Abstract: 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.

Abstract: 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.

Abstract: A system and/or method may be provided to silently authenticate a user. An example method of silently authenticating a user includes receiving a request to complete a transaction associated with a merchant application. The request includes a data file including an identifier from the user device. The request is from a user device. The method also includes determining whether the data file includes a refresh token and determining whether the refresh token is valid if the data file includes the refresh token. The method further includes receiving an access token from the user device if the refresh token is valid. The access token includes an authorization scope. The method also includes determining whether the transaction is within the authorization scope. The method further includes authenticating a user if the transaction is within the authorization scope.

Abstract: 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.

Abstract: 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.

Abstract: There are provided systems and methods for on-device accessibility options for checkout data using short-range wireless transmissions without needing a user or device identifier. A user may engage in a transaction with another user, such as a purchase of goods, services, or other items from a merchant at a physical merchant location. The merchant may provide options to receive checkout data on a mobile device so that the checkout data may be presented with accessibility options that assist a user in consuming the data. The checkout data may be transmitted via data exchanges using short-range wireless communications. A message may be generated that includes a webpage address or another identifier allowing for retrieval of a webpage or user interface data for the checkout data. The checkout data may also be updated by the user on the mobile device, which may be provided to the merchant device.

Abstract: Methods and systems are presented for providing a framework for analyzing graphs that exhibit non-homophilous behavior. Under the framework, a structural analysis and a feature-based analysis will be performed on a sequence of graphs. When performing the feature-based analysis, various features are extracted from each node in the sequence of graphs, and clusters of nodes are identified from each graph based on the features. A set of evolving prototypes is generated to represent evolving characteristics of the clusters of nodes, and a set of persistent prototypes is generated to represent persistent characteristics of the clusters of nodes. Information derived from the structural analysis of the graphs, the set of evolving prototypes, and the set of persistent prototypes are embedded within the nodes of the graphs. The embedded information is then used to classify the nodes.

Abstract: There are provided systems and methods for dynamic autoscaling of server resources using intelligent demand analytic systems. A service provider, such as an electronic transaction processor for digital transactions, may utilize different computing resource to provide computing resources to users. During use of such computing resources by end users and their computing devices, different demand and needs may be required by such users and devices. The service provider may utilize an intelligent machine learning system to predict computing resource needs and demands at different future time periods based on past usages over similar time periods, computing requests and demands, and network communications. The machine learning engines may identify one or more usages curves, which may be of one or more degrees of curvature, to determine potential future usage. Using these past analytics, the service provider may dynamically scale automatic provision of computing resources.

Abstract: 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.

Abstract: Methods and systems are presented for providing a framework for facilitating token sharing with contract issuers based on processing of smart contract transactions. The framework provides a mechanism for allowing one or more computer nodes within a blockchain network to opt-in to a token sharing agreement with a contract issuer and a mechanism for an opted-in computer node to share tokens with the contract issuer in a decentralized manner. When a computer node has opted-in to the token sharing agreement, the computer node, by executing a smart contract, performs a set of computer procedures that is non-productive to the processing of the smart contract transaction. The execution of the set of non-productive computer procedures provides an additional amount of processing fee based on the processing of the smart contract transaction. The computer node then transfers a portion of the additional amount of processing fee to an account of the contract issuer as part of the processing of the smart contract.

Abstract: A method for facilitating secured execution of blockchain transactions includes generating and training one or more models using real-world blockchain transaction data corresponding to one or more blockchains, wherein each of the one or more models corresponds to a respective blockchain of the one or more blockchains, receiving a request to perform a simulation of one or more transactions on a first blockchain of the one or more blockchains, and in response to receiving the request, identifying a first model of the one or more models that corresponds to the first blockchain, determining a transactional stress level for the simulation, determining a number of nodes for the simulation, and executing, using the first model, the simulation based on the request to predict a result of the one or more transactions if they were executed on the first block-chain.

