A method and related system may establish communication guardrails during multi-turn conversations. The method may include constructing a graph indicating a set of intents and a set of intent states by inputting a set of transcripts to a language model and determining a set of metrics of events by traversing a path through nodes of the graph based on intent states indicated by the graph.
In some implementations, a device may obtain a request including a secondary credential. The device may identify a primary credential associated with the secondary credential, the secondary credential being one of a set of secondary credentials associated with the primary credential. The device may retrieve health information associated with a plurality of cells indicated in a cell registry. The device may select a target cell for processing the request, the target cell being one of the plurality of cells and being selected based on the primary credential, the cell registry, and the health information. The device may provide the request to the target cell.
In some implementations, a device may retrieve a configuration associated with processing a request. The device may perform, based on service call information included in the configuration, one or more service calls to retrieve input data associated with processing the request. The device may perform, based on the input data and field mapping information included in the configuration, one or more field mappings to create output data. The device may evaluate the output data based on one or more rule sets indicated in the configuration to determine a decision associated with the request. The device may provide a response based on the decision.
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
4.
SYSTEMS AND METHODS FOR VERIFYING AND MANAGING MULTIPLE CARD VERSIONS
The disclosed systems and methods are directed to secure retrieval of proprietary data associated with converted applet file stored on a contactless card. The data is stored in a hidden file not referenced in the capability container file. The file may then be directly read by a NFC command specifying the name of the file, for example, as a command parameter. One aspect of the disclosed systems and methods involves an access control bit associated with a specific file. The access setting can be statically set at personalization time. Another aspect involves resetting the flag bit following every read operation directed at the proprietary file. The access state can then be reset to an active state by an explicit write instruction generated as an NDEF encapsulated write command. Another aspect may include a command sequence for setting the access flag and returning the version number during a single read operation.
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
H04L 9/14 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité utilisant plusieurs clés ou algorithmes
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
H04W 12/47 - Dispositions de sécurité utilisant des modules d’identité utilisant la communication en champ proche [NFC] ou des modules d’identification par radiofréquence [RFID]
5.
LOGICAL REPLICATION OF DATA ASSOCIATED WITH A CREDENTIAL IN A CELL-BASED AUTHORIZATION REQUEST PROCESSING SYSTEM
In some implementations, a device may obtain a request including transaction information, the request being associated with one or more credentials. The device may add, based on the transaction information, a record to an authorization data structure associated with the one or more credentials. The device may adjust, based on the transaction information, one or more sub-aggregate data structures associated with the one or more credentials. The device may provide change information to enable logical replication of the addition to the data structure and the adjustment of the one or more sub-aggregate data structures on one or more cells.
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
6.
SYSTEMS AND METHODS FOR ACHIEVING NEAR ZERO IMPACT DURING NODE FAILURE IN A CLUSTER SYSTEM
Disclosed are systems and methods for node management performed by a client driver of a client device comprising receiving cluster topology data from the cluster system; transmitting, a data request to each of a plurality of nodes in a cluster system; receiving a reply from each of the nodes that are responsive; assigning, by the client driver, based on the received replies, a responsive status for each of the nodes that are responsive or a non-responsive status for each of nodes that are non-responsive; updating a listing of management data, wherein the management data includes: an identification of each of the plurality of nodes, and a current status of each of the plurality of nodes; and routing, by the client driver, a client request to one of the plurality of nodes that are responsive based on the cluster topology data.
Disclosed embodiments may include a system for providing customized recommendations via data analysis. The system may receive transaction data associated with a user. The system may cause a user device associated with the user to display a notification prompting the user to provide image data. The system may receive the image data. The system may identify, from the image data via computer vision, first object(s). The system may generate, via an MLM, first item recommendation(s) based on the first object(s). The system may cause the user device to display, via the GUI, the first item recommendation(s). The system may receive type(s) of travel data. The system may generate, via the MLM, second item recommendation(s) based on the type(s) of travel data. The system may transmit, to a merchant system, a request to purchase at least one item at a predefined location based on the second item recommendation(s).
In some implementations, a system may receive, via a data element portal, a data element for analysis. The system may generate, based on the first set of parameters, an enhanced data element. The system may analyze the enhanced data element to identify one or more approval processes. The system may obtain information identifying a set of outputs responsive to the request for the approval associated with the data element. The system may transmit the information identifying the set of outputs to fulfill the request for the approval associated with the data element.
In some implementations, a system may receive user context information indicating that a user is associated with a first stage of a multi-stage user procedure. The system may identify, based on the user context information, a first graphical element state associated with a first graphical element complexity level. The system may identify a first graphical element associated with the first graphical element state and transmit an indication to display the first graphical element. The system may receive user interaction information indicating that the user is associated with a second stage of the multi-stage user procedure. The system may identify, based on the user interaction information, a second graphical element state associated with a second graphical element complexity level. The system may identify a second graphical element that is associated with the second graphical element state and transmit an indication to display the second graphical element.
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
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 13/80 - Animation bidimensionnelle [2D], p. ex. utilisant des motifs graphiques programmables
In some implementations, a display layer configuration system may display, in a preview pane, a first display layer including a first display element indicating first content information and a second display element indicating second content information. The display layer configuration system may display a display layers palette including: a first graphical representation of the first display layer including a first graphical representation of the first display element indicating the first content information and a first graphical representation of the second display element indicating the second content information, and a first graphical representation of a second display layer including a second graphical representation of the first display element indicating third content information different from the first content information and a second graphical representation of the second display element indicating fourth content information different from the second content information.
The systems and methods disclosed herein allow the cloud services to better apportion cloud resources between multiple cloud services recipients. The systems and methods accomplish this by both accessing the relevant data (e.g., despite existing security and/or access restrictions) as well as providing it in a short-form communication (e.g., in a format that is usable). The systems and methods accomplish this by first determining a dataset for generating a response to a query and then determining a portion of the dataset that is available to the user.
In some embodiments, a method includes segmenting updates associated with a record into a set of update subsets and generating first and second vectors based on first and second update subsets using a first neural network. The first update subset is associated with a first session and a timestamp, and the second update subset is associated with a second session. The method includes determining a first output using a second neural network based on the first and second vectors and a time difference between the first and second sessions. The method includes selecting a segment of a periodic time interval based on the timestamp, determining a second output using a third neural network based on a ratio based on the segment and the periodic time interval, and generating a characterizing vector using a fourth neural network based on the first and second outputs.
G06N 3/063 - Réalisation physique, c.-à-d. mise en œuvre matérielle de réseaux neuronaux, de neurones ou de parties de neurone utilisant des moyens électroniques
13.
Template-based automated data pipeline execution with source ingestion triggers and cluster provisioning
In some implementations, a data pipeline execution system may obtain, in accordance with a data source ingestion configuration, an indication of an event-based data source ingestion trigger associated with the first data source and an indication of a time-based data source ingestion trigger associated with the second data source. The data pipeline execution system may provision a cluster in response to one or more of the event-based data source ingestion trigger or the time-based data source ingestion trigger. The data pipeline execution system may execute, using the cluster, a data pipeline associated with a data pipeline template in accordance with a data pipeline file, including: read data from the first data source or the second data source, perform a transformation of the data, and write the transformed data to a target data storage location. The data pipeline execution system may generate a data pipeline execution completion notification.
