A system and/or method may be provided to authenticate a user. An example method of authenticating a user includes receiving, by a merchant application, a user request to complete a transaction using a payment service provider. The method also includes in response to receiving the user request to complete the transaction, retrieving, by the merchant application, a browser cookie stored on a user device and associated with one or more user interactions with a browser included in the user device and the payment service provider. The method further includes in response to receiving the user request to complete the transaction, launching, by the merchant application, an instance of the browser that reads the browser cookie and authenticates the user based on the browser cookie.
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/12 - Architectures de paiement spécialement adaptées aux systèmes de commerce électronique
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06Q 20/38 - Architectures, 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 magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
Techniques are disclosed for generating multi-dimensional variable matrices for predicting abnormality of electronic communications. After receiving the request for a new communication from a given entity, a system retrieves attributes of the entity. The system generates, a variable matrix for the entity with a variable dimension corresponding to a number of variables determined for the entity based on the entity's attributes and an entity dimension corresponding to a number of other entities with which the entity performed communications. The system inputs the variable matrix into a trained machine learning model and determines, based on an abnormality score output by the model, whether the communication requested by the entity corresponds to anomalous behavior. The disclosed techniques may advantageously provide a greater distribution of data for an entity using a multi-dimensional variable matrix for anomaly prediction e.g., using machine learning relative to traditional techniques that compress the distribution to a one-dimensional statistic.
Techniques are disclosed for automatically retraining a machine learning model based on the performance of this model falling below a performance threshold. In some embodiments, a computer system compares output of a new machine learning model for a new set of examples with known labels for examples in the new set of examples, wherein the new set of examples includes one or more new features. In some embodiments, the computer system determines, based on the comparing, whether a current performance of the new machine learning model satisfies a performance threshold for machine learning models, where the performance threshold is based on output of a benchmark machine learning model. In some embodiments, the computer system automatically triggers, in response to determining that the current performance of the new model does not satisfy the performance threshold, retraining of the new model.
G06F 18/21 - Conception ou mise en place de systèmes ou de techniques; Extraction de caractéristiques dans l'espace des caractéristiques; Séparation aveugle de sources
G06F 18/213 - Extraction de caractéristiques, p.ex. en transformant l'espace des caractéristiques; Synthétisations; Mappages, p.ex. procédés de sous-espace
G06F 18/214 - Génération de motifs d'entraînement; Procédés de Bootstrapping, p.ex. ”bagging” ou ”boosting”
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
G06N 5/04 - Modèles d’inférence ou de raisonnement
Techniques are disclosed relating to a graphical user interface (GUI) that is operable to depict data lineage information in levels. In some embodiments, data lineage information may specify a directed graph that is indicative of a data lineage associated with a plurality of data elements. For example, in the data lineage information, the plurality of data elements may be represented as a corresponding plurality of nodes and, in the directed graph, the plurality of nodes may be connected by edges in a manner that is indicative of the data lineage relationships between the plurality of data elements. In various embodiments, the disclosed techniques may generate a data lineage GUI that, for a selected data element of the plurality of elements, is usable to navigate different levels of the data lineage in an upstream and downstream direction relative to a particular level of the selected data element.
Methods and systems for creating and analyzing low-dimensional representation of webpage sequences are described. Network traffic history data associated with a particular website is retrieved and a word embedding algorithm is applied to the network traffic history data to produce a low dimensional embedding. A prediction model is created based on the low-dimensional embedding. Browsing activity on the particular website is monitored. A set of sessions in the current browsing activity is flagged based on a result of applying the prediction model to the monitored browsing activity.
H04L 67/145 - Interruption ou inactivation de sessions, p.ex. fin de session contrôlée par un événement en évitant la fin de session, p.ex. maintien en vie, battements de cœur, message de reprise ou réveil pour une session inactive ou interrompue
A cryptography agent is implemented to serve as an intermediary for a client application executing on an unsecured portion of a machine to bring greater hardware-based security to the client application. The cryptography agent does so by generating a public/private key pair for the client application and sealing the key pair inside an enclave that resides on a secured portion of the machine. The cryptography agent fetches confidential information for the client application from a secure server, where the confidential information is encrypted using the public key. The cryptography agent seals the confidential information using seal keys that are directly fused into hardware of the machine on which the enclave resides, which prevents the client application from accessing the confidential information in plaintext form. The client application sends commands to the cryptography agent, which performs operations within the enclave according to the commands once the client application is validated.
Systems, methods, and computer program products are directed to machine learning techniques that use a separate embedding layer. This can allow for continuous monitoring of a processing system based on events that are continuously generated. Various events may have corresponding feature data associated with at least one action relating to a processing system. Embedding vectors that correspond to the features are retrieved from an embedding layer that is hosted on a separate physical device or a separate computer system from a computer that hosts the machine learning system. The embedding vectors are processed though the machine learning model, which may then make a determination (e.g. whether or not a particular user action should be allowed). Generic embedding vectors additionally enable the use of a single remote embedding layer for multiple different machine learning models, such as event driven data models.
Techniques for performing secure contactless payments are disclosed. An example apparatus includes an external contactless card reader, including a first short-distance communication antenna, connected to a card reader terminal using a feed line. The first short-distance communication antenna is configured to convert an electromagnetic signal including data received from a contactless payment card into an analog signal. The card reader terminal includes a short-distance receiver circuit and a secure controller, both located within a secure area, and a second short-distance communication antenna, wherein the first and second short-distance communication antennas are connected via separate paths to the short-distance receiver circuit. An example method includes receiving an electromagnetic signal including card data via a short-distance communication antenna in an external contactless card reader, converting the electromagnetic signal into an analog signal and transferring the analog signal to a card reader terminal.
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06K 7/10 - Méthodes ou dispositions pour la lecture de supports d'enregistrement par radiation corpusculaire
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 magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
Techniques are disclosed that relate to identifying a set of outlier data points from a set of time-series data. A computer system may receive time-series data that includes a plurality of data points. The computer system scores, using a first scoring function, a plurality of candidate outlier detection models to evaluate their ability to accurately predict outlier data points in the time-series data. The computer system selects, based on results of the scoring, a selected one of the plurality of candidate outlier detection models that predicts a preliminary set of outlier data points. Weak outliers may be removed and missing outliers added to generate a final set of outlier data points. The computer system may output one or messages explaining the rationale for including one or more of the final set of outlier data points.
A method according to the present disclosure may include providing an embedded service in an application, in response to receiving an input via the embedded service, determining an applicable module of a plurality of modules based on a characteristic of at least one of the input or of the embedded service, processing the input via the applicable module, and controlling the application based on the processed input. Another method according to the present disclosure may include providing an embedded service in an application, training a monitoring model via federated learning based on data derived locally from the application, monitoring, via the trained monitoring model, a plurality of interactions with the embedded service, determining, by the trained monitoring model, that at least one of the plurality of interactions comprises an anomalous interaction, and in response to the determination, restricting further usage of the application.
G06F 16/9535 - Adaptation de la recherche basée sur les profils des utilisateurs et la personnalisation
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
11.
SYSTEMS AND METHODS FOR LEVERAGING EMBEDDED SERVICES
A method according to the present disclosure may include providing an embedded service in an application, in response to receiving an input via the embedded service, determining an applicable module of a plurality of modules based on a characteristic of at least one of the input or of the embedded service, processing the input via the applicable module, and controlling the application based on the processed input. Another method according to the present disclosure may include providing an embedded service in an application, training a monitoring model via federated learning based on data derived locally from the application, monitoring, via the trained monitoring model, a plurality of interactions with the embedded service, determining, by the trained monitoring model, that at least one of the plurality of interactions comprises an anomalous interaction, and in response to the determination, restricting further usage of the application.
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
12.
Locking tablet display with configured charging cord placement
A method for transferring a digital asset from a first user to a second user includes the first user's computing device scanning a token on physical media, such as a QR code on a greeting card, to launch a digital asset transfer interface. In the interface, the first user identifies a digital asset to be transferred, and the digital asset is transferred from the first user's account or other digital storage to a digital escrow. The first user conveys the physical media to a second user. The second user scans the same token to launch the digital asset transfer interface and is presented with the digital asset and a greeting or other content selected by the first user. The second user selects a destination account, and the digital asset is transferred from escrow to the destination account.
H04L 67/146 - Marqueurs pour l'identification sans ambiguïté d'une session particulière, p.ex. mouchard de session ou encodage d'URL
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p.ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06K 7/14 - Méthodes ou dispositions pour la lecture de supports d'enregistrement par radiation corpusculaire utilisant la lumière sans sélection des longueurs d'onde, p.ex. lecture de la lumière blanche réfléchie
14.
SHORT-RANGE TRANSMISSION OF RECEIPT DATA WITHOUT CONTACT IDENTIFIERS
There are provided systems and methods for short-range transmission of receipt data without contact identifiers. A user may engage in a transaction with another user, such as a purchase of goods, services, or other items from a merchant at a physical merchant location. The merchant may provide options to receive a receipt, where conventional digital receipt transmission would require the user to enter their contact information, such as an email address or phone number. Instead, the user may be provided with an option to receive a digital version of the receipt via short-range wireless communications without entering contact information. A message may be generated having a webpage address or another identifier allowing for retrieval of the digital receipt from a storage system, and the message may then be broadcast locally to the user's device. The broadcast may the cause the user's device to load and present the digital receipt.
