The cannabis industry's evolution depends on cannabis 3.0, the future of blockchain, and AI in the Cannabis industry.

FREMONT, CA: Cannabis 1.0, 2.0, and 3.0 consists of the industry's progress and gives a roadmap forward into true consumerism. This path will be determined using cutting-edge technologies, including machine learning algorithms, blockchain, regulatory technologies, and consumer input devices, all functioning together in a big data system. 

Regulatory needs and global market conditions have accelerated the arrival of 3.0

Cannabis 3.0 is seen as collectivization of individual opinions, biometric tracking, tracing of the products from the origin to the consumer, the quality of the products in the marketplace, and brand recognition. When someone wants to know who has the best strain of medical cannabis or CBD oil for headaches, joint pain, knees, or anxiety, individuals will have verified real-time data opinions and facts about the market. Brand efficacy will form the division between mass producers of gummy edibles, which serves one kind of mass benefit, versus craft or smaller-scale manufacturing, which may be measured to meet customer needs to be improved than mass production.

Being able to trace the efficacy as to how the products are being made, what ailment they are assisting, and the effectiveness of those products, 3.0 will be the true test for creating trust in the market. Cannabis 3.0 is critical to the cannabis sector, as the doctors can view real-time data and make real-time recommendations based on the quantitively and qualitatively gathered information on end-users and the delivery of quality patient care and consumer protection.

Formulating methodology, system, and apparatus for data storage and data access to medical cannabis products using blockchain

Over the past two years, Global Cannabis Applications Corporation (GCAC) has been a Cannabis 3.0 early innovator. GCAC has developed a patent-pending methodology of collecting and storing data related to a cannabis product and consumer feedback on that product's consumption throughout a distributed node validation system. The method consists of associating data plurality to a record categorized by a one-of-a-kind digital identifier stored on the blockchain for access by one or more authorized users. The records are then analyzed with the help of non-directed machine learning algorithms to determine the quality and quantity of desired components and undesired components in the cannabis product. The output is a determination of an end-user experience followed by storing this data on the blockchain.

Anovora

Axeb Technologies