Abstract: A device that includes a model training engine implemented by a processor. The model training engine is configured to select a first sub-string correlithm object and a second sub-string correlithm object from a set of sub-string correlithm objects. The model training engine is further configured to compute a Hamming distance between the first sub-string correlithm object and the second sub-string correlithm object and to compare the Hamming distance to a bit difference threshold value. The model training engine is further configured to determine that the Hamming distance is less than the bit difference threshold value and to compute an average of the first sub-string correlithm object and the second sub-string correlithm object in the n-dimensional space in response to the determination. The model training engine is further configured to train the machine learning model to define the average as a centroid for the first cluster.

Abstract: A device comprising a cluster engine implemented by a processor. The cluster engine is configured to obtain a reference correlithm object and compute a set of Anti-Hamming distances between the reference correlithm object and the set of correlithm objects. The cluster engine is further configured to identify a subset of correlithm objects from the set of correlithm objects that are associated with an Anti-Hamming distance that is greater than a first bit threshold value. The cluster engine is further configured to compute a set of Hamming distances between the reference correlithm object and the subset of correlithm objects and to identify correlithm objects associated with a Hamming distance that exceeds a second bit threshold value. The cluster engine is further configured to remove the identified correlithm objects that are associated with a Hamming distance that exceeds the second bit threshold value and generate the cluster.

Abstract: A device that includes a model training engine implemented by a processor. The model training engine is configured to obtain a set of data values associated with a feature vector. The model training engine is further configured to generate a set of gradients by dividing separation distances by an average separation distance and to compare each gradient to a gradient threshold value. The model training engine is further configured to identify a boundary in response to determining a gradient exceeds the gradient threshold value, to determine a number of identified boundaries, and to determine a number of clusters based on the number of identified boundaries. The model training engine is further configured to train the machine learning model to associate the determined number of clusters with the feature vector.

Abstract: A device that includes a model training engine implemented by a processor. The model training engine is configured to obtain a set of data values associated with a feature vector. The model training engine is further configured to transform a first data value and a second data value from the set of data value into sub-string correlithm objects. The model training engine is further configured to compute a Hamming distance between the first sub-string correlithm object and the second sub-string correlithm object and to identify a boundary in response to determining that the Hamming distance exceeds a bit difference threshold value. The model training engine is further configured to determine a number of identified boundaries, to determine a number of clusters based on the number of identified boundaries, and to train the machine learning model to associate the determined number of clusters with the feature vector.

Abstract: A device comprising a cluster engine implemented by a processor. The cluster engine is configured to obtain a reference correlithm object and compute a set of Anti-Hamming distances between the reference correlithm object and the set of correlithm objects. The cluster engine is further configured to identify a subset of correlithm objects from the set of correlithm objects that are associated with an Anti-Hamming distance that is greater than a first bit threshold value. The cluster engine is further configured to compute a set of Hamming distances between the reference correlithm object and the subset of correlithm objects and to identify correlithm objects associated with a Hamming distance that exceeds a second bit threshold value. The cluster engine is further configured to remove the identified correlithm objects that are associated with a Hamming distance that exceeds the second bit threshold value and generate the cluster.

Abstract: A device that includes a model training engine implemented by a processor. The model training engine is configured to obtain a set of data values associated with a feature vector. The model training engine is further configured to generate a set of gradients by dividing separation distances by an average separation distance and to compare each gradient to a gradient threshold value. The model training engine is further configured to identify a boundary in response to determining a gradient exceeds the gradient threshold value, to determine a number of identified boundaries, and to determine a number of clusters based on the number of identified boundaries. The model training engine is further configured to train the machine learning model to associate the determined number of clusters with the feature vector.

Abstract: A device that includes a model training engine implemented by a processor. The model training engine is configured to obtain a set of data values associated with a feature vector. The model training engine is further configured to transform a first data value and a second data value from the set of data value into sub-string correlithm objects. The model training engine is further configured to compute a Hamming distance between the first sub-string correlithm object and the second sub-string correlithm object and to identify a boundary in response to determining that the Hamming distance exceeds a bit difference threshold value. The model training engine is further configured to determine a number of identified boundaries, to determine a number of clusters based on the number of identified boundaries, and to train the machine learning model to associate the determined number of clusters with the feature vector.

Abstract: A device that includes a model training engine implemented by a processor. The model training engine is configured to select a first sub-string correlithm object and a second sub-string correlithm object from a set of sub-string correlithm objects. The model training engine is further configured to compute a Hamming distance between the first sub-string correlithm object and the second sub-string correlithm object and to compare the Hamming distance to a bit difference threshold value. The model training engine is further configured to determine that the Hamming distance is less than the bit difference threshold value and to compute an average of the first sub-string correlithm object and the second sub-string correlithm object in the n-dimensional space in response to the determination. The model training engine is further configured to train the machine learning model to define the average as a centroid for the first cluster.

Abstract: A system for analyzing vendor interactions by inspecting message logs is disclosed. The system is configured to identify a plurality of messages logs associated with an attachment comprising an indication of an accepted invitation to a calendar event. The system further identifies a sender and a recipient associated with each of the message logs, and determines that one of the sender and the recipient is identified as an employee and the other one is identified as a vendor. Next, the system extracts personal information about the identified employee and public information about the identified vendor, and stores the extracted information in an interaction data entry. Additionally, the system creates an index associated with the interaction data entry to facilitate query of the data stored in the interaction data entry.

Abstract: According to one embodiment, an apparatus is provided that comprises a memory, an interface, and a processor communicatively coupled to the memory and to the interface. The memory can store a conversion rule. The interface can receive an audio signal and receive a file. The file indicates a start time, an end time, a key, and a password. The processor can clip the audio signal from the start time to the end time to produce a portion of the audio signal. The processor can convert, based at least in part upon the conversion rule, the portion of the audio signal using the key to form a converted portion of the audio signal. The processor can determine that the converted portion of the audio signal matches the password. The interface can communicate a response indicating that the converted portion of the audio signal matches the password.

Abstract: According to one embodiment, an apparatus is provided that comprises a memory, an interface, and a processor communicatively coupled to the memory and to the interface. The memory can store a conversion rule. The interface can receive an audio signal and receive a file. The file indicates a start time, an end time, a key, and a password. The processor can clip the audio signal from the start time to the end time to produce a portion of the audio signal. The processor can convert, based at least in part upon the conversion rule, the portion of the audio signal using the key to form a converted portion of the audio signal. The processor can determine that the converted portion of the audio signal matches the password. The interface can communicate a response indicating that the converted portion of the audio signal matches the password.