Methods and systems are presented for providing a framework for dynamically managing computer resources in processing transactions by selectively applying one or more techniques to improve the computer resource usage efficiency of processing different transactions. When a request for processing a first transaction is received, an estimated frequency of future transactions that are related to the first transaction is predicted. Based on the estimated frequency of future transactions, one or more actions that improves the computer resource usage efficiency for processing the first transaction are applied. The action may include suspending the processing of the first transaction and subsequently performing a batch process including the first transaction and a second transaction related to the first transaction. The action may include storing data associated with the processing of the first transaction in a cache memory for use in the subsequent processing of a second transaction.
Techniques are disclosed relating to maintaining a high availability (HA) database. In some embodiments, a computer system receives, from a plurality of host computers, a plurality of requests to access data stored in a database implemented using a plurality of clusters. In some embodiments, the computer system responds to the plurality of requests by accessing data stored in an active cluster. The computer system may then determine, based on the responding, health information for ones of the plurality of clusters, wherein the health information is generated based on real-time traffic for the database. In some embodiments, the computer system determines, based on the health information, whether to switch from accessing the active cluster to accessing a backup cluster. In some embodiments, the computer system stores, in respective clusters of the database, a changeover decision generated based on the determining.
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
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
There are provided systems and methods for detection of similar machine learning features in real-time for declarative feature engineering. A service provider, such as an electronic transaction processor for digital transactions, may utilize computing services that implement machine learning models for decision-making of data including real-time data in production computing environments. Machine learning models may utilize features or variables that may correspond to coded logic that provides a measurable datum, property, or the like to the models for intelligent outputs. When creating features, preexisting features may accomplish the same or similar function. Thus, the service provider may provide machine learning clustering of features for similarity detection in real-time. Feature clusters may be precomputed and loaded for comparison using representative vectors for clusters. Each feature may have a declarative definition of parameters that may be used for comparison, and similar detected features may be output during feature engineering.
There are provided systems and methods for procedural pattern matching in audio and audiovisual files using voice prints. A user may utilize a computing device to interact with online service providers via voice communications. Based on audio and/or audiovisual data provided during the voice communications, voice prints may be generated, such as by determine audio signals from audio and/or audiovisual data, extracting audio features from such signals, and identifying voice and other audio dimensions in the audio and/or audiovisual data. The voice print may be generated based on an algorithmic calculation or other function that hides or obscures personal data for the corresponding user and/or masks the users voice and identity. The voice print may then be stored and used as a key for data associated with the user, which allows the data to be scrubbed or masked of the user's personal data to protect their privacy.
G10L 17/06 - Techniques de prise de décision; Stratégies d’alignement de motifs
G10L 17/02 - Opérations de prétraitement, p.ex. sélection de segment; Représentation ou modélisation de motifs, p.ex. fondée sur l’analyse linéaire discriminante [LDA] ou les composantes principales; Sélection ou extraction des caractéristiques
G10L 17/04 - Entraînement, enrôlement ou construction de modèle
19.
REDUCING LATENCY THROUGH PROPENSITY MODELS THAT PREDICT DATA CALLS
There are provided systems and methods for reducing latency through propensity models that predict data calls. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users including those for electronic transaction processing. In order to provide sequence-based forecasting of computing events and processing requests for users, accounts, and/or activities associated with the service provider, the service provider may provide a machine learning model, such as a deep neural network, that predicted potential occurrences and likelihoods of computing events occurring at future times. When predicting the events, the service provider's machine learning predictive framework may further predict data calls required to be executed to retrieve data needed for processing during the events. These predicted calls may then be batched together into a batch processing job, which may be executed to retrieve the data prior to the predicted events.
G06N 3/0442 - Réseaux récurrents, p.ex. réseaux de Hopfield caractérisés par la présence de mémoire ou de portes, p.ex. mémoire longue à court terme [LSTM] ou unités récurrentes à porte [GRU]
There are provided systems and methods for scalable service discover and load balancing using direct client connections to servers. A service provider, such as an electronic transaction processor for digital transactions, may provide different computing services to users through client devices, which utilize server instances from server pools and the like to provide the computing services to users. This may include providing servers to handle client requests and process data with users. When client devices connect to the service provider's system, service discovery may be performed to find an available server instance to handle client requests. To provide scalable service discovery, load balancers may, instead of managing client requests through the load balancers, ping server instances from a server pool to identify a network address of an available server. This may be returned to the client device and a direction connection may be made between the device and server.
A device may collect environmental information surrounding the device. Based on the collected environmental information, the device may automatically identify a potentially secured location that has lower security risk. When a potentially secured location is identified, the device may prompt the user to setup a security profile having reduced security requirement for the secured location. The device may store and associate the security profile with the secured location. The device may activate the security profile with reduced security requirement when the device is in the secured area. Further, the security profile may require that certain features of the device be disabled when the device is in the secured location.
H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
H04W 4/80 - Services utilisant la communication de courte portée, p.ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
There are provided systems and methods for automated data tokenization through networked sensors. A network of data sensors may detect that a user is likely to engage in electronic transaction processing with a merchant device, for example, based on an action performed by the user and/or with a user device. One or more of the sensors may connect with the user's device and retrieve financial data from the device and/or data necessary to issue a token to the device for transaction processing. The sensor may perform a background process to issue the token to the device, and once onboarded, may store the token to the user's device and/or the merchant device that the user is likely to interact with for transaction processing. The token may be limited in use by location and/or amount, or may be used to fully onboard the user with the token service provider.
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06Q 20/38 - Architectures, 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 magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
H04W 4/021 - Services concernant des domaines particuliers, p.ex. services de points d’intérêt, services sur place ou géorepères
23.
DYNAMICALLY RENDERED INTERFACE ELEMENTS DURING ONLINE CHAT SESSIONS
There are provided systems and methods for dynamically rendered interface elements during online chat sessions. A user may engage in online communications with another user, such as a communication session between a customer and a customer representative or agent of a merchant. During this communication session, the agent may navigate to particular data on the agent's device, which may be desirable to provide to the customer. For example, the agent may view a particular item of interest to the customer. An application programming interface of the merchant may detect one or more actions or calls associated with this data and may dynamically provide an interface element to transmit data displayed on the agent's device to the customer's device during the communication session. The customer may view a dynamically rendered interface element that allows for processing data during the communication session with the agent.
There are provided systems and methods for monitoring device application usage for completion of checkout data processing. A computing device may be utilized to perform one or more actions while utilizing an application executable by the device, including a browser application or merchant application that allows a user to view an online marketplace and purchase items in a transaction. Prior to checkout and transaction processing, the device may be used to browse items, and items may be added to a shopping cart. However, the device may not finish electronic transaction processing for the items, for example, where the device does not enter transaction processing details and/or navigates away from the items or cart. A service provider may utilize past actions to determine whether the action indicates that the device is abandoning use of the application, including electronic transaction processing. If so, an incentive may be provided to continue use.
Techniques are disclosed that relate to generating a rule for performing predictions of a characteristic of computer operations. A computer system may receive historical data that describes executed computer operations, including variables associated with those executed computer operations, and user input specifying desired properties of the performed predictions. The computer system determines, for a given variable, bins having ranges specified using the variable. The bins may be formed such that, when the executed computer operations are grouped into the bins, a prevalence of the characteristic monotonically increases or decreases from bin to bin across a bin ordering that is based on the ranges. The computer system then determines one or more cutoffs for one or more of the variables based on the desired properties from user's inputs and the determined bins. The computer system generates the rule based on the one or more cutoffs and the one or more variables.
There are provided systems and methods for automated generation of conversational workflows for automation chatbots. A service provider, such as an electronic transaction processor for digital transactions, may provide self-service channels for assistance through chatbot and other automated computing processes. In order to facilitate deployment of new automated skills, a conversational workflow generator may be provided to internal users of the service provider that allows for automated construction of conversational workflows. The generator may process input REST endpoints, request and response parameters, and workflow diagram to generate Java classes and code. The generator may then connect to a conversational AI platform using the code, which may generate the conversational workflow through dialog and a dialog tree that is mapped to the workflow diagram. The conversational workflow may then be implemented in a chatbot to provide automated services through conversational dialog.
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
Systems and methods for identifying patterns in data including determining a set of first attributes for a first dataset corresponding to a plurality of vertices, performing a first join operation based on the set of first attributes, classifying the first dataset to a collection of buckets based on the set of first attributes, each respective bucket including one or more vertices of the plurality of vertices, determining, for a respective bucket, a set of second attributes, performing a second join operation on the set of second attributes to generate a second dataset, and determining a cluster based on applying a first threshold to the second dataset, the cluster including one or more respective vertices in the second dataset exceeding the first threshold. The first threshold corresponding to a minimum number of vertices within a distance parameter of a given vertex.
