Abstract: A spending analysis display is provided that includes first, second, and third display regions, wherein an uncategorized financial transaction of the first display region is selectable by a user, and a sweeping motion toward either the second or third display region categorizes the selected uncategorized financial transaction under a financial transaction category represented by the second or third display region that is positioned in the direction of the sweeping motion.

Abstract: Systems and methods for forecasting cashflows across one or more accounts of a user disclosed. One example method may include retrieving a data set for each of a plurality of accounts from a database, constructing a graph including a plurality of nodes linked together by a multitude of edges, wherein each node identifies a time series value corresponding to one of the accounts, and each edge indicates a time series value of a corresponding set of transactions occurring between a corresponding pair of accounts, determining a plurality of constraints, determining a specified loss function based on the plurality of constraints, back-propagating a derivative of the specified loss function into a deep neural network (DNN) to determine a set of neural network parameters, forecasting, using the DNN, a time sequence for one or more of the nodes and one or more of the edges, and providing the forecasted time sequences to the user.

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: This disclosure relates to extracting entities from unstructured text. The unstructured text is segmented into structured segments with one or more instances, that belong to different topics, with a topic segmentation model. Each instances of the structured segment is operated on by an entity extraction model to extract entities, and the extracted entities associated with each topic is produced in a computer-readable format. The relations between extracted entities associated with each topic may be identified.

Abstract: Systems and methods are disclosed for classifying objects by a machine learning (ML) model. The ML model includes one or more layer level classification models to generate classifications and uncertainty metrics in the classifications and a meta-model to generate a final classification and confidence based on the underlying classifications and uncertainty metrics. In some implementations, the ML model provides an object to be classified to one or more layer level classification models, and the layer level classification models generate a classification for the object and an uncertainty metric in the classification. The meta-model receives the classifications and uncertainty metrics from the one or more layer level classification models and generates the final classification and confidence in the final classification. The uncertainty metrics may also be output by the ML model or used to adjust the meta-model to improve the final classification and confidence.

Abstract: Systems and methods for validation of transaction amounts with a predictive model are disclosed. An example method may be performed by one or more processors of a system and include retrieving data indicating attributes for each of a plurality of transactions, assigning a label to each of the transactions based on whether an original amount entered changed, defining predictive features suggesting an extent to which final amounts stored for a particular set of similar transactions tend to vary, defining one or more interaction features suggesting a probability of a particular predictive feature value being generated for a transaction having particular attributes, generating, using a machine learning process, an anomaly scoring algorithm based on the predictive features and the one or more interaction features, and training, using the labeled transactions, a predictive model to predict, using the anomaly scoring algorithm, whether an amount entered for a given transaction will be changed.

Abstract: A method including receiving first and second natural language texts. A distance metric is generated from the first and second natural language texts. A first machine learning system is executed, the first machine learning system taking, as a first input, the distance metric and generating, as a first output, a first probability that the first natural language text matches the second natural language text. A second machine learning system is executed, the second machine learning system taking as a second input the first natural language text and as a third input the second natural language text, and generating, as a second output, a second probability that the first natural language text matches the second natural language text. A third probability that the first natural language text matches the second natural language text is generated. Generating includes combining the first probability and the second probability.

Abstract: A system that communicates information is described. This system includes: a network interface, a proxy device coupled to the network interface, and an interface node coupled to the proxy device and configured to couple to a channel. Note that the network interface is configured to transmit outbound messages from the system to a location and to receive inbound messages to the system from the location, and the channel is configured to convey the outbound messages and the inbound messages. Moreover, the proxy device is configured to inspect a given message inbound or outbound based on a pre-determined profile of the location and pre-defined communication rules. Then, the proxy device is configured to restrict the given message based on a result of the inspection, where the restriction occurs after the system begins a communication session with the location and is performed for the duration of the communication session.

Abstract: Certain aspects of the present disclosure provide techniques for intelligent grouping of travel data for review through a user interface. In one example, a method for providing grouped travel data to a user interface of an application includes receiving travel data from an application running on a remote device; generating one or more travel data-based features from the travel data thereby creating featurized travel data; applying a pattern mining technique to the featurized travel data to detect a plurality of patterns in the featurized travel data; for each trip record in the featurized travel data: determining a plurality of trip record groups in which the trip record falls based on the plurality of patterns; and adding the trip record to a trip record group of the plurality of trip record groups according to a prioritization scheme; and transmitting the trip record group to the application running on the remote device.

Abstract: A method including receiving events from different data sources for a service automatically executing in an enterprise system. A first event is enriched by providing the first event with first metadata that associates the first event with a first application used by the service. The first event is assigned to a time slice associated with the first application. A second event is enriched in a similar manner. A correlation graph of nodes and edges is built using the enriched events, with nodes representing the events and edges indicating relationships between the edges. A third event indicating a fault in the first application associated with the first node is received. The source of the error for the third event is identified using the second updated correlation graph and the time slice. The source of error is then mitigated.

