SYSTEMS AND METHODS FOR A MACHINE LEARNING BASED PERSONALIZED VIRTUAL STORE WITHIN A VIDEO GAME USING A GAME ENGINE

1 . A system comprising:

one or more computer processors;

one or more computer memories;

a dynamic personalized AI store module incorporated into the one or more computer memories, the AI store module configuring the one or more computer processors to perform operations for optimizing a Lifetime Value (LTV) of a player of a plurality of computer-implemented games, the operations comprising:

collecting data from a game of the plurality of games, the data including game event data associated with the player, a playing environment within the game, and store action data;

analyzing the data with a first machine-learning (ML) system to create a time-dependent state representation of the game, the player, and the playing environment;

providing the state representation as input to a second ML system to create and optimize an ML policy over time, the ML policy including a functional relationship proposing a selection of one or more store actions within a store to maximize the LTV; and

choosing and implementing within the store environment one or more of the store actions from the proposed selection in accordance with the ML policy.

2 . The system of claim 1 , wherein store actions include one or more of changing a price, changing a visual layout, or changing content of one or more virtual items within a store template, the template providing rules for the price, the visual layout and the content.

3 . The system of claim 2 , wherein the second ML system is used to modify the store template.

4 . The system of claim 1 , wherein the choosing of the one or more of the store actions includes operations for performing an auction, the operations for performing the auction comprising:

providing an environment for the auction wherein a plurality of advertising entities and a plurality of IAP entities place one or more bids for a placeholder impression, the placeholder impression including instructions on defining a price, a visual layout, and content within a store action;

choosing one of the one or more bids so as to optimize the LTV in accordance with the policy; and

extracting the instructions for the store action for the chosen bid.

5 . The system of claim 1 , wherein the first ML system or the second ML system is a recurrent neural network with long short-term memory and gated recurrent units.

6 . The system of claim 1 , wherein the playing environment includes one of a 3D virtual environment, a 2D virtual environment, or an augmented reality environment.

7 . The system of claim 1 , wherein the state representation includes a history of time-ordered game events and context data for the player.

8 . The system of claim 1 , wherein the game event data includes at least one of device and operating system (OS) information, player gameplay behavior data, application performance data, or game metadata.

9 . The system of claim 1 , wherein the store action data comprises data that describes the store action, the store action including at least one of changing a price, changing a visual layout, or changing content of one or more virtual items within a store

10 . A method comprising:

performing operations for optimizing a Lifetime Value (LTV) of a player of a plurality of computer-implemented games, the operations comprising:

collecting data from a game of the plurality of games, the data including game event data associated with the player, a playing environment within the game, and store action data;

analyzing the data with a first machine-learning (ML) system to create a time-dependent state representation of the game, the player, and the playing environment;

providing the state representation as input to a second ML system to create and optimize an ML policy over time, the ML policy including a functional relationship proposing a selection of one or more store actions within a store to maximize the LTV; and

choosing and implementing within the store environment one or more of the store actions from the proposed selection in accordance with the ML policy.

11 . The method of claim 10 , wherein store actions include one or more of changing a price, changing a visual layout, or changing content of one or more virtual items within a store template, the template providing rules for the price, the visual layout and the content.

12 . The method of claim 11 , wherein the second ML system is used to modify the store template.

13 . The method of claim 10 , wherein the choosing of the one or more of the store actions includes operations for performing an auction, the operations for performing the auction comprising:

providing an environment for the auction wherein a plurality of advertising entities and a plurality of IAP entities place one or more bids for a placeholder impression, the placeholder impression including instructions on defining a price, a visual layout, and content within a store action;

choosing one of the one or more bids so as to optimize the LTV in accordance with the policy; and

extracting the instructions for the store action for the chosen bid.

14 . The method of claim 10 , wherein the first ML system or the second ML system is a recurrent neural network with long short-term memory and gated recurrent units.

15 . The method of claim 10 , wherein the playing environment includes one of a 3D virtual environment, a 2D virtual environment, or an augmented reality environment.

16 . The method of claim 10 , wherein the state representation includes a history of time-ordered game events and context data for the player.

17 . A non-transitory machine-readable medium having a set of instructions stored thereon, the set of instructions configuring one or more computer processors to perform operations for optimizing a Lifetime Value (LTV) of a player of a plurality of computer-implemented games, the operations comprising:

collecting data from a game of the plurality of games, the data including game event data associated with the player, a playing environment within the game, and store action data;

analyzing the data with a first machine-learning (ML) system to create a time-dependent state representation of the game, the player, and the playing environment;

providing the state representation as input to a second ML system to create and optimize an ML policy over time, the ML policy including a functional relationship proposing a selection of one or more store actions within a store to maximize the LTV; and

choosing and implementing within the store environment one or more of the store actions from the proposed selection in accordance with the ML policy.

18 . The non-transitory machine-readable medium of claim 17 , wherein store actions include one or more of changing a price, changing a visual layout, or changing content of one or more virtual items within a store template, the template providing rules for the price, the visual layout and the content.

19 . The non-transitory machine-readable medium of claim 18 , wherein the second ML system is used to modify the store template.

20 . The non-transitory machine-readable medium of claim 17 , wherein the choosing of the one or more of the store actions includes operations for performing an auction, the operations for performing the auction comprising:

providing an environment for the auction wherein a plurality of advertising entities and a plurality of IAP entities place one or more bids for a placeholder impression, the placeholder impression including instructions on defining a price, a visual layout, and content within a store action;

choosing one of the one or more bids so as to optimize the LTV in accordance with the policy; and

extracting the instructions for the store action for the chosen bid.