Aspects disclosed provide system and method validating data integrity. Whether a data object represents a synthetic user may be determined based on a value of a most significant bit in a predetermined field of a universally unique identifier (UUID) associated with the data object. The data object may comprise encoded attributes of the synthetic user which may be used to validate accuracy of data transformations performed by a sub-system on the encoded attributes. Validating the accuracy of the data transformations and routing of the data object may be done in real-time and during a production session of the sub-system. Results of the data transformations may be received and compared with expected results based on a pairwise comparison of the results with the expected results in the patterns of attributes of the data object indicative of how the data object is to be tested may be detected at a desired destination.
Aspects disclosed provide system and method validating data integrity. Whether a data object represents a synthetic user may be determined based on a value of a most significant bit in a predetermined field of a universally unique identifier (UUID) associated with the data object. The data object may comprise encoded attributes of the synthetic user which may be used to validate accuracy of data transformations performed by a sub-system on the encoded attributes. Validating the accuracy of the data transformations and routing of the data object may be done in real-time and during a production session of the sub-system. Results of the data transformations may be received and compared with expected results based on a pairwise comparison of the results with the expected results in the patterns of attributes of the data object indicative of how the data object is to be tested may be detected at a desired destination.
In some implementations, a system may monitor one or more user interactions with a web page presented on a user device, wherein the web page is associated with a call-to-action (CTA) element having a set of engagement properties. The system may generate behavior information based on the one or more user interactions. The system may obtain decision information defining one or more conditions for updating one or more engagement properties of the set of engagement properties, wherein each condition of the one or more conditions is associated with a user intent level. The system may update one or more engagement properties of the set of engagement properties based on the behavior information satisfying one or more conditions indicated by the decision information. The system may provide an updated CTA element to the user device, wherein the updated CTA element is associated with the one or more updated engagement properties.
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
17.
SYSTEMS AND METHODS FOR GENERATING RELIABLE LANGUAGE MODEL OUTPUTS USING UNCERTAINTY QUANTIFICATION
A device may obtain a plurality of responses of a language model to a prompt, where each of the plurality of responses comprises a binary response portion and a reasoning portion. The device may determine an uncertainty quantification for the language model based on the plurality of responses. The uncertainty quantification may be based on variations across reasoning portions of the plurality of responses, or variations in binary option confidences in binary response portions of the plurality of responses. The device may output the binary response portion of one of the plurality of responses based on the uncertainty quantification indicating certainty in the plurality of responses, or an uncertainty indication based on the uncertainty quantification indicating uncertainty in the plurality of responses.
G06F 16/338 - Présentation des résultats des requêtes
G06F 18/2415 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p. ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
Systems and methods use requests with triangulating time stamps. In particular, each request includes an effective date time stamp, an active date time stamp, and a deployment date time stamp. The use of the triangulating time stamps allows the system to use synchronous exeution, but also allow for requests to be reversed and/or back dated. For example, the effective date time stamp is used to indicate when a request is first processed. The active date time stamp indicates when the request should be used. The deployment date time stamp indicates when the request was first deployed (e.g., received), which may be used for audit or reference purposes.
Methods and systems are described herein for facilitating queryless information retrieval during a live conversation via a bifurcated model. For example, the system may receive, a first set of decrypted utterances spoken during the live conversation. The system may provide the first set of decrypted utterances as input to a bifurcated model comprising (i) a first bifurcated model portion associated with a dynamic utterance embedding space and (ii) a second bifurcated model portion associated with a static computer file embedding space. The system may then generate, via the bifurcated model, a first intermediate output from the first bifurcated model portion indicating a first utterance embedding and a second intermediate output from the second bifurcated model portion indicating a first computer file embedding. The system may then generate during the live conversation, a graphical representation of a computer file associated with the first computer file embedding.
G10L 25/54 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation pour la recherche
G06F 16/14 - Détails de la recherche de fichiers basée sur les métadonnées des fichiers
G10L 17/02 - Opérations de prétraitement, p. ex. sélection de segmentReprésentation ou modélisation de motifs, p. ex. fondée sur l’analyse linéaire discriminante [LDA] ou les composantes principalesSélection ou extraction des caractéristiques
G10L 17/04 - Entraînement, enrôlement ou construction de modèle
Systems and methods are described herein for generating a human-interpretable context report associated with anomalous computer network event logs of users. For example, the system receives a set of data packets comprising a set of computer network events associated with a first user. The system provides the set of computer network events as input to an artificial intelligence (AI) model to generate, (i) a first output identifying a subset of computer network events contributing to an identified anomaly associated with the set of computer network events and (ii) a second output indicating a context associated with the subset of computer network events, the first output being generated via a first portion of the AI model, the second output being generated via a second portion of the AI model. The system generates, for display on a user interface, a graphical representation of a context report associated with the identified anomaly.
SYSTEMS AND METHODS FOR GENERATING PLAIN TEXT DESCRIPTIONS BY TRANSLATING PREDICTIONS BASED ON SPEECH SIGNAL OR DATA PROCESSING RESULTS FROM SIMILAR USERS
Systems and methods for generating plain text descriptions by translating predictions based on speech signal or data processing from similar users. The system may retrieve a first embedding set, wherein the first embedding set comprises a plurality of user embeddings, wherein each user embedding of the plurality of user embeddings, correspond to respective user query of a plurality of user queries. The system may process the plurality of user embeddings with a first classifier model to generate a first subset of user embeddings corresponding to a first prediction of a prediction pairing and a second subset of user embeddings corresponding to a second prediction of the prediction pairing. The system may determine, using the first classifier model, that a first user query corresponds to the first prediction. The system may determine a first response to the first user query based on the prediction pairing.
G10L 15/22 - Procédures utilisées pendant le processus de reconnaissance de la parole, p. ex. dialogue homme-machine
23.
SYSTEMS AND METHODS FOR GENERATING EXTERNAL OPENSOURCE DATASETS USING DATASET TUNING OF ENCRYPTED INTERNAL DATASETS WHILE MAINTAINING SECURITY OF THE ENCRYPTED INTERNAL DATASETS
Systems and methods for dataset tuning that provide a method to approximate internal data using external, potentially opensource, datasets. As one example, systems and methods for generating public datasets with similar statistics to internal datasets, providing an opensource replacement to internal data. In particular, the systems and methods provide the internal replacement data by computing corpus n-gram statistics of the internal. The system may then determine the n-gram statistics of the tokenized data as opposed to the raw data.
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
24.
SYSTEMS AND METHODS FOR IMPROVING EVALUATION IN SUPERVISED FINE-TUNING PROCESSES
Methods and systems are described herein for evaluating SFT processes for large language models (LLMs). For example, a request to evaluate a performance of a trained large language model to execute a computing task may be received. The request may indicate an evaluation data set including a plurality of evaluation samples. A prompt template may be retrieved based on the request. For each evaluation sample of the plurality of evaluation samples, the system may generate a sample prompt by incorporating a query and context from an evaluation sample into the prompt template. The sample prompt may be input into the large language model to obtain a sample evaluation result. An evaluation metric score may be determined based on the sample evaluation result and a reference evaluation result associated with the evaluation sample. A model performance score may be generated based on the evaluation score for each evaluation sample.
