Video game inventory coach

1. A device, comprising:

at least one computer storage that is not a transitory signal and that comprises instructions executable by at least one processor to:

analyze, using an inventory coach model executing at a server, a video game player's playing style in relation to a particular video game that is being played by the video game player; and

communicate with the server to, based on the analysis performed using the inventory coach model executing at the server, provide, via a graphical user interface (GUI) presented on a display, at least two different types of suggestions of video game assets to discard, obtain, and/or use, the GUI providing a first suggestion indicating a first video game asset to discard, obtain, and/or use as determined based on past gameplay data for elite players that share the same playstyle as the video game player's playing style, the GUI providing a second suggestion indicating a second video game asset to discard, obtain, and/or use as determined based on past gameplay data for elite players from a broader class of players than those sharing the same playstyle as the video game player's playing style.

2. The device of claim 1 , wherein the instructions are executable to:

use the inventory coach model and at least one reference player's data to make the first and second suggestions, the reference player being different from the video game player.

3. The device of claim 2 , wherein the at least one reference player's data is assigned via the inventory coach model into a cluster defined by at least one common gameplay characteristic, the at least one common gameplay characteristic also identified as exhibited by the video game player while playing the particular video game.

4. The device of claim 1 , wherein the first and second video game assets each comprise one or more of: a virtual weapon, virtual ammunition, virtual armor, a virtual craft, a virtual potion, a virtual plant.

5. The device of claim 1 , wherein the suggestions are presented external to the video game environment itself.

6. The device of claim 1 , wherein the first and second video game assets are different from each other.

7. The device of claim 1 , wherein the broader class comprises elite players generally.

8. The device of claim 1 , wherein the GUI is a first GUI, and wherein the instructions are executable to:

present a second GUI on the display, the second GUI being different from the first GUI, the second GUI comprising an option that is selectable to set the at least one processor to in the future provide suggestions, during gameplay, to the video game player based on the video game player's playing style.

9. The device of claim 1 , wherein the GUI comprising the first and second suggestions is presented responsive to a pause command.

10. A method, comprising:

presenting a settings graphical user interface (GUI) on a display, the settings GUI establishing a first GUI, the settings GUI comprising an option that is selectable to set a device for in the future:

analyzing, at the device, a video game player's gameplay in relation to a particular video game that is being played by the video game player; and

based on the analysis, suggesting at least one video game asset to discard, obtain, and/or use;

wherein the analyzing and suggesting are performed by an inventory coach model executing at a server, the server establishing at least part of the device, the server communicating with a video game console to present the suggestion via a second GUI that is presented on the display.

11. The method of claim 10 , wherein analysis of the video game player's gameplay comprises analysis of the video game player's combat style for engaging in video game combat.

12. The method of claim 10 , wherein the video game console facilitates gameplay of the particular video game.

13. The method of claim 10 , wherein the second GUI is presented external to the video game's environment.

14. The method of claim 10 , wherein the video game player is a first video game player, and wherein the inventory coach model is trained through machine learning to tailor suggestions of video game assets based on the learned habits of a threshold number of other video game players besides the first video game player.

15. The method of claim 10 , wherein the method comprises:

based on the analysis, providing, via the second GUI presented on the display, at least two different types of suggestions of video game assets to discard, obtain, and/or use, the second GUI providing a first suggestion indicating a first video game asset to discard, obtain, and/or use as determined based on past gameplay data for elite players that share the same playstyle as the video game player's playing style, the second GUI providing a second suggestion indicating a second video game asset to discard, obtain, and/or use as determined based on past gameplay data for elite players from a broader class of players than those sharing the same playstyle as the video game player's playing style.

16. The method of claim 10 , comprising:

presenting the second GUI responsive to a pause command.

17. An assembly, comprising:

at least one computer comprising at least one processor programmed with instructions to:

identify a first video game player's collection of assets for a particular video game that is being played by the first video game player;

associate, using a machine learning model executing at a server, the first video game player with a cluster of other video game players; and

communicate with the server to, based on the association and responsive to receipt of a pause command to pause the particular video game, provide, via a graphical user interface (GUI) presented on a display, one or more suggestions of assets to discard and/or use, the one or more suggestions identified from the communication with the server to provide the one or more suggestions via the GUI, the assets being from the first video game player's collection of assets.

18. The assembly of claim 17 , wherein the processor is programmed to:

associate the first video game player with the cluster based on the first video game player and members of the cluster having similar gameplay styles for playing the particular video game as determined by the machine learning model.

19. The assembly of claim 17 , wherein the GUI is a first GUI, and wherein the at least one processor is programmed with instructions to:

present a second GUI on the display, the second GUI being different from the first GUI, the second GUI comprising an option that is selectable to set the at least one processor to in the future provide suggestions, during gameplay, of assets to discard and/or use.

20. The assembly of claim 17 , wherein the GUI comprises:

a first suggestion indicating a first asset to discard and/or use as determined based on past gameplay data for elite players that share the same playstyle as the first video game player's playing style; and

a second suggestion indicating a second asset to discard and/or use as determined based on past gameplay data for elite players from a broader class of players than those sharing the same playstyle as the first video game player's playing style, the second suggestion being different from the first suggestion.