Abstract: The present disclosure provides techniques for extracting structural information using machine learning. One example method includes receiving electronic data indicating one or more pages, constructing, for each page of the one or more pages, a tree based on the page, wherein each level of the tree includes one or more nodes corresponding to elements in a level of elements in the page, encoding, for each page of the one or more pages, a value of each node of the tree for the page into a vector using a first machine learning model, sampling a plurality of pairs of vectors from the one or more trees for the one or more pages, wherein a given pair of vectors corresponds to values of nodes in a same tree, training a second machine learning model using the plurality of pairs, and combining each vector with weights of the second machine learning model.

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 computer-implemented method and system are provided to utilize machine learning technology to process user financial transaction data to predict a personalized payment screen architecture. A plurality of feature datasets associated with transaction data of a plurality of electronic invoices are obtained by a computing device. Each feature dataset comprises a plurality of features, a payment screen and a payment method configured to be presented on at least one payment screen. The computing device is configured to train a machine learning model with the feature datasets to produce a probability matrix with probabilities of each payment method used to pay the invoices through each payment screen. The computing device may weigh the probability matrix to generate a recommendation matrix and determine a prediction of a payment screen based on the recommendation matrix.

Abstract: Techniques are disclosed to predict experience degradation in a microservice-based application comprising a plurality of microservices. Quality of service metrics are derived for each node from the historical event log data of nodes forming a plurality of directed acyclic graph (DAG) paths in the multiple-layer nodes. A clustering model clusters the plurality of quality of service metrics according to multiple levels of quality of experience and determines respective value ranges of each quality of service metric for the multiple levels of quality of experience. Each quality of service metric is labeled with one of the multiple levels of quality of service according to the respective value ranges. A support vector machine model predicts various experience degradation events which are expected to occur during the operation of the microservice-based application.

Abstract: The present disclosure provides a composite machine learning system for a transaction labeling service. A transaction labeling service receives at least one descriptive string describing a transaction associated with a user. The service identifies a preliminary grouping from a generalized scheme. The service extracts a set of N-grams from the descriptive string and converts the N-grams and the preliminary grouping into a set of features. A machine learning model determines a label from a labeling scheme for the transaction based on the features. User input related to the label includes an accuracy indicator and a reliability indicator. If the reliability indicator satisfies a reliability condition, a set of training data for the machine learning model is updated based on the descriptive string and the label. The machine learning model is then trained against the updated set of training data.

Abstract: Dynamic state-space modeling within a special purpose hardware platform to determine non-conversion risks for each trial user and churn risks for each active subscriber having exhibited a sequence of behaviors. The state-space model may be operable to determine a loss risk for each of a provider's active trial users and/or subscribers.

Abstract: An ensemble of machine learning models used for real-time prediction of text for an electronic chat with an expert user. A global machine learning model, e.g., a transformer model, trained with domain specific knowledge makes a domain specific generalized prediction. Another machine learning model, e.g., an n-gram model, learns the specific style of the expert user as the expert user types to generate more natural, more expert user specific text. If specific words cannot be predicted with a desired probability level, another word level machine learning model, e.g., a word completion model, completes the words as the characters are being typed. The ensemble therefore produces real-time, natural, and accurate text that is provided to the expert user. Continuous feedback of the acceptance/rejection of predictions by the expert is used to fine tune one or more machine learning models of the ensemble in real time.

Abstract: At least one processor may capture a plurality of image snapshots containing information about a monitored system at a plurality of sequential times, each snapshot having the same vertical and horizontal dimensions. The processor may label the plurality of image snapshots as indicative of an event that took place in the monitored system, may receive additional data describing the event, may cluster the labeled plurality of image snapshots and the additional data using at least one machine learning clustering algorithm, and may merge the clustered plurality of image snapshots and the clustered additional data into merged data. The processors may create a model by processing the merged data using at least one neural network, the model being configured to detect future events of a same type as the event in the monitored system. The processor may store the model in a memory in communication with the processor.

Abstract: The present disclosure relates to a trustworthy data exchange. Embodiments include receiving, from a device, a query, wherein the query comprises a question. Embodiments include identifying particular information related to the query. Embodiments include receiving credentials from a user for retrieving the particular information related to the query. Embodiments include retrieving, using the credentials, the particular information related to the query from one or more data repositories that are part of a distributed database comprising an immutable data store that maintains a verifiable history of changes to information stored in the distributed database. Embodiments include determining, based on the particular information related to the query, an answer to the query. Embodiments include providing the answer to the device.

