Abstract: Operating an encoder with double decoder machine learning models include executing, on a transcript, an encoder machine learning model to generate an encoder output, and executing a situation decoder machine learning model on the encoder output to obtain a situation model output having a situation identifier, and executing a trouble decoder machine learning model using the encoder output to obtain a trouble identifier. The method further includes outputting the situation identifier and the trouble identifier.

Abstract: This disclosure relates to systems and methods for constructing a customized debt reduction plan for a user. In some implementations, a customized debt reduction system obtains a plurality of financial attributes of the user and a plurality of other users, where the plurality of financial attributes are indicative of credit card debt, and identifies users from the plurality of other users who successfully repaid their credit card debt based on their respective financial attributes and one or more repayment techniques that resulted in successful repayment of their credit card debt. The customized debt reduction system correlates the plurality of financial attributes of the user with the plurality of financial attributes of a number of the identified users and determines a personalized score for the user, using a trained machine learning model, based on the correlation to determine a customized debt reduction plan for the user based on the personalized score.

Abstract: Systems and methods here may be used for pre-processing images, including using a computer for receiving a pixelated image of a paper document of an original size, downscaling the received pixelated image, employing a neural network algorithm to the downscaled image to identify four corners of the paper document in the received pixelated image, re-enlarging the downscaled image to the original size, identifying each of four corners of the paper document in the pixelated image, determining a quadrilateral composed of lines that intersect at four angles at the four corners of the paper document in the pixelated image, defining a projective plane of the pixelated image, and determining an inverse transformation of the pixelated image to transform the projective plane quadrilateral into a right angled rectangle.

Abstract: This disclosure provides systems, methods and apparatuses for recommending items to users of a recommendation system. In some implementations, the recommendation system determines a plurality of contribution values based on interactions between a plurality of users and a plurality of items. Each of the plurality of contribution values represents a confidence level that a respective user prefers a respective item. The recommendation system further determines a capacity of each of the plurality of items. The capacity of each item represents a maximum number of users to which the item can be recommended. The recommendation system recommends one or more items of the plurality of items to each of the plurality of users based at least in part on the plurality of contribution values and the capacities of the plurality of items.

Abstract: Systems and models are disclosed for determining a value over replacement feature (VORF) for one or more features of a machine learning model. An example method includes selecting one or more features used in the machine learning model, determining a comparison set of unused features not used in the machine learning model, for each unused feature in the comparison set, determining a difference in a specified metric when the selected one or more features are replaced by a corresponding unused feature from the comparison set, and determining the VORF to be the smallest difference in the specified metric.

Abstract: This disclosure describes converting computer-executable predicate-argument structures for a specific field to field-specific predicated-argument structures to improve execution. In some implementations, a method can be performed by one or more processors of a computing device, and can include receiving one or more predicate-argument structures (PASs) associated with taxation-specific text and converting the one or more PASs into one or more tax-specific predicate-argument structures (TPASs). Converting the one or more PASs to one or more TPASs may include one or more of: defining terms in a segment based on a definition of the term from a different segment or line description (including from a different document); reordering nodes, replacing nodes, or removing nodes of a segment (such as based on one or more single segment tree traversal rules); or combining multiple PASs for multiple segments of a single line description based on one or more multiple segment tree traversal rules.

Abstract: Certain aspects of the present disclosure provide techniques for generating an application programming interface (API) for a plurality of associated services. The method generally includes retrieving a declarative schema for a service of a plurality of services in an application programming interface (API). A language object for the service is generated from a data object definition in the declarative schema associated with the service. Generally, the language object is configured to handle data events generated by the service. A service controller for the service is generated from a service definition in the declarative schema associated with the respective service. Generally, the service controller is fronted by a gateway for external sources to invoke one or more functions implemented by the service. An application programming interface (API) is exposed for the service independently of details of APIs for the associated services to one or more external services.

Abstract: This disclosure provides systems, methods and apparatuses for detecting anomalous activity in an electronic system. In some implementations, a system generates a set of model parameters based on a number (n) of historical datapoints in a dataset, where each datapoint represents activity detected in the electronic system over a respective period of time. The system receives a first new data point for the data set and generates a first test parameter based on a value of the first new datapoint and an average and a measure of spread of the n historical datapoints. The system further compares the first test parameter to the set of model parameters and determines whether the first new datapoint represents an anomaly based at least in part on the comparison.

Abstract: A method is disclosed. The method includes: obtaining a help request associated with a user operating an application and a click stream of the user within the application; generating a feature data structure based on the help request and the click stream; generating, by applying the feature data structure to a machine learning model, a topic data structure including a plurality of scores corresponding to a plurality of topics; obtaining a plurality of topic distributions corresponding to a plurality of articles; identifying, by applying the topic data structure to the plurality of topic distributions, a subset of the plurality of articles for the user; and displaying, in response to the help request, a graphical user interface (GUI) including a plurality of links to the subset of the plurality of articles.

Abstract: Systems and methods that may be used to provide an inference-based incident detection and reporting process. In one or more embodiments, an inference model may be used to track and analyze changes and incident data from various sources of record to quickly predict potential sources of errors and help focus in on the root cause of a software incident.

