Abstract: Systems and methods for customizing business applications based upon user intent scores is described. A machine learning model trained to specifically predict when a user is likely to engage in a specific activity while interacting with the business application may be trained using data regarding prior interactions between a business application and a plurality of users. The machine learning model may thereafter provide a probability score for a particular user, the probability score indicating the likelihood that the user will engage in the specific activity for which the model has been trained to predict. The probability may be combined with a business value factor to produce a user intent score indicating the relative value of the user engaging in the specific activity.

Abstract: In one aspect, a method of utilization analysis of network devices include receiving a set of information associated with performance of the network devices operating in a network, processing using a trained machine learning model the set of information to identify one or more variables indicative of performance of the network devices, the machine learning model being trained to receive as input the set of device utilization information, identify the one or more variables, and provide as output an analysis of the performance of one or more of the network devices, and generating a user interface to display on a user device a visual representation of the output of the trained machine learning model.

Abstract: In one aspect, a method of identifying network features includes receiving a first-time definition of a feature, the feature representing a user query for analytics associated with the feature based on data collected on a plurality of devices in one or more networks, generating the analytics associated with the feature, determining, using a trained machine learning model, a likelihood of adoption of at least the feature by one or more users of the plurality of devices, wherein the trained machine learning model receives as input the first-time definition and provides, as output, the likelihood of adoption of at least the feature, and configuring a user interface on a terminal to provide a visualization of at least one of the likelihood of adoption of at least the feature and the analytics associated with the feature.

Abstract: A transaction information aggregation system includes raw transaction and enriched transaction databases. The raw transaction database comprises transaction information for a plurality of transactions, including for each transaction an account holder identifier and at least one transaction parameter. The enriched transaction database comprises an indexed transaction record for each of the plurality of transactions, each record including a plurality of search terms associated with each of the at least one transaction parameter.

Abstract: Systems and methods are provided for authorizing an electronic transaction. In one implementation at least one processor is programmed to receive electronic transaction data and historical transaction data, the electronic transaction data including an entity identifier component and an amount component of an electronic transaction; determine, based on the entity identifier component and the amount component, a location of the electronic transaction in a space of a distributed representation space, the distributed representation space comprising a mapping of electronic transaction components in a high-order space; determine locations of the historical transaction data in the distributed representation space; determine a decision boundary in the distributed representation space based on the locations of the historical transaction data; and authorize the electronic transaction based on the location of the electronic transaction being within the decision boundary.

Abstract: Systems and methods for customizing business applications based upon user intent scores is described. A machine learning model trained to specifically predict when a user is likely to engage in a specific activity while interacting with the business application may be trained using data regarding prior interactions between a business application and a plurality of users. The machine learning model may thereafter provide a probability score for a particular user, the probability score indicating the likelihood that the user will engage in the specific activity for which the model has been trained to predict. The probability may be combined with a business value factor to produce a user intent score indicating the relative value of the user engaging in the specific activity.

Abstract: Systems and methods for customizing business applications based upon user intent scores is described. A machine learning model trained to specifically predict when a user is likely to engage in a specific activity while interacting with the business application may be trained using data regarding prior interactions between a business application and a plurality of users. The machine learning model may thereafter provide a probability score for a particular user, the probability score indicating the likelihood that the user will engage in the specific activity for which the model has been trained to predict. The probability may be combined with a business value factor to produce a user intent score indicating the relative value of the user engaging in the specific activity.

Abstract: Systems and methods are provided for authorizing an electronic transaction. In one implementation at least one processor is programmed to receive electronic transaction data and historical transaction data, the electronic transaction data including an entity identifier component and an amount component of an electronic transaction; determine, based on the entity identifier component and the amount component, a location of the electronic transaction in a space of a distributed representation space, the distributed representation space comprising a mapping of electronic transaction components in a high-order space; determine locations of the historical transaction data in the distributed representation space; determine a decision boundary in the distributed representation space based on the locations of the historical transaction data; and authorize the electronic transaction based on the location of the electronic transaction being within the decision boundary.

Abstract: Systems and methods are provided for authorizing an electronic transaction. In one implementation at least one processor is programmed to receive electronic transaction data and historical transaction data, the electronic transaction data including an entity identifier component and an amount component of an electronic transaction; determine, based on the entity identifier component and the amount component, a location of the electronic transaction in a space of a distributed representation space, the distributed representation space comprising a mapping of electronic transaction components in a high-order space; determine locations of the historical transaction data in the distributed representation space; determine a decision boundary in the distributed representation space based on the locations of the historical transaction data; and authorize the electronic transaction based on the location of the electronic transaction being within the decision boundary.

Abstract: Disclosed are systems and methods for monitoring user-defined metrics. A method may include: receiving, from a user device, a metric definition usable to generate queries to obtain data for a metric to be monitored; receiving, from the user device, a monitoring configuration indicative of a manner in which a metric monitoring process associated with the metric definition is to be repeatedly performed; storing the metric definition in a metric definition database; and repeatedly performing the metric monitoring process in accordance with the monitoring configuration. The metric monitoring process may include: retrieving the metric definition from the metric definition database; generating a database query based on the metric definition, the database query including one or more executable database statements defined by the metric definition; executing the database query to obtain query result data, the query result data being data for the metric; and storing the query result data.

