Abstract: Certain aspects of the present disclosure provide techniques for detecting errors in account numbers. One example method generally includes receiving, from a user device, an entered number associated with a user and determining, based on a first portion of the entered number, an entity associated with the entered number. The method further includes obtaining, from an account number database, a plurality of account numbers associated with the entity and generating, from the plurality of account numbers, an account number matrix. The method further includes attempting to solve a multiplication equation of the account number matrix, wherein a solution of the multiplication equation is a vector of constants, upon determining a solution to the multiplication equation, determining whether the entered vector is a valid number for the entity and upon determining the entered vector is a valid number for the entity, storing the entered number in the account number database.

Abstract: The present disclosure includes computer implemented techniques for providing a survey to a user. In one embodiment, a software application receives an audio stream including utterances spoken by the user interacting with a voice-user interface of the application. The application then determines a set of paralinguistic features from the audio stream and estimates at least a first attribute of the user based on the set of paralinguistic features. The application selects survey content based on a value of the first attribute and transmits a survey including the selected survey content to the user.

Abstract: Techniques are disclosed for dynamically generating a data set representative of search results in response to a query and using the data set to accurately rank search results in response to a domain specific search query. Upon receiving the search query, features of the query and features of each search result are extracted. A relevance ranking may be assigned to each search result based on a comparison of the features of the query and each search result. The relevance ranking of each search result may be adjusted based on metrics related to user interactions. A data set may be created which includes the query, search results, extracted features, and metrics. The data set may be used to train a machine learning model to accurately determine a ranking of search results in response to a subsequent search query.

Abstract: A method and system assists experts associated with an electronic bookkeeping system to increase a rate at which users select to receive the services of the expert. The method and system retain information regarding the selection rates of a plurality of experts, and the profiles of the plurality of experts. The method and system trains an analysis model with a machine learnings process to learn how the attributes in the profiles relate to the selection rates. The method and system then utilizes the analysis model to predict changes to the profile that an expert can make to increase the selection rate of the expert. The method and system outputs recommendation data to the expert recommending one or more changes in the expert's profile that will likely result in an increase in the expert's selection rate.

Abstract: Systems and methods that may be used to provide in-context editing of a webpage-based user interface (UI).

Abstract: A browser executing on a computer terminal associated with a merchant receives a payment request for a purchase by a customer from the merchant. The computer terminal transmits, over a communications network to a mobile communication device associated with the customer, a push notification including the payment request and a set of instructions. The payment request indicates an identifier and amount of the purchase. The set of instructions causes the mobile communication device to activate a card reader using a mobile payment app, present the purchase amount to the customer on a touch-sensitive display of the mobile computing device, receive an acceptance of the purchase amount from the customer via the touch-sensitive display, and authorize the card reader to process payment for the purchase amount based on the acceptance. The computer terminal receives, over the communications network, a confirmation of the processed payment from the mobile computing device.

Abstract: Systems and methods for training machine learning models based on domain constraints are disclosed. An example method includes receiving a plurality of images, each image associated with a cluster of a plurality of clusters, the plurality of clusters representing an output of a second machine learning model, assigning a label to each cluster of the plurality of clusters based at least in part on a plurality of constraints, identifying, based at least in part on the plurality of constraints, a first label mismatch for a first image, the first label mismatch indicating that the first image belongs to a first cluster but should be assigned to a second cluster different from the first cluster, reassigning the first image to the second cluster, and training the first machine learning model, based on the labeled clusters of the plurality of clusters, to predict labels associated with subsequently received image data.

Abstract: Certain aspects of the present disclosure provide techniques for training and using machine learning models to extract key-value sets from a document. An example method generally includes identifying regions of a document including key-value sets corresponding to inputs to a data processing application based on a first machine learning model and an electronic version of the document. One or more keys and one or more values are identified in the document based on a second machine learning model. One or more key-value sets are generated based on matching keys of the one or more keys and values of the one or more values in the region of the document. The one or more key-value sets are provided to a data processing application for processing.

Abstract: Aspects of the present disclosure provide techniques for improved automated parsing and display of electronic documents. Embodiments include identifying a set of topics in a first electronic document based on one or more rules related to one or more keywords in the first electronic document. Embodiments include providing one or more inputs to a machine learning model based on the set of topics and a second electronic document related to the first electronic document. Embodiments include receiving, from the machine learning model in response to the one or more inputs, one or more outputs related to formatting the second electronic document for display. Embodiments include generating a formatted version of the first electronic document based on the set of topics and generating a formatted version of the second electronic document based on the one or more outputs.

Abstract: A method for making binary predictions for a subject involves obtaining historical data for multiple subjects, the historical data including, for each subject, a feature set and a binary outcome, generating training data from the historical data, and training a predictive model using the training data to predict the outcomes based on the feature sets. The method further includes obtaining historical data including a feature set for a subject under consideration, and predicting a binary outcome for the subject under consideration, based on the feature set associated with the subject under consideration.

Abstract: A method may include obtaining a knowledge graph including entities, and determining, for the knowledge graph, a first state including a first selectable entity subset of the entities that are selectable by a user. The first selectable entity subset may include an entity. The method may further include receiving, from the user and via a graphical user interface (GUI), a selection of the entity from the first selectable entity subset, and responsive to the selection, adding the entity to a report schema. The method further includes restricting, by the GUI, a space of selectable entities addable to the knowledge graph to the first selectable entity subset. The report schema may be used to populate a report. The method may further include, responsive to the selection, transitioning the knowledge graph to a second state including a second selectable entity subset of the entities that are selectable by the user.

