Computing systems, computing apparatuses, computing methods, and computer program products are disclosed for optimizing a webpage. An example computing method includes determining a first average number of clicks (ANC) value for a first set of webpage nodes based on first webpage decision tree data and historical usage data. The example computing method further includes generating semantic grouping data for the first set of webpage nodes based on the first webpage decision tree data and webpage node description data. The example computing method further includes determining a second ANC value based on the first set of webpage nodes. The example computing method further includes generating, based on the second ANC value and the semantic grouping data, second webpage decision tree data.

A computing system for managing a contact center having agent instances includes processor(s) in a management network serving an end-user network that includes the contact center and a communication distributor server operable to receive communications from customers to the end-user network and assign one or more of the agent instances to service the communications from the customers. The processor(s) are configured to perform tasks including (a) receiving, from the end-user network, data associated with processes of the server(s) including the communication distributor server, (b) determining, based on a specification, operations to be performed by the one or more servers, and (c) providing, to the one or more servers, the operations, wherein the operations include changing a state of a particular agent instance of the agent instances or modifying an assigned schedule or assigned queue for the particular agent instance. A method and article of manufacture are also provided.

An example computing device is configured to receive, from a customer device, an indication of a plurality of resources and an indication of a plurality of customer services, each of the plurality of customer services being associated with a corresponding at least one requirement and a corresponding at least one constraint. The computing device is configured to automatically determine, for each requirement and each constraint, whether the requirement or the constraint can only be satisfied by a particular resource of the plurality of resources, and allocate, based on the determining, at least one resource of the plurality of resources to at least one customer service of the plurality of customer services. The example computing device is configured to provide, to the customer device and subsequent to the determining for every requirement and for every constraint, information to enable the customer device to provision the at least one customer service.

An example computing device is configured to receive, from a customer device, an indication of a plurality of resources and an indication of a plurality of customer services, each of the plurality of customer services being associated with a corresponding at least one requirement and a corresponding at least one constraint. The computing device is configured to automatically determine, for each requirement and each constraint, whether the requirement or the constraint can only be satisfied by a particular resource of the plurality of resources, and allocate, based on the determining, at least one resource of the plurality of resources to at least one customer service of the plurality of customer services. The example computing device is configured to provide, to the customer device and subsequent to the determining for every requirement and for every constraint, information to enable the customer device to provision the at least one customer service.

System trains machine learning model to determine content data, metadata, and context data for support ticket communications, in response to receiving support ticket communications. Machine learning model receives communication associated with support ticket, and determines content data, metadata, and context data for communication. System converts content data, metadata, and context data for communication into first impulse for first channel and second impulse for second channel. System determines first channel value based on first type of conversion of first impulse and any impulses for first channel that are converted from data that is determined for support ticket event. System determines second channel value based on second type of conversion of second impulse and any impulses for second channel that are converted from data that is determined for support ticket event. System uses first channel value and second channel value to generate priority associated with support ticket, and outputs priority.

Disclosed herein are systems, methods, and non-transitory computer-readable storage media for collecting call data, feeding call data to applications, and providing advanced call features.

A computer system that updates a pretrained predictive model is described. During operation, the computer system may receive, from an electronic device, information specifying user feedback about a network incident. Then, the computer system may update, based at least in part on the user feedback, the pretrained predictive model that outputs severity classifications of network incidents, where a difference between a severity classification of the updated pretrained predictive model and a user severity classification associated with the user feedback is reduced relative to an initial difference between an initial severity classification of the pretrained predictive model and the user severity classification. Moreover, the computer system may receive information specifying a second network incident. Next, the computer system may compute a second severity classification of the second network incident using the updated pretrained predictive model.

One example method includes various processes performed by a vendor system including discovering infrastructure assets of a subscriber system, determining that the vendor system is permitted by the subscriber system to access information about the infrastructure assets of the subscriber system, accessing the information about the infrastructure assets of the subscriber system, assessing an ability of the infrastructure assets of the subscriber system to implement a service offering of the vendor system, and the assessing is based on the information about the infrastructure assets, and implementing the service offering in the infrastructure assets of the subscriber system when (i) given permission to do so by the subscriber system and (ii) the infrastructure assets have been determined to be able to implement the service offering.

A method of an automatic support service that includes receiving a request for additional assistance for an error from a user interface, retrieving error information from a logging system for the error, generating an error information collection interface, in response to the request, populating the error information collection interface with the error information from the logging system, sending the error information collection interface to be displayed to the user by the user interface, and receiving additional error information from the user via the error information collection interface.

A system coordinates with remote hardware to execute customer workloads. The system uses an architecture for ensuring trust to ensure that debugging is not performed at the remote hardware while the customer workload is being executed on the remote hardware without customer consent. For example, debugging at the remote hardware may enable an entity performing the debugging to view certain aspects of the customer's workload. The architecture for ensuring trusts uses a shared secret to ensure customer consent is given before debugging can be performed while the customer's workload is being executed on the remote hardware.