Systems and methods for improvements to network routing to different network components. As one example, systems and methods for a bifurcated routing system using datastream specific trainable embedding (e.g., an embedding table) to optimize resource allocation and model efficiency. For example, the system divides the network model into specialized components, or “experts,” each trained to handle specific types of inputs or tasks. A gating mechanism (e.g., a first router of the bifurcated routing system) dynamically determines which subset of components to activate for a given input, resulting in sparse activation.
Systems and methods for determining security vulnerabilities that represent edge cases within a distributed computing system are disclosed. For example, a system can be configured to obtain requirement data associated with requirement updates in response to one or more events, the one or more events involving execution of first network operations in a first network environment. The system can determine first deficiencies associated with the execution of the first network operations based on the requirement updates and the first network operations. In response to comparing the first deficiencies to the execution of second network operations in a second network environment, the system can determine second deficiencies associated with the second network environment. The system can provide an indication of the second deficiencies to cause one or more updates to be performed, the updates indicating one or more reconfigurations for the second network environment.
Described herein are systems, methods, and programming for determining a context source of a large language model. For example, the techniques leverage a token accessible large language model to extract token activations associated with answers generated by the model in response to prompts. The token activations can be passed to a trained classification model that learns patterns of token activations associated with context-based model outputs. The trained classification model can assign a label to the model-produced answer indicating whether the answer was generated by the large language model using the input context included by the prompt or model memory of the large language model.
Disclosed are a system and methods enabling updating of a card number of a payment card and thereby reissuing the payment card utilizing a reissue application associated with the payment card. The reissue application, when executed communicates with a service provider that manages the payment card. The payment card includes processing circuity and a rewriteable visual display. Using cryptographic techniques, the reissue application and payment card are authenticated to the service provider. Upon verification, an updated card number is obtained and provided via near-field communication to the payment card. In response to the update payment card number received from the reissue application, the rewriteable visual display on the payment card is updated with the updated card number. Other information may also be presented on the rewriteable visual display of the payment card based on user preferences.
G06Q 20/34 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des cartes, p. ex. cartes à puces ou cartes magnétiques
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
29.
SCHEMA MANAGEMENT FOR A FEDERATED GRAPH WITH AT LEAST THREE LAYERS
Some implementations described herein relate to a system for schema management of a federated graph. The system may be configured to obtain unit information. The system may be configured to generate, based on the unit information, a unit schema. The system may be configured to identify, based on generating the unit schema, a particular subgraph schema, of a plurality of subgraph schemas, that is to include the unit schema. The system may be configured to cause the particular subgraph schema to include the unit schema. The system may be configured to cause, based on causing the particular subgraph schema to include the unit schema, a supergraph schema to include the particular subgraph schema.
Systems and methods are disclosed herein for building contextual transcripts. A computing system may receive a textual transcript of a meeting that contains a variety of statements made by various attendees of the meeting, select the first statement made during the meeting, and determine which meeting attendee made the statement. A machine learning model corresponding to the particular attendee that has been trained using previously received statements by the particular attendee may be used on the utterance to determine the tone of the utterance. That tone may be recorded within the transcript and this process may be repeated for each utterance to build a contextual transcript.
Methods and systems are described herein for improving information retrieval during a live conversation via context tokens and real-time natural language utterances. For example, the system may receive a first set of decrypted utterances spoken during a live conversation. The system may determine a token comprising token data associated with a second set of decrypted utterances spoken during the live conversation prior to the first set of decrypted utterances. The system may generate, via a secured model, a first query and a subset of utterances corresponding to the first set of utterances and the token data. The system may perform a first search based on the first query and a second search based on the subset of utterances to retrieve a set of relevant computer files. The system may generate, during the live conversation, a graphical representation of the set of relevant computer files.
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
32.
SYSTEMS AND METHODS FOR PROCESSING SERIAL COMMUNICATIONS USING BIFURCATED APPLICATION PROGRAMMING SUBROUTINES
The system may receive, via a bifurcated serial communication processing API, a first serial communication for a first account, wherein the bifurcated serial communication processing API adds a first subroutine identifier to the first serial communication based on a first function for completing. The system may process the first serial communication using a first API subroutine based on the first subroutine identifier, wherein the first API subroutine comprises an account locking function. The system may determine a status of the first account based on the account locking function.
Methods and systems are described herein for optimizing the fine-tuning and evaluation thereof for large language models (LLMs). For example, a request to (i) execute a supervised fine tuning (SFT) process to train a large language model and (ii) evaluate a performance of the large language model after execution can be received. The request can indicate (a) a training data set to be used for the SFT process, (b) a test data set to be used to evaluate the performance of the large language model, and (c) a set of evaluation metrics to evaluate the performance of the large language model after the SFT process has been executed. A trained model can be obtained based on executing the SFT process using the training data set and a prompt template. A set of evaluation metric scores can be obtained based on evaluating the trained model using the test data set.
Methods and systems are described herein for managing prompts used for executing and evaluating supervised fine tuning (SFT) processes. For example, a model identifier and a first description of a first computing task may be extracted from a first request to execute an SFT process to perform the first computing task. A candidate prompt template and associated metadata including a candidate prompt name may be generated, and the candidate prompt template may be uploaded to a prompt database with the associated metadata. A second request to execute an SFT process indicating the candidate prompt name may be received. The candidate prompt template may be used to execute the SFT process on a large language model associated with the second request, resulting in a trained first large language model. The trained first large language model may be evaluated using a test data set and the candidate prompt template.
A device may receive an input indicating a prompt for a language model. The device may determine, using the language model, an output responsive to the prompt. The device may determine a degree by which the output relies on memory of the language model. The device may output, responsive to the degree by which the output relies on memory indicating an over-reliance on memory, a modified output that differs from the output.
Aspects of the disclosure may comprise a method for encrypting parameters of nodes (e.g. neurons) within an artificial neural network such that private and/or confidential information may be used for training and/or embedded within the artificial neural network with minimal risk of exposure. Aspects of the disclosure further comprise encrypting the node parameters such that the artificial neural network may be trained on the encrypted node parameters and, at runtime, decrypted such that output values reflect the unencrypted node parameters. Aspects of the disclosure may further comprise encrypting the artificial neural network after training has already taken place with unencrypted values to protect private and/or confidential data that may have already been used to train the artificial neural network.
H04L 9/30 - Clé publique, c.-à-d. l'algorithme de chiffrement étant impossible à inverser par ordinateur et les clés de chiffrement des utilisateurs n'exigeant pas le secret
37.
REVERSIBLE SECRET KEY ENCRYPTION OF NODE PARAMETERS IN NEURAL NETWORKS
Aspects of the disclosure may comprise a method for encrypting parameters of nodes (e.g. neurons) within an artificial neural network such that private and/or confidential information may be used for training and/or embedded within the artificial neural network with minimal risk of exposure. Aspects of the disclosure further comprise encrypting the node parameters such that the artificial neural network may be trained on the encrypted node parameters and, at runtime, decrypted such that output values reflect the unencrypted node parameters. Aspects of the disclosure may further comprise encrypting the artificial neural network after training has already taken place with unencrypted values to protect private and/or confidential data that may have already been used to train the artificial neural network.
In some implementations, a device may detect an attempt by a code deployment pipeline to use a deployment agent to deploy code to a computing environment. The code may be from a code repository that relates to a software application. The device may retrieve a list of change requests associated with the software application. The device may determine whether a change request, from the list of change requests, validates the attempt. The device may selectively terminate or cause deployment of the code to the computing environment by the deployment agent based on whether the change request validates the attempt.
