A server computer system comprises a communications module; at least one processor coupled to the communications module; and a memory coupled to the at least one processor and storing processor-executable instructions which, when executed by the at least one processor, configure the at least one processor to obtain data associated with a plurality of assets; group the assets into one or more asset buckets; within at least one of the asset buckets, identify at least one representative asset based at least on stored selection criteria; obtain at least one image associated with the at least one representative asset; and send, via the communications module and to a computing device, an asset summary interface that includes the at least one image associated with the at least one representative asset. The plurality of assets may include one or more assets generated using an artificial intelligence engine trained on asset data.
A computer implemented system and method is provided for responding to textual reviews. The method comprises: receiving harvested content comprising a text segment and associated with an entity; determining a primary intent of the text segment determined by reviewing a set of utterances in the text segment of the harvested content and comparing the set of utterances to example utterances associated with a set of pre-defined intents, the primary intent having a highest similarity to the set of utterances in the text segment as compared to other deduced intents and associated utterances. The method comprises assigning a confidence score associated with determining the primary intent. If the confidence score exceeds a first threshold, generating an automated response, based on the primary intent and automatically responding to the harvested content with the automated response having the customized sentence segment where the confidence score exceeds a second threshold score.
G06Q 30/0282 - Notation ou évaluation d’opérateurs commerciaux ou de produits
G06F 40/35 - Représentation du discours ou du dialogue
G06F 40/40 - Traitement ou traduction du langage naturel
G06F 40/58 - Utilisation de traduction automatisée, p. ex. pour recherches multilingues, pour fournir aux dispositifs clients une traduction effectuée par le serveur ou pour la traduction en temps réel
3.
SYSTEM AND METHOD FOR DEFINING A WEIGHTING SCHEME FOR A DATASET
A computer system comprises a communications module; at least one processor coupled to the communications module; and a memory coupled to the at least one processor and storing processor-executable instructions which, when executed by the at least one processor, configure the at least one processor to send, via the communications module, a request for a proposed weight value for at least one data point in a dataset; receive, via the communications module, the proposed weight value for the at least one data point based on analysis of the at least one data point; define a weighting scheme for the dataset based at least on the proposed weight value for the at least one data point; and apply the weighting scheme to the dataset. Artificial intelligence may be used to identify biases within the dataset.
The present disclosure relates to systems and methods for generating summaries about context of data transfers using trained machined learning models. There is provided a computer system, comprising a processor, a communications module coupled to the processor, and a memory coupled to the processor. The memory stores instructions that, when executed, configure the processor to receive an indication to view a record of a data transfer on a device, collect metadata associated with the data transfer and device data associated with the data transfer from the device, generate a context summary of the data transfer based on the metadata and the device data using a trained machine learning model, and transmit a signal to the device to display the context summary in association with the record of the data transfer.
G06F 16/383 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
5.
SYSTEM AND METHOD FOR CONTINUOUS TESTING WITHIN A DEVSECOPS PIPELINE
A computer system comprising at least one processor; and a memory coupled to the at least one processor and storing processor-executable instructions which, when executed by the at least one processor, configure the at least one processor to define at least one test automation code base associated with a continuous testing cycle; for the at least one test automation code base, define at least one automated test and criteria associated with the at least one automated test; and map the at least one automated test to a command and parameter to trigger the at least one automated test.
Computing platforms, methods, and storage media for determining a root cause of a logged defect in a software environment are disclosed. Exemplary implementations may: obtain, by the apparatus, a defect record associated with the logged defect; convert the categorical-format defect record data to numerical-format defect record data suitable for use by a machine learning model; generate, using the machine learning model, numerical-format model prediction data based on the numerical-format defect record data; convert the numerical-format model prediction data to categorical-format defect root cause data comprising categorical data; and generate a defect root cause report based on the defect root cause data and on the categorical-format defect record data.
The present disclosure generally relates to a computer device, method and system utilizing machine learning for capturing and analyzing profile data communicated across a computing environment including but not limited to: each user's profile, online behaviors and career progression path and provides dynamic recommendations of online actions to be performed to reach a desired target state.
Systems and methods for sending a transfer message based on unstructured text data are disclosed. A method may receive unstructured text data associated with an account, and based on the unstructured text data, identify an intent to transfer data. The unstructured text data may then be sent to a Large Language Model (LLM) via a first prompt engine and an LLM Application Programming Interface (API). First LLM output data may then be received form the LLM, and the transfer message may be sent based on the first LLM output data. The LLM may be a type of artificial intelligence model designed to understand and generate natural-language input.
G10L 13/08 - Analyse de texte ou génération de paramètres pour la synthèse de la parole à partir de texte, p. ex. conversion graphème-phonème, génération de prosodie ou détermination de l'intonation ou de l'accent tonique
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
36 - Services financiers, assurances et affaires immobilières
41 - Éducation, divertissements, activités sportives et culturelles
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Downloadable computer software and mobile applications for electronically trading securities and managing financial portfolios; downloadable computer software and mobile applications for online, internet and mobile banking; downloadable computer software and mobile applications for financial management and financial planning; downloadable software and mobile applications for requesting insurance quotes, managing insurance policies, and for the submission of insurance claims for administrative processing; magnetically encoded credit and debit cards; software and mobile applications in the field of user identification and verification for granting access to banking, investment, loan, securities trading and insurance accounts; software and mobile applications in the field of assistive technology for improving access to websites and digital content. (1) Business consulting services in the field of business acquisitions and mergers; statement production, namely, preparation of financial statements for others; preparing business reports containing financial and economic information; credit and debit card reward program services; arranging and conducting incentive reward programs to promote the sale of products and services of others; business management and administration services and business management consulting; business consultancy in the field of banking leads; business consultancy services in relation to corporate social responsibility; strategic business analysis consulting; business consulting services in the field of business acquisitions and mergers; online payroll preparation.
(2) Banking, online banking and mobile banking services; investment and financial asset management services; financial planning and investment advisory services; mutual fund and mutual fund brokerage services; securities brokerage services; creating, managing and administering investment funds; financial placement of private equity funds for others; private financial placement of hedge funds for others; private financial placement of securities and derivatives for others; venture capital advisory services; venture capital financing services; private equity financing; financing of acquisitions; merchant banking; corporate lending services; securities lending; commodity exchange services; lending and secured lending services; mortgage and online mortgage services; real estate financing; payment gateway services; processing electronic payments made through debit, credit and gift cards, online transfers and units of crypto-currency; bill payment services; stock exchange quotation and listing services; monetary and currency exchange services; automobile financing services; credit and debit card services; insurance claim processing and insurance underwriting services in the fields of life, health, travel, accident, fire, home, pet and automobile insurance; insurance brokerage; providing financial grants to charitable organizations and governmental bodies for education, health, civic and community support, conservation of natural resources and support of philanthropy; philanthropic and charitable services, namely supporting environmental community initiatives and raising environmental awareness; financial sponsorship of sporting, music and entertainment events; financial sponsorship of e-sports events and contests.
(3) Entertainment services, namely, organizing and hosting exclusive receptions at sporting events and concerts; educational services, namely, conducting training classes, seminars, conferences and workshops in the fields of banking, financial services, strategy, leadership and customer service; providing on-line non-downloadable electronic publications in the nature of magazines, newsletters and brochures all in the fields of banking, financial advisory, investment management, real estate and credit card services providing information via a website and educational services in the field of supporting environmental community initiatives and raising environmental awareness; providing information via a website in the field of financial advisory, investment management, mutual fund and securities brokerage services; providing non-downloadable instructional videos in the field of finance via a web site; providing on-line news in the field of finance; entertainment services, namely, online and virtual reality game services; entertainment services, namely, online video games.
(4) Data encryption and decoding services; secure electronic storage of banking, financial transaction and payment, insurance and securities trading data and metadata; computer software design; computer security consultancy and software security consultancy; computer network security services; computer security consultancy; computer security threat analysis for protecting personal and financial data; data security consultancy; electronic monitoring of personally identifying information to detect identity theft; internet security consultancy in the field of detecting and preventing phishing and cyber-security attacks against personal and financial information; software as a service (SAAS) featuring non-downloadable software for computer network security and for data security; credit, debit and gift card transaction processing services; cryptocurrency payment processing; processing electronic payments made through prepaid cards; user authentication services using biometric hardware and software technology for granting access to banking, investment, loan, securities trading and insurance accounts; platform as a service (PAAS) offering online portals and computer software platforms for use in the field of financial services for private equity trading and for the trading of derivatives and commodities; providing temporary use of non-downloadable computer software for tracking and managing financial transactions and for tracking and managing private equity, derivatives and commodity trades; platform as a service (PAAS) offering online portals and computer software platforms to conduct research in the field of the creation, management and trading of financial instruments, private equity funds, securities, derivatives and commodities and to provide financial information in the field of the creation, management and trading of financial instruments, private equity funds, securities, derivatives and commodities.
