Computer architecture for emulating an asynchronous correlithm object processing system

• US 10,019,650 B1
• Filed: 11/28/2017
• Issued: 07/10/2018
• Est. Priority Date: 11/28/2017
• Status: Active Grant

• Current Assignee: Bank of America Corp.
• Original Assignee: Bank of America Corp.
• Inventors: Lawrence, Patrick N.
• Primary Examiner(s): Nguyen, Linh
• Application Number:
  US15/824,782
• Time in Patent Office: 224 Days
• Field of Search: 341 50, 341 51, 341 87
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing
• CPC Class Codes: G06F 18/10 Pre-processing; Data cleansing
• CPC Class Codes: G06F 18/2111 by using evolutionary computational techniques, e.g. genetic algorithms
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06N 10/00 Quantum computing, i.e. information processing based on quantum-mechanical phenomena
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 3/126 Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects

Abstract

A device that includes a node engine configured to emulate a first node, a second node, and a third node. The first node is configured to receive a first correlithm object, fetch a second correlithm object based on the first correlithm object, and output the second correlithm object to the second node and the third node. Each correlithm object is a point in an n-dimensional space represented by a binary string. The second node is configured to receive the second correlithm object, fetch a third correlithm object based on the second correlithm object, and output the third correlithm object to the third node. The third node is configured to receive the second correlithm object, receive the third correlithm object, fetch a fourth correlithm object based on the second correlithm object and the third correlithm object, and output the fourth correlithm object.

Claims

1. A device configured to emulate an asynchronous correlithm object processing system, comprising:

a node engine configured to emulate;

a first node linked with a first node table, wherein;

the first node table that identifies;

a first plurality of correlithm objects, wherein each correlithm object in the first plurality of correlithm objects is a point in a first n-dimensional space represented by a binary string; and

a second plurality of correlithm objects linked with the first plurality of correlithm objects, wherein each correlithm object in the second plurality of correlithm objects is a point in a second n-dimensional space represented by a binary string; and

the first node is configured to;

receive a first correlithm object;

fetch a second correlithm object from among the second plurality of correlithm objects based on the first correlithm object; and

output the second correlithm object to a second node and a third node;

the second node linked with a second node table, wherein;

the second node table that identifies;

a third plurality of correlithm objects, wherein each correlithm object in the third plurality of correlithm objects is a point in the second n-dimensional space represented by a binary string; and

a fourth plurality of correlithm objects linked with the third plurality of correlithm objects, wherein each correlithm object in the fourth plurality of correlithm objects is a point in a third n-dimensional space represented by a binary string; and

the second node is configured to;

receive the second correlithm object;

fetch a third correlithm object from among the fourth plurality of correlithm objects based on the second correlithm object; and

output the third correlithm object to the third node; and

the third node linked with a third node table, wherein;

the third node table that identifies;

a fifth plurality of correlithm objects, wherein each correlithm object in the fifth plurality of correlithm objects is a point in the second n-dimensional space represented by a binary string;

a sixth plurality of correlithm objects, wherein each correlithm object in the sixth plurality of correlithm objects is a point in the third n-dimensional space represented by a binary string; and

a seventh plurality of correlithm objects linked with the fifth plurality of correlithm objects and the sixth plurality of correlithm objects, wherein each correlithm object in the fifth plurality of correlithm objects is a point in a fourth n-dimensional space represented by a binary string; and

the third node is configured to;

receive the second correlithm object;

receive the third correlithm object;

fetch a fourth correlithm object from among the seventh plurality of correlithm objects based on the second correlithm object and the third correlithm object; and

output the fourth correlithm object.

2. The device of claim 1, wherein fetching the second correlithm object comprises:

determining hamming distances between the first correlithm object and each of the first plurality of correlithm objects;

identifying a correlithm object with the shortest hamming distance between the first correlithm object and each of the first plurality of correlithm objects; and

identifying an entry in the first node table that corresponds with the identified correlithm object.

3. The device of claim 1, wherein fetching the second correlithm object comprises:

determining distances between the first correlithm object and each of the first plurality of correlithm objects, wherein determining the distances comprises;

performing an XOR operation between the first correlithm object and each of the first plurality of correlithm objects to generate a resulting binary string;

counting the number of logical high values in the resulting binary string; and

identify a correlithm object with the smallest number of logical high values; and

identifying an entry in the first node table that corresponds with the identified correlithm object.

