NEURAL NETWORK DATA FILTERING AND MONITORING SYSTEMS AND METHODS

• US 20120324565A1
• Filed: 06/17/2011
• Published: 12/20/2012
• Est. Priority Date: 06/17/2011
• Status: Active Grant

• Current Assignee: Trace-Wilco Incorporated
• Original Assignee: Elliot D. Cohen
• Inventors: Cohen, Elliot D.
• Granted Patent: US 8,516,568 B2
• Time in Patent Office: Days
• US Class Current: 726/12
• CPC Class Codes: G06F 3/011 Arrangements for interaction with the human body, e.g. for user immersion in virtual reality blind teaching G09B21/00
• CPC Class Codes: G06N 3/02 Neural networks

Abstract

Systems and methods are disclosed for filtering data in a neural network environment to filter out inappropriate content. In some embodiments, a data signal including a sensible representation is received. The sensible representation included in the data signal is produced in a sensible format. From the sensible representation in the sensible format, a clean copy of the sensible representation can be generated such that any inappropriate content present within the received data signal is not reproduced in the clean copy. Optionally, additional filtering can occur before and/or after the generating of the clean copy. The (filtered) clean copy of the sensible representation is sent to a network. Embodiments can permit the filtering of input to and/or output from a network.

Claims

1. A method for filtering data in a neural network environment comprising the steps of:

receiving a data signal including a sensible representation;

producing the sensible representation included in the data signal in a sensible format;

copying the sensible representation in the sensible format to generate a clean copy of the sensible representation;

sending the clean copy of the sensible representation to a network; and

whereby the copying prevents inappropriate content potentially present within the data signal from being reproduced in the clean copy and sent to the network.

2. The method of claim 1 further comprising:

filtering out inappropriate content potentially inherent within the sensible representation included in the data signal.

3. The method of claim 1 further comprising:

filtering out inappropriate content potentially present within the data signal by using a set of defined matching criteria.

4. The method of claim 1 further comprising:

filtering out inappropriate content potentially present within the data signal by applying an anti-malware technique.

5. The method of claim 1 wherein the receiving of the data signal including the sensible representation by a receiver is from a BMI.

6. The method of claim 1 wherein the sending of the clean copy of the sensible representation to the network by a transmitter is via a BMI interfaced with the network.

7. The method of claim 1 wherein the sensible representation is a visual representation and the sensible format is a visible format.

8. The method of claim 1 wherein the sensible representation is an auditory representation and the sensible format is an audible format.

9. The method of claim 1 wherein the sensible representation is a gustatory representation and the sensible format is a format capable of being tasted.

10. The method of claim 1 wherein the sensible representation is a tactile representation and the sensible format is a format capable of being measured by applied force.

11. The method of claim 1 wherein the sensible representation is a cognitive representation and the sensible format is a format being at least one of:

visible, audible, or measurable by applied force.

12. The method of claim 1 wherein the sensible representation is an olfactory representation and the sensible format is a format capable of being smelled.

13. The method of claim 1 wherein the sensible representation is a kinesthetic representation and the sensible format is a format capable of being sensed by a motion sensor.

14. The method of claim 1 wherein the sensible representation is a motor representation and the sensible format is a format capable of being recognized by command recognizer.

15. A system for filtering data in a neural network environment comprising:

a transceiver for receiving a data signal including a sensible representation and for sending a clean copy of the sensible representation to a network;

a sensible representation sensor for copying the sensible representation in a sensible format and capable of being utilized to generate a clean copy of the sensible representation;

a processor for facilitating system communication and interaction; and

whereby generating the clean copy of the sensible representation prevents inappropriate content potentially present within the data signal from being reproduced in the clean copy and sent to the network.

16. The system of claim 15 further comprising:

a filter for filtering out inappropriate content potentially inherent within the sensible representation included in the data signal.

17. The system of claim 15 further comprising:

a filter for filtering out inappropriate content potentially present within the data signal by using a set of defined matching criteria.