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
36 - Services financiers, assurances et affaires immobilières
41 - Éducation, divertissements, activités sportives et culturelles
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Downloadable computer software and mobile applications for electronically trading securities and managing financial portfolios; downloadable computer software and mobile applications for online, internet and mobile banking; downloadable computer software and mobile applications for financial management and financial planning; downloadable software and mobile applications for requesting insurance quotes, managing insurance policies, and for the submission of insurance claims for administrative processing; magnetically encoded credit and debit cards; software and mobile applications in the field of user identification and verification for granting access to banking, investment, loan, securities trading and insurance accounts; software and mobile applications in the field of assistive technology for improving access to websites and digital content. (1) Business consulting services in the field of business acquisitions and mergers; statement production, namely, preparation of financial statements for others; preparing business reports containing financial and economic information; credit and debit card reward program services; arranging and conducting incentive reward programs to promote the sale of products and services of others; business management and administration services and business management consulting; business consultancy in the field of banking leads; business consultancy services in relation to corporate social responsibility; strategic business analysis consulting; business consulting services in the field of business acquisitions and mergers; online payroll preparation.
(2) Banking, online banking and mobile banking services; investment and financial asset management services; financial planning and investment advisory services; mutual fund and mutual fund brokerage services; securities brokerage services; creating, managing and administering investment funds; financial placement of private equity funds for others; private financial placement of hedge funds for others; private financial placement of securities and derivatives for others; venture capital advisory services; venture capital financing services; private equity financing; financing of acquisitions; merchant banking; corporate lending services; securities lending; commodity exchange services; lending and secured lending services; mortgage and online mortgage services; real estate financing; payment gateway services; processing electronic payments made through debit, credit and gift cards, online transfers and units of crypto-currency; bill payment services; stock exchange quotation and listing services; monetary and currency exchange services; automobile financing services; credit and debit card services; insurance claim processing and insurance underwriting services in the fields of life, health, travel, accident, fire, home, pet and automobile insurance; insurance brokerage; providing financial grants to charitable organizations and governmental bodies for education, health, civic and community support, conservation of natural resources and support of philanthropy; philanthropic and charitable services, namely supporting environmental community initiatives and raising environmental awareness; financial sponsorship of sporting, music and entertainment events; financial sponsorship of e-sports events and contests.
(3) Entertainment services, namely, organizing and hosting exclusive receptions at sporting events and concerts; educational services, namely, conducting training classes, seminars, conferences and workshops in the fields of banking, financial services, strategy, leadership and customer service; providing on-line non-downloadable electronic publications in the nature of magazines, newsletters and brochures all in the fields of banking, financial advisory, investment management, real estate and credit card services providing information via a website and educational services in the field of supporting environmental community initiatives and raising environmental awareness; providing information via a website in the field of financial advisory, investment management, mutual fund and securities brokerage services; providing non-downloadable instructional videos in the field of finance via a web site; providing on-line news in the field of finance; entertainment services, namely, online and virtual reality game services; entertainment services, namely, online video games.
(4) Data encryption and decoding services; secure electronic storage of banking, financial transaction and payment, insurance and securities trading data and metadata; computer software design; computer security consultancy and software security consultancy; computer network security services; computer security consultancy; computer security threat analysis for protecting personal and financial data; data security consultancy; electronic monitoring of personally identifying information to detect identity theft; internet security consultancy in the field of detecting and preventing phishing and cyber-security attacks against personal and financial information; software as a service (SAAS) featuring non-downloadable software for computer network security and for data security; credit, debit and gift card transaction processing services; cryptocurrency payment processing; processing electronic payments made through prepaid cards; user authentication services using biometric hardware and software technology for granting access to banking, investment, loan, securities trading and insurance accounts; platform as a service (PAAS) offering online portals and computer software platforms for use in the field of financial services for private equity trading and for the trading of derivatives and commodities; providing temporary use of non-downloadable computer software for tracking and managing financial transactions and for tracking and managing private equity, derivatives and commodity trades; platform as a service (PAAS) offering online portals and computer software platforms to conduct research in the field of the creation, management and trading of financial instruments, private equity funds, securities, derivatives and commodities and to provide financial information in the field of the creation, management and trading of financial instruments, private equity funds, securities, derivatives and commodities.
11.
System and Method for Controlling Access to Project Data and To Computing Resources Therefor
A server device, system, method, and for controlling access to project resources is disclosed. The disclosure includes a processor, and a communications module and a memory coupled to the processor. The memory, when executed by the processor, causes the processor to generate a plurality of zones for a project, each zone defining a set of access rights to: i) a database; and ii) at least one tool. The processor configures each set of access rights to allow a proxy service to access the zones, and receives, from a client device and via the proxy service, an access query to access at least one zone. The processor provides the client device access to, via the proxy service, the at least one dataset and at least one tool of the at least one zone.
H04L 47/762 - Contrôle d'admissionAllocation des ressources en utilisant l'allocation dynamique des ressources, p. ex. renégociation en cours d'appel sur requête de l'utilisateur ou sur requête du réseau en réponse à des changements dans les conditions du réseau déclenchée par le réseau
H04L 47/78 - Architectures d'allocation des ressources
H04L 47/783 - Allocation distribuée des ressources, p. ex. courtiers en bande passante
H04L 47/80 - Actions liées au type d'utilisateur ou à la nature du flux
12.
COMPUTING SYSTEMS AND METHODS FOR IDENTIFYING SOFTWARE TEST CASES USING NATURAL LANGUAGE PROCESSING
A server system for identifying test cases is provided. The server system obtains a group of test cases, each test case including a name, a description and one or more steps for testing. For each test case, the server system processes at least the description and the one or more steps using a Natural Language Processing (NLP) pre-trained model to output a vector of numerical values across n-number of dimensions. The server system compiles a group of vectors corresponding to the group of test cases. The server system applies a clustering process to the group of vectors to identify a subset of vectors from the group of vectors. The server system then outputs a subset of test cases corresponding to the subset of vectors.
A computing system may be configured to: receive an instruction to provision a logical storage area; perform a first identity authentication based on a received representation of an identification credential and biometric data captured at the remote computing device; in response to successfully performing the first identity authentication, provision the logical storage area with an unlocked first software feature and a locked second software feature; after provisioning the logical storage area: determine that a second identity authentication has been successfully performed for the identification data, the second identity authentication using a different authentication technique than the first identity authentication; and in response to determining that the second identity authentication has been successfully performed, unlock the second software feature to grant access to additional software functionality in association with the logical storage area. Machine learning may be used in the authentication.
A system and method are provided for authenticating client devices communicating with an enterprise system. The method includes providing a policy enforcement interceptor to intercept API calls and enabling the policy enforcement interceptor to communicate with a policy information point to query the at least one endpoint for entitlements associated with an account. The method also includes intercepting an API call to the application API, communicating with the policy information point to determine entitlements associated with the account by having the policy information point query an entitlements database and, when the entitlements returned to the policy enforcement interceptor are valid, invoking a policy decision point to validate the client device. The method also includes, when the client device is validated, permitting invocation of the API. The method also includes providing an API response to the client device to permit access to the application via the API.
A system, device and method are provided for managing data from different data sources. The illustrative method includes receiving data files from a plurality of data sources and processing each of the received plurality of data files to detect whether data within a respective data file is associated with one or more data models or a respective downstream model. The method includes processing data associated with the one or more data models to generate first portion of a hybrid data file based on the one or more data models. The method includes processing data associated with the respective downstream model to generate a second portion of a hybrid data file, the respective downstream model defining data other than data associated with one or more data models, the respective downstream model being one of a plurality of downstream models. The model includes combining the first and second portions of the hybrid data file and provide the combined hybrid data file to a related downstream application.
A computing system uses a large language model (LLM) to generate one or more synthetic queries for each document of a set of documents. For a user query, the computing system: selects one or more of the synthetic queries related to the user query; generates an adaptive few-shot prompt to instruct the LLM to generate a response to the query, wherein the adaptive few-shot prompt comprises an example query-response pair for each of the selected one more synthetic queries; provides the adaptive few-shot prompt to the LLM as an input; and generates an amended query based on the output of the LLM in response to the adaptive few-shot prompt.
A system, method, and computer readable medium (CRM) for transitioning a computer integration system are disclosed. Illustratively, the method includes providing an integration layer having an interface positioned between a plurality of consumer applications and a plurality of back-end services. The interface is for performing a plurality of functionalities. The method includes providing a target integration scheme to decompose the interface, the target integration scheme comprising a plurality of groupings. For at least one grouping of the plurality of groupings, the method includes providing a service that implements a subset of the plurality of functionalities. The method includes updating the interface to handle requests for the subset via activating the service.
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
H04L 41/5019 - Pratiques de respect de l’accord du niveau de service
A system, method, and computer readable medium for forecasting multiple scenarios are disclosed. Illustratively, the method includes providing data files each comprising a plurality of assumption metrics used for forecasting. Each of the data files can be associated with different scenarios or with the same scenario with different assumption metrics. Data models may be used and trained using machine learning. The method includes receiving a request to evaluate an entity with at least two of the plurality of data files. The method includes determining at least one entity interrelated to the entity. The at least one entity can at least in part owned by one or more owners common to the entity. The method includes generating at least two forecasts based on the entity, the at least one entity, and the at least two of the plurality of data files and providing an output.
