Patent Publication Number: US-10331444-B2

Title: Computer architecture for emulating a hamming distance measuring device for a correlithm object processing system

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation of U.S. Non-provisional application Ser. No. 15/701,314 filed Sep. 11, 2017, by Patrick N. Lawrence and entitled “COMPUTER ARCHITECTURE FOR EMULATING A HAMMING DISTANCE MEASURING DEVICE FOR A CORRELITHM OBJECT PROCESSING SYSTEM,” which is hereby incorporated by reference as if reproduced in its entirety. 
    
    
     TECHNICAL FIELD 
     The present disclosure relates generally to computer architectures for emulating a processing system, and more specifically to computer architectures for emulating a correlithm object processing system. 
     BACKGROUND 
     Conventional computers are highly attuned to using operations that require manipulating ordinal numbers, especially ordinal binary integers. The value of an ordinal number corresponds with its position in a set of sequentially ordered number values. These computers use ordinal binary integers to represent, manipulate, and store information. These computers rely on the numerical order of ordinal binary integers representing data to perform various operations such as counting, sorting, indexing, and mathematical calculations. Even when performing operations that involve other number systems (e.g. floating point), conventional computers still resort to using ordinal binary integers to perform any operations. 
     Ordinal based number systems only provide information about the sequence order of the numbers themselves based on their numeric values. Ordinal numbers do not provide any information about any other types of relationships for the data being represented by the numeric values such as similarity. For example, when a conventional computer uses ordinal numbers to represent data samples (e.g. images or audio signals), different data samples are represented by different numeric values. The different numeric values do not provide any information about how similar or dissimilar one data sample is from another. Unless there is an exact match in ordinal number values, conventional systems are unable to tell if a data sample matches or is similar to any other data samples. As a result, conventional computers are unable to use ordinal numbers by themselves for comparing different data samples and instead these computers rely on complex signal processing techniques. Determining whether a data sample matches or is similar to other data samples is not a trivial task and poses several technical challenges for conventional computers. These technical challenges result in complex processes that consume processing power which reduces the speed and performance of the system. The ability to compare unknown data samples to known data samples is crucial for many security applications such as face recognition, voice recognition, and fraud detection. 
     Thus, it is desirable to provide a solution that allows computing systems to efficiently determine how similar different data samples are to each other and to perform operations based on their similarity. 
     SUMMARY 
     Conventional computers are highly attuned to using operations that require manipulating ordinal numbers, especially ordinal binary integers. The value of an ordinal number corresponds with its position in an set of sequentially ordered number values. These computers use ordinal binary integers to represent, manipulate, and store information. These computers rely on the numerical order of ordinal binary integers representing data to perform various operations such as counting, sorting, indexing, and mathematical calculations. Even when performing operations that involve other number systems (e.g. floating point), conventional computers still resort to using ordinal binary integers to perform any operations. 
     Ordinal based number systems only provide information about the sequence order of the numbers themselves based on their numeric values. Ordinal numbers do not provide any information about any other types of relationships for the data being represented by the numeric values such as similarity. For example, when a conventional computer uses ordinal numbers to represent data samples (e.g. images or audio signals), different data samples are represented by different numeric values. The different numeric values do not provide any information about how similar or dissimilar one data sample is from another. Unless there is an exact match in ordinal number values, conventional systems are unable to tell if a data sample matches or is similar to any other data samples. As a result, conventional computers are unable to use ordinal numbers by themselves for comparing different data samples and instead these computers rely on complex signal processing techniques. Determining whether a data sample matches or is similar to other data samples is not a trivial task and poses several technical challenges for conventional computers. These technical challenges result in complex processes that consume processing power which reduces the speed and performance of the system. The ability to compare unknown data samples to known data samples is crucial for many applications such as security application (e.g. face recognition, voice recognition, and fraud detection). 
     The system described in the present application provides a technical solution that enables the system to efficiently determine how similar different objects are to each other and to perform operations based on their similarity. In contrast to conventional systems, the system uses an unconventional configuration to perform various operations using categorical numbers and geometric objects, also referred to as correlithm objects, instead of ordinal numbers. Using categorical numbers and correlithm objects on a conventional device involves changing the traditional operation of the computer to support representing and manipulating concepts as correlithm objects. A device or system may be configured to implement or emulate a special purpose computing device capable of performing operations using correlithm objects. Implementing or emulating a correlithm object processing system improves the operation of a device by enabling the device to perform non-binary comparisons (i.e. match or no match) between different data samples. This enables the device to quantify a degree of similarity between different data samples. This increases the flexibility of the device to work with data samples having different data types and/or formats, and also increases the speed and performance of the device when performing operations using data samples. These technical advantages and other improvements to the device are described in more detail throughout the disclosure. 
     In one embodiment, the system is configured to use binary integers as categorical numbers rather than ordinal numbers which enables the system to determine how similar a data sample is to other data samples. Categorical numbers provide information about similar or dissimilar different data samples are from each other. For example, categorical numbers can be used in facial recognition applications to represent different images of faces and/or features of the faces. The system provides a technical advantage by allowing the system to assign correlithm objects represented by categorical numbers to different data samples based on how similar they are to other data samples. As an example, the system is able to assign correlithm objects to different images of people such that the correlithm objects can be directly used to determine how similar the people in the images are to each other. In other words, the system is able to use correlithm objects in facial recognition applications to quickly determine whether a captured image of a person matches any previously stored images without relying on conventional signal processing techniques. 
     Correlithm object processing systems use new types of data structures called correlithm objects that improve the way a device operates, for example, by enabling the device to perform non-binary data set comparisons and to quantify the similarity between different data samples. Correlithm objects are data structures designed to improve the way a device stores, retrieves, and compares data samples in memory. Correlithm objects also provide a data structure that is independent of the data type and format of the data samples they represent. Correlithm objects allow data samples to be directly compared regardless of their original data type and/or format. 
     A correlithm object processing system uses a combination of a sensor table, a node table, and/or an actor table to provide a specific set of rules that improve computer-related technologies by enabling devices to compare and to determine the degree of similarity between different data samples regardless of the data type and/or format of the data sample they represent. The ability to directly compare data samples having different data types and/or formatting is a new functionality that cannot be performed using conventional computing systems and data structures. 
     In addition, correlithm object processing system uses a combination of a sensor table, a node table, and/or an actor table to provide a particular manner for transforming data samples between ordinal number representations and correlithm objects in a correlithm object domain. Transforming data samples between ordinal number representations and correlithm objects involves fundamentally changing the data type of data samples between an ordinal number system and a categorical number system to achieve the previously described benefits of the correlithm object processing system. 
     Using correlithm objects allows the system or device to compare data samples (e.g. images) even when the input data sample does not exactly match any known or previously stored input values. For example, an input data sample that is an image may have different lighting conditions than the previously stored images. The differences in lighting conditions can make images of the same person appear different from each other. The device uses an unconventional configuration that implements a correlithm object processing system that uses the distance between the data samples which are represented as correlithm objects and other known data samples to determine whether the input data sample matches or is similar to the other known data samples. Implementing a correlithm object processing system fundamentally changes the device and the traditional data processing paradigm. Implementing the correlithm object processing system improves the operation of the device by enabling the device to perform non-binary comparisons of data samples. In other words, the device is able to determine how similar the data samples are to each other even when the data samples are not exact matches. In addition, the device is able to quantify how similar data samples are to one another. The ability to determine how similar data samples are to each others is unique and distinct from conventional computers that can only perform binary comparisons to identify exact matches. 