Systems and methods for managing concurrent secure elements on a mobile device to coordinate with an application or “app” running on the mobile device and an appropriate communications protocol for conducting transactions using the mobile device include: informing, by the processor, the reader device of a preferred app and a communication protocol usable by the preferred app; receiving, by the processor, information about which apps and communication protocols are supported by a reader for processing a transaction; locating, by the processor, a secure element supporting an app and a communication protocol supported by the reader; channeling the communication protocol for the specific configuration of the app and the supporting secure element; activating the secure element that supports the app; and processing, with the activated secure element, using the supported app and communication channel, the transaction with the reader.
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 magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
H04L 67/10 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau
H04L 67/1095 - Réplication ou mise en miroir des données, p.ex. l’ordonnancement ou le transport pour la synchronisation des données entre les nœuds du réseau
H04W 4/60 - Services basés sur un abonnement qui utilisent des serveurs d’applications ou de supports d’enregistrement, p.ex. boîtes à outils d’application SIM
H04W 4/80 - Services utilisant la communication de courte portée, p.ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
29.
SYSTEMS AND METHODS FOR CONFIGURING AND TRAINING MACHINE LEARNING MODELS BASED ON INPUT VALUE CHARACTERISTICS
There are provided systems and methods for configuring and training a machine learning model based on data characteristics associated with input data usable for the machine learning model. First measures are derived for the machine learning model based the input data usable for the machine learning model. The first measures represent data characteristics associated with the input data. The first measures are compared against measures associated with other previously built machine learning models. Based on a comparison, a particular previously built machine learning model having data characteristics most similar to the data characteristics calculated for the machine learning model is selected. A machine learning model configuration setting and training parameters may be determined based on the particular previously built machine learning model. The machine learning model is configured and trained based on the configuration setting and training parameters.
A payment service provider device determines that a first geolocation of a user device is within a predetermined proximity to a second geolocation associated with a merchant Point-Of-Sale (POS) device that is coupled to a network and provides, via the network to the merchant POS device, a communication that includes a user identifier associated with a user of the user device, such that the communication causes the user identifier to be displayed on a display device associated with the merchant POS device and the user is checked-in at a merchant location associated with the merchant POS and the second geolocation. The payment service provider device receives, via the network, a payment request that is associated with the user identifier, and then completes the payment request using a user account associated with the user identifier.
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/10 - Architectures de paiement spécialement adaptées aux systèmes de banque à domicile
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
H04L 67/02 - Protocoles basés sur la technologie du Web, p.ex. protocole de transfert hypertexte [HTTP]
H04W 4/021 - Services concernant des domaines particuliers, p.ex. services de points d’intérêt, services sur place ou géorepères
31.
UNSUPERVISED MACHINE LEARNING MODEL FRAMEWORK FOR KEYWORD ANALYSIS IN REAL-TIME OF MULTILINGUAL DOCUMENTS
There are provided systems and methods for sentence level dialogue summaries using unsupervised machine learning for keyword selection and scoring. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users, which may be used to engage in interactions with other users and entities. When utilizing these services, memoranda may be generated that includes text from different online interactions. To provide summarization and searching of the memoranda, the service provider may implement an unsupervised machine learning framework that utilizes machine learning models to perform text preprocessing, keyword extraction, and keyword weighting when ranking and outputting relevant keywords. Once extracted and ranked, the keywords may be used to provide different insights to the memoranda. The keywords may be selected based on domains and tasks and the framework may be made pluggable and customizable for different systems and search operations.
A method for storing data respective of a time series of electronic transactions respective of a user includes receiving a plurality of indications of data for a plurality of electronic transactions associated with a user, generating a plurality of tokens by generating, for each of the indications, a respective token, each token encoding a respective pointer to a storage location of the respective data, receiving a request for a time series of electronic transactions associated with the user, in response to the request, generating a time series of the plurality of electronic transactions by decoding the plurality of tokens and retrieving the plurality of data according to the pointers.
Apparatus, systems, and methods are disclosed that operate to receiving an authentication request at a server associated with an authenticating entity from a requesting party responsive to a request being provided to the requesting party by a client terminal associated with an unauthenticated individual purporting to be an individual account owner previously authenticated with the authenticating entity. A token from the client terminal associated with the unauthenticated individual is received, and the token includes information associated with the unauthenticated individual and a user permission authorizing the authenticating entity to share a selected portion of the information with a plurality of selected requesting parties. The server associated with the authenticating entity authenticates the unauthenticated individual as the individual account owner based on, inter alia, matching the token to a pre-registered identity uniquely associated with the individual account owner. Additional apparatus, systems, and methods are disclosed.
G06Q 20/10 - Architectures de paiement spécialement adaptées aux systèmes de banque à domicile
G06Q 20/38 - Architectures, 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 magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
A transaction processing method includes receiving data respective of an electronic transaction, the data including an account involved in the transaction and determining an entity graph according to the account. The entity graph includes a plurality of first-order connections, each including a primary node representative of the account, a secondary node representative of a secondary entity, and an edge from the primary node to the secondary node, and a plurality of second-order connections, each including one of the secondary nodes, a tertiary node representative of a tertiary entity, and an edge from the secondary node to the tertiary node. The method includes determining a weight associated with each first-order connection edge based on a quantity of second-order connections associated with the secondary node in the first-order connection, calculating a risk associated with the electronic transaction according to the respective weights, and processing the electronic transaction according to the calculated risk.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
A system and method for investigating trust scores. A trust score is calculated based on peer transfers, a graphical user interface displays actuatable elements associated with a first peer transfer from the peer transfers, in response to receiving an indication the first actuatable element has been actuated, recalculating the trust score without the first peer transfer.
G06F 3/04842 - Sélection des objets affichés ou des éléments de texte affichés
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
Methods and systems for content distribution and management are presented. A transaction flow for conducting different stages of a transaction is determined in response to a request for the transaction from an application executable at a user device. The transaction flow includes a sequence of content pages to be displayed within a graphical user interface (GUI) of the application over the different stages of the transaction. The content associated with a tagged UI element of at least one content page is identified. The content is validated for the tagged UI element of the at least one content page, based on a software and hardware configuration of the user device. The validated content is provided via a network to the application at the user device to be displayable with the tagged UI element on the at least one content page of the transaction flow during a corresponding stage of the transaction.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06Q 30/018 - Certification d’entreprises ou de produits
37.
Evaluating User Status Via Natural Language Processing and Machine Learning
A request is received from a first user of a social interaction platform. The request is a request to acquire a status. In response to the receiving of the request, a database is accessed. The database contains first electronic data pertaining to previous interactions between the first user and other entities of the social interaction platform. The first electronic data is analyzed via one or more Natural Language Processing (NLP) techniques. A first result is obtained based on the analyzing. A machine learning process is executed based at least in part on the first result. Based on the executing of the machine learning process, a determination is made whether to grant or deny the request received from the first user.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06F 40/35 - Représentation du discours ou du dialogue
There are provided systems and methods for establishing digital account usage in digital wallets during cross-platform data processing. A user may engage in a transaction with another user, such as a purchase of goods, services, or other items a merchant using a digital wallet, such as through a software application. An online transaction processor may provide a digital account for payment processing, which may provide financing and installment loan payment services. Where the payment account may not be compatible with the protocols and procedures for use with the digital wallet, a specific identifier may be provisioned, and proxy data may be generated between the transaction processor's system and the digital wallet's system through exchanged calls and data. The identifier may imitate data used by the digital wallet and may also allow for tokenization and processing with token service providers on payment networks.
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
G06Q 20/10 - Architectures de paiement spécialement adaptées aux systèmes de banque à domicile
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
39.
ESTABLISHING DIGITAL ACCOUNT USAGE IN DIGITAL WALLETS DURING CROSS-PLATFORM DATA PROCESSING
There are provided systems and methods for establishing digital account usage in digital wallets during cross-platform data processing. A user may engage in a transaction with another user, such as a purchase of goods, services, or other items a merchant using a digital wallet, such as through a software application. An online transaction processor may provide a digital account for payment processing, which may provide financing and installment loan payment services. Where the payment account may not be compatible with the protocols and procedures for use with the digital wallet, a specific identifier may be provisioned, and proxy data may be generated between the transaction processor's system and the digital wallet's system through exchanged calls and data. The identifier may imitate data used by the digital wallet and may also allow for tokenization and processing with token service providers on payment networks.
A system or a method may be provided that may detect a movement or activity of a user via the user's mobile and/or wearable devices. The system may adjust the display interface based on the user's detected movement or activity. When a user is very active (biking or jogging), the user may have very limited amount of time or attention to interact with a display interface. The movement or activity of the user may be detected by a motion detection device installed on the mobile device or on the wearable device. When the user is active, the display interface may adjust to enlarge the information to make it easier for the user to view, read, or interact with. The system may also select and display important information, without other peripheral information (less important information) when the user is active.
