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: A computer-implemented system and method for predicting and flagging an anomaly entered in a digital form. A server computing device classifies a plurality of data fields of the digital form to identify a set of non-zero value data fields; and obtains an anomaly detection model comprising a statistical tree structure associated with the data field of the digital form. The server computing device receives datasets including a target value of a data field and values of a set of cohorting data features; traverses a statistical tree structure of the anomaly detection model with the target dataset to form a set of target cohorts to determine a target statistic value for the data field; flags the data field value of the target dataset as an anomaly item; and generates one or more confidence scores for a runtime prediction based on one or more variance changes for the data field.

Abstract: A method including embedding, by a trained issue MLM (machine learning model), a new natural language issue statement into an issue vector. An inner product of the issue vector with an actions matrix is calculated. The actions matrix includes centroid-vectors calculated using a clustering method from a second output of a trained action MLM which embedded prior actions expressed in natural language action statements taken as a result of prior natural issue statements. Calculating the inner product results in probabilities associated with the prior actions. Each of the probabilities represents a corresponding estimate that a corresponding prior action is relevant to the issue vector. A list of proposed actions relevant to the issue vector is generated by comparing the probabilities to a threshold value and selecting a subset of the prior actions with corresponding probabilities above the threshold. The list of proposed actions is transmitted to a user device.

Abstract: This disclosure relates to systems and methods for processing electronic payments for customer purchases. In some implementations, a mobile computing device receives a payment request identifying a sales transaction between a merchant and a customer. The payment request indicates a purchase amount owed by the customer to the merchant, and carries a set of instructions. The mobile computing device activates a card reader based at least in part on the set of instructions, receives customer authorization for electronic payment of the purchase amount, instructs the card reader to process a credit card for the purchase amount based on receiving the customer authorization, and receives confirmation of payment of the purchase amount from the card reader.

Abstract: Systems and methods for recognizing domain specific named entities are disclosed. An example method may be performed by one or more processors of a text incorporation system and include extracting a number of terms from a text under consideration, identifying, among the number of terms, a set of unmatched terms that do not match any of a plurality of known terms, passing each respective unmatched term to a vectorization module, embedding a vectorized version of each respective unmatched term in a vector space, comparing each vectorized version to known term vectors, passing, to a machine learning model, candidate terms corresponding to known term vectors closest to the vectorized versions, identifying, using the machine learning model, a best candidate term for each respective unmatched term, mapping the best candidate terms to unmatched terms in the text under consideration, and incorporating the text under consideration into the system based on the mappings.

Abstract: A method for mitigating cold starts in recommendations includes receiving a request that identifies a requested page and identifying a content vector of the requested page. The content vector is generated based on providing text of the requested page to a neural network text encoder. The method further includes selecting, based on the content vector, a link to a cold start page that does not satisfy a threshold level of interaction data. The selected link is ranked above a second link to a warm page that does satisfy the threshold level of the interaction data. The method further includes presenting the requested page with the selected link.

Abstract: Aspects of the present disclosure provide techniques for machine learning and rules integration. Embodiments include receiving input values corresponding to a subset of a set of input variables associated with an automated determination. Embodiments include generating a directed acyclic graph (DAG) representing a set of constraints corresponding to the set of input variables. The set of constraints relate to one or more machine learning models and one or more rules. Embodiments include receiving one or more outputs from the one or more machine learning models based on one or more of the input values. Embodiments include determining outcomes for the one or more rules based on at least one of the input values. Embodiments include populating the DAG based on the input values, the one or more outputs, and the outcomes. Embodiments include making the automated determination based on logic represented by the DAG.

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 method implements last mile churn prediction. The method includes retrieving data during a user session in response to a trigger. The data includes a list of screen identifiers and a corresponding list of timestamps. The method further includes converting the list of timestamps to a list of time deltas. The list of time deltas includes a time delta that identifies an amount of time between two timestamps of the list of timestamps. The method further includes generating a churn risk from the list of screen identifiers and the list of time deltas. The churn risk identifies a probability that the user session will be terminated. The method further includes transmitting an intervention to intervene in the user session based on the churn risk.

Abstract: A method extracts explainable corpora embeddings. The method includes constructing a graph with nodes representing terms from a text sequence and edges that include pointwise values generated between pairs of terms. The method further includes generating a rank vector from the graph. Elements of the rank vector correspond to the edges of the graph. A rank value, of the rank values, corresponds to a term from the text sequence. The method further includes selecting the term by comparing the rank vector to a previous rank vector generated for a previous text sequence.

