US 12,290,754 B2
Automated artificial intelligence (AI) personal assistant
Steven Osman, San Francisco, CA (US); Jeffrey R. Stafford, Redwood City, CA (US); Javier F. Rico, Pacifica, CA (US); Michael G. Taylor, San Mateo, CA (US); and Todd S. Tokubo, Newark, CA (US)
Assigned to Sony Interactive Entertainment Inc., Tokyo (JP)
Filed by Sony Interactive Entertainment Inc., Tokyo (JP)
Filed on Dec. 22, 2020, as Appl. No. 17/131,048.
Application 17/131,048 is a continuation of application No. 16/268,384, filed on Feb. 5, 2019, granted, now 10,870,057.
Application 16/268,384 is a continuation of application No. 15/467,557, filed on Mar. 23, 2017, granted, now 10,195,531, issued on Feb. 5, 2019.
Claims priority of provisional application 62/357,248, filed on Jun. 30, 2016.
Prior Publication US 2021/0106919 A1, Apr. 15, 2021
Int. Cl. A63F 13/67 (2014.01); A63F 13/335 (2014.01); A63F 13/5375 (2014.01); A63F 13/795 (2014.01); A63F 13/798 (2014.01); A63F 13/85 (2014.01); G06N 5/04 (2023.01)
CPC A63F 13/67 (2014.09) [A63F 13/335 (2014.09); A63F 13/5375 (2014.09); A63F 13/795 (2014.09); A63F 13/798 (2014.09); A63F 13/85 (2014.09); G06N 5/04 (2013.01); A63F 2300/407 (2013.01); A63F 2300/535 (2013.01); A63F 2300/6027 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
establishing at a game server a multi-player gaming session of a gaming application, wherein a first client device of a first player and a second client device of a second player are communicatively coupled to the game server via a network to enable participation in the multi-player gaming session;
monitoring at the game server a first game play of the first player and a second game play of the second player in the multi-player gaming session of the gaming application to determine a current game context using a first artificial intelligence (AI) model configured to learn a plurality of game contexts of the of the gaming application;
executing the first AI model to identify a plurality of tasks potentially presented in the multi-player gaming session based on the current game context, wherein the plurality of tasks correspond with a plurality of task types;
dynamically determining at the game server during the multi-player gaming session a plurality of player proficiency scores for the first player and the second player in association with the plurality of tasks types based on first data collected by the game server from a first plurality of game plays of a first plurality of gaming applications by the first player and second data collected by the game server from a second plurality of game plays by a second plurality of gaming applications of the second player, wherein the first plurality of game plays includes the first game play of the first player and the second plurality of game plays includes the second game play of the second player;
executing a second AI model configured to predict rates of success when accomplishing tasks encountered in the gaming application to determine at the game server during the multi-player gaming session a plurality of predictive rates of success for different combinations of task pairs from the plurality of tasks that could be performed by the first player and the second player based on the plurality of player proficiency scores;
assigning at the game server during the multi-player gaming session a first task of a first task type to the first player and a second task of a second task type to the second player that has the maximum predictive rate of success from the plurality of predictive rates of success, wherein the plurality of tasks include the first task and the second task;
sending a first notification over the network to the first client device that the first task to be performed in the multi-player gaming session is assigned to the first player; and
sending a second notification over the network to the second client device that the second task to be performed in the multi-player gaming session is assigned to the second player,
wherein the game server is configured to control the multi-player gaming session.