Abstract: A system for account mapping includes functionality for obtaining more than one labeled accounts labeled by more than one accountant; pre-processing more than one labeled accounts using natural language processing, using the more than one pre-processed labeled accounts to train an account mapping model that performs multinomial classification; receiving an account name from an accounting application where the account name includes a text label for an account included in a chart of accounts; generating an account mapping by applying the account mapping model to the account name, where the account mapping includes a type of the account, a sub-type of the account, a code, and a series associated with an accounting form; returning the account mapping to the accounting application through an Application Programming Interface (API); and receiving a corrected account mapping from an accountant and using the corrected account mapping as a new text label to incrementally update the account mapping model.

Abstract: Systems and methods for quantifying saved time during data entry.

Abstract: A processor may receive a request to display a user interface (UI) for a user account. The processor may determine a context for the UI from information specific to the user account. The processor may select one of a plurality of UI elements based on the determining. In some embodiments, the selecting may include evaluating a value function taking the information specific to the user account and information about the context as inputs. The processor may cause the UI including the selected one of the plurality of UI elements to be displayed in response to the request.

Abstract: A content editor for generating content including root blocks and nested blocks is disclosed. The content editor can generate a deployment that includes the content. The content editor can generate user interface code configured to edit the content. The content editor can receive updates to the content and update the root blocks and nested blocks. The updated root blocks and nested blocks can be used to generate updated content for editing and/or can be deployed to end-users.

Abstract: Systems and methods for generating augmented search results are disclosed. An example method may be performed by one or more processors of a ranking system and include receiving a search query, generating, for each respective data asset of a plurality of data assets, a query relevancy score indicating a likelihood that the respective data asset is relevant to the search query, retrieving, for each of a number of potentially relevant data assets, a utility score indicating a likelihood that the potentially relevant data asset is useful, generating, for each of the number of potentially relevant data assets, an augmented query score indicating a likelihood that the potentially relevant data asset is relevant to and useful for the search query based on the query relevancy scores and the utility scores, and generating augmented search results for the search query based on the augmented query scores.

Abstract: At least one processor may obtain a document comprising text tokens. The at least one processor may determine, based on a pre-trained language model, word embeddings corresponding to the text tokens. The at least one processor may determine, based on the word embeddings, named entities corresponding to the text tokens; and one or more accuracy predictions corresponding to the named entities. The at least one processor may compare the one or more accuracy predictions with at least one threshold. The at least one processor may associate, based on the comparing, the named entities with one or more confidence levels. The at last one processor may deliver the named entities and the one or more confidence levels.

Abstract: A method may include extracting training tokens from a training corpus, generating training token features and training contextual features for each of the training tokens, training a probabilistic graphical model using classes, the training token features, and the training contextual features, extracting, from actual data, actual tokens including an actual token, generating actual token features and actual contextual features for each of the actual tokens, and classifying the actual token as a class using the probabilistic graphical model, the actual token features, and the actual contextual features.

Abstract: A computer-implemented method of managing questions and answers on a computer-hosted service. The method includes a computing device receiving text based tax question and answer pairings and inputting the tax question and answer pairings into a content model executed by the device and outputting a content score for each tax question and answer pairing based on the model. The content score comprises a number within a range. One end of the range corresponds to product content and another end of the range corresponds to general tax content. The device outputs an answer quality score for the tax question and answer pairings based at least in part on the content score and votes assigned to each respective question and answer pair, wherein votes comprises up votes and down votes. The device may generate a FAQ list stored in a database based at least in part on the answer quality score.

Abstract: Systems and methods for generating augmented search results are disclosed. An example method may be performed by one or more processors of a ranking system and include receiving a search query, generating, for each respective data asset of a plurality of data assets, a query relevancy score indicating a likelihood that the respective data asset is relevant to the search query, retrieving, for each of a number of potentially relevant data assets, a utility score indicating a likelihood that the potentially relevant data asset is useful, generating, for each of the number of potentially relevant data assets, an augmented query score indicating a likelihood that the potentially relevant data asset is relevant to and useful for the search query based on the query relevancy scores and the utility scores, and generating augmented search results for the search query based on the augmented query scores.

Abstract: A method including transcribing, into digital tokens, utterances from a conversation between an agent and a person. The method also includes embedding the digital tokens into an utterances tensor including sequences of the digital tokens. The method also includes obtaining a metadata tensor by encoding metadata related to the utterances into the metadata tensor. The method also includes executing a machine learning model which takes, as input, the utterances tensor and the metadata tensor, and which outputs a predicted source article predicted to be related to the utterances. The method also includes generating an interactive link to the predicted source article.

