System and method for meeting volume optimizer

A computer implemented method for processing a scheduling request, comprising:

• receiving first training data as an input to a machine learning system, wherein the first training data includes multiple scheduling instances, and wherein the scheduling instances specify a plurality of scheduling parameters upon which to make a prediction of a future schedule;
• receiving second training data as an input to the machine learning system, wherein the second training data includes multiple productivity measurement instances, and wherein the productivity measurement instances specify a plurality of productivity parameters upon which to make a prediction of a future productivity, wherein the plurality of productivity parameters are based on work product measurements;
• training, by the machine learning system, a productivity predictive model with the first training data and the second training data, wherein the productivity predictive model includes one or more algorithms to predict a current productivity change based on either accepting a new scheduling request or alternatively, rescheduling the new scheduling request;
• receiving a current schedule and current productivity measurements of a user;
• receiving a scheduling request for the user, wherein the scheduling request includes one or more proposed scheduling parameters;
• predicting a future productivity change based on using the productivity predictive model with the current schedule, the current productivity measurements of the user, the one or more proposed scheduling parameters and accepting the scheduling request;
• and
• generating a graphic revealing the future productivity change of the user based on accepting the scheduling request.

A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:

• receiving first training data as an input to a machine learning system, wherein the first training data includes multiple scheduling instances, and wherein the scheduling instances specify a plurality of scheduling parameters upon which to make a prediction of a future schedule;
• receiving second training data as an input to the machine learning system, wherein the second training data includes multiple productivity measurement instances, and wherein the productivity measurement instances specify a plurality of productivity parameters upon which to make a prediction of a future productivity, wherein the plurality of productivity parameters are based on work product measurements;
• training, by the machine learning system, a productivity predictive model with the first training data and the second training data, wherein the productivity predictive model includes one or more algorithms to predict a current productivity change based on either accepting a new scheduling request or alternatively, rescheduling the new scheduling request;
• receiving a current schedule and current productivity measurements of a user;
• receiving a scheduling request for the user, wherein the scheduling request includes one or more proposed scheduling parameters;
• predicting a future productivity change based on using the productivity predictive model with the current schedule, the current productivity measurements of the user, the one or more proposed scheduling parameters and accepting the scheduling request;
• and
• generating a graphic revealing the future productivity change of the user based on accepting the scheduling request.