Abstract: A method of blank detection involves receiving a document from a user, where the document includes derived text; applying a trained blank detection model to the document to make a first prediction, where the first prediction indicates whether at least one field in the document is blank; comparing the first prediction with a second prediction, where the second prediction is made by an extraction model; and extracting the at least one field using the extraction model.

Abstract: A method collects word-based data corresponding to a first identifier. A first phrase vector is generated for the first identifier by extracting frequency data from the word-based data. A similarity metric is generated corresponding to the first identifier and a second identifier by comparing the first phrase vector of the first identifier to a second phrase vector of the second identifier. A tuple is generated that includes the first identifier and the second identifier using the similarity metric. A machine learning model is trained with the tuple to generate an embedded vector corresponding to the first identifier.

Abstract: Systems and methods of the present disclosure provide processes for determining how much to adjust machine-learning parameter values in a direction of a gradient for gradient-descent steps in training processes for machine-learning models. Current parameter values of a machine-learning model are vector components that define an initial estimate for a local extremum of a cost function used to measure how well the machine-learning model performs. The initial estimate and the gradient of the cost function for the initial estimate are used to define an auxiliary function. A root estimate is determined for the auxiliary function of the gradient. The parameters are adjusted in the direction of the gradient by an amount specified by the root estimate.

Abstract: Systems and methods for a boosted Latent Dirichlet Allocation model with predefined clusters and repelling force. Herein, clusters are pre-populated with seed words with amplified weights to predefine the characteristics of clusters. A repel force is also applied while assigning words to specific clusters in order to assign words more accurately to clusters in instances where clusters have similar characteristics. In addition to generating improved clustering of words, systems and methods may analyze words within queries to provide explainability output related to the performance of content or data.

Abstract: This disclosure relates to systems and methods for processing electronic payments for customer purchases. In some implementations, a system receives a payment request from a merchant, and transmits payment information to a mobile computing device associated with the merchant. The payment information includes a transaction ID to identify the purchase, a purchase amount, and instructions that cause the mobile computing device to activate a card reader, present a notification of the purchase amount to the customer, receive an acceptance of the purchase amount from the customer, and authorize the card reader to process an electronic payment for the purchase amount. The system receives payment confirmation from the mobile computing device, and provides the parameter confirmation to the merchant.

Abstract: A method including monitoring, using a machine learning model, network events of a network. The machine learning model generates fraud scores representing a corresponding probability that a corresponding network event is fraudulent. The method also includes detecting a failure of the machine learning model to generate, within a threshold time, a given fraud score for a given network event. The method also includes determining, by the machine learning model and after the threshold time, the given fraud score. The method also includes logging, responsive to detecting the failure, the given network event in a first table, including logging the given fraud score. The method also includes determining a metric based on comparing the first table to a second table which logs at least the given fraud score and the fraud scores. The method also includes generating an adjusted machine learning model based on the metric.

Abstract: Systems and methods that may be used to implement overlapping data caching for object application program interfaces (APIs). A unique identifier may be assigned to each sub-component of an object API request. The unique identifiers may be used to determine if sub-components of one object API request overlaps with a sub-component of a prior different request such that a data response for the prior different request can be retrieved from the cache and used as part of the data response for the current object API request.

Abstract: Systems and methods for dynamic presentation of content items in a user interface are disclosed. An example method includes presenting a plurality of content items on the user interface in a predetermined geometric arrangement, receiving a user selection of a first content item of the plurality of content items, in response to receiving the user selection of the first content item, expanding the first content item while moving the first content item to a predetermined focus position within the geometric arrangement, and presenting, in the expanded first content item, additional information associated with the first content item, where the additional information is not displayed before receiving the user selection of the first content item.

Abstract: A method for generating a synthetic dataset involves generating discretized synthetic data based on driving a model of a cumulative distribution function (CDF) with random numbers. The CDF is based on a source dataset. The method further includes generating the synthetic dataset from the discretized synthetic data by selecting, for inclusion into the synthetic dataset, values from a multitude of entries of the source dataset, based on the discretized synthetic data, and providing the synthetic dataset to a downstream application that is configured to operate on the source dataset.

