Abstract: A system configured to emulate a correlithm object processing system includes an input node, a first output node, and a second output node. The input node receives a real-world numeric value comprising an amplitude component and a phase component. The first output node receives the amplitude component and generates a first correlithm object associated with the amplitude component. The second output node receives the phase component and generates a second correlithm object associated with the phase component. A string correlithm object engine maps the first correlithm object to a first sub-string correlithm object of a string correlithm object, and maps the second correlithm object to a second sub-string correlithm object of the string correlithm object.

Abstract: A device configured to emulate an actor in a correlithm object processing system includes a memory and a processor. The memory stores an actor table that includes a string correlithm object comprising a plurality of sub-string correlithm objects. The actor is implemented by the processor and receives an input correlithm object, determines n-dimensional distances between the input correlithm object and at least a portion of the plurality of sub-string correlithm objects, and identifies a sub-string correlithm object from the actor table with the shortest determined n-dimensional distance. The actor outputs the real-world output value associated with the identified sub-string correlithm object in the actor table.

Abstract: A system that includes a first device configured to send a set of real world values to a second device using an unsecure channel and to send a correlithm object algorithm and a correlithm object key to the second device using a secure channel. The first device is further configured to input the set of real world values and the correlithm object key into the correlithm object algorithm to generate a sensor table that identifies the set of real world values and a set of correlithm objects corresponding with the set of real world values. The first device is further configured to store the sensor table in a first memory.

Abstract: A system includes a memory and a node. The memory stores first and second log string correlithm objects. The node aligns the first and second log string correlithm objects such that a sub-string correlithm object from the first log string correlithm object associated with the logarithmic value of ten aligns with a sub-string correlithm object from the second log string correlithm object representing the logarithmic value of one. The node receives a first real-world numerical value and identifies a first sub-string correlithm object from the first log string correlithm object that corresponds to the first real-world numerical value. The node determines which sub-string correlithm object from the second log string correlithm object aligns in n-dimensional space with the first sub-string correlithm object from the first log string correlithm object, and outputs the determined sub-string correlithm object.

Abstract: A device configured to emulate a bidirectional string correlithm object generator includes multiple processing stages that operate together to output a bidirectional string correlithm object. The bidirectional string correlithm object includes sub-string correlithm objects that extend in different n-dimensional directions from a central sub-string correlithm object.

Abstract: A system configured to emulate a correlithm object processing system includes an input node, a first output node, and a second output node. The input node receives a real-world numeric value comprising a plurality of numerical digits, and a flag indicating a numeric system associated with the numeric value. The first output node receives a first one of the plurality of numerical digits and generates a first correlithm object associated with the first numerical digit. The second output node receives a second one of the plurality of numerical digits and generates a second correlithm object associated with the second numerical digit. A string correlithm object engine maps the first correlithm object to a first sub-string correlithm object of a string correlithm object, and maps the second correlithm object to a second sub-string correlithm object of the string correlithm object.

Abstract: A device configured to emulate a quadrilateral lattice correlithm object generator includes multiple processing stages that operate together to output a quadrilateral lattice correlithm object. A quadrilateral lattice correlithm object has a quadrilateral geometric shape and is formed by at least four sub-lattice correlithm objects that are some number of bits away from each other in n-dimensional space.

Abstract: A string correlithm object generator is configured to output a string correlithm object comprising a plurality of sub-string correlithm objects. A node is configured to receive a plurality of data values. A memory is configured to store a node table that associates sub-string correlithm objects with the data values such that a first sub-string correlithm object is associated with a first data value and a second sub-string correlithm object is associated with a second data value. A processor is configured to receive a third data value that is between the first data value and the second data value, determine a third sub-string correlithm object that is interpolated between the first sub-string correlithm object and the second sub-string correlithm object, and associate the third sub-string correlithm object with the third data value.

Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: A device includes a memory and a node engine. The memory stores a table that includes input values represented by first correlithm objects, previous values represented by second correlithm objects, and output values represented by third correlithm objects. Each output value in the table is the difference between a corresponding input value and a corresponding previous value. The node engine receives an input correlithm object and a previous value correlithm object. The node engine determines a first correlithm object in the table based on the input correlithm object, and a second correlithm object in the table based on the previous value correlithm object. The node engine then determines a third correlithm object from the table based on the determined first correlithm object and the determined second correlithm object, and outputs the third correlithm object as an output correlithm object.

Abstract: A correlithm object processing system that includes a trainer configured to send a node entry request to a node engine that triggers the node engine to generate an entry in a node table. The trainer is further configured to receive a source correlithm object and a target correlithm object in response to sending the node entry request. The trainer is further configured to send a real world input value and the source correlithm object to a sensor engine which triggers the sensor engine to generate an entry in a sensor table linking the real world input value and the source correlithm object. The trainer is further configured to send a real world output value and the target correlithm object to an actor engine which triggers the actor engine to generate an entry in an actor table linking the real world output value and the target correlithm object.

