Abstract: A computing system receives, from a client device, a first request for access to a single page application associated with the computing system. A gateway of the computing system intercepts the first request before it reaches a shell service associated with the single page application. The gateway sends a second request for the single page application to the shell service. Based on the single page application request, the gateway determines that the shell service is experiencing a failure. Responsive to determining that the shell service is experiencing a failure, the gateway retrieves, from a content delivery network, a uniform resource locator (URL) associated with a time-lapse hypertext markup language (HTML) of the single page application. The gateway redirects the client device to the time-lapse HTML of the single page application using the URL.

Abstract: A processor may receive a call transcript including text and form a text string including at least a portion of the text. The processor may generate a situation description of the call transcript, which may comprise processing the text string using a transformer-based machine learning model. The processor may generate a trouble description of the call transcript, which may comprise creating a sentence embedding of the situation description, creating sentence embeddings for a plurality of utterances within the portion of the text, determining respective similarities between the sentence embedding of the situation description and each of the sentence embeddings for each respective one of the plurality of utterances, and selecting at least one of the plurality of utterances having at least one highest determined respective similarity as the trouble description. The processor may store a call summary comprising the situation description and the trouble description in a non-transitory memory.

Abstract: A method implements calibrated risk scoring and sampling. Features are extracted from a record. A risk score, associated with the record, is generated from the features using a machine learning model. The record is mapped to a risk bucket using the risk score. The risk bucket may include multiple risk bucket records. The record is selected from the risk bucket records with a sampling threshold corresponding to the risk bucket. A form prepopulated with values from the record is presenting to a client device.

Abstract: A method may include generating a vector from unstructured data included in an untransformed transaction, and determining, for the vector, a cluster ID of cluster IDs by matching the vector with a matching cluster vector of cluster vectors. The method may further include generating a query using the cluster ID and the untransformed transaction, and transforming, using the cluster IDs, untransformed transactions to transformed transactions. The transformed transactions may each include a cluster ID. The method may further include generating, using the query, a query result from features of the transformed transactions, generating a fraud score using the query result, and presenting the fraud score and the cluster ID.

Abstract: A method of determining whether a user has a self-control problem. A computing system receives, from a plurality of financial institutions associated with a target user, a plurality of transactions associated with the target user. The computing system groups the plurality of transactions into a category or subcategory based on transaction data associated with each respective transaction. The computing system determines, based on the plurality of transactions, that the target user has a self-control problem. The computing system, responsive to determining that the target user has the self-control problem, generates an alert or recommendation to the target user. The alert or recommendation notifies the target user of the self-control problem.

Abstract: A computer-implemented system and method for predicting rule-based compliance scenarios to implement rule-based topic determinations. A server computing device generates a compliance scenario prediction model by training a machine learning model for a topic with historical user data and cohort labels created by analyzing the scenarios in a completeness graph to predict a set of scenario cohorts that constitute a set of most probable compliance scenarios. The server computing device executes the scenario prediction model to process a user profile including data features associated with the topic to predict a scenario cohort and a compliance scenario corresponding to the predicted cohort for the user. The server computing device automatically infers one or more personalized responses to at least one question of the respective decision node based on the predicted compliance scenario.

Abstract: A processor may receive log data generated by a chat application. The processor may identify at least one positive sentiment in at least one portion of the log data using a first machine learning (ML) process. The processor may also identify content of the at least one portion of the log data relevant to an external application different from the chat application using a second ML process, and/or topic data in the at least one portion of the log data using a third ML process. The processor may form an extraction of the log data that includes at least a portion of the content and at least a portion of the topic data. The extraction may have a format processed by the external application. The processor may export the extraction to the external application.

Abstract: Systems and methods for parsing and publishing messages corresponding to changes in a database are disclosed. An example method includes receiving a first plurality of messages from the database, parsing the messages in the first plurality of messages into at least a first group and a second group of messages, each message in a group sharing a common identifier, and forwarding the first group of messages and the second group of messages to an event streaming platform.

Abstract: Systems and methods for cleaning and publishing event messages. An example method includes receiving messages corresponding to a plurality of events, identifying, for a first event of the plurality of events, one or more changed tables, assigning an event type to the first event based at least in part on the one or more changed tables, and publishing the first event on an event streaming platform.

