Methods and systems are presented for bot detection. A movement of a pointing device is tracked via a graphical user interface (GUI) of an application executable at a user device. Movement data associated with different locations of the pointing device within the GUI is obtained. The movement data is mapped to functional areas corresponding to a range of the different locations of the pointing device within the GUI over consecutive time intervals. At least one vector representing a sequence of movements for at least one trajectory of the pointing device through one or more of the functional areas and a duration the pointing device stays within each functional area is generated. At least one trained machine learning model is used to determine whether the sequence of movements of the pointing device was produced through human interaction with the pointing device by an actual user of the user device.
Techniques for providing access to scope-delimited sensitive data are disclosed. A user provides sensitive data to a first party associated with a payment service provider. The first party stores the sensitive data with the payment service provider, and the payment service provider provides the first party merchant with an encoding of the payment data. The first party provides a purchasing opportunity to the user for goods offered by a third party also associated with the payment service provider. The first party transmits a sensitive data grant request to the payment service provider. In response, the payment service provides a scope-delimited encoding of the sensitive data. The first party provides the scope-delimited encoding of the payment data to the third party. The third party merchant creates a transaction using the scope-delimited encoding of the sensitive data. At some time later, access to the scope-delimited encoding of the sensitive data is revoked.
Techniques are disclosed relating to generating real-time suggested actions for a user based on their user profile attributes. In various embodiments, a server system may select, in real-time, a particular action to suggest to a user based on profile attributes associated with a user account of the user. The server system may then provide a message indicative of this particular action to a user device associated with the user. In some embodiments, the server system may then receive an indication that the user has initiated the particular action. In response to this indication, the server system may update the profile attributes associated with the user account to indicate that the user has initiated the particular action. Using these updated profile attributes, the server system may then select, in real-time, an updated action to suggest to the user that is different from the particular action.
G06F 9/451 - Dispositions d’exécution pour interfaces utilisateur
G06F 16/955 - Recherche dans le Web utilisant des identifiants d’information, p. ex. des localisateurs uniformisés de ressources [uniform resource locators - URL]
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
Downloadable and recorded software for processing electronic
payments and for transferring funds to and from others;
downloadable and recorded software for remittance, transfer
of funds, stored value accounts, debit card transactions,
credit card transactions, and financial transactions and
account notifications; downloadable and recorded software
for creating, preparing, managing, sending, processing,
tracking, and reconciling invoices; downloadable and
recorded software for issuing receipts regarding mobile
payment transactions; downloadable and recorded
authentication software for controlling access to and
communications with computers and computer networks;
downloadable and recorded software for creating and managing
a business and an online store, namely, inventory
management, order processing, order tracking, fulfillment of
orders, sales tracking, collection of sales data, and sales
analytics; downloadable and recorded computer software for
generating and soliciting business funding and investments;
downloadable and recorded software for managing customer
relations (CRM) and loyalty programs, namely, software to
create, manage, and analyze customer contact and account
information, generate and track customer loyalty programs,
and generate reports; downloadable and recorded software
used for point of sale transactions; downloadable and
recorded computer software used to train and manage
employees, record employee hours worked, and generate
payroll processing; downloadable software development kits
(SDK); computer hardware for making, authenticating,
facilitating, operating, managing, and processing payment
transactions with credit cards, debit cards, prepaid cards,
payment cards, gift cards, and other payment forms;
electronic devices, namely, point of sale terminals, chip
card readers, credit card readers, payment card readers,
magnetic encoded and coded card readers, and mobile card
readers; charging stands adapted for use with handheld
digital electronic devices namely, cell phones, MP3 players,
personal digital assistants, point of sale terminals, chip
card readers, credit card readers, payment card readers, and
mobile card readers; credit card reader stands; tablet and
mobile device stands; barcode scanners; receipt printers;
cash drawers. Promoting the sale of goods and services of others by means
of contests and incentive award programs; providing
incentive award programs through issuance and processing of
loyalty points for purchase of the goods and services of
others; providing incentive award programs through the
issuance of gift cards, prepaid gift cards, prepaid stored
value cards for the purpose of promoting and rewarding
loyalty, discounts, offers, deals, coupons, rebates,
rewards, and vouchers to participants for the purchase of
the goods and services of others; customer loyalty services
for commercial, promotional and/or advertising purposes,
namely, administration of a program that allows participants
to obtain and redeem points or awards for goods and/or
services; business consulting services in the field of
online payments; managing and tracking credit card, debit
card, ACH, prepaid cards, payment cards, and other forms of
payment transactions via electronic communications networks
for business purposes; business information management,
namely, electronic reporting of business analytics relating
to payment processing, authentication, tracking, and
invoicing; business management, namely, optimization of
payments for businesses; reconciling financial transactions. Financial services, namely, electronic funds transfer;
clearing financial transactions; financial services, namely,
payment collection, payment transactions and information
processing; providing a wide variety of payment and
financial services, namely, issuing credit cards and lines
of credit, electronic payment services involving electronic
processing and subsequent transmission of bill payment data,
bill payment services with guaranteed payment delivery, all
conducted via a global communications network; credit card
transaction processing services; debit card transaction
processing services; electronic foreign exchange payment
processing; payment processing services, namely, providing
virtual currency transaction processing services for others;
processing electronic payments made through prepaid cards;
providing electronic mobile payment services for others;
providing electronic processing of electronic funds
transfer, ACH, credit card, debit card, check transactions
and payments; credit services, namely, providing revolving
credit account services; bill payment services; fund wiring
services; providing electronic payment services via ATM
machines and point-of-sale (POS) merchants, namely,
providing secure commercial transactions and payment options
using a mobile device at a point of sale; providing payment
collection, payment transaction processing, financial
remittance, transfer of funds to make payments, payment
services using stored value accounts, and debit card
transaction processing to make payments via an online
portal; financial risk management services; financial
transaction analysis, namely, financial analysis of payment
transactions and stored value card and debit card
transactions; providing purchase protection services for
goods and services purchased by others via a global computer
network and wireless networks, namely, providing fraud
reimbursement services in the field of credit card purchases
and electronic payment purchases, and providing secure
commercial transactions for credit card purchases and
electronic payment purchases; reimbursement of funds for
disputed items in the field of electronic payment purchases;
loyalty program and incentive award program payment
processing services. Providing temporary use of online non-downloadable software
for processing electronic payments and for transferring
funds to and from others; providing temporary use of online
non-downloadable software for remittance, transfer of funds,
stored value accounts, debit card transactions, credit card
transactions, financial transactions and account
notifications; providing temporary use of online
non-downloadable software for creating, preparing, managing,
sending, processing, tracking, and reconciling invoices;
providing temporary use of online non-downloadable software
for issuing receipts regarding mobile payment transactions;
providing temporary use of online non-downloadable
authentication software for controlling access to and
communications with computers and computer networks;
providing temporary use of online non-downloadable software
for creating and managing a business and an online store,
namely, inventory management, order processing, order
tracking, fulfillment of orders, sales tracking, collection
of sales data, and sales analytics; providing temporary use
of online non-downloadable computer software for managing
customer relations (CRM) and loyalty programs, namely,
software to create, manage, and analyze customer contact and
account information, generate and track customer loyalty
programs, and generate reports; providing online temporary
use of non-downloadable computer software used for point of
sale transactions; providing temporary use of online
non-downloadable computer software used to train and manage
employees, record employee hours worked, and generate
payroll processing; application service provider featuring
application programming interface (API) software for payment
collection, payment transactions, and information
processing; providing temporary use of online
non-downloadable software for customizing application
programming interfaces (APIs), integrating pay in methods to
digital wallets, sharing payment data between users and
generating reports, managing disputed charges, and
automating chargebacks; application service provider (ASP)
featuring application programming interface (API) software
for payment collection, payment transactions, forwarding
data and information processing; electronic monitoring of
financial transactions for fraud, money laundering, and
illegality in the field of electronic fund transfer and
electronic payment processing services; providing temporary
use of online non-downloadable computer software for
tracking and analyzing payment activity; providing temporary
use of online non-downloadable software to evaluate and
detect fraud and illegality in payment transactions, and
manage compliance validation.
5.
SERVER DEVICE CONFIGURATIONS BASED ON MACHINE LEARNING
A system, a medium, and a method are provided to exchange data packets over a communications network and perform machine learning operations. A network server device receives account data from client devices that correspond to account profiles. An account engine of the network server device segments the account profiles into profile groups based on a respective balance associated with each account profile. The account engine determines target accounts from profile groups based on behavioral data. Further, data processing components of the network server device determine a method of contact for each target account. The data processing components determine a respective time to communicate with a respective device for each target account. Further, communication components of the network server device initiate communications to the respective devices at the respective times for each target account.
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
H04W 4/21 - Signalisation de servicesSignalisation de données auxiliaires, c.-à-d. transmission de données par un canal non destiné au trafic pour applications de réseaux sociaux
6.
SYSTEMS AND METHODS EMPLOYING A ROUTER FOR ELECTRONIC TRANSACTIONS
A system, including: a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations including: receiving a transaction request from an endpoint device on a network, wherein the endpoint device is registered with a transaction service provider; locating, based on the transaction request, an authorization token corresponding to a payment mechanism, wherein the authorization token is stored to a memory device of the router; in response to receiving the transaction request, transmitting the authorization token to the transaction service provider to retrieve transaction information from the transaction service provider, wherein the transaction information includes payment data for a user of the endpoint device; and transmitting the transaction information to the upstream network location, wherein the upstream network location includes a merchant server.
