Abstract: A method that involves establishing a network connection with a plurality of servers executing at least one web application; executing, via the network connection, a set of computer executable scraping instructions on the at least one web application to obtain a set of scraped data; receiving a plurality of electronic messages transmitted via a network; generating a first set of records from the set of scraped data; generating a second set of records from the plurality of electronic messages; validating the set of computer executable scraping instructions by locating a record in the second set of records that identifies a transaction missing in the first set of records to obtain a validation result; and generating, based on the validation result, an alert to update the set of computer executable scraping instructions.

Abstract: A method for presence based mobile payment comprises receiving, from a user, a request for registration with a mobile payment service, registering, by the mobile payment service, the user based on the request, broadcasting, by a point of sale (PoS) system and through an advertising channel, an identification of a transaction, wherein the transaction comprises an item, wherein the advertising channel is used for discovery of available devices, and wherein the user is purchasing the item at the PoS system, receiving, from a mobile device of the user, a payment authorization for the transaction, and facilitating, by the mobile payment service, payment for the transaction.

Abstract: A method identifies a plurality of product categories by applying a categorization model to product data that includes product names and product descriptions. The product data is obtained from internal sources and external sources. The method creates a knowledge graph for a plurality of business entities from inputs that include the plurality of product categories, published data that includes merchant categorizations, and geographical data. The method generates cohort clusters of similar business entities by applying a clustering algorithm to the knowledge graph. The method uses the cohort clusters to learn a first extrapolation model for predicting inventory. And the method provides access to the first extrapolation model to predict inventory level at a specific point in time. The access is provided via a merchant application that runs on an online merchant platform.

Abstract: A method that involves receiving a set of first log records. The set of first log records are duplicated using key operation pairs to generate a set of second log records from the set of first log records. The set of second log records are duplicated using a second set of intervals to generate a set of third log records from the set of second log records. The set of third log records are aggregated using the second set of intervals to generate a set of aggregated log records. The set of aggregated log records includes an aggregated log record comprising a number indicating a number of events from the set of first log records that have the key, that have the operation, and that occurred during an interval of the second set of intervals. The operation is identified by comparing the aggregated log record to a server log record.

Abstract: A location system of a mobile device may detect a first location and a second location of the mobile device. A processor of the mobile device may determine that the first location and second location are within a geofence surrounding the location of interest. The processor may determine a speed at which the mobile device traveled from the first location to the second location. The processor may determine that the mobile device is loitering at the location of interest in response to the speed being below a threshold value. In response to determining that the mobile device is loitering at the location of interest, the processor may perform check in processing to check a user of the mobile device as being on site at the location of interest.

Abstract: A data management system trains an analysis model with a machine learning process to understand the semantic meaning of queries received from users of the data management system. The machine learning process includes retrieving assistance documents that each include a query and an answer to the query. A training model analyzes each answer and generates first topic distribution data indicating, for each answer, how relevant each of a plurality of topics is to the answer. The queries are passed to the analysis model and the analysis model is trained to generate second topic distribution data that converges with the first topic distribution data based on analysis of the queries.

Abstract: A method of accounting workflow integration includes receiving, by a workflow user interface, a first request from a first worker to generate a project including multiple accounting tasks. The first request includes an assignment of an accounting task in the accounting tasks to a second worker. The method further includes generating the project in response to the request, and providing, by the workflow user interface to the second worker, the accounting task and a deadline to complete the accounting task. The method further includes accounting software of the second worker completing the accounting task, and updating a status of the accounting task in response to completing the accounting task. The method further includes receiving a second request from the first worker, the second request to display a status of the project, and providing, to the first worker in the workflow user interface, an updated status of the project.

Abstract: Systems and methods configured to provide dynamic and deep navigation of web pages using keyboard navigation.

Abstract: A method and system provides a data management system that provides data management services and products to users. The method and system provides a predictive model that generates probability scores indicating the likelihood that current users of the data management system would select promotional messages if the promotional messages are presented to the current users.

Abstract: Certain aspects of the present disclosure provide techniques for interacting with a graph database structure. In one embodiment, a method includes receiving, at an application, information regarding a first entity; transmitting, to a graph database, a query regarding the first entity; receiving, at the application, query results based on one or more relationships between the first entity and other entities in the graph database; making, by the application, an inference based on the query results; modifying, by the application, a user interface of the application based on the inference by displaying at least one user interface element suggesting a selection of an application option; and receiving, by the application, a user selection of the suggested application option.