In some implementations, an adaptive cursor or tooltip management system may receive first user interaction information associated with a first user device indicating a user interaction with a first web page. The adaptive cursor or tooltip management system may receive first user context information that indicates user data associated with the first user device. The adaptive cursor or tooltip management system may identify, based on the first user interaction information and the first user context information, one or more of first cursor display information or first tooltip display information. The adaptive cursor or tooltip management system may transmit the one or more of the first cursor display information or the first tooltip display information. The adaptive cursor or tooltip management system may receive user feedback information associated with the one or more of the first cursor display information or the first tooltip display information.
In some implementations, a system may receive coarse location information based on a user device being in an activation zone. The system may receive fine location information based on a determination of at least one of a proximity event or an interaction event occurring in a proximity zone within the activation zone. The system may obtain decision information indicating one or more conditions for selecting one or more content elements according to at least one of the coarse location information or the fine location information, wherein the decision information is associated with one or more respective time stamps for the coarse location information and the fine location information. The system may select one or more content elements according to the decision information. The system may provide the selected one or more content elements to the user device.
In some implementations, a data analysis system may detect a performance gap associated with the data analysis model. The data analysis system may generate a model adjustment factor for the data analysis model based on the performance gap associated with the data analysis model, wherein the model adjustment factor is a scalar value applicable to one or more predictions associated with the data analysis model. The data analysis system may generate a data file for the data analysis model based on the model adjustment factor. The data analysis system may execute the data analysis model with the data file to generate a third dataset of predictions associated with the data analysis model. The data analysis system may output information associated with the third set of predictions.
Systems and methods for reducing data storage requirements across computer networks. For example, the system may receive, from a first vector database, a first vector chunk. The system may store, in a first vector cache, the first vector chunk. The system may receive a first processing request. While the first vector chunk is in the first vector cache, the system may retrieve a first usage trigger from the first vector chunk, wherein the first usage trigger indicates a first condition for using the first vector chunk. The system may determine, by a vector cache manager, whether to subject the first vector chunk to the first processing request based on the first usage trigger. The system may execute the first processing request using the first vector chunk based on determining to subject the first vector chunk to the first processing request.
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
43.
SOURCE DETECTION FOR TARGET LLMS USING TOKEN ACCESSIBLE LLMS FOR TOKEN ACTIVATIONS
Described herein are systems, methods, and programming for determining a context source of a target large language model by leveraging token activations of a large language model having an accessible final layer for token activation extraction. For example, the techniques leverage a token accessible large language model to extract token activations associated with query-context-answer triplets, where the answer may be generated by the target large language model. The token activations can be passed to a trained classification model that learns patterns of token activations associated with context-based model outputs. The trained classification model can assign a label to the model-produced answer indicating whether the answer was generated by the target large language model using the input context included by the prompt or model memory of the target large language model.
Described herein are systems, methods, and programming for generating training data to train a classification model to determine, using token activations associated with an answer generated by a large language model, whether the large language model use provided context or model memory to generate the answer. For example, the techniques can include creating, from query-context-answer (QCA) triplets, controlled conflicts by perturbing the answer to test whether the large language model uses the context to generate the answer, as opposed to model memory. These controlled conflicts can be labeled to indicate the ones that the model recognized as being based on context and those based on memory. Token activations associated with those answers can be generated and used, in conjunction with the labels, to train the classification 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
45.
AUTHENTICATING A USER IN LIVENESS TESTING USING A TRUSTED CAMERA
A method for performing enhanced user authentication using a trusted camera for determining access for a user account includes initiating a live identity verification challenge based on detecting a trigger event associated with an authentication event; generating one or more prompts for the live identity verification challenge, the one or more prompts indicating one or more tasks to be performed by the access requester using a camera of a trusted device; obtaining from the trusted device, digital evidence of the access requester performing the one or more tasks, the digital evidence including live camera data captured by the camera; analyzing, using a machine learning model, the digital evidence to verify whether or not the access requester is an authorized user of the user account; and authenticating or denying the access attempt based on whether or not the access requester is verified as the authorized user of the user account.
G06F 21/32 - Authentification de l’utilisateur par données biométriques, p. ex. empreintes digitales, balayages de l’iris ou empreintes vocales
G06V 10/70 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique
G06V 40/16 - Visages humains, p. ex. parties du visage, croquis ou expressions
G06V 40/40 - Détection d’usurpation, p. ex. détection d’activité
G06V 40/50 - Traitement de données biométriques ou leur maintenance
G06V 40/60 - Moyens statiques ou dynamiques permettant d’aider l’utilisateur à positionner une partie du corps pour l’acquisition de données biométriques
46.
AUTHENTICATING USER REQUESTS VIA INTERACTIONLESS AUTHENTICATION
Methods and systems for reducing network traffic associated with authenticating user requests via interactionless authentication are disclosed. For example, in connection with a user device transmitting a user request to access a resource, the system determines a first address associated with the user device. The system then transmits the first address to a third-party authentication platform that determines a Uniform Resource Locator (URL) that is specific to a mobile carrier. The system then transmits a verification token request associated with the mobile carrier. The system receives a verification token from the URL, and transmits an authentication request to the third-party authentication platform comprising the verification token. The system then receives, from the third-party authentication platform, an authentication response comprising a user device identifier. In response to receiving the authentication response, the system performs an authentication action associated with the user request.
An authenticated data sharing system may include a contactless card comprising a processor and a memory including one or more applets and an application comprising instructions for execution on a device. The application is configured to determine a capability associated with the contactless card after a first entry into a communication field. The application is configured to request additional information based on the determination. The one or more applets are configured to transmit, to the application, a generated tokenized link after a second entry into the communication field based on the requested additional information. The application is configured to transmit the link to obtain the requested additional information.
G06F 16/955 - Recherche dans le Web utilisant des identifiants d’information, p. ex. des localisateurs uniformisés de ressources [uniform resource locators - URL]
G06K 7/08 - Méthodes ou dispositions pour la lecture de supports d'enregistrement avec des moyens de perception des modifications d'un champ électrostatique ou magnétique, p. ex. par perception des modifications de la capacité entre des électrodes
In some implementations, a system may obtain, based on a graphical user interface (GUI) that includes a first field identifying a plurality of transformation objects, first input that indicates selection of a transformation object of the plurality of transformation objects. The system may obtain, based on a set of one or more second fields of the GUI that are associated with identifying parameter information associated with the transformation object, second input that indicates one or more parameters associated with the transformation object. The system may generate a transformation data structure, to be processed in a transformation sequence, that indicates the transformation object and the one or more parameters associated with the transformation object.
A method for performing object-based authentication for determining access to a user account includes detecting an enrollment event associated with the user account; obtaining, based on detecting the enrollment event, one or more enrollment images of an authentication object; classifying, using a classification machine learning model, the authentication object as unique or non-unique based on the one or more enrollment images; selecting an object authentication machine learning model for use during user authentication based on whether the authentication object is unique or non-unique, including selecting a first object authentication machine learning model based on the authentication object being unique, or selecting a second object authentication machine learning model based on the authentication object being non-unique; and configuring one or more processors to use the object authentication machine learning model for authenticating the user account during an authentication event.