Abstract: Systems and methods for customizing business applications based upon user intent scores is described. A machine learning model trained to specifically predict when a user is likely to engage in a specific activity while interacting with the business application may be trained using data regarding prior interactions between a business application and a plurality of users. The machine learning model may thereafter provide a probability score for a particular user, the probability score indicating the likelihood that the user will engage in the specific activity for which the model has been trained to predict. The probability may be combined with a business value factor to produce a user intent score indicating the relative value of the user engaging in the specific activity.

Abstract: A transaction information aggregation system includes raw transaction and enriched transaction databases. The raw transaction database comprises transaction information for a plurality of transactions, including for each transaction an account holder identifier and at least one transaction parameter. The enriched transaction database comprises an indexed transaction record for each of the plurality of transactions, each record including a plurality of search terms associated with each of the at least one transaction parameter.

Abstract: Disclosed are systems and methods for monitoring user-defined metrics. A method may include: receiving, from a user device, a metric definition usable to generate queries to obtain data for a metric to be monitored; receiving, from the user device, a monitoring configuration indicative of a manner in which a metric monitoring process associated with the metric definition is to be repeatedly performed; storing the metric definition in a metric definition database; and repeatedly performing the metric monitoring process in accordance with the monitoring configuration. The metric monitoring process may include: retrieving the metric definition from the metric definition database; generating a database query based on the metric definition, the database query including one or more executable database statements defined by the metric definition; executing the database query to obtain query result data, the query result data being data for the metric; and storing the query result data.

Abstract: A transaction information aggregation system includes raw transaction and enriched transaction databases. The raw transaction database comprises transaction information for a plurality of transactions, including for each transaction an account holder identifier and at least one transaction parameter. The enriched transaction database comprises an indexed transaction record for each of the plurality of transactions, each record including a plurality of search terms associated with each of the at least one transaction parameter.

Abstract: Systems and methods are provided for authorizing an electronic transaction. In one implementation at least one processor is programmed to receive electronic transaction data and historical transaction data, the electronic transaction data including an entity identifier component and an amount component of an electronic transaction; determine, based on the entity identifier component and the amount component, a location of the electronic transaction in a space of a distributed representation space, the distributed representation space comprising a mapping of electronic transaction components in a high-order space; determine locations of the historical transaction data in the distributed representation space; determine a decision boundary in the distributed representation space based on the locations of the historical transaction data; and authorize the electronic transaction based on the location of the electronic transaction being within the decision boundary.

Abstract: Disclosed are systems and methods for monitoring user-defined metrics. A method may include: receiving, from a user device, a metric definition usable to generate queries to obtain data for a metric to be monitored; receiving, from the user device, a monitoring configuration indicative of a manner in which a metric monitoring process associated with the metric definition is to be repeatedly performed; storing the metric definition in a metric definition database; and repeatedly performing the metric monitoring process in accordance with the monitoring configuration. The metric monitoring process may include: retrieving the metric definition from the metric definition database; generating a database query based on the metric definition, the database query including one or more executable database statements defined by the metric definition; executing the database query to obtain query result data, the query result data being data for the metric; and storing the query result data.

Abstract: Computer-implemented methods and systems are provided for generating a distributed representation of electronic transaction data. Consistent with disclosed embodiments, generation may include receiving electronic transaction data including first and second entity identifiers. Generation may also include generating an output distributed representation by iteratively updating a distributed representation using the electronic transaction data. The distributed representation may include rows corresponding to first entity identifiers and rows corresponding to second entity identifiers. An iterative update may include generating a training sample and an embedding vector using the components and the distributed representation; determining, by a neural network, a predicted category from the embedding vector; and updating the distributed representation using the predicted category and the training sample.

Abstract: A transaction information aggregation system includes raw transaction and enriched transaction databases. The raw transaction database comprises transaction information for a plurality of transactions, including for each transaction an account holder identifier and at least one transaction parameter. The enriched transaction database comprises an indexed transaction record for each of the plurality of transactions, each record including a plurality of search terms associated with each of the at least one transaction parameter.

Abstract: Various embodiments are generally directed to techniques to determine contextual search terms. For example, embodiments include receiving a search query including a search term, the search query to cause performance of a search within a website. Embodiments also include determining a rule comprising one or more criteria is met, the rule associated with the search term and to link the search term with a contextual search term when the one or more criteria are met, appending the contextual search term with the search term in the search query to perform the search within the website, and performing the search within the website utilizing the search query comprising the search term and the contextual search term. Finally, embodiments include returning a result to the website.

Abstract: Systems and methods for customizing business applications based upon user intent scores is described. A machine learning model trained to specifically predict when a user is likely to engage in a specific activity while interacting with the business application may be trained using data regarding prior interactions between a business application and a plurality of users. The machine learning model may thereafter provide a probability score for a particular user, the probability score indicating the likelihood that the user will engage in the specific activity for which the model has been trained to predict. The probability may be combined with a business value factor to produce a user intent score indicating the relative value of the user engaging in the specific activity.