G06Q 10/04 - Prévision ou optimisation spécialement adaptées à des fins administratives ou de gestion, p. ex. programmation linéaire ou "problème d’optimisation des stocks"
19.
SYSTEM AND METHODS FOR REAL-TIME PROCESSING OF ACCOUNT OPERATIONS OF A RESOURCE ACCOUNT
A computer-implemented method is disclosed. The method includes: determining at least one trigger condition associated with a resource account; detecting the at least one trigger condition based on real-time analysis of resource transfers in connection the resource account; determining a set of actions based on the resource transfer data, the at least one trigger condition, and customer information; generating text prompts for a large language model to obtain recommendations output for the customer in connection with one or more of the actions of the determined set; obtaining customer input of responses to the recommendations data; and submitting requests, via API calls, to one or more third-party service providers in order to perform the one or more actions, the API calls being generated based on the customer-inputted responses.
Methods and computer systems for notifications. Generating anonymous information and associating the anonymous information with a particular profile in a plurality of profiles. During an authenticated state of a software application installed on a computing device, provisioning the software application with the anonymous information. During an unauthenticated state of the software application, receiving, from the software application, the anonymous information and an indication of detection of the computing device at a premises. In response to receiving the anonymous information and the indication of detection, selecting the particular profile based on the anonymous information and transmitting a notification based on the selected particular profile.
A computer-implemented method is disclosed. The method includes: receiving, via a user interface on a first computing device at a first time, an access request for accessing a specified account; obtaining data capturing account activity of the specified account prior to the first time; determining a first status of a first requesting account associated with the access request based on the obtained data, the first status indicating a determined relationship between the specified account and the first requesting account; and configuring the user interface to selectively enable account features of the specified account based on the first status of the first requesting account.
Computing platforms, methods, and storage media for creating service provisions for related service products are disclosed. Exemplary implementations may: obtain a service provision associated with a first service product; replace a grandfathering logic of the service product design tool with service provision availability parameters defined in a format independent of the service product design tool; and configure an availability script in the service product design tool based on the service provision availability parameters to enable implementation of the service provision in a second service product such that the service provision works differently with the first service product and the second service product.
An example operation may include one or more of receiving interaction content from a communication session between a source device and a service provider device of a service provider, identifying a plurality of contextual attributes of the communication session based on execution of at least one large language models (LLMs) on the interaction content, converting the interaction content and the plurality of contextual attributes of the communication session into vectorized data based on execution of an additional LLM, labelling the vectorized data with identifiers of the plurality of contextual attributes, and storing the vectorized data within a vector database.
An example operation may include one or more of receiving interaction content from a communication session between a source device and a service provider device of a service provider, identifying a search criteria from the interaction content, retrieving a subset of vectors from a plurality of vectors stored in a vector database based on the search criteria of the interaction content, wherein the subset of vectors includes previous interaction content with the service provider, generating a response for the communication session based on execution of a large language model (LLM) on the subset of vectors, and outputting the response to at least one of the source device and the service provider device during the communication session.
An example operation may include one or more of receiving interaction content from a communication session between a source device and a service provider device, executing a large language model (LLM) on the interaction content, wherein the LLM comprises a plurality of attention heads which are configured to simultaneously identify a mood and an item of interest from the interaction content, generating a response to the interaction content based on the mood and the item of interest, and outputting the response to at least one of the source device and the service provider device during the communication session.
An example operation may include one or more of receiving interaction content from an interaction session between devices of internal participants of a service provider, determining contextual values of the interaction content based on execution of one or more machine learning (ML) models on the interaction content, annotating the interaction content with the contextual values, aggregating the interaction content with previously received and annotated interaction content to generate aggregated content, and training an ML model to output responses from the service provider based on execution of the ML model on the aggregated content.
An example operation may include one or more of storing a plurality of vectors corresponding to a plurality of interactions of an organization within a vector database, wherein the plurality of vectors is labeled with policies of the organization based on policies discussed in the plurality of interactions, receiving an identifier of a policy, identifying a subset of vectors in the vector database based on a comparison of the identifier of the policy to labels of the subset of vectors, determining a drift between a current implementation of the policy and content of the policy of the organization based on execution of a machine learning (ML) model on the subset of vectors and the content of the policy, and generating training content based on the drift between the current implementation of the policy and the content of the policy.
The disclosed embodiments include computer-implemented apparatuses and processes that perform causal inferencing in distributed computing environments using trained double machine learning and trained classifiers. For example, an apparatus may receive, from a device, a request that includes identifier associated with the device and exception data that includes a requested modification to a value of a parameter of a data exchange. The apparatus may also obtain labelling data based on an application of a trained classifier to a first input dataset that includes a value of an elasticity parameter associated with the request, may generate elements of decision data associated with the requested modification based on the labelling data and on the exception data, and may transmit, to the device, a response to the request that includes the elements of decision data.
Systems and methods are provided for receiving a first request to access a first borrowed resource in association with a first account, determining that a second account that is associated with the first account has access to a first owned resource corresponding to the first borrowed resource, and in response to determining that the second account has access to the first owned resource, allowing access to the first borrowed resource via the first account; and restricting the first owned resource via the second account.
There is provided a computer-implemented method, device and system for automatically configuring a computer device located at a particular location of an entity having an associated local network. The method, system and device further comprises: detecting from another computer device metadata characterizing the particular location; determining from the metadata whether the computer device requires configuration and if so, performing configuration steps of: automatically determining a device type for the computer device based on one or more connected peripheral devices, the device type defining a role of the computer device within the particular location; sending metadata comprising the particular location and the device type to a central server for requesting configuration of the computer device and in response, receiving configuration information for the computer device. The configuration information comprises configuration settings for configuring the computer device to allow said one or more transactions to be performed thereon.
REAL-TIME PROVISIONING OF DIRECTED DIGITAL CONTENT BASED ON DECOMPOSED STRUCTURED MESSAGING DATA AND TRAINED MACHINE LEARNING OR ARTIFICIAL INTELLIGENCE PROCESSES
The disclosed embodiments include computer-implemented systems and processes that generate and provision, in real time, directed digital content based on decomposed structured messaging data and trained machine learning or artificial intelligence processes. For example, an apparatus may receive messages that characterize first data exchanges initiated between a first counterparty and second counterparties during a first temporal interval. Each of the messages includes elements of message data associated with a real-time payment requested from the first counterparty by a corresponding one of the second counterparties. Based on the elements of message data, that apparatus may predict an occurrence of a second exchange of data that involves the first counterparty during a second temporal interval, and may transmit notification data that includes product data characterizing an available product associated with the predicted occurrence of the second data exchange to a device operable by the first counterparty for presentation within a digital interface.
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
G06F 16/9537 - Recherche à dépendance spatiale ou temporelle, p. ex. requêtes spatio-temporelles
G06F 16/955 - Recherche dans le Web utilisant des identifiants d’information, p. ex. des localisateurs uniformisés de ressources [uniform resource locators - URL]
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
32.
SYSTEM AND METHOD FOR DYNAMICALLY MANAGING A DIGITAL CARD IN AN ELECTRONIC WALLET
A mobile computing device configured to manage a digital wallet card within a mobile wallet on the device by communicating with a central server. The mobile computing device continually communicates with the central server that stores policy information for an account associated with the digital wallet card. If the central server detects an update to the digital wallet card then, based on such indication, the mobile computing device generates an alert such as a notification to be displayed on a user interface of the mobile computing device indicating the existence of the update. The mobile computing device displays concurrently the notification of an update and an embedded link that when selected triggers downloading the update and replacing the digital wallet card with an updated digital wallet card in the mobile wallet.
A system and method are provided for implementing process workflows. The method includes receiving, from a designer client, a process model payload, the process model payload providing a graphical representation of a process workflow; storing the process model payload in a workflow graph database; and, during execution of the process workflow, retrieving the stored process model payload from the workflow graph database. The method also includes translating the process model payload from a workflow graph to a data interchange format to determine a task associated with the process workflow, and indicating the task to a routing service to execute at least part of the process workflow.
A file tokenization application is provided that obtains a data record, such as a table, comprising groupings of data elements. The file tokenization application may be used to tokenize and detokenize data for use in artificial intelligence applications. Each one of the groupings of data elements are associated with a data type. The application selects a first template for tokenization based on at least a first data type associated with a first given grouping of data elements from amongst the groupings of data elements. The first template comprises a first tokenization rule and a first detokenization rule. The file tokenization application tokenizes at least a portion of each of the data elements in the first grouping of data elements to generate a tokenized data record and associated tokenization file, which comprises the first detokenization rule and the data record. The file tokenization application then transmits the tokenized data record.
Methods and computer systems for digital doorbell-based notifications. Determining that a computing device is located at a premises. Determining that a profile associated with the computing device corresponds to a scheduled appointment at the premises with an entity. In response to determining that the profile is associated with the scheduled appointment, causing the computing device to provide a selectable option to send an indication signal. Receiving an indication of actuation of the selectable option to send the indication signal and, in response, sending a notification to a terminal associated with the entity indicating that the computing device is at the premises.