     The problems associated with comparing data sets and identifying matches based on the comparison are problems necessarily rooted in computer technologies. As described above, conventional systems are limited to a binary comparison that can only determine whether an exact match is found. Emulating a correlithm object processing system provides a technical solution that addresses problems associated with comparing data sets and identifying matches. Using correlithm objects to represent data samples fundamentally changes the operation of a device and how the device views data samples. By implementing a correlithm object processing system, the device can determine the distance between the data samples and other known data samples to determine whether the input data sample matches or is similar to the other known data samples. In addition, the device is able to determine a degree of similarity that quantifies how similar different data samples are to one another. 
     Certain embodiments of the present disclosure may include some, all, or none of these advantages. These advantages and other features will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings and claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of this disclosure, reference is now made to the following brief description, taken in connection with the accompanying drawings and detailed description, wherein like reference numerals represent like parts. 
         FIG. 1  is a schematic view of an embodiment of a special purpose computer implementing correlithm objects in an n-dimensional space; 
         FIG. 2  is a perspective view of an embodiment of a mapping between correlithm objects in different n-dimensional spaces; 
         FIG. 3  is a schematic view of an embodiment of a correlithm object processing system; 
         FIG. 4  is a protocol diagram of an embodiment of a correlithm object process flow; 
         FIG. 5  is a schematic diagram of an embodiment a computer architecture for emulating a correlithm object processing system; 
         FIG. 6A  is a schematic diagram of an embodiment of a distance measuring process for a correlithm object processing system; 
         FIG. 6B  is a schematic diagram of another embodiment of a distance measuring process for a correlithm object processing system based on hamming distances; 
         FIG. 7A  is a flowchart of an embodiment of a distance measuring process flow; 
         FIG. 7B  is a flowchart of another embodiment of a distance measuring process flow based on hamming distances; 
         FIG. 8  is a schematic diagram of an embodiment of a process for emulating an image input adapter for a correlithm object processing system; 
         FIG. 9  is a flowchart of an embodiment of an image input adapting emulation method; 
         FIG. 10  is a schematic diagram of an embodiment of a process for emulating an image output adapter for a correlithm object processing system; and 
         FIG. 11  is a flowchart of an embodiment of an image output adapter emulation method. 
     
    
    
     DETAILED DESCRIPTION 
       FIGS. 1-5  describe various embodiments of how a correlithm object processing system may be implemented or emulated in hardware, such as a special purpose computer.  FIGS. 6A, 6B, 7A, and 7B  describe processes for determining distances between correlithm objects in a correlithm object processing system.  FIGS. 8 and 9  describe an embodiment of an image input adapter for a correlithm object processing system.  FIGS. 10 and 11  describe an image output adapter for a correlithm object processing system. 
       FIG. 1  is a schematic view of an embodiment of a user device  100  implementing correlithm objects  104  in an n-dimensional space  102 . Examples of user devices  100  include, but are not limited to, desktop computers, mobile phones, tablet computers, laptop computers, or other special purpose computer platform. The user device  100  is configured to implement or emulate a correlithm object processing system that uses categorical numbers to represent data samples as correlithm objects  104  in a high-dimensional space  102 , for example a high-dimensional binary cube. Additional information about the correlithm object processing system is described in  FIG. 3 . Additional information about configuring the user device  100  to implement or emulate a correlithm object processing system is described in  FIG. 5 . 
     Conventional computers rely on the numerical order of ordinal binary integers representing data to perform various operations such as counting, sorting, indexing, and mathematical calculations. Even when performing operations that involve other number systems (e.g. floating point), conventional computers still resort to using ordinal binary integers to perform any operations. Ordinal based number systems only provide information about the sequence order of the numbers themselves based on their numeric values. Ordinal numbers do not provide any information about any other types of relationships for the data being represented by the numeric values, such as similarity. For example, when a conventional computer uses ordinal numbers to represent data samples (e.g. images or audio signals), different data samples are represented by different numeric values. The different numeric values do not provide any information about how similar or dissimilar one data sample is from another. In other words, conventional computers are only able to make binary comparisons of data samples which only results in determining whether the data samples match or do not match. Unless there is an exact match in ordinal number values, conventional systems are unable to tell if a data sample matches or is similar to any other data samples. As a result, conventional computers are unable to use ordinal numbers by themselves for determining similarity between different data samples, and instead these computers rely on complex signal processing techniques. Determining whether a data sample matches or is similar to other data samples is not a trivial task and poses several technical challenges for conventional computers. These technical challenges result in complex processes that consume processing power which reduces the speed and performance of the system. 
     In contrast to conventional systems, the user device  100  operates as a special purpose machine for implementing or emulating a correlithm object processing system. Implementing or emulating a correlithm object processing system improves the operation of the user device  100  by enabling the user device  100  to perform non-binary comparisons (i.e. match or no match) between different data samples. This enables the user device  100  to quantify a degree of similarity between different data samples. This increases the flexibility of the user device  100  to work with data samples having different data types and/or formats, and also increases the speed and performance of the user device  100  when performing operations using data samples. These improvements and other benefits to the user device  100  are described in more detail below and throughout the disclosure. 
     For example, the user device  100  employs the correlithm object processing system to allow the user device  100  to compare data samples even when the input data sample does not exactly match any known or previously stored input values. Implementing a correlithm object processing system fundamentally changes the user device  100  and the traditional data processing paradigm. Implementing the correlithm object processing system improves the operation of the user device  100  by enabling the user device  100  to perform non-binary comparisons of data samples. In other words, the user device  100  is able to determine how similar the data samples are to each other even when the data samples are not exact matches. In addition, the user device  100  is able to quantify how similar data samples are to one another. The ability to determine how similar data samples are to each others is unique and distinct from conventional computers that can only perform binary comparisons to identify exact matches. 
     The user device&#39;s  100  ability to perform non-binary comparisons of data samples also fundamentally changes traditional data searching paradigms. For example, conventional search engines rely on finding exact matches or exact partial matches of search tokens to identify related data samples. For instance, conventional text-based search engine are limited to finding related data samples that have text that exactly matches other data samples. These search engines only provide a binary result that identifies whether or not an exact match was found based on the search token. Implementing the correlithm object processing system improves the operation of the user device  100  by enabling the user device  100  to identify related data samples based on how similar the search token is to other data sample. These improvements result in increased flexibility and faster search time when using a correlithm object processing system. The ability to identify similarities between data samples expands the capabilities of a search engine to include data samples that may not have an exact match with a search token but are still related and similar in some aspects. The user device  100  is also able to quantify how similar data samples are to each other based on characteristics besides exact matches to the search token. Implementing the correlithm object processing system involves operating the user device  100  in an unconventional manner to achieve these technological improvements as well as other benefits described below for the user device  100 . 
     Computing devices typically rely on the ability to compare data sets (e.g. data samples) to one another for processing. For example, in security or authentication applications a computing device is configured to compare an input of an unknown person to a data set of known people (or biometric information associated with these people). The problems associated with comparing data sets and identifying matches based on the comparison are problems necessarily rooted in computer technologies. As described above, conventional systems are limited to a binary comparison that can only determine whether an exact match is found. As an example, an input data sample that is an image of a person may have different lighting conditions than previously stored images. In this example, different lighting conditions can make images of the same person appear different from each other. Conventional computers are unable to distinguish between two images of the same person with different lighting conditions and two images of two different people without complicated signal processing. In both of these cases, conventional computers can only determine that the images are different. This is because conventional computers rely on manipulating ordinal numbers for processing. 
