Patent ID: 11934979
Assignee: AMAZON TECHNOLOGIES, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 9:
10. A system comprising:
at least one processor; and
non-transitory computer-readable memory storing instructions that, when executed by the at least one processor, are effective to:
receive labor order data specifying a target number of new hires;
determine annotated training data comprising a target number of new hires, first candidate fallout data, first seasonality data, and first incentive data, wherein the annotated training data is annotated with a first number of new hire appointments;
determine, for the annotated training data by inputting the target number of new hires, the first candidate fallout data, the first seasonality data, and the first incentive data into a first machine learning model comprising a neural network, a prediction of a second number of new hire appointments;
determine a gradient using a difference between the first number of new hire appointments and the second number of new hire appointments;
update weights of the neural network using the gradient to generate a first trained machine learning model;
predict, by the first trained machine learning model, a third number of new hire appointments to offer based at least in part on the labor order data and based at least in part on historical candidate fallout data;
allocate the third number of new hire appointments to respective time slots based at least in part on candidate preferences, historical candidate arrival pattern data, and the third number of new hire appointments predicted by the first trained machine learning model, wherein the third number of new hire appointments are allocated to respective time slots by solving an optimization problem; and
send data to a first application effective to display a list of available new hire appointment time slots of the respective time slots.