H04M 1/72457 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles avec des moyens permettant d’adapter la fonctionnalité du dispositif dans des circonstances spécifiques en s’appuyant sur la localisation géographique
H04L 51/046 - Interopérabilité avec d'autres applications ou services réseau
H04M 1/72451 - Interfaces utilisateur spécialement adaptées aux téléphones sans fil ou mobiles avec des moyens permettant d’adapter la fonctionnalité du dispositif dans des circonstances spécifiques basés sur des horaires, p. ex. utilisant des applications de calendrier
A computer server system comprises at least one processor; and a memory coupled to the at least one processor and storing processor-executable instructions which, when executed by the at least one processor, configure the at least one processor to define a class that includes at least one generic element control method; implement the class for a specific user interface type that requires the at least one generic element control method; generate a unique name for at least one element on a user interface of the specific user interface type; map the unique name to a locator for the at least one element; and execute a test script to perform automation testing on the user interface based at least on the mapping and the implemented class.
A computer-implemented method is disclosed. The method includes: determining that a time-limited action is active; detecting a trigger condition representing a change in interaction focus; in response to detecting the trigger condition: generating a notification indicating remaining time associated with the time-limited action; providing the notification to an output interface associated with a computing device.
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
G06F 3/04886 - 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 exercée enregistrée par une tablette numérique utilisant un écran tactile ou une tablette numérique, p. ex. entrée de commandes par des tracés gestuels par partition en zones à commande indépendante de la surface d’affichage de l’écran tactile ou de la tablette numérique, p. ex. claviers virtuels ou menus
38.
RESPONSE DETERMINATION BASED ON CONTEXTUAL ATTRIBUTES AND PREVIOUS CONVERSATION CONTENT
An example operation may include at least one of storing first interaction content with a service provider; receiving second interaction content from a communication session between a source device and a service provider device of the service provider; identifying at least one contextual attribute associated with the source device; determining a response based on execution of at least one large language models (LLMs) on the second interaction content, the at least one contextual attribute associated with the source device, and the first interaction content with the service provider; and outputting the response to at least one of the source device and the service provider device during the communication session.
An example operation may include at least one of retrieving vectors from a vector database, where the vectors include previous communication content between a source device and a service provider device, identifying an item of interest that has not been discussed in the previous communication content based on execution of a large language model (LLM) on the vectors, generating content about the item of interest, and outputting the content about the item of interest to at least one of the source device and the service provider device during an active communication session between the source device and the service provider device.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
G06F 16/22 - IndexationStructures de données à cet effetStructures de stockage
G06F 40/35 - Représentation du discours ou du dialogue
G06Q 40/06 - Gestion de biensPlanification ou analyse financières
An example operation may include one or more of receiving communication content from an interaction session between participants of an organization, identifying a plurality of subsets of content within the communication content that correspond to a plurality of different geographic locations based on execution of a machine learning (ML) model on the communication content, converting the plurality of subsets of content into a plurality of vectors and labelling the plurality of vectors with the plurality of different geographic locations, respectively, and identifying a subset of content within the interaction session that is directed to a common topic based on the execution of the ML model, wherein the identifying comprises identifying a plurality of subsets of posted content that correspond to the plurality of different geographic locations within the subset of content that is directed to the common topic.
An example operation may include one or more of receiving interaction content from a interaction session between devices associated with an organization, identifying contextual attributes of one or more of the interaction content and the interaction session, matching the interaction content to a subset of vectors within a vector storage based on labels previously assigned to the subset of vectors, augmenting a machine learning (ML) model based on the subset of vectors to generate an augmented ML model, and generating a response for the interaction session based on execution of the augmented ML model on the interaction content and outputting the response to a device participating in the interaction session.
An execution system enables flexible execution of machine learning process pipelines by generating machine learning workflows with dispatchable workflow components. The execution system identifies process logic components of machine learning process pipelines, where each process logic component is a machine learning model or other data processing function. The execution system generates a machine learning workflow including dispatchable workflow components. Each dispatchable workflow component includes a process logic component, execution wrapper, and dispatch configuration, each of which is logically separate and may be individually modified. The execution system coordinates execution of the dispatchable workflow components by transmitting instructions to worker environments to execute the components. The worker environments may be selected based on requirements or performance of each dispatchable workflow component.
A generative model is evaluated by combining the generative model with an encoder architecture to form an autoencoder. The encoder architecture is trained with the autoencoder while fixing parameters of the generative model, enabling the encoder to learn parameters for reproducing data samples. The generative model is scored by determining the similarity of data points when processed by the trained autoencoder, such as a reconstruction error of the data points when reproduced by the autoencoder. The same encoder architecture may be used to evaluate multiple generative models, such that the different generative models may train different parameters for the encoder architecture. The generative models that are more effective at training the encoder to reproduce the data samples may be considered a higher-quality generative model. This generative model quality score may also provide an effective, calculable upper bound on the Wasserstein distance.
A system and method are provided for examining data from a source. The method is executed by a device having a processor and includes receiving a set of historical data and a set of current data to be examined, from the source. The method also includes generating multiple statistical models based on the historical data and a forecast for each model. The method also includes selecting one of the multiple statistical models based on at least one criterion, and generating a new forecast using the selected model. The method also includes comparing the set of current data against the new forecast to identify any data points in the set of current data with unexpected values. The method also includes outputting a result of the comparison, the result comprising any data points with unexpected values.
A method may include: sending a first signal representing a message identifying an entity from a server computer system to a resource usage tracking server; in response to the message, receiving, from the resource usage tracking server, a second signal representing historical resource usage data for the entity; identifying a first data transfer recipient associated with the historical resource usage data for the entity by using a database of supported data transfer recipients; sending a third signal, the third signal causing a client device associated with the entity to display a selectable option for data transfer to the identified first data transfer recipient; receiving a fourth signal, the fourth signal indicating selection of the selectable option for data transfer to the identified first data transfer recipient; and in response to receiving the fourth signal, configuring an account associated with the entity based on the first data transfer recipient.
G06Q 20/10 - Architectures de paiement spécialement adaptées aux systèmes de transfert électronique de fondsArchitectures de paiement spécialement adaptées aux systèmes de banque à domicile
G06Q 20/14 - Architectures de paiement spécialement adaptées aux systèmes de facturation
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
One example method includes generating an aggregate risk score for an object deployed within a networked environment. The aggregate risk score can be based on a number of threat events and a corresponding set of severity scores for the object, and based on an aggregation of a product of a severity score of each type of threat event and a number of each type of threat event. An overall risk score for the object can be based on a modification of the aggregate risk score. Controlling access of the object to system resources can be based on whether the overall risk score exceeds a predetermined risk threshold value.
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
A system and method are provided for reconciling data used by a data management system. The method includes obtaining an input dataset, the input dataset being replicated from a baseline dataset to enable the data management system to operate on the input dataset. The method also includes comparing the input dataset to the baseline data set to determine discrepancies between the input and baseline datasets, by, for each of a plurality of database pools, process data assigned to that pool by concurrently checking for the discrepancies and executing statements without waiting for all pools to have finished processing. The method also includes creating a delta table for each pool to identify extracted data associated with the discrepancies; and combining delta pools from the plurality of database pools and process columns in the delta table.
An example operation may include one or more of onboarding an account with a software application hosted by a host platform, wherein the onboarding comprises storing an account characteristic within an account profile in a storage device of the host platform, receiving a request for a user interface of the software application from a device, determining a current account characteristic at a time of the request based on the account characteristic of the account stored within the account profile and a clock of the host platform, and dynamically generating visual content based on the current account characteristic of the account at the time of the request and displaying the visual content within the user interface of the software application. An artificial intelligence (AI) model can be trained and/or executed when performing at least one portion of the example operation.
An example operation may include one or more of onboarding an account with a software application, wherein the onboarding comprises storing a characteristic of the account within an account profile of the software application, creating an avatar for the account within the software application, generating and displaying pages of visual aids including the avatar on a user interface of the software application as the account interacts with the pages of the visual aids, determining that the account has interacted with a visual aid and implemented training described within the visual aid based on the account interaction with the software application, and in response, changing a position of the avatar within the pages of the visual aids to reflect the implemented training. An artificial intelligence (AI) model can be trained and/or executed when performing at least one portion of the example operation.
An example operation may include one or more of executing an interaction event with an account device and a chatbot within a chat element based on chatbot responses determined by an artificial intelligence (AI) model, wherein the interaction event comprises an exchange of content between the account device and the chatbot through the chat element, determining that a related response to the content has not been output by the chatbot within the chat element, in response, executing a second AI model on the content received from the account device within the chat element to generate a new chatbot response to the content, and outputting the new chatbot response within the chat element.
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
An example operation may include one or more of executing an interaction with an account device about a first topic of interest and a chatbot within a chat element, wherein the interaction comprises an exchange of content between the account device and the chatbot within the chat element, receiving an interaction data from the account device within the chat element; determining that the interaction data is a second topic of interest based on an execution of an artificial intelligence model on the interaction and the first topic of interest, generating a chatbot response to the interaction data based on the execution of the artificial intelligence model on a state of the interaction prior to receipt of the interaction data, and outputting the chatbot response within the chat element.