Abstract: Aspects of using augment reality for service projects are described. In some implementations, an augmented reality (AR) system receives one or more images of a real-world space, detects a physical object in the real-world space from the one or more images, and identifies the physical object for a service project. The AR system also generates a virtual object associated with the identified physical object and outputs the virtual object for display in an AR environment. The AR system further receives a selection of one or more portions of the virtual object associated with one or more portions of the physical object to be serviced and generates information for performing the service project based on the one or more portions of the physical object.

Abstract: Techniques are disclosed for determining application experience of a user. One embodiment presented herein includes a computer-implemented method, which includes receiving, at a computing device, eye tracking data of a user interacting with at least a first page of an application. The computer-implemented method further includes determining, based at least on the eye tracking data, at least a current user experience regarding the first page. The computer-implemented method further includes predicting, based on evaluating the current user experience, that the user is likely to discontinue use of the application. The computer-implemented method further includes determining, based at least on the prediction, an intervention that reduces a likelihood of the user discontinuing use of the application, and interacting with the user according to the intervention.

Abstract: A method and system learns functions to be associated with data fields of forms to be incorporated into an electronic document preparation system. The functions are essentially sets of operations required to calculate the data field. The method and system receive form data related to a data field that expects data values resulting from performing specific operations. The method and system utilize machine learning and training set data to generate, test, and evaluate candidate functions to determine acceptable functions.

Abstract: A method may include receiving, from a source application, partial data that is incomplete for use by a target application, obtaining a set of contextual data correlated with the partial data, assigning, using a ranking model, confidence levels to the set of contextual data, selecting, from the set of contextual data, contextual data according to the confidence levels assigned to the set of contextual data, generating augmented partial data using the contextual data and the partial data, determining that the augmented partial data satisfies a transaction completion criterion of a transaction schema, and in response to determining that the augmented partial data satisfies the transaction completion criterion, sending the augmented partial data to a transaction store used by the target application.

Abstract: Computer software product management systems that operate in a networked computing environment and including a computer database, an intermediate publishing service and spoke computing systems. The database includes an entitlement-monetization data model that includes an entitlement definition and a monetization definition that are inextricably tied to each other in the entitlement-monetization data model. The entitlement-monetization data model is published to spoke computing systems for runtime processing of customer requests made through a customer facing application concerning a computer software product configured according to the entitlement-monetization data model.

Abstract: Aspects of the disclosure describe improving identification of product solutions. An example method includes transcribing in real-time a conversation between a user and an agent into a speech text, processing digital data of the speech text associated with a topic, including parsing the speech text into one or more words and determining collocation information among the one or more words in the speech text. The method also includes providing the one or more words and the collocation information as a first input set to a machine learning engine configured to recommend one or more product solutions from a library of product solutions, generating a recommendation of one or more product solutions for a user based on recommendation parameters for the library of product solutions, and providing the recommendation.

Abstract: A computer-implemented method for inferring or estimating user-related data for use with tax preparation software is disclosed. The method uses a computer that connects to the one or more remotely located data sources and executing a data capture utility, the data capture utility capturing at least some tax data pertaining to the user. The computing device executes an estimation module that receives the captured tax data pertaining to the user and generates one or more estimates and stores the one or more estimates in a data store associated with the tax preparation software, the data store configured to store user-specific tax data therein. The computing device executes a tax calculation engine of the tax preparation software configured to read the user-specific tax data contained in the data store and compute an intermediate or final tax liability or refund amount.

Abstract: This disclosure relates to systems and methods for identifying risky merchants associated with an electronic payment service. In some implementations, a risk assessment system receives a set of features indicative of one or more risks posed by a merchant enrolled in the electronic payment service, where each feature of the set of features indicative of one or more financial attributes of the merchant. The risk assessment system determines a risk score for the merchant based on the set of features using a trained machine learning model, determines a Shapely additive explanation (SHAP) score for each feature of the set of features, and then divides the set of features into multiple groups of features based on a mapping between the features and their respective indicated financial attributes.

Abstract: Systems and methods for generating a custom document template are disclosed. An example method may be performed by one or more processors of a system and include retrieving a user document including a user data entry in a user data field, identifying a set of system data fields within a plurality of system documents potentially relevant to the user document, determining, for each of the set of system data fields, a weighted value indicative of a likelihood that the system data field is relevant to the user data field, identifying a most relevant system data field of the set of system data fields, the most relevant system data field having a highest weighted value of the determined weighted values, and generating a custom document template including a dynamic data region for the user data entry, the dynamic data region mapped to the most relevant system data field.

Abstract: A user device associated with a user may receive a document associated with the user. The user device may encrypt the received document. The user device may perform patch-based document segmentation on the received document to form a plurality of patches on the received document. The user device may extract text from each patch of the plurality of patches. The user device may analyze the extracted text from each patch to detect a field title and a field value. The user device may encrypt the extracted text and its associated field value for each patch of the plurality of patches. The user device may send the encrypted extracted text and its associated field value to the user device and instructions to display the extracted text and its associated field value on a user interface.