Computer architecture for emulating a correlithm object processing system

• US 10,380,221 B2
• Filed: 09/11/2017
• Issued: 08/13/2019
• Est. Priority Date: 09/11/2017
• Status: Active Grant

• Current Assignee: Bank of America Corp.
• Original Assignee: Bank of America Corp.
• Inventors: Lawrence, Patrick N.
• Primary Examiner(s): Caldwell, Andrew
• Assistant Examiner(s): Larocque, Emily E
• Application Number:
  US15/701,258
• Publication Number:
  US 20190079901A1
• Time in Patent Office: 701 Days
• CPC Class Codes: G06F 16/2462 Approximate or statistical queries
• CPC Class Codes: G06F 16/288 Entity relationship models
• CPC Class Codes: G06F 17/153 Multidimensional correlation or convolution
• CPC Class Codes: G06F 7/00 Methods or arrangements for processing data by operating upon the order or content of the data handled logic circuits H03K19/00
• CPC Class Codes: G06N 20/20 Ensemble learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: H04L 67/12 specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/565 Conversion or adaptation of application format or content adding application control or application functional data H04L67/561
• CPC Class Codes: H04L 67/566 Grouping or aggregating service requests, e.g. for unified processing

Abstract

A device that includes a sensor engine configured to receive an input signal, to identify a real world value entry in a sensor table based on the input signal, to identify an input correlithm object linked with the real world value entry, and to send the identified input correlithm object to a node engine. The node engine is configured to identify a source correlithm object from a node table with the shortest distance from the input correlithm object, to identify a target correlithm object linked with the identified source correlithm object, and to send the identified target correlithm object to an actor engine. The actor engine is configured to identify an output correlithm object from an actor table with the shortest distance from the target correlithm object, to identify a real world output value linked with the identified output correlithm object, and to output the real world output value.

Claims

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

a memory operable to store;

a sensor table that identifies;

a plurality of real world value entries; and

a plurality of input correlithm objects, wherein;

each input correlithm object is a point in a first n-dimensional space represented by a binary string, and

each input correlithm object is linked with a real world value entry from among the plurality of real world value entries;

a node table that identifies;

a plurality of source correlithm objects, wherein each source correlithm object is a point in the 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;

an actor table that identifies;

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

a plurality of real world output values, wherein each real world output value is linked with an output correlithm object from among the plurality of output correlithm objects;

a sensor engine operably coupled to the memory, configured to emulate a sensor configured to;

receive an input signal representing a data sample;

identify a real world value entry in the sensor table based on the input signal;

fetch an input correlithm object in the sensor table linked with the real world value entry;

send the identified input correlithm object to a node engine;

the node engine operably coupled to the memory, configured to emulate a node configured to;

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;

send the identified target correlithm object to an actor engine; and

the actor engine operably coupled to the memory, configured to emulate an actor configured to;

determine distances between the target correlithm object and each of the output correlithm objects in the actor table in response to receiving target correlithm object, wherein the distance between the target correlithm object and an output correlithm object is based on differences between a binary string representing the target correlithm object and binary strings linked with each of the output correlithm objects;

identify an output correlithm object from the actor table with the shortest distance;

fetch a real world output value from the actor table linked with the identified output correlithm object; and

output the real world output value.

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 real world value entries and the real world output values have different data types.

7. The device of claim 1, wherein outputting the real world output value comprises sending the real world output value to a peripheral device.

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

receiving, by a sensor engine, an input signal representing a data sample;

identifying, by the sensor engine, a real world value entry in a sensor table based on the input signal, wherein the sensor table identifies;

a plurality of real world value entries; and

a plurality of input correlithm objects, wherein;

each input correlithm object is a point in a first n-dimensional space represented by a binary string, and

each input correlithm object is linked with a real world value entry from among the plurality of real world value entries;

fetching, by the sensor engine, an input correlithm object in the sensor table linked with the real world value entry;

sending, by the sensor engine, the identified input correlithm object to a node engine;

determining, by the node engine, distances between the input correlithm object and each of the source correlithm objects in a 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; and

the node table identifies;

a plurality of source correlithm objects, wherein each source correlithm object is a point in the 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;

identifying, by the node engine, a source correlithm object from the node table with the shortest distance;

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

sending, by the node engine, the identified target correlithm object to an actor engine; and

determining, by the actor engine, distances between the target correlithm object and each of the output correlithm objects in an actor table in response to receiving target correlithm object, wherein;

the distance between the target correlithm object and an output correlithm object is based on differences between a binary string representing the target correlithm object and binary strings linked with each of the output correlithm objects; and

the actor table identifies;

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

a plurality of real world output values, wherein each real world output value is linked with an output correlithm object from among the plurality of output correlithm objects;

identifying, by the actor engine, an output correlithm object from the actor table with the shortest distance;

fetching, by the actor engine, a real world output value from the actor table linked with the identified output correlithm object; and

outputting, by the actor, the real world output value.

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 the real world value entries and the real world output values have different data types.

14. The method of claim 8, wherein outputting the real world output value comprises sending the real world output value to a peripheral device.

15. A system configured to emulate a correlithm object processing system, comprising:

a first device configured to emulate a sensor in a correlithm object processing system, comprising;

a first memory operable to store a sensor table that identifies;

a plurality of real world value entries; and

a plurality of input correlithm objects, wherein;

each input correlithm object is a point in a first n-dimensional space represented by a binary string, and

each input correlithm object is linked with a real world value entry from among the plurality of real world value entries; and

a sensor engine operably coupled to the first memory, configured to;

receive an input signal representing a data sample;

identify a real world value entry in the sensor table based on the input signal;

fetch an input correlithm object in the sensor table linked with the real world value entry; and

send the identified input correlithm object to a node engine;

a second device in signal communication with the first device and configured to emulate a node in the correlithm object processing system, comprising;

a second memory operable to store a node table that identifies;

a plurality of source correlithm objects, wherein each source correlithm object is a point in the 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

the node engine operably coupled to the second memory, configured to;

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

send the identified target correlithm object to an actor engine; and

a third device in signal communication with the second device and configured to emulate an actor in the correlithm object processing system, comprising;

a third memory operable to store an actor table that identifies;

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

a plurality of real world output values, wherein each real world output value is linked with an output correlithm object from among the plurality of output correlithm objects; and

the actor engine operable coupled to the third memory, configured to;

determine distances between the target correlithm object and each of the output correlithm objects in the actor table in response to receiving target correlithm object, wherein the distance between the target correlithm object and an output correlithm object is based on differences between a binary string representing the target correlithm object and binary strings linked with each of the output correlithm objects;

identify an output correlithm object from the actor table with the shortest distance;

fetch a real world output value from the actor table linked with the identified output correlithm object; and

output the real world output value.

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

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

18. The system 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 system of claim 15, 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.

20. The system of claim 15, wherein the real world value entries and the real world output values have different data types.