Abstract: A method including receiving a data structure describing transactions between electronic user accounts associated with users. A relationship graph is constructed from the data in the data structure. The relationship graph has nodes representing entities described in the transactions. The relationship graph has edges representing connections between the nodes. The method also includes clustering groups of nodes within the nodes to form clusters among the nodes. The edges are labeled as relationships types. Labeling is performed by receiving, as input to a machine learning model, a vector having attributes representing the clusters, the nodes, and the edges. Labeling is also performed by outputting, from the machine learning model, probabilities. Each of the probabilities corresponds to a corresponding probability that an edge in the edges represents a relationship type between two nodes in the nodes. Labeling is also performed by labeling, based on the output, the edges as the relationship types.

Abstract: A data management system manages secured data for a plurality of users. The data management system utilizes an access authorization system to authenticate users seeking access to the data management system. The access authorization system provides access tokens to authenticated users. The access tokens enable the authenticated users to access the data management system without again providing authentication data. The access authorization system includes, for each user, an access policy that governs whether the users can use the access tokens to access the data management system. The access tokens have a finite lifetime. If the users use the access tokens within the finite lifetime and if the users satisfy all of the access rules of the access policies, then the lifetime of the access tokens can be extended a finite number of times.

Abstract: A method to predict a delay involves receiving an open invoice sent by a company to a customer, extracting, using a programmable interface, a set of company data, a set of customer data, and a set of invoice data from a management application (MA) and the open invoice; refining, using a set of algorithms, the set of company data, the set of customer data, and the set of invoice data into a set of invoice attributes, a set of company features, and a set of customer features; predicting a delay in processing the open invoice using a trained model analyzing the set of company features and the set of customer features; updating, at an expiration of a predetermined timespan, the MA to add the delay to a due date of the open invoice by including the set of company features; and updating, at the expiration of the predetermined timespan, the MA to add the delay in processing the open invoice by including the set of customer features.

Abstract: Systems and methods are provided that, in some embodiments that extract user data from at least one data warehouse. The user data is sorted within each dimension, and partitions each dimension into bins. Clusters are defined as each bin that includes user data for a number of users that exceeds a threshold. Clusters are determined for every combination of dimensions. Each combination of clusters that exceed the threshold is defined as clusters that are formed from multiple dimensions. All clusters and other clusters are stored into a cluster definition table. The clusters are used to analyze the profile of specific users.

Abstract: A method and system provides a tax refund confidence indicator to a user of a tax return preparation system, according to one embodiment. The method and system include receiving user current tax related data from a user and receiving historical tax related data associated with previously prepared tax returns. The method and system further includes generating estimated tax refund data and confidence score data indicative of the reliability of the estimated tax refund data. The method and system include providing the estimated tax refund data and the confidence score data to the user.

Abstract: Aspects of the present disclosure provide techniques for dynamic location tracking. Embodiments include receiving a plurality of location records associated with a site, wherein each respective location record of the plurality of location records comprises respective location coordinates of a respective device associated with the respective location record. Embodiments include determining respective distances from a center point of the site to the respective location coordinates in each respective location record of the plurality of location records. Embodiments include determining a radius of a region definition for the site based on the respective distances. Embodiments include receiving a device location from a device associated with a user. Embodiments include performing, based on the device location and the region definition, one or more location-based operations.

Abstract: A data management system manages secured data for a plurality of users. The data management system utilizes an access authorization system to authenticate users seeking access to the data management system. The access authorization system provides access tokens to authenticated users. The access tokens enable the authenticated users to access the data management system without again providing authentication data. The access authorization system includes, for each user, an access policy that governs whether the users can use the access tokens to access the data management system. The access tokens have a finite lifetime. If the users use the access tokens within the finite lifetime and if the users satisfy all of the access rules of the access policies, then the lifetime of the access tokens can be extended a finite number of times.

Abstract: A method including obtaining terms that are specific to a domain. First and second sets of the terms are obtained from first and second users. The first set do not adhere to a standard; the second terms do adhere to the standard. Tokens are obtained from the terms. First and second topics, representing terms, are identified within the domain. The terms are assigned to exactly one corresponding topic. The terms are assigned to the topics. First and second topic names are identified for the first and second topics. Identifying includes analyzing, for relevance, ones of the tokens. Identifying also includes selecting a particular token as a selected topic name for a selected one of the first topics and the second topics. A similarity value is determined between the first and the second selected topics. A mapping is established, based on the similarity value, between the first and second selected topic.

Abstract: A method and system generate customized financial document templates in a document preparation system, according to one embodiment. The method and system receive document data from a user corresponding to a document including a plurality of custom data fields in a custom template. The method and system map the custom data fields to system data fields from a relatively small subset of data fields selected from a system data field pool based on the characteristics of the user. The method and system generate a custom form template based on the document data and the mapping of the custom data fields to system data fields.