Abstract: Aspects of the present disclosure relate to watermarks and watermarking techniques for data streaming pipelines. Time stamp and offset timeline data is shared by computing instances along the pipeline to enable improved watermarking of the data stream through the pipeline. The improved watermarks enable better determination of completeness for the data stream and improve materialization of the results. The watermarking techniques can include periodically publishing watermark data by processing units of a vertex, fetching a merged watermark for a vertex by a vertex, and/or watching a data storage for the watermark data for events. Consensus algorithms can be used to maintain consensus among vertices for the watermark data.

Abstract: The present disclosure relates to processing support data to increase a self-support knowledge base. In some embodiments, assisted support data is received comprising a record of an interaction between a user and a support professional. In certain embodiments, a support data set is extracted from the assisted support data. In some embodiments, feedback related to the support data set is received. The feedback may include an indication that the support data set is ready to be included in the self-support knowledge base. In some embodiments, upon determining, based on the feedback, that the support data set is ready to be used for self-support, the support data set is added to the self-support knowledge base. The self-support knowledge base may be accessible by a plurality of users.

Abstract: This disclosure provides systems, methods and apparatuses for converting natural language text in financial documents to mathematical operations for a financial management program. In some implementations, a computing device may obtain a first segment of computer readable text associated with a text capture in a financial document, parse only a portion of the first segment to generate a parsed segment, and determine translation information associated with the parsed segment to translate the parsed segment. The computing device may also store the parsed segment and the translation information in a data structure in memory, translate the parsed segment in the data structure based on the translation information, and output one or more computer readable mathematical operations corresponding to the translated segment. In this manner, the computing device may output the one or more computer readable mathematical operations without parsing all of the segment.

Abstract: A machine learning system executed by a processor may generate predictions for a variety of natural language processing (NLP) tasks. The machine learning system may include a single deployment implementing a parameter efficient transfer learning architecture. The machine learning system may use adapter layers to dynamically modify a base model to generate a plurality of fine-tuned models. Each fine-tuned model may generate predictions for a specific NLP task. By transferring knowledge from the base model to each fine-tuned model, the ML system achieves a significant reduction in the number of tunable parameters required to generate a fine-tuned NLP model and decreases the fine-tuned model artifact size. Additionally, the ML system reduces training times for fine-tuned NLP models, promotes transfer learning across NLP tasks with lower labeled data volumes, and enables easier and more computationally efficient deployments for multi-task NLP.

Abstract: Systems and methods for predicting account action failures are disclosed. An example method is performed by one or more processors of a system and includes detecting a character sequence being entered for identifying an account to be used in an account action, extracting a set of subsequences from the character sequence, determining, for the character sequence and each subsequence extracted from the character sequence, a feature value for each of a predefined set of features for characterizing sequences, the determining based on a number of characters included in at least one portion of the character sequence, and generating, using the feature values as input to a model trained in conjunction with a machine learning (ML) algorithm, a predictive value suggestive of a likelihood that the detected character sequence, if submitted, will result in the account action failing.

Abstract: A method for computer estimations based on statistical tree structures involves obtaining a statistical tree structure for reference elements. The statistical tree structure includes leaf nodes segmenting a statistic for a data label according to data features in the reference elements, and intermediate nodes connecting a first node to the leaf nodes. Each of the first node and the intermediate nodes provide a branching based on one of the data features. The method further includes obtaining target data, including values for the data features, and a value for the data label. The method also includes selecting the first node, associated with a first data feature, traversing the statistical tree structure to a leaf node by matching the values of the data features to the branching of the intermediate nodes, and assessing the value for the data label in the target data based on the statistic associated with the leaf node.

Abstract: Aspects of the present disclosure provide techniques for document classification through signal processing. Embodiments include receiving a document for classification. Embodiments include generating an image of the document. Embodiments include producing a signal representation of the document based on numbers of non-white pixels in each horizontal scan line or vertical scan line of the image of the document. Embodiments include comparing the signal representation of the document to signal representations of previously-classified documents. Embodiments include determining, based on the comparing, a classification for the document. Embodiments include performing additional processing with respect to the document based on the classification for the document.

Abstract: A method may include receiving, for a package, shipment details including attributes, obtaining, for a subset of the attributes, logistic preferences, applying the logistic preferences to the shipment details to obtain modified shipment details, training a classifier using shipment transactions each including values for the attributes and labeled with a vendor logistic service, generating, by applying the classifier to the modified shipment details, scores for vendor logistic services, and recommending a vendor logistic service from the vendor logistic services using the scores.

Abstract: Certain aspects of the present disclosure provide techniques for operation of a feature management platform. A feature management platform is an end-to-end platform developed to manage the full lifecycle of data features. For example, to create a feature the feature management platform can receive a processing artifact (e.g., a configuration file and code fragment) from a computing device. The processing artifact defines the feature, including the data source to retrieve event data from, when to retrieve the event data, the type of transform to apply, etc. Based on the processing artifact, the feature management system generates a processing job, which when initiated generates a vector that encapsulates the feature data. The vector is transmitted to the computing device that locally hosts a model, which generates a prediction. The prediction is transmitted to the feature management platform and can be transmitted to other computing devices, upon request.