Abstract: A device configured to emulate a triangle lattice correlithm object generator includes multiple processing stages that operate together to output a triangle lattice correlithm object. A triangle lattice correlithm object has a generally triangular shape and is formed by a first sub-lattice correlithm object, a second sub-lattice correlithm object that is some number of bits away from the first sub-lattice correlithm object in n-dimensional space, and a third sub-lattice correlithm object that is some number of bits away from the second sub-lattice correlithm object in n-dimensional space.

Abstract: A correlithm object processing system includes a reference table that stores a plurality of correlithm objects, and a first node communicatively coupled to a second node by a communication channel. The first node is configured to receive a particular one of the plurality of correlithm objects from the second node over the communication channel. The first node determines distances between the received correlithm object and each of the plurality of correlithm objects stored in the reference table. The first node further identifies one of the plurality of correlithm objects from the reference table with the shortest distance, and outputs the identified correlithm object.

Abstract: A device configured to link correlithm objects to string correlithm objects in a correlithm object processing system includes a string correlithm object generator configured to output a string correlithm object comprising a plurality of sub-string correlithm objects. Each sub-string correlithm object is adjacent in n-dimensional space to a preceding sub-string correlithm object and a subsequent sub-string correlithm object. A node is configured to receive a plurality of non-string correlithm objects, each non-string correlithm object representing data. A memory is configured to store a node table that associates sub-string correlithm objects with non-string correlithm objects such that a first sub-string correlithm object is associated with a first non-string correlithm object and a second sub-string correlithm object is associated with a second non-string correlithm object.

Abstract: A correlithm object processing system includes a memory that stores a sample two-dimensional data object represented by a horizontal string correlithm object and a vertical string correlithm object, and an example two-dimensional object represented by a horizontal string correlithm object and a vertical string correlithm object. The system determines a horizontal composite anti-Hamming distance by comparing the horizontal string correlithm objects to each other in n-dimensional space. The system determines a vertical composite anti-Hamming distance by comparing the vertical string correlithm objects to each other in n-dimensional space. The system sums the horizontal composite anti-Hamming distance with the vertical composite anti-Hamming distance to determine a composite anti-Hamming distance. The system determines the quality of a match between the example two-dimensional data object and the sample two-dimensional data object based on the composite anti-Hamming distance.

Abstract: A device configured to emulate an irregular lattice correlithm object generator includes multiple processing stages that operate together to output an irregular lattice correlithm object. The irregular lattice correlithm object includes a first sub-lattice correlithm object with a plurality of first cluster sub-lattice correlithm objects surrounding it in an n-dimensional sphere. One of the first cluster sub-lattice correlithm objects serves as a second sub-lattice correlithm object surrounded by a plurality of second cluster sub-lattice correlithm objects in an n-dimensional sphere. An irregular lattice correlithm object may be used to represent multiple data elements that are clustered around a central data element in an n-dimensional sphere. Irregular lattice correlithm object may further be used to represent multiple n-dimensional spheres that are related to each other.

Abstract: A system configured to emulate a correlithm object processing system, includes a first input node, a second input node, and an output node. The first input node receives a first correlithm object and generates a first amplitude component of a frequency domain signal associated with the first correlithm object. The second input node receives a second correlithm object and generates a first phase component of the frequency domain signal associated with the second correlithm object. The output node receives the first amplitude component and the first phase component and generates a first real-world numeric value comprising the first amplitude component and the first phase component.

Abstract: A device includes a memory and a node engine. The memory stores a table that includes input values represented by first correlithm objects, previous values represented by second correlithm objects, and output values represented by third correlithm objects. Each output value in the table is the difference between a corresponding input value and a corresponding previous value. The node engine receives an input correlithm object and a previous value correlithm object. The node engine determines a first correlithm object in the table based on the input correlithm object, and a second correlithm object in the table based on the previous value correlithm object. The node engine then determines a third correlithm object from the table based on the determined first correlithm object and the determined second correlithm object, and outputs the third correlithm object as an output correlithm object.

Abstract: A correlithm object processing system includes a reference table that stores a plurality of correlithm objects, a demultiplexer configured to split a particular one of the plurality of correlithm objects into a first portion of the binary string and a second portion of the binary string, and a multiplexer communicatively coupled to the demultiplexer by at least first and second communication channels. The multiplexer receives the first and second portions of the particular correlithm object over the first and second communication channels, respectively, and combines the first and second portions into a received correlithm object. A node communicatively coupled to the multiplexer node determines distances between the received correlithm object and each of the plurality of correlithm objects stored in the reference table, identifies one of the plurality of correlithm objects from the reference table with the shortest distance, and outputs the identified correlithm object.