Abstract: Systems and processes herein integrate a crude separation unit, a steam cracker unit, a hydrocracker unit, an aromatics processing unit, and a pyrolysis gasoline hydrogenation unit for separating a whole crude oil or other wide boiling hydrocarbon mixtures for producing olefins and aromatics.

Abstract: Embodiments provide artificial intelligence-based methods and systems for generating scalable task-agnostic embeddings for training task-specific models. Method performed by server system includes accessing historical transaction data from database. Historical transaction data includes entity-specific data, cardholder-specific data, and transaction-specific data. Method includes generating one or more pseudo-objective models for each of plurality of entities based on historical transaction data and one or more pseudo-objectives. Plurality of entities includes acquirer, merchant, and issuer. Method includes determining via one or more pseudo-objective models, entity-specific embeddings for each entity of plurality of entities based on entity-specific data. Entity-specific embeddings includes acquirer-specific embeddings, merchant-specific embeddings, and issuer-specific embeddings. Upon receiving request to induct new entity, method includes accessing new entity data associated with new entity from database.

Abstract: Provided is a system and method for enhancing media. The method includes: identifying whether a scene of a first preview frame captured by at least one primary camera was captured in low light based on at least one pre-defined parameter of the first preview frame; based on identifying that the first preview frame was captured in low light, triggering at least one secondary camera to capture a second preview frame of the scene, wherein the second preview frame has an exposure time higher than an exposure time of the first preview frame, and wherein the second preview frame includes one of a higher field of view (FOV) and a same FOV as compared to the first preview frame; and generating an output frame based on the first preview frame, the second preview frame, and at least one of the at least one pre-defined parameter.

Abstract: Embodiments provide methods and systems for temporal graph representation learning based on node-level temporal point processes. Method performed by the server system includes accessing historical interaction data, generating temporal graph based on historical transaction data and predicting likelihoods of future interaction occurrences among entities based on a pre-trained TPP based graph neural network (GNN) model. Method includes determining edge embeddings of each node based on node features of each node and direct neighbor nodes of each node. Method includes generating edge-contextualized node embeddings of each node corresponding to the edge embeddings based on a neural network model and computing a likelihood of future interaction occurrences associated with the each node based on edge-contextualized node embeddings and a conditional intensity function.

Abstract: A method for handling a display device in an extended reality (XR) environment, includes: obtaining at least one media of a scene comprising a plurality of display devices; determining at least one pixel group comprising locations of the plurality of display devices in the at least one media; determining at least one parameter based on the at least one determined pixel group, wherein the at least one parameter comprises at least one of a frequency and a duty cycle; extracting a pulse width modulation (PWM) signal corresponding to the display device from the plurality of display devices based on the at least one parameter; and recognizing the display device from the plurality of display devices by correlating the extracted PWM signal with an identifier (ID) of the display device stored in at least one memory of the XR device.

Abstract: A modelling platform comprising at least one processor in communication with at least one memory device and a payment processor is disclosed. The at least one processor is programmed to apply one or more data fields of an authorization request message received from a payment processor as one or more inputs to at least one trained machine learning model to generate a first output and a second output. The at least one processor is further programmed to transmit, to an issuer computing device, in real-time as part of an enhanced authorization request message, the first output and the second output. The enhanced authorization request message instructs the issuer computing device to cause a value to be displayed that is viewable by the account holder based upon the first output for a period of time that is based upon the second output.

Abstract: The disclosure relates to AI-based machine-learning and natural language modeling to identify semantic similarities between sets of content having natural language text. For example, a system may generate a relevance classification that indicates whether content such as articles are non-specifically relevant to charities without identifying a particular charity. If the content is non-specifically relevant to charities, the system may apply a natural language model to generate sentence embeddings based on the content and determine a level similarity between the sentence embeddings and a query embedding generated from a charity query. The charity query may itself be generated from a full description of the charity through an encoder-decoder architecture with reinforcement learning.