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
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 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
G06Q 40/02 - Opérations bancaires, p. ex. calcul d'intérêts ou tenue de compte
H04W 88/00 - Dispositifs spécialement adaptés aux réseaux de télécommunications sans fil, p. ex. terminaux, stations de base ou points d'accès
Latency, response times, and efficiency improvements for data querying are provided herein, particularly in the context of querying large database systems and data tables from disparate data sources. There are provided systems and methods for automatic query and data retrieval optimization through procedural generation of data tables from query patterns. A service provider may utilize different computing services for query processing and data retrieval for different applications and services used by internal and/or external users. Instead of querying large database systems and numerous data tables, pre-aggregated data tables may instead be used and searched by procedurally generating such tables based on precomputation rules and query patterns. Once patterns have been identified in queries, corresponding data may be aggregated from data sources in a pre-aggregated data table. Query optimization rules may then be used to have these data tables queried in place of their original sources.
A computer-implemented method may include receiving an image to analyze for potential objects; providing the image as input to a pretrained machine learning model, where the pretrained machine learning model is trained to detect a known object that corresponds to a predefined category within the pretrained machine learning model or a generic object; determining based on output from the pretrained machine learning model, that the image comprises an instance of a generic object; performing, based on determining that the image comprises an instance of a generic object, a similarity search between the image and a library of images; and determining, for the instance of the generic object, a specific category defined by the library of images based at least in part on the similarity search. Various other methods and systems are also disclosed.
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
Systems and techniques for facilitating detection of data duplication issues relating to generation of non-fungible tokens are provided. In various embodiments, a computer system can access a digital artwork image. In various aspects, the computer system can generate a set of plagiarism probabilities by comparing the digital artwork image to a set of cached digital artwork images. In various instances, a given plagiarism probability in the set of plagiarism probabilities can indicate a likelihood that the digital artwork image was derived from a given cached digital artwork image in the set of cached digital artwork images. In various cases, the computer system can calculate an authenticity score for the digital artwork image based on the set of plagiarism probabilities. In various aspects, the computer system can determine whether the authenticity score for the digital artwork image satisfies a threshold authenticity value.
A method and system for detecting slow page load is provided. An example system comprises a page request detector, a time-out module, a time-out monitor, and a lightweight page requestor. The page request detector may be configured to detect a request for a web page. The time-out module may be configured to commence a time-out period in response to a request for a web page. The time-out module cooperates with the time-out monitor that may be configured to determine that rendering of a rich version of the requested web page has not commenced at an expiration of the time-out period. The lightweight page requestor may be configured to cause a lightweight version of the requested page to be provided to the client system when the time-out monitor determines that the rendering of a rich version of the requested web page has not commenced at an expiration of the time-out period.
Systems and methods for real-time electronic service processing adjustments are disclosed. In an embodiment, a computer system may determine that a user account activity has triggered an assessment checkpoint from a plurality of assessment checkpoints in a life cycle of a user account. The computer system may retrieve data from the assessment checkpoint and update a lifetime score for the user account based on the retrieved data. The computer system may update the lifetime score by weighting the retrieved data as one or more features in a linear-weighted lifetime score model, for the life cycle. The computer system may adjust a response threshold for the assessment checkpoint based on the updated lifetime score.
A machine learning engine may be trained using artificial intelligence techniques and used according to techniques discussed herein. While an initial electronic transaction for a resource may be permitted, a subsequent related transaction to the initial electronic transaction may be analyzed in view of additional electronic information that was not available at the time of the initial transaction. Analysis of the subsequent related transaction, using the machine learning engine, may indicate a new classification related to the resource and/or the acquisition of the resource. Based on this new classification, usage of the resource may be restricted and/or denied, and the initial transaction for the resource may even be canceled retroactively.
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 16/955 - Recherche dans le Web utilisant des identifiants d’information, p. ex. des localisateurs uniformisés de ressources [uniform resource locators - URL]
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
There are provided systems and methods for a guided web crawler for automated identification and verification of webpage resources. A service provider, such as an online transaction processor, may provide a guided web crawler and/or resources for such crawler for execution by computing devices of users. Users may load different pluggable modules to the guided web crawler, which are associated with specific web crawling tasks. Web crawling tasks may correspond to identification and verification of webpage resources on a webpage, such as a location, placement, use of, and/or number of appearances of the resource. The web crawler may use code from the pluggable module being executed to parse and/or crawl webpage data for a webpage and identify requested resources. Thereafter, the guided web crawler may automate resources to use, display, and/or interact with the identified and verified resource.
There are provided systems and methods for configuration-driven efficient transformation of formats and object structures for data specifications in computing services. A service provider, such as an electronic transaction processor for digital transactions, may utilize different computing services that implement rules and artificial intelligence models for decision-making of data including data in production computing environment. Different services may process data in different data formats and structures. However, transformation of data between different services, such as a gateway service that may receive data processing requests and/or data objects and downstream services that may process such requests and objects, may take significant time and resources. A configuration-driven data transformation platform may intelligently create code for and select from transformers that may be used for data transformations. When selected, the transformers may transform data between services faster and more efficiently by being specifically selected based on past performances and code configurations.
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
36 - Services financiers, assurances et affaires immobilières
Produits et services
Cryptocurrency and stablecoin payment processing; cryptocurrency and stablecoin exchange services; electronic wallet services for trading, storing, sending, receiving, validating, verifying, accepting, tracking, transferring, and transmitting virtual currency, and managing virtual currency payment and exchange transactions; fraud and mistaken transaction reimbursement services, namely, reimbursement of payment purchase processing for transactions concerning lost, damaged, misrepresented, or incorrect products or services; providing purchase protection services, namely, providing payment dispute resolution services and reimbursement services for disputed transactions in the field of electronic payment purchases; financial and banking services, namely, debit card and credit card transaction processing services featuring cash back rewards to customers that make incentivized purchases; electronic funds transfer; processing electronic payment transactions and electronic funds transfers via automated clearing house (ACH), credit card, debit card, electronic check and electronic, mobile and online payment platforms; electronic currency transfer services, namely, electronic splitting and sharing of funds between users
16.
AUTOMATED GENERATION OF PROMPTS FOR RESEARCH SUMMARIES USING GENERATIVE ARTIFICIAL INTELLIGENCE
A method according to the present disclose may include presenting, on a graphical user interface (GUI), an interactive element; receiving, via the interactive element on the GUI, a research target and a type of research; autonomously retrieving, from a search engine, search results related to the research target; identifying, using a predictive machine learning model, at least one relevant portion of the search results, the at least one relevant portion comprising information related to the research target and responsive to the type of research; generating a prompt based on the type of research and the at least one relevant portion of the search results; and receiving, from a generative machine learning model in response to receipt of the generated prompt, a report indicative of the research target.
Techniques are disclosed relating to determining a minimum search region with a threshold number of entities within the minimum search region. In some embodiments, a system selects, based on a location of a user device, a first region, where the location of the device is at a center of the first region. The system executes, based on the first region, a query on a database storing entity locations. The system increases the first region by a specified amount to generate a second search region, where the increasing is based on determining that entities returned by the first query does not satisfy an entity threshold requirement. The system executes, based on the second region, a second query on the database. In response to the second query returning a number of entities that satisfy the threshold requirement, the system causes display, at the device, of the entities returned by the second query.
Techniques are disclosed relating to improving the efficiency of geolocation queries via geospatial cells and query parameter caching. In various embodiments, a system receives a request from a user device to access geolocation information of entities and determine a first geographic location of the device. The system identifies that the first location is within a geospatial cell stored in a database cache and determines whether query parameters corresponding to the cell are stored in the cache. The system may retrieve the cached query parameters corresponding to the geospatial cell and execute a geolocation query on a database storing location information for different entities based on the cached query parameters. Results of the geolocation query may be different from query results of one or more other queries executed, based on the cached query parameters, for other user computing devices located within the given geospatial cell based on the cached query parameters.
