Abstract: Disclosed herein are systems and methods for replicating data across deployments in a routing constrained environment. To replicate data, a processor may detect a modification that changes data for a source entity within a source environment hosting a source deployment of an application. The processor may then update a target environment hosting a target deployment of the application to mirror the modification within the source environment. To update the target environment, the processor may generate a mapping artifact that identifies the source entity having changed data and the target entity within the target environment receiving the changed data. The processor may then create a mapping infrastructure including one or more compute instances that replicate the changed data for the source entity in the target entity.

Abstract: Disclosed are techniques for classifying inquiries and providing recommended actions. These techniques identify or generate a data model that receives and classifies an inquiry into at least one class in real-time or nearly real-time. One or more actions may be determined based at least in part on the at least one class and presented in a user interface to a user who first submitted the inquiry.

Abstract: Computer-implemented method, tax return preparation systems and computer program products for presenting an explanation for a tax calculation or operation performed by a tax return preparation system when the system is in “forms” mode. An electronic version of a tax authority form that has been modified by inclusion of interface elements is displayed to a user. In response to the user selecting an interface element while in forms mode, presentation of an explanation regarding a tax calculation or operation for the associated field is invoked. The user interface controller provides data in response to selection of the interface element to an explanation engine, which determines an explanation based at least in part upon the tax calculation graph. The explanation is provided to the user interface controller for presentation to the user.

Abstract: Systems and methods for authenticating a user are disclosed.

Abstract: A system including a data repository storing metrics describing operational behavior of software programs executing in an enterprise system. The system also includes an application programming interface (API) gateway configured to receive the metrics. The system also includes an ingestion layer configured to ingest the metrics to form the ingested metrics. The system also includes a tumbling window processor configured to process the ingested metrics and the events into heat maps, sort the heat maps into the time slices, and populate the time slices with the ingested metrics.

Abstract: A method of presenting transactions to a user via a client device for review. Each of the transactions and transactions details are presented in a transaction cell. The user swipes the corresponding transaction cell, thus assigning the associated transaction to a category. In detecting a swipe across a transaction cell, a temporary (yet visible) pause of the swipe interaction is performed, alongside the presentation of an action prompt within the transaction cell directed to performing actions relevant to the transaction. Constrained by a swipe pause timer (SPT), the user is provided a short time window in which to engage the action prompt in order to pursue the actions disclosed by the action prompt. If engagement of the action prompt is not pursued by the user prior to the elapsing of the SPT, the swipe interaction proceeds without hindering the speed with which the user typically reviews a transaction.

Abstract: User data from users/consumers is transformed into machine learning training data including historical offer attribute model training data, historical offer performance model training data, and user attribute model training data associated with two or more users/consumers, and, in some cases, millions, tens of millions, or hundreds of millions or more, users/consumers. The machine learning training data is then used to train one or more offer/attribute matching models in an offline training environment. A given current user's data and current offer data are then provided as input data to the offer/attribute matching models in an online runtime/execution environment to identify current offers predicted to have a threshold level of user interest. Recommendation data representing these offers is then provided to the user and the current user's actions with respect to the recommended offers is monitored and used as online training data.

Abstract: A method and system authenticates a user of a data management system. The method and system store an identification tag in the transaction description for a transaction between the user and the data management system. When the user attempts to access the data management system at a later date, the method and system requests that the user provide access to the transaction description. If the user is able to provide access to a transaction description that includes the identification tag, then the method and system can authenticate the user.

Abstract: Automatic keyphrase labeling and machine learning training may include a processor extracting a plurality of keywords from at least one search query that resulted in a selection of a document appearing in a search result. For each of the plurality of keywords, the processor may determine a probability that the keyword describes the document. The processor may generate one or more keyphrases by performing processing including selecting each of the plurality of keywords having a probability greater than a predetermined threshold value for insertion into at least one of the one or more keyphrases and assembling the one or more keyphrases from the selected plurality of keywords. The processor may label the document with the keyphrase.

Abstract: A method may include clustering form elements into line objects and columns of a table of a structured representation by applying a trained multi-dimensional clustering model to spatial coordinates of the form elements, and assigning a table header line type to a table header line object of the line objects based on a spatial coordinate of the table header line object relative to a spatial coordinate of a topmost table data line object of the line objects, and a determination that a number of columns of the table header line object is within a threshold of a number of columns of the topmost table data line object. The topmost table data line object may be assigned a table data line type. The method may further include presenting the structured representation to a user.

Abstract: A method collects word-based data corresponding to a first identifier. A first phrase vector is generated for the first identifier by extracting frequency data from the word-based data. A similarity metric is generated corresponding to the first identifier and a second identifier by comparing the first phrase vector of the first identifier to a second phrase vector of the second identifier. A tuple is generated that includes the first identifier and the second identifier using the similarity metric. A machine learning model is trained with the tuple to generate an embedded vector corresponding to the first identifier.