A system may include, as part of a doubly robust large language model (LLM) architecture for data summarization, a pre-trained LLM in addition to first and second fine-tuned LLMs derived from the pre-trained LLM and fine-tuned with training datasets collected over different time periods. In response to receiving a request to summarize a dataset, the dataset is provided as input to each of the LLMs to obtain, as output, a first summary, a second summary, and a third summary generated by the pre-trained LLM, first fine-tuned LLM, and second fine-tuned LLM, respectively. Each of the second and third summary may be compared to the first summary relative to a similarity threshold to determine whether a hallucination associated with the first or second fine-tuned LLM is indicated and, based on the determination, select one of the second or third summary as a response to the request or halt the data summarization.
Various embodiments are generally directed to authenticating a user for non-payment purposes utilizing a payment protocol, a computer device and a contactless card. The payment protocol may be consistent with an EMV standard. An application may determine that authorization or verification of a user may be required to access non-payment features of another application associated with the user and the computer device. The application may then receive and/or facilitate transmission of encrypted data from a communications interface of a contactless card associated with an account and utilizing either an offline or online technique to do so. The offline or online technique may involve one or more operations that can verify the identity of the user and/or otherwise authorize the user to have access to various aspects of the other application.
G06Q 20/34 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des cartes, p. ex. cartes à puces ou cartes magnétiques
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
H04L 9/30 - Clé publique, c.-à-d. l'algorithme de chiffrement étant impossible à inverser par ordinateur et les clés de chiffrement des utilisateurs n'exigeant pas le secret
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
H04W 12/033 - Protection de la confidentialité, p. ex. par chiffrement du plan utilisateur, p. ex. trafic utilisateur
In some implementations, a test system may cause, for a microservice system that includes multiple microservices, one or more test workflows to be performed. The test system may obtain, based on the one or more test workflows, result information indicating results of the one or more test workflows, wherein the result information indicates error information for an error associated with at least one test workflow of the one or more test workflows. The test system may determine a microservice, of the multiple microservices, that is a cause of the error based on a comparison of an identifier of the error to one or more known identifiers of respective known errors, wherein the identifier of the error is based on the error information. The test system may provide report information for display, wherein the report information indicates the error and that the microservice is the cause of the error.
In some implementations, a test system may cause, for a microservice system that includes multiple microservices, one or more test workflows to be performed. The test system may obtain, based on the one or more test workflows, result information indicating results of the one or more test workflows, wherein the result information indicates error information for an error associated with at least one test workflow of the one or more test workflows. The test system may determine a microservice, of the multiple microservices, that is a cause of the error based on a comparison of an identifier of the error to one or more known identifiers of respective known errors, wherein the identifier of the error is based on the error information. The test system may provide report information for display, wherein the report information indicates the error and that the microservice is the cause of the error.
Systems and methods for improving network security across computer networks when granting access entitlements to a plurality of applications. For example, the system may receive, from a first account, a first access request for a first application entitlement. The system may retrieve a first access credential profile for the first account. The system may determine a first embedding for the first access credential profile. The system may retrieve a first required embedding for the first application entitlement. The system may determine a first dimensionality conflict between the first embedding and the first required embedding. The system may determine a dependency between the first dimensionality conflict and a credential attribute. The system may query the first account for the credential attribute.
Systems, methods, and apparatuses are described for validating compliance in disaster recovery exercises (DREs). In conducting a DRE, a method may collect network data and may enrich the collected network data with application data. The method may simulate the unavailability of a cloud network node, collect network data in the new configuration, compare the two collected network data sets, and determine applications that rely on the simulated unavailable cloud network node. The method may indicate which application was resilient to the simulated unavailable network cloud node based on its determination. By enriching, the method may create a mapping to trace each traffic packet back to a specific application and verify whether the specific application's performance complies with the DRE. Unlike the current methods that lead to time-consuming manual analyses that are prone to false positives, the method may validate compliance in an automated way in a near-real-time fashion.
H04L 43/062 - Génération de rapports liés au trafic du réseau
H04L 43/0817 - Surveillance ou test en fonction de métriques spécifiques, p. ex. la qualité du service [QoS], la consommation d’énergie ou les paramètres environnementaux en vérifiant la disponibilité en vérifiant le fonctionnement
In some implementations, a response system may receive, from a user device, at least one file encoding the audit request. The audit request may be represented by a first set of text in the at least one file. The response system may provide the first set of text to a large language model, trained on a set of previous audit requests and previous responses, in order to receive the draft response. The draft response may be represented by a second set of text. The response system may generate at least one file encoding the draft response by inserting the second set of text into a template. The response system may output, to the user device, the at least one file encoding the draft response.
G06F 40/40 - Traitement ou traduction du langage naturel
57.
SYSTEMS AND METHODS FOR TRANSLATING DIFFERENT VULNERABILITY SCAN RESULTS INTO A STANDARDIZED FORMAT AND FOR CERTIFYING TARGET RESOURCES AGAINST DETECTION OF VULNERABILITIES
A computer-implemented method includes: receiving a request to scan a resource for detection of a vulnerability, the resource comprising one or more of: a component of an application, a code-base of the application, or a third-party library associated with the application; determining one or more matching vulnerability scanning operators among a plurality of vulnerability scanning operators based at least in part on one or more properties associated with the request; transmitting the request to the one or more matching vulnerability scanning operators, the one or more matching vulnerability scanning operators having access to the resource; receiving an initial result associated with the request from the one or more matching vulnerability scanning operators; obtaining a predetermined format for the initial result; generating a translated result by modifying the initial result based on the predetermined format; and transmitting the translated result to a source of the request.
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
Embodiments disclosed are directed to a computing system that performs steps to automatically identify risk control features and entities in a risk control document. The computing system regenerates, by a semantic prediction machine learning (ML) model, phrases in a risk control document. The computing system then classifies, by the semantic prediction ML model, risk control features associated with the regenerated phrases. Subsequently, the computing system corrects, by a discriminative natural language processing (NLP) model, the classified risk control features based on the phrases and the regenerated phrases.
Systems and methods for the creation of human-readable cyber incident reports from cyber incident logs, in which the cyber incident reports may link cyber incidents recorded in a cyber incident log to the existing knowledge sources. To do so, the systems and methods overcome the technical problems of conventional systems as well as the technical problems inherent in adapting artificial intelligence solutions to the creation of cyber incident reports.
Methods, systems, and apparatuses are described herein for using machine learning processes to improve synthetic monitoring of, e.g., websites. Training data may comprise sitemap information for a plurality of different websites and monitoring information that indicates whether, for each of a plurality of different portions of those websites, one or more synthetic monitoring scripts are executed. A machine learning model may be trained using that training data to output whether one or more portions of an input website should be monitored using synthetic monitoring. A sitemap of a first website may be determined and provided as input to the trained first machine learning model. Based on output from the trained first machine learning model, a synthetic monitoring script may be determined and executed to monitor at least a first portion of the first website.
Aspects described herein may relate to a transaction exchange platform using a streaming data platform (SDP) and microservices to process transactions according to review and approval workflows. The transaction exchange platform may receive transactions from origination sources, which may be added to the SDP as transaction objects. As the transactions are processed, the transactions may require access to a resource (e.g., a key value in a database). A microservice processing the transaction may request, from a locking microservice, a lock for the resource. The locking microservice may query a local cache to determine whether a lock exists for the resource. If the local cache determines that no lock exists for resource, the locking mechanism may employ a consensus protocol to obtain a lock for the resource from a plurality of clusters. If consensus is reached, a lock for the resource may be granted to the requesting microservice.