G06F 1/16 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES - Détails non couverts par les groupes et - Détails ou dispositions de structure
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06F 3/0346 - Dispositifs de pointage déplacés ou positionnés par l'utilisateur; Leurs accessoires avec détection de l’orientation ou du mouvement libre du dispositif dans un espace en trois dimensions [3D], p.ex. souris 3D, dispositifs de pointage à six degrés de liberté [6-DOF] utilisant des capteurs gyroscopiques, accéléromètres ou d’inclinaiso
G06F 3/04845 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p.ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs pour la transformation d’images, p.ex. glissement, rotation, agrandissement ou changement de couleur
G06F 3/04847 - Techniques d’interaction pour la commande des valeurs des paramètres, p.ex. interaction avec des règles ou des cadrans
G06F 3/0488 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p.ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p.ex. des gestes en fonction de la pression exer utilisant un écran tactile ou une tablette numérique, p.ex. entrée de commandes par des tracés gestuels
G06F 3/147 - Sortie numérique vers un dispositif de visualisation utilisant des panneaux de visualisation
41.
SYSTEMS AND METHODS FOR DYNAMICALLY MODIFYING CONTENT OF A WEBSITE
Systems and methods for interfacing with a third-party website. In one embodiment, a computer system is configured to directly interface with a website via a webpage to change certain numerical values through the use of digital codes. The digital codes are applied to a data entry interface on the webpage, and the responses are monitored and transmitted back to a server system.
Methods and systems for digital hot wallet protection are provided. A payment channel is established via a Layer-2 network of a cryptocurrency blockchain for transferring a cryptocurrency balance from a first digital wallet of a service provider to a second digital wallet of a trusted entity over a plurality of commitment transactions. A transaction receipt for each commitment transaction is transmitted to the trusted entity via a secure communication channel previously established between the service provider and the trusted entity outside of the Layer-2 network. A transaction log of the service provider is modified so that it no longer represents the current transaction state of the payment channel. Responsive to detecting a breach of the first wallet, a transaction is broadcast to a Layer-1 network of the blockchain for transferring the total cryptocurrency balance from the first wallet to the second wallet.
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06Q 20/38 - Architectures, 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 magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
H04L 9/00 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité
43.
Methods And Systems For Transferring Unspent Transaction Output (Utxo) Tokens In A Blockchain Network
Mechanisms for efficiently transferring multiple unspent transaction output (UTXO) tokens in a blockchain network operating a UTXO-based token transaction model are disclosed herein. These methods allow for the use of less computer processing and network bandwidth resources in the transfer of blockchain items, particularly in blockchain schemes with lineage tracking mechanisms (e.g. as may allow for clawback of tokens). Some embodiments comprise generating a delegated account and transferring the UTXO tokens into the delegated account. The ownership of the delegated account can then be transferred to another use of the blockchain network in a single transaction, thereby avoiding the need to individually transfer the UTXO tokens and incur the memory and computational resource burden and costs associated with therewith.
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06Q 20/06 - Circuits privés de paiement, p.ex. impliquant de la monnaie électronique utilisée uniquement entre les participants à un programme commun de paiement
H04L 9/00 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité
H04L 9/06 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité l'appareil de chiffrement utilisant des registres à décalage ou des mémoires pour le codage par blocs, p.ex. système DES
Methods and systems are presented for providing a multi-modal machine learning model framework for using enriched data to improve the accuracy of a machine learning model in classifying transactions. Upon receiving a request to process a transaction associated with a purchase of an item, a classification system extracts text data associated with the transaction from the request. Based on the text data, the classification system retrieves additional data related to the item. The additional data is of different modality than the text data. The classification system may transform the text data and the additional data into respective vectors, and merge the vectors for use as input data for the machine learning model. Based on the merged vectors, the classification system obtains multiple classification scores from the machine learning model. The classification system then classifies the transaction based on the multiple classification scores, and processes the transaction according to the classification.
Techniques are disclosed for updating an incremental cache with merged features generated by merging new, incremental features with the existing features. After retrieving source data including attributes from a source database, a system identifies, based on a known set of historical attributes included in the source data, new attributes in the source data. Using feature algorithms, the system generates new features from the new attributes. The system retrieves existing features from the incremental cache storing existing features generated from historical attributes in the source data. Using aggregation procedures, the system merges the new features and the existing features generated based on the historical attributes. Using the merged features, the system updates the incremental cache. The disclosed techniques may advantageously decrease time to retrieve a set of features e.g., for machine learning relative to traditional techniques that recalculate features from an entire source dataset when new source data is released.
A system and/or method may be provided to silently authenticate a user. An example method of silently authenticating a user includes receiving a request to complete a transaction associated with a merchant application. The request includes a data file including an identifier from the user device. The request is from a user device. The method also includes determining whether the data file includes a refresh token and determining whether the refresh token is valid if the data file includes the refresh token. The method further includes receiving an access token from the user device if the refresh token is valid. The access token includes an authorization scope. The method also includes determining whether the transaction is within the authorization scope. The method further includes authenticating a user if the transaction is within the authorization scope.
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06F 21/33 - Authentification de l’utilisateur par certificats
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/38 - Architectures, 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 magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
Techniques are disclosed relating to a delayed presentation of authentication challenge for users, such as in the context of a chat session. In various embodiments, a server system receives an indication of a request for service initiated by a user in a chat session within an application executed by a client device. The request for service involves an authentication of the user that is dependent on the authentication being successfully completed within a particular time period after the authentication is initiated. The server system delays the initiation of authentication for the request for service until a readiness condition is satisfied. The readiness condition includes the server system being available to process the request for service, as well as subsequently detecting engagement with the user relating to the request for service. In response to the readiness condition being satisfied, the server system initiates the authentication of the user.
Techniques are disclosed relating to monitoring network traffic of an embeddable browser displayed by an application executing on a mobile computing device. In some embodiments, a first layer of the application manipulates one or more user interface elements displayed in the embeddable browser. The first layer of the application then detects network requests made by one or more application programming interfaces (APIs) executed by the embeddable browser in response to the manipulating. In some embodiments, the first layer sends to a second layer of the application results of observing network requests. In some embodiments, the second layer of the application displays, in real-time, information corresponding to the results of observing network requests. The disclosed techniques for monitoring activity on an embeddable browser included in mobile applications despite mobile security restrictions may advantageously reduce or remove wait times associated with manipulating and observing content of the embeddable browser.
H04L 43/08 - Surveillance ou test en fonction de métriques spécifiques, p.ex. la qualité du service [QoS], la consommation d’énergie ou les paramètres environnementaux
G06F 3/04842 - Sélection des objets affichés ou des éléments de texte affichés
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
H04L 41/22 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p.ex. des réseaux de commutation de paquets comprenant des interfaces utilisateur graphiques spécialement adaptées [GUI]
H04L 67/02 - Protocoles basés sur la technologie du Web, p.ex. protocole de transfert hypertexte [HTTP]
49.
RISK DETERMINATION ENABLED CRYPTO CURRENCY TRANSACTION SYSTEM
Systems and methods for providing risk determination in a crypto currency transaction include receiving, through a network via a broadcast by a first payer device, a first crypto currency transaction that includes a first payee public address. A first request for a determination of risk associated with the first crypto currency transaction is then identified in the first crypto currency transaction, with the first request including risk criteria. A first payee involved in the first crypto currency transaction is then identified using the first payee public address, and first payee risk information is accessed via at least one external risk information database based on the identification of the first payee. If it is determined that the first payee risk information satisfies the at least one risk criteria in the first request, the first crypto currency transaction is provided for addition to a block in a crypto currency public ledger.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/06 - Circuits privés de paiement, p.ex. impliquant de la monnaie électronique utilisée uniquement entre les participants à un programme commun de paiement
G06Q 20/10 - Architectures de paiement spécialement adaptées aux systèmes de banque à domicile
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
50.
SEMI-SUPERVISED MACHINE LEARNING MODEL FRAMEWORK FOR UNLABELED LEARNING
Methods and systems are presented for providing a semi-supervised machine learning framework for training a machine learning model using partly mislabeled training data sets. Using the semi-supervised machine learning framework, an iterative training process is performed on the machine learning model, wherein the training data is being adjusted continuously in each iteration for training the machine learning model. During each iteration, the machine learning model is evaluated based on its ability to identify training data that has been mislabeled. The labeling of identified mislabeled training data is corrected before feeding back to the machine learning model in the next training iteration.
A search engine determines a number of pages to present to a user based on one or more of a variety of factors. The search engine may send the search results to a client machine, which may present a subset of the results along with a pagination control in a scrollable interface. The pagination control may present the number of pages determined by the search engine. The search engine may also determine a number of search results to present prior to the presentation of non-scrollable user interface (UI) elements based on the same or different factors. After the amount of scrolling exceeds a threshold, a non-scrollable UI element may be displayed. Additional thresholds may exist, such that additional non-scrollable UI elements are added as the user continues to scroll.
