MACHINE-LEARNED SEAT PREDICTION AND ASSIGNMENT

1 . A computer-implemented method comprising:

accessing, for a set of users, historical attendance data for each user for events at a venue during a season of events at the venue, each user associated with a subscription to the season of events such that a seat at the venue is not assigned for the user until the user arrives at the venue, the historical attendance data comprising at least a historical seat quality of each seat assigned to the user for events attended by the user;

generating a set of training data based on the accessed historical attendance data;

training a machine-learned model using the generated set of training data, the machine-learned model configured to identify a seat quality based on characteristics and historical attendance data of a user;

receiving a request from a target user for a seat at the venue for the event when the target user arrives at the venue; and

assigning a target seat at the venue to the target user by applying the machine-learned model to characteristics and historical attendance data associated with the target user to identify a target seat quality and selecting the target seat based on the identified target seat quality.

2 . The computer-implemented method of claim 1 , wherein assigning the target seat at the venue further comprises:

accessing real-time seat status information for the venue; and

selecting the target seat for assignment based on the identified target seat quality and the real-time seat status information.

3 . The computer-implemented method of claim 1 , further comprising modifying a user interface of a user device to include information identifying the target seat.

4 . The computer-implemented method of claim 1 , further comprising updating the assigned target seat based on a request of the target user.

5 . The computer-implemented method of claim 1 , further comprising updating the assigned target seat based on a size of a group of individuals attending the event with the target user.

6 . The computer-implemented method of claim 1 , further comprising updating the assigned target seat mid-game based on real-time seat status information for the venue.

7 . The computer-implemented method of claim 1 , wherein the machine learning model comprises a regression model, a random forest classifier, a support vector machine, a neural network, or a model trained by an unsupervised approach.

8 . The computer-implemented method of claim 1 , wherein the characteristics and historical attendance data of the user comprises:

data indicating demographics of the user;

average seat quality assigned to the user at prior events;

data associated with an account for the user;

data associated with groups and/or individuals who attended prior events with the user;

number of games attended by the user;

data indicating a user's past seat locations;

data indicating a user's seating preferences;

arrival times of the user;

number of no-shows by the user;

data indicating advance notice of event attendance by the user;

data associated with historical purchases of the user at prior events;

user data indicating user feedback for prior events;

data associated with user social media engagement; and

data indicating teams, match, rivalry, and game preferences of the user.

9 . The computer-implemented method of claim 1 , wherein the event is one of a basketball game, a baseball game, a football game, a volleyball game, a soccer game, a tennis match, a hockey game, and a rugby game.

10 . The computer-implemented method of claim 1 , wherein the subscription to the season of events comprises a fixed number of events or a ticket package.

11 . A non-transitory computer-readable storage medium comprising instructions executable by a processor, the instructions comprising:

instructions for accessing, for a set of users, historical attendance data for each user for events at a venue during a season of events at the venue, each user associated with a subscription to the season of events such that a seat at the venue is not assigned for the user until the user arrives at the venue, the historical attendance data comprising at least a historical seat quality of each seat assigned to the user for events attended by the user;

instructions for generating a set of training data based on the accessed historical attendance data;

instructions for training a machine-learned model using the generated set of training data, the machine-learned model configured to identify a seat quality based on characteristics and historical attendance data of a user;

instructions for receiving a request from a target user for a seat at the venue for the event when the target user arrives at the venue; and

instructions for assigning a target seat at the venue to the target user by applying the machine-learned model to characteristics and historical attendance data associated with the target user to identify a target seat quality and selecting the target seat based on the identified target seat quality.

12 . The non-transitory computer-readable storage medium of claim 11 , wherein the instructions for assigning the target seat at the venue further comprise:

instructions for accessing real-time seat status information for the venue; and

instructions for selecting the target seat for assignment based on the identified target seat quality and the real-time seat status information.

13 . The non-transitory computer-readable storage medium of claim 11 , further comprising instructions for modifying a user interface of a user device to include information identifying the target seat.

14 . The non-transitory computer-readable storage medium of claim 11 , further comprising instructions for updating the assigned target seat based on a request of the target user.

15 . The non-transitory computer-readable storage medium of claim 11 , further comprising instructions for updating the assigned target seat based on a size of a group of individuals attending the event with the target user.

16 . The non-transitory computer-readable storage medium of claim 11 , further comprising instructions for updating the assigned target seat mid-game based on real-time seat status information for the venue.

17 . The non-transitory computer-readable storage medium of claim 11 , wherein the machine learning model comprises a regression model, a random forest classifier, a support vector machine, a neural network, or a model trained by an unsupervised approach.

18 . The non-transitory computer-readable storage medium of claim 11 , wherein the characteristics and historical attendance data of the user comprises:

data indicating demographics of the user;

average seat quality assigned to the user at prior events;

data associated with an account for the user;

data associated with groups and/or individuals who attended prior events with the user;

number of games attended by the user;

data indicating a user's past seat locations;

data indicating a user's seating preferences;

arrival times of the user;

number of no-shows by the user;

data indicating advance notice of event attendance by the user;

data associated with historical purchases of the user at prior events;

user data indicating user feedback for prior events;

data associated with user social media engagement; and

data indicating teams, match, rivalry, and game preferences of the user.

19 . The non-transitory computer-readable storage medium of claim 11 , wherein the event is one of a basketball game, a baseball game, a football game, a volleyball game, a soccer game, a tennis match, a hockey game, and a rugby game.

20 . A computer system comprising:

a computer processor; and

a non-transitory computer-readable storage medium storage instructions that when executed by the computer processor perform actions comprising:

accessing, for a set of users, historical attendance data for each user for events at a venue during a season of events at the venue, each user associated with a subscription to the season of events such that a seat at the venue is not assigned for the user until the user arrives at the venue, the historical attendance data comprising at least a historical seat quality of each seat assigned to the user for events attended by the user;

generating a set of training data based on the accessed historical attendance data;

training a machine-learned model using the generated set of training data, the machine-learned model configured to identify a seat quality based on characteristics and historical attendance data of a user;

receiving a request from a target user for a seat at the venue for the event when the target user arrives at the venue; and

assigning a target seat at the venue to the target user by applying the machine-learned model to characteristics and historical attendance data associated with the target user to identify a target seat quality and selecting the target seat based on the identified target seat quality.