Live Nation Entertainment, Incorporated

Mark Roden

Dan Cernoch

Victoria Chung

Chris Smith

Spencer Brown

Scott Kline

Ish Rishabh

Krisha Zagura

Lee Ha

James Healy

Systems and methods may use models to generate predictions of specific access rights for users. Further, systems and methods may generate the predictions in an environment in which the availability of the specific access rights change frequently. The access rights, predicted using embodiments described herein, may be both available and associated with user affinities. An interface associated with the primary load management system may be configured to display the predicted access rights for a user operating a user device.

Dynamic model-based access right predictions

Certain embodiments of the present disclosure relate to systems and methods that control access to system resources, such as interfaces, access rights to events, query systems, and other suitable system resources. Further, certain embodiments of the present disclosure relate to a collision detection technique that is implemented to control which and/or a number of queue positions within a queue that are processed. In some implementations, a collision may be detected when two or more users request the same access right within a defined time period.

Systems and methods for dynamic queue control using machine learning techniques

The present disclosure generally relates to a primary load management system configured to execute machine learning and artificial intelligence techniques to generate predictions of access-right requests that are or are likely to be invalid before the access-right requests are processed for assignment to users or user devices. The present disclosure relates to systems and methods that collect a data set representing characteristics of user devices as the user devices interact with various systems of the primary load management system and train a machine-learning model to predict invalid access-right requests using the collected data set. The collected data set may include a log line that represents each user device, and each log line may be labeled based on an invalidity evaluation. New access-right requests can be processed using the trained machine-learning model to determine whether or not to assign access rights in response to the access-right request.

Enhanced validity modeling using machine-learning techniques

Growing community of inventors

Los Angeles, CA, United States of America

Mark Roden