     In contrast, the user device  100  uses an unconventional configuration that uses correlithm objects to represent data samples. Using correlithm objects to represent data samples fundamentally changes the operation of the user device  100  and how the device views data samples. By implementing a correlithm object processing system, the user device  100  can determine the distance between the data samples and other known data samples to determine whether the input data sample matches or is similar to the other known data samples, as explained in detail below. Unlike the conventional computers described in the previous example, the user device  100  is able to distinguish between two images of the same person with different lighting conditions and two images of two different people by using correlithm objects  104 . Correlithm objects allow the user device  100  to determine whether there are any similarities between data samples, such as between two images that are different from each other in some respects but similar in other respects. For example, the user device  100  is able to determine that despite different lighting conditions, the same person is present in both images. 
     In addition, the user device  100  is able to determine a degree of similarity that quantifies how similar different data samples are to one another. Implementing a correlithm object processing system in the user device  100  improves the operation of the user device  100  when comparing data sets and identifying matches by allowing the user device  100  to perform non-binary comparisons between data sets and to quantify the similarity between different data samples. In addition, using a correlithm object processing system results in increased flexibility and faster search times when comparing data samples or data sets. Thus, implementing a correlithm object processing system in the user device  100  provides a technical solution to a problem necessarily rooted in computer technologies. 
     The ability to implement a correlithm object processing system provides a technical advantage by allowing the system to identify and compare data samples regardless of whether an exact match has been previous observed or stored. In other words, using the correlithm object processing system the user device  100  is able to identify similar data samples to an input data sample in the absence of an exact match. This functionality is unique and distinct from conventional computers that can only identify data samples with exact matches. 
     Examples of data samples include, but are not limited to, images, files, text, audio signals, biometric signals, electric signals, or any other suitable type of data. A correlithm object  104  is a point in the n-dimensional space  102 , sometimes called an “n-space.” The value of represents the number of dimensions of the space. For example, an n-dimensional space  102  may be a 3-dimensional space, a 50-dimensional space, a 100-dimensional space, or any other suitable dimension space. The number of dimensions depends on its ability to support certain statistical tests, such as the distances between pairs of randomly chosen points in the space approximating a normal distribution. In some embodiments, increasing the number of dimensions in the n-dimensional space  102  modifies the statistical properties of the system to provide improved results. Increasing the number of dimensions increases the probability that a correlithm object  104  is similar to other adjacent correlithm objects  104 . In other words, increasing the number of dimensions increases the correlation between how close a pair of correlithm objects  104  are to each other and how similar the correlithm objects  104  are to each other. 
     Correlithm object processing systems use new types of data structures called correlithm objects  104  that improve the way a device operates, for example, by enabling the device to perform non-binary data set comparisons and to quantify the similarity between different data samples. Correlithm objects  104  are data structures designed to improve the way a device stores, retrieves, and compares data samples in memory. Unlike conventional data structures, correlithm objects  104  are data structures where objects can be expressed in a high-dimensional space such that distance  106  between points in the space represent the similarity between different objects or data samples. In other words, the distance  106  between a pair of correlithm objects  104  in the n-dimensional space  102  indicates how similar the correlithm objects  104  are from each other and the data samples they represent. Correlithm objects  104  that are close to each other are more similar to each other than correlithm objects  104  that are further apart from each other. For example, in a facial recognition application, correlithm objects  104  used to represent images of different types of glasses may be relatively close to each other compared to correlithm objects  104  used to represent images of other features such as facial hair. An exact match between two data samples occurs when their corresponding correlithm objects  104  are the same or have no distance between them. When two data samples are not exact matches but are similar, the distance between their correlithm objects  104  can be used to indicate their similarities. In other words, the distance  106  between correlithm objects  104  can be used to identify both data samples that exactly match each other as well as data samples that do not match but are similar. This feature is unique to a correlithm processing system and is unlike conventional computers that are unable to detect when data samples are different but similar in some aspects. 
     Correlithm objects  104  also provide a data structure that is independent of the data type and format of the data samples they represent. Correlithm objects  104  allow data samples to be directly compared regardless of their original data type and/or format. In some instances, comparing data samples as correlithm objects  104  is computationally more efficient and faster than comparing data samples in their original format. For example, comparing images using conventional data structures involves significant amounts of image processing which is time consuming and consumes processing resources. Thus, using correlithm objects  104  to represent data samples provides increased flexibility and improved performance compared to using other conventional data structures. 
     In one embodiment, correlithm objects  104  may be represented using categorical binary strings. The number of bits used to represent the correlithm object  104  corresponds with the number of dimensions of the n-dimensional space  102  where the correlithm object  102  is located. For example, each correlithm object  104  may be uniquely identified using a 64-bit string in a 64-dimensional space  102 . In other examples, correlithm objects  104  can be identified using any other suitable number of bits in a string. In this configuration, the distance  106  between two correlithm objects  104  can be determined based on the differences between the bits of the two correlithm objects  104 . In other words, the distance  106  between two correlithm objects can be determined based on how many individual bits differ between the correlithm objects  104 . The distance  106  between two correlithm objects  104  can be computed using hamming distance or any other suitable technique. An example of a process for computing the distance between a pair of correlithm objects  104  is described in  FIGS. 6A, 6B, 7A, and 7B . 
     In another embodiment, the distance  106  between two correlithm objects  104  can be determined using a Minkowski distance such as the Euclidean or “straight-line” distance between the correlithm objects  104 . For example, the distance  106  between a pair of correlithm objects  104  may be determined by calculating the square root of the sum of squares of the coordinate difference in each dimension. 
     The user device  100  is configured to implement or emulate a correlithm object processing system that comprises one or more sensors  302 , nodes  304 , and/or actors  306  in order to convert data samples between real world values or representations and to correlithm objects  104  in a correlithm object domain. Sensors  302  are generally configured to convert real world data samples to the correlithm object domain. Nodes  304  are generally configured to process or perform various operations on correlithm objects in the correlithm object domain. Actors  306  are generally configured to convert correlithm objects  104  into real world values or representations. Additional information about sensors  302 , nodes  304 , and actors  306  is described in  FIG. 3 . 
     Performing operations using correlithm objects  104  in a correlithm object domain allows the user device  100  to identify relationships between data samples that cannot be identified using conventional data processing systems. For example, in the correlithm object domain, the user device  100  is able to identify not only data samples that exactly match an input data sample, but also other data samples that have similar characteristics or features as the input data samples. Conventional computers are unable to identify these types of relationships readily. Using correlithm objects  104  improves the operation of the user device  100  by enabling the user device  100  to efficiently process data samples and identify relationships between data samples without relying on signal processing techniques that require a significant amount of processing resources. These benefits allow the user device  100  to operate more efficiently than conventional computers by reducing the amount of processing power and resources that are needed to perform various operations. 
       FIG. 2  is a schematic view of an embodiment of a mapping between correlithm objects  104  in different n-dimensional spaces  102 . When implementing a correlithm object processing system, the user device  100  performs operations within the correlithm object domain using correlithm objects  104  in different n-dimensional spaces  102 . As an example, the user device  100  may convert different types of data samples having real world values into correlithm objects  104  in different n-dimensional spaces  102 . For instance, the user device  100  may convert data samples of text into a first set of correlithm objects  104  in a first n-dimensional space  102  and data samples of audio samples as a second set of correlithm objects  104  in a second n-dimensional space  102 . Conventional systems require data samples to be of the same type and/or format in order to perform any kind of operation on the data samples. In some instances, some types of data samples cannot be compared because there is no common format available. For example, conventional computers are unable to compare data samples of images and data samples of audio samples because there is no common format. In contrast, the user device  100  implementing a correlithm object processing system is able to compare and perform operations using correlithm objects  104  in the correlithm object domain regardless of the type or format of the original data samples. 