Abstract: Detect duplicated questions using reverse gradient adversarial domain adaptation includes applying a general network to multiple general question pairs to obtain a first set of losses. A target domain network is applied to multiple domain specific network pairs to obtain a second set of losses. Further, a domain distinguishing network is applied to a set of domain specific questions and a set of general questions to obtain a third set of losses. A set of accumulated gradients is calculated from the first set of losses, the second set of losses, and the third set of losses. Multiple features are updated according to the set of accumulated gradients to train the target domain network.

Abstract: A processor of a remote crypto cluster (RCC) may obtain an encrypted specific key from at least one data source through at least one network. The processor of the RCC may derive intermediate data in blind based on the encrypted specific key. The intermediate data may include information from which a derived key is derived. The processor of the RCC may send the intermediate data in blind to a client device.

Abstract: A processor may generate a plurality of vectors from an original text by processing the original text with at least one unsupervised learning algorithm. Each of the plurality of vectors may correspond to a separate portion of a plurality of portions of the original text. The processor may determine respective segments to which respective vectors belong. The processor may minimize a distance between at least one vector belonging to the segment and a known vector from among one or more known vectors and applying a label of the known vector to the segment.

Abstract: The present disclosure provides a composite machine-learning system for a transaction labeling service. A transaction labeling service receives at least one descriptive string describing a transaction associated with a user. The service identifies a preliminary grouping from a generalized scheme. The service extracts a set of N-grams from the descriptive string and converts the N-grams and the preliminary grouping into a set of features. A machine-learning model determines a label from a labeling scheme for the transaction based on the features. User input related to the label includes an accuracy indicator and a reliability indicator. If the reliability indicator satisfies a reliability condition, a set of training data for the machine-learning model is updated based on the descriptive string and the label. The machine-learning model is then trained against the updated set of training data.

Abstract: Certain aspects of the present disclosure provide techniques for determining similarities between businesses. One example method generally includes receiving a similarity query and receiving transaction data associated with a plurality of businesses for comparing the plurality of businesses. The method further includes generating a set of vectors representing the plurality of businesses based on the transaction data and generating a set of compact vectors based on the vectors by applying a dimensionality reduction technique. The method further includes generating based on the set of compact vectors, a similarity index and determining a response to the similarity query using the similarity index.

Abstract: A method that involves receiving, from a host server in a serverless computing architecture, a list of functions comprising a deployed function on the host server, wherein the functions execute within a function as a service (FaaS) platform; receiving validation data for each of the functions; executing a validation process for each of the functions using the validation data; and generating a validation alert in response to the deployed function failing the validation process.

Abstract: A method and system identify assistance offerings that are likely to be relevant to users of a data management system. The method and system utilize a multivariate random forest regression machine learning process to train an assistance offerings recommendation model to recommend relevant assistance offerings to users of the data management system. The multivariate random forest regression machine learning process replaces zero values in the training set data with negative numbers to increase the accuracy of the machine learning process.

Abstract: Computer-implemented methods, system and computer program products for determining what to present to a user of a tax preparation application. A tax compliance or rule module receives runtime data of the electronic tax return and tax rules specifying tax authority requirements. The rule module executes at least one tax rule utilizing the runtime data to generate a non-binding suggestion, which is provided as an input to loosely coupled interface controller. The interface controller determines content of an interview screen for display to the user based at least in part upon factors including the non-binding suggestion, and presents the interview screen including the determined content to the user via a user interface. The content may or may not include content based on the non-binding suggestion depending on processing by the interface controller.

Abstract: An intermediate computer receives or accesses current year electronic payroll data of employees including a taxpayer and a prior year electronic tax return of the taxpayer. During preparation of an electronic tax return utilizing a tax return preparation application, the intermediate computer receives an image of a current year electronic tax document from a taxpayer computing device, processes the image to transform the image into current year electronic tax data and executes specific rules to validate the current year electronic tax data for entry into the electronic tax return based at least in part upon certain current year electronic tax return data derived from the image, the data of the prior year electronic tax return and data of the current year electronic payroll data. A discrepancy within these different data sources may indicate a life event, and screens and questions regarding a life event are presented to the taxpayer.

Abstract: Computer-implemented methods, systems and articles of manufacture for uniquely identifying instances during preparation of an electronic tax return. A user interface (UI) controller receives inputs related to respective electronic tax return data for the same element of a schema, e.g., different data for two different Form W-2 documents. The UI controller generates different instance identifiers, which are written to a data store with the electronic tax return data. A first instance for the first electronic tax return data is generated from the schema element for Form W-2 and identified by a first instance identifier, and a second instance for the second electronic tax return data is generated from the same schema element and identified by the different, second instance identifier. The instance identifiers are used by a logic agent that generates non-binding suggestions for the UI controller indicating a question nor topic to present to the user.