Patent ID: 11900292
Assignee: FLOQQUE, LLC
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 13:
14. A computer-implemented method performed by at least one processor to automatically prioritize predicted events in a dynamic predicted queueing profile data structure for a service provider for a finite future time period, the method comprising:
generate, in response to a request signal originating from a first device associated with a first service seeking entity and representing a request for service from a first service seeking entity seeking service from the first service provider at a first future time period, a first dynamic queueing event profile associating the first service seeking entity with a first predicted queueing profile for the first service provider at the first future time period, wherein the first predicted queueing profile corresponds to a single time block for providing a service sought by the service seeking entity, and wherein the first predicted queueing profile comprises associations with a plurality of dynamic queueing event profiles associating a corresponding service seeking entity and the first service provider with the first predicted queueing profile for the first future time period, wherein the plurality of dynamic queueing event profiles comprises the first dynamic queueing event profile;
generate, by an execution confidence engine, for each of the plurality of dynamic queueing event profiles associated with the first predicted queueing profile, a confidence level of execution that the service seeking entity will receive service from the service provider in the first future time period, for the first service provider at the first future time period based at least on an execution confidence model, current geolocation coordinates of the first service seeking entity in a predetermined time window before the future time period of the first predicted queueing profile, and a historical behavior profile of the corresponding service seeking entity; and,
apply a dynamic queueing engine to each of the plurality of dynamic queueing event profiles to automatically determine, based on the corresponding confidence level of execution for each of the plurality of competing dynamic queueing event profiles, a priority of the first dynamic queueing event profile in the first predicted queueing profile;
generate a display signal configured to induce the first device to generate a graphical display based on the confidence level of execution corresponding to at least one of the plurality of predicted queueing profiles corresponding to at least one future finite time period; and,
in response to a training signal, perform training operations to retrain the execution confidence model by a machine learning algorithm, wherein the training operations are applied repeatedly until an accuracy score of the execution confidence model meets at least one predetermined accuracy criterion, wherein the accuracy score is generated as a function of an output generated by the retrained executed confidence model based on a set of requests for service and a target outcome of the set of requests.