Abstract: A diabetes monitor can provide diabetes information is invented in this invention. It comprises an input module, a storage module, and a knowledge base system module. This invention breaks through the design idea of traditional diabetes monitors. It not only can monitor the blood glucose values but also can generate the diabetes state of the patient. So it makes patients recognizing their own diabetes state in time, and can take effective control and treatment accordingly.

Abstract: Deductive Object-Oriented Data Mining System (Doodms) is a predictive data mining system. Its goal is generating prediction rules from given databases or any given data sets. In Doodms, the difficult data mining problem is reduced to a series of probability problems, each of which is not only much easier to be solved but also has much better mathematical basis. The generate-count-and-test methodology, which contains generate, count, and test processes, is applied in Doodms. Since it is object-oriented, each instance is treated as an object. Since it is deductive, the generate-process is from more general cases to less general cases. Since an important theorem is proved and applied, the data mining process is greatly sped up. Since no heuristics are applied, the generated results are mathematical rigorous and the tolerance for each case can be set by the user.

Abstract: The Object-Oriented Data Mining and Decision Making System is invented and disclosed. This system is an integration of two subsystems, the object-oriented data mining subsystem which is an object-oriented machine learning system, and the object-oriented decision making subsystem which is also called the object-oriented expert system. In this invention, object-oriented technology, a new technology in software design and development is applied. The difference between the methodology used in this invention and that used in all other learning systems and expert systems is that in this invention, all objects have the same attributes and actions and are in the same class in the whole process. At the same time, only objects themselves, nothing else are processed, without adding any additional structures, such as trees, hierarchies, and relations to them. Therefore a single class can cover all working objects in the whole process. Besides, statistical methodology is introduced to each object in the class.