     In  FIG. 2 , a first set of correlithm objects  104 A are defined within a first n-dimensional space  102 A and a second set of correlithm objects  104 B are defined within a second n-dimensional space  102 B. The n-dimensional spaces may have the same number dimensions or a different number of dimensions. For example, the first n-dimensional space  102 A and the second n-dimensional space  102 B may both be three dimensional spaces. As another example, the first n-dimensional space  102 A may be a three dimensional space and the second n-dimensional space  102 B may be a nine dimensional space. Correlithm objects  104  in the first n-dimensional space  102 A and second n-dimensional space  102 B are mapped to each other. In other words, a correlithm object  104 A in the first n-dimensional space  102 A may reference or be linked with a particular correlithm object  104 B in the second n-dimensional space  102 B. The correlithm objects  104  may also be linked with and referenced with other correlithm objects  104  in other n-dimensional spaces  102 . 
     In one embodiment, a data structure such as table  200  may used to map or link correlithm objects  194  in different n-dimensional spaces  102 . In some instances, table  200  is referred to as a node table. Table  200  is generally configured to identify a first plurality of correlithm objects  104  in a first n-dimensional space  102  and a second plurality of correlithm objects  104  in a second n-dimensional space  102 . Each correlithm object  104  in the first n-dimensional space  102  is linked with a correlithm object  104  is the second n-dimensional space  102 . For example, table  200  may be configured with a first column  202  that lists correlithm objects  104 A as source correlithm objects and a second column  204  that lists corresponding correlithm objects  104 B as target correlithm objects. In other examples, table  200  may be configured in any other suitable manner or may be implemented using any other suitable data structure. In some embodiments, one or more mapping functions may be used to convert between a correlithm object  104  in a first n-dimensional space and a correlithm object  104  is a second n-dimensional space. 
       FIG. 3  is a schematic view of an embodiment of a correlithm object processing system  300  that is implemented by a user device  100  to perform operations using correlithm objects  104 . The system  300  generally comprises a sensor  302 , a node  304 , and an actor  306 . The system  300  may be configured with any suitable number and/or configuration of sensors  302 , nodes  304 , and actors  306 . An example of the system  300  in operation is described in  FIG. 4 . In one embodiment, a sensor  302 , a node  304 , and an actor  306  may all be implemented on the same device (e.g. user device  100 ). In other embodiments, a sensor  302 , a node  304 , and an actor  306  may each be implemented on different devices in signal communication with each other for example over a network. In other embodiments, different devices may be configured to implement any combination of sensors  302 , nodes  304 , and actors  306 . 
     Sensors  302  serve as interfaces that allow a user device  100  to convert real world data samples into correlithm objects  104  that can be used in the correlithm object domain. Sensors  302  enable the user device  100  compare and perform operations using correlithm objects  104  regardless of the data type or format of the original data sample. Sensors  302  are configured to receive a real world value  320  representing a data sample as an input, to determine a correlithm object  104  based on the real world value  320 , and to output the correlithm object  104 . For example, the sensor  302  may receive an image  301  of a person and output a correlithm object  322  to the node  304  or actor  306 . In one embodiment, sensors  302  are configured to use sensor tables  308  that link a plurality of real world values with a plurality of correlithm objects  104  in an n-dimensional space  102 . Real world values are any type of signal, value, or representation of data samples. Examples of real world values include, but are not limited to, images, pixel values, text, audio signals, electrical signals, and biometric signals. As an example, a sensor table  308  may be configured with a first column  312  that lists real world value entries corresponding with different images and a second column  314  that lists corresponding correlithm objects  104  as input correlithm objects. In other examples, sensor tables  308  may be configured in any other suitable manner or may be implemented using any other suitable data structure. In some embodiments, one or more mapping functions may be used to translate between a real world value  320  and a correlithm object  104  is a n-dimensional space. Additional information for implementing or emulating a sensor  302  in hardware is described in  FIG. 5 . 
     Nodes  304  are configured to receive a correlithm object  104  (e.g. an input correlithm object  104 ), to determine another correlithm object  104  based on the received correlithm object  104 , and to output the identified correlithm object  104  (e.g. an output correlithm object  104 ). In one embodiment, nodes  304  are configured to use node tables  200  that link a plurality of correlithm objects  104  from a first n-dimensional space  102  with a plurality of correlithm objects  104  in a second n-dimensional space  102 . A node table  200  may be configured similar to the table  200  described in  FIG. 2 . Additional information for implementing or emulating a node  304  in hardware is described in  FIG. 5 . 
     Actors  306  serve as interfaces that allow a user device  100  to convert correlithm objects  104  in the correlithm object domain back to real world values or data samples. Actors  306  enable the user device  100  to convert from correlithm objects  104  into any suitable type of real world value. Actors  306  are configured to receive a correlithm object  104  (e.g. an output correlithm object  104 ), to determine a real world output value  326  based on the received correlithm object  104 , and to output the real world output value  326 . The real world output value  326  may be a different data type or representation of the original data sample. As an example, the real world input value  320  may be an image  301  of a person and the resulting real world output value  326  may be text  327  and/or an audio signal identifying the person. In one embodiment, actors  306  are configured to use actor tables  310  that link a plurality of correlithm objects  104  in an n-dimensional space  102  with a plurality of real world values. As an example, an actor table  310  may be configured with a first column  316  that lists correlithm objects  104  as output correlithm objects and a second column  318  that lists real world values. In other examples, actor tables  310  may be configured in any other suitable manner or may be implemented using any other suitable data structure. In some embodiments, one or more mapping functions may be employed to translate between a correlithm object  104  in an n-dimensional space and a real world output value  326 . Additional information for implementing or emulating an actor  306  in hardware is described in  FIG. 5 . 
     A correlithm object processing system  300  uses a combination of a sensor table  308 , a node table  200 , and/or an actor table  310  to provide a specific set of rules that improve computer-related technologies by enabling devices to compare and to determine the degree of similarity between different data samples regardless of the data type and/or format of the data sample they represent. The ability to directly compare data samples having different data types and/or formatting is a new functionality that cannot be performed using conventional computing systems and data structures. Conventional systems require data samples to be of the same type and/or format in order to perform any kind of operation on the data samples. In some instances, some types of data samples are incompatible with each other and cannot be compared because there is no common format available. For example, conventional computers are unable to compare data samples of images with data samples of audio samples because there is no common format available. In contrast, a device implementing a correlithm object processing system uses a combination of a sensor table  308 , a node table  200 , and/or an actor table  310  to compare and perform operations using correlithm objects  104  in the correlithm object domain regardless of the type or format of the original data samples. The correlithm object processing system  300  uses a combination of a sensor table  308 , a node table  200 , and/or an actor table  310  as a specific set of rules that provides a particular solution to dealing with different types of data samples and allows devices to perform operations on different types of data samples using correlithm objects  104  in the correlithm object domain. In some instances, comparing data samples as correlithm objects  104  is computationally more efficient and faster than comparing data samples in their original format. Thus, using correlithm objects  104  to represent data samples provides increased flexibility and improved performance compared to using other conventional data structures. The specific set of rules used by the correlithm object processing system  300  go beyond simply using routine and conventional activities in order to achieve this new functionality and performance improvements. 
     In addition, correlithm object processing system  300  uses a combination of a sensor table  308 , a node table  200 , and/or an actor table  310  to provide a particular manner for transforming data samples between ordinal number representations and correlithm objects  104  in a correlithm object domain. For example, the correlithm object processing system  300  may be configured to transform a representation of a data sample into a correlithm object  104 , to perform various operations using the correlithm object  104  in the correlithm object domain, and to transform a resulting correlithm object  104  into another representation of a data sample. Transforming data samples between ordinal number representations and correlithm objects  104  involves fundamentally changing the data type of data samples between an ordinal number system and a categorical number system to achieve the previously described benefits of the correlithm object processing system  300 . 