Abstract: A method including receiving a first command including both a data extraction expression and a first report configuration expression. The data extraction expression includes program code for extracting fields of a dataset of a data source. The first report configuration expression includes program code configured to populate cells of first dimensions of a first report and to generate a first tree including subset nodes including records of the dataset. The first command is executed by executing the data extraction expression on the dataset to generate the records. Executing the first command also includes executing the first report configuration expression on the records to generate the first tree. Executing the first command also includes populating, using the first report configuration expression and the first tree, the cells. Executing the first command also includes generating, in response to receiving the first command and by traversing the first tree, the first report.

Abstract: Described herein are example implementations for generating a perturbation seed for the perturbation of electronic data. A system obtains a plurality of datapoints (with one or more statistics calculated from the plurality of datapoints to be perturbed based on a perturbation seed). The system calculates one or more metrics from the plurality of datapoints. The system also generates, for each of the one or more metrics, a rounded metric by rounding the metric. The system further generates the perturbation seed. Generating the perturbation seed includes hashing the one or more rounded metrics. Rounding a metric may be to a defined place value (such as the second most significant place value), and a binary output of hashing the one or more rounded metrics may be converted to a number. The system may perturb one or more statistics based on the perturbation seed and output the one or more perturbed statistics.

Abstract: Methods and systems for efficiently generating tagged training data for machine learning models. In conventional systems, all of the raw data (e.g., each sentence) has to be manually tagged. Instead, the methods and systems generate a representative sample for multiple portions of raw data, e.g., a representative sentence for multiple, similar sentences. Only the representative sample is tagged and used for training, thereby realizing a significant efficiency in both tagging the data and training the machine learning models.

Abstract: Certain aspects of the present disclosure provide techniques for training and using a machine learning model to extract relevant textual content for custom fields in a software application from freeform text samples. An example method generally includes generating, via a natural language processing pipeline, a training data set from a data set of freeform text samples and field entries for a plurality of custom fields defined in a software application. A first machine learning model is trained to identify custom fields for which relevant data is included in freeform text. A second machine learning model is trained to extract content from the freeform text into one or more custom fields of the plurality of custom fields defined in the software application and identified by the first machine learning model as custom fields for which relevant data is included in the freeform text.

Abstract: Techniques for detecting fraud may include mapping routing numbers of one or more financial institutions with geolocation data of the financial institutions; obtaining a geolocation of a user based on the user's internet protocol (IP) address; obtaining a first user input from the user indicating a first financial institution; generating a match score for each of the one or more financial institutions that indicates a level of match between the first user input and the respective financial institution; boosting the match score for each financial institution based on its location with respect to the geolocation of the user; generating a list of financial institutions having the boosted match score above a threshold; obtaining a second user input from the user indicating at least one second financial institution; and presenting search results to the user based on the second user input, wherein the search results are boosted.

Abstract: A processor may receive an image of a user and an avatar representing the user within a computing environment. The processor may generate a score for the avatar on the basis of its resemblance to the image using a machine learning (ML) process. The processor may configure at least one option for action by the user within the computing environment according to the score.

Abstract: Embodiments disclosed herein select a content message to present to a user on a page of an application based on paralinguistic features of audio input received from the user for the application. The audio input is received via a microphone associated with a computing device. A feature extractor extracts paralinguistic features from the audio input. A predictive model determines a label indicating a measure of receptiveness to product placement (e.g., a predicted marketing outcome) based on the paralinguistic features. A content-selection component selects a content message to present to the user based on the label and based on a profile of the user.

Abstract: Certain aspects of the present disclosure provide techniques and systems for screening chat attachments. A chat attachment screening system monitors a chat window of a first computing device associated with a first user during an interaction session between the first user and a second user. An upload of an attachment is detected based on the monitoring. Access to the attachment from a second computing device associated with the second user is blocked, in response to detecting the upload. Content from the attachment is identified and extracted. A type of the attachment is determined based on the content. A determination is made as to whether the second user is authorized to access the type of the attachment. An indication of the determination is presented on at least one of the first computing device or the second computing device during the interaction session.

Abstract: Embodiments disclosed herein provide language-agnostic routing prediction models. The routing prediction models input text queries in any language and generate a routing prediction for the text queries. For a language that may have sparse training text data, the models, which are machine learning models, are trained using a machine translation to a prevalent language (e.g., English) to the language having sparse training text data -with the original text corpus and the translated text corpus being an input to multi-language embedding layers. The trained machine learning model makes routing predictions for text queries for the language having sparse training text data.