An estimate is provided for how long a guest will have to wait to enter a ride, restaurant, restroom, or other point of interest at a venue. Wait times are computed using one or more algorithms (e.g., Bayesian) that can combine direct measurements around the point of interest, historical measurements around the point of interest, and measurements taken from other systems not directly connected to the point of interest. Supporting systems will vary by venue and may include entry ticketing systems, network traffic, point of sale registers, event schedules, and proximity detectors in other parts of the venue. By comparing probabilities generated from this disparate data, current and future wait times can be estimated and provided to front-end devices corresponding to guests or staff.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for estimating queue wait time, comprising: receiving mobile device location data associated with a plurality of guest mobile devices corresponding to a plurality of venue guests within a venue; receiving event schedule data corresponding to schedules associated with a plurality of points of interest located within the venue; generating an estimated wait time based at least on the received mobile device location data and the received event schedule data, the estimated wait time corresponding to a first point of interest of the plurality of points of interest; and transmitting a wait time dataset identifying the estimated wait time to a front-end device, thereby displaying the estimated wait time via the front-end device. 2. The method of claim 1, wherein the first point of interest is one of an amusement park ride, a restaurant, a cafeteria, a restroom, a concession stand, a store, a library, a theme park ride, a theatre stage, a movie theater, a circus stage, an animal display showing, or some combination thereof. 3. The method of claim 1, wherein the estimated wait time is a prediction of a time that a guest would currently wait before entering the first point of interest. 4. The method of claim 1, wherein the estimated wait time is a prediction of a time that a guest would wait at a predetermined future time before entering the first point of interest. 5. The method of claim 1, further comprising receiving network traffic data, and wherein generating the estimated wait time is further based on the received network traffic data. 6. The method of claim 1, further comprising receiving entitlement redemption data, and wherein generating the estimated wait time is further based on the received entitlement redemption data. 7. The method of claim 1, further comprising receiving weather data, and wherein generating the estimated wait time is further based on the received weather data. 8. The method of claim 1, further comprising generating a plurality of additional estimated wait times at predetermined intervals from each other. 9. The method of claim 8, further comprising generating a graph charting the estimated wait time and the plurality of additional estimated wait times. 10. The method of claim 1, wherein the front-end device is a guest mobile device corresponding to a venue guest. 11. The method of claim 1, wherein the front-end device is a staff device corresponding to a venue staff member. 12. The method of claim 1, further comprising generating a map user interface that identifies the estimated wait time at a location on a map corresponding to a location of the first point of interest. 13. The method of claim 12, wherein the map is distorted so that a first region is drawn at a first scale and a second region is drawn at a second scale. 14. The method of claim 13, wherein the map includes a third region between the first region and the second region, the third region having a gradient scale between the first scale and the second scale. 15. The method of claim 1, further comprising generating a list user interface that identifies the estimated wait time next to a name corresponding to the first point of interest, the name being one of a plurality of names within a list of the list user interface. 16. The method of claim 1, wherein generating an estimated wait time is performed using at least one of a Bayesian algorithm, a nearest-neighbor algorithm, an alternating squares algorithm, a neural network, or some combination thereof. 17. A system for estimating queue wait time, the system comprising: a communication transceiver, the communication transceiver receiving mobile device location data and event schedule data, the mobile device location data associated with a plurality of guest mobile devices corresponding to a plurality of venue guests within a venue, the event schedule data corresponding to schedules associated with a plurality of points of interest located within the venue; a memory; and a processor coupled to the memory and to the communication transceiver, wherein execution of instructions stored in the memory by the processor: generates an estimated wait time based at least on the received mobile device location data and the received event schedule data, the estimated wait time corresponding to a first point of interest of the plurality of points of interest, and triggers transmission of a wait time dataset to a front-end device via the communication transceiver, the wait time dataset identifying the estimated wait time, thereby displaying the estimated wait time via the front-end device. 18. The system of claim 17, wherein the communication transceiver further receives network traffic data, and wherein generating the estimated wait time is further based on the received network traffic data. 19. The system of claim 17, wherein the communication transceiver further receives entitlement redemption data, and wherein generating the estimated wait time is further based on the received entitlement redemption data. 20. A non-transitory computer-readable storage medium, having embodied thereon a program executable by a processor to perform a method for estimating queue wait time, the method comprising: receiving mobile device location data associated with a plurality of guest mobile devices corresponding to a plurality of venue guests within a venue; receiving event schedule data corresponding to schedules associated with a plurality of points of interest located within the venue; generating an estimated wait time based at least on the received mobile device location data and the received event schedule data, the estimated wait time corresponding to a first point of interest of the plurality of points of interest; and transmitting a wait time dataset identifying the estimated wait time to a front-end device, thereby displaying the estimated wait time via the front-end device.