       FIG. 4  is a protocol diagram of an embodiment of a correlithm object process flow  400 . A user device  100  implements process flow  400  to emulate a correlithm object processing system  300  to perform operations using correlithm object  104  such as facial recognition. The user device  100  implements process flow  400  to compare different data samples (e.g. images, voice signals, or text) are to each other and to identify other objects based on the comparison. Process flow  400  provides instructions that allows user devices  100  to achieve the improved technical benefits of a correlithm object processing system  300 . 
     Conventional systems are configured to use ordinal numbers for identifying different data samples. Ordinal based number systems only provide information about the sequence order of numbers based on their numeric values, and do not provide any information about any other types of relationships for the data samples being represented by the numeric values such as similarity. In contrast, a user device  100  can implement or emulate the correlithm object processing system  300  which provides an unconventional solution that uses categorical numbers and correlithm objects  104  to represent data samples. For example, the system  300  may be configured to use binary integers as categorical numbers to generate correlithm objects  104  which enables the user device  100  to perform operations directly based on similarities between different data samples. Categorical numbers provide information about how similar different data sample are from each other. Correlithm objects  104  generated using categorical numbers can be used directly by the system  300  for determining how similar different data samples are from each other without relying on exact matches, having a common data type or format, or conventional signal processing techniques. 
     A non-limiting example is provided to illustrate how the user device  100  implements process flow  400  to emulate a correlithm object processing system  300  to perform facial recognition on an image to determine the identity of the person in the image. In other examples, the user device  100  may implement process flow  400  to emulate a correlithm object processing system  300  to perform voice recognition, text recognition, or any other operation that compares different objects. 
     At step  402 , a sensor  302  receives an input signal representing a data sample. For example, the sensor  302  receives an image of person&#39;s face as a real world input value  320 . The input signal may be in any suitable data type or format. In one embodiment, the sensor  302  may obtain the input signal in real-time from a peripheral device (e.g. a camera). In another embodiment, the sensor  302  may obtain the input signal from a memory or database. 
     At step  404 , the sensor  302  identifies a real world value entry in a sensor table  308  based on the input signal. In one embodiment, the system  300  identifies a real world value entry in the sensor table  308  that matches the input signal. For example, the real world value entries may comprise previously stored images. The sensor  302  may compare the received image to the previously stored images to identify a real world value entry that matches the received image. In one embodiment, when the sensor  302  does not find an exact match, the sensor  302  finds a real world value entry that closest matches the received image. 
     At step  406 , the sensor  302  identifies and fetches an input correlithm object  104  in the sensor table  308  linked with the real world value entry. At step  408 , the sensor  302  sends the identified input correlithm object  104  to the node  304 . In one embodiment, the identified input correlithm object  104  is represented in the sensor table  308  using a categorical binary integer string. The sensor  302  sends the binary string representing to the identified input correlithm object  104  to the node  304 . 
     At step  410 , the node  304  receives the input correlithm object  104  and determines distances  106  between the input correlithm object  104  and each source correlithm object  104  in a node table  200 . In one embodiment, the distance  106  between two correlithm objects  104  can be determined based on the differences between the bits of the two correlithm objects  104 . In other words, the distance  106  between two correlithm objects can be determined based on how many individual bits differ between a pair of correlithm objects  104 . The distance  106  between two correlithm objects  104  can be computed using hamming distance or any other suitable technique. An example of a distance measuring process for a pair of correlithm objects  104  is described in  FIGS. 7A and 7B . In another embodiment, the distance  106  between two correlithm objects  104  can be determined using a Minkowski distance such as the Euclidean or “straight-line” distance between the correlithm objects  104 . For example, the distance  106  between a pair of correlithm objects  104  may be determined by calculating the square root of the sum of squares of the coordinate difference in each dimension. 
     At step  412 , the node  304  identifies a source correlithm object  104  from the node table  200  with the shortest distance  106 . A source correlithm object  104  with the shortest distance from the input correlithm object  104  is a correlithm object  104  either matches or most closely matches the received input correlithm object  104 . 
     At step  414 , the node  304  identifies and fetches a target correlithm object  104  in the node table  200  linked with the source correlithm object  104 . At step  416 , the node  304  outputs the identified target correlithm object  104  to the actor  306 . In this example, the identified target correlithm object  104  is represented in the node table  200  using a categorical binary integer string. The node  304  sends the binary string representing to the identified target correlithm object  104  to the actor  306 . 
     At step  418 , the actor  306  receives the target correlithm object  104  and determines distances between the target correlithm object  104  and each output correlithm object  104  in an actor table  310 . The actor  306  may compute the distances between the target correlithm object  104  and each output correlithm object  104  in an actor table  310  using a process similar to the process described in step  410 . 
     At step  420 , the actor  306  identifies an output correlithm object  104  from the actor table  310  with the shortest distance  106 . An output correlithm object  104  with the shortest distance from the target correlithm object  104  is a correlithm object  104  either matches or most closely matches the received target correlithm object  104 . 
     At step  422 , the actor  306  identifies and fetches a real world output value in the actor table  310  linked with the output correlithm object  104 . The real world output value may be any suitable type of data sample that corresponds with the original input signal. For example, the real world output value may be text that indicates the name of the person in the image or some other identifier associated with the person in the image. As another example, the real world output value may be an audio signal or sample of the name of the person in the image. In other examples, the real world output value may be any other suitable real world signal or value that corresponds with the original input signal. The real world output value may be in any suitable data type or format. 
     At step  424 , the actor  306  outputs the identified real world output value. In one embodiment, the actor  306  may output the real world output value in real-time to a peripheral device (e.g. a display or a speaker). In one embodiment, the actor  306  may output the real world output value to a memory or database. In one embodiment, the real world output value is sent to another sensor  302 . For example, the real world output value may be sent to another sensor  302  as an input for another process. 
       FIG. 5  is a schematic diagram of an embodiment a computer architecture  500  for emulating a correlithm object processing system  300  in a user device  100 . The computer architecture  500  comprises a processor  502 , a memory  504 , a network interface  506 , and an input-output (I/O) interface  508 . The computer architecture  500  may be configured as shown or in any other suitable configuration. 
     The processor  502  comprises one or more processors operably coupled to the memory  504 . The processor  502  is any electronic circuitry including, but not limited to, state machines, one or more central processing unit (CPU) chips, logic units, cores (e.g. a multi-core processor), field-programmable gate array (FPGAs), application specific integrated circuits (ASICs), graphics processing units (GPUs), or digital signal processors (DSPs). The processor  502  may be a programmable logic device, a microcontroller, a microprocessor, or any suitable combination of the preceding. The processor  502  is communicatively coupled to and in signal communication with the memory  204 . The one or more processors are configured to process data and may be implemented in hardware or software. For example, the processor  502  may be 8-bit, 16-bit, 32-bit, 64-bit or of any other suitable architecture. The processor  502  may include an arithmetic logic unit (ALU) for performing arithmetic and logic operations, processor registers that supply operands to the ALU and store the results of ALU operations, and a control unit that fetches instructions from memory and executes them by directing the coordinated operations of the ALU, registers and other components. 
     The one or more processors are configured to implement various instructions. For example, the one or more processors are configured to execute instructions to implement sensor engines  510 , node engines  512 , and actor engines  514 . In an embodiment, the sensor engines  510 , the node engines  512 , and the actor engines  514  are implemented using logic units, FPGAs, ASICs, DSPs, or any other suitable hardware. The sensor engines  510 , the node engines  512 , and the actor engines  514  are each configured to implement a specific set of rules or process that provides an improved technological result. 