G06F 40/35 - Représentation du discours ou du dialogue
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
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
An example operation may include one or more of establishing a link between a mobile device and a head unit within a vehicle, retrieving, by the head unit, one or more data routing tokens from a secure element stored on the mobile device via the link, displaying identifiers of the one or more data routing tokens on a user interface of the head unit in association with a data exchange, receiving an input from the user interface of the head unit which selects a target data routing token from among the one or more data routing tokens, and transmitting a request for the data exchange that includes the target data routing token and the data exchange. An artificial intelligence (AI) model can be trained and/or executed when performing at least one portion of the example operation.
H04W 4/40 - Services spécialement adaptés à des environnements, à des situations ou à des fins spécifiques pour les véhicules, p. ex. communication véhicule-piétons
An example operation may include one or more of communicating with a service provider via a head unit of a vehicle, capturing sensor data from an interior of the vehicle during an interaction between the service provider and an occupant within the vehicle, determining a location of the occupant within the vehicle based on the sensor data captured from the interior of the vehicle, and displaying data related to the interaction between the service provider and the occupant on a user interface from among a plurality of available user interfaces within the vehicle based on the location of the occupant within the vehicle. An artificial intelligence (AI) model can be trained and/or executed when performing at least one portion of the example operation.
B60K 35/65 - Instruments spécialement adaptés à des types de véhicules ou d’utilisateurs spécifiques, p. ex. pour la conduite à gauche ou à droite
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
A computer server system comprises at least one processor; a communications module coupled to the at least one processor; and a memory coupled to the at least one processor and storing processor-executable instructions which, when executed by the at least one processor, configure the at least one processor to define a context model that includes at least one command for performing a specific action; map the at least one command to at least one corresponding command of one or more automation testing tools; obtain a test script and an indication of selection of a first automation testing tool, the test script generated within the context model and including the at least one command; and translate the test script into a first format compliant with the first automation testing tool based at least on the mapping.
A computer-implemented method is disclosed. The method includes: identifying one or more tradeable objects based on performing a contextual scan of document data of a first web document; graphically presenting supplementary display data with webpage content of the first web document, wherein the graphically presenting the supplementary display data comprises displaying, for each identified tradeable object, user interface elements corresponding to available account actions for the tradeable object determined based on permission levels for the account actions and inputted user authentication information; detecting user selection of a user interface element corresponding to a first account-related action in connection with a first one of the identified tradeable objects; determining that further authentication is required for initiating performance of the first account-related action; and responsive to successful further authentication of the user, generating a request to process performance of the first account-related action.
G06F 16/9538 - Présentation des résultats des requêtes
G06F 16/955 - Recherche dans le Web utilisant des identifiants d’information, p. ex. des localisateurs uniformisés de ressources [uniform resource locators - URL]
G06Q 40/04 - TransactionsOpérations boursières, p. ex. actions, marchandises, produits dérivés ou change de devises
56.
Computer System And Method For Automating Support Operations In A Data Management System
A system and method are provided for executing supporting operations in a data management system. The method includes assigning a support pipeline to each of at least one repetitive data treatment task; automatically generate a database query for each support pipeline, each database query applying a corresponding operation to data in a database used by the data management system; and initiating each support pipeline to be triggered by database operations.
An example operation may include one or more of onboarding a user with a child version of a software application, wherein the onboarding comprises storing an account characteristic within an account profile of the software application, connecting a parent version of the software application to the child version of the software application, wherein the connecting comprises enabling the parent version of the software application to control functionality within the child version of the software application, determining that a current account characteristic of the user with the child version of the software application has reached a predetermined account characteristic threshold based on the account characteristic stored within the account profile and a system clock of a host platform of the software application, and in response, automatically disconnecting the parent version of the software application from the child version of the software application. An artificial intelligence (AI) model can be trained and/or executed when performing at least one portion of the example operation.
An example operation may include one or more of executing an interaction with an account via a chatbot within a chat element running on an account device, determining a next response for the chatbot to output within the chat element based on an execution of an artificial intelligence (AI) model on content within the chat element, determining an interaction attribute of the next response based on an execution of a second AI model on the content from the chat element and on historical chat content of the account device and the chatbot, and outputting the next response via the chatbot within the chat element based on the determined interaction attribute.
G06F 40/35 - Représentation du discours ou du dialogue
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
59.
MULTI-TABLE DATA STORAGE WITH AUDITABLE DATA CHANGES
A data management system stores data in a plurality of data tables in relation to unique transaction identifiers stored in a transaction table. The transaction table manages a record for transactions, such that individual transactions may be marked as valid or invalid without modifying or deleting data, thus preserving an auditable data log. When data is transmitted to the data management system for storage, such as from machine-learning model applications, the data management system appends the received data to multiple data tables. When the received data is successfully appended, a corresponding transaction table is updated to include a record of a transaction identifier for the data, the record indicating that the transaction is valid. Subsequent queries are executed on valid transactions, while invalidated or outdated data is still maintained by the data management system for audit purposes.
G06F 16/215 - Amélioration de la qualité des donnéesNettoyage des données, p. ex. déduplication, suppression des entrées non valides ou correction des erreurs typographiques
G06F 16/2458 - Types spéciaux de requêtes, p. ex. requêtes statistiques, requêtes floues ou requêtes distribuées
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
36 - Services financiers, assurances et affaires immobilières
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Computer software and mobile applications for electronically trading securities and managing financial portfolios; computer software and mobile applications for online, internet and mobile banking; computer software and mobile applications for financial management and financial planning. (1) Business consulting services in the field of business acquisitions and mergers.
(2) Banking services; investment and financial asset management services; financial planning and investment advisory services; mutual fund and mutual fund brokerage services; securities brokerage services; financial services, namely money lending and credit and loan services secured by assets; commercial lending, mortgage lending services, real estate lending services and securities lending; real estate financing; stock exchange quotation and listing services; investment of funds; investment fund management; creating, managing and administering investment funds; financial placement of private equity funds for others; private financial placement of hedge funds for others; private financial placement of securities and derivatives for others; venture capital advisory services; venture capital financing services; private equity financing; management of private equity funds; leveraged buy outs and investments in financially distressed or underperforming companies; financing of acquisitions; merchant banking; corporate lending services; securities lending; securities trading services; commodity exchange services; commodity brokerage; currency trading and exchange services; clearing trades for commodities, futures and foreign exchange; corporate lending services; loan syndication; securitization services, namely the structuring, financing, administration and collection of income streams from loans, mortgages, the proceeds of conditional sales contracts and accounts receivable streams; structured finance services; trading of financial instruments; trading of equity derivatives; research services in the field of the creation, management and trading of financial instruments, private equity funds, securities, derivatives and commodities; providing financial information in the field of the creation, management and trading of financial instruments, private equity funds, securities, derivatives and commodities.
(3) Platform as a service (PAAS) offering online portals and computer software platforms for use in the field of financial services for private equity trading and for the trading of derivatives and commodities; providing temporary use of non-downloadable computer software for tracking and managing financial transactions and for tracking and managing private equity, derivatives and commodity trades; platform as a service (PAAS) offering online portals and computer software platforms to conduct research in the field of the creation, management and trading of financial instruments, private equity funds, securities, derivatives and commodities and to provide financial information in the field of the creation, management and trading of financial instruments, private equity funds, securities, derivatives and commodities.
09 - Appareils et instruments scientifiques et électriques
35 - Publicité; Affaires commerciales
36 - Services financiers, assurances et affaires immobilières
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
(1) Computer software and mobile applications for electronically trading securities and managing financial portfolios; computer software and mobile applications for online, internet and mobile banking; computer software and mobile applications for financial management and financial planning. (1) Business consulting services in the field of business acquisitions and mergers.
(2) Banking services; investment and financial asset management services; financial planning and investment advisory services; mutual fund and mutual fund brokerage services; securities brokerage services; financial services, namely money lending and credit and loan services secured by assets; commercial lending, mortgage lending services, real estate lending services and securities lending; real estate financing; stock exchange quotation and listing services; investment of funds; investment fund management; creating, managing and administering investment funds; financial placement of private equity funds for others; private financial placement of hedge funds for others; private financial placement of securities and derivatives for others; venture capital advisory services; venture capital financing services; private equity financing; management of private equity funds; leveraged buy outs and investments in financially distressed or underperforming companies; financing of acquisitions; merchant banking; corporate lending services; securities lending; securities trading services; commodity exchange services; commodity brokerage; currency trading and exchange services; clearing trades for commodities, futures and foreign exchange; corporate lending services; loan syndication; securitization services, namely the structuring, financing, administration and collection of income streams from loans, mortgages, the proceeds of conditional sales contracts and accounts receivable streams; structured finance services; trading of financial instruments; trading of equity derivatives; research services in the field of the creation, management and trading of financial instruments, private equity funds, securities, derivatives and commodities; providing financial information in the field of the creation, management and trading of financial instruments, private equity funds, securities, derivatives and commodities.
