Abstract: A method including receiving a request for a report on a data set. The method also includes providing the report. The report includes a macro page having a subset of the data set. The method also includes converting the macro page into a primary tree data structure having levels. The method also includes buffering the primary tree data structure in a buffer to form a buffered tree data structure. The buffered tree data structure is buffered in a level order of the levels. The method also includes selecting a first micro page from the buffered tree data structure. The first micro page is configured for display on a user interface. The method also includes transmitting, to the user interface, the first micro page.

Abstract: This disclosure relates to increasing database functionality in non-relational databases using an object relational mapper to provide updates to table field values across a plurality of separate tables and provide virtual table functionality through compound search operations and condensed table structures.

Abstract: Systems and methods for validating credentials are disclosed. One example method, performed by one or more processors of a computing device associated with a neural network, includes training the neural network to infer validity information for encrypted credentials received from a credential source, wherein the validity information is inferred without decrypting the encrypted credentials, receiving a first encrypted credential from the credential source, generating an encrypted validity indicator for the first encrypted credential based on the validity information inferred by the neural network, and providing the encrypted validity indicator to the credential source.

Abstract: This disclosure relates to forecasting when and whether an invoice is to be paid and indicating such forecasts to a user. An example system is configured to perform operations including determining, by a classification model, a first confidence as to whether an invoice is to be paid, determining, by a regression model associated with the classification model, a first time associated with a second confidence as to when the invoice is likely to be paid, and indicating, to a user, whether the invoice is to be paid based on the first confidence and the first time when the invoice is likely to be paid based on the second confidence. The regression model may include one or more gradient boosted trees to determine the first time. Different times associated with different confidences can be determined by different gradient boosted trees, with the specific tree corresponding to the associated confidence.

Abstract: A method includes executing an encoder machine learning model on multiple token values contained in a document to create an encoder hidden state vector. A decoder machine learning model executing on the encoder hidden state vector generates raw text comprising an entity value and an entity label for each of multiple entities. The method further includes generating a structural representation of the entities directly from the raw text and outputting the structural representation of the entities of the document.

Abstract: Systems and methods for anonymizing content suggestive of a particular characteristic while preserving relevant content are disclosed.

Abstract: A processor of a remote crypto cluster (RCC) may receive a public key from a client device through at least one network. The processor of the RCC may obtain an encrypted specific key and a blinded project key from at least one data source through the at least one network. The processor of the RCC may derive a derived key in blind based on the encrypted specific key and the blinded project key. The processor of the RCC may send the derived key in blind to the client device.

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: Methods and systems classify and cluster invoice data. An invoice is obtained. A category vector is generated from an invoice string of the invoice with a dense layer of a machine learning model that includes an embedding layer, a neural network layer, and the dense layer. A suggestion is selected with a selection engine and in response to comparing the category vector to a set of clusters. The suggestion is presented.

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: A transceiver of a mobile device may receive a beacon transmitted by a mesh network member mobile device. A processor of the mobile device may extract a crew ID from the beacon. The processor may determine that the crew ID matches a crew ID of a user logged into the mobile device. In response to determining that the crew ID matches the crew ID of the user, the processor may generate a reply beacon. The transceiver may transmit the reply beacon. The transceiver may receive a response to the reply beacon. The response may include a job ID enabling the user to clock into a job. The processor may clock the user into the job. As a result of clocking the user into the job, the mobile device may become a member of the mesh network.

Abstract: A method for rule-based composition of user interfaces involves obtaining a user identity (ID) of a user accessing an application using a user interface and obtaining a user interface (UI) state of the user interface. Based on the UI state and based on the user ID, a plurality of rule-based recipes are obtained. Each rule-based recipe specifies a UI content suitable for an interaction between the user and the user interface. The method further includes ranking each of the rule-based recipes of the plurality of rule-based recipes based on a likeliness that the rule-based recipe is suitable, given the UI state and the user ID, identifying, from the ranked plurality of rule-based recipes, a highest-ranked rule-based recipe, obtaining the UI content specified by the highest-ranked rule-based recipe, and updating the user interface with the UI content.

Abstract: A processor may generate a plurality of intermediate feature layers of an image using convolutional neural network (CNN) processing. For each intermediate feature layer, the processor may generate a plurality of text proposals using a region proposal network (RPN). Each text proposal may comprise a portion of the intermediate feature layer that is predicted to contain text. The processor may perform OCR processing on image data within a plurality of regions of the image to generate a text result for each region. Each region may comprise at least one of the text proposals. The processor may assemble the text results into a text string comprising the text results ordered according to a spatial order in which the plurality of regions appear within the image.