     In one embodiment, the sensor engine  510  is configured to receive a real world value  320  as an input, to determine a correlithm object  104  based on the real world value  320 , and to output the correlithm object  104 . Examples of the sensor engine  510  in operation are described in  FIGS. 4 and 8 . 
     In one embodiment, the node engine  512  is configured to receive a correlithm object  104  (e.g. an input correlithm object  104 ), to determine another correlithm object  104  based on the received correlithm object  104 , and to output the identified correlithm object  104  (e.g. an output correlithm object  104 ). The node engine  512  is also configured to compute distances between pairs of correlithm objects  104 . Examples of the node engine  512  in operation are described in  FIGS. 4, 7A, and 7B . 
     In one embodiment, the actor engine  514  is configured to receive a correlithm object  104  (e.g. an output correlithm object  104 ), to determine a real world output value  326  based on the received correlithm object  104 , and to output the real world output value  326 . Examples of the actor engine  514  in operation are described in  FIGS. 4 and 11 . 
     The memory  504  comprises one or more non-transitory disks, tape drives, or solid-state drives, and may be used as an over-flow data storage device, to store programs when such programs are selected for execution, and to store instructions and data that are read during program execution. The memory  504  may be volatile or non-volatile and may comprise read-only memory (ROM), random-access memory (RAM), ternary content-addressable memory (TCAM), dynamic random-access memory (DRAM), and static random-access memory (SRAM). The memory  504  is operable to store sensor instructions  516 , node instructions  518 , actor instructions  520 , counting tables  522 , sensor tables  308 , node tables  200 , actor tables  310 , mask tables  524 , and/or any other data or instructions. The sensor instructions  516 , the node instructions  518 , and the actor instructions  520  comprise any suitable set of instructions, logic, rules, or code operable to execute the sensor engine  510 , node engine  512 , and the actor engine  514 , respectively. 
     The sensor tables  308 , the node tables  200 , and the actor tables  310  may be configured similar to the sensor tables  308 , the node tables  200 , and the actor tables  310  described in  FIG. 3 , respectively. 
     Counting tables  522  are configured to link a plurality of binary strings with a plurality of numeric values. In one embodiment, the numeric value identifies the number of bits set to a logical high or logical one in a corresponding binary string. For example, a binary string with eight bits set to a logical one will be linked with a numeric value of eight. As an example, a counting table  522  may be configured with a first column that lists binary strings as input values and a second column that lists numeric values as output values. In other examples, counting tables  522  may be configured in any other suitable manner or may be implemented using any other suitable data structure. An example of a counting table  522  in operation is described in  FIGS. 6A and 7A . 
     Each mask table  524  is linked with a mask that defines an array of pixels in an image. In one embodiment, each mask at least partially overlaps with at least one other mask. In this configuration, each mask has at least one pixel in common with another mask. In other embodiments, the masks are configured to not overlap with other masks. In this configuration, the masks do not have any pixels in common with each other. Each mask table  524  identifies a plurality of correlithm object location indexes that are linked with a portion of an aggregated correlithm object. An aggregated correlithm object is a correlithm object  104  that is composed of a plurality of correlithm objects  104 . For example, an aggregated correlithm object may be formed from five, ten, fifteen, or more correlithm objects  104 . An aggregated correlithm object may be formed from any other suitable number of correlithm objects  104 . As an example, a first correlithm object location index may be linked with a first correlithm object represented by the first 8-bits of an aggregated correlithm object, a second correlithm object location index is linked with a second correlithm object represented by the second 8-bits of the aggregated correlithm object, and so on. Each mask table  524  is further configured to link each of the plurality of correlithm object location indexes with a pixel location in a mask. For example, a mask table  524  may link a first portion of an aggregated correlithm object that defines a correlithm object  104  with the first pixel defined by the mask. An example of a mask table  524  in operation is described in  FIGS. 10 and 11 . 
     The network interface  506  is configured to enable wired and/or wireless communications. The network interface  506  is configured to communicate data with any other device or system. For example, the network interface  506  may be configured for communication with a modem, a switch, a router, a bridge, a server, or a client. The processor  502  is configured to send and receive data using the network interface  506 . 
     The I/O interface  508  may comprise ports, transmitters, receivers, transceivers, or any other devices for transmitting and/or receiving data with peripheral devices as would be appreciated by one of ordinary skill in the art upon viewing this disclosure. For example, the I/O interface  508  may be configured to communicate data between the processor  502  and peripheral hardware such as a graphical user interface, a display, a mouse, a keyboard, a key pad, and a touch sensor (e.g. a touch screen). 
     When implementing a correlithm object processing system  300 , user devices  100  measure the distance between different correlithm objects  104  to determine how similar the correlithm objects  104  and the data samples they represent are to each other.  FIGS. 6A, 6B, 7A, and 7B  described examples of distance measuring processes that can be implemented by a user device  100  to compute the distance between a pair of correlithm objects  104  in the correlithm object domain. 
       FIGS. 6A and 7A  combine to describe a distance measuring process for the correlithm object processing system  300 .  FIG. 6A  is a schematic diagram of an embodiment of a distance measuring process for the correlithm object processing system  300 .  FIG. 7A  is a flowchart of an embodiment of a distance measuring process flow  700 . Process  700  provides instructions that allows user devices  100  to achieve the previously described improved technical benefits of a correlithm object processing system  300 . The distance measuring process may be used by a node  304  or an actor  306  to determine the distance between a pair of correlithm objects  104 . For example, a node  304  may implement process  700  to perform step  410  in  FIG. 4 . As another example, an actor  306  may implement process  700  to perform step  418  in  FIG. 4 . The distance between correlithm objects  104  is proportional to how similar the correlithm objects  104  and the objects they represent are to each other. The shorter the distance between a pair correlithm objects  104  indicates the more similar the correlithm objects  104  and the objects they represent are to each other. 
     In  FIG. 7A  at step  702 , a node  304  receives an input binary string. For example, referring to  FIG. 6A , an exclusive-or (XOR) logic gate  601  is connected to the node  304 . In one embodiment, the XOR  601  or XOR functionality is integrated with the node  304 . In other embodiments, the XOR  601  is a device external to the node  304 . The XOR  601  is configured to receive a pair of correlithm objects  104 , for example, as a pair of categorical binary integer strings. The output of the XOR  601  is passed to the node  304  as an input binary string  602 . The input binary string may be any suitable length. For instance, the input binary string may be 16-bits, 32-bits, 64-bits, 128-bits, or any other suitable number of bits. 
     At step  704 , the node  304  masks a portion of the input binary string. Referring to  FIG. 6A , the node  304  masks a first portion  604  of the input binary string  602 . When the node  304  masks the first portion  604  of the input binary string  602  at least a portion of the input binary string  602  not masked or modified. For example, the node  304  may mask a first portion  604  of the input binary string  602  and leaves a second portion  606  of the input binary string  602  unmasked. In one embodiment, the node  304  may use shift registers to extract the unmasked portion of the input binary string  602 . 
     At step  706 , the node  304  identifies a binary string in a counting table  522  matching an unmasked portion of the input binary string. In other words, the node  304  identifies an entry in the counting table  522  that matches the unmasked portion of the input binary string. The counting table  522  is configured similar to the counting table  522  described in  FIG. 5 . Referring to  FIG. 6A , the node  304  identifies an input value entry  608  in the counting table  522  that matches the unmasked second portion  606  of the input binary string  602 . 