(3) Platform as a service (PAAS) offering online portals and computer software platforms for use in the field of financial services for private equity trading and for the trading of derivatives and commodities; providing temporary use of non-downloadable computer software for tracking and managing financial transactions and for tracking and managing private equity, derivatives and commodity trades; platform as a service (PAAS) offering online portals and computer software platforms to conduct research in the field of the creation, management and trading of financial instruments, private equity funds, securities, derivatives and commodities and to provide financial information in the field of the creation, management and trading of financial instruments, private equity funds, securities, derivatives and commodities.
62.
System and Method for Executing A Process Workflow
A system and method are provided for executing process workflows. The method includes obtaining via a communications module, a representation of a workflow as a graph, the graph including a plurality of interconnected workflow tasks. The method also includes storing the graph in a graph database, navigating through the workflow tasks in the graph as the process is executed, and publishing via the communications module, a workflow state change with a topic for the current workflow task. The method also includes receiving via the communications module, a document for the current workflow task, wherein a state of the process is implied by the topic position in the graph, and wherein the topic determines at least one microservice to be employed. The method also includes having at least one workflow task associated with the current workflow task executed by instructing a corresponding one or more microservices via the communications module.
Apparatus and methods for expanding a data transfer framework are disclosed. Exemplary implementations may: provide an ETL framework comprising a plurality of ETL modules and comprising code including variables; obtain a configuration file including data values to replace the variables for executing selected ETL modules, and including external command data configured to execute a new data transformation external to and absent from the ETL framework, the external command data including a reference to an external module generated in relation to an external interface; and execute the one or more selected ETL modules based on the code, the data values and the external command data. Exemplary implementations provide a flexible and expandable ETL framework that enable a new type of data transformation that is not currently supported by the framework, without having to modify the framework. The framework may impart native properties and characteristics of the framework to the external module.
A device, method, and system for treating data from legacy infrastructure is disclosed. Illustratively, the device memory stores computer executable instructions that when executed by the processor cause the processor to provide a dataset comprising a plurality of characters and provide a token table for tokenizing datasets. The token table includes mappings that define replacement tokens for characters in datasets. The instructions cause the processor to generate a tokenized dataset based on the dataset by (1) for each contiguous sequence of letter characters of the plurality of characters, determining a respective letter token having the same length as the respective contiguous sequence, and (2) generate the tokenized dataset by replacing each contiguous sequence of letter characters of the plurality of characters with the determined respective letter token.
The disclosed embodiments include computer-implemented systems and processes that perform reconciliation processing in real-time based on structured messaging data. For example, an apparatus may receive elements of decomposed message data that include a first parameter value of a data exchange involving first and second counterparties, and that characterize a real-time payment requested from the second counterparty by the first counterparty. The apparatus may transmit a notification to a first device operable by the second counterparty and receive a response to the notification from the first device that includes a second parameter value of the data exchange. Based on at least the first and second parameter values, the apparatus may perform operations that reconcile the response with the elements of decomposed message data, and transmitting data indicative of an outcome of the reconciliation to a second device operable by the first counterparty for presentation within a digital interface.
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
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
66.
USING GENERATIVE ARTIFICIAL INTELLIGENCE TO IMPROVE USER INTERACTIONS
The present disclosure generally relates to systems, software, and computer-implemented methods for using generative artificial intelligence to improve user interactions. One example method includes receiving a notification from a contact center application that user interaction events have been generated during an interaction session. Event descriptions for events generated in the session are located in a contact center application use case definition. Event descriptions are enhanced with event information for to generate contextualized event information. The contextualized event information to is added to a generative large language model artificial intelligence context that is provided to a generative large language model artificial intelligence engine. A query is provided to the generative large language model artificial intelligence engine. A query response is received from the generative large language model artificial intelligence engine and the query response is used in the interaction session.
The present disclosure relates to systems and methods for pre-checking transfer information for data transfers using trained machined learning models. There is provided a computer system, comprising a processor; a communications module coupled to the processor; and a memory coupled to the processor. The memory stores instructions that, when executed, configure the processor to monitor input of transfer input for a data transfer in an input field of an interface displayed on a device in real-time, determine a format protocol that applies to the input field, determine whether the transfer input complies with the format protocol using a trained machine learning model, generate and transmit a signal to the device receiving the transfer input in real time, the signal indicating whether the transfer input complies with the format protocol, and receive modification to the transfer input in the input field prior to execution of the data transfer.
A device, method, and system for managing multi-user development systems is disclosed. Illustratively, the method includes providing a platform that enables customization of objects associated with the platform through record based configurations and providing a plurality of configuration files, each configuration file associated with a distinct functionality of the platform. The method includes, for each configuration file of the plurality of the configuration files, structuring the respective configuration file to be an objected oriented and a feature-based data file for entering record based configuration changes for the respective functionality. The method includes accepting requests for customizations of objects in the platform through the plurality of configuration files.
The present disclosure relates to systems and methods for enhancing call center features using generative AI. There is provided a server computer system, comprising: a processor, a communications module coupled to the processor, and a memory coupled to the processor. The memory stores a playbook of a call center and instructions that, when executed, configure the processor to monitor a call in real-time during the call with a caller, identify, from the call, a caller issue in real-time using a trained machine learning model, obtain a response to the caller issue based on execution of a generative artificial intelligence (GenAI) model and the playbook stored in the memory, and implement the response during the call.
The disclosed embodiments include computer-implemented apparatuses and processes that dynamically predict future occurrences of events using adaptively trained artificial-intelligence processes and contextual data. For example, an apparatus may generate an input dataset based on first interaction data and contextual data associated with a prior temporal interval, and may apply an adaptively trained, gradient-boosted, decision-tree process to the input dataset. Based on the application of the adaptively trained, gradient-boosted, decision-tree process to the input dataset, the apparatus may generate output data representative of a predicted likelihood of an occurrence of an event during a future temporal interval, which may be separated from the prior temporal interval by a corresponding buffer interval. The apparatus may also transmit a portion of the generated output data to a computing system, and the computing system may be configured to generate or modify second interaction data based on the portion of the output data.
A computer server system comprises at least one processor; a communications module coupled to the at least one processor; and a memory coupled to the at least one processor and storing processor-executable instructions which, when executed by the at least one processor, configure the at least one processor to receive, via the communications module and from a client device, a hypertext transfer protocol (HTTP) request; determine that the HTTP request includes a large data request; generate a unique identifier for the large data request and store the unique identifier in a database; route the HTTP request to at least one handler function to perform one or more tasks associated with the large data request; and log updates associated with the large data request based on completion or failure of the one or more tasks in the database in association with the unique identifier.
The present disclosure generally relates to systems, software, and computer-implemented methods for using generative artificial intelligence to improve user interactions. One example method includes receiving a notification from a contact center application that user interaction events have been generated during an interaction session. Event descriptions for events generated in the session are located in a contact center application use case definition. Event descriptions are enhanced with event information for to generate contextualized event information. The contextualized event information to is added to a generative large language model artificial intelligence context that is provided to a generative large language model artificial intelligence engine. A query is provided to the generative large language model artificial intelligence engine. A query response is received from the generative large language model artificial intelligence engine and the query response is used in the interaction session.
A system and method are provided for integrating selectable inputs with real-time data pipelines to execute actions. The method includes obtaining, via the data interface, at least one dataset comprising real-time data used to execute an action; rendering, to a client device coupled to the system, a graphical user interface that presents the real-time data from the at least one dataset; enabling selection of a datapoint on the real-time data from the graphical user interface; displaying information associated with the action based on the selection; enabling the selection to be confirmed as an input to execute the action; providing, via the data interface, the input to a real-time data pipeline; and executing a workflow comprising execution of the action using the input.
Methods and computer systems for executing an authentication module. Receiving a first request from a first system executing a first software application. In response to receiving the first request, execute an authentication module. Receiving a second request from a second system executing a second software application. In response to receiving the second request, execute the authentication module, wherein executing the authentication module configures the authentication module to authenticate a credential received from the second system as being associated with a particular profile; identify one or more accounts to be used in fulfilling an associated request, the one or more accounts corresponding to the particular profile; and obtain an indication of consent to perform an operation associated with the associated request.
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
An example operation may include one or more of storing a plurality of articles of content within a data store, ingesting user data from an external data source, wherein the user data comprises contextual attributes of a user, generating content based on content from an article among the plurality of articles and the user data to generate a fused article based on execution of an artificial intelligence (AI) model on the contextual attributes of the user and the content from the article.
An example operation may include one or more of generating an article of content based on execution of an artificial intelligence (AI) model on contextual attributes of a user and a plurality of articles stored within a data store, embedding a product within the article of content, displaying the article of content via a user interface of a user device and embedding an add to cart function associated with the product into the user interface, detecting an input with respect to the add to cart function displayed within the user interface, and in response to the detected input, displaying a notification on the user interface with a cart that includes the product therein.