G06Q 20/42 - Confirmation, p. ex. contrôle ou autorisation de paiement par le débiteur légal
G06F 11/07 - Réaction à l'apparition d'un défaut, p. ex. tolérance de certains défauts
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
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
Aspects described herein may allow keystroke logs to be monitored. A computing device may receive a plurality of keystroke logs and provide, to a machine learning model, the plurality of keystroke logs. The computing device may receive, as output from the machine learning model, a value representing a likelihood that a first keystroke log comprises a first command to change a computing system. The computing device may retrieve, from a database, one or more change orders, each indicating an authorization to change the computing system. The computing device may send, to a second computing device and based on determining that the first command does not match the one or more change orders, an alert indicating the first keystroke log. In this way, unauthorized change to the computing system may be detected.
Methods and devices for routing authentication using a switchboard network are disclosed. A computing device accesses a merchant server hosting a website or application requiring personal data associated with a user account to process a transaction, the personal data being stored on an issuer server associated with a contactless card associated with the user account. The computing device receives a request from the merchant server to authenticate the user account and the contactless card responds and indicates the authentication method to be used, which is forwarded to a node in a switching network that extracts information from the response and determines the issuer server associated with the contactless card. The node initiates authentication of the user with an issuer server and retrieves personal data for the merchant server.
G06Q 20/12 - Architectures de paiement spécialement adaptées aux systèmes de commerce électronique
G06Q 20/34 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des cartes, p. ex. cartes à puces ou cartes magnétiques
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
64.
DETERMINING QUALITY OF MACHINE LEARNING MODEL OUTPUT
In some aspects, a computing system may generate uninformative features that may be added to a dataset of real features to use as a baseline for determining the quality of an explanation of model output. The uninformative features may be features that do not correlate with what a model is tasked with predicting (e.g., the uninformative features may be random values), and the real features may be informative and correlate with what the model is tasked with predicting (e.g., variables of a dataset sample). A machine learning model may be trained on a dataset that includes both the real features and the uninformative features. The computing system may generate feature attributions for model output, which may include feature attributions for the uninformative features and the real features in the dataset.
In some implementations, a user device may obtain an input stream, wherein the input stream is obtained via a virtual keyboard provided by the user device. The user device may process the input stream to determine that the input stream includes particular information. The user device may cause, based on determining that the input stream includes the particular information, one or more security parameters associated with an account of a user of the user device to be modified.
Wireless communication technologies, a dynamic transaction card, and a mobile application may be utilized to facilitate multi-factor authentication and secure electronic checkout of any website. A wireless connection between a dynamic transaction card and a user device may be utilized to authenticate a user. A user device application may be triggered to call, via an application programming interface (API), an account provider system, and this unique pairing may automatically facilitate payment to a merchant system associated with the electronic check out page. The account provider system may send a push notification to a browser extension associated with the checkout page to prompt the browser extension to populate fields on the electronic checkout page using user account information transmitted from the account provider system, providing a novel digital authentication framework that utilizes digital authentication techniques enables by user devices and dynamic transaction cards to seamlessly facilitate electronic checkout.
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/34 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des cartes, p. ex. cartes à puces ou cartes magnétiques
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
Aspects described herein may allow for the application of stochastic gradient boosting techniques to the training of deep neural networks by disallowing gradient back propagation from examples that are correctly classified by the neural network model while still keeping correctly classified examples in the gradient averaging. Removing the gradient contribution from correctly classified examples may regularize the deep neural network and prevent the model from overfitting. Further aspects described herein may provide for scheduled boosting during the training of the deep neural network model conditioned on a mini-batch accuracy and/or a number of training iterations. The model training process may start un-boosted, using maximum likelihood objectives or another first loss function. Once a threshold mini-batch accuracy and/or number of iterations are reached, the model training process may begin using boosting by disallowing gradient back propagation from correctly classified examples while continue to average over all mini-batch examples.
Methods, systems, and apparatuses are described herein for automatically recommending cloud server configuration changes. A machine learning model may be trained to output infrastructure modification recommendations based on a history of performance measurements of a server environment that executes one or more applications. Input data indicating a current configuration of the server environment may be provided to the trained machine learning model. In response, the trained machine learning model may output a recommended change to the server environment. Based on comparing the risk score to a threshold associated with the one or more applications, users might be provided with the option to implement the recommended change by modifying one or more operating parameters of one or more servers of the server environment. Additionally and/or alternatively, the recommended change may be automatically implemented.
H04L 41/08 - Gestion de la configuration des réseaux ou des éléments de réseau
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 47/78 - Architectures d'allocation des ressources
69.
SECURE MAPPING OF AGGREGATED PURCHASED ITEMS TO VERIFIABLE USER IDENTITIES FOR CONSOLIDATED PICKUP AND DELIVERY
Systems and methods are directed to secure mapping and aggregation of purchase transactions. A common identifier associated with all purchase transactions, conducted within a pre-determined time window, is generated. One example utilizes a hashed identifier generated from PAN retrieved from a user EMV transaction card. The hashed PAN identifier can be incorporated with a timestamp associated with a prescribed time window that accommodates a duration of a user shopping activity at a merchant collective. The identifier code may be replicated for retrieval of purchased items within the allotted time window using the same PAN.
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
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
70.
PROVIDING VIRTUAL IDENTIFIERS, WITH EXPIRY PARAMETERS, TO MOBILE DEVICES
In some implementations, a mobile device may receive, from an identifier manager, a token encoding the virtual identifier and an indication of the expiry parameter to associate with the token. The mobile device may output a user interface (UI) including a graphical representation of the token. The mobile device may determine, based on the expiry parameter, that the token has expired. The mobile device may modify the UI to hide the graphical representation of the token in response to determining that the token has expired.
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
The disclosed methods and devices are directed to pre-registering Fast Identity Online (FIDO) security keys on contactless cards for providing security in transactions and web resource access. A user attempts to log in to their account on an application, and instead of using their normal password, a FIDO key is used. In some cases, the FIDO key is pre-registered on a contactless card associated with the user and the user taps their card to their mobile device to log in to the application using FIDO. An authentication server sends a FIDO challenge to the card, which signs a FIDO response to the FIDO challenge. A FIDO public key associated with the user account is used by the authentication server to verify the signed FIDO response. The user is logged in if the FIDO response is verified. The FIDO key is pre-registered on the card at personalization.
G06Q 20/12 - Architectures de paiement spécialement adaptées aux systèmes de commerce électronique
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/34 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des cartes, p. ex. cartes à puces ou cartes magnétiques
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
72.
SECURE MAPPING OF AGGREGATED PURCHASED ITEMS TO VERIFIABLE USER IDENTITIES FOR CONSOLIDATED PICKUP AND DELIVERY
Systems and methods are directed to secure mapping and aggregation of purchase transactions. A common identifier associated with all purchase transactions, conducted within a pre-determined time window, is generated. One example utilizes a hashed identifier generated from a primary account number (PAN) retrieved from a user transaction card. The hashed PAN identifier can be incorporated with a timestamp associated with a prescribed time window that accommodates a duration of a user shopping activity at a merchant collective. The identifier code may be replicated for retrieval of purchased items within the allotted time window using the same PAN.