     At step  708 , the node  304  identifies and fetches a numeric value linked with the identified binary string in the counting table  522 . Referring to  FIG. 6A , the node  304  identifies an output value entry  610  in the counting table  522  that is linked with the identified input value entry  608 . 
     At step  710 , the node  304  increments a counter by the numeric value. In one embodiment, the counter or counter functionality may be integrated with the node  304 . In other embodiments, the counter may be an external device connected to the node  304 . Referring to  FIG. 6A , the numeric value  612  of the identified output numeric value entry  610  in the counting table  522  is passed to a counter  616  that increments its current count value  618  by the numeric value  612 . For example, when a numeric value  612  of seven is passed to the counter  616 , the counter  616  will increase its current count value  618  by seven. 
     At step  712 , the node  304  determines whether there are anymore portions of the input binary string to mask. The node  304  returns to step  704  when the node  304  determines there are more portions of the input binary string to mask. The node  304  proceeds to step  714  when the node  304  determines there are no more portions of the input binary string to mask. 
     Referring to  FIG. 6A , after incrementing the counter  616  based on the unmasked second portion  606  of the input binary string  602 , the node  304  returns to step  704  to process the first portion  604  of the input binary string  602  that was previously masked. In this example, the node  304  unmasks the first portion  604  of the input binary string  602  and masks the second portion  606  of the input binary string  602 . The node  304  repeats the process of identifying an entry in the counting table  522  that matches the unmasked portion of the input binary string  602  and passing a corresponding numeric value  614  to the counter  616 . The counter  616  increments its current count value  618  by the numeric value  614 . This process may be repeated one or more times for any additional portions of the input binary string  602 . 
     At step  714 , the node  304  outputs the current count value  618  of the counter  616 . The current count value  618  indicates the distance between the pair of correlithm objects  104  that were provided to the XOR  601 . The current count value  618  may be used by nodes  304  and/or actor  306  to determine whether a received correlithm object  104  is similar to any of the previously known correlithm objects  104 . 
       FIGS. 6B and 7B  combine to describe another distance measuring process for a correlithm object processing system  300  based on hamming distances.  FIG. 6B  is a schematic diagram of another embodiment of a distance measuring process for a correlithm object processing system  300  based on hamming distances.  FIG. 7B  is a flowchart of another embodiment of a distance measuring process flow  750  based on hamming distances. Process  750  provides instructions that allows user devices  100  to achieve the previously described improved technical benefits of a correlithm object processing system  300 . The distance measuring process described in  FIG. 6B  may be used by a node  304  or an actor  306  to determine the distance between a pair of correlithm objects  104 . For example, a node  304  may implement the distance measuring process to perform step  410  in  FIG. 4 . As another example, an actor  306  may implement the distance measuring process to perform step  418  in  FIG. 4 . 
     In  FIG. 7B  at step  752 , a node  304  obtains a pair of correlithm objects  104 . The node  304  obtains the pair of correlithm objects  104  to compute the distance between the correlithm objects  104 . As an example, the node  304  may receive one correlithm object  104  from a sensor  302 , node  304 , or actor  306  and one correlithm object  104  from a node table  200 . 
     At step  754 , the node  304  performs an XOR operation on the pair of correlithm objects to generate a binary string. Referring to  FIG. 6B , an XOR  601  is connected to the node  304 . In one embodiment, the XOR  601  or XOR functionality is integrated with the node  304 . In other embodiments, the XOR  601  is a device external to the node  304 . The XOR  601  is configured to receive the pair of correlithm objects  104  as a pair of categorical binary integer strings. The XOR  601  is configured to output a binary string  602 . The binary string  602  may be any suitable length. For instance, the binary string  602  may be 16-bits, 32-bits, 64-bits, 128-bits, or any other suitable number of bits. 
     At step  756 , the node  304  transfers the binary string to a counter using a shift register. Referring to  FIG. 6B , the output of the XOR  601  is passed to a shift register  603  as a binary string  602 . In one embodiment, the shift register  603  or binary data shifting functionality is integrated with the node  304 . In other embodiments, the shift register  603  is a device external to the node  304 . In one embodiment, the shift register  603  is configured transfer the binary string to the counter  616  one bit at a time. 
     At step  758 , the node  304  determines whether the current input to the counter is a logical high value. For example, the node  304  determines whether the input bit  605  that is passed to the counter  616  is a logical high value (e.g. a logical one). The node  304  proceeds to step  760  in response to determining the current input to the counter is a logical high value. 
     The node  304  proceeds to step  762  in response to determining the current input to the counter in not a logical high value. In other words, the node proceeds to step  762  in response to determining the input to the counter is a logical low value (e.g. a logical zero). 
     At step  760 , the node  304  increments a current count value of the counter. In other words, the node  304  adds one to the current count value of the counter when the input bit  605  that is passed to the counter  616  is a logical high value. In one embodiment, the counter or counter functionality may be integrated with the node  304 . In other embodiments, the counter may be an external device connected to the node  304 . 
     At step  762 , the node  304  determines whether there are any more bits in the binary string to shift into the counter. The node  304  returns to step  756  in response to determining there are more bits in the binary string to shift into the counter. The node  304  proceeds to step  764  in response to determining there are no more bits in the binary string to shift into the counter. 
     At step  764 , the node  304  outputs the current count value of the counter. The current count value  618  indicates the distance between the pair of correlithm objects  104  that were provided to the XOR  601 . The current count value  618  may be used by nodes  304  and/or actor  306  to determine whether a received correlithm object  104  is similar to any of the previously known correlithm objects  104 . 
     When implementing a correlithm object processing system  300 , user devices  100  implement various types of sensors  302  and actors  304  in order to convert real world data samples into and out of the correlithm object domain. Examples of sensors  302  and actors  306  that are implemented by a user device  100  to convert images into correlithm objects  104  and to convert correlithm objects  104  into other types of data sample representations are described in  FIGS. 8-11 . 
       FIGS. 8 and 9  combine to describe a process for using a sensor  302  to emulate an image input adapter for the correlithm object processing system  300 .  FIG. 8  is a schematic diagram of an embodiment of a process for emulating an image input adapter for the correlithm object processing system  300  using a sensor  302 .  FIG. 9  is a flowchart of an embodiment of an image input adapting emulation method  900 . Method  900  provides instructions that allows user devices  100  to achieve the previously described improved technical benefits of a correlithm object processing system  300 . An image input adapter is generally configured to convert an image to a correlithm object  104 . Once an image is converted to a correlithm object  104 , the correlithm object  104  can be used for other processes or applications in the correlithm object domain by a node  304  and/or an actor  306  such as facial recognition. 
     In  FIG. 9  at step  902 , a sensor  302  receives an image formed by an array of pixels. For example, referring to  FIG. 8 , the sensor  302  receives image  802  which is made up of an array of pixels  804 . The image may comprise any number of pixels  804 . The image  802  may be any suitable data type or format. In one embodiment, the sensor  302  may obtain the image  802  in real-time from a peripheral device (e.g. a camera). In another embodiment, the sensor  302  may obtain the image  802  from a memory or database. 
     At step  904 , the sensor  302  determines the dimensions of the array of pixels. In other words, the sensor  302  determines the size of the image in terms of pixels. For example, the sensor  302  may determine the image is a 10 by 10 array of pixels. The sensor  302  may employ any suitable technique for determining the size of the image. 
     At step  906 , the sensor  302  defines a plurality of masks. The masks may be configured similar to the masks described in  FIG. 5 . In one embodiment, each mask at least partially overlaps with at least one other mask. In this configuration, each mask has at least one pixel in common with another mask. In other embodiments, the masks are configured to not overlap with other masks. In this configuration, the masks do not have any pixels in common with each other. 