An AI-driven recommendation system utilizes a machine learning model and a dynamically updated knowledge graph to generate personalized product recommendations. The system constructs a knowledge graph with nodes and edges representing relationships between users, prior product selections, and historical interactions. A supervised learning framework trains the machine learning model using labeled data from the knowledge graph to predict relevant products based on multi-dimensional constraints. A graphical user interface (GUI) presents dynamically adjusted interactive elements to capture user preferences. User responses are processed using natural language processing (NLP) to refine predictions and generate recommendations. The system continuously updates the knowledge graph with real-time user feedback and external data, retraining the machine learning model to enhance future recommendations. This adaptive approach enables personalized, context-aware recommendations that evolve based on user interactions and external influences.
The disclosed embodiments include computer-implemented apparatuses and processes that train and deploy hybrid artificial intelligence processes and coupled extrapolation processes within distributed computing environments. For example, an apparatus obtains event data and indicator data associated with a first temporal interval, and based on an application of a trained artificial intelligence process to portions of the event data and the indicator data, the apparatus generates first output data indicating an expected number of occurrences of a first event during each of a plurality of second temporal intervals. Further, and based on an application of an extrapolation process to the output data, the apparatus generates second output data indicating an expected number of occurrences of a second event during each of the second temporal intervals and modifies an allocation of a computational resource at a computing system in accordance with the first output data and the second output data.
A method may include performing segmentation on unstructured data to generate a number of data segments; providing at least a subset of the data segments to a machine learning; associating each of a plurality of data segments with topics using machine learning; and preparing the training data set based on an output of the machine learning system; and training the specialized machine learning system using the training data set to configure the specialized machine learning system to detect one or more topics represented in one or more further data segments.
Methods and computer systems for locking in offsets associated with resource requests. Receiving input of a resource request amount for a resource request, the resource request associated with a defined period of time and a property. Determining, based on data indicating one or more characteristics of the property, a metric representing an expected quantity of emissions over the defined period of time. Identifying an application programming interface capable of processing a request to lock in an amount for an offset product. Using the application programming interface and the metric to determine an offset amount to offset the expected quantity of emissions. Determining a periodic data transfer amount for the resource request that is based on the resource request amount and the offset amount. Outputting the periodic data transfer amount. Receiving an instruction to initiate the resource request and using the application programming interface to lock in the offset amount.
H04L 47/70 - Contrôle d'admissionAllocation des ressources
G06Q 40/04 - TransactionsOpérations boursières, p. ex. actions, marchandises, produits dérivés ou change de devises
H04L 47/762 - Contrôle d'admissionAllocation des ressources en utilisant l'allocation dynamique des ressources, p. ex. renégociation en cours d'appel sur requête de l'utilisateur ou sur requête du réseau en réponse à des changements dans les conditions du réseau déclenchée par le réseau
An emissions model generating system includes an identifier decoder, an emissions database, and an emissions modeller. The identifier decoder receives indefinite article identifiers and translates the indefinite article identifiers into attribute sets. Each indefinite article identifier non-uniquely identifies an article of manufacture. Each attribute set includes article attributes associated with the respective article of manufacture. The emissions database comprises records each including greenhouse gas emissions data indexed by the article attributes. The emissions modeller receives the attribute sets and translates each received attribute sets into an emissions model by locating a respective matching emissions record in the emissions database. Amongst the plurality of emissions records, the article attributes of the received attribute set most closely match the article attributes of the respective matching emissions record.
A device, method, and system for treating data from legacy infrastructure is disclosed. The method, illustratively, includes receiving a request to treat a data set with a treatment provider. The data set is processed by legacy architecture prior to being provided to the treatment provider. The method includes identifying whether a format of the data set is, or processing by the legacy architecture results in formats that are compatible with the treatment provider. The method includes adjusting the data set with a conversion utility to convert the data set into an adjusted data set in response to determining incompatibility with the treatment provider. The method includes providing the adjusted data set to the treatment provider for treatment.
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
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
In accordance with one aspect of the present disclosure, there is provided a computer-implemented method. The method may include: performing segmentation on text data to generate a number of data segments; providing each of the data segments to a machine learning system; obtaining, as output of the machine learning system, an indication of a sentiment associated with each of the data segments; obtaining an indication of overall sentiment for a plurality of data segments represented by the text data; and providing the indication of the overall sentiment to a device, together with a selectable option to retrieve one or more of the data segments having an indication of sentiment corresponding to the indication of the overall sentiment.
Computing platforms, methods, and storage media for data movement are disclosed. Exemplary implementations may: obtain a data transfer command including a date specification of a set of data to be transferred; automatically determine, based on the date specification of the data to be transferred, a set of files to be transferred; and initiate transfer of the set of files. In an implementation, data is moved based on the date specification, and the files associated with the specified date range are automatically determined. A single command, which may reference a CSV file, may be used to efficiently and reliably transfer a large amount of data without a user having to specify the specific files to be transferred.
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
85.
SYSTEM AND METHOD FOR OPTIMIZED TRANSFER OF DIGITAL ASSETS
A computer-implemented method is disclosed. The method includes: generating a unique code for a recipient of a value storage token; sending, to a messaging address associated with the recipient, a first message including a link for accessing a token provider selection interface; detecting activation of the link by the recipient; in response to detecting activation of the link by the recipient, providing, on a computing device associated with the recipient, the token provider selection interface; receiving, via the token provider selection interface, a selection of a first token provider; and in response to receiving the selection of the first token provider, sending a digital representation of the value storage token to the messaging address associated with the recipient.
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
86.
IDENTIFYING AND MITIGATING DISPARATE GROUP IMPACT IN DIFFERENTIAL-PRIVACY MACHINE-LEARNED MODELS
A model evaluation system evaluates the extent to which privacy-aware training processes affect the direction of training gradients for groups. A modified differential-privacy (“DP”) training process provides per-sample gradient adjustments with parameters that may be adaptively modified for different data batches. Per-sample gradients are modified with respect to a reference bound and a clipping bound. A scaling factor may be determined for each per-sample gradient based on the higher of the reference bound or a magnitude of the per-sample gradient. Per-sample gradients may then be adjusted based on a ratio of the clipping bound to the scaling factor. A relative privacy cost between groups may be determined as excess training risk based on a difference in group gradient direction relative to an unadjusted batch gradient and the adjusted batch gradient according to the privacy-aware training.
36 - Services financiers, assurances et affaires immobilières
Produits et services
(1) Loyalty program services namely the operation of a credit card program involving discounts, incentives or points for the purchase of selected goods or services; operation of customer incentive, award and loyalty programs; promoting the sale of goods and services of others by awarding purchase points for credit card use
(2) Banking services; credit card services
89.
SYSTEM AND METHODS FOR VALIDATING RESOURCE TRANSFERS IN A COMPUTER NETWORK
A computer-implemented method is disclosed. The method includes: receiving, via a computing device, a first request to digitally certify resources for transfer, the first request including an identifier of a first resource account, a recipient identifier, a first quantity of resources to certify, and an expiry time; causing a transfer of the first quantity of resources from the first resource account to a defined intermediate resource account; generating an electronic proof document associated with the digital certification of the resources, the electronic proof document being digitally signed; responsive to receiving, via the computing device, a second request to transfer the digitally certified resources: transmitting, to the recipient entity, a message comprising the electronic proof document and an indication of the expiry time; and processing transfer of the first quantity of resources from the intermediate resource account to the first resource account based on a response action of the recipient entity.
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
G06Q 20/10 - Architectures de paiement spécialement adaptées aux systèmes de transfert électronique de fondsArchitectures de paiement spécialement adaptées aux systèmes de banque à domicile
36 - Services financiers, assurances et affaires immobilières
Produits et services
(1) Loyalty program services namely the operation of a credit card program involving discounts, incentives or points for the purchase of selected goods or services; operation of customer incentive, award and loyalty programs; promoting the sale of goods and services of others by awarding purchase points for credit card use
(2) Banking services; credit card services
91.
DYNAMIC MANAGEMENT AND IMPLEMENTATION OF CONSENT AND PERMISSIONING PROTOCOLS USING CONTAINER-BASED APPLICATIONS
The disclosed exemplary embodiments include computer-implemented systems, devices, apparatuses, and processes that dynamically implement and manage consent and permissioning protocols using container-based applications. By way of example, a device may receive a request for an element of data that includes a first digital token associated with an executed application program. The device may load a second digital token from a portion of the memory that is inaccessible to the executed application program, and when the first digital token is consistent with the second digital token, the device may present, within a digital interface, an interface element that confirms a verification of a digital signature associated with the data element.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
H04L 9/30 - Clé publique, c.-à-d. l'algorithme de chiffrement étant impossible à inverser par ordinateur et les clés de chiffrement des utilisateurs n'exigeant pas le secret
92.