Abstract: A method for improving consensus in a blockchain network through decentralized grouping includes: identifying, by each node of a plurality of nodes in a blockchain network that manages a blockchain, a plurality of groups, where each is comprised of a subset of nodes; generating, by each node in each subset of nodes, a new block for the blockchain; performing, by each subset of nodes, a first consensus operation among all nodes in the subset of nodes for the new block generated by in the subset of nodes to identify a group consensus block, where each node in the subset of nodes receives the group's group consensus block; and performing, by the blockchain network, a second consensus operation among all groups for the identified group consensus block to identify an overall consensus block, where a majority of groups of the plurality of groups receives the overall consensus block.

Abstract: A method for processing a multi-account check includes: receiving a first authorization request including a transaction amount, an account identifier, and a check number; identifying a blockchain associated with the account identifier including a plurality of blocks, each block including a block header and blockchain data values, and a blockchain data value included in one of the blocks including the check number; generating two or more additional authorization requests including a payment amount, an alternate account number, and an alternate check number, where the alternate account number included in each of the additional authorization requests is unique among the additional authorization requests and a total of the payment amount included in the additional authorization requests is based on the transaction amount; and transmitting each of the additional authorization requests to a financial institution associated with the alternate account number included in the respective additional authorization request

Abstract: Embodiments provide methods and systems for merchant data cleansing in payment network. Method performed by server system includes accessing electronic payment transaction records from transaction database. Each electronic payment transaction record includes merchant data fields. Method includes determining set of electronic payment transaction records with ambiguous merchant data fields having matching probability scores less than predetermined threshold value computed by probabilistic matching model and identifying at least one issue for non-matching of each of set of electronic payment transaction records. Method includes determining data model based on at least one issue of each of set of electronic payment transaction records. Data model is one of: phone-to-city model, payment aggregator model, and merchant name normalization model.

Abstract: Methods and server systems for improving accuracy of authorization optimizer are described herein. Method performed by server system includes receiving a Non-Sufficient Funds (NSF) error message from an acquirer server. Method includes accessing historical transaction data from a transaction database. The historical transaction data includes transaction related information associated with a plurality of users. Method includes generating a plurality of transaction features associated with the user based on the historical transaction data. Method includes determining via an authorization optimizer model, an optimal time slot from a plurality of time slots for the user based on the plurality of transaction features associated with the user. The optimal time slot indicates an optimal time window for the acquirer server to transmit an upcoming recurring payment request to the payment account of the user.

Abstract: A method for generating digital certificates for anonymous users in blockchain transactions includes: storing a blockchain comprised of a plurality of blocks, each block including a block header and transaction values, where each transaction value includes data related to a blockchain transaction including a sending address, recipient address, and transaction amount; receiving a certificate request from a computing device, the request including a user public key of a cryptographic key pair; identifying a subset of transaction values in the blockchain where the sending address or recipient address was generated using the user public key; determining a confidence level based on the data included in each transaction value included in the subset; generating a digital certificate based on the determined confidence level; and transmitting the generated digital certificate to the computing device.

Abstract: A method for preventing unauthorized repeat purchases of a product includes: storing a plurality of purchase data values, each related to a completed financial transaction and including a purchase identifier; receiving an authorization request for a new financial transaction from an external system, the authorization request including a specific identifier; identifying a number of purchase data values where the included purchase identifier matches the specific identifier; and transmitting an authorization response to the external system, the authorization response including (i) an approval of the new financial transaction if the number of purchase data values is below a predetermined number, or (ii) a denial of the new financial transaction if the number of purchase data values is equal to or above the predetermined number.

Abstract: A method for detecting fraudulent transactions includes generation of a graph including a plurality of nodes and a plurality edges between the plurality of nodes based on historical transaction data of a plurality of historical transactions. The plurality of nodes include a set of merchant nodes and a set of consumer nodes. A set of static features and a plurality of dynamic features are determined based on the historical transaction data and the generated graph, respectively. A neural network is trained based on the set of static features and the plurality of dynamic features for detection of transaction fraud. The neural network is used to detect a first transaction as one of a fraudulent transaction or a legitimate transaction based on first transaction data of the first transaction.