09 - Appareils et instruments scientifiques et électriques
36 - Services financiers, assurances et affaires immobilières
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Advertising, marketing, and promotion services; administering promotional and incentive rewards programs, namely, rewards, discounts, and promotional programs featuring discounts and the ability to earn rewards on third-party products and services; debit and credit card incentive reward programs, namely, providing cash back rewards to debit card and credit card account holders; promoting the goods and services of others by providing information regarding rewards, discounts, coupons, rebates, vouchers and special offers for goods and services; promoting the goods and services of others, namely, providing links to the websites of others; promotion services, namely, providing websites featuring links to the online retail and e-commerce websites of others; promoting the goods and services of others via offering rewards, discounts, and promotions through a mobile application Downloadable software for sending, receiving, accepting, buying, selling, storing, transmitting, trading and exchanging digital currency, virtual currency, cryptocurrency, stablecoins, digital and blockchain financial assets, digitized assets, digital tokens, crypto tokens and utility tokens; downloadable software for use in processing payments, purchases, and investments using digital currency, virtual currency, cryptocurrency, stablecoins, digital and blockchain financial assets, digitized assets, digital tokens, crypto tokens and utility tokens; downloadable software for use as an electronic wallet; magnetically encoded debit cards and credit cards; downloadable software that promotes the goods and services of others by providing information regarding rewards, discounts, cash back offers, coupons, rebates, vouchers and special offers for goods and services; downloadable software for the electronic transfer of money; downloadable software that allows users to transfer money, split money, and share money; downloadable software for processing electronic payment transactions and funds transfers made via automated clearing house (ACH), credit card, debit card, electronic check and electronic, mobile and online payment platforms Cryptocurrency and stablecoin payment processing; cryptocurrency and stablecoin exchange services; electronic wallet services for trading, storing, sending, receiving, validating, verifying, accepting, tracking, transferring, and transmitting virtual currency, and managing virtual currency payment and exchange transactions; fraud and mistaken transaction reimbursement services, namely, reimbursement of payment purchase processing for transactions concerning lost, damaged, misrepresented, or incorrect products or services; providing purchase protection services, namely, providing payment dispute resolution services and reimbursement services for disputed transactions in the field of electronic payment purchases; financial and banking services, namely, debit card and credit card transaction processing services featuring cash back rewards to customers that make incentivized purchases; electronic funds transfer; processing electronic payment transactions and electronic funds transfers via automated clearing house (ACH), credit card, debit card, electronic check and electronic, mobile and online payment platforms; electronic currency transfer services, namely, electronic splitting and sharing of funds between users Software as a service (SaaS) services featuring software for sending, receiving, accepting, buying, selling, storing, transmitting, trading and exchanging digital currency, virtual currency, cryptocurrency, stablecoins, digital and blockchain financial assets, digitized assets, digital tokens, crypto tokens and utility tokens; Software as a service (SaaS) services featuring software for use in processing payments, purchases, and investments using digital currency, virtual currency, cryptocurrency, stablecoins, digital and blockchain financial assets, digitized assets, digital tokens, crypto tokens and utility tokens; Software as a service (SaaS) services featuring software that promotes the goods and services of others by providing information regarding rewards, discounts, cash back offers, coupons, rebates, vouchers and special offers for goods and services; Software as a service (SaaS) services featuring software for the electronic transfer of money; Software as a service (SaaS) services featuring software that allows users to transfer money, split money, and share money; software as a service (SaaS) services featuring software for processing electronic payment transactions and funds transfers made via automated clearing house (ACH), credit card, debit card, electronic check and electronic, mobile and online payment platforms; software as a service (SaaS) services featuring software for use as an electronic wallet
36 - Services financiers, assurances et affaires immobilières
Produits et services
Cryptocurrency and stablecoin payment processing; cryptocurrency and stablecoin exchange services; electronic wallet services for trading, storing, sending, receiving, validating, verifying, accepting, tracking, transferring, and transmitting virtual currency, and managing virtual currency payment and exchange transactions; fraud and mistaken transaction reimbursement services, namely, reimbursement of payment purchase processing for transactions concerning lost, damaged, misrepresented, or incorrect products or services; providing purchase protection services, namely, providing payment dispute resolution services and reimbursement services for disputed transactions in the field of electronic payment purchases; financial and banking services, namely, debit card and credit card transaction processing services featuring cash back rewards to customers that make incentivized purchases; electronic funds transfer; processing electronic payment transactions and electronic funds transfers via automated clearing house (ACH), credit card, debit card, electronic check and electronic, mobile and online payment platforms; electronic currency transfer services, namely, electronic splitting and sharing of funds between users
Techniques are disclosed relating to improving the efficiency of geolocation queries via geospatial cells and query parameter caching. In various embodiments, a system receives a request from a user device to access geolocation information of entities and determine a first geographic location of the device. The system identifies that the first location is within a geospatial cell stored in a database cache and determines whether query parameters corresponding to the cell are stored in the cache. The system may retrieve the cached query parameters corresponding to the geospatial cell and execute a geolocation query on a database storing location information for different entities based on the cached query parameters. Results of the geolocation query may be different from query results of one or more other queries executed, based on the cached query parameters, for other user computing devices located within the given geospatial cell based on the cached query parameters.
Techniques for detecting a fraudulent attempt by an adversarial user to voice verify as a user are presented. An authenticator component can determine characteristics of voice information received in connection with a user account based on analysis of the voice information. In response to determining the characteristics sufficiently match characteristics of a voice print associated with the user account, authenticator component can determine a similarity score based on comparing the characteristics of the voice information and other characteristics of a set of previously stored voice prints associated with the user account. Authenticator component can determine whether the similarity score is higher than a threshold similarity score to indicate whether the voice information is a replay of a recording or a deep fake emulation of the voice of the user. Above the threshold can indicate the voice information is fraudulent, and below the threshold can indicate the voice information is valid.
G10L 25/51 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation
Systems and methods for determining control objective for electronic documents using models may include obtaining electronic documents and a control objectives library, determining a first set of summaries based on the electronic documents, extracting a set of embeddings from the control objectives library, and determining a set of control objectives based on the summaries and the embeddings. The method may also include determining control objective candidates based on the summaries and embeddings, ranking the control objective candidates based on a confidence score, filtering the control objective candidates based on the ranking, categorizing the control objectives candidates into a second and third set of control objectives, updating the control objectives library to include one or more control objectives from the third set of control objectives, and validating control objectives in the third set of control objectives based on a test plan and updating the control objectives library that pass validation.
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
24.
INTELLIGENT PRE-EXECUTION OF DECISION SERVICE STRATEGIES FOR AVAILABILITY DURING DATA REQUESTS
There are provided systems and methods for intelligent pre-execution of decision service strategies for availability during data requests. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules and artificial intelligence models for decision-making of data including data in production computing environment. A decision service may normally be invoked to execute a strategy for processing a request when that request is received or arrives at the decision service. To provide faster processing and higher availability of such services, an intimation call may be received at the decision service from an upstream service, which initiates a process to pre-execute the strategy based on available data at the time of the intimation call. Pathways of rules capable of being completed may be executed using the available data and the result may be stored for later merging with further strategy execution.
There are provided systems and methods for streamlined and privacy protected data flows for entity onboarding with online data platforms. An online transaction processor or other service provider may provide computing services and platforms to entities including merchants for electronic transaction processing and other account services. To onboard entities with the transaction processor, the transaction processor may provide a merchant or user-specific experience and recommendations using a conversational AI service and chatbot. An AI engine may be trained to engage with users via chat dialogue, which may interact with users during onboarding and/or account lifecycle events based on the available services and products of the service provider. An intent of the entity may be classified by the AI engine and may be used to generate a recommendation including an action plan or the like of activities or steps for the entity to take.
From a plurality of sources, data pertaining to one or more users is accessed. Based on the data, one or more original underwriting model scores are determined for the users. Based on the one or more original underwriting model scores, an initial approval decision is generated for one or more credit applications associated with the one or more users. One or more macro environmental criteria is monitored. Based on the monitoring indicating that the one or more macro environmental criteria has exceeded a specified threshold, the one or more macro environmental criteria and the one or more original underwriting model scores are inputted into a hyper model. Via the hyper model, one or more scaled underwriting model scores are determined for the one or more users. Based on the one or more scaled underwriting model scores, a revised approval decision is generated for one or more credit applications.
A system may include a processor and a non-transitory computer readable medium having stored thereon instructions that are executable by the processor to cause the system to receive a conversation log between a first user and a second user, derive, via a first machine learning model, at least one text chunk from the conversation log, process, via a second machine learning model, the at least one text chunk, the second machine learning model trained using previous conversation logs to determine whether the at least one text chunk indicates a vulnerability, in response to the at least one text chunk indicating the vulnerability, classify a type of the indicated vulnerability, and automatically execute a remedial action based on the classified type.
G06F 21/55 - Détection d’intrusion locale ou mise en œuvre de contre-mesures
G06F 21/57 - Certification ou préservation de plates-formes informatiques fiables, p. ex. démarrages ou arrêts sécurisés, suivis de version, contrôles de logiciel système, mises à jour sécurisées ou évaluation de vulnérabilité
G06F 40/289 - Analyse syntagmatique, p. ex. techniques d’états finis ou regroupement
Methods, systems, and computer program products are included for load balancing processing of a data stream that is communicated over a network. An exemplary method includes establishing a communication session over a network between a source endpoint device and a destination endpoint device. A network device in the network receives a data stream that is communicated to the destination endpoint device from the source endpoint device during the communication session. The network device provides data corresponding to the data stream to a processing device. The processing device identifies a portion of the data that is assigned to the processing device and performs operations to process the portion of the data. After performing the operations, the processing device communicates a response corresponding to the processing to the source endpoint device by way of the destination endpoint device.
H04L 67/1008 - Sélection du serveur pour la répartition de charge basée sur les paramètres des serveurs, p. ex. la mémoire disponible ou la charge de travail
H04L 67/141 - Configuration des sessions d'application
H04L 67/56 - Approvisionnement des services mandataires
H04L 69/16 - Implémentation ou adaptation du protocole Internet [IP], du protocole de contrôle de transmission [TCP] ou du protocole datagramme utilisateur [UDP]
H04L 69/163 - Adaptation dans la bande de l'échange de données TCPProcédures de commande intra-bande
29.
CONVERSATIONAL ARTIFICIAL INTELLIGENCE SERVICE AND CHAT ASSISTANT FOR PERSONALIZED ENTITY ONBOARDING WITH DIGITAL PLATFORMS
There are provided systems and methods for streamlined and privacy protected data flows for entity onboarding with online data platforms. An online transaction processor or other service provider may provide computing services and platforms to entities including merchants for electronic transaction processing and other account services. To onboard entities with the transaction processor, the transaction processor may provide a merchant or user-specific experience and recommendations using a conversational AI service and chatbot. An AI engine may be trained to engage with users via chat dialogue, which may interact with users during onboarding and/or account lifecycle events based on the available services and products of the service provider. An intent of the entity may be classified by the AI engine and may be used to generate a recommendation including an action plan or the like of activities or steps for the entity to take.
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
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
The disclosed computer-implemented method includes detecting an unavailability of a primary cryptocurrency exchange system and queuing received cryptocurrency transaction requests. The method also includes connecting to a backup cryptocurrency exchange system and completing the queued cryptocurrency transaction requests with a backup asset pool using the backup cryptocurrency exchange system. The method further includes detecting an availability of the primary cryptocurrency exchange system and reconnecting to the primary cryptocurrency exchange system. Various other methods, systems, and computer-readable media are also disclosed.