Abstract: Systems and methods for generating one or more comparative features for machine learning based classification are disclosed. A system may be configured to obtain time series data and forecast one or more predicted values based on the time series data. The system may also be configured, for each predicted value of the one or more predicted values, to compare an actual value of the time series data to the predicted value and generate a comparative value of a comparative feature based on the comparison. The comparative feature is to be provided to a machine learning model for a classification task associated with the time series data. The classification task may include determining whether one or more data values in the time series data is fraudulent based on the comparative feature.

Abstract: A processor may launch a common layer including a browser and a shell. The processor may identify a request for data required by the software platform. The processor may determine a context in which the request was generated by detecting, by the shell, whether the software platform is an on-premise solution or a cloud solution; examining, by the shell, the request to determine whether it requires local resources or cloud-based resources; and designating, by the shell, the context as a cloud-based context or an on-premise context based on the detecting and the examining. In response to the determining, the processor may perform processing related to a request generated in the on-premise context using at least one local resource stored in a local data store; and communicate, by the browser, with at least one cloud-based resource to perform processing related to a request generated in the cloud-based context.

Abstract: An intermediate computer hosts a tax return preparation application and generates an interview screen presented to a preparer through a display of the preparer computing device executing a browser. In response, the preparer acquires images of tax documents to begin preparation of an electronic tax return, e.g., by sequentially taking photographs of tax documents using a camera of a mobile communication device, which transmits the images or results of image processing such as Optical Character Recognition(OCR) to the intermediate computer. The intermediate computer, as necessary, performs OCR processing and automatically populates a plurality of electronic tax forms with the first electronic tax data and the second electronic tax data. The electronic tax return may be completed by tax document imaging and manual corrections to OCR results as necessary, in contrast to traditional tax return preparation applications structured according to a pre-determined and programmed sequence of interview screens.

Abstract: A method for recommending offerings to a business may include: receiving a request for recommended business offerings from a device; receiving business data associated with a business from the device, the business data comprising invoice data associated with the business; embedding the business data to a vector space to obtain a business vector, the vector space comprising a plurality of other vectors associated with other businesses; calculating a relation metric between the business vector and a vector of the plurality of other vectors, the vector being associated with a second business, the relation metric representing a degree of relation between the business and the second business; determining that the relation metric is above a pre-defined threshold value; and responsive to the determining, sending business data associated with the second business to the device, the business data associated with the second business comprising invoice data associated with the second business.

Abstract: A method including receiving, in a machine learning model (MLM), a corpus including words. The MLM includes layers configured to extract keywords from the corpus, plus a retrospective layer. A first keyword and a second keyword from the corpus are identified in the layers. The first and second keywords are assigned first and second probabilities. Each probability is a likelihood that a keyword is to be included in a key phrase. A determination is made, in the retrospective layer, of a first probability modifier that modifies the first probability based on a first dependence relationship between the second keyword being placed after the first keyword. The first probability is modified using the first probability modifier. The first modified probability is used to determine whether the first keyword and the second keyword together form the key phrase. The key phrase is stored in a non-transitory computer readable storage medium.

Abstract: A method includes executing a Optical Character Recognition (OCR) preprocessor on training images to obtain OCR preprocessor output, executing an OCR engine on the OCR preprocessor output to obtain OCR engine output, and executing an approximator on the OCR preprocessor output to obtain approximator output. The method further includes iteratively adjusting the approximator to simulate the OCR engine using the OCR engine output and the approximator output, and generating OCR preprocessor losses using the approximator output and target labels. The method further includes iteratively adjusting the OCR preprocessor using the OCR preprocessor losses to obtain a customized OCR preprocessor.

Abstract: Certain embodiments of the present disclosure provide techniques for detecting anomalous activity in a computing system. The method generally includes receiving a request to perform an action in a computing system. The request is added to a historical time-series data set. A portion of the historical time-series data set is selected for use in determining whether the received request is an anomalous request, and a set of previously identified outliers are removed from the selected portion of the historical time-series data set. An anomaly score is calculated based on a statistical analysis of the received request and the selected portion of the historical time-series data set, wherein the anomaly score comprises a predicted number of operations executed to isolate the received request from the selected portion of the historical time-series data set. One or more actions are taken to process the received request based on the calculated anomaly score.