An information handling system may receive a request from a user for expert assistance associated with a gaming application. The information handling system may determine a recommendation of one or more experts from a plurality of available experts based, at least in part, on the gaming application and one or more gaming characteristics of each of the plurality of available experts. The information handling system may initiate a gaming assistance session between the user and at least one of the determined experts.

An electronic information system which enables email based transactions comprises an information database storing information regarding a plurality of individuals in a group and selections available to individuals in the group. A first email message with a mailto hyperlink having a plurality of fields including actionable parameter is generated and sent. A reply email message is received in response to selection of the mailto hyperlink. The received email message includes the plurality of fields and the actionable parameter that indicates that a specific selection has been made. A processor performs the action indicated by the actionable parameter in response to the received email message, including updating the information database to indicate the specific selection that has been made.

Described are platforms, systems, and methods for determining quantifiable measures of dyadic ties. In one aspect, a method comprises receiving contextual data for a user from at least one data source; processing the contextual data through a first machine-learning model to determine quantifiable measures of dyadic ties between the user and each of a plurality of individuals, the first machine-learning model trained with previously received contextual data of a plurality of other users; determining a grouping for the user based on the determined quantifiable measures, the grouping comprising at least one of the individuals; and providing, through a user-interface, access to the determined quantifiable measures to members of the grouping.

A third-party server, delegated by organizations to manage application environment, may maintain a plurality of guided workflow plans. At least one of the guided workflow plans may include one or more steps associated with setting up an interaction control policy. The third-party server may receive an interaction report associated with the organization. The interaction report may include metadata of one or more devices that interacted with other devices. The third-party server may identify a particular device to which existing interaction control policies of the organization are inapplicable. The third-party server may search for additional out-of-band information of the particular device using the metadata in the interaction report. The third-party server may select an applicable guided workflow plan for setting up an applicable interaction control policy for the particular device. A guided workflow may be presented via a graphical user interface according to the applicable guided workflow plan.

A computer system including a processor in communication with a memory and a database may be provided. The processor may be programmed to: (i) execute a query on the database including a list of user identifiers associated with a plurality of users, (ii) receive license data associated with the user identifiers including a list of licenses and respective license renewal data associated with each user, (iii) determine, from the license data, that one or more licenses of a group of users is in a renewal period, (iv) notify each user of the group of users of the one or more licenses in the renewal period, (v) pre-populate a license renewal application for the one or more licenses in the renewal period the group of users, (vi) transmit the pre-populated application to be approved by the group of users, and (vii) receive the approved pre-populated application from the group of users.

Systems and methods for tailoring a single prescription for different users for completing tasks on equipment are provided. A prescription representative of a flowchart or decision tree for performing a main set of the tasks is obtained. Characteristics of the equipment and/or a user of the prescription are obtained. Content of the prescription that is displayed to the user during performance of at least some of the tasks on the equipment is tailored based on the characteristics. The content of the prescription is tailored by hiding one or more of the tasks that are not applicable to the equipment from display to the user. This can occur without any data or information being removed from or added to the prescription. Instead, the prescription can be tailored by changing what information is automatically and/or initially displayed to the user.

A task management platform generates an interactive display tasks based on multi-team activity data of different geographic locations across a plurality of distributed guided user interfaces (GUIs). Additionally the task management platform uses a distributed machine-learning based system to determine a suggested task item for a remote team based on multi-team activity data of different geographic locations.

Methods, systems, and devices for worker ranking, worker scheduling, and enterprise labor sharing are described. A method includes: receiving objective evaluation data associated with members of a workforce; receiving subjective evaluation data associated with the members; generating composite evaluation data associated with the first members based on the objective evaluation data, the subjective evaluation data, or both; and generating scheduling information associated with the members based on the composite evaluation data. Another method includes: assigning members of a workforce to candidate temporal periods of a work schedule, where the assigning is based on a priority order corresponding to composite evaluation data associated with the members. Another method includes: identifying a work task associated with a first member of a network; selecting a worker associated with a second member of the network based on the work task or worker data; and outputting a notification at a device associated with the worker.

Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured accurately and programmatically train a responder prediction machine learning model for generating response team predictions based on the systematic collection of one or more responder prediction training corpuses comprising one or more alert related datasets in a responder prediction server system. For example, the responder prediction server system may extract one or more alert attributes for each of the one or more alert related datasets for training one or more responder prediction machine learning models and/or one or more prioritization machine learning models. The responder prediction machine learning model and prioritization machine learning models may process one or more alerts, in real-time, to generate one or more response team prediction objects for rendering in a response team suggestion interface.

A processor may identify a first task of a set of tasks. The processor may identify features of the first task. The processor may generate a reputation assessment for a first user related to the features of the first task. The processor may match the first user to the first task based on the reputation assessment.