Visa : embedded payment tokens in digital media

Voici une opportunité pour centraliser le traitement des NFT sur une plateforme "sérieuse"

Visa’s latest filing looks at trying to solve the problem of copyrighted photographs being sold or shared without the owner receiving payment.

Visa’s solution involves cameras embedding payment tokens into the metadata of each photograph taken, along with an optional price. If a photo agency wants to purchase a photograph, they can just drag the photo onto an application and all of the token data can be extracted from it, and a payment can then be made.

This solution is interesting in a few ways.

Firstly, this makes it easier for people to buy rights for digital media. At the moment, if you find a photo online, it can be difficult finding who the actual copyright owner is and how to make a payment to use the content. In theory, Visa’s filing helps simplify the process with all of the core details being embedded as a token into the photograph.

Secondly, where things could become really interesting is if we start to think about what’s happening in the space of NFTs (or non-fungible tokens) – here’s a good primer. If digital media minted onto the blockchain had a token embedded into it, we could start to automate new behaviours. For example, when tokenised digital media is used on a website, the browser or web platform could automatically bring out who the creator is and who the owner is. Ad revenue from that the page that hosts the image could automatically be distributed to creators and owners of the tokenised image. If you wanted to own the image, there could be a ‘bid’ button that lets you easily bid a price to become a new owner of the image, and therefore receive any future revenues in an automated way.

Visa’s filing is currently narrowly focusing on the embedding a payment token into copyrighted photographs, but the idea of embedding other forms of tokens into digital media could unlock a lot more interesting behaviours around monetisation and ownership.

via Patent Drop : lire l’article source

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