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
There are provided systems and methods of bot detection through explainable deep learning and rule violation codebooks from generative AI. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users for processing various requests and interactions with those users. However, malicious users may utilize bots, such as automated scripts and software applications, that attempt to conduct fraud, compromise systems and data, and the like. To provide better bot and bot activity detection, the service provider may implement an explainable deep learning system that may generate rule violations of rules indicating bot activity or presence in computing logs and interactions using a generative AI. The violations may have a corresponding explanation in codebooks to automate bot detection. When bot activity is detected, the explanation may provide a reason for the bot activity detection.
Blockchain latency is improved by unclogging a mempool, which frees up electronic memory and reduces CPU usage and network bandwidth. Mining data of one or more initial blocks of a blockchain is accessed. The mining data reveals, for each miner, the time delay between individual transactions mined by that miner. A subset of miners is then determined to have lower time delays than miners not in the subset. Thereafter, a different random number is generated for each new block of the blockchain system to be mined. Based on a comparison of this random number and a predefined threshold, either an exploitation phase or an exploration phase is entered for the mining of each new block. In the exploitation phase, mining tasks are assigned only to the subset of the miners. In the exploration phase, mining tasks are assigned to both miners within the subset and miners not in the subset.
H04L 9/06 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité l'appareil de chiffrement utilisant des registres à décalage ou des mémoires pour le codage par blocs, p. ex. système DES
G06F 9/46 - Dispositions pour la multiprogrammation
H04L 9/00 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité
H04L 9/32 - Dispositions pour les communications secrètes ou protégéesProtocoles réseaux de sécurité comprenant des moyens pour vérifier l'identité ou l'autorisation d'un utilisateur du système
33.
CONTEXT-BASED GENERATIVE ARTIFICIAL INTELLIGENCE SYSTEM
Systems, methods, and computer program products for using a generative artificial intelligence system to generate answers or summaries is provided. During the ingestion stage, the system receives documents and transcripts that include data associated with a theme. The data is converted into a common format and is divided into chunks. The chunks are associated with metadata tags that include chunk and data information. From the chunks, the system generates embedding vectors. During the inference stage, the system receives an information request. If the information request is a question, the system generates a vector from the question, and uses a similarity search to identify similar vectors. From the similar vectors, the system identifies chunks. If the information request includes a summary request, the system uses the metadata tags to identify chunks with summary information. The system generates an answer or a summary from the identified chunks.
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
34.
NETWORK SPILLOVER DETECTION AND GLOBAL HOLDOUT CREATION USING GRAPH PARTITIONING
Methods and systems are presented for facilitating computer software feature experimentation by using one or more graph analyses to partition users into different experimentation groups. A graph is generated to represent users of a service provider and relationships among the users. When a request to perform a software feature experiment on the users of the service provider, the graph is analyzed to determine an estimated spillover effect. The graph is then coarsened across multiple levels using a label propagation technique based on one or more coarsening parameters. After the graph has been coarsened, the graph is partitioned into multiple partitions based on one or more partitioning parameters. The coarsening parameters and/or the partitioning parameters may be determined based on the estimated spillover effect. A subset of users is selected for performing the software feature experiment based on the partitioning of the coarsened graph.
Accuracy and speed improvements for data computing results are provided herein, particularly in the context of data event streaming services and downstream data computing processes. There are provided systems and methods for failure tracking with real-time data event streaming for data quality checks. A service provider may utilize different computing services for event processing and storing for downstream applications and services in a production computing environment. Due to issues in data loading and/or processing, certain events when streamed may fail to be processed and/or stored for availability to further system components. A failed event tracker may be implemented where, when events fail to process in an original processing queue, the tracker may detect the failure and write an identifier for the event to a table in an accessible database. The tracker may the republish the event via a retry processing queue using the identifier and may track for completion.
A method for remote device updates includes receiving a selection of a terminal device having an existing operational configuration and capable of operating in a development mode and a production mode. The method further includes receiving a new operational configuration for the terminal device, causing the terminal device to enter development mode with the new operational configuration, receiving an indication that the new operational configuration should be deployed, and, in response to the indication, formatting the new operational configuration into one or more files usable by the terminal in production mode, and causing the one or more files to be transmitted to the terminal with an instruction for the terminal device to enter production mode operating according to the one or more files.
There are provided systems and methods of bot detection through explainable deep learning and rule violation codebooks from generative AI. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users for processing various requests and interactions with those users. However, malicious users may utilize bots, such as automated scripts and software applications, that attempt to conduct fraud, compromise systems and data, and the like. To provide better bot and bot activity detection, the service provider may implement an explainable deep learning system that may generate rule violations of rules indicating bot activity or presence in computing logs and interactions using a generative AI. The violations may have a corresponding explanation in codebooks to automate bot detection. When bot activity is detected, the explanation may provide a reason for the bot activity detection.
A method for remote device updates includes receiving a selection of a terminal device having an existing operational configuration and capable of operating in a development mode and a production mode. The method further includes receiving a new operational configuration for the terminal device, causing the terminal device to enter development mode with the new operational configuration, receiving an indication that the new operational configuration should be deployed, and, in response to the indication, formatting the new operational configuration into one or more files usable by the terminal in production mode, and causing the one or more files to be transmitted to the terminal with an instruction for the terminal device to enter production mode operating according to the one or more files.
G06F 15/177 - Commande d'initialisation ou de configuration
H04L 41/50 - Gestion des services réseau, p. ex. en assurant une bonne réalisation du service conformément aux accords
H04L 41/22 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets comprenant des interfaces utilisateur graphiques spécialement adaptées [GUI]
H04L 41/00 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets
39.
INTELLIGENT ORCHESTRATION OF PROCESSING RESOURCES FOR PIPELINES
Systems and methods for predicting computer resources to fulfil pipeline requests includes obtaining a processing pipeline request defining a first set of computing resources for allocation during a first time period, obtaining a first dataset corresponding to historical data and scheduled pipelines, determining an availability of computing resources during a second time period based on the request, and predicting a recommendation corresponding to a second set of computing resources and a third time period to fulfill the request, the prediction being determined based on the first dataset and the available computing resources. The operations may further include generating a second dataset as output, sending the second dataset to a second computing device, obtaining a third dataset corresponding to a user selection of one or more computing resources during the third time period, and coordinating with one or more other computing devices in the system to fulfill the request.
Methods and systems are presented for providing a framework for analyzing and classifying audio data using a split-and-merge approach. Audio data is split into multiple audio tracks that correspond to different characteristics. Each audio track is segmented, and features are extracted from each segment of the audio track. Features extracted from audio segments of each audio track is analyzed. One or more correlations between the different audio tracks are determined based on comparing features extracted from audio segments of a first audio track against features extracted from audio segments of a second audio track. The audio data is classified based on the one or more correlations.
G10L 25/51 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes spécialement adaptées pour un usage particulier pour comparaison ou différentiation
41.
SPLIT-AND-MERGE FRAMEWORK FOR AUDIO CONTENT PROCESSING
Methods and systems are presented for providing a framework for analyzing and classifying audio data using a split-and-merge approach. Audio data is split into multiple audio tracks that correspond to different characteristics. Each audio track is segmented, and features are extracted from each segment of the audio track. Features extracted from audio segments of each audio track is analyzed. One or more correlations between the different audio tracks are determined based on comparing features extracted from audio segments of a first audio track against features extracted from audio segments of a second audio track. The audio data is classified based on the one or more correlations.
G11B 20/10 - Enregistrement ou reproduction numériques
G11B 27/02 - Montage, p. ex. variation de l'ordre des signaux d'information enregistrés sur, ou reproduits à partir des supports d'enregistrement ou d'information
H04H 60/04 - Équipement de studioInterconnexion des studios
G10L 25/00 - Techniques d'analyse de la parole ou de la voix qui ne se limitent pas à un seul des groupes
G10L 21/00 - Techniques de traitement du signal de parole ou de voix pour produire un autre signal audible ou non audible, p. ex. visuel ou tactile, afin de modifier sa qualité ou son intelligibilité
42.
ACCOUNT-CENTRIC EVALUATION FOR AUTOMATED CLEARING HOUSE APPROVALS
A system may include a processor and a non-transitory computer readable medium having stored thereon instructions that are executable by the processor to cause the system to receive a clearing house request that may include an amount owed to a first party by a second party and an indication of an account associated with the second party, and retrieve a risk score associated with the indicated account. The risk score may have been generated by a trained machine learning model configured to receive, as input, a plurality of account activities and to generate, as output, risk scores for a plurality of accounts indicative of a probability that the associated account is solvent. The system may further determine that the associated risk score exceeds a threshold value, and in response to the associated risk score exceeding the threshold value, execute a transfer of the amount to the first party via a clearing house.
Systems, methods, and computer program products for using machine learning to determine whether to dispute a chargeback request are provided. A dispute processing system receives a chargeback request for a transaction, transaction data and service provider data associated with the transaction. The dispute processing system incorporates the transaction and service provider data into a template. Next, the dispute processing system uses a machine learning framework to generate machine learning scores that indicates a likelihood of successfully winning the chargeback request and avoiding pre-arbitration. Using the machine learning scores, the transaction data, the service provider data, and at least one dispute processing rule, the dispute processing system determines a likelihood of successfully challenging the chargeback request. Based on the likelihood of successfully contesting the chargeback request, the dispute processing system generates a contestation document from the template, and submits the contestation document to contest the chargeback request.
G06Q 30/016 - Fourniture d’une assistance aux clients, p. ex. pour assister un client dans un lieu commercial ou par un service d’assistance après-vente
The disclosed computer-implemented method includes detecting an unavailability of a primary cryptocurrency exchange system and queuing received cryptocurrency transaction requests. The method also includes connecting to a backup cryptocurrency exchange system and completing the queued cryptocurrency transaction requests with a backup asset pool using the backup cryptocurrency exchange system. The method further includes detecting an availability of the primary cryptocurrency exchange system and reconnecting to the primary cryptocurrency exchange system. Various other methods, systems, and computer-readable media are also disclosed.
