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Patent for Blockchain Augmented Reality Tool Filed by IBM

This week, IBM filed paperwork to obtain a patent for their BC-based tool that aims to prevent augmented reality players from overstepping physical boundaries.


 

The company cites examples of physical regions it wishes to help players avoid, such as danger zones, culturally important or sensitive locations, areas owned by private individuals, and more.

AR is a type of tech that adds digital layers to the physical world. One popular example of this is Pokemon Go, a revolutionary AR game that came out in 2016. Another is Zombie Go, which makes zombie figures appear in reality. AR can be applied to many other types of applications, not only games, such as virtual tours of historical locations.

IBM aims at utilizing the tech to progress the use of augmented reality particularly in the physical world. They hope to do so to prevent conflict between AR and undesired physical regions. The company will add an “attack vector” in certain AR games that base gameplay on physical locations. The vector will appear when users report a location that is being used for malicious purposes such as misleading, taking advantage of or ambushing players, and the like.

Staying out of Trouble

BC tech will be utilized in the new network in order to document data about different locations being used in games and other platforms. As such, it is crucial to verify any location-based action.

The blockchain system will track and maintain these locations along with other types of metadata. A BC is a secure database that consists of a linearly growing record of data that prevents any sort of manipulation of revisions. It is composed of a system of blocks, each holding a certain set of data and marked with a timestamp alongside other information - the hash of the block that came before it - which links it to the former block.

The system also aims to include a neural network that can adapt to solutions for interactions in different physical locations, and record information in the block used.

This way, it is easier to predict risks based on the adapted rules from previous transactions within the chain, such as patterns from various user actions in combination with subtle results, like reports, complaints, and the like. By scanning through rules learned from previous actions, the network can pinpoint risks with different degrees of confirmations.

Although it may be years until IBM’s technology is released, it is nonetheless a step in the right direction for AR tech.

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