There are provided systems and methods for a guided web crawler for automated identification and verification of webpage resources. A service provider, such as an online transaction processor, may provide a guided web crawler and/or resources for such crawler for execution by computing devices of users. Users may load different pluggable modules to the guided web crawler, which are associated with specific web crawling tasks. Web crawling tasks may correspond to identification and verification of webpage resources on a webpage, such as a location, placement, use of, and/or number of appearances of the resource. The web crawler may use code from the pluggable module being executed to parse and/or crawl webpage data for a webpage and identify requested resources. Thereafter, the guided web crawler may automate resources to use, display, and/or interact with the identified and verified resource.
A method for offering at least one credit product by at least one credit issuer to a consumer at a point-of-sale between a merchant and the consumer. The method includes the steps of: providing a credit issuer data set including a plurality of data fields to a central database; initiating a transaction between the consumer and the merchant at the point-of-sale; offering, to the consumer at the point-of-sale, the at least one credit product; and presenting, to the consumer at the point-of-sale, at least one data field in the credit issuer data set. The at least one data field presented to the consumer is populated with data directed to the credit product, the credit issuer, or any combination thereof. An apparatus and system are also disclosed.
G06Q 20/02 - Architectures, schémas ou protocoles de paiement impliquant un tiers neutre, p.ex. une autorité de certification, un notaire ou un tiers de confiance
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G06Q 30/0207 - Remises ou incitations, p.ex. coupons ou rabais
A method according to the present disclosure may include receiving, from a user device, an update associated with a document, generating an update log event based on the update, appending metadata to the update log event, the metadata indicative of a property of the update, storing the update log event with at least one other log event to generate a plurality of log events, receiving an indication of a type of compression, labelling metadata of each of the plurality of log events based on the indicated type of compression, and compressing the plurality of log events based on the labels.
Historical outlier data corresponding to a plurality of user accounts is accessed. The historical outlier data are extracted from historical outlier events, which may correspond to fraud trends or malicious activities. Based on the historical outlier data and using a minority oversampling technique, synthetic outlier data associated with the user accounts is generated. The synthetic outlier data mimics data associated with potential future outlier events that may be similar, but not identical, to any of the historical outlier events. The historical outlier data, at least a subset of the synthetic outlier data, and historical non-outlier data associated with the user accounts are combined into a unified dataset, which may be used to train a machine learning model. Based on the trained machine learning model, new data associated with the user accounts is classified as either outlier data or non-outlier data.
Techniques are disclosed for detecting whether an entity associated with a node of a summary graph is suspicious by retrieving, from a graph database storing a network graph representing a plurality of electronic communications, a portion of the network graph that includes a set of target nodes. Based on the target nodes included in the portion of the network graph, the server system generates community graphs that includes at least a target node and nodes connected to the target node. The server system assigns, based on similarities between the community graphs, the community graphs to clusters and generates a closure graph for clusters, including combining two or more community graphs within respective clusters. Based on respective closure graphs, the server system performs preventative actions relative to entities represented by nodes included in respective closure graphs and connected to the target nodes.
A system includes a memory and a processor configured to execute computer instructions stored in the memory that when executed cause the system to perform operations. The operations include receiving transaction data associated with a transaction via a transaction component. The operations include incorporating at least a portion of the transaction data into a security process associated with challenge-response authentication of a data block for the transaction data. The data block includes cryptographic hash data for another data block in a blockchain associated with the data block. The operations include validating the data block associated with the blockchain based on the security process.
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
H04L 9/00 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité
H04L 9/06 - Dispositions pour les communications secrètes ou protégées; Protocoles 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 9/32 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
Techniques are disclosed relating to automatically resolving an error in a user interaction with a user page without the user having to disengage from the user page to resolve the error. A monitoring agent may interface with the user page. The monitoring agent may provide an error signal to an error resolution module in response to detecting an error in the user interaction with the user page. The error resolution module may determine a causal factor for the error based on the error signal and contextual data at the time of the error. A resolution flow may be determined based on the causal factor. The resolution flow may be implemented by the monitoring agent contextually within the user page to resolve the error without the user disengaging from the user page.
There are provided systems and methods for a data integration framework that provides an institutional or organizational user data enrichment capability locally. Specifically, instead of relying on the fraud detection platform to constantly updating and/or building new data connectors to intake data from updated or a new data provider, an institutional user, such as a financial institution, may receive a software development kit (SDK) from the fraud detection platform, using which the institutional user may build its own data connector deployed at the institutional user.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06F 9/455 - Dispositions pour exécuter des programmes spécifiques Émulation; Interprétation; Simulation de logiciel, p.ex. virtualisation ou émulation des moteurs d’exécution d’applications ou de systèmes d’exploitation
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
Techniques for predicting whether a submission includes a forged image. A computer system receives a submission from a user that includes an image and image metadata, such as an identifier for the user and a User-Agent string value. An image pixel embedding is generated from the image, and a profile embedding is generated from the image metadata. The image embedding is indicative of whether the image is similar to known image forgeries. The profile embedding is generated from a user activity embedding indicative of User-Agent values associated with the user identifier. The profile embedding is generated using a machine learning model that uses stored parameters to associate user activity, device information, and forgery groups. The profile embedding thus indicates whether the user is associated with known image forgeries. The image pixel embedding and profile embedding are then used by a neural network to output a forgery prediction.
A first machine learning task having a first data size is executed via virtualized computing resource units in a research workspace. The first machine learning task is associated with the virtualized computing resource units and with an amount of execution time. A second machine learning task is executed in a production workspace having a plurality of physical computing resource units. The second machine learning task has a same algorithm as the first machine learning task and a second data size greater than the first data size. A subset of the physical computing resource units is allocated for the execution of the second machine learning task in the production workspace. The allocating is based on the virtualized computing resource units used during an execution of the first machine learning task in the research workspace and the amount of execution time of the first machine learning task in the research workspace.
Systems and/or techniques for facilitating image forgery detection via headpose estimation may include a system that can receive a document from a client device. The system can identify, by executing a first trained machine learning model, an object that is depicted in the document. The system can determine, by executing a second trained machine learning model, a pose of the object. The system can determine, by executing a third trained machine learning model, whether the document is authentic or forged based on the pose of the object. The system can, in response to determining that the document is forged, transmit an unsuccessful validation message to the client device.
Methods and systems are presented for providing a framework for analyzing graphs that exhibit non-homophilous behavior. Under the framework, a structural analysis and a feature-based analysis will be performed on a sequence of graphs. When performing the feature-based analysis, various features are extracted from each node in the sequence of graphs, and clusters of nodes are identified from each graph based on the features. A set of evolving prototypes is generated to represent evolving characteristics of the clusters of nodes, and a set of persistent prototypes is generated to represent persistent characteristics of the clusters of nodes. Information derived from the structural analysis of the graphs, the set of evolving prototypes, and the set of persistent prototypes are embedded within the nodes of the graphs. The embedded information is then used to classify the nodes.
G06N 5/02 - Représentation de la connaissance; Représentation symbolique
G06N 5/022 - Ingénierie de la connaissance; Acquisition de la connaissance
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
Systems and methods for facilitating a purchase are described. A user logs in to a payment service provider site. The user provides authorization to use a one-page checkout service. The user's information is captured during the session, and a cookie is placed on the user's device. When the user goes on a merchant website and checks out using the payment service provider, the payment service provider detects the cookie on the user device. The payment service provider uses the cookie, and in one embodiment, centrally stored information, to populate the one-page checkout page with the last used payment. In various embodiments, the one-page checkout page also displays a shipping address.
Techniques are disclosed relating to operating, by a computer system, a semantic search engine to retrieve records from a data store. The technique includes training, by the computer system using a plurality of training data sets that include queries and corresponding records, a retrieval model for use in the semantic search engine. The technique may further include generating, by the trained retrieval model, a particular output vector representing a received semantic search query, and generating, using the particular output vector, a respective similarity score for ones of candidate records identified in the data store. The trained retrieval model may send the particular output vector to a late interaction model, and the late interaction model may sort, using the particular output vector, candidate records with respective similarity scores that satisfy a threshold score.
There are provided systems and methods for a computational platform using machine learning for integration data sharing platforms. A user may engage in a transaction with another user, such as a purchase of goods, services, or other items from a merchant. A service provider may provide a data feed to the user via integrated computational platforms that allows the user to post data including information regarding the processed transaction. The post may include a share code that links back to the user and their corresponding transaction. Thereafter, the post may be viewed by other users and the share code may be used by the other users in order to perform similar transaction processing, where these later transactions are linked back to the original user. Tracking of these later transactions may be done through application extensions that allow the computational platforms to track user data over different online interactions.
A computer system detects the reception of a first token associated with a first transaction. The computer system determines that a first Payments Reward Identifier (PRI) is associated with the first token by accessing a PRI database. In response to determining that the first PRI is associated with the first token, the computer system accesses the PRI database and determines that the first PRI is associated with a record within the PRI database that corresponds to a first rewards ID (RID). In response to determining that the first PRI is associated with a record within the PRI database that corresponds to the first rewards, the computer system determines a first rewards amount corresponding to the first transaction, and updates a total rewards amount, in a rewards database, corresponding to the determined first RID based on the first rewards amount.