Abstract: A method for optimized validation of a blockchain transaction through the use of a parallel database and date management includes: receiving a new blockchain transaction that includes unspent transaction outputs; identifying, in a parallel database, a start date for a blockchain wallet involved in the new transaction; identifying a subset of blocks in the blockchain that have been added to the blockchain since the start date; validating the new blockchain transaction by validating the unspent transaction outputs using the subset of blocks; and, if the validation is successful, adding the new transaction to the blockchain, or, if the validation is unsuccessful, notifying the submitter of the failed validation.

Abstract: A method for risk scoring a blockchain transaction includes: storing blockchain data associated with a blockchain, the data including a plurality of blocks, each block including a block header and transaction values, each transaction value including a sending address, recipient address, and transaction amount; receiving a new transaction value related to a proposed blockchain transaction and a node identifier associated with a node included in a blockchain network associated with the blockchain, the new transaction value including a user address, transacting address, and blockchain amount; identifying transaction values in the blockchain where the sending address or recipient address is associated with a transacting cryptographic key pair also associated with the transacting address; determining a risk score for the proposed blockchain transaction based on at least the data included in each of the identified transaction values and the new transaction value; and transmitting the determined risk score.

Abstract: A method for conveyance of payment credentials through a third party data source includes: storing, in a memory of a computing device, at least a first public key and payment credentials associated with a transaction account; generating, by a generation module of the computing device, a machine-readable code encoded with at least the first public key; displaying, by a display device interfaced with the computing device, the generated machine-readable code; generating, by the generation module of the computing device, a key pair comprised of at least a private key and a second public key; encrypting, by an encryption module of the computing device, the payment credentials using the private key; and electronically transmitting, by a transmitting device of the computing device, the encrypted payment credentials to a publicly accessible data source.

Abstract: A method for offline transmission of blockchain details includes: storing, in a computing device, a first private key and a currency amount; receiving a first destination address associated with a blockchain network and a transaction amount; generating a second private key; generating a second destination address associated with the blockchain network using the second private key; generating a blockchain transaction including at least the first destination address, the transaction amount, the second destination address, and a remainder amount based on at least the currency amount and the transaction amount; signing the generated blockchain transaction using the first private key; executing a query to replace the first private key with the second private key, wherein replacement of the first private key includes deletion of the first private key from the computing device; and transmitting the generated blockchain transaction.

Abstract: Embodiments provide artificial intelligence methods and systems for evaluating vulnerability risks of issuer authorization system. Method performed by a server system includes accessing a set of payment transaction data including subset of fraudulent transaction data. Method includes generating via a machine learning model, set of synthetic transaction data based on the subset of fraudulent transaction data. Method includes accessing set of historical card velocity features and collating the set of synthetic transaction data and the set of historical card velocity features to generate set of enriched synthetic transaction data. Method includes extracting via a classifier, subset of feasible fraudulent transaction data from the set of enriched synthetic transaction data. Method includes generating simulated authorization model based on the set of payment transaction data.

Abstract: A method for dynamic selection of transactions for inclusion in new blocks added to a blockchain includes: receiving, by a blockchain node in a blockchain network that manages a blockchain, a plurality of proposed transactions; sorting, by the blockchain node, each proposed transaction into one of a plurality of transaction groups; identifying, by the blockchain node, a selection of proposed transactions based on one or more dynamic selection criteria, where the selection of proposed transactions includes one or more proposed transactions from one or more of the plurality of transactions groups based on the one or more dynamic selection criteria; generating, by the blockchain node, a new block including a new block header and the identified selection of proposed transactions; and distributing, by the blockchain node, the generated new block to a plurality of additional nodes included in the blockchain network.