GBT Tokenize is Seeking to Develop an Expert System Based Flow qTerm Vitals’ Device Mobile Application
GBT Technologies, announced that its Joint Venture, GBT Tokenize Corp, is currently developing an expert system based flow for its qTerm device mobile application. An expert system is an artificial intelligence based computer program that solves problems that would usually require a human expert. An expert system typically relies on a knowledge base and fact analysis resources.
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The knowledge database is an organized collection of facts and rules about a topic. In qTerm, the database will be the user’s vitals data and measurement that is taken over time. The facts analysis engine interprets and evaluates the knowledge base facts in order to provide the best answer for a query. Typical implementations of expert systems are data analysis, diagnostics, gaming, classifications or specific tasks. Upon a query the analysis engine fetches the relevant knowledge from the knowledge base, interprets it and to find a solution that is the most relevant to the problem. In an efficient expert system many facts are acquired and evaluated. The system provides the best answer to the query based on the provided knowledge.
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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
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