Abstract: A testing system and method for testing application code against various failure scenarios. The testing system and method generate a test workflow including test source code implementing a series of actions that affect an application component and or an infrastructure component included in application code. The testing system and method execute the test workflow to determine the performance of the application code during one or more failure scenarios caused by the series of actions included in the test workflow. Performance data generated by the test code is analyzed by a performance analysis service or method to identify limitations of the application code and build resiliency patterns that address the limitations and improve the performance of the application code.

Abstract: A processor may receive user interaction data of a user for a plurality of electronically-presented offers. The processor may generate a plurality of labels, the generating comprising generating a label for each respective offer according to a comparison of the quality of the user interactions of the respective offer to the frequency of the user interactions of the respective offer. Each label may be a positive label or a negative label. The processor may determine whether the generating produced both positive and negative labels. The processor may select one of a plurality of available ML models, wherein a two-class ML model is chosen in response to determining that the generating produced both positive and negative labels and a one-class ML model is chosen in response to determining that the generating did not produce both positive and negative labels. The selected ML model may be trained and/or may be used to process user profile data and provide recommendations.

Abstract: Systems and methods for throttling requests to web services are disclosed. A system is configured to receive, at a host, one or more requests during a first time period. Each request is for a web service hosted on a backend. The host is one of a plurality of hosts of an application programming interface (API) gateway to receive a plurality of requests for the web services. The system is further configured to: process at least a portion of the one or more requests for the one or more web services; count, by a local counter in a local cache of the host, the one or more requests received at the host during the first time period; compare a local count of the local counter to a local bucket size associated with the host; and provide an instruction to update a remote count of a remote counter based on the comparison.

Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.

Abstract: Predictive modeling within a special purpose hardware platform to determine scenarios that are most likely to increase conversion potential for each trial user and retention potential for each active subscriber of a service, collectively referred to as a propensity score. The predictive models are integrated with a contextual marketing system that uses a loss risk assessment to learn user behavior and optimize content messaging designed to improve actual conversion or retention behavior for the user.

Abstract: Embodiments disclosed herein may extract trending topics from phone call transcripts or any type of text data. The phone call transcripts may be collected for a time period and the time period may be divided into time spans. For each time span having more than a threshold number of phone call transcripts, n-grams from the phone call transcripts may be extracted. The extracted n-grams may be contextually clustered by converting the n-grams into their embedding vectors, reducing the dimensionality of the embedding vectors, and clustering similar reduced dimensionality embedding vectors. Normalized occurrences of one or more clusters may be generated. The recent mean of the number of occurrences of the normalized clusters may be compared with the historical mean and offset by historical standard deviation to generate a modified Z-score. N-grams corresponding to the clusters with high Z-scores may be identified as trending topics.

Abstract: Systems and methods may be used to generate and use a merchant community graph generated based on merchant financial transaction data. Connections between merchants and other data within the merchant community graph can be used to detect fraud, target product offerings and or other advertisements, detect similar communities, generate dynamic attributes that may be used to develop machine learning models, and develop new user interfaces (UIs) and other features of an information service.

Abstract: A method including receiving, at a mobile device, a request to provision a first geofence. The first geofence has a first predicted duration that expires before a second predicted geofence is provisioned by the mobile device. Provisioning the first geofence and the second predicted geofence together consume a first amount of power. The method also includes determining a predictive geofence radius based on a predicted speed of the mobile device. The method also includes provisioning a first predictive geofence having the predictive geofence radius and further having a second predicted duration that is different than the first predicted duration and that expires before a second predictive geofence is provisioned by the mobile device. Provisioning the first predictive geofence and the second predictive geofence together consume a second amount of power, the second amount of power less than the first amount of power.

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: Certain aspects of the present disclosure provide techniques for generalized metric for machine learning model evaluation for unsupervised classification including: for each unsupervised machine learning model of one or more unsupervised machine learning models: generating a first set of synthetic inputs for the model of the one or more unsupervised machine learning models; providing the first set of synthetic inputs to the model trained to output a prediction for each input of the first set of synthetic inputs, wherein the prediction indicates whether the input is of a first class; identifying, based on an output of the model, a second set of synthetic inputs predicted to be of the first class; determining, based on a set of expected normal inputs for the model and the second set of synthetic inputs, an accuracy score for the unsupervised machine learning model; and providing the accuracy score for display.