System and method for enhanced virtual queuing

A system for enhanced virtual queuing, comprising:

• a computing device comprising a memory, a processor, and a non-volatile data storage device;
• an entity database stored on the non-volatile data storage device, the entity database comprising historical data for virtual queues;
• a machine learning algorithm operating on the computing device, the machine learning algorithm configured to predict wait times and low throughput times for a plurality of times within a time period for virtual queues;
• a queue manager comprising a first plurality of programming instructions stored in the memory which, when operating on the processor, causes the processor to:
• establish plurality of virtual queues;
• request and receive predicted wait times and predicted low throughput times from a prediction module for each of the plurality of virtual queues;
• continuously or periodically receive requests to join a queue, each request comprising a requestor and contact information for the requestor;
• assign each requestor to one of the queues of the plurality of virtual queues using the based in part on the predicted wait times;
• send an initial update notification indicating the queue assignment to the requestor using the contact information;
• during each period of predicted low throughput times, request and receive queue load balancing from a queue load balancer module;
• send periodic update notifications to each requestor based on reassignment of requestors among the plurality of virtual queues by the queue load balancer;
• and
• the queue load balancer module comprising a second plurality of programming instructions stored in the memory which, when operating on the processor, causes the processor to:
• continuously measure actual wait times across the plurality of virtual queues;
• receive the request for queue load balancing from the queue manager;
• calculate a current queue throughput for each of the plurality of virtual queues;
• for each of the plurality of virtual queues, compare the current queue throughput to the predicted low throughput time to confirm that the current queue throughput is a low throughput time;
• process the actual wait times for each of the plurality of virtual queues through the machine learning algorithm to predict revised wait times and low throughput times for a plurality of times within a time period for each of the plurality of virtual queues;
• and
• reassign requestors among the plurality of virtual queues to minimize wait times across the plurality of virtual queues;
• and
• the prediction module comprising a third plurality of programming instructions stored in the memory which, when operating on the processor, causes the processor to:
• retrieve historical data for each of the plurality of virtual queues from the entity database;
• process the historical data for each of the plurality of virtual queues through the machine learning algorithm to predict wait times and low throughput times for a plurality of times within a time period for each of the plurality of virtual queues;
• and
• send the predicted wait times to the queue manager.