H04L 9/28 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité utilisant un algorithme de chiffrement particulier
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
74.
Display screen or portion thereof with animated graphical user interface for card communication
Methods, systems, and apparatuses are described herein for efficiently identifying and processing sensitive data. A computing device may receive text content, such as one or more words. The computing device may select characters of that text content by identifying, using regular expressions corresponding to sensitive data categories, matches and select various characters including and around such matches. The computing device may provide the selected characters as input to a machine learning model trained to identify sensitive data, and that trained machine learning model may output information about a sensitivity of the characters. Such output might be used to modify all or portions of the text content.
In some implementations, a device may receive information identifying a cloud platform associated with generating a data log. The device may identify an application programming interface (API) associated with the cloud platform. The device may establish, using the API associated with the cloud platform, a listener service associated with receiving the data log asynchronously to an execution of a particular type of function in a cloud environment associated with the cloud platform. The device may receive, based on establishing the listener service and based on the execution of the particular type of function, the data log. The device may forward, to a primary endpoint associated with a data log platform and a failover endpoint associated with a data storage platform, and based on receiving the data log, information associated with the data log.
In some implementations, a system may determine, based on respective reference information associated with a set of one or more machine images of a plurality of machine images associated with a cloud computing environment, respective lifecycle management actions for the set of one or more machine images. The system may cause the respective lifecycle management actions to be performed for the set of one or more machine images.
G06F 9/455 - ÉmulationInterprétationSimulation de logiciel, p. ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
82.
SYSTEMS AND METHODS FOR IDENTIFYING NETWORK OPERATIONS THAT ARE INDICATIVE OF AT LEAST ONE CYBERSECURITY EVENT WHEN MONITORING NETWORK ACTIVITY
Systems and methods for identifying network operations that are indicative of at least one cybersecurity event when monitoring network activity are disclosed. For example, a system can be configured to contain a data set representing a set of network operations, determine that a subset of network operations from the set of network operations are indicative of irregularities, and generate alert data associated with one or more alerts. In an example, the system can generate a query instruction that is based on the subset of network operations, and provide the query instruction to a database search system to cause the system to generate a set of query results. In this example, the system can update the alert data based on the set of query results.
A zero-trust gateway system is provided for enforcing a zero-trust principle for granting access to one or more single sign-on (SSO) service provider entities provided in a zero-trust network. The zero-trust gateway system includes a plurality of interfaces for communicating with the one or more SSO service provider entities, one or more SSO identity provider entities, and one or more data sources provided in the zero-trust network; and one or more processors configured to: detect an authentication event associated with an access attempt for an SSO service provider entity of the one or more SSO service provider entities, the access attempt being indicated by an SSO identity provider entity of the one or more SSO identity provider entities; and authenticate or limit the access attempt based on information associated with the access attempt, the information being obtained from the one or more data sources.
Systems and methods for cataloging secured data in encrypted clusters based on received authorizations are described. For example, the system may retrieve dataset, wherein the dataset comprises non-native data, wherein the dataset has a first data cluster corresponding to a first approved-use authorization applied to a first non-native data source used to populate the first data cluster, and wherein the dataset has a second data cluster corresponding to a second approved-use authorization applied to a second non-native data source used to populate the first data cluster.
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
85.
METHODS AND DEVICES FOR PREREGISTERED PARALLEL FIDO KEYS FOR ISSUER AUTHENTICATION
The disclosed methods and devices are directed to pre-registering Fast Identity Online (FIDO) security keys on contactless cards for providing security in transactions and web resource access. A user attempts to log in to their account on an application, and instead of using their normal password, a FIDO key is used. In some cases, the FIDO key is pre-registered on a contactless card associated with the user and the user taps their card to their mobile device to log in to the application using FIDO. An authentication server sends a FIDO challenge to the card, which signs a FIDO response to the FIDO challenge. A FIDO public key associated with the user account is used by the authentication server to verify the signed FIDO response. The user is logged in if the FIDO response is verified. The FIDO key is pre-registered on the card at personalization.
G06F 21/34 - Authentification de l’utilisateur impliquant l’utilisation de dispositifs externes supplémentaires, p. ex. clés électroniques ou cartes à puce intelligentes
Disclosed are methods and systems for identifying on-line reductions. For instance, a user interaction may be monitored to detect a first on-line platform being launched, and a user input may be received associated with the first on-line platform. An item associated with the first on-line platform, and one or more potential reductions associated with the item are automatically identified. Responsive to the identification of the one or more potential reductions, an electronic application is displayed including an indication of the one or more potential reductions at a display of the user input device, the electronic application operating in conjunction with the first on-line platform and including a user-selectable link that updates the first on-line platform to display the one or more potential reductions for the item on the first platform, or causes display of a second on-line platform to display the one or more potential reductions.
A device may receive an input that indicates a request to initiate a transaction at an ATM device. The device may instruct the user to capture one or more images of the ATM device. The device may determine that an image has been captured and process the image to determine first information that identifies the ATM device. The device may send the first information to a server device and receive a signal that indicates the ATM device has been validated. The device may cause an augmented reality (AR) overlay to be displayed, wherein the AR overlay includes second information related to authenticating the user to the ATM device. The device may determine whether a user action is performed with respect to the second information included in the AR overlay, and perform a device action related to the second information, the ATM device, or the AR overlay.
G06F 21/36 - Authentification de l’utilisateur par représentation graphique ou iconique
G06F 3/044 - Numériseurs, p. ex. pour des écrans ou des pavés tactiles, caractérisés par les moyens de transduction par des moyens capacitifs
G06F 21/35 - Authentification de l’utilisateur impliquant l’utilisation de dispositifs externes supplémentaires, p. ex. clés électroniques ou cartes à puce intelligentes communiquant sans fils
G06F 21/83 - Protection des dispositifs de saisie, d’affichage de données ou d’interconnexion dispositifs de saisie de données, p. ex. claviers, souris ou commandes desdits claviers ou souris
G06Q 20/10 - Architectures de paiement spécialement adaptées aux systèmes de transfert électronique de fondsArchitectures de paiement spécialement adaptées aux systèmes de banque à domicile
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06T 19/00 - Transformation de modèles ou d'images tridimensionnels [3D] pour infographie
G07F 19/00 - Systèmes bancaires completsDispositions à déclenchement par carte codée adaptées pour délivrer ou recevoir des espèces ou analogues et adresser de telles transactions à des comptes existants, p. ex. guichets automatiques
88.
DATA PROTECTION FOR MACHINE LEARNING MODELS TRAINED ON CLIENT DATA
Methods and systems are described herein for protecting client data while training machine learning models. The system may transmit, to client devices, simple models to be trained on a respective client device to generate predictions based on a respective subset of respective client data of the respective client device. The system may receive the trained simple models from the client devices. The system may input, into an ensemble model including the simple models, an unlabeled synthetic dataset. This may cause the ensemble model to aggregate a set of predictions generated by each simple model to generate labels for the unlabeled synthetic dataset. The system may then input, into a new model, the unlabeled synthetic dataset and the labels to train the new model to predict the labels for the unlabeled synthetic dataset.
