System and method for dynamic knowledge construction

• US 20090018984A1
• Filed: 06/16/2008
• Published: 01/15/2009
• Est. Priority Date: 06/30/2000
• Status: Abandoned Application

• Current Assignee: James C. Solinsky
• Original Assignee: James C. Solinsky
• Inventors: Solinsky, James C.
• Application Number:
  US12/213,203
• Publication Number:
  US 20090018984A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 18/213 Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
• CPC Class Codes: G06N 3/02 Neural networks
• CPC Class Codes: G06V 10/7715 Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods

Abstract

A system and method responsive to input stimuli is provided by incorporating a computer software program, hardware processing engine, or a specialized ASIC chip processor apparatus to capture concurrent inputs that are responsive to training stimulation, store a model representing a synthesis of the captured inputs, and use the stored model to generate outputs in response to real-world stimulation. Human user forced-choice approval/disapproval generated descriptions and decisions may be dynamically mapped with conventionally presented information and sensor and control data. The model mapping is stored into and out of a conventional mass storage device, such as is used in a relational database for use in generating a response to the stimuli. By accessing commonly stored mappings, the system can be incorporated into a mixture of multiple domains and disciplines of users and can create a common understanding of knowledge and design concept contained within it through mutual interaction, and subsequent automatic modifications to a common relational database. The system and method is applicable to conventional storage and presentation devices, making it easily incorporated into a variety of commercial products, utilizing current commercial human-machine interfaces (e.g. Human-Machine Interface graphical user interface, or Graphical User Interface) and current mass storage devices. The system uses N-dimensional descriptions of observations and concepts in an infinitely expandable space, embracing elements of human thought. This allows the user to tailor this system to control operation of automated devices and appliances to reflect the individual'"'"'s wishes and desires as a dynamic representation and mapping of user descriptions and decisions with information, sensor data, and device controls.

Claims

1. A method of generating outputs in response to real world stimulation comprising:

capturing concurrent inputs that are responsive to training stimulation;

storing a model representing a synthesis of the captured inputs; and

using the stored model to generate outputs in response to real-world stimulation.

2. The method according to claim 1, further comprising:

using a forced choice interaction to generate one or more additional inputs;

capturing the additional inputs; and

incorporating the additional inputs into the model.

3. The method according to claim 1, wherein the model comprises a worldline of linked object diagram exemplars in an N-dimensional space.

4. The method according to claim 1, wherein

the real world stimulation comprises concurrent inputs that are compared to the stored model, and

the outputs are based on the results of the comparison.

5. A computer readable medium for storing computer-executable instructions for performing the method of claim 1.

6. A hardware processing engine configured to perform the method of claim 1.

7. An application specific integrated circuit configured to perform the method of claim 1.

8. A net list integrated into other integrated circuits to perform the method of claim 1.

9. A method of generating outputs in response to control command stimulation comprising:

capturing concurrent inputs that are responsive to training stimulation;

storing a model representing a synthesis of the captured inputs; and

using the stored model to generate outputs in response to control command stimulation.

10. The method according to claim 9, further comprising:

using forced choice interaction to generate one or more additional inputs;

capturing the additional inputs; and

incorporating the additional inputs into the model.

11. The method according to claim 9, wherein the model comprises a worldline of linked object diagram exemplars in an N-dimensional space.

12. The method according to claim 9, wherein

the real world stimulation comprises concurrent inputs that are compared to the stored model, and

the outputs are based on the results of the comparison.

13. A computer readable medium for storing computer-executable instructions for performing the method of claim 9.

14. A hardware processing engine configured to perform the method of claim 9.

15. An application specific integrated circuit configured to perform the method of claim 9.

16. A net list integrated into other integrated circuits to perform the method of claim 9.

17. A system for generating an outputs in response to real world stimulation comprising:

input capture circuitry that captures concurrent system inputs that are responsive to training stimulation;

a memory that stores a model representing a synthesis of the captured inputs; and

an output generator that uses the stored model to generate outputs in response to real world stimulation.

18. The system according to claim 17, wherein the input capture circuitry further captures one or more additional inputs generated from a forced choice interaction and the additional inputs are incorporated into the model.

19. The method according to claim 17, wherein the model comprises a worldline of linked object diagram exemplars in an N-dimensional space.

20. The system according to claim 17, wherein the real world stimulation comprises concurrent inputs that are compared to the stored model, and the outputs are based on the results of the comparison.

21. The system according to claim 17, wherein at least part of said system is implemented in a computer software program.

22. The system according to claim 17, wherein at least part of said system is implemented as a hardware processing engine.

23. The system according to claim 17, wherein at least part of said system is implemented as an application specific integrated circuit.

24. The system according to claim 17, wherein at least part of said system is implemented as a net list integrated into other integrated circuits.

25. A system for generating an output in response to control command stimulation comprising:

input capture circuitry that captures concurrent system inputs that are responsive to training stimulation;

a memory that stores a model representing a synthesis of the captured inputs; and

an output generator that uses the stored model to generate outputs in response to control command stimulation.

26. The system according to claim 25, wherein the input capture circuitry further captures one or more additional inputs generated from a forced choice interaction and the additional inputs are incorporated into the model.

27. The method according to claim 25, wherein the model comprises a worldline of linked object diagram exemplars in an N-dimensional space.

28. The system according to claim 25, wherein the real world stimulation comprises concurrent inputs that are compared to the stored model, and the outputs are based on the results of the comparison.

29. The system according to claim 25, wherein at least part of said system is implemented in a computer software program.

30. The system according to claim 25, wherein at least part of said system is implemented as a hardware processing engine.

31. The system according to claim 25, wherein at least part of said system is implemented as an application specific integrated circuit.

32. The system according to claim 25, wherein at least part of said system is implemented as a net list integrated into other integrated circuits.