G06Q 20/06 - Circuits privés de paiement, p. ex. impliquant de la monnaie électronique utilisée uniquement entre les participants à un programme commun de paiement
45.
Interface for Constructing Smart Protocols for Execution on Blockchain Platforms
A method for generating a smart protocol includes providing, by a server computer system, a user interface to one or more of a plurality of users. The server computer system may receive, via the user interface, input specifying terms corresponding to a smart protocol that is to be deployed on a particular blockchain platform. The specified terms may include the plurality of users associated with the smart protocol and a web resource to be used to identify one or more external data. An execution of the smart protocol may be based on a value of the external data. Based on the specified terms, the server computer system may generate, without further input from the plurality of users, the smart protocol. The server computer system may deploy the smart protocol to the particular blockchain platform.
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
G06Q 40/04 - TransactionsOpérations boursières, p. ex. actions, marchandises, produits dérivés ou change de devises
H04L 69/00 - Dispositions, protocoles ou services de réseau indépendants de la charge utile de l'application et non couverts dans un des autres groupes de la présente sous-classe
46.
AUTOMATED CHATBOTS THAT DETECT PRIVACY DATA SHARING AND LEAKAGE BY OTHER AUTOMATED CHATBOT SYSTEMS
There are provided systems and methods for automated chatbots that detect privacy data sharing and leakage by other automated chatbot systems. A service provider and other chatbot services, including an electronic transaction processor, may provide self-service channels for assistance through chatbot and other automated computing processes. However, these chatbots may be regulated by compliance with privacy protection requirements, and therefore use of the chatbots and/or user data when responding to users via chatbots in chat sessions may be noncompliant and/or attacked by malicious users to compromise privacy protected data. In order to facilitate detection of leaks and shares by chatbots, an AI for a privacy protection chatbot may be trained on regulations for chatbot use and/or data security and privacy protection. The chatbot may then interact with other chatbots and question the other chatbots using the trained AI model to detect whether privacy protected data is exposed.
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
47.
AUTOMATICALLY GENERATING AN ELECTRONIC CONVERSATION VIA NATURAL LANGUAGE MAP MODELING
A determination is made that a customer event has been initiated by a customer. The customer event is associated with a plurality of words. The plurality of words are parsed. Based on a result of the parsing of the plurality of words, an intent of the customer corresponding to the customer event is predicted. Based on the predicted intent, a first simulated service agent of a plurality of service agents is associated with the customer event. Based on the predicted intent, a first set of Natural Language Map (NLM) models for the customer and a second set of NLM models for the first simulated service agent are accessed. Based on the first set of NLM models and the second set of NLM models, a simulated conversation between the customer and the first simulated service agent is generated. The simulated conversation involves the predicted intent.
H04L 51/02 - Messagerie d'utilisateur à utilisateur dans des réseaux à commutation de paquets, transmise selon des protocoles de stockage et de retransmission ou en temps réel, p. ex. courriel en utilisant des réactions automatiques ou la délégation par l’utilisateur, p. ex. des réponses automatiques ou des messages générés par un agent conversationnel
H04L 51/216 - Gestion de l'historique des conversations, p. ex. regroupement de messages dans des sessions ou des fils de conversation
48.
SYSTEMS AND METHODS FOR FORMATTING INFORMAL UTTERANCES
Methods and systems are presented for translating informal utterances into formal texts. Informal utterances may include words in abbreviation forms or typographical errors. The informal utterances may be processed by mapping each word in an utterance into a well-defined token. The mapping from the words to the tokens may be based on a context associated with the utterance derived by analyzing the utterance in a character-by-character basis. The token that is mapped for each word can be one of a vocabulary token that corresponds to a formal word in a pre-defined word corpus, an unknown token that corresponds to an unknown word, or a masked token. Formal text may then be generated based on the mapped tokens. Through the processing of informal utterances using the techniques disclosed herein, the informal utterances are both normalized and sanitized.
A method of preventing account takeover fraud includes assigning a user account to a target set accounts, determining matric values for past and present computing actions of the target set of accounts, determining, for at least one of the metrics, that present metric value is an outlier with respect to the past metric values and, in response, transmitting an outlier notification to the user. The method further includes receiving a computing action request involving the account, the request including computing action characteristic values, determining that the characteristic values match a combination of characteristic values associated with a risk that exceeds a threshold, the risk determined according to past computing actions and, in response, requiring a second authentication factor of the user or declining the computing action associated with the 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
50.
LEVERAGING GRAPH NEURAL NETWORKS, COMMUNITY DETECTION, AND TREE-BASED MODELS FOR TRANSACTION CLASSIFICATIONS
Methods and systems are presented for providing a machine learning model framework that uses multiple models that analyze different aspects of graph data to perform transaction classification. A graph is generated to represent relationships among transactions and fuzzy attributes. The framework includes a graph neural network that generates embeddings for each transaction based on the graph. The framework further includes a machine learning model that generates an initial classification score for a particular transaction based on the embeddings generated for the particular transaction and the actual attributes associated with the particular transaction. One or more communities are identified within the graph based on the connections among various fuzzy attributes. Characteristics associated with a particular community corresponding to the particular transaction are used to modify the initial risk score. A classification is determined for the particular transaction based on the modified risk score.
G06N 3/043 - Architecture, p. ex. topologie d'interconnexion fondée sur la logique floue, l’appartenance floue ou l’inférence floue, p. ex. systèmes d’inférence neuro-floue adaptatifs [ANFIS]
A system according to the present disclosure may include a processor, a centralized data warehouse, and a non-transitory computer readable medium storing thereon instructions that are executable by the processor to cause the system to perform operations. The operations may include comprising training a machine learning model based on enterprise data from the centralized data warehouse, the machine learning model trained to determine a value corresponding to a metric, receiving, from at least one external source, updated enterprise data, determining, in real-time by the machine learning model, a predicted change to the value based on the updated enterprise data, and presenting, via a graphical user interface (GUI), a suggested action based on the predicted change.
Various techniques are disclosed for providing gateway services between a client system and downstream service systems for a service system. The disclosed gateway service system is capable of implementing a new service for a client system in response to a request from the client system. The gateway service system internally determines which downstream services are needed to implement the new service. In various instances, the gateway service system utilizes machine learning algorithms to determine the downstream services suitable for providing the output needed for the new service. The gateway service system is also capable of determining whether an existing application programming interface (API) is able to be used for the new service or whether a new API needs to be created for the new service. By internally determining the downstream services and APIs, the gateway service system has more efficient utilization of its computational resources.
H04L 67/61 - 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 en tenant compte de la qualité de service [QoS] ou des exigences de priorité
Various techniques are disclosed for providing gateway services between a client system and downstream service systems for a service system. The disclosed gateway service system is capable of implementing a new service for a client system in response to a request from the client system. The gateway service system internally determines which downstream services are needed to implement the new service. In various instances, the gateway service system utilizes machine learning algorithms to determine the downstream services suitable for providing the output needed for the new service. The gateway service system is also capable of determining whether an existing application programming interface (API) is able to be used for the new service or whether a new API needs to be created for the new service. By internally determining the downstream services and APIs, the gateway service system has more efficient utilization of its computational resources.
G06F 21/53 - Contrôle des utilisateurs, des programmes ou des dispositifs de préservation de l’intégrité des plates-formes, p. ex. des processeurs, des micrologiciels ou des systèmes d’exploitation au stade de l’exécution du programme, p. ex. intégrité de la pile, débordement de tampon ou prévention d'effacement involontaire de données par exécution dans un environnement restreint, p. ex. "boîte à sable" ou machine virtuelle sécurisée
There are provided systems and methods for an automated domain crawler and checkout simulator for proactive and real-time scam website detection. A service provider, such as an online transaction processor, may provide a domain crawler and checkout simulator that may be used to detect scam websites when registered and/or configured to utilize intermediary webpages of other domains to perpetrate fraud or scams on customers. The domain parser may detect new domain registrations and may parse through their corresponding websites to identify those that include checkout options or other processing flows that proceed through steps and use a service or feature of a service provider, including account usage for electronic transaction processing. The checkout simulator may then simulate a user's experience through the checkout to determine if domain redirections occur. If so, those redirections may be checked to identify usage of a merchant account having been flagged for scams.
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
A computer-implemented method for utilizing a machine learning model configured to determine synthetic identity theft may include processing a plurality of user datasets to generate a set of features for each user dataset, with each set of features being representative of a particular user. The method may further include generating a plurality of embeddings sets, with each embedding set being representative of a respective set of features, generating a plurality of synthetic user datasets, combining the plurality of embeddings sets and the plurality of synthetic user datasets to generate a training dataset, the training dataset comprising a plurality of user profiles, training the machine learning model based on the generated training dataset, and determining, via the machine learning model and in response to receiving a new user profile, a determination of whether the new user profile is real or synthetic.
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
There are provided systems and methods for an automated domain crawler and checkout simulator for proactive and real-time scam website detection. A service provider, such as an online transaction processor, may provide a domain crawler and checkout simulator that may be used to detect scam websites when registered and/or configured to utilize intermediary webpages of other domains to perpetrate fraud or scams on customers. The domain parser may detect new domain registrations and may parse through their corresponding websites to identify those that include checkout options or other processing flows that proceed through steps and use a service or feature of a service provider, including account usage for electronic transaction processing. The checkout simulator may then simulate a user's experience through the checkout to determine if domain redirections occur. If so, those redirections may be checked to identify usage of a merchant account having been flagged for scams.