18. The system of claim 15 further comprising:

a filter for filtering out inappropriate content potentially present within the data signal by applying an anti-malware technique.

19. The system of claim 15 wherein the sensible representation sensor is a software component implementing an algorithm for copying the sensible representation in the sensible format and constructing the clean copy of the sensible representation.

20. The system of claim 15 wherein the sensible representation is a visual representation and the sensible representation sensor is a camera.

21. The system of claim 15 wherein the sensible representation is a visual representation and the sensible representation sensor is a hybrid or artificial eye.

22. The system of claim 15 further comprising:

a BMI interfacing with a neural network; and

wherein the sensible representation is a visual representation and the sensible representation sensor is a biological eye, the biological eye capable of being utilized to generate the clean copy for sending to the BMI interfaced with the network.

23. The system of claim 15 wherein the sensible representation is an auditory representation and the sensible representation sensor is a microphone.

24. The system of claim 15 wherein the sensible representation is an auditory representation and the sensible representation sensor is a hybrid or artificial ear.

25. The system of claim 15 further comprising:

a BMI interfacing with a neural network; and

wherein the sensible representation is an auditory representation and the sensible representation sensor is a biological ear, the biological ear capable of being utilized to generate the clean copy for sending to the BMI interfaced with the network.

26. The system of claim 15 wherein the sensible representation is a gustatory representation and the sensible representation sensor is a hybrid or artificial tongue.

27. The system of claim 15 further comprising:

a BMI interfacing with a neural network; and

wherein the sensible representation is a gustatory representation and the sensible representation sensor is a biological tongue, the biological tongue capable of being utilized to generate the clean copy for sending to the BMI interfaced with the network.

28. The system of claim 15 wherein the sensible representation is a tactile representation and the sensible representation sensor is a hybrid or artificial pressure sensor.

29. The system of claim 15 further comprising:

a BMI interfacing with a neural network; and

wherein the sensible representation is a tactile representation and the sensible representation sensor is a biological pressure sensor, the biological pressure sensor capable of being utilized to generate the clean copy for sending to the BMI interfaced with the network.

30. The system of claim 15 wherein the sensible representation is a cognitive representation and the sensible representation sensor includes at least one of:

a camera, a microphone, or a pressure sensor.

31. The system of claim 15 wherein the sensible representation is a cognitive representation and the sensible representation sensor includes at least one of:

a hybrid or artificial eye, a hybrid or artificial ear, or a hybrid or artificial pressure sensor.

32. The system of claim 15 further comprising:

a BMI interfacing with a neural network; and

wherein the sensible representation is a cognitive representation and the sensible representation sensor includes at least one of a biological eye, a biological ear, or a biological pressure sensor, the sensible representation sensor capable of being utilized to generate the clean copy for sending to the BMI interfaced with the network.

33. The system of claim 15 wherein the sensible representation is an olfactory representation and the sensible representation sensor is a hybrid or artificial nose.

34. The system of claim 15 further comprising:

a BMI interfacing with a neural network; and

wherein the sensible representation is an olfactory representation and the sensible representation sensor is a biological nose, the biological nose capable of being utilized to generate the clean copy for sending to the BMI interfaced with the network.

35. The system of claim 15 wherein the sensible representation is a kinesthetic representation and the sensible representation sensor is a hybrid or artificial motion sensor.

36. The system of claim 15 further comprising:

a BMI interfacing with a neural network; and

wherein the sensible representation is a kinesthetic representation and the sensible representation sensor is a biological motion sensor, the biological motion sensor capable of being utilized to generate the clean copy for sending to the BMI interfaced with the network.

37. The system of claim 15 wherein the sensible representation is a motor representation and the sensible representation sensor is a hybrid or artificial command recognizer.

38. The system of claim 15 further comprising:

a BMI interfacing with a neural network; and

wherein the sensible representation is a motor representation and the sensible representation sensor is a biological command recognizer, the biological command recognizer capable of being utilized to generate the clean copy for sending to the BMI interfaced with the network.