US 11,657,173 B2
Systems and methods for dynamic queue control using machine learning techniques
Mark Roden, Los Angeles, CA (US); Dan Cernoch, Los Angeles, CA (US); and Victoria Chung, Irvine, CA (US)
Assigned to Live Nation Entertainment, Inc., Beverly Hills, CA (US)
Filed by Live Nation Entertainment, Inc., Beverly Hills, CA (US)
Filed on Apr. 22, 2021, as Appl. No. 17/237,985.
Application 17/237,985 is a continuation of application No. 16/740,910, filed on Jan. 13, 2020, granted, now 11,010,488.
Application 16/740,910 is a continuation of application No. 16/195,568, filed on Nov. 19, 2018, granted, now 10,534,928, issued on Jan. 14, 2020.
Prior Publication US 2021/0365578 A1, Nov. 25, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 21/62 (2013.01); G06N 20/00 (2019.01)
CPC G06F 21/6218 (2013.01) [G06N 20/00 (2019.01)] 17 Claims
OG exemplary drawing
 
1. A method for managing assignment of access rights to a resource, comprising:
receiving, at a primary load management system, a communication from a user device, wherein the communication corresponds to a request for the access rights to the resource;
determining a user identifier associated with the user device based on the communication;
accessing one or more data points associated with the user identifier, wherein the one or more data points correspond to an attribute of the user identifier;
generating a resource-affinity parameter comprises:
accessing a first data set that includes one or more first data points associated with a user,
accessing a second data set that includes one or more second data points associated with the user,
inputting the first data set into a first trained machine-learning model,
inputting the second data set into a second trained machine-learning model, and
generating the resource-affinity parameter based on a combination of a first output of the first trained machine-learning model and a second output of the second trained machine-learning model, wherein:
the resource-affinity parameter represents a likelihood that the user associated with the user device will meet an objective, and
the resource-affinity parameter is generated by inputting the one or more data points into a machine learning model;
determining a current system load, wherein the current system load represents a load of requests received at the primary load management system during a current time period;
determining a first throttle factor based on the resource-affinity parameter and the current system load;
controlling a first workflow associated with the resource, wherein:
controlling the first workflow comprises enabling the user device to query the access rights as a part of the first workflow controlled by the first throttle factor, and
the query includes a constraint for querying the access rights;
determining a second throttle factor based on the resource-affinity parameter; and
controlling a second workflow based on the second throttle factor, wherein the second workflow comprises assigning the access rights to the user device.