4. The device of claim 1, wherein fetching the fourth correlithm object comprises:

determining hamming distances between the second correlithm object and each of the fifth plurality of correlithm objects;

identifying a first input correlithm object with the shortest hamming distance between the second correlithm object and each of the fifth plurality of correlithm objects;

determining hamming distances between the third correlithm object and each of the sixth plurality of correlithm objects;

identifying a second input correlithm object with the shortest hamming distance between the third correlithm object and each of the sixth plurality of correlithm objects; and

identifying an entry in the third node table that corresponds with the first input correlithm object and the second input correlithm object.

5. The device of claim 1, wherein the first node is configured to receive the first correlithm object from a sensor configured to convert a real world value into the first correlithm object.

6. The device of claim 1, wherein outputting the fourth correlithm object comprises sending the fourth correlithm object to an actor configured to output a real world value based on the fourth correlithm object.

7. The device of claim 1, wherein the first n-dimensional space, the second n-dimensional space, the third n-dimensional space, and the fourth n-dimensional space all have the same number of dimensions.

8. A method for emulating an asynchronous correlithm object processing system, comprising:

receiving, by a first node implemented by a node engine, a first correlithm object;

fetching, by the first node, a second correlithm object from a first node table based on the first correlithm object, wherein the first node table identifies;

a first plurality of correlithm objects, wherein each correlithm object in the first plurality of correlithm objects is a point in a first n-dimensional space represented by a binary string; and

a second plurality of correlithm objects linked with the first plurality of correlithm objects, wherein each correlithm object in the second plurality of correlithm objects is a point in a second n-dimensional space represented by a binary string;

outputting, by the first node, the second correlithm object to a second node and a third node implemented by the node engine;

fetching, by the second node, a third correlithm object from a second node table based on the second correlithm object in response to receiving the second correlithm object, wherein the second node table identifies;

a third plurality of correlithm objects, wherein each correlithm object in the third plurality of correlithm objects is a point in the second n-dimensional space represented by a binary string; and

a fourth plurality of correlithm objects linked with the third plurality of correlithm objects, wherein each correlithm object in the fourth plurality of correlithm objects is a point in a third n-dimensional space represented by a binary string;

outputting, by the second node, the third correlithm object to the third node;

fetching, by the third node, a fourth correlithm object from a third node table based on the second correlithm object and the third correlithm object in response to receiving the second correlithm object and the third correlithm object, wherein the third node table identifies;

a fifth plurality of correlithm objects, wherein each correlithm object in the fifth plurality of correlithm objects is a point in the second n-dimensional space represented by a binary string;

a sixth plurality of correlithm objects, wherein each correlithm object in the sixth plurality of correlithm objects is a point in the third n-dimensional space represented by a binary string; and

a seventh plurality of correlithm objects linked with the fifth plurality of correlithm objects and the sixth plurality of correlithm objects, wherein each correlithm object in the fifth plurality of correlithm objects is a point in a fourth n-dimensional space represented by a binary string; and

outputting, by the third node, the fourth correlithm object.

9. The method of claim 8, wherein fetching the second correlithm object comprises:

determining hamming distances between the first correlithm object and each of the first plurality of correlithm objects;

identifying a correlithm object with the shortest hamming distance between the first correlithm object and each of the first plurality of correlithm objects; and

identifying an entry in the first node table that corresponds with the identified correlithm object.

10. The method of claim 8, wherein fetching the second correlithm object comprises:

determining distances between the first correlithm object and each of the first plurality of correlithm objects, wherein determining the distances comprises;

performing an XOR operation between the first correlithm object and each of the first plurality of correlithm objects to generate a resulting binary string;

counting the number of logical high values in the resulting binary string; and

identify a correlithm object with the smallest number of logical high values; and

identifying an entry in the first node table that corresponds with the identified correlithm object.

11. The method of claim 8, wherein fetching the fourth correlithm object comprises:

determining hamming distances between the second correlithm object and each of the fifth plurality of correlithm objects;

identifying a first input correlithm object with the shortest hamming distance between the second correlithm object and each of the fifth plurality of correlithm objects;

determining hamming distances between the third correlithm object and each of the sixth plurality of correlithm objects;

identifying a second input correlithm object with the shortest hamming distance between the third correlithm object and each of the sixth plurality of correlithm objects; and

identifying an entry in the third node table that corresponds with the first input correlithm object and the second input correlithm object.