Abstract: A method comprising receiving real time data associated with a transaction, accessing, from an online bank account after receiving the real time data, processed data associated with the transaction, matching the real time data with the processed data, and displaying an alert when the processed data and the real time data are inconsistent.

Abstract: An anomaly detection service executed by a processor may receive multivariate time series data and format the multivariate time series data into a final input shape configured for processing by a generative adversarial network (GAN). The anomaly detection service may generate a residual matrix by applying the final input shape to a generator of the GAN, the residual matrix comprising a plurality of tiles. The anomaly detecting service may score the residual matrix by identifying at least one tile of the plurality of tiles having a value beyond a threshold indicating an anomaly. The processor may perform at least one remedial action for the anomaly in response to the scoring.

Abstract: Methods, systems and articles of manufacture for allowing taxpayers to utilize mobile communication or communication devices such as smartphones and tablet devices to upload tax document images and process tax document images such that resulting recognized tax data is incorporated into an electronic tax return. Authentication data is generated and provided to the taxpayer to establishing a connection with an image processor, which provides recognized tax data to the on-line tax preparation application for incorporation into the electronic tax return. Authentication data may be embodied within a URL address to the image processor that is transmitted as a SMS message to the mobile communication or computing device or encoded within a QR code such that the mobile communication or computing device can be used to take an image of and decode the QR code to determine the URL address and authentication data to establish a connection with the image processor.

Abstract: Certain aspects of the present disclosure provide techniques for updating database records while maintaining accessible temporal history. One example method generally includes receiving a request, at a database, to select an instance of a record from the database at a specific point in time and reading the instance of the record from a snapshot of the database. The method further includes loading one or more deltas associated with the record from the database and chronologically applying the one or more deltas to the instance of the record to create the instance of the record. The method further includes returning the instance of the record, determining that the request has made a percentage of recent requests exceed a threshold for requests for most-current data and creating a new snapshot of the database.

Abstract: A method and system provides personalized search results to users of a data management system. The method and system receives a search query from a user and generate initial search results including a plurality of assistance documents relevant to the query data. The method and system utilizes natural language analysis and machine learning processes to analyze the query data, user attributes data, and the assistance documents in order to generate personalized previews of the assistance documents for the user. The method and system output personalized search results to the user including the personalized previews of the assistance documents.

Abstract: A method for increasing computer network security. A first request to attach a first user account for a software program to an external secure account is received. The first request includes a first set of user credentials for accessing the external secure account and a first unverified email address, the external secure account controlled by a second external computer. After receiving the first request, features having metrics is received. The metrics describe at least creation of the first user account and include at least the first unverified email address. A machine learning model processing the features calculates a probability score that the first request is fraudulent. The probability score is compared to a threshold to form a comparison result. A security action is performed with respect to attaching the first user account to the external secure account in response to the comparison result.

Abstract: A method and system provides efficient data searches. The method and system include receiving search request data that requests a search to be made and identifying multiple search terms or clauses in the search request data. The method and system includes determining an expected cost in resources for searching each search term. The method and system then searches for each search term in order from least resource intensive to most resource intensive, with each search after the first search being performed only among the results of the previously searched term.

Abstract: Computer-implemented methods, articles of manufacture and computerized systems for identifying or alerting a user of certain data in electronic tax returns. A computerized tax return preparation system including a tax return preparation software application executed by a computing device receives first and second tax data and populates respective fields of the electronic tax return. The system executes a predictive model such as logistic regression, naive bayes, K-means clustering, clustering, k-nearest neighbor, and neural networks. First tax data is an input into the predictive model, which generates an output, which is compared with second tax data. An alert is generated when the second tax data does not satisfy pre-determined criteria relative to the first output generated by the predictive model. The same or other predictive model may be used as additional tax data is received for subsequent tax data analysis.

Abstract: Systems, methods, and articles of manufacture for associated tax data in a tax data item with a tax entity to which the tax data item pertains. A computerized tax return system is configured to access a tax data item having tax data regarding a tax entity. The system stores tax entity record(s) for one or more tax entities related to a tax return. The system has a set of matching rules for matching tax data from the tax data item with key attributes of the tax entity record(s) for a respective tax topic, and a matching algorithm for utilizing the matching rules to compare utilize the matching rules to compute a matching score. The tax return system determines a matching tax entity using the matching score. The system associates the matching tax entity with the tax data and generates a normalized data set for the tax data item.

Abstract: Certain aspects of the present disclosure provide techniques for providing an automated callback service to a user. An example technique includes receiving an indication of a product installation failure, which includes an error code, a context of the computing device, and a product identifier. Based on the product identifier, a phone number is retrieved that is associated with the user of the computing device. A set of solutions predicted to resolve the installation failure is retrieved, based on the error code and the context. A callback is established to the user, and the user is connected with a virtual agent that will provide solutions from the set of solutions in the ranking order retrieved until a solution is determined to resolve the product installation error. The ranking of the predicted solutions is updated for other users in the future who may face a similar product installation error.