$GTCH GBT/Tokenize is developing a learning knowle

$GTCH GBT/Tokenize is developing a learning knowledge flow to be implemented within its qTerm mobile application. The goal of the flow will be to integrate machine learning functions to allow the expert system to acquire more and more knowledge from past experience and various external sources. The information will be categorized and classified for every user and stored in the user's private knowledge base. In this way each user will have personal own health history and private records. Furthermore, the new goal of the new flow will be to provide an explanation, statistics and alerts in case a potential health issue is predicted. For example, assuming the finalization of design and implementation, based on specific user measurements data, the expert system heuristic engine may reach a conclusion to recommend an immediate professional medical consultation which can be significant in early detection of diseases. It is the goal of the expert system to work as an integral part with a backend program, and will be constantly studying the user's health. The system will be self-learned just like a human being and improve from experience. The flow will provide feedback through the mobile application and a web application.

"We are preparing qTerm infrastructure for our qTerm vitals device and our goal is to develop an advanced flow for its mobile application. The mobile application flow is targeted to work with a backend server and together to run an expert system that will use a knowledge database and an inference engine. We believe that our qTerm device will become a health monitor device for users, enabling health-aware life. We believe this type of system can save lives for users with existing conditions," stated Danny Rittman, GBT’s CTO

https://www.wsj.com/articles/gbt-tokenize-is-...07?tesla=y

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