12. The method of claim 8, wherein receiving the first correlithm object comprises receiving the first correlithm object from a sensor configured to convert a real world value into the first correlithm object.

13. The method of claim 8, wherein outputting the fourth correlithm object comprises sending the fourth correlithm object to an actor configured to output a real world value based on the fourth correlithm object.

14. The method of claim 8, wherein the first n-dimensional space, the second n-dimensional space, the third n-dimensional space, and the fourth n-dimensional space all have the same number of dimensions.

15. A computer program product comprising executable instructions stored in a non-transitory computer readable medium such that when executed by a processor causes the processor to emulate an asynchronous correlithm object processing system configured to:

implement a first node, a second node, and a third node using a node engine, receive, using the first node, a first correlithm object;

fetch, using the first node, a second correlithm object from a first node table based on the first correlithm object, wherein the first node table identifies;

a first plurality of correlithm objects, wherein each correlithm object in the first plurality of correlithm objects is a point in a first n-dimensional space represented by a binary string; and

a second plurality of correlithm objects linked with the first plurality of correlithm objects, wherein each correlithm object in the second plurality of correlithm objects is a point in a second n-dimensional space represented by a binary string;

output, using the first node, the second correlithm object to the second node and the third node;

fetch, using the second node, a third correlithm object from a second node table based on the second correlithm object in response to receiving the second correlithm object, wherein the second node table identifies;

a third plurality of correlithm objects, wherein each correlithm object in the third plurality of correlithm objects is a point in the second n-dimensional space represented by a binary string; and

a fourth plurality of correlithm objects linked with the third plurality of correlithm objects, wherein each correlithm object in the fourth plurality of correlithm objects is a point in a third n-dimensional space represented by a binary string;

output, using the second node, the third correlithm object to the third node;

fetch, using the third node, a fourth correlithm object from a third node table based on the second correlithm object and the third correlithm object in response to receiving the second correlithm object and the third correlithm object, wherein the third node table identifies;

a fifth plurality of correlithm objects, wherein each correlithm object in the fifth plurality of correlithm objects is a point in the second n-dimensional space represented by a binary string;

a sixth plurality of correlithm objects, wherein each correlithm object in the sixth plurality of correlithm objects is a point in the third n-dimensional space represented by a binary string; and

a seventh plurality of correlithm objects linked with the fifth plurality of correlithm objects and the sixth plurality of correlithm objects, wherein each correlithm object in the fifth plurality of correlithm objects is a point in a fourth n-dimensional space represented by a binary string; and

output, using the third node, the fourth correlithm object.

16. The computer program product of claim 15, wherein fetching the second correlithm object comprises:

determining hamming distances between the first correlithm object and each of the first plurality of correlithm objects;

identifying a correlithm object with the shortest hamming distance between the first correlithm object and each of the first plurality of correlithm objects; and

identifying an entry in the first node table that corresponds with the identified correlithm object.

17. The computer program product of claim 15, wherein fetching the second correlithm object comprises:

determining distances between the first correlithm object and each of the first plurality of correlithm objects, wherein determining the distances comprises;

performing an XOR operation between the first correlithm object and each of the first plurality of correlithm objects to generate a resulting binary string;

counting the number of logical high values in the resulting binary string; and

identify a correlithm object with the smallest number of logical high values; and

identifying an entry in the first node table that corresponds with the identified correlithm object.

18. The computer program product of claim 15, wherein fetching the fourth correlithm object comprises:

determining hamming distances between the second correlithm object and each of the fifth plurality of correlithm objects;

identifying a first input correlithm object with the shortest hamming distance between the second correlithm object and each of the fifth plurality of correlithm objects;

determining hamming distances between the third correlithm object and each of the sixth plurality of correlithm objects;

identifying a second input correlithm object with the shortest hamming distance between the third correlithm object and each of the sixth plurality of correlithm objects; and

identifying an entry in the third node table that corresponds with the first input correlithm object and the second input correlithm object.

19. The computer program product of claim 15, wherein receiving the first correlithm object comprises receiving the first correlithm object from a sensor configured to convert a real world value into the first correlithm object.

20. The computer program product of claim 15, wherein outputting the fourth correlithm object comprises sending the fourth correlithm object to an actor configured to output a real world value based on the fourth correlithm object.