Abstract: Systems and methods for automated transactional offers are disclosed. In one embodiment, a system can be configured to register a uniform resource locator (URL) of a merchant system as a verified merchant URL. An extension communication can be received from a browser extension executed on a client device. A captured merchant URL and a user account of a user can be determined based on the extension communication. A merchant ID is associated with the captured merchant URL is determined based on a comparison of the captured merchant URL and a plurality of verified merchant URLs. An available offer is retrieved based on the merchant ID. The available offer is displayed on the client device. An offer enrollment request from the browser extension of the client device is received. A user offer enrollment is transmitted to an issuer system.

Abstract: Systems and methods for the calculation of a dynamic trust score are disclosed. The dynamic trust score may indicate a likelihood that the consumer will complete the transaction in a positive manner. The system may calculate the dynamic trust score based on various static and dynamic variables including digital identity data, internal data, third-party data, private data, and/or data from the transaction initiated by the consumer.

Abstract: Disclosed are various embodiments for data anomaly detection. A variable profile is generated for each variable in source data. Then, the variable profiles are provided to each of a plurality of machine learning models. Next, it is determined, with each of the plurality of machine learning models, whether each variable profile is anomalous. The determination, from each of the plurality of machine learning models, whether each variable profile is anomalous is provided to an ensemble model. The ensemble model then generates a final determination whether each variable profile is anomalous. The final determination is then reported to an analysis service.

Abstract: A system and method for generating product analytics insights for machine learning platforms is disclosed. The system and method can receive a log file including data derived while executing a software program. Software packages and the package sub-modules logged in the log file may be categorized into a plurality of package categories. A notebook in which the software code is written, and identified in the log file, may be categorized into a notebook category. A consumable insight may be generated based on: the categorized software packages and package sub-modules, and the categorized notebook. A natural language summary may be generated describing the consumable insight. The natural language summary may be transmitted for display on a graphical user interface (GUI) in response to a user query.

Abstract: A blockchain-based loyalty point system that comprises a host computing device for interfacing with authorized devices and loyalty partner websites in a loyalty point network. In one example, the loyalty point network includes a blockchain network and a host computing device. The host computing device is configured to receive a registration request from a client device for access to the loyalty point network. The host computing device causes a registration proposal for the client device to be written to the blockchain network and receives a point adjustment request from a loyalty partner website. The host computing device executes a smart contract in response to receiving the point adjustment request from the loyalty partner website, and the smart contract is executed to adjust the account balance of the customer account based at least in part on the point adjustment request.

Abstract: Disclosed are various embodiments for providing non-fungible tokens (NFT) as a mechanism for verifying ownership of payment instruments such as stored value payment instruments. First, a computing device can receive a purchase notification, the purchase notification indicating that a first user has purchased a stored value payment instrument. Then, the computing device can create a non-fungible token (NFT) on a distributed data store in response to receipt of the purchase notification. Next, the computing device can associate a unique identifier of the NFT with the stored value payment instrument. Subsequently, the computing device can receive a first public key associated with the first user. Then, the computing device can update an owner identifier of the NFT with the first public key associated with the first user.

Abstract: Disclosed are various embodiments for generating payment accessibility notifications for individuals with impairments. In one non-limiting example, a system includes a first computing device configured to identify a second computing device using a wireless communication protocol in a store location. A first notification for the first computing device is activated based at least in part on an accessibility type. A user interface is displayed for receiving client data, and the client data includes a product entered by a user. An expected amount is determined for the product based at least in part on the client data and transmitted to the second computing device. A pending amount for the product is received from the second computing device. A second notification is activated for the first computing device prior to a completion of the purchase based at least in part on the pending amount and the expected amount.

Abstract: Systems and methods for the maintenance of merchant-stored transaction account data are disclosed. The system may include various merchant systems and issuer systems in communication via a blockchain network. The system provides a process for collaboration between various issuer systems and merchant systems to update and maintain merchant stored transaction account data in response to changes, cancellations, updates, or the like in various stored transaction account.

Abstract: A computing device (e.g., a server, a cloud-based device, a network device, an item analysis device, etc.) may receive location information indicating a geographic location of a user device and image data captured by the user device of a listing (e.g., a menu, an event ticket, a content source, etc.) for a selectable item. The computing device may determine an entity offering the selectable item based on the location information. An intent indicator for the selectable item may be determined based on the entity and the information for the selectable item. An instruction to the user device may cause modification of the image data to indicate the intent indicator based on the intent indicator satisfying an intent threshold that defines a level at which an intent is classified as a positive intent.

Abstract: Disclosed are various embodiments for garbage collection for object-based storage systems. A first set of objects stored in a data object store is identified as not meeting criteria for deletion or archiving, the criteria specified in a retention policy. A second set of objects is identified by performing a search of the object data store for data objects belonging to a data structure with the first set of objects. The second set of objects is recorded as not meeting the criteria for deletion or archiving. The second set of data objects are retained, based on the recording, to prevent the data structure from being corrupted.

Abstract: Systems and methods for automated travel itinerary management based on real-time event aggregation are disclosed. The system may aggregate event data, in real-time, from multiple distributed data sources to generate an automated itinerary based on travel-related transactions. The system may update the automated itinerary as additional travel-related transactions are completed. The system may also include travel data from travel systems, concierge systems, and manual input in the automated itinerary.