Methods, systems, devices, and computer-readable media for orchestrating the sharing of data between accounts that are hosted by a cloud-based data warehousing system on different cloud platforms or in different cloud regions of a cloud platform, and where such accounts may be associated with different organizations. Sharing of data in the multi-cloud platform and/or multi-cloud region environments may be facilitated by the on-demand creation of one or more data collection accounts.
G06F 16/25 - Systèmes d’intégration ou d’interfaçage impliquant les systèmes de gestion de bases de données
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
90.
SYSTEMS AND METHODS FOR ROUTING INTERNAL COMMUNICATIONS WHILE MAINTAINING A SECURED ENVIRONMENT AND SECURITY OVER INTERNAL DATA USING A BIFURCATED SECURITY PROTOCOL
Systems and methods for routing internal communications while maintaining a secured environment and security over internal data are described. For example, the system may process, by a first cloud-based collaboration and communication platform, a first secured input using a large language model, wherein the large language model is trained using a first training routine, of a bifurcated security protocol, for weighting the large language model to determine routing instructions or data retrieval locations for content retrieval operations, wherein the large language model is trained using a second training routine for weighting the large language model to determine intents or contexts of received inputs, and wherein the first training routine comprises filtering data from a first dataset based on whether the data has an indicium of user feedback.
In some implementations, a device may detect an event associated with performing data synchronization. The device may fetch a plurality of datasets, each dataset having a plurality of data entries. The device may cross-correlate data entries to generate a single merged dataset. The device may identify at least one group of data entries for which there is a discrepancy with respect to a data entry. The device may identify a source of truth for resolving the discrepancy. The device may resolve the discrepancy and identify a value for the data entry. The device may update the single merged dataset to include the value for the data entry. The device may publish the single merged dataset to a configuration management database that is accessible to the plurality of data platforms.
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
92.
FACILITATING SECURE, SCALABLE, SEGMENTED LAYER TWO COMMUNICATIONS
Methods and systems are described herein for reducing data storage requirements associated with facilitating secure, scalable, segmented layer two communications. For example, the system may receive a message, from a first device, at a first port mapped to a first Virtual Local Area Network (VLAN), to communicate with a second device. The system may insert, based on receiving the message, a Security Group Tag (SGT) into the message. The system may determine a second port, and in response to determining, based on an assigned SGT to the second port, that a second VLAN is different than the first VLAN, the system may: forward the message to an enforcement point. The system may verify, at the enforcement point, whether the network traffic between the first and second device is valid. In response to the network traffic being valid, the system may forward the message to a gateway address.
Methods and devices for routing authentication using a switchboard network are disclosed. A computing device accesses a merchant server hosting a website or application requiring personal data associated with a user account to process a transaction, the personal data being stored on an issuer server associated with a contactless card associated with the user account. The computing device receives a request from the merchant server to authenticate the user account and the contactless card responds and indicates the authentication method to be used, which is forwarded to a node in a switching network that extracts information from the response and determines the issuer server associated with the contactless card. The node initiates authentication of the user with an issuer server and retrieves personal data for the merchant server.
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/34 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des cartes, p. ex. cartes à puces ou cartes magnétiques
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
94.
DYNAMIC PARTITIONING FOR NETWORK CONTROL FOR NETWORKED SYSTEM
A system may increase measurement retrieval by using multiple partitions for data. Some embodiments may determine how many partitions to allocate to a user. Furthermore, some embodiments may send warm-up requests to the partitions before using them in a production environment.
Disclosed herein is a computing platform configured to (i) for a deep-learning AI model, determine a respective fairness-importance score of a respective parameter for at least a subset of the deep-learning AI model's parameters that quantifies how much the respective parameter influences generating fair predictions across a plurality of demographic groups, (ii) carry out an optimization process that produces and evaluates different quantized versions of the deep-learning AI model, (iii) based on the optimization process, select a given quantized version of the deep-learning AI model for deployment, (iv) fine-tune the given quantized version of the deep-learning AI model, and after fine-tuning the given quantized version of the deep-learning AI model, deploying the given quantized version of the deep-learning AI model.
In some implementations, a search system may receive, from a first user device, a first search. The search system may convert the first search into a first query. The search system may return, to the first user device and from an application layer of the search system, one or more first matching log files, from the plurality of standardized log files, using the first query.
In some implementations, a search system may receive, from a first user device, a first search. The search system may convert the first search into a first query. The search system may return, to the first user device and from an application layer of the search system, one or more first matching log files, from the plurality of standardized log files, using the first query.
The search system may receive, from a second user device, a second search. The search system may convert the second search into a second query. The search system may return, to the second user device and from an enterprise layer of the search system, one or more second matching log files, from the plurality of standardized log files, using the second query.
Disclosed herein are system, method, and computer program product embodiments for generating a segmentation mask of an image object; determining a sizing of the segmentation mask, wherein the sizing includes a bounding box around the image object; based on determining the sizing to be outside a threshold sizing ratio, resizing the segmentation mask to be equal to or within the threshold sizing ratio; determining, based on a center point of the image object, that the resized image object is not centered; centering the resized segmentation mask resizing, centering the digital imagery such that the image object is of a same size and occupies a same position as the resized and centered segmentation mask; and rendering, from the resized and centered digital imagery, a final image. A final image may also include background blurring, padding and whitening.
In some implementations, an application server may establish a proxy service component associated with a metadata source associated with a first version of a metadata service and having a first type of metadata requests. The application server may configure a packet filtering component. The application server may receive a first metadata request. The application server may determine that the first metadata request is associated with the second type of the second version of the metadata service. The application server may authorize the first metadata request. The application server may generate a second metadata request associated with the first type of the first version of the metadata service. The application server may transmit the second metadata request. The application server may receive a metadata response. The application server may transmit information identifying a content of the metadata response.
A device may retrieve data representing a plurality of activity sequences associated with a plurality of channels. Each activity sequence may include a plurality of activity entries having data values for a plurality of fields. The plurality of activity entries may be associated with respective timestamps. The plurality of activity sequences may have respective temporal resolutions. The device may flatten the data values for the plurality of activity sequences, across the plurality of activity entries, the plurality of fields, and the plurality of channels, into a single time-ordered activity sequence. The device may generate activity data embeddings using the single time-ordered activity sequence, generate, using an encoder module, an encoder output based on the activity data embeddings and temporal information based on the respective timestamps, input the encoder output into a decoder module, and generate, using the decoder module, a decoder output based on the encoder output.
G06F 16/9537 - Recherche à dépendance spatiale ou temporelle, p. ex. requêtes spatio-temporelles
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
100.
PERSONALIZED RESPONSES TO CHATBOT PROMPT BASED ON EMBEDDING SPACES BETWEEN USER AND SOCIETY
Systems described herein may provide responses to chatbot prompts that correspond to both a user's preferences and accepted views of society. A chat recommendation server may receive a prompt from a user device. The chat recommendation server may determine a general Overton window and a user-specific Overton window associated with the prompt. The chat recommendation server may generate a plurality of candidate responses using the first machine learning model, input the prompt and the plurality of candidate responses to a second machine learning model, and receive, as output from the second machine learning model, a polarization score for each of the plurality of candidate responses. Based on the polarization scores, a recommended response may be selected which minimizes a distance between the user-specific Overton window and the general Overton window. Accordingly, the recommended response may be displayed on the user device.
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