Abstract: Systems and methods for generating a multiple source audit log are disclosed. An example method includes receiving a first plurality of event messages from a first source, the first plurality of event messages respectively corresponding to a first plurality of events, receiving a second plurality of event messages from a second source, the second plurality of event messages respectively corresponding to a second plurality of events, identifying a first event of the first plurality of events and a second event of the second plurality of events for a first event of the plurality of events, the first event and the second event corresponding to a common transaction, and generating a first entry in an audit log corresponding to the common transaction based at least in part on a first message corresponding to the first event and a second message corresponding to the second event.

Abstract: A computer-implemented method including tracking data describing a new population of users of a software application including different graphical user interfaces (GUIs). The method also includes generating a distribution by mapping the data to lookalike cohorts. The method also includes extracting, using a random sampling algorithm, samples from the distribution. The method also includes generating, from the samples, a normal distribution of predicted long term values of the new population of users. The method also includes selecting an expected long term value from the normal distribution. The method also includes generating, from the normal distribution, an estimated distribution, around the expected long term value, of estimated long-term values for the new population. The method also includes selecting, using the expected long term value and the estimated distribution, a selected GUI from among the different GUIs. The method also includes modifying the software application by presenting the selected GUI.

Abstract: A method of score prediction uses hierarchical attention. Word features, positioning features, participant embedding features, and metadata are extracted from a transcript of a conversation. A word encoder vector is formed by multiplying weights of a word encoder layer to one or more word features. A sentence vector is formed by multiplying weights of a word attention layer to word encoder vectors. An utterance encoder vector is formed by multiplying weights of an utterance encoder layer to the sentence vector. A conversation vector is formed by multiplying weights of an utterance attention layer to utterance encoder vectors. The utterance encoder vector is combined with one or more positioning features and one or more participant embedding features. A predicted net promoter score is generated by multiplying weights of an output layer to the conversation vector combined with the metadata. The predicted net promoter score is presented in a list of conversations.

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: Systems and methods may be used to generate and use a structured form representation and structured metadata. The structured form representation and structured metadata may include information relevant to a particular context and may be used to update document templates, import new documents and update document versions into software, automate data entry for document completion, update records to include new and or updated information, and provide other functionality of an information service.

Abstract: A system and method for detecting anomalies in mission-critical environments. The method includes: parsing at least one received data set into a text structure; isolating a protocol language of the at least one received data set, wherein the protocol language is a standardized pattern for communication over at least one protocol; generating at least one document from the contents of the received at least one data set, wherein the at least one document includes at least one parsed text structure referencing a unique identifier, and wherein the at least one parsed text structure is organized within the at least one document according to a natural language scheme; detecting insights in the generated documents; extracting rules from the detected insights; and detecting anomalies by applying the extracted rules.

Abstract: A method and system for detecting anomalies in mission-critical environments using word representation learning are provided. The method includes parsing at least one received data set into a text structure; isolating a protocol language of the at least one received data set, wherein the protocol language is a standardized pattern for communication over at least one communication protocol; generating at least one document from the contents of the received at least one data set, wherein the at least one document includes at least one parsed text structure referencing a unique identifier; detecting insights in the at least one generated document, wherein insights are detected in at least one representation having at least one dimension, wherein the representation is mapped to at least one learned hyperspace; extracting rules from the detected insights; and detecting anomalies by applying the extracted rules on patterns for communication over at least one communication protocol.

Abstract: A computer-implemented system and method for generating heterogeneous graph feature embeddings for feature learning and prediction. An application server may receive and process a plurality of feature datasets to generate a graph data structure comprising a plurality of interconnected transaction pairs. The application server processes the graph data structure to determine a first-order transaction pair corresponding to a maximum transaction frequency based on a user identifier; executes a jumping probability algorithm to process the graph data structure to determine a second-order transaction pair jumping from a first-order transaction pair; and generates a transaction sequence associated with the user identifier.

Abstract: A computing system receives, from a client device, an image of a content item uploaded by a user of the client devices. The computing system divides the image into one or more overlapping patches. The computing system identifies, via a first machine learning model, one or more distortions present in the image based on the image and the one or more overlapping patches. The computing system determines that the image meets a threshold level of quality. Responsive to the determining, the computing system corrects, via a second machine learning model, the one or more distortions present in the image based on the image and the one or more overlapping patches. Each patch of the one or more overlapping patches are corrected. The computing system reconstructs the image of the content item based on the one or more corrected overlapping patches.

Abstract: A method uses private information with a shared single source of truth. A message is received responsive to adding a first shared block to a shared object ledger of a shared object. The message includes message data. A private block is added to a private object ledger to update a private object. The private block includes private data from the message data. A second shared block is added to the shared object ledger to update the shared object. The second shared block includes shared data from the message data.