Abstract: Systems and apparatuses are disclosed for extracting information from document images. An example method includes segmenting a document image into multiple segments and determining formatting information for each segment. Determining formatting information for a segment includes determining one or more features of the segment and comparing the one or more features of the segment to one or more clusters of features associated with different document types. The formatting information for the segment is based on the comparison. The method also includes, for each segment, storing the formatting information in a data structure associated with the segment. The method further includes, for each segment including text to be identified during information extraction, applying OCR to the segment to generate machine-encoded text and storing the machine-encoded text in the associated data structure.

Abstract: A method including generating a first overlap matrix from a first attribute having first measurements of data items, and generating a second overlap matrix from a second attribute having second measurements of the data items. Samples of weights are generated, each of the samples of weights including a corresponding first weight for the first overlap matrix and a corresponding second weight for the second overlap matrix. For each of the samples of weights, the first overlap matrix is combined with the corresponding first weight and the second overlap matrix is combined with the corresponding second weight. Similarity matrices are generated by combining, for each of the samples of weights, a weighted first overlap matrix with a weighted second overlap matrix. A cluster analysis is performed on the similarity matrices to generate groupings the similarity matrices. The groupings represent groups of the data items. A selected grouping is chosen.

Abstract: Systems and methods for predicting one or more field values using machine learning in a knowledge engineering (KE) data model are disclosed. An example method may include identifying a first field in the KE data model which lacks a value and for which one or more machine learning models are defined, the first field being associated with one or more dependent field, determining that each dependent field of the first field has a corresponding value in the KE data model, executing each of the one or more machine learning models to predict one or more values for the first field, selecting one of the one or more predicted values as the representative value of the first field, identifying one or more further fields in the KE data model for which the first field is a dependent field, none of the one or more further fields defining any machine learning models, and calculating values for one or more further fields based at least in part on the representative value of the first field.

Abstract: A method converts a request received from a client application to a query by a serverless function operating on a virtual machine instance provisioned responsive to the request. The query is transmitted to an application database corresponding to the client application. A result is received from the application database. The result is converted into a response and the response is transmitted to the client application in response to the request. Display of the client application is updated based on response with the result.

Abstract: Systems and methods that may be used to implement overlapping data caching for object application program interfaces (APIs). A unique identifier may be assigned to each sub-component of an object API request. The unique identifiers may be used to determine if sub-components of one object API request overlaps with a sub-component of a prior different request such that a data response for the prior different request can be retrieved from the cache and used as part of the data response for the current object API request.

Abstract: A method may include detecting, in transactions of initial users, open recurring expense sequences each having expense sequence attributes, deriving, using the expense sequence attributes of the open recurring expense sequences, recurring expense groups each including a subset of the initial users, generating a prediction that the open recurring expense sequences of a recurring expense group will terminate within a period of a current period, grouping, using personal attributes of the users in the recurring expense group, the recurring expense group into recurring expense subgroups, generating, using a trained model, scores for the recurring expense subgroups each indicating a probability that the open recurring expense sequences of the respective recurring expense subgroup are extendable beyond the current period, and selecting, using the scores for the recurring expense subgroups, a recurring expense subgroup to attempt an extension of the open recurring expense sequences of the recurring expense subgroup.

Abstract: A method may include generating a source transaction description, encoding, by an encoder model of a machine translation model executing on a computer processor, the source transaction description to create a context vector, decoding, by a decoder model of the machine translation model, the context vector to predict a target entity description, generating a transaction including the target entity description, detecting an acceptance, by a user, of an action performed on the transaction, in response to detecting the acceptance, updating a translation accuracy metric for the target entity description, determining that the updated translation accuracy metric satisfies a translation accuracy criterion, and in response to determining that the updated translation accuracy metric satisfies the translation accuracy criterion, adding the target entity description to golden entity descriptions.

Abstract: A computer-implemented system and method for generating a minimum-cost circulation topology in a mesh network is provided. The system comprises one or more processors configured to receive a service request from a client computer and identify a group of services for the service request. The one or more processors are further configured to iteratively query a global registry for each of the group of services to obtain respective groups of dependent service instances, generate an adjacency table including a plurality of service dependency paths identified from the respective groups of dependent service instances. The system determines a minimum-cost service dependency path by applying a predetermined cost algorithm on the adjacency table and executing a first service instance of the minimum-cost service dependency path and route the service request to the second service instance in the dependency path.

Abstract: A method involves: receiving a request to access a first online financial application from a client device, where the first online financial application is one of a many online financial applications made available by an online financial service, and where the many online applications includes a second online application; attempting to generate a browser fingerprint for a browser application on the client device by applying a fingerprinting algorithm to one or more items of browser metadata; associating the browser fingerprint, if generated, with external user data obtained from a web analytics tool and internal user data obtained from the online financial service; receiving login credentials for the first online financial application from a user of the client device; retrieving, using the login credentials, additional internal user data maintained by the online financial service; personalizing, upon detecting the browser fingerprint and using the external user data, the internal user data, and the additional