     At step  908 , the sensor  302  overlays the plurality of masks with the image to partition the image into a plurality of sub-arrays of pixels. Referring to  FIG. 8 , a mask  806  is overlaid with the image  802  to define a sub-array of pixels  807 . In one embodiment, the plurality of masks may be overlaid with the image simultaneously to partition the image into a plurality of sub-arrays of pixels. In another embodiment, the masks may be overlaid with the image sequentially such that less than all of the masks are overlaid with the image at any given time. For example, the sensor  302  may apply one mask at a time with the image. 
     At step  910 , the sensor  302  determines binary values for each pixel in a sub-array of pixels. Referring to  FIG. 8 , the sub-array of pixels  807  defined by the mask  806  is initially populated with different pixel values that each describe the color (e.g. red-green-blue (RGB) color) or intensity of a pixel in the sub-array of pixels. The pixel values may be in any number units such as decimal. The sensor  302  converts the sub-array of pixels  807  with pixel values to a sub-array of pixels  808  where each pixel value is described as a binary string. In one embodiment, the sensor  302  converts the pixel values to a correlithm object  104  represented as a categorical binary string using a sensor table  308 . The sensor  302  may use a process similar to the process described in steps  402 - 406  in  FIG. 4  to convert from pixel values to correlithm objects  104 . In other embodiments, the sensor  302  converts the pixel values to binary strings using any other suitable technique. 
     At step  912 , the sensor  302  serialize the correlithm objects  104  for the sub-array of pixels to form an aggregated correlithm object for the sub-array of pixels. Referring to  FIG. 8 , the sensor  302  serializes the correlithm objects  104  of the sub-array of pixels  808  to generate an aggregated correlithm object  810 . In other words, the sensor  302  sequentially appends the binary values of the correlithm objects  104  for each pixel in the sub-array of pixels  808  to form the aggregated correlithm object  810 . 
     At step  914 , the sensor  302  determines whether to generate an aggregated correlithm object for another sub-array of pixels. The sensor  302  returns to step  910  when the sensor  302  determines to generate more aggregated correlithm objects. The sensor  302  proceeds to step  916  when the sensor  302  determines to not generate anymore aggregated correlithm objects  104 . The sensor  302  returns to step  910  for each mask to repeat the process of converting sub-arrays of pixels with pixel values to sub-arrays of pixels where each pixel value is described as a correlithm object  104 . The sensor  302  also repeats the process of serializing binary strings of correlithm objects  104  to generate an aggregated correlithm object for a mask. The sensor  302  proceeds to step  916  when the sensor  302  has completed converting the image into a plurality of aggregated correlithm objects. 
     At step  916 , the sensor  302  outputs the aggregated correlithm objects. Each aggregated correlithm object is a categorical binary integer string. The sensor  302  sends the binary string representing to the aggregated correlithm object to a node  304  and/or an actor  306  for further processing. In some embodiments, the sensor  302  outputs the aggregated correlithm object to a memory. 
       FIGS. 10 and 11  combine to describe a process for using an actor  306  to emulate an image output adapter for the correlithm object processing system  300 .  FIG. 10  is a schematic diagram of an embodiment of a process for emulating an image output adapter for a correlithm object processing system  300  using an actor  306 .  FIG. 11  is a flowchart of an embodiment of an image output adapter emulation method  1100 . Method  1100  provides instructions that allows user devices  100  to achieve the previously described improved technical benefits of a correlithm object processing system  300 . An image output adapter is generally configured to convert a correlithm object  104  to an image or a representation of an image. For example, the actor  306  may generate an image based on the correlithm object  104 . As another example, the actor  306  may generate a voice sample that identifies the image and/or elements in the image. As another example, the actor  306  may generate a text description of the image and/or elements in the image. 
     In  FIG. 11  at step  1102 , an actor  306  receives an aggregated correlithm object corresponding with a mask. For example, referring to  FIG. 10 , the actor  306  receives an aggregated correlithm object  810  that is composed of a plurality of correlithm objects  104 . The actor  306  may receive the aggregated correlithm object  810  from either a sensor  302  or a node  304 . 
     At step  1104 , the actor  306  identifies the plurality of correlithm objects  104  in the aggregated correlithm object. The actor  306  may parse out or identify each of the correlithm objects  104  within the aggregated correlithm object. 
     At step  1106 , the actor  306  populates each pixel location in a mask with a correlithm object  104  from the plurality of correlithm objects  104 . Referring to  FIG. 10 , the actor  306  uses a mask table  524  to identify a sub-array of pixels that is defined by a corresponding mask  806  in the image  802 . The actor  306  populates each pixel in the sub-array of pixels  808  with one of the correlithm objects  104  obtained from the aggregated correlithm object  810 . 
     At step  1108 , the actor  306  determines a pixel value for each pixel location in the mask based on the correlithm object  104  at each pixel location. Referring to  FIG. 10 , the actor  306  converts each binary string or correlithm object  104  at each pixel location into a corresponding pixel value. In one embodiment, the actor  306  converts each binary string or correlithm object  104  at each pixel location into a corresponding pixel value using an actor table  310 . The actor  306  may use a process similar to the process described in steps  418 - 422  in  FIG. 4  to convert from correlithm objects  104  to pixels values. 
     At step  1110 , the actor  306  outputs a representation of a portion of the image based on the mask populated with pixel values at each pixel location. Referring to  FIG. 10 , the actor  306  may output the sub-array of pixels  807  that is populated with pixel values. For example, the actor  306  may generate a portion of image based on the sub-array of pixels  807 . In one embodiment, the actor  306  may output the sub-array of pixels  807  in real-time to a peripheral device (e.g. a display). In one embodiment, the actor  306  may output the sub-array of pixels  807  to a memory or database. In one embodiment, the sub-array of pixels  807  is sent to a sensor  302 . For example, the sub-array of pixels  807  may be sent to a sensor  302  as an input for another process. As another example, the actor  306  may generate a voice sample based on the sub-array of pixels  807  that identifies the image and/or elements in the image. As another example, the actor  306  may generate a text description of the image and/or elements in the image based on the sub-array of pixels  807 . 
     At step  1112 , the actor  306  determines whether there are anymore aggregated correlithm objects available to process. The actor  306  returns to step  1102  when the actor  306  determines there are more aggregated correlithm objects available. The actor  306  returns to step  1102  for each mask to repeat the process of converting aggregated correlithm objects to sub-arrays of pixels populated with pixel values. Otherwise, the actor  306  terminates method  1100  when there are no more aggregated correlithm objects available to convert for the image. 
     While several embodiments have been provided in the present disclosure, it should be understood that the disclosed systems and methods might be embodied in many other specific forms without departing from the spirit or scope of the present disclosure. The present examples are to be considered as illustrative and not restrictive, and the intention is not to be limited to the details given herein. For example, the various elements or components may be combined or integrated in another system or certain features may be omitted, or not implemented. 
     In addition, techniques, systems, subsystems, and methods described and illustrated in the various embodiments as discrete or separate may be combined or integrated with other systems, modules, techniques, or methods without departing from the scope of the present disclosure. Other items shown or discussed as coupled or directly coupled or communicating with each other may be indirectly coupled or communicating through some interface, device, or intermediate component whether electrically, mechanically, or otherwise. Other examples of changes, substitutions, and alterations are ascertainable by one skilled in the art and could be made without departing from the spirit and scope disclosed herein. 
     To aid the Patent Office, and any readers of any patent issued on this application in interpreting the claims appended hereto, applicants note that they do not intend any of the appended claims to invoke 35 U.S.C. § 112(f) as it exists on the date of filing hereof unless the words “means for” or “step for” are explicitly used in the particular claim.