At&t Intellectual Property I, L.p.

Kelly Dowd

Prateek Baranwal

Emily Williams

Eric Martin Zavesky

James H Pratt

Martin Patrick McEnroe

Ni An

Jami Daly

The technologies described herein are generally directed to allocating a resource to perform a task based on a predicted capacity of the resource. For example, a method described herein can include identifying a selected capacity associated with completion of tasks of a particular task type. Further, the method can include, evaluating a result of work by a worker resource performing a task of the particular task type. The method can further include, based on the result, predicting that the worker resource has the selected capacity and, based on this prediction, assign the worker resource to perform another task of the particular task type.

Utilization of a resource to perform a task based on a predicted capacity of the resource

Concepts and technologies disclosed herein are directed to automated optimization of events by engagement and format. According to one aspect disclosed herein, an event management system can create an event for a specified topic based upon input provided, at least in part, by an event creator. The input can identify initial event content, a plurality of event attendees, a location for the event, and an event presenter. The event management system can receive a selection of a desired audience for the event. The event management system can accept the initial event content to be presented during the event and can determine a time for the event. The event management system can perform engagement sampling during the event. The event management system can utilize the selection, the time, and the engagement sampling to optimize the event. The event management system can determine a follow-up action to be performed after the event.

Automated optimization of events by engagement and format

An example method, executed by a processing system, includes receiving data from a data source, routing the data to a plurality of different data labelers to label the data, determining that an inflection point associated with an accuracy of the plurality of different data labelers is reached, re-routing additional data from the data source to a machine learning model, and activating the machine learning model to label the additional data.

Machine learning models with accurate data labeling

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Dallas, TX, United States of America

Kelly Dowd