DEVICE FOR PREDICTING NON-LINEAR, TIME-DEPENDENT RESPONSES OF A COMPONENT OF A SYSTEM TO AN INPUT INTO THE SYSTEM

• US 20140172383A1
• Filed: 08/09/2013
• Published: 06/19/2014
• Est. Priority Date: 12/17/2012
• Status: Active Grant

• Current Assignee: Arrapoi, Inc.
• Original Assignee: Arrapoi, Inc.
• Inventors: WILLIAMS, GLENN A.
• Granted Patent: US 9,460,246 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G06F 17/17 Function evaluation by approximation methods, e.g. inter- or extrapolation, smoothing, least mean square method G06F17/18 takes precedence ; interpolation for numerical control G05B19/18
• CPC Class Codes: G06F 2111/10 Numerical modelling
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16C 20/30 Prediction of properties of chemical compounds, compositions or mixtures

Abstract

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.

Claims

1. 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 method comprising:

identifying the system, the component, the input, and the non-linear, time-dependent response;

wherein, the input includes a set of actual inputs and a test input, and the non-linear time-dependent response includes a set of non-linear, time-dependent actual responses and a non-linear, test response;

obtaining the set of non-linear, time-dependent actual responses of the component to the set of actual inputs;

using the set of actual inputs and the set of non-linear, time-dependent actual responses to provide a model for predicting the non-linear, test response to the test input, the model comprising the formula

2. The method of claim 1, wherein the system is an environmental system and the component is selected from the group consisting of air, water, and soil.

3. The method of claim 1, wherein the system is a mammal, and the component is selected from the group consisting of a cell, a tissue, an organ, a DNA, a virus, a protein, an antibody, a bacteria.

4. The method of claim 1, wherein the system is a chemical system.

5. The method of claim 1, wherein the system is a mechanical system.

6. The method of claim 1, wherein the system is an electrical system.

7. A non-compartmental method of predicting a non-linear, time-dependent response of a component of a mammalian system to an input into the system, the method comprising:

selecting a component of the system, the component selected from the group consisting of a cell, a tissue, an organ, a DNA, a virus, a protein, an antibody, a bacteria;

selecting a set of actual inputs, the set of actual inputs having an element selected from the group consisting of a DNA, a virus, a protein, an antibody, a bacteria, a chemical, a dietary supplement, a nutrient, and a drug;

obtaining a set of non-linear, time-dependent actual responses of the component to the set of actual inputs;

using the set of actual inputs and the set of non-linear, time-dependent actual responses to provide a model for predicting a non-linear, test response to a test input, the model comprising the formula

8. The method of claim 7, wherein the component is blood.

9. The method of claim 7, wherein the component is a tumor cell.

10. The method of claim 7, wherein the component is a virus.

11. The method of claim 7, wherein the component is a bacteria.

12. The method of claim 7, wherein the non-linear, test response is a bacterial load.

13. The method of claim 7, wherein the non-linear, test response is a viral load.

14. The method of claim 7, wherein the non-linear, test response is a tumor marker.

15. The method of claim 7, wherein the non-linear, test response is a blood chemistry.

16. The method of claim 7, wherein the set of actual inputs includes a set of dosages of a drug.

17. The method of claim 7, wherein the set of actual inputs includes a set of drugs.

18. The method of claim 7, wherein the input is a diabetes drug, and the non-linear, time-dependent response is glucose in the bloodstream.

19. A device for predicting a non-linear, time-dependent response of a component of a physical system to an input into the system, the device comprising:

a processor;

a database for storing a set of actual input data, a set of non-linear, time-dependent actual response data, test input data, and non-linear, time-dependent test response data on a non-transitory computer readable medium;

an enumeration engine on a non-transitory computer readable medium to parameterize a non-compartmental model for predicting a non-linear, test response to a test input, the non-compartmental model comprising the formula

20. The device of claim 19, wherein the system is an environmental system and the component is selected from the group consisting of air, water, and soil.

21. A device for predicting a non-linear, time-dependent response of a component of a mammalian system to an input into the system, the device comprising:

a processor;

a database for storing a set of actual input data, a set of non-linear, time-dependent actual response data, test input data, and non-linear, time-dependent test response data on a non-transitory computer readable medium;

an enumeration engine on a non-transitory computer readable medium to parameterize a non-compartmental model for predicting a non-linear, test response to a test input, the non-compartmental model comprising the formula

22. The device of claim 21, wherein the component is blood.

23. The device of claim 21, wherein the component is a tumor cell.

24. The device of claim 21, wherein the component is a virus.

25. The device of claim 21, wherein the component is a bacteria.

26. The device of claim 21, wherein the non-linear, time-dependent response is a bacterial load.

27. The device of claim 21, wherein the non-linear, time-dependent response is a viral load.

28. The device of claim 21, wherein the non-linear, time-dependent response is a tumor marker.

29. The device of claim 21, wherein the non-linear, time-dependent response is a blood chemistry.

30. The device of claim 21, wherein the device is a handheld device.