G06Q 30/0226 - Systèmes d’incitation à un usage fréquent, p.ex. programmes de miles pour voyageurs fréquents ou systèmes de points
G06Q 20/38 - Architectures, 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 magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
68.
MULTI-LAYER ARTIFICIAL INTELLIGENCE MODELS FOR PROGRESSIVE PREDICTIONS DURING DATA PIPELINE MANAGEMENT
There are provided systems and methods for multi-layer artificial intelligence models for progressive predictions during data pipeline management. A service provider may provide AI functionalities, such as through a multi-layer ML model framework that employs multiple layers for different ML models that process different features. The features in one layer and ML model may process data for static features, where an output from this layer may be used as an input with data for dynamic feature that provide a predictive score or output for the input data. The static features may only be required to be processed once or a few times in the first layer and may not be required to be further processed again at later times. With the second layer, the data for the dynamic features may change, and thus the second layer may process new data without being required to reprocess the static features.
Techniques are disclosed relating to a computer system identifying usage of a plurality of individual instances of a common computation task by a plurality of users of a networked service. These individual instances of the common computation task may generate a respective data set. Techniques also include creating, by the computer system, a global process to perform the common computation task. Execution of the global process may include generation of a global data set that includes at least portions of the respective data sets. Additionally, techniques include modifying, by the computer system, respective accounts of a subset of the plurality of users to use the global process in place of using a respective instance of the common computation task, as well as providing, by the computer system, the global data set generated by the global process to the respective accounts of the subset of users.
G06F 9/48 - Lancement de programmes; Commutation de programmes, p.ex. par interruption
H04L 67/60 - Ordonnancement ou organisation du service des demandes d'application, p.ex. demandes de transmission de données d'application en utilisant l'analyse et l'optimisation des ressources réseau requises
Methods and systems are provided for facilitating shopping for products that have been liked to a social media network. A product can be seen on a merchant's website, for example. The product can be liked by user to the social media website. When the same or a different user subsequently sees the liked product on the social media website, the same or different user can purchase the product from the social media website. Thus, the user is not required to visit the merchant's website to perform the purchase transaction. Upon completion of the purchase, the user can be allowed to leave feedback to rate the item according to a predefined scale, which can be visible to all those who view the item, as exhibited by a vendor on the social media platform.
G06Q 50/00 - Systèmes ou procédés spécialement adaptés à un secteur particulier d’activité économique, p.ex. aux services d’utilité publique ou au tourisme
Methods and systems for a device identification system may be provided. The device identification system may determine an identity of a user device associated with a transaction. The identity may be determined by network address information, hard link information, soft link information, and/or other such information. The network address information may include IPv4 information, IPv6 information, a device ID, and/or other such information. The identity of the user device may be determined and a transaction conducted from the user device may be assigned a fraudulent transaction risk score according to the information. Transactions that are determined to be at a high risk of fraud may be reviewed or otherwise flagged and/or canceled.
Systems, methods and computer-readable media for advertising cannibalization management are provided. An example embodiment includes a processor configured to perform operations including monitoring one or more navigation metrics associated with navigating a website that includes multiple internal pages, wherein one or more of the internal pages include a plurality of interface elements associated with a plurality of navigation links. The operations further include determining, based on the navigation data, presentation values for at least a portion of the interface elements. In addition, the operations include, based on the presentation values, selecting a particular interface element of the at least a portion of the interface elements and causing a real time presentation of the particular interface element on a user interface of a client device.
Methods and systems are presented for improving the accuracy performance and utilization rates of a cascade machine learning model system. The cascade machine learning model system includes multiple machine learning models configured to process transactions according to a cascade operation scheme. Hyperparameter values usable to configure the multiple machine learning models are determined collectively such that the hyperparameter values are selected to optimize the performance of the multiple machine learning models when the models operate according to the cascade operation scheme. Furthermore, an efficacy determination model is used to determine an efficacy of the cascade machine learning model in processing a given transaction. Based on an output of the efficacy determination model, one or more characteristics of the cascade machine learning model are modified for processing the transaction.
Systems, methods, and computer-readable media disclosed herein relate to reducing computation and computing resources for certain blockchain related transactions. Specifically, software algorithms and architecture allow some transactions to avoid the need for recordation on a blockchain, which can be computationally expensive both for a requesting device and for various nodes on the blockchain. Thus, a computer system may receive indications of incoming transactions transferring digital assets to particular user accounts, and in response to requests from user accounts, the computer system facilitates one or more internal transactions between those accounts. In response to a request from a particular internal user account, the computer system may perform an outgoing transaction transferring one or more digital assets to an external user account from one or more internal user accounts. The incoming transactions and outgoing transaction are recorded on the blockchain, but the internal transactions are recorded on an internal ledger rather than the blockchain, saving computational power and improving computer operations.
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06Q 20/06 - Circuits privés de paiement, p.ex. impliquant de la monnaie électronique utilisée uniquement entre les participants à un programme commun de paiement
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
H04L 9/00 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité
H04L 9/06 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité l'appareil de chiffrement utilisant des registres à décalage ou des mémoires pour le codage par blocs, p.ex. système DES
75.
MACHINE LEARNING SYSTEM FOR AUTOMATED RECOMMENDATIONS OF EVIDENCE DURING DISPUTE RESOLUTION
There are provided systems and methods for a machine learning system for automated recommendations of evidence during dispute resolution. A service provider, such as an electronic transaction processor for digital transactions, may provide a dispute resolution system, which allows adjudication of disputes between merchants and customers. The dispute resolution system may employ a machine learning engine that performs evidence classification and recommendation using one or more machine learning models. A first model may be trained to classify evidence based on text and evidence categories. Using the classified evidence, a second model may be trained to recommend evidence that has a highest probability of winning a dispute from past dispute resolutions and those evidence categories submitted to the dispute. The second model may provide one or more evidence categories for a dispute party to submit via a user interface and may rank or otherwise suggest evidence by the user interface.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/02 - Architectures, schémas ou protocoles de paiement impliquant un tiers neutre, p.ex. une autorité de certification, un notaire ou un tiers de confiance
Systems, methods, and computer program products for identifying a fraudulent device. A device analytics engine receives device data from a computing device, the device data including parameters associated with the computing device. The device analytics engine selects a set of rules in a plurality of rules that indicate at least one parameter in the plurality of parameters in the device data for determining a device identifier. The set of rules are evaluated in order until the device identifier is determined from the at least one parameter indicated in the set of rules, the device data, and previously stored data from multiple computing devices. A score is generated for the computing device using one or more of the device identifier, device data, a set of rules, and previously receive device data that corresponds to the device identifier. A computing device is identified as a fraudulent computing device based on the score.
There are provided systems and methods for an account rebalancing daemon for use with secure digital asset custodians. A service provider server, such as an electronic transaction processor, may provide cryptocurrency functions and operations associated with use of an online digital platform that trades and secures cryptocurrencies. The service provider may have a digital wallet that allows for storage of cryptocurrency on the online platform. However, these digital wallets may be vulnerable to computing exploits that may obtain cryptocurrencies through computing attacks, hacking operations, and the like. Thus, the service provider may provide for an automated daemon application or process that utilizes a secure digital wallet using secure computing hardware and authorizations to maintain a balance between the digital wallets to reduce risk. This daemon may automatically detect balance data and may execute operations to rebalance the digital wallets.
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06Q 20/06 - Circuits privés de paiement, p.ex. impliquant de la monnaie électronique utilisée uniquement entre les participants à un programme commun de paiement
Methods and systems for using dispersed cached data stored in multiple database nodes for serving database access requests are described herein. Upon receiving a request for data from a requesting device, a first application server determines whether the requested data is stored in a local cache memory. If it is determined that the requested data is not stored in the local cache memory, without accessing a local, first database, the first application server determines that the requested data is stored in a cache memory of a second application server, wherein the second application server stores at least a portion of the data from a second database in its cache memory. The first application server retrieves the requested data from the cache memory of the second application server and provides the retrieved data to the requesting device.
G06F 16/22 - Indexation; Structures de données à cet effet; Structures de stockage
H04L 65/1063 - Serveurs d'applications fournissant des services réseau
H04L 67/1097 - Protocoles dans lesquels une application est distribuée parmi les nœuds du réseau pour le stockage distribué de données dans des réseaux, p.ex. dispositions de transport pour le système de fichiers réseau [NFS], réseaux de stockage [SAN] ou stockage en réseau [NAS]
H04L 67/568 - Stockage temporaire des données à un stade intermédiaire, p.ex. par mise en antémémoire
H04L 67/60 - Ordonnancement ou organisation du service des demandes d'application, p.ex. demandes de transmission de données d'application en utilisant l'analyse et l'optimisation des ressources réseau requises
79.
DYNAMIC DATA QUERY ROUTING FOR MANAGING ACCESS TO DATASETS HAVING MULTIPLE VERSIONS
There are provided systems and methods for dynamic data query routing for managing access to datasets having multiple versions. A service provider, such as an electronic transaction processor for digital transactions, may provide data for different applications including local device-side software applications and web-based or server-side applications. The data may be read and provided within the application, for example, for output or processing, as well as operated on in the applications to update, add to, or delete from corresponding records. When retrieving or loading data, a data quantum may be used in a data mesh to abstract database physical locations and utilize logical names that allow for applications to code and query for data without direct entanglement with databases and corresponding stored datasets for the data. A router may then be used to route requests and queries for the data to optimized databases for the data.