Abstract: Techniques are disclosed relating to maintaining a point in time (PIT) database. A database system updates a snapshot table included in the PIT database according to a snapshot table time-to-live (TTL) value of 3K/2. In some embodiments, the updated snapshot table is configured to provide accurate data for queries up to K years prior to points in time at which the queries are executed. The system may update a binlog table included in the PIT database according to a binlog table TTL value of K. The system may receive a request to access PIT data stored in the PIT database. Based on a timestamp specified in the request, the system may access the PIT database. The system transmits a set of PIT data that corresponds to the timestamp and includes accurate data for K years prior to the timestamp.

Abstract: Methods and systems for fault tolerant token based transactions are described. A user may perform a transaction with a mobile device. The mobile device may be in an offline mode or may be lacking a network connection to servers or cloud devices of a mobile payment system. The transaction may be processed through a transaction device, which may aid in the sending and receiving of transaction data while the mobile device is in the offline mode. The transaction device may communicate with the server or cloud device and the server or cloud device may process the transaction and communicate with the transaction device, which may forward data from the server or cloud device to the mobile device.

Abstract: A system and/or method may be provided to authenticate a user. An example method of authenticating a user includes receiving, by a merchant application, a user request to complete a transaction using a payment service provider. The method also includes in response to receiving the user request to complete the transaction, retrieving, by the merchant application, a browser cookie stored on a user device and associated with one or more user interactions with a browser included in the user device and the payment service provider. The method further includes in response to receiving the user request to complete the transaction, launching, by the merchant application, an instance of the browser that reads the browser cookie and authenticates the user based on the browser cookie.

Abstract: A system may perform operations including, for a given seed file, obtaining a first graph query to extract a first set of feature values from a first dataset, extracting, based on a feature calculation time, a sub-graph from the first dataset, obtaining, based on a second logic, a second graph query to extract a second set of feature values from a second dataset, and calculating a parity between the first set of feature values and second set of feature values. The operations may also include obtaining the events from a first data store, the events being filtered based on the given seed file, replaying the events backwards and extracting a third set of feature values, determining whether any of the second set of feature values do not match the second set of feature values, and updating the second set of feature values based on third set of feature values.

Abstract: Methods and systems are presented for providing a framework for dynamically managing computer resources in processing transactions by selectively applying one or more techniques to improve the computer resource usage efficiency of processing different transactions. When a request for processing a first transaction is received, an estimated frequency of future transactions that are related to the first transaction is predicted. Based on the estimated frequency of future transactions, one or more actions that improves the computer resource usage efficiency for processing the first transaction are applied. The action may include suspending the processing of the first transaction and subsequently performing a batch process including the first transaction and a second transaction related to the first transaction. The action may include storing data associated with the processing of the first transaction in a cache memory for use in the subsequent processing of a second transaction.

Abstract: Some embodiments may provide a method comprising receiving, from a remote machine, entity-identifying data and form identifying data, the form identifying data to identify a sequence of one or more electronic forms, the sequence including a target electronic form having a form element, determining an entity identifier, based on the entity-identifying data, accessing, from a data store, an entity attribute value based on the entity identifier, and transmitting, to the remote machine, form-filling instructions operable to cause a machine to automatically associate an entity attribute value with the form element.

Abstract: Methods and systems are presented for providing a computer platform that manages the impacts of government regulations on existing software processes of an online service provider. A regulation document is obtained from a government agency. The regulation document is processed, and legal obligations relevant to an online service provider are extracted from the regulation document. An ensemble machine learning model is used to recommend, for each of the legal obligations, software controls that can be implemented within one or more software processes of the online service provider to mitigate a risk of the legal obligations. The ensemble machine learning model may include an attribute-based model and a text-based model. An explainable visual interface is provided to present the recommended software controls and context that indicates to a user how the software controls are determined for the legal obligations.

Abstract: 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.

Abstract: Methods and systems are presented for providing an artificial intelligence (AI)-based conversation system for facilitating a conversation with users and processing transactions for the users. The AI-based conversation system includes an AI model coupled with different backend modules. Based on an utterance submitted by a user during a chat session, the AI model is configured to generate instructions for a backend module to perform a transaction for the user based on a prompt template. The AI model also communicates the instructions to the backend module using a protocol specified in the prompt template. Upon receiving an output from the backend module, the AI model is configured to generate content for the chat session based on the output, and provide the content to the user.