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: 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: 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.

Abstract: In various embodiments, a micro registry may be a mobile communication platform that is configured to connect to users and service hosts. The micro registry may facilitate advertisements, payments, communication, loyalty, behavior tracking, social media interaction, and/or any other suitable communication advertising, or transaction between a user and a service host. The micro registry may employ BLE beacons that enable the micro-registry to identify micro-locations and facilitate connections (e.g., interfaces, interactions, communications, etc.) between user devices and service hosts and/or merchants. 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.

Abstract: The present disclosure presents systems and related methods for customer defined limited use authorization. One such method comprises implementing a customer defined generation control associated with a parent transaction account; receiving a request to generate the electronic payment token from at least one of an issuer web app or an issuer native app; verifying that the generation control condition has been satisfied; generating via a tokenization engine, the electronic payment token in response to the request to generate the electronic payment token, wherein generating the electronic payment token further comprises storing an expanded set of token controls comprising customer defined authorization control as token data; and transmitting, via the tokenization engine, the electronic payment token to the customer mobile device responsive to the generation control condition being verified as being satisfied.

Abstract: Transaction account holders are able to attach additional data to a transaction in concert with the transaction being initiated, prior to the merchant posting the transaction. Transaction account holders do not have to wait and/or opt for an external service provider to assist with the compiling and/or attaching of data at a later point in time (e.g., after the merchant posts the transaction). Transaction account holders may upload and access electronic receipts associated with a transaction authorization almost immediately after the sale and/or authorization of the transaction.

Abstract: At least some embodiments are directed to an entity classification system that receives informational data associated with an entity. The informational data includes sentences associated with the entity. The entity classification system utilizes a first machine learning model to determine a first contextual meaning among words of a sentence associated with the entity based on a first word embedding technique, and determines at least one category associated with the entity based at least in part on the first contextual meaning. The entity classification system utilizes a second machine learning model to determine a second contextual meaning shared by a set of sentences based on a second embedding technique, and determines a subcategory of the category associated with the entity based at least in part on the second contextual meaning. The entity classification system generates an output including the category and subcategory associated with the entity.

Abstract: A consumer may enroll in an event gamification system. An event listener may detect an event. The event gamification system may determine that the event qualifies for a game in which the consumer is enrolled. The event gamification system may transmit an achievement notification to the consumer as soon as the event occurs. The game may be displayed in a GUI on a consumer device. The consumer may be rewarded for completing the achievements in the game. Also, a server computer receiving a plurality of data sets that represent visually perceptible elements for a plurality of host web pages, wherein each of the plurality of host web pages displays an active link associated with a product on a merchant web page. The server automatically generating composite web page from: i) the particular data set associated with the particular host web page and the particular set of visually perceptible elements and ii) the related electronic content associated with the product from the merchant web page.

Abstract: Disclosed are various embodiments for using machine learning models to identify appropriate security patterns to follow during the application development process. A computing device can receive a request to identify a collection of security patterns to apply to an application and then identify a plurality of features associated with the application. Next, the computing device can submit the plurality of features to a random forest machine learning model and receive a first set of security patterns in response. Then, the computing device can submit the plurality of features to a k-nearest neighbor (KNN) machine learning model and receive a second set of security patterns in response. Then, the computing device can identify a subset of the security patterns that is included in both the first set of security patterns and the second set of security patterns and return the subset of the security patterns.

Abstract: Disclosed are various embodiments for securely conducting online in-application purchases. In one example, among others, a system comprises a computing device that is configured to identify a purchase request from a merchant application executed on the computing device and authenticate an account with a wallet provider based at least in part on a selection of the account. The computing device is configured to receive an instruction from the wallet provider to execute a security library and determine that the computing device is secure by executing a security library. The execution of the security library generates a device response for the wallet provider. A security code is determined data received from the wallet provider. A token is transmitted for the purchase request to the wallet provider based at least in part on the security code.

Abstract: Disclosed are various embodiments for authenticating a user using non-fungible tokens (NFTs). A trusted token issuer verifies a user's identity according to identifying credentials (e.g., government issued identification, passport, driver's license, etc.) presented by the user and creates a non-fungible token in response to verifying the credentials. The non-fungible token is associated with a user identifier and can be used by an access provider to authenticate a user requesting access to restricted content provided by the access provider. For example, when a client device associated with the user requests access from an access provider to an access-restricted website or other type of access-restricted area (e.g., building, concert venue, network, etc.), the access provider (e.g., website server, building computing device, venue system, etc.) uses the properties of the non-fungible token to verify one's identity and permit access upon verification.

Abstract: Disclosed are various embodiments for securely sharing confidential data between applications. A deep link request can be received from a first computing device, the deep link request identifying a set of data to be shared and a deep link schema. A token that represents the set of data can then be created. Also, a deep link that complies with the deep link schema and includes the token can also be created. The deep link and the token can then be returned to the first computing device. Later, a data request containing the token can be received from a second computing device. The set of data associated with the token can then be return to the second computing device.

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 by an object storage service that have been accessed within a previously defined date range is identified. Then, a second set of objects stored by the object storage service is identified based at least in part on a relationship to one or more objects in the first set of objects. Next, a third set of objects stored by the object storage service that have been created prior to a predefined date is identified. Then, a subset of objects which are members of the third set of objects and not members of the first set of objects or the second set of objects is identified. Finally, a retention action is performed on individual members of the subset of objects based at least in part on a retention policy.

Abstract: Embodiments for intelligently matching a user to an agent for a communication session are disclosed. In one example, a system that comprises a computing device that is configured to initiate a first communication session with a first agent client device and generate interaction data associated with the user from the first communication session. A user segment classification is determined for a user account. A user request is received to initiate a second communication. The computing device identifies an agent account of a second agent to participate in the second data communication session. T second communication session is initiated between a client device and a second agent client device associated with the agent account.

Abstract: In various embodiments, a system, method, and computer readable medium of the present disclosure comprises authenticating, by a processor of a computing device, credential information of a user of the computing device; releasing, by a processor of the computing device and in response to authenticating the credential information, a first secret element to a secure element processor of the computing device; verifying, by the processor of the computing device from the secure element processor of the computing device, that the user is an authorized user of a first transaction account by comparing the first secret element with an issuer element associated with the first transaction account; and activating, by the processor of the computing device, the first transaction account for a transaction on the computing device.

Abstract: Disclosed are various embodiments for verifying the presentation of media represented by non-fungible tokens (NFTs). A presentation device can receive a smart contract address and a non-fungible token (NFT) identifier from a client device. The presentation device can then execute a function of a smart contract located at the smart contract address to obtain the owner address for the NFT, wherein the NFT identifier is provided as an argument to the function of the smart contract. Subsequently, the presentation device can validate the owner address for the NFT. Then, in response to validation of the owner address for the NFT, the presentation device can present the media file represented by the NFT.