Arrapoi, Inc. Patent applications |
Patent application number | Title | Published |
20150100286 | Using input properties and system properties to predict a time-dependent response of a component of a system to an input into the system - Systems, methods, and devices are provided to facilitate non-mechanistic, differential-equation-free approaches to predict a response of a system to a given input, wherein the response is defined in terms of at least one property of the system and at least one property of the input. The systems, methods, and devices provide the ability to (i) reduce the cost of research and development by offering an accurate modeling of heterogeneous and complex physical systems; (ii) reduce the cost of creating such systems and methods by simplifying the modeling process; (iii) accurately capture and model inherent nonlinearities in cases where sufficient knowledge does not exist to a priori build a model and its parameters; and, (iv) provide one-to-one relationships between model parameters and model outputs, addressing the problem of the ambiguities inherent in the current, state-of-the-art systems and methods. | 04-09-2015 |
20150100283 | IN VITRO AND EX VIVO METHODS OF USING INPUT PROPERTIES AND SYSTEM PROPERTIES TO PREDICT A TIME-DEPENDENT RESPONSE OF A COMPONENT OF A SYSTEM TO AN INPUT INTO THE SYSTEM - Systems, methods, and devices are provided to facilitate non-mechanistic, differential-equation-free approaches to predict a response of a system to a given input, wherein the response is defined in terms of at least one property of the system and at least one property of the input. The systems, methods, and devices provide the ability to (i) reduce the cost of research and development by offering an accurate modeling of heterogeneous and complex physical systems; (ii) reduce the cost of creating such systems and methods by simplifying the modeling process; (iii) accurately capture and model inherent nonlinearities in cases where sufficient knowledge does not exist to a priori build a model and its parameters; and, (iv) provide one-to-one relationships between model parameters and model outputs, addressing the problem of the ambiguities inherent in the current, state-of-the-art systems and methods. | 04-09-2015 |
20140172765 | PREDICTING PHARMACOKINETIC AND PHARMACODYNAMIC RESPONSES OF A COMPONENT OF A SYSTEM TO AN INPUT INTO THE SYSTEM - Non-mechanistic, differential-equation-free approaches for predicting a particular pharmacokinetic and pharmacodynamic responses of a system to a given input are provided in the form of systems, methods, and devices. These approaches are generally directed to a non-compartmental method of predicting a time-dependent pharmacokinetic response, or pharmacodynamics response, of a component of a system to an input into the system. The systems, methods, and devices provide the ability to (i) reduce the cost of research and development by offering an accurate modeling of heterogeneous and complex physical systems; (ii) reduce the cost of creating such systems and methods by simplifying the modeling process; (iii) accurately capture and model inherent nonlinearities in cases where sufficient knowledge does not exist to a priori build a model and its parameters; and, (iv) provide one-to-one relationships between model parameters and model outputs, addressing the problem of the ambiguities inherent in the current, state-of-the-art systems and methods. | 06-19-2014 |
20140172383 | DEVICE FOR PREDICTING NON-LINEAR, TIME-DEPENDENT RESPONSES OF A COMPONENT OF A SYSTEM TO AN INPUT INTO THE SYSTEM - Non-mechanistic, differential-equation-free approaches for predicting a particular non-linear, response of a system to a given input are provided in the form of systems, methods, and devices. These approaches are generally directed to a non-compartmental method of predicting a non-linear, time-dependent response of a component of a system to an input into the system. The systems, methods, and devices provide the ability to (i) reduce the cost of research and development by offering an accurate modeling of heterogeneous and complex physical systems; (ii) reduce the cost of creating such systems and methods by simplifying the modeling process; (iii) accurately capture and model inherent nonlinearities in cases where sufficient knowledge does not exist to a priori build a model and its parameters; and, (iv) provide one-to-one relationships between model parameters and model outputs, addressing the problem of the ambiguities inherent in the current, state-of-the-art systems and methods. | 06-19-2014 |