SYSTEM AND METHOD FOR EXECUTING DATA PROCESSING TASKS
Computing platforms, methods, and storage media for executing data processing tasks are disclosed. Exemplary implementations may: receive a plurality of serially executable software object tasks; obtain thread data associated with a plurality of available threads and machine data associated with a plurality of available machines; distribute the plurality of serially executable software object tasks for parallel execution via the plurality of available threads and on the plurality of available machines; and obtain and store shared thread data for the plurality of available threads, such that first thread status data associated with a first thread from the plurality of available threads is made available to a second thread from the plurality of available threads. Executing software tasks in parallel, even when the tasks were not designed to be executed in parallel, increases speed and execution of the software tasks, which uses less processing power and less memory compared to known approaches.
09 - Appareils et instruments scientifiques et électriques
11 - Appareils de contrôle de l'environnement
14 - Métaux précieux et leurs alliages; bijouterie; horlogerie
16 - Papier, carton et produits en ces matières
18 - Cuir et imitations du cuir
20 - Meubles et produits décoratifs
21 - Ustensiles, récipients, matériaux pour le ménage; verre; porcelaine; faience
24 - Tissus et produits textiles
25 - Vêtements; chaussures; chapellerie
26 - Articles de fantaisie; mercerie; fleurs artificielles, cheveux postiches
28 - Jeux, jouets, articles de sport
35 - Publicité; Affaires commerciales
36 - Services financiers, assurances et affaires immobilières
38 - Services de télécommunications
41 - Éducation, divertissements, activités sportives et culturelles
43 - Services de restauration (alimentation); hébergement temporaire
Produits et services
(1) Fridge magnets; sports whistles; sunglasses; cases for sunglasses; chains and chords for sunglasses; mouse pads; cases and covers for cell phones; cell phone holders; cell phone skins; cell phone straps; docking stations for cell phones; hands-free holders for cell phones; phone ring holders; stands for cell phones; lanyards for encoded identity cards.
(2) Key chains; charms for key chains; lanyard for holding keys, lapel pins; decorative hat pins.
(3) Souvenir programs, sports trading cards, pens, notepads, printed notebooks, printed notepads; Bookmarks; Padfolios; Pencils; Stickers; Bumper stickers; Paper coasters; calendars and diaries; souvenir books.
(4) Tote bags, duffle bags, Umbrellas; Wallets; leads for pets; collars for pets; backpacks, book bags, sports bags, bum bags, wallets and handbags; all purpose carrying bags; baggage tags; clothing for pets; cosmetic bags sold empty; wristlet bags.
(5) Non-electric fans for personal use; Non-metal dog tags; plastic pet identification tags; corks for bottles; personal compact mirrors.
(6) Mugs; sports water bottles; plastic bottles; drinking straws; drink coasters; shaker bottles sold empty; napkin holders; corkscrews; bento boxes; candle holders; coin banks; serving dishes; statues and figurines of ceramic; tea cups; tea infusers; thermally insulated beverage bottles; travel mugs; insulated sleeve holder for bottles; Flasks; Bottle openers; Lunch boxes; Pet bowls; Shot glasses; drinking glasses; Water bottles sold empty; neoprene zippered bottle holders; sippy cups.
(7) Towels of textile; banners and flags of textile; table clothes; blankets.
(8) Caps; baseball hats; hats and toques; casual clothing, t-shirts, promotional t-shirts; sports jerseys; scarves; toques; t-shirts, polos, hoodies, sweatshirts, track pants, sweat pants, caps, shoes; athletic clothing; children's clothing; beach clothing; sun protective clothing; wristbands, bandanas, sportswear, casual clothing, rainwear, head scarves, golf wear; sun visors.
(9) Novelty pins and badges; patches for clothing.
(10) Novelty foam fingers and hands, inflatable balloon cheering sticks; small toys; glowsticks; beach balls; pet toys; collectable toy figures; fidget toys; plastic toys; soft toys; stuffed toys; wooden toys. (1) Beverage concession stands; food concession stands; retail and online retail store services featuring, sports collectibles, toys, novelty casual clothing, headwear, beverage glassware, and novelty items; online retail services featuring sports and music memorabilia.
(2) Banking; financial advisory services, namely financial planning and investment management services; insurance services; financial sponsorship of charitable and philanthropic activities; financial sponsorship of sports events.
(3) Broadcasting of radio and television programs related to sports and sporting events; broadcasting of music concerts over the Internet.
(4) Entertainment in the form of live musical concerts; entertainment services in the nature of live sporting events transmitted via the internet; leasing stadium facilities and suites for sporting events and concerts; presentation of musical concerts; providing exclusive reception and waiting areas for customers at entertainment and sporting events; operating an indoor arena.
(5) Restaurant and bar services; food kiosk services being services for providing food and drink.
94.
SYSTEM AND METHODS FOR SECURE PROCESSING OF REAL-TIME RESOURCE TRANSFERS
A computer-implemented method is disclosed. The method includes: receiving a request to initiate a first resource transfer for transferring a first defined quantity of resources from a transferor data record to a transferee data record; verifying that the transferee data record is associated with an intended recipient of the first resource transfer based on: transmitting, to a first server associated with the transferee data record, a transfer solicitation request for requesting a second resource transfer to be initiated from the transferee data record to the transferor data record; receiving, from the first server, a response message including an indication of approval of the second resource transfer by the intended recipient; and verifying ownership of the transferee data record based on the response message, and after verifying that the transferee data record is associated with the intended recipient, initiating a third resource transfer for transferring a third defined quantity of resources from the transferor data record to the transferee data record.
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
An example operation may include one or more of storing a database of payment card data, receiving an identifier of a product from a digital wallet on a user device, identifying one or more payment cards stored within the digital wallet on the user device, determining the benefits that will be obtained by using each of the one or more payment cards to purchase the product via execution of an LLM on the identifier of the product and the database of payment card data, and displaying a chat message within a chat window on the user device with a description of the determined benefits that will be obtained.
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
H04W 4/029 - Services de gestion ou de suivi basés sur la localisation
96.
CONTEXT OPTIMIZATION FOR CONTEXT-BASED TABULAR CLASSIFICATION
A tabular modeling system uses a tabular data model to predict data sample classification for input data samples. When applied, the tabular data model receives a context and an input data point and outputs a classification of the input data. When the tabular data model is applied to a new training set, the tabular modeling system optimizes the context for the new training set by fixing model parameters while modifying context points with respect to the training data set. This enables the tabular data model to learn effective contexts for different data sets.
An example method includes receiving, via a network interface, data relating to a user and a processing pipeline relating to obtaining a first item; determining a current state of the user in the processing pipeline; inputting the received data and the current state into a machine learning model that is trained to receive such inputs for a particular user and generate an output specifying a propensity that the particular user will obtain a particular item; in response to inputting the received data and current state, obtaining, from the machine learning model, a model output specifying a propensity that the user will obtain the first item; and performing, based on the propensity that the user will obtain the first item, a corrective action that mitigates for risks of changing conditions and corresponding impact on an electronic platform when the user obtains the first item.
The present disclosure relates to system and methods for managing permission sharing. There is provided a computer system configured to provide, in an account view of an account, a selectable option to manage sharing permissions for third-party systems defined for the account. The computer system is configured to receive an indication via the selectable option to manage the sharing permissions of the third-party systems, and in response to receiving the indication, display a listing including only the third-party systems for which the sharing permissions for the account have been previously configured, the listing having one or more interface elements for receiving a listing instruction in association with the listing. The computer system is further configured to receive the listing instruction via the one or more interface elements, and in response to receiving the listing instruction, provide a management interface related to the listing.
G06F 3/04817 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p. ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comportement ou d’aspect utilisant des icônes
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
99.
SYSTEM AND METHOD FOR INTEGRATED APPLICATION AND PROVISIONING
The present disclosure involves systems, software, and computer implemented methods for generating new credit accounts for immediate availability for use in current online transaction. One example process includes identifying, at a merchant application, a request to perform a credit application process associated with a particular user via a client application. The credit application process is associated with a financial institution, and the merchant application stores a set of user accounts, each associated with a merchant-specific identifier identifying the particular user profile at the merchant. An interactive application is presented within the merchant application, and information associated with the completed application is transmitted to an API associated with the financial institution with the merchant-specific identifier of the associated user. A payment credential for a newly created credit account is received, and can be associated with the user profile corresponding to the merchant-specific identifier.
G06Q 20/10 - Architectures de paiement spécialement adaptées aux systèmes de transfert électronique de fondsArchitectures de paiement spécialement adaptées aux systèmes de banque à domicile
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
100.
TOKEN MANAGEMENT SERVER AND METHOD OF PROCESSING LIMITED-USE TOKENS
A computer server includes a memory and a data processor. The memory stores a token database and computer processing instructions. The computer processing instructions cause the data processor to receive from a POS station via a payment network a token authorization request that includes a limited-use token and an authorization value, locate in the token database a token record that stores a subledger identifier in association with the limited-use token, and extract the subledger identifier from the located token record. The computer processing instructions also cause the data processor to locate in a subledger database a subledger that is associated with the subledger identifier, confirm that a balance value associated with the located subledger is at least equal to the authorization value, and initiate a transfer from a pooling ledger, distinct from the subledger, of a transfer amount that is equal to the authorization value.
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G06Q 20/34 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des cartes, p. ex. cartes à puces ou cartes magnétiques
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