Abstract: A system includes a categorization application including a personal categorization model for execution on a user device, the personal categorization model configured to assign a personal assigned category and a personal assigned categorization confidence value to transaction record. The system also includes a global categorization model executing on a central server. The global categorization model is configured to receive the transaction record from the user device, and process the transaction record through the global categorization model to obtain a global assigned category and a global assigned categorization confidence value. The system is configured to select a transaction category from the personal assigned category and the global assigned category using the personal assigned categorization confidence value and the global assigned categorization confidence value, and categorize the transaction with the transaction category.

Abstract: Systems and methods for updating a data catalog in real-time with changes to metadata are disclosed. An example method may be performed by one or more processors of a system and include detecting changes to metadata in a metadata store, generating an event queue indicating a plurality of change events associated with system objects based on the changes, each of the plurality of change events indicating a number of event attributes and a system object associated with the change event, identifying recent events among the plurality of change events, identifying relevant events among the recent events, extracting unique identifiers from the relevant events, identifying priority objects among the system objects, obtaining, from the metadata store, current metadata for each of the priority objects, and updating information associated with the priority objects in a data catalog, the updating occurring in real-time with the changes to the metadata in the metadata store.

Abstract: A processor may generate a cluster of at least two (or any other minimal cluster size) of a plurality of vectors using a density-based clustering algorithm. The generating may include optimizing at least one hyperparameter of the density-based clustering algorithm by minimizing a loss function to increase a cluster count and decrease a cluster variance. The processor may select a vector closest to the center of the cluster as a representative vector.

Abstract: Systems and methods are provided to structure event description data.

Abstract: In one embodiment a finite rank deep kernel learning method includes: receiving a training dataset; forming a plurality of training data subsets from the training dataset; for each respective training data subset of the plurality of training data subsets: calculating a subset-specific loss based on a loss function and the respective training data subset; and optimizing a model based on the subset-specific loss; determining a set of embeddings based on the optimized model; determining, based on the set of embeddings, a plurality of dot kernels; combining the plurality of dot kernels to form a composite kernel for a Gaussian process; receiving live data from an application; and predicting a plurality of values and a plurality of uncertainties associated with the plurality of values simultaneously using the composite kernel.

Abstract: Certain aspects of the present disclosure provide techniques for communicating between an application executing on a client device and a server using a persistent connection. An example method generally includes initializing a persistent connection between an application executing on a client device and a server. Information about an event within the application is received. Communications between the application and the server are performed via streaming data related to the information about the event carried on the persistent connection. Generally, the streaming data may be translated from an application-native format to a platform-agnostic format and may include application-specific information. One or more actions are taken within the application based on the streaming data related to the event and carried on the persistent connection.

Abstract: Certain aspects of the present disclosure provide techniques for entering user credentials through a proxy. One example method generally includes receiving, at a user device, a push request for user data from a cloud server and receiving a request file from an aggregation system. The method further includes injecting user credentials stored on the user device into the request file, wherein when injected the user credentials replace at least one dummy entry of the request file, and transmitting the request file to a data source associated with the request file. The method further includes receiving user data from the data source and transmitting the user data to the aggregation system.

Abstract: A processor may receive an image and determine a number of foreground pixels in the image. The processor may obtain a result of optical character recognition (OCR) processing performed on the image. The processor may identify at least one bounding box surrounding at least one portion of text in the result and overlay the at least one bounding box on the image to form a masked image. The processor may determine a number of foreground pixels in the masked image and a decrease in the number of foreground pixels in the masked image relative to the number of foreground pixels in the image. Based on the decrease, the processor may modify an aspect of the OCR processing for subsequent image processing.

Abstract: Systems and methods for synchronizing cloud resources are disclosed. An example method may include receiving a first request to synchronize first target cloud resources to a first specified state defined in a configuration repository, generating one or more first configuration commands corresponding to the first request, the one or more first configuration commands associated with a first cloud provider and a first cloud configuration framework, and executing the one or more first configuration commands to set a state of the first target cloud resources to the first specified state.

Abstract: A method including receiving, at a platform and from a first user using a first user device, selection of a uniform resource indicator (URI) unique to a second user using a second user device. The method also includes generating, automatically by the platform in response to receiving the URI, a conference session unique to the first user and the second user. The method also includes transmitting, automatically by the platform, a message to the second user, the message indicating that the conference session is initiated. The method also includes receiving, by the platform, an indication from the second user device that the second user joins the conference session. The method also includes joining, automatically by the platform, the first user device and the second user device in the conference session.