A method may include receiving, via an interface respective of a third party, a first request respective of a user to access a payment application, prompting the user, in response to the first request, to provide user credentials, receiving, from the user, user credentials, processing, via a first set of modules, the user credentials to determine a validity of the user credentials, in response to determining that the user credentials are valid, retrieving transaction details from the third party, the transaction details comprising a profile of the third party and a profile of a subject of the transaction, processing, via a second set of modules, the transaction details to determine a validity of the transaction details, and in response to determining that the transaction details are valid, transmitting an approval of the user to the third party.
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
58.
SYSTEMS AND METHODS FOR RULE AGNOSTIC REJECT INFERENCING
There are provided systems and methods for rule agnostic reject inferencing. An example method may receive a request for processing a transaction, and determine, using a machine learning model, a classification for the transaction based on data associated with the transaction. The machine learning model may be trained using first training data having verified labels and second training data having inferred labels, and the inferred labels of the second training data may be generated based on a distribution of classifications associated with the first training data. The example method may further process the request based on the classification.
G06N 20/20 - Techniques d’ensemble en apprentissage automatique
G06F 18/2415 - Techniques de classification relatives au modèle de classification, p. ex. approches paramétriques ou non paramétriques basées sur des modèles paramétriques ou probabilistes, p. ex. basées sur un rapport de vraisemblance ou un taux de faux positifs par rapport à un taux de faux négatifs
59.
CENTRALIZED PROFILE FOR VERIFICATION WITHOUT COMPROMISE
A system according to the present disclosure may include a processor, a centralized data warehouse, and a non-transitory computer readable medium storing thereon instructions that are executable by the processor to cause the system to perform operations. The operations may include receiving, from a user, identifying information, generating, based on the identifying information, a central profile for the user, generating, from the central profile, a first verification token configured to verify the user for a first service, and generating, from the central profile, a second verification token configured to verify the user for a second service. The first verification token and the second verification token are separately revokable and severable from the central profile, and neither the first verification token nor the second verification token comprise the identifying information of the user.
Methods and systems are presented for providing a large language model-based query optimizer to interface between program developers and database systems. The query optimizer receives programming code corresponding to a set of queries intended for a database system from a program developer. The query optimizer then uses a machine learning model to analyze the programming code and to determine a set of strategies for executing the set of data queries corresponding to the programming code. To determine the set of strategies, the machine learning model analyzes dependencies among the set of data queries and retrieves sample data from the database system. The machine learning model implement the set of strategies by incorporating additional instructions in the programming code for the database system such that the database system would execute the set of data queries according to the set of strategies.
There are provided systems and methods for data security systems for controlling access to restricted data and data processing flows to prevent comprising data and flow abuses. A service provider, such as an electronic transaction processor for digital transactions, may provide a restricted access controller and dynamic permission calculator to enforce more granular and dynamic permissions of data access and restricted such access to prevent unauthorized access through flow abuse. To prevent malicious actors from circumventing required authorizations to data, the restricted access controller may provide permissions for data based on the particular data portion and elements, which may be determined based on the context of the data when entering the system or when requested by users. Further, permissions may be dynamically calculated when users request data instead of static permissions, which may be based on the flow, such as how the user arrives at the data being requested.
Techniques are disclosed relating to using graph neural networks to identify locations in a hierarchical data set. In various embodiments, a computing system receives a request to identify, in a data set having a hierarchical structure, one or more locations corresponding to a description specified by the request. The computing system assembles, from the data set, a graph data structure that includes nodes corresponding to locations in the data set and interconnected by edges preserving the hierarchical structure. The computing system applies a graph neural network algorithm to the graph data structure to generate location embeddings for the nodes and identifies the one or more locations by determining similarities between the generated location embeddings and a description embedding representative of the description.
Various systems, mediums, and methods may involve a provider application configured to access a user account associated with a provider server. For example, a system may perform operations to identify one or more other applications installed on the mobile device that may be configured to communicate with the provider server. The system may determine data required by the other applications to perform one or more data transfers with the user account. The system may cause the provider application to send the data required to the other applications. As such, the other applications may be enabled to perform one or more data transfers with the user account based on the data sent to the other applications.
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
H04W 4/60 - Services basés sur un abonnement qui utilisent des serveurs d’applications ou de supports d’enregistrement, p. ex. boîtes à outils d’application SIM
H04W 8/18 - Traitement de données utilisateur ou abonné, p. ex. services faisant l'objet d'un abonnement, préférences utilisateur ou profils utilisateurTransfert de données utilisateur ou abonné
Expedited virtual currency transactions are provided by identifying a first user primary wallet associated with a virtual currency and including a first user primary wallet private key. First user secondary wallets are created that each include a respective first user secondary wallet private key, and a respective virtual currency transaction is performed using the first user primary wallet private key to transfer predefined amounts of the virtual currency from the first user primary wallet to each of the first user secondary wallets such that first user secondary wallets are provided with different predefined amounts of the virtual currency. Subsequently, an instruction is received to transfer a payment amount to a second user, and the second user is allocated a subset of the first user secondary wallet private keys included in respective first user secondary wallets that are associated with predefined amounts of the virtual currency that equal the payment amount.
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
G06Q 20/06 - Circuits privés de paiement, p. ex. impliquant de la monnaie électronique utilisée uniquement entre les participants à un programme commun de paiement
G06Q 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
65.
METHOD, NON-TRANSITORY MACHINE-READABLE MEDIUM, AND SYSTEM FOR MERCHANT-SIDE INTEGRATED SHOPPING CART RECOVERY
A method for merchant-side tracking of customer selections includes receiving, via a user list script on a merchant webpage, a user selection of a product representation displayed on a UI of a user device. The method includes updating a user list with the user selection, the user list associated with a generic user representation. The method includes determining, based on a type of identifier associated with a user of the user device, to use a generic user representation with the user list. The method includes selecting, based on the generic user representation, an anonymous user flow type for tracking the user as an anonymous user of the merchant webpage. The anonymous user flow type associates the user device with the user list independently of associating the user with the merchant.
There are provided systems and methods for determining processing weights of rule variables for rule processing optimization. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules for decision-making of data including real-time data in production computing environments. Rules may correspond to coded statements that perform an automated decision-making service for the computing services and platforms of the service provider. When writing rules, different variables for data objects may be utilized, where each variable may perform a different operation and/or utilize a different operation for fetching and retrieving data used during rule processing. Each variable may therefore have a different data processing weights based on processing requirements of the data. Thus, optimization of rule authoring may be performed by mapping variables to other similar variables and showing a processing weight of each variable.
H04L 67/125 - Protocoles spécialement adaptés aux environnements propriétaires ou de mise en réseau pour un usage spécial, p. ex. les réseaux médicaux, les réseaux de capteurs, les réseaux dans les véhicules ou les réseaux de mesure à distance en impliquant la commande des applications des terminaux par un réseau
Techniques are disclosed relating to speculatively processing transactions. A transaction processing system may receive an indication of a trigger event associated with an electronic transaction not yet initiated by a client computing device. In some embodiments, in response to the indication of the trigger event and prior to receiving an indication of the electronic transaction being initiated, the computer system begins speculative processing of the electronic transaction. In some embodiments, the speculative processing includes identifying the client computing device based on device authentication information received from the client device, determining a user account based on the identifier client computing device, retrieving account information for the determined account, and storing the retrieved account information. In response to receiving an indication of the electronic transaction being initiated, in some embodiments, the computer system performs one or more operations using the stored account information to complete the transaction.
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 20/42 - Confirmation, p. ex. contrôle ou autorisation de paiement par le débiteur légal
68.
Real-time Media Alteration Using Generative Techniques
Techniques are disclosed for generating an altered version of an original version of media using a generative model. A system detects, at a computing device, a trigger event indicating a user of the computing device has requested to view media at a user interface of the computing device. In response to detecting the trigger event, the system: retrieves, an original version of the media from a backend server and identifies, using an object detection model, a bounding region of content within the original version, generates, using a generative model, an altered version of the media based on the bounding region, and transmits to the computing device, the altered version for display at the computing device in place of the original version, where the generative model generates the altered version of the media based on historical transaction information and user information of the user of the computing device.
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06F 16/954 - Navigation, p. ex. en utilisant la navigation par catégories
69.
REDUCE RECURRING ISSUES AND INCIDENTS BY REMEDIATION ARTIFICIAL INTELLIGENCE
Systems, methods, and computer program products for using artificial intelligence (AI) models, machine learning, and large language models to identify root causes of issues in a computing environment where multiple applications and computing devices operate. One or more AI models may determine adverse trends from one or more issue metrics, where an issue metric corresponds to issues occurring in a computing system. The AI models may identify issues corresponding to the adverse trends. From the identified issues, the AI models may determine root causes from the issues and the impacted area information and process information from the issues. From the issues and the impacted area information and process information from the issues the AI models may determine recommendations for rectifying the issues. The relations between the root causes, the impacted area information, the process information, and the issues may be formatted and displayed as a traversable network graph.
Systems, methods, and computer program products for a video verification system with generative artificial intelligence are provided. A generative artificial intelligence model generates a dynamic set of questions based on account, identity, risk, and compliance associated with a user. The questions in the set correspond to different difficulty levels for answering the question. A question from the set is communicated to a computing device of a user over a video call with an artificial intelligence bot. In response, the video verification system receives an answer to the question, and one or more of a telemetry, an audio, and a video. The answer, telemetry, audio, and video data are assessed to determine a difficultly level of a subsequent question. The subsequent question is selected from the dynamic set of questions using the difficulty level and provided to the computing device of a user over the video call.
Based on detecting a selection, by a first user, of a group payment option on a merchant interface, the computer system launches a user selection user interface that includes one or more user selection interface elements that correspond to one or more users associated with the first user. Based on detecting a selection of one or users, the computer system launches a group payment user interface that includes one or more payment allocation user interface elements that corresponds to a payment allocation for the selected users. Based on detecting a confirmation of a first payment allocation plan, the computer system processes a payment for the purchase and further transmits invoices to the selected users which include an allocated payment amount.
