Computer task management system and method

A method comprising:

• detecting an issue regarding a first computer device in a group of monitored computer devices;
• determining, via one or more processors, a remedial action to address the issue, the issue and the remedial action constituting a first task item;
• grouping the first task item with task items associated with other computer devices in the group of the monitored computer devices to compile a task set, wherein the task set is compiled based on both a common remedial action and a shared location of the other computer devices associated with the task set;
• accessing a scoring rubric listing weight values assigned to multiple different users for factors comprising firmware update proficiency, configuration compliance, data center access, networking proficiency, storage proficiency, and hardware proficiency;
• and
• determining an assignment of the task set to a first user of the multiple different users for managing the task set based on a comparison of a weighted score of the first user with weighted scores of the other users in the multiple different users.

A computer program product comprising non-transitory computer readable storage medium, the non-transitory computer readable storage medium comprising computer executable code configured to be executed by one or more processors to:

• detect issues regarding a group of monitored computer devices;
• determine one or more remedial actions to address the issues, the issues and the remedial actions constituting task items;
• grouping some of the task items to compile task sets, wherein the task sets are compiled based on both a common remedial action and a shared location of the monitored computer devices associated with the task sets;
• run a machine learning algorithm that receives, as input, at least an identified issue regarding a first computer device of the group of monitored computer devices, a determined action for the identified issue, information about the task sets, and information about a group of prospective users including weight values for multiple factors including two or more of firmware update proficiency, configuration compliance, data center access, networking proficiency, storage proficiency, or hardware proficiency;
• and
• output, via the machine learning algorithm, at least an identification of one of the task sets into which a new task item is compiled and a ranked selection of the prospective users for managing the task set that is identified.