| CPC G06Q 10/063114 (2013.01) [G06F 3/04817 (2013.01); G06N 20/00 (2019.01); G06Q 10/063118 (2013.01); G06Q 10/06312 (2013.01); G06Q 10/06313 (2013.01); G06Q 10/06316 (2013.01); G06Q 10/0633 (2013.01)] | 20 Claims |
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1. A device, comprising:
a processing system including a processor; and
a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
receiving, from an operator for a process, wherein the process is performed remotely under control of an artificial intelligence system of a control computer, first information defining one or more tasks, the one or more tasks to perform the process;
receiving, from technical personnel, respective rulesets associated with respective tasks of the one or more tasks, each respective ruleset defining procedures to complete a respective task;
initiating a machine learning process, the machine learning process operative to monitor progress of tasks of the one or more tasks based on completion of selected rulesets associated with a selected task, wherein the machine learning process is operative to predict a current status of the process when there is no actual knowledge of the process due to operation of the artificial intelligence system, the machine learning process being trained on inputs and output of the artificial intelligence system to make future predictions about operation of the artificial intelligence system;
communicating second information about a task between the task and an associated ruleset, wherein the communicating comprises exchanging parameters and parameter values for the task or procedures of the associated ruleset;
preventing communication of information between a selected task and any ruleset other than a selected ruleset associated with the selected task and preventing communication of information between any two or more rulesets of the respective rulesets to enable one or more portions of the process to be isolated, tested and corrected if necessary;
displaying, to the operator, third information about the one or more tasks on a graphical user interface during performance of the process, wherein the third information is based on task information received from the machine learning process;
displaying the graphical user interface to enable the operator to monitor and interact visually with the one or more tasks via the graphical user interface, wherein the graphical user interface includes an operator selectable popup menu, wherein the operator selectable popup menu is presented to the operator to select the one or more tasks;
prompting, via the graphical user interface, the operator to drag-and-drop to instantiate tasks for the process, wherein the graphical user interface allows the operator to specify dependencies among various tasks of the one or more tasks;
displaying fourth information on the graphical user interface about the respective rulesets during the performance of the process, wherein the fourth information is based on rule information received from the machine learning process;
receiving, from the machine learning process, information about an error condition of a failed ruleset;
displaying on the graphical user interface for the operator an indication of the error condition of the failed ruleset with a task associated with the failed ruleset of the process on the graphical user interface; and
receiving, from the operator at the graphical user interface, an instruction to initiate corrective action to correct the error condition, wherein the initiating the corrective action comprises identifying an abnormal condition in the process based on the information associated with the machine learning process and sending a reset signal to reset operation of the control computer, the reset signal operative to reset a task associated with the failed ruleset and thereafter to resume performance of the task associated with the failed ruleset.
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