There are provided systems and methods for targeted authentication queries based on detected user actions. A user may perform various actions during a day, including online, electronic, or digital actions, such as social networking, messaging, and media consumption, as well as real-life actions, such as exercise, travel, and purchases. The actions may be used to determine a user history for the user by a service provider. When the user wishes to login to an account or otherwise authenticate the identity of the user, the user may provide login or authentication credentials. The credentials may be used to look up the user history and cause the service provider to generate an authentication-query for the user based on events associated with the user in the user history. The query may be utilized to further authenticate the user by requiring the user to respond with the event associated with the user.
There are provided systems and methods for an acknowledgement process for message transmissions to connectable processing terminals for point-of-sale (POS) devices. A user may engage in a transaction with a merchant at a POS device, such as a purchase of items from a merchant at a physical merchant location and using payment terminals with the POS device. An online transaction processor may provide an acknowledgement process to determine if the payment terminal is online and receiving communications so that the user may view transaction details and complete transaction processing. This may be done through lookup of a previous connection of the payment terminal over a cloud computing system with the transaction processor and transmission of a request for acknowledgement over that connection. If the payment terminal responds with an acknowledgement, the transaction processor may then notify the POS device that data processing may proceed.
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
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 20/38 - 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
Disclosed methods and systems include monitoring, by a computer system, online activity associated with a plurality of entities and a plurality of user devices that have respective pluralities of tokens provided by the token management system. The computer system may detect particular online activity related to a first token of a first of the pluralities of tokens, associated with a first user device. The computer system may determine that the particular online activity affects a status of the first token. In response to the determining, the computer system may modify data within the first token using information within the particular online activity. In response to identifying a second token of the first plurality of tokens, the computer system may determine that the particular online activity also affects a status of the second token, and modify, without receiving input from the first user device, the second token using the information.
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
There are provided systems and methods for an acknowledgement process for message transmissions to connectable processing terminals for point-of-sale (POS) devices. A user may engage in a transaction with a merchant at a POS device, such as a purchase of items from a merchant at a physical merchant location and using payment terminals with the POS device. An online transaction processor may provide an acknowledgement process to determine if the payment terminal is online and receiving communications so that the user may view transaction details and complete transaction processing. This may be done through lookup of a previous connection of the payment terminal over a cloud computing system with the transaction processor and transmission of a request for acknowledgement over that connection. If the payment terminal responds with an acknowledgement, the transaction processor may then notify the POS device that data processing may proceed.
Systems and/or techniques for facilitating image forgery detection via pixel-metadata consistency analysis are provided. In various embodiments, a system can receive an electronic image from a client device. In various cases, the system can obtain a pixel vector and/or an image metadata vector that correspond to the electronic image. In various aspects, the system can determine whether the electronic image is authentic or forged, based on analyzing the pixel vector and the image metadata vector via at least one machine learning model.
Methods and systems are presented for providing multi-tiered cache system that works with an artificial intelligence (AI)-based conversation system for facilitating a conversation with users and processing transactions for the users. The multi-tiered cache system includes multiple tiers of cache modules that use different structures for caching and/or querying data. As a new utterance is received, the cache system uses each of the cache modules in sequence to determine whether a cache hit occurs. If a cache miss occurs at a first cache module, the cache system determines if a cache hit occurs at a second cache module. When a response is obtained from one of the cache modules and/or the AI model, the cache system updates the cache modules using the response.
A computer-implemented method may include receiving a set of security signatures for analysis; correlating the set of security signatures with corresponding computing traffic data within which the set of security signatures have appeared; extracting from the computing traffic data a set of features describing the computing traffic data; correlating the set of features with the set of security signatures; and generating a new security signature based at least in part on a correlation between the set of features and the set of security signatures. Various other methods and systems are also disclosed.
Disclosed methods and systems include monitoring, by a computer system, online activity associated with a plurality of entities and a plurality of user devices that have respective pluralities of tokens provided by the token management system. The computer system may detect particular online activity related to a first token of a first of the pluralities of tokens, associated with a first user device. The computer system may determine that the particular online activity affects a status of the first token. In response to the determining, the computer system may modify data within the first token using information within the particular online activity. In response to identifying a second token of the first plurality of tokens, the computer system may determine that the particular online activity also affects a status of the second token, and modify, without receiving input from the first user device, the second token using the information.
Methods and systems are presented for classifying a digital image of a document using a machine learning model framework. The machine learning model framework is configured to provide a classification output based on a fusion of features corresponding to different modalities and extracted from the digital image. The machine learning model framework includes multiple encoders. Each encoder is configured to encode features corresponding to a distinct modality into a respective embedding. Different embeddings generated by the multiple encoders are fused together using one or more fusion techniques. The fused embedding is provided to a machine learning model for classifying the document.
G06N 3/084 - Rétropropagation, p. ex. suivant l’algorithme du gradient
G06V 30/19 - Reconnaissance utilisant des moyens électroniques
G06V 30/414 - Extraction de la structure géométrique, p. ex. arborescenceDécoupage en blocs, p. ex. boîtes englobantes pour les éléments graphiques ou textuels
G06V 30/416 - Extraction de la structure logique, p. ex. chapitres, sections ou numéros de pageIdentification des éléments de document, p. ex. des auteurs
G06V 30/418 - Appariement de documents, p. ex. d’images de documents
81.
MULTI-LABEL SHALLOW NEURAL NETWORK MODEL FOR TABULAR DATA
The disclosed computer-implemented method includes normalizing tabular data corresponding to a query target, and inputting the normalized tabular data into a shallow neural network corresponding to a fully-connected three-layer model comprising an input layer, a hidden layer, and an output layer. Normalizing the tabular data may replace feature detection for the shallow neural network. The method may further include predicting a plurality of classifications for the query target, wherein a plurality of nodes of the output layer respectively correspond to the plurality of classifications. Various other methods, systems, and computer-readable media are also disclosed.
There are provided systems and methods for dynamic creation of data specification-driven AI-based executable strategies for high availability of evaluation services. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules and artificial intelligence models for decision-making of data including data in production computing environment. A decision service may normally be used for data processing and decision-making. However, at certain times, the decision service may fail or the services and/or a gateway for such services may be inaccessible. To provide higher availability and better SLA times, a client-side executable strategy for decision service execution may be determined using the pathways for strategy execution and available data from called resources. This strategy may be loaded in parallel to calling the decision service, and when failure occurs, may be used as a fallback to request processing.
H04L 41/5009 - Détermination des paramètres de rendement du niveau de service ou violations des contrats de niveau de service, p. ex. violations du temps de réponse convenu ou du temps moyen entre l’échec [MTBF]
H04L 41/16 - Dispositions pour la maintenance, l’administration ou la gestion des réseaux de commutation de données, p. ex. des réseaux de commutation de paquets en utilisant l'apprentissage automatique ou l'intelligence artificielle
83.
MULTI-LABEL SHALLOW NEURAL NETWORK MODEL FOR TABULAR DATA
The disclosed computer-implemented method includes normalizing tabular data corresponding to a query target, and inputting the normalized tabular data into a shallow neural network corresponding to a fully-connected three-layer model comprising an input layer, a hidden layer, and an output layer. Normalizing the tabular data may replace feature detection for the shallow neural network. The method may further include predicting a plurality of classifications for the query target, wherein a plurality of nodes of the output layer respectively correspond to the plurality of classifications. Various other methods, systems, and computer-readable media are also disclosed.
G06Q 40/00 - FinanceAssuranceStratégies fiscalesTraitement des impôts sur les sociétés ou sur le revenu
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
G06Q 10/0635 - Analyse des risques liés aux activités d’entreprises ou d’organisations
There are provided systems and methods for dynamic user interfaces and content with reduced code development using an intelligent scenario simulation engine. A service provider, such as an online transaction processor, may provide computing services to users, such as electronic transaction processing, which may be facilitated and/or assisted by agents of the service provider. To provide dynamic user interfaces and content tailored to a user experience and based on different simulated scenarios of the user experience, the service provider may utilize a simulation engine to simulate permutations of user interfaces and content from combinations of the user and/or agents data and service states of available services that may be accessed and used through the user interfaces. This may utilize a large language model and/or generative AI to simulate such scenarios, as well as computing code for the user interfaces and calls to provide content via the services.
A method includes receiving, by a processor of a transaction processing entity, a transaction attempt. The method includes receiving a risk score from a risk strategy decision model, the risk score being determined from a machine learning model. The method includes in response to receiving the risk score, determining, whether the risk score exceeds a threshold indicating the transaction attempt is potentially fraudulent. In response to determining the risk score exceeds the threshold: the method includes determining, whether to approve or decline the transaction attempt; and determining a reason for approving or declining the transaction attempt based on one or more variables contributing to the risk score. The method includes outputting an indication to approve or decline the transaction attempt in response to determining whether to approve or decline the transaction attempt and the reason for approving or declining the transaction attempt.
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
A method according to the present disclosure may include receiving information associated with a user; generating, via a computing device, a limited account for the user, the limited account having at least one restriction, according to the received information; causing a code associated with the limited account to be applied to a physical token; providing, to a mobile device that scans the code, limited access to the limited account; receiving, from the mobile device, an instruction to transfer an asset to or from the limited account; and causing the asset to be transferred according to the instruction.
There are provided systems and methods for providing a merchant recommendation using multiple models. An example system may receive, at a product predictor, an input including a series of products and generate an output including a target product associated with the series of products. The system may generate, at a value evaluator, a value change associated with the target product based at least in part on the output, and generate, at the product predictor, a recommendation including the target product and the value change associated with the target product. The system may further generate, at a seasonal predictor, a seasonal prediction for the output in a future time frame based on the output, and generate, at the product predictor, an updated output including an updated target product based on the seasonal prediction for the output in the future time frame.
