In-game asset tracking using NFTs that track impressions across multiple platforms
Data processing/GUIs for NFT block chain data to “tell the story” of ownership or highlight “cool” aspects of a computer game-related NFT in simplified way. Machine learning (ML) may be used to boil down the complexity of data to what people need or want to understand. The displayed timeline of ownership as presented in a GUI can be interactive. Types of metadata to encapsulate in the NFT are discussed.
1. A system comprising:
at least one computer medium that is not a transitory signal and that comprises instructions executable by at least one processor to:
input to at least one machine learning (ML) model at least one non-fungible token (NFT) representing at least one digital asset related to at least one computer simulation;
receive from the at least one ML model information from the at least one NFT indicating inferred interesting past aspects in a lifetime of the at least one NFT but not indicating all past aspects in the lifetime of the at least one NFT; and
present on at least one computer display the information indicating the inferred past interesting aspects;
wherein the information is presented as part of at least one user interface (UI) that presents a visual timeline with time increasing along an X axis, the visual timeline comprising upward spikes according to a Y axis, each spike representing an important event in the at least one NFT as inferred by the at least one ML model.
2. The system of claim 1 , comprising the at least one processor.
3. The system of claim 1 , wherein the information from the at least one NFT is derived from some but not all metadata associated with the at least one NFT as stored in a block chain of the at least one NFT.
4. The system of claim 1 , wherein the information presented as part of the at least one UI indicates one or more past owners of the at least one NFT.
5. The system of claim 4 , wherein the at least one UI indicates a respective period for which the one or more past owners owned the at least one NFT.
6. The system of claim 4 , wherein the at least one UI indicates a respective game and/or game scene associated with acquisition of the at least one NFT by a respective past owner.
7. The system of claim 4 , wherein the at least one UI presents a selector that is selectable to present a recording of a past play session of a computer game, the past play session being one in which the at least one digital asset was used.
8. The system of claim 4 , wherein the at least one UI on which the visual timeline is presented is a first UI, and wherein the instructions are executable to:
responsive to a selection from the first UI, present a second UI surfacing a list of computer gamers who wielded the at least one digital asset while playing a computer game.
9. The system of claim 1 , wherein the at least one UI on which the visual timeline is presented is a first UI, and wherein the instructions are executable to:
receive selection of a first spike in the visual timeline; and
responsive to the selection, present a second UI presenting information associated with the first spike.
10. A method comprising:
inputting to at least one machine learning (ML) model at least one training set of data comprising first metadata of non-fungible tokens (NFT) associated with computer simulation assets and ground truth interesting elements therein;
training the at least one ML model using the training set;
subsequent to training, inputting to the at least one ML model at least one NFT comprising second metadata; and
presenting third metadata output by the at least one ML model for a user to visualize important events in a life of the at least one NFT, the third metadata presented on at least one computer display as part of a first user interface (UI), the first UI presenting a visual timeline with time increasing along an X axis, the visual timeline comprising upward spikes according to a Y axis, each spike representing an important event in the at least one NFT as inferred by the at least one ML model.
11. The method of claim 10 , wherein the ground truth interesting elements comprise name of at least one owner of the at least one NFT, name of at least one computer game, and activity in the at least one computer game.
12. The method of claim 10 , wherein the first UI indicates at least some past interesting owners of the at least one NFT but not all past owners of the at least one NFT.
13. The method of claim 12 , wherein the first UI indicates a respective period for which each respective past interesting owner owned the at least one NFT.
14. The method of claim 12 , wherein the first UI presents a selector that is selectable to initiate the playing of a past recording of a respective game and/or game scene associated with the at least one NFT.
15. The method of claim 12 , comprising:
responsive to a selection from the first UI, presenting a second UI surfacing a list of computer gamers who wielded the at least one digital asset while playing a computer game.
16. The method of claim 10 , comprising:
receiving selection of a first spike in the visual timeline; and
responsive to the selection, presenting a second UI presenting information associated with the first spike.
17. An assembly comprising:
at least one display; and
at least one processor programmed with instructions to:
present on the at least one display a visual history of interesting past events for at least one non-fungible token (NFT), the at least one NFT derived to represent at least one digital asset in a computer game, the visual history of interesting past events not showing all past events for the at least one NFT, the visual history comprising a visual timeline with time increasing along an X axis, the visual timeline comprising spikes according to a Y axis, each spike representing a respective interesting event related to the at least one NFT as inferred by the at least one ML model.
18. The assembly of claim 17 , wherein the at least one processor is programmed with instructions to:
provide, as input to at least one machine learning (ML) model, data associated with the at least one NFT; and
receive, as output from the at least one ML model, inferred interesting past events for the at least one NFT, the inferred interesting past events used for the visual history.
19. The assembly of claim 17 , wherein the at least one processor is programmed with instructions to:
receive a selection of a first spike on the visual timeline; and
responsive to the selection, present additional information associated with the first spike.
20. The assembly of claim 17 , wherein one or more of the interesting past events indicated by a respective spike on the visual timeline relates to a change in ownership of the at least one NFT.