Computer architecture for emulating n-dimensional workspaces in a correlithm object processing system

Patent metadata

Field	Value
Publication number	US-10366141-B2
Application number	US-201715701294-A
Country	US
Kind code	B2
Filing date	Sep 11, 2017
Priority date	Sep 11, 2017
Publication date	Jul 30, 2019
Grant date	Jul 30, 2019

Abstract

A device configured to emulate a node in a correlithm object processing system that includes a node engine. The node engine is configured to receive an input correlithm object and to determine distances between the input correlithm object and source correlithm objects in a node table. A correlithm object is a point in an n-dimensional space represented by a binary string. The distance between the input correlithm object and a source correlithm object is determined based on differences between a binary string representing the input correlithm object and binary strings linked with the source correlithm objects. The node engine is configured to identify a source correlithm object from the node table with the shortest distance, to fetch a target correlithm object from the node table linked with the identified source correlithm object, and to output the identified target correlithm object.

First claim

The invention claimed is:

1. A device configured to emulate a node in a correlithm object processing system, comprising:
a memory operable to store:
a node table that identifies:
a plurality of source correlithm objects, wherein each source correlithm object is a point in a first n-dimensional space represented by a binary string; and
a plurality of target correlithm objects, wherein:
each target correlithm object is a point in a second n-dimensional space represented by a binary string, and
each target correlithm object is linked with a source correlithm object from among the plurality of source correlithm objects; and

a node engine operably coupled to the memory, configured to emulate a node configured to:
receive an input correlithm object;
determine distances between the input correlithm object and each of the source correlithm objects in the node table in response to receiving the input correlithm object, wherein the distance between the input correlithm object and a source correlithm object is determined based on differences between a binary string representing the input correlithm object and binary strings linked with each of the source correlithm objects;
identify a source correlithm object from the node table with the shortest distance;
fetch a target correlithm object from the node table linked with the identified source correlithm object; and
output the identified target correlithm object.

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

3. The device of claim 1 , wherein the first n-dimensional space and the second n-dimensional space have different numbers of dimensions.

4. The device of claim 1 , wherein determining distances between the input correlithm object and each of the source correlithm objects in the node table comprises determining a hamming distance between the input correlithm object and a source correlithm object.

5. The device of claim 1 , wherein determining distances between the input correlithm object and each of the source correlithm objects in the node table comprises:
performing an XOR operation between the input correlithm object and a source correlithm object to generate a binary string; and
counting the number of logical high values in the binary string.

6. The device of claim 1 , wherein the node engine is configured to output the target correlithm object to an actor engine configured to convert the target correlithm object into a real world output value.

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

8. A method for emulating a node in a correlithm object processing system, comprising:
receiving, by a node engine, an input correlithm object;
determining, by the node engine, distances between the input correlithm object and each of the source correlithm objects in the node table in response to receiving the input correlithm object, wherein the distance between the input correlithm object and a source correlithm object is determined based on differences between a binary string representing the input correlithm object and binary strings linked with each of the source correlithm objects;
identifying, by the node engine, a source correlithm object from a node table with the shortest distance, wherein the node table identifies:
a plurality of source correlithm objects, wherein each source correlithm object is a point in a first n-dimensional space represented by a binary string; and
a plurality of target correlithm objects, wherein:
each target correlithm object is a point in a second n-dimensional space represented by a binary string, and
each target correlithm object is linked with a source correlithm object from among the plurality of source correlithm objects;

fetching, by the node engine, a target correlithm object from the node table linked with the identified source correlithm object; and
outputting, by the node engine, the identified target correlithm object.

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

10. The method of claim 8 , wherein the first n-dimensional space and the second n-dimensional space have different numbers of dimensions.

11. The method of claim 8 , wherein determining distances between the input correlithm object and each of the source correlithm objects in the node table comprises determining a hamming distance between the input correlithm object and a source correlithm object.

12. The method of claim 8 , wherein determining distances between the input correlithm object and each of the source correlithm objects in the node table comprises:
performing an XOR operation between the input correlithm object and a source correlithm object to generate a binary string; and
counting the number of logical high values in the binary string.

13. The method of claim 8 , wherein outputting the target correlithm object comprises sending the target correlithm object to an actor engine configured to convert the target correlithm object into a real world output value.

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

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 a node in a correlithm object processing system configured to:
receive an input correlithm object;
determine distances between the input correlithm object and each of the source correlithm objects in the node table in response to receiving the input correlithm object, wherein the distance between the input correlithm object and a source correlithm object is determined based on differences between a binary string representing the input correlithm object and binary strings linked with each of the source correlithm objects;
identify a source correlithm object from a node table with the shortest distance, wherein the node table identifies:
a plurality of source correlithm objects, wherein each source correlithm object is a point in a first n-dimensional space represented by a binary string; and
a plurality of target correlithm objects, wherein:
each target correlithm object is a point in a second n-dimensional space represented by a binary string, and
each target correlithm object is linked with a source correlithm object from among the plurality of source correlithm objects;

fetch a target correlithm object from the node table linked with the identified source correlithm object; and
output the identified target correlithm object.

16. The computer program product of claim 15 , wherein the first n-dimensional space and the second n-dimensional space have the same number of dimensions.

17. The computer program product of claim 15 , wherein the first n-dimensional space and the second n-dimensional space have different numbers of dimensions.

18. The computer program product of claim 15 , wherein determining distances between the input correlithm object and each of the source correlithm objects in the node table comprises determining a hamming distance between the input correlithm object and a source correlithm object.

19. The computer program product of claim 15 ,

Assignees

• Bank Of America
• Bank Of American Corp

Inventors

• Lawrence Patrick N

Classifications

• G06N5/022

  Knowledge engineering; Knowledge acquisition · CPC title

• G06F17/153 Primary

  Multidimensional correlation or convolution · CPC title

• G06N20/20 Primary

  Ensemble learning · CPC title

• G06F7/00

  Methods or arrangements for processing data by operating upon the order or content of the data handled (logic circuits H03K19/00) · CPC title

• G06F15/00

  Digital computers in general (details G06F1/00 – G06F13/00); Data processing equipment in general · CPC title