Systems and techniques that facilitate computing touchpoint journey recommendations are provided. In various embodiments, an input component can receive a computing context of a client and a computing profile of a client. In various instances, the client can be engaged in a computing touchpoint journey. In various embodiments, a prediction component can predict, via a first machine learning classifier, a negative event likely to occur on the computing touchpoint journey. In various cases, the first machine learning classifier can receive as input the computing context and the computing profile and can generate as output the predicted negative event. In various embodiments, a decision component can recommend in real-time, via a second machine learning classifier, a computing touchpoint to which to transfer the client. In various aspects, the second machine learning classifier can receive as input the computing context, the computing profile, and the predicted negative event and produce as output the recommended computing touchpoint. In various embodiments, an execution component can transfer the client to the recommended computing touchpoint. In various embodiments, a computing touchpoint journey component can record computing touchpoint journeys traversed by various clients and trains the first and second machine learning classifiers on the recorded computing touchpoint journeys.
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
G06F 3/04883 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] utilisant des caractéristiques spécifiques fournies par le périphérique d’entrée, p.ex. des fonctions commandées par la rotation d’une souris à deux capteurs, ou par la nature du périphérique d’entrée, p.ex. des gestes en fonction de la pression exer utilisant un écran tactile ou une tablette numérique, p.ex. entrée de commandes par des tracés gestuels pour l’entrée de données par calligraphie, p.ex. sous forme de gestes ou de texte
G06F 11/34 - Enregistrement ou évaluation statistique de l'activité du calculateur, p.ex. des interruptions ou des opérations d'entrée–sortie
G06F 17/18 - Opérations mathématiques complexes pour l'évaluation de données statistiques
Methods and systems are presented for configuring, training, and utilizing a machine learning model that includes different experts corresponding to different domains, such that the machine learning model may facilitate transfer of knowledge acquired from one domain to another domain and to use different mixtures of experts to perform tasks across the different domains. The machine learning model includes individual domain experts configured to process input values corresponding to features that are unique to the corresponding domains. The machine learning model also includes a common expert configured to process input values corresponding to features that are common to the different domains. By training the machine learning model using training data associated with a first domain, both a first domain expert and the common expert are trained. The knowledge acquired by the common expert can then be utilized when processing tasks associated with a second domain.
A load balancer receives a sequence of requests for computing service and distributes the requests for computing service to a computing node in an ordered list of computing nodes until the computing node reaches its maximum allowable compute capability. Responsive to an indication that the computing node has reached its maximum allowable compute capability, the load balancer distributes subsequent requests for computing service to another computing node in the ordered list. If the computing node is the last computing node in the ordered list, the load balancer distributes a subsequent request for computing service to a computing node other than one of the computing nodes in the ordered list of computing nodes. If the computing node is not the last computing node in the ordered list, the load balancer distributes a subsequent request for computing service to another computing node in the ordered list of computing nodes.
H04L 47/726 - Réservations de ressources sur plusieurs routes utilisées simultanément
H04L 41/5019 - Pratiques de respect de l’accord du niveau de service
H04L 67/1012 - Sélection du serveur pour la répartition de charge basée sur la conformité des exigences ou des conditions avec les ressources de serveur disponibles
H04L 67/1031 - Commande du fonctionnement des serveurs par un répartiteur de charge, p.ex. en ajoutant ou en supprimant de serveurs qui servent des requêtes
Systems and methods for providing a resource-based distributed public crypto currency blockchain include system provider device(s) that receive first crypto currency transaction information for a first crypto currency transaction that is configured to provide for the transfer of a crypto currency to a payee via a primary distributed public crypto currency blockchain maintained by computing devices. The system provider device(s) identify resource information provided by each computing device and use the resource information to select a subset of the computing devices for processing the first crypto currency transaction. The system provider device(s) then broadcast, via the network to each computing device, the first crypto currency transaction information for the first crypto currency transaction in order to cause a first computing device to process the first crypto currency transaction as part of a first block that is added to the primary distributed public crypto currency blockchain.
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
H04L 9/06 - Dispositions pour les communications secrètes ou protégées; Protocoles réseaux de sécurité l'appareil de chiffrement utilisant des registres à décalage ou des mémoires pour le codage par blocs, p.ex. système DES
84.
ADAPTABLE QR CODES TO LAUNCH CUSTOMIZED EXPERIENCES
Systems, computer-implemented methods, apparatus, and/or computer program products that can facilitate adaptable QR codes to launch customized experiences are provided. In various embodiments, a system can receive, from a client device, a quick response (QR) code, a client identifier, and location data associated with the client device. In various aspects, the system can identify, from a plurality of merchants, a first merchant that corresponds to the QR code, based on identifying that the first merchant corresponds to the location data. In various instances, the system can identify, from a plurality of client profiles, a first client profile that corresponds to the client identifier. In various cases, the system can identify a digital content based on the first merchant and the first client profile. In various aspects, the system can cause the digital content to be provided to the client device.
G06K 19/06 - Supports d'enregistrement pour utilisation avec des machines et avec au moins une partie prévue pour supporter des marques numériques caractérisés par le genre de marque numérique, p.ex. forme, nature, code
G06Q 30/0226 - Systèmes d’incitation à un usage fréquent, p.ex. programmes de miles pour voyageurs fréquents ou systèmes de points
An indication is received that a first online platform has undergone/is undergoing a first electronic attack made by one or more actors engaged in online malicious actions with the first online platform. Responsive to the indication of the first electronic attack, one or more vulnerability characteristics of the first online platform are determined, where the vulnerability characteristics are associated with the first electronic attack. A plurality of other online platforms are analyzed to identify a second online platform that shares at least one of the vulnerability characteristics with the first online platform. Based on the determining and/or the analyzing, the second online platform is predicted to be a potential target for a second electronic attack having an attack vector in common with the first electronic attack that corresponds to the shared vulnerability characteristics. An action is performed to mitigate potential damage of the second electronic attack.
There are provided systems and methods for load balancing for map application route selection and output. A user may utilize a device application to map or route between two or more endpoints, such as geo-locations entered or detected by the device. During calculation of a travel route between the endpoints, real-time data, user preferences, and requesting entities may provide criteria data that may cause determination of a particular travel route, where the travel route may be longer than a most efficient route but within a pre-defined variable time or distance allotment and match the criteria data. Use of the route may accrue a form of compensation for the user. The user may view an application interface displaying the route, which may further include one or more executable processes to cause recalculation of the route. Recalculation of the route may require the user to provide credits or compensation.
Aspects of the present disclosure involve systems, methods, devices, and the like for tokenizing offers. The current disclosure presents a system and method that can present a tokenized offer for saving and retrieving from a digital wallet. The tokenized offer may be presented on a client site for selection by a user interested in taking advantage of the promotion without having to leave the current site. The current disclosure also presents a system that can retrieve the tokenized offer saved for use with a purchase. The tokenized offer may be automatically applied and on display at the merchant site and/or retrieved from the digital wallet.
Methods and systems for managing loyalty programs using a decentralized blockchain are provided. Transactions between users and a merchant are monitored via an application programming interface (API) of a service provider. Based on the monitoring, it may be determined that at least one of the users has satisfied reward criteria for a loyalty program associated with the merchant. The loyalty program includes a plurality of tokens to be distributed to users who satisfy the reward criteria configured by the merchant via a merchant interface. At least one token from among the plurality of tokens may be selected for the user, based on at least one smart contract associated with the merchant. The smart contract is stored on the blockchain in association with a unique identifier for the merchant. A transaction is broadcasted to the decentralized blockchain for transferring the at least one token to a digital wallet of the user.
G06Q 30/0226 - Systèmes d’incitation à un usage fréquent, p.ex. programmes de miles pour voyageurs fréquents ou systèmes de points
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06Q 20/38 - Architectures, schémas ou protocoles de paiement - leurs détails
A payment button on a device, such as a mobile phone, allows the user to remain on the window or page from which an item was selected for purchase. When the user is ready to purchases, the button is selected, and the user simply enters an identifier, such as a password or PIN, and the transaction is processed. The button remains on the same screen and changes during different stages of the payment process.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/10 - Architectures de paiement spécialement adaptées aux systèmes de banque à domicile
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
G07F 7/10 - Mécanismes actionnés par des objets autres que des pièces de monnaie pour déclencher ou actionner des appareils de vente, de location, de distribution de pièces de monnaie ou de papier-monnaie, ou de remboursement par carte d'identité codée ou carte de crédit codée utilisée simultanément avec un signal codé
90.
