrative generation techniques can be used in connection with data visualization tools to automatically generate narratives that explain the information conveyed by a visualization of a data set. In example embodiments, new data structures and artificial intelligence (AI) logic can be used by narrative generation software to map different types of visualizations to different types of story configurations that will drive how narrative text is generated by the narrative generation software.
Disclosed are examples of systems, apparatus, methods and computer program products providing network security orchestration and management across different clouds. In some implementations, network security information includes a set of security policies indicating permitted communications between or among computing resources. The network security information is converted to a cloud-independent representation. From the cloud-independent representation, policy sets can be generated, where each policy set is specific to a different cloud.
A computer-implemented method for exposing a software component through a predetermined protocol is disclosed. The method may include receiving a software component including at least one of a configuration, an operation, a trigger, and a parameter, and receiving a metamodel describes the configuration, the operation, the trigger, and the parameter. The method may also include generating a microservice and an API specification entirely based on the metamodel without additional coding. The computer-implemented method may further include deploying the microservice such that the microservice accepts incoming requests described by the API specification, and receiving a formatted response from the deployed microservice. The method may also include receiving a new configuration of the software component created on the deployed microservice.
Implementation(s) for multi-factor network segmentation are described. A plurality of packets at a higher layer of a network stack is processed, where at least one packet of the plurality of packets was previously determined, as part of processing the at least one packet at lower layers of the network stack, to be authorized to be processed by the higher layer. Specifically, responsive to successful authentication of a cryptographic certificate received during the handshake process, a second service is identified from the cryptographic certificate. It is determined, based on a security policy, that the second service is authorized to access the first service. Responsive to the determination, a configuration is caused such that packets sent using the source address are now authorized to be processed by the higher layer.
The present disclosure is related to automatically, based on contextual information and without needing explicit input from a user, modifying one or more settings associated with presenting a notification. In examples, settings may include automatically suspending notification presentation or automatically overriding a notification setting that suspends notification presentation. In addition, contextual information may include, among other things, information related to a computing device (e.g., device location or network signal strength), a rate of user interaction or engagement with an application (e.g., rate of information sharing, user reactions, etc.), and/or a calendar or schedule of a user. In examples, the contextual information may be analyzed (e.g., based on comparison to a threshold) to determine whether a condition is met, and based on the analysis, the one or more settings may be modified.
H04L 51/222 - Surveillance ou traitement des messages en utilisant des informations de localisation géographique, p. ex. des messages transmis ou reçus à proximité d'un certain lieu ou d'une certaine zone
H04L 51/224 - Surveillance ou traitement des messages en fournissant une notification sur les messages entrants, p. ex. des poussées de notifications des messages reçus
H04M 1/72451 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles avec des moyens permettant d’adapter la fonctionnalité du dispositif dans des circonstances spécifiques basés sur des horaires, p. ex. utilisant des applications de calendrier
H04M 1/72454 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles avec des moyens permettant d’adapter la fonctionnalité du dispositif dans des circonstances spécifiques en tenant compte des contraintes imposées par le contexte ou par l’environnement
H04M 1/72457 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles avec des moyens permettant d’adapter la fonctionnalité du dispositif dans des circonstances spécifiques en s’appuyant sur la localisation géographique
H04M 1/72463 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles avec des moyens permettant d’adapter la fonctionnalité du dispositif dans des circonstances spécifiques pour limiter la fonctionnalité du dispositif
H04W 4/02 - Services utilisant des informations de localisation
H04W 68/02 - Dispositions pour augmenter l'efficacité du canal d'avertissement ou de messagerie
H04W 72/54 - Critères d’affectation ou de planification des ressources sans fil sur la base de critères de qualité
H04W 72/563 - Critères d’affectation ou de planification des ressources sans fil sur la base de critères de priorité des ressources sans fil
Techniques are disclosed relating to a database system. The database system includes multiple coordinator nodes storing replicas of a partition. Each partition describes the state of locks and transactions for keys covered by that partition of keys. Each partition is, in turn, replicated. The multiple coordinator nodes receive, from multiple worker nodes, requests to grant a lock for a key to permit a worker node to write a record for the key as part of executing a transaction. A given coordinator node of the multiple coordinator nodes sends an approval response for the lock to at most one of the worker nodes. A single worker node acquires the lock in response to receiving approval responses from a majority of the multiple coordinator nodes, and none of the multiple worker nodes acquire the lock in response to none of them receiving approval responses from a majority of the multiple coordinator nodes.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuéesArchitectures de systèmes de bases de données distribuées à cet effet
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
Applied Artificial Intelligence Technology for Performing Natural Language Generation (NLG) Using Composable Communication Goals and Ontologies to Generate Narrative Stories
Artificial intelligence (AI) technology can be used process natural language statements to facilitate the automated generation of narratives about data sets that achieve a desired communication goal without any need for a user to directly author computer code. This AI technology permits NLG systems to determine the appropriate content for inclusion in the narrative in a manner that will satisfy the desired communication goal.
Techniques described herein support component maintenance that accounts for a similarity between characteristics of different components. To identify components with similar characteristics, one or more techniques described herein support generation of a data structure (e.g., a tree) that represents a component, where each characteristic may be represented in a different leaf node. The system may generate a similarity score, or value (e.g., as a percentage), between multiple components based on comparing individual nodes of a tree representing each component, respectively. If the similarity score satisfies a threshold, then the system may display, at the UI, a message indicating to a user to preferentially implement one component over another.
A method generates analytic asset recommendations using graph neural networks. The method obtains a data graph that includes a plurality of nodes. Each node stores metadata for a respective analytic asset of a plurality of analytic assets. The data graph encodes relationships between the plurality of analytic assets. The method extracts a set of features for each node of the data graph. Each node has the same features as other nodes. The method derives corresponding node embeddings for two nodes of the data graph using a two-layer graph neural network based on the data graph and the set of features. The method predicts a link between the two nodes of the data graph based on the corresponding node embeddings. The method also generates a recommendation for an analytic asset when the probability for the link is above a predetermined threshold.
A method generates data visualizations for interactive recommender systems for analytic assets. The method obtains recommendations to destination nodes for a source node of an input graph, which includes nodes including the source node and a destination node. Each node stores metadata for a respective analytic asset. The input graph encodes asset lineage that captures relationships between the analytic assets. The method also generates a data visualization for the recommendations. The data visualization includes (i) a summary of the recommendations, (ii) a comparison of the destination nodes, and (iii) a set of factors that contributed to one or more recommendations. The method also includes displaying the data visualization using a graphical user interface. The graphical user interface includes a data region that includes the summary, a recommendation overview region that includes the comparison, and a recommendation detail region that includes the set of factors.
Disclosed herein are system, method, and computer program product embodiments for automatic non-code test suite generation of an application programming language (API) specification. An embodiment operates by receiving a specification of an API, wherein the API comprises a plurality of endpoints. The embodiment generates, using a parser, an abstraction model corresponding to the specification of the API, wherein the abstraction model comprises a plurality of entities corresponding to the plurality of endpoints. The embodiment identifies, based on the abstraction model, an operation that is applicable to an entity of the plurality of entities. The embodiment then generates a functional test based on a use case corresponding to the entity and the operation.
