Lawrence Technologies, LLC

Other

Syngence Corporation

P Nick Lawrence

Kenneth Loafman

Stephen A Benno

Douglas J Matzke

Chandler L Burgess

Irvin R Jackson, Jr, Ii

A system for identifying relationships between database records includes a memory operable to store a plurality of records comprising a first record and at least one second record. Each record comprises at least one of a plurality of tokens. The system also includes one or more processors collectively operable to determine a weight associated with each of the tokens, compare at least one second record to the first record, and determine at least one relationship indicator based on the comparison and at least one of the weights. The at least one relationship indicator identifies a level of relationship between the first record and at least one second record.

System and method for identifying relationships between database records

Performing operations using quantum correlithm objects includes establishing real states, where each real state comprises an element of a real space, and encoding the real states as quantum objects representing a correlithm object. The correlithm object is projected to the real space using a measurement basis, and measurement values corresponding to the measurement basis are determined. The projected correlithm object is retrieved according to the measurement values.

Representing and manipulating correlithm objects using quantum objects

Encoding bits includes receiving a bit set to encode. An encoding lookup table associates correlithm objects of a space with bit sets. The space refers to an N-dimensional space, a correlithm object refers to a point of the space. The correlithm object corresponding to the received bit set is identified. The received bit set is encoded as the identified correlithm object. The identified correlithm object is imposed to encode the received bit set and subsequently decoded with table lookup using the reverse process.

Imposing and recovering correlithm objects in conjunction with table lookup

In one aspect of the invention, a system for processing data includes a memory operable to store a plurality of correlithm objects. Each correlithm object includes a plurality of values defining a point in a particular space. The particular space is defined by a plurality of dimensions and includes a plurality of points. The system also includes a processor operable to generate the values in at least a portion of the correlithm objects. A distance between a first point associated with one of the correlithm objects and each of the plurality of points in the particular space defines a distribution having a mean and a standard deviation such that a ratio of the mean to the standard deviation increases with the number of dimensions of the particular space. A distance between the first point and a second point associated with another of the correlithm objects is substantially larger than the mean of the distribution.

Systems and methods for generating string correlithm objects

A methodology for producing efficient implementations of constructs such as feature tables is disclosed. The method of superimposed coding and the method of inverted list tables are combined. Although they both accomplish similar things, each method has advantages over the other under particular circumstances. The method of superimposed coding is used where feature table rows are &ldquo;dense&rdquo; or contain many records of interest, while the method of inverted list tables is used where feature table rows are &ldquo;sparse&rdquo; or contain few records of interest. The combination of the two methods results in a synergistic method that loses no accuracy and is generally faster in operation than what could be achieved by the application of either method alone.

Efficient implementations of constructs such as feature tables

A system for processing data includes a memory coupled to a processor. The memory stores a correlithm object that defines a point in a particular space, wherein the particular space is defined by a plurality of dimensions and including a plurality of points. The correlithm object is associated with each point using a metric. The memory further stores data associated with the correlithm object. The processor applies the metric to the correlithm object and each of the plurality of points in the particular space to generate a plurality of values defining a distribution having a mean and a standard of deviation such that the ratio of the mean to the standard of deviation increases with the number of dimensions of the particular space.

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Roanoke, TX, United States of America

P Nick Lawrence