Ecosystem for NFT trading in public media distribution platforms

1. A computer-implemented method comprising:

obtaining, from a first server, a scene of a media asset;

automatically extracting image metadata from the scene;

inputting the image metadata to a machine learning (ML) algorithm;

receiving an output from the ML algorithm;

identifying, based on the output from the ML algorithm, a product in the scene;

requesting, from a second server comprising a database of a plurality of non-fungible tokens (NFTs), an indication of whether a NFT based on the product exists, wherein the request includes first metadata based on the identified product of the media asset;

determining whether the metadata of an NFT in the database matches the first metadata by comparing the first metadata with metadata of the plurality of NFTs in the database;

receiving the indication of whether the NFT based on the product exists based on determining whether the metadata of an NFT in the database matches the first metadata;

if the indication indicates that the NFT based on the product does exist:

matching the NFT to a first advertiser of the product based on the identified product; and

presenting a first option to purchase the NFT to the first advertiser of the product;

if the indication indicates that the NFT based on the product does not exist:

transmitting, to the second server, a request for generation of the NFT;

obtaining, from the second server, the generated NFT based on the product;

matching the generated NFT to an advertiser of the product based on the identified product; and

presenting an option to purchase the generated NFT to the advertiser of the product.

2. The computer-implemented method of claim 1 , wherein metadata comprises any one of:

an NFT type;

a scene start time;

a scene end time;

ownership of the scene; and

a bidding price for the NFT.

3. The computer-implemented method of claim 1 , wherein matching the generated NFT to the advertiser of the product further comprises:

receiving, using control circuitry, metadata based on an advertiser's preference;

determining, using machine learning, an NFT suitability score based on the advertiser's preferences; and

in response to determining that the NFT suitability score is above a threshold, matching, using control circuitry, the NFT to the advertiser.

4. The computer-implemented method of claim 1 , wherein presenting an option to purchase the generated NFT to the advertiser of the product further comprises:

generating, using control circuitry, an indication/notification to purchase the generated NFT;

determining, using control circuitry, that additional NFTs related to the generated NFT are available for purchase at the second server;

receiving, using control circuitry, a request to purchase the generated NFT, the additional NFTs, or a combination of the two;

generating, using control circuitry, a payment request to a third server;

receiving, using control circuitry, an acknowledge notification from the third server that payment has been accepted; and

receiving, using control circuitry, the purchased generated NFT, additional NFTs, or combination of the two from the second server.

5. The computer-implemented method of claim 1 , wherein the NFT comprises a cinemagraph.

6. The computer-implemented method of claim 1 , wherein the identified product comprises any one of:

a brand name; or

a brand product.

7. The computer-implemented method of claim 1 , wherein the scene comprises any one of:

an image;

a video; or

a text.

8. The method of claim 1 , wherein the trained model is trained by metadata related to the scene, and where the metadata comprises any one of an NFT type, a scene start time, a scene end time, ownership of the scene, or a bidding price for the NFT.

9. An apparatus comprising:

a memory storing instructions;

communication paths; and

control circuitry coupled to the communication paths and the memory and configured to execute the instructions to:

obtain, from a first server via the communication paths, a scene of a media asset;

automatically extract image metadata from the scene;

input the image metadata to a machine learning (ML) algorithm;

receive an output from the ML algorithm;

identify, based on the output from the ML algorithm, a product;

request, from a second server comprising a database of a plurality of non-fungible tokens (NFTs), via the communication paths, an indication of whether a NFT based on the product exists, wherein the request includes first metadata based on the identified product of the media asset;

determine whether the metadata of an NFT in the database matches the first metadata by comparing the first metadata with metadata of the plurality of NFTs in the database;

receive the indication of whether the NFT based on the product exists based on determining whether the metadata of an NFT in the database matches the first metadata;

if the indication indicates that the NFT based on the product does exist:

match the NFT to a first advertiser of the product based on the identified product; and

present a first option to purchase the NFT to the first advertiser of the product;

if the indication indicates that the NFT based on the product does not exist:

transmit, to the second server, a request for generation of the NFT;

obtain, from the second server, the generated NFT based on the product;

match the generated NFT to an advertiser of the product based on the identified product; and

present an option to purchase the generated NFT to the advertiser of the product.

10. The apparatus of claim 9 , wherein metadata comprises any one of:

an NFT type;

a scene start time;

a scene end time;

ownership of the scene; and

a bidding price for the NFT.

11. The apparatus of claim 9 , wherein to match the generated NFT to the advertiser of the product further comprises the control circuitry to execute the instructions to:

receive, using control circuitry, metadata based on an advertiser's preference;

determine, using machine learning, an NFT suitability score based on the advertiser's preferences; and

in response to determining that the NFT suitability score is above a threshold, match, using control circuitry, the NFT to the advertiser.

12. The apparatus of claim 9 , wherein to present an option to purchase the generated NFT to the advertiser of the product further comprises the control circuitry to execute the instructions to:

generate, using control circuitry, an indication/notification to purchase the generated NFT;

determine, using control circuitry, that additional NFTs related to the generated NFT are available for purchase at the second server;

receive, using control circuitry, a request to purchase the generated NFT, the additional NFTs, or a combination of the two;

generate, using control circuitry, a payment request to a third server;

receive, using control circuitry, an acknowledge notification from the third server that payment has been accepted; and

receive, using control circuitry, the purchased generated NFT, additional NFTs, or combination of the two from the second server.

13. The apparatus of claim 9 , wherein the NFT comprises a cinemagraph.

14. The apparatus of claim 9 , wherein the identified product comprises any one of:

a brand name; or

a brand product.

15. The apparatus of claim 9 , wherein the scene comprises any one of:

an image;

a video; or

a text.

16. The apparatus of claim 9 , where the trained model is trained by metadata related to the scene, and where the metadata comprises any one of an NFT type, a scene start time, a scene end time, ownership of the scene, or a bidding price for the NFT.