Abstract: A blockchain-based multi-merchant loyalty point partnership system may include a blockchain API host that is configured to generate a reward based at least in part on transaction data received from a web site. In one example, a system is configured to receive a loyalty account and transaction data for a completed purchase from a loyalty partner site. A partnership smart contract is executed to validate that the transaction data meets a purchase requirement parameter of the partnership smart contract. A transaction record is stored which indicates that the loyalty account has the transaction data for meeting the purchase requirement parameter. The transaction record is transmitted to consensus participants associated with the blockchain network.

Abstract: A transaction card can be manufactured from rigid materials, such as glass, stone, or ceramics. First, a rigid layer of a transaction card can be placed within a first recess of a first metallic sheet. The first recess can comprise a lip and an opening. Next, a binding layer can be placed on top of the first rigid layer of the transaction card where the binding layer comprises a binding medium. A second rigid layer can be placed on top of the binding layer. A second metallic sheet can be placed on top of the first metallic sheet where the second metallic sheet has a second recess with a lip and an opening. Next, the first metallic sheet can be fused together with the second metallic sheet to form a fused metal sheet. The fused metal sheet can be cut along a border of the opening of the first recess.

Abstract: Disclosed are various embodiments for a service for decentralized browser based wallets. Various embodiments of the present disclosure can receive a first passcode and a second passcode from a security provider device. Various embodiments can use a cryptowallet to decrypt a first encrypted private key using the first passcode to generate a decrypted private key. Various embodiments can use a cryptowallet to sign a transaction request with the decrypted private key. Various embodiments can use a cryptowallet to generate a second encrypted private key by encrypting the decrypted private key using the second passcode.

Abstract: Systems and methods for identifying data of interest are disclosed. The system may retrieve unstructured data from an internet data source via an alert system or RSS feed. The system may input the unstructured data into various models and scoring systems to determine whether the data is of interest. The models and scoring systems may be executed in order or in parallel. For example, the system may input the unstructured data into a Naïve Bayes machine learning model, a long short-term memory (LSTM) machine learning model, a named entity recognition (NER) model, a semantic role labeling (SRL) model, a sentiment scoring algorithm, and/or a gradient boosted regression tree (GBRT) machine learning model. Based on determining that the unstructured data is of interest, a data alert may be generated and transmitted for manual review or as part of an automated decisioning process.

Abstract: Disclosed are various embodiments for executing entity-specific cryptographic code in a cryptographic coprocessor. In one embodiment, an exemplary method comprises transmitting, from an entity service to a client device, a key identifier and encrypted code that implements a cryptographic algorithm, wherein the encrypted code further includes an encryption key, wherein the key identifier identifies a particular root key that is associated with the entity service and is stored with a cryptographic coprocessor of the client device; receiving, by the entity service from the client device, a cryptogram that includes information encrypted using the cryptographic algorithm and the encryption key; and decrypting, by the entity service, the cryptogram using the encryption key.

Abstract: At least some embodiments are directed to a large-scale data standardization system. The system receives a set of documents with records formatted according to a third-party data schema. The system utilizes a first machine learning model to select a document from the set of documents. The system utilizes a machine learning model to select data classification labels formatted according to the third-party data schema. The classification labels are associated with a set of records. The system utilizes a second machine learning model to generate a canonical data structure constructed according to a standardized data schema based on the classification labels and the records associated with the classification labels.

Abstract: Disclosed are various embodiments for inferring a schema for a flat file when a corresponding schema file defining the structure of the data included in the flat file is not available. In some examples, a trained schema inference model can be used to determine the corresponding schema defining the data structure and relationships of the data include the flat file. In other examples, statistical analysis and pattern detection is used to determine the corresponding schema of the flat file. Once a flat file schema is defined, a compliance analysis can be performed to ensure that the data of the flat file conforms with various privacy and compliance regulations. If a compliance issue is detected, the flat file can be flagged for manual review and/or modified to rectify the compliance issue.

Abstract: Disclosed herein are system, method, and computer program product embodiments for converting queries and scripts used in big data systems. A script conversion system converts a script and its queries from a first big data format to a second big data format. The script conversion system uses a configuration mapping that includes direct and/or indirect function mappings. For the direct mapping, the script conversion system replaces the function with a new function in the second big data format. For the indirect mapping, the script conversion system constructs an equivalent function in the second big data format using information extracted from parsing queries in the first big data format. The script conversion system also formats a converted query using a project mapping and/or generates a conversion report indicating successful or unsuccessful conversions. Scripts may be converted from an on-premises data warehouse system to a cloud-based data warehouse system.

Abstract: At least some embodiments are directed to a prediction system of software objects. The prediction system predicts a first aspect of a user profile utilizing a categorization machine learning model and a user activity profile, the user profile and the user activity profile are associated with a user. The user activity profile comprises a plurality of values associated with demographics and historical activity data of the user. The prediction system predicts a software object associated with the user profile utilizing an optimization machine learning model, the first aspect of the user profile, and a second aspect of the user profile. The software object is optimized with respect to at least one competitive interest between the user associated with the user profile and an entity associated with the software object. The prediction system outputs the software object to a client computing device of the user.