Abstract: Systems and methods that may be used to provide policies and protocols for blocking decryption capabilities in symmetric key encryption using a unique protocol in which key derivation may include injecting a random string into each key derivation. For example, a policy may be assigned to each client device indicating whether the client device has been assigned encryption only permission or full access permission to both encrypt and decrypt data. The disclosed protocol prevents client devices with encryption only permission from obtaining keys for decryption.

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: Aspects of the present disclosure provide techniques for automated data classification error correction through machine learning. Embodiments include receiving a set of predicted labels corresponding to a set of consecutive text strings that appear in a particular order in a document, including: a first text string corresponding to a first predicted label; a second text string that follows the first text string in the particular order and corresponds to a second predicted label; and a third text string that follows the second text string in the particular order and corresponds to a third predicted label. Embodiments include providing inputs to a machine learning model based on: the third text string; the second text string; the second predicted label; and the first predicted label. Embodiments include determining a corrected third label for the third text string based on an output provided by the machine learning model in response to the inputs.

Abstract: This disclosure relates to widget integration. Embodiments include receiving a workflow definition specifying one or more widgets to be loaded into a shell executing within a given application, the shell comprising a runtime environment, the one or more widgets comprising at least a first cross-platform widget. Embodiments include instantiating the runtime environment for the first cross-platform widget, the runtime environment including a bridge interface for facilitating communications between each widget in the one or more widgets and the given application. Embodiments include loading the first cross-platform widget into the runtime environment. Embodiments include processing, through the bridge interface, a communication from the first cross-platform widget running within the shell, wherein the communication comprises a result of executing a function.

Abstract: A document extraction system executed by a processor, may process documents using manual and automated systems. The document extraction system may efficiently route tasks to the manual and automated systems based on a predicted probability that the results generated by the automated system meet some baseline level of accuracy. To increase document processing speed, documents having a high likelihood of accurate automated processing may be routed to an automated system. To ensure a baseline level of accuracy, documents having a smaller likelihood of accurate automated processing may be routed to a manual system.

Abstract: Systems and methods are used to generate contact type predictions that route user customer service requests within a support platform. The contact type predictions are generated using a hybrid model that includes a deep learning component and a business logic component. The deep learning component may generate a multi-channel output based on text features and context features. The multi-channel output is modified based on one or more business rules to generate the contact type predictions.

Abstract: Certain aspects of the present disclose provide techniques for generating a knowledge engine module collection. Techniques for generating the module collection include receiving input data comprising a first identifier, a second identifier, and a third set of fields. Based on the input data, a UI builder tool can retrieve a first set of artifact files and a second set of artifact files corresponding to a first module and a second module. The UI builder tool can generate a third set of artifact files based on the first set of artifact files, the second set of artifact files, and the input data.

Abstract: Aspects of the present disclosure provide techniques for automated data classification through machine learning. Embodiments include providing first inputs to a first machine learning model based on a column header of a column from a table and receiving a first output from the first machine learning model in response to the first inputs, wherein the first output indicates a first likelihood that the column relates to a given classification. Embodiments include providing second inputs to a second machine learning model based on a value from the column and receiving a second output from the second machine learning model in response to the second inputs, wherein the second output indicates a second likelihood that the value relates to the given classification. Embodiments include determining whether to associate the value with the given classification based on the first output and the second output.

Abstract: Systems of the present disclosure generate database queries for financial information requested in a natural-language form. A natural-language processing (NLP) financial aggregator receives a request for financial information and extracts NLP features of the request, including keywords. The NLP financial aggregator determines a type of the request based on the features and creates a query in a database-query language for the financial information based on the type and on the features. The NLP financial aggregator submits the query to a database where the financial information is stored. The software then receives the financial information from the database and sends the information to the user.

Abstract: A method for using piecewise forecasts involves obtaining, by a model discovery service, a plurality of models and generating, by a demand prediction service, a plurality of values for a time series variable. The plurality of values corresponding to a plurality of days to be predicted. The method further involves inputting the plurality of values for the time series variable as part of a piecewise forecast to a headcount estimation service and generating, by the headcount estimation service with the piecewise forecast, an estimated headcount from the time series variable.

Abstract: Systems and methods for optimally formatting item identifiers (ID) are disclosed. An example method is performed by one or more processors of a system and includes obtaining descriptions of items, identifying, for each item, one or more attributes of the item described in the item's description, extracting a value for each of the identified attributes, identifying a set of common attributes among the identified attributes for which values were extracted for more than a threshold ratio of the items, assigning a priority weight to each of the common attributes using an optimization algorithm, identifying a set of optimum attributes among the set of common attributes based on the priority weights, mapping an optimum code to each unique value extracted for the optimum attributes, and generating an optimum ID format that provides, for each item, a unique ID including the optimum codes mapped to the values of the item's optimum attributes.