Abstract: A processor may receive a request for access to a first resource from a client. The processor may retrieve a decision token indicating a plurality of resource decisions for the client, each of the plurality of resource decisions including a decision permitting or forbidding access to at least one resource. The processor may identify, among the plurality of resource decisions, a first decision for the first resource. On the basis of the first decision for the first resource, the processor may enable or block access to the first resource by the client. The decision token may have been generated by the processor generating a plurality of resource decisions for the client, the plurality of resource decisions including a first decision permitting or forbidding access to the first resource and at least one additional decision permitting or forbidding access to at least one additional resource.

Abstract: The invention relates to a method for mapping topics. The method includes obtaining terms, obtaining tokens from each term, and identifying a first and a second set of topics. Each of the topics represents one or more of the terms. The method further includes identifying first and second topic names for the first and the second sets of topics. For each topic, the tokens associated with the terms assigned to the topic are analyzed for relevance, and a token with a high relevance is selected as the topic name. The method also includes selecting one of the first and one of the second sets of topics to obtain first and second selected topics, determining, based on the one or more terms, a similarity value between each of the first and the second selected topics, and establishing a mapping between similar first and second selected topics.

Abstract: Certain aspects of the present disclosure provide techniques for detecting and preventing web service usage anomalies. Embodiments include forecasting, based on a model, a number of resource instances for one or more web services for a time period. Embodiments include receiving, after the time period has elapsed, a detected number of resource instances for the one or more web services for the time period. Embodiments include comparing the detected number of resource instances to the forecasted number of resource instances. Embodiments include determining, based on the comparing, a usage anomaly. Embodiments include determining an impact of the usage anomaly. Embodiments include outputting an indication of the usage anomaly and the impact of the usage anomaly.

Abstract: The present disclosure provides techniques for recommending vendors using machine learning models. One example method includes receiving transaction data indicative of a transaction, identifying, based on the transaction data, an associated merchant of one or more merchants and an associated time window of one or more time windows, receiving historical transaction data indicative of one or more historical transactions, wherein the one or more historical transactions are associated with the associated merchant and the associated time window, generating features based on the transaction data and the historical transaction data, and determining, using a classification model, that the transaction is fraudulent, based on the generated features.

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 stateful feature, the feature management platform can receive a processing artifact from a computing device. The processing artifact defines the stateful 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 (e.g., the API defines a pipeline), which when initiated generates a vector that encapsulates the stateful feature. The vector is transmitted to the computing device that locally hosts a model, which generates a prediction that is transmitted to the feature management platform. Subsequently, the predication and stateful feature can be transmitted to other computing devices.

Abstract: The instant systems and methods are directed to a contextual bandits machine learning model configured to enable granular synchronized ecosystem personalization and optimization. The system and methods determine an objective and feed the objective and one more lifecycle model propensity scores as inputs to the contextual bandits machine learning model. The contextual bandits machine learning model then generates one or more potential weighted model rewards, wherein each potential weighted model reward includes at least a desired user action, a weight, a channel, and an expected change to the objective, and selects a weighted model reward that optimizes the objective. An action recommendation is subsequently transmitted to a user device based on the weighted model reward, wherein the action recommendation is presented in a selected channel associated with the weighted model reward. Feedback associated with the action recommendation is collected and used in training and fine-tuning of the model.

Abstract: Certain aspects of the present disclosure provide techniques for vectorization of transactions including: receiving electronic transaction information of one or more transactions of a user; for each transaction of the one or more transactions: segmenting the electronic transaction information of the transaction into one or more transaction words; generating a second transaction description related to the transaction; and identifying a category of the transaction; generating, based on the corresponding identified categories of the one or more transactions, a set of transaction history data of the user; providing the set of transaction history data of the user as an input to a machine learned model trained to output a set of word embedding vectors; determining, based on an output of the machine learned model comprising a set of word embedding vectors, a set of similar merchants; and providing the set of similar merchants for display to the user.

Abstract: A method includes receiving a set of text documents. The method also includes generating a summary of the set of text documents by a set of large language machine learning models. The method further includes generating a set of keywords from the summary by the set of large language machine learning models. The method additionally includes generating an image prompt from the set of keywords by the set of large language machine learning models. The method also includes generating a set of images from the image prompt by a text-to-image machine learning model. The method further includes generating a video clip from the set of images. The method additionally includes presenting the video clip.

Abstract: Systems and methods of generating synthetic training data for machine learning models. First, line items in source documents such as bills, invoices, and or receipts are identified and labeled. The identification and labeling generate labeled documents. Then, in the labeled documents, the line items are augmented by adding, deleting, and or swapping line items to generate synthetic training documents. An addition operation randomly selects one or more line items and adds the selected line item(s) to the same labeled document or another labeled document. A deletion operation randomly deletes one or more line items. A swapping operation randomly swaps line items in a single labeled document or across different labeled documents. These operations can generate synthetic labeled documents of any length, which form synthetic training data for training the machine learning models.

Abstract: Certain aspects of the present disclosure provide techniques for establishing a web session between a computing device and a web-based integrated development environment to create and/or update files (e.g., content files and/or resource files). In the web session, content file(s) can be evaluated in real-time with one or more resource files. Further, in the web session, a set of validation rules of a resource file can be updated by the computing device. To stablish the web session, a first communication connection is established between the computing device and the web-based integrated development environment. With the first communication established, a second communication connection is established for real-time, bi-directional communication between the computing device and a language server instance of the web-based integrated development environment.