Artificial intelligence (AI) engine for dynamic content distribution and management
Methods and systems for content distribution and management are presented. A transaction flow for conducting different stages of a transaction is determined in response to a request for the transaction from an application executable at a user device. The transaction flow includes a sequence of content pages to be displayed within a graphical user interface (GUI) of the application over the different stages of the transaction. The content associated with a tagged UI element of at least one content page is identified. The content is validated for the tagged UI element of the at least one content page, based on a software and hardware configuration of the user device. The validated content is provided via a network to the application at the user device to be displayable with the tagged UI element on the at least one content page of the transaction flow during a corresponding stage of the transaction.
Systems, methods, apparatus, processes, computer program code and means for conducting transactions are described which allow a first party to a transaction to identify a second party to a transaction.
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
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 magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
One or more Bluetooth® low energy (BLE) beacons in communication with a remote server that provides check in capabilities and payment capabilities may be installed at a location. The BLE beacons may connect with a user's mobile device when the user enters the location and allow the user to check in to the location and authorize payments to be made at the location. Once the user is checked in to the location, the user may be provided with additional functionality, benefits, offers, and applications related to the location and facilitated by the check in. Further, the user may be pre-checked in into a next location when the user is at a current location.
H04W 4/80 - Services utilisant la communication de courte portée, p.ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
G06F 21/10 - Protection de programmes ou contenus distribués, p.ex. vente ou concession de licence de matériel soumis à droit de reproduction
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
G06Q 20/12 - Architectures de paiement spécialement adaptées aux systèmes de commerce électronique
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
H04B 7/26 - Systèmes de transmission radio, c. à d. utilisant un champ de rayonnement pour communication entre plusieurs postes dont au moins un est mobile
H04L 9/06 - Dispositions pour les communications secrètes ou protégées; Protocoles 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 67/52 - Services réseau spécialement adaptés à l'emplacement du terminal utilisateur
H04L 67/54 - Gestion de la présence, p.ex. surveillance ou enregistrement pour la réception des informations de connexion des utilisateurs ou état de connexion des utilisateurs
H04W 4/02 - Services utilisant des informations de localisation
H04W 4/06 - Répartition sélective de services de diffusion, p.ex. service de diffusion/multidiffusion multimédia; Services à des groupes d’utilisateurs; Services d’appel sélectif unidirectionnel
H04W 4/21 - Signalisation de services; Signalisation de données auxiliaires, c. à d. transmission de données par un canal non destiné au trafic pour applications de réseaux sociaux
H04W 8/00 - Gestion de données relatives au réseau
H04W 12/02 - Protection de la confidentialité ou de l'anonymat, p.ex. protection des informations personnellement identifiables [PII]
H04W 12/04 - Gestion des clés, p.ex. par architecture d’amorçage générique [GBA]
H04W 12/0431 - Distribution ou pré-distribution de clés; Mise en accord de clés
H04W 40/24 - Gestion d'informations sur la connectabilité, p.ex. exploration de connectabilité ou mise à jour de connectabilité
H04W 48/10 - Distribution d'informations relatives aux restrictions d'accès ou aux accès, p.ex. distribution de données d'exploration utilisant des informations radiodiffusées
H04W 76/11 - Attribution ou utilisation d'identifiants de connexion
H04W 76/34 - Libération sélective de connexions en cours
93.
EDGE COMPUTING STORAGE NODES BASED ON LOCATION AND ACTIVITIES FOR USER DATA SEPARATE FROM CLOUD COMPUTING ENVIRONMENTS
There are provided systems and methods for edge computing storage nodes based on location and activities for user data separate from cloud computing environments. A service provider, such as an online transaction processor, may provide additional services for to users via edge computing systems and edge computing storage nodes. The service may be for data that may be predictively loaded to the edge computing storage node for a particular location, where the edge computing storage node may reside more locally to the location on a network so that data may be served quicker and with less network resource consumption than providing data from a remote cloud computing storage. The data may be predicted to be needed or useful to the user at the location using a user profile for the user, monitored user activities, and/or one or more machine learning models that predict user behaviors at the location.
G06F 16/27 - Réplication, distribution ou synchronisation de données entre bases de données ou dans un système de bases de données distribuées; Architectures de systèmes de bases de données distribuées à cet effet
G06F 16/9035 - Filtrage basé sur des données supplémentaires, p.ex. sur des profils d'utilisateurs ou de groupes
Methods and systems are provided for electronically managing coupons. The user no longer has to clip and organize coupons. The need for hand processing of coupons is substantially reduced. Rebates or payments for coupons can be substantially hastened and can be deposited directly into a user's bank, credit card, or payment provider account. Merchants and manufactures no longer have to be concerned with receiving bad (e.g., invalid, expired, or counterfeit) coupons. Thus, the electronic coupon management system benefits the users, merchants, and manufactures.
G06Q 30/0207 - Remises ou incitations, p.ex. coupons ou rabais
G06K 7/10 - Méthodes ou dispositions pour la lecture de supports d'enregistrement par radiation corpusculaire
G06K 7/14 - Méthodes ou dispositions pour la lecture de supports d'enregistrement par radiation corpusculaire utilisant la lumière sans sélection des longueurs d'onde, p.ex. lecture de la lumière blanche réfléchie
95.
POWER USAGE AND RESOURCE OPTIMIZATION USING MACHINE LEARNING
Various systems, computer-implemented methods, and computer program products are disclosed that use improved machine learning models and techniques for allocating physical resources such as electricity and HVAC systems. These techniques may use data, such as image data, text data, location data, or other similar types of data, indicative of a movement of objects associated with a time period. A machine learning model may extract a first set of features from the data and may determine a physical resource allocation based on the first set of features and a reference dataset. A machine learning model may determine a dynamic configuration of one or more physical resources associated with a physical building space based on the physical resource allocation. These techniques may dynamically configure usage of the one or more physical resources associated with the physical building space based on the physical resource allocation.
There are provided systems and methods for providing application notifications for computing application limitations. A user may engage in a transaction with another user, such as a purchase of goods, services, or other items a merchant using a payment account and/or payment card, such as through a software application. An online transaction processor may provide a monitoring operation to enforce an electronic transaction processing limit on use of one or more account and/or payment limitations via the application. This limit may correspond to a processing limit or throttle, which may limit use of a spending limit in the application below a maximum amount of usage allowed. When the limit is utilized, the online transaction processor may determine that the throttle is approved or violated and may allow a user to configure such limitation through slidable and/or adjustable application icons and interfaces.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
Systems and methods related to point-of-sale devices are disclosed. In an embodiment, a point-of-sale system includes a base stand having a receiving interface that includes first electrical contacts. The POS system may further include a computing device having a user interface, a docking interface comprising second electrical contacts, wherein the docking interface is configured to removably dock the computing device to the receiving interface of the base stand such that the first electrical contacts of the computing device are in contact with the second electrical contacts of the base stand. The computing device may further include a card reader configured to receive and read a card when inserted into the card reader and a scanner configured to scan machine-readable codes. The computing device may be configured to extend or enable various functions of the base stand when the computing device is docked to the base stand.
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G06F 1/16 - TRAITEMENT ÉLECTRIQUE DE DONNÉES NUMÉRIQUES - Détails non couverts par les groupes et - Détails ou dispositions de structure
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
H02J 7/00 - Circuits pour la charge ou la dépolarisation des batteries ou pour alimenter des charges par des batteries
H04W 4/80 - Services utilisant la communication de courte portée, p.ex. la communication en champ proche, l'identification par radiofréquence ou la communication à faible consommation d’énergie
Techniques for using a data confidence index are presented herein. In one embodiment, a method includes maintaining, by a server system, confidence index values corresponding to a set of computing assets that are available to the server system to provide a computing service over a network to remote client devices. The confidence index values are indicative of a readiness of the corresponding computing assets in providing the computing service. The method further includes receiving from a first client device a request for a computing resource associated with the computing service and comparing one or more first confidence index values for a first computing asset of the set of computing assets to one or more second confidence index values for a second computing asset of the set of computing assets. The method also includes selecting data from the one of the first computing asset of second computing asset having confidence index values indicating a greater readiness for providing data and using the selected data to respond to the request.
There are provided systems and methods for dynamic authentication through user information and intent. A user may wish to purchase an item that they view on a merchant marketplace using a computer of mobile phone. The merchant for the merchant marketplace may register the user's intent to purchase the item by receiving the user's actions while browsing the marketplace. The user may further provide user information with the merchant, such as a biometric reading, identifier, or other information. When the user then arrives at a merchant location to purchase the item and complete a transaction using a payment instrument, the merchant may process the user's intent and information to determine how confident the merchant is that the user is entitled to utilize the payment method. Such confidence rating may correspond to whether the merchant believes the transaction is fraudulent or if the user is misrepresenting their identity.
G06Q 20/40 - Autorisation, p.ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasin; Examen et approbation des payeurs, p.ex. contrôle des lignes de crédit ou des listes négatives
Image-based processing of sources of financial information for financial transactions is provided. A device is enabled to acquire one or more images including a representation of a source of financial information. The device can validate the source, independent of the financial information in a preliminary analysis. If the source is validated, one or more images are used to acquire the financial information. The financial information is then validated. If the financial information is valid, authorization of the financial transaction can be initiated.
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