There are provided systems and methods for multi-token application functionality for wider availability of digital token processing. A user may engage in a transaction with another user, such as a purchase of goods, services, or other items a merchant using a digital wallet, such as through a software application. An online transaction processor may provide a digital account for payment processing, which may include processing through mobile user devices at physical merchant locations using digital tokens and contactless payment terminals. To provide multi-token functionality, an application on a user device may be setup using a software development kit to utilize multiple different digital tokens with different token processor networks. When interacting with a POS device or payment terminal, the application may make each token available with preferences for use so that a primary preferred token may be identified but a fallback token may be used if needed.
G06Q 20/40 - Autorisation, p. ex. identification du payeur ou du bénéficiaire, vérification des références du client ou du magasinExamen et approbation des payeurs, p. ex. contrôle des lignes de crédit ou des listes négatives
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
89.
NEAR REAL-TIME VECTOR INDEX BUILDING AND SERVING SOLUTION
Systems, methods, and computer program for generating and using embedding vectors associated with real-time data are provided. A streaming platform receives streaming data associated with events occurring in a network environment. At least one neural network generates embedding vectors from the streaming data associated with the events. The analytical models analyze the embedding vectors and are updated with the result from the analysis. Embedding vectors are also associated with one or more indexes. The streaming platform may receive a query with an embedding vector associated with data from another event that is occurring in real-time in the network. Based on the embedding vector in the query, the streaming platform may use the one or more indexes to provide, in real-time, similar embedding vectors, which are indicative of similar events in the network environment.
H04L 41/0604 - Gestion des fautes, des événements, des alarmes ou des notifications en utilisant du filtrage, p. ex. la réduction de l’information en utilisant la priorité, les types d’éléments, la position ou le temps
H04L 41/069 - Gestion des fautes, des événements, des alarmes ou des notifications en utilisant des journaux de notificationsPost-traitement des notifications
Methods and systems are presented for providing a knowledge bot configurable to interact with users across multiple domains. The knowledge bot includes at least a text-based search engine and a semantic-based search engine. Each of the search engine is configured to retrieve documents from a corpus of documents based on the user query. The user query is in a natural language format. The retrieved documents may be ranked according to how relevant the documents are to the user query. A subset of the documents is used as the search results based on the ranking. The search results from the search engine are combined with the user query to generate a prompt for an artificial intelligence model. Based on the prompt, a response in the natural language format is generated by the artificial intelligence model.
Techniques are disclosed that relate to a computer system implementing an interactive voice response (IVR) transcoder. The computer system may receive voice input from an interactive voice response (IVR) system, or other channel. The computer system converts the received input to a request having a common format supported by an artificial intelligence (AI) core including one or more virtual agents. The computer system routes the request to the AI core operable to handle requests specified in the common format. The computer system receives, by the interface module, response data including data (e.g. text data) responsive to the second request. The computer system may implement a conversational artificial intelligence (CAI) platform. The computer system may receive text input from a chat-based input source. The computer system may route another request based on the text input to the AI core, the request being specified in the common text format.
H04M 3/493 - Services d'information interactifs, p. ex. renseignements sur l'annuaire téléphonique
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
G10L 15/26 - Systèmes de synthèse de texte à partir de la parole
Techniques are disclosed that relate to a computer system implementing a conversational artificial intelligence platform. The computer system may receive a request that includes text data specifying an utterance of a user. The computer system determines, form the utterance, an intent of the user. The computer system selects a first virtual agent to handle the request based on the determined intent. The computer system routes the text data and subsequent text data of the request to the first virtual agent for handling. The computer system receives, from the first virtual agent, a message that the determined intent is incorrect, the message including state information indicative of actions taken by the first virtual agent relative to the request. The computer system selects a second virtual agent to handle the request based on the state information. The computer system may receive input from an interactive voice response system or another channel.
Systems and methods for providing merchant/customer interaction include determining that a tablet computer is in a merchant orientation, retrieving merchant product information according to a received instruction and merchant orientation information, and displaying a merchant screen on the tablet computer that includes the merchant product information according to the merchant orientation information. A change in the orientation of the tablet computer enclosure/stand is then detected from the merchant orientation to a customer orientation. In response, the merchant screen is transitioned on the tablet computer display to a customer screen as the tablet computer enclosure/stand changes orientations by moving the merchant screen and the customer screen linearly while in a stacked orientation. The customer screen includes the merchant product information displayed according to customer orientation information such that the merchant product information is displayed differently on the customer screen relative to the merchant screen.
G06F 3/01 - Dispositions d'entrée ou dispositions d'entrée et de sortie combinées pour l'interaction entre l'utilisateur et le calculateur
G06F 3/03 - Dispositions pour convertir sous forme codée la position ou le déplacement d'un élément
G06F 3/0346 - Dispositifs de pointage déplacés ou positionnés par l'utilisateurLeurs accessoires avec détection de l’orientation ou du mouvement libre du dispositif dans un espace en trois dimensions [3D], p. ex. souris 3D, dispositifs de pointage à six degrés de liberté [6-DOF] utilisant des capteurs gyroscopiques, accéléromètres ou d’inclinaison
G06F 3/0481 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p. ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comportement ou d’aspect
G06F 3/0487 - 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
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
Methods and systems are provided for making secure financial transactions, such as purchase payments, using rich Internet applications (RIA) running an RIA runtime (also referred to as a platform or framework) on the user's smart phone or other mobile device. Embodiments differ from the usual way of re-directing a user from a third-party application and authenticating the user by providing secure in-line payments from a rich Internet application running on an RIA runtime. A system includes: a mobile device executing a rich Internet application running on an RIA runtime; a payment library communicating with the RIA runtime and a service provider, for which the payment library communicates with the service provider to authenticate the rich Internet application; and in response to authentication by the service provider, facilitates secure financial transactions via the rich Internet application.
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 21/52 - Contrôle des utilisateurs, des programmes ou des dispositifs de préservation de l’intégrité des plates-formes, p. ex. des processeurs, des micrologiciels ou des systèmes d’exploitation au stade de l’exécution du programme, p. ex. intégrité de la pile, débordement de tampon ou prévention d'effacement involontaire de données
G06Q 20/20 - Systèmes de réseaux présents sur les points de vente
G06Q 20/32 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des dispositifs sans fil
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 20/36 - Architectures, schémas ou protocoles de paiement caractérisés par l'emploi de dispositifs spécifiques utilisant des portefeuilles électroniques ou coffres-forts électroniques
G06Q 20/38 - Protocoles de paiementArchitectures, schémas ou protocoles de paiement leurs détails
Systems and methods of assisting in an electronic shopping experience are disclosed. A request may be received, from a user on a device having a user interface, to search for an item listed for sale in a marketplace. A balance amount of an account associated with or available to the user at the marketplace is displayed in the user interface. The search results are limited to at least one item having a sales price less than the displayed balance amount and the at least one item is displayed in the user interface. The account may be a user account at a payment service provider.
Techniques for an ultra-fact software compilation of source code are provided. A compiler receives software code and may divide it into code sections. A map of ordered nodes may be generated, such that each node in the map may include a code section and the order of the nodes indicates an execution order of the software code. Each code section may be compiled into an executable object in parallel and independently from other code sections. A binary executable may be generated by linking executable objects generated from the code sections. The methodology significantly differs from existing source code compilation techniques because conventional compilers build executable sequentially, whereas the embodiments divide the source code into multiple smaller code sections and compile them individually and in parallel. Compiling multiple code sections improves the compilations in order of magnitude from conventional techniques.
Novel technical ways of analyzing a blockchain system using machine learning are presented. In various embodiments, A system can deploy, by a first entity, a policy smart contract on a blockchain to analyze a first smart contract deployed by a second entity, wherein the policy smart contract is governed by a set of rules, wherein the policy smart contract performs a first assessment that includes analyzing a set of functionalities of the first smart contract and detects a set of vulnerabilities associated with the first smart contract based on the set of rules. The system can determine at a first time a risk score corresponding to the first smart contract based on the analyzing and the detecting. In response to determining that the risk score is above a threshold score, the system can restrict users of a first platform corresponding to the first entity from accessing the first smart contract.
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
Systems, methods, and computer program products for determining an attack on a neural network. A data sample is received at a first classifier neural network and at a watermark classifier neural network, wherein the first classifier neural network is trained using a first dataset and a watermark dataset. The first classifier neural network determines a classification label for the data sample. A watermark classifier neural network determines a watermark classification label for the data sample. A data sample is determined as an adversarial data sample based on the classification label for the data sample and the watermark classification label for the data sample.
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06T 1/00 - Traitement de données d'image, d'application générale
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
99.
UNIFIED ARTIFICIAL INTELLIGENCE MODEL FOR MULTIPLE CUSTOMER VALUE VARIABLE PREDICTION
A unified model for a neural network can be used to predict a particular value, such as a customer value. In various instances, customer value may have particular sub-components. Taking advantage of this fact, a specific learning architecture can be used to predict not just customer value (e.g. a final objective) but also the sub-components of customer value. This allows improved accuracy and reduced error in various embodiments.
A method may include allocating a number of public keys, where each respective public key is allocated to a respective entity of a number of entities; storing a number of private keys, where each respective private corresponds to a respective public key; storing one or more decryption algorithms, where each respective decryption algorithm is configured to decrypt data previously encrypted using at least one encryption algorithm of the encryption algorithms. Each respective encryption algorithm may be configured to encrypt data using at least one public key. Each respective decryption algorithm may be configured to decrypt data using at least one private key. The method may include receiving encrypted data, where the encrypted data is encrypted using a first public key and a first encryption algorithm, and the encrypted data is provided over a network.
