1. A computer implemented method comprising: receiving a first content set, wherein the first content set is organized according to a rules set; using the rules set to parse the first content set to generate a first pattern set having a plurality of members; assigning a weighted value to each member of the first and a second pattern set based on a frequency of occurrence of each member in the first and second pattern sets, wherein each member of the first and second pattern sets includes digital content; and determining a relevancy score linking each of the members of the first and second pattern sets in a one to one mapping of the members of the first pattern set to each of the members of the second pattern set, wherein the relevancy score is based upon the weighted value assigned to each member of the first and second pattern sets and represents a relationship between the members of the first and second pattern sets, wherein determining includes using at least one of a contextual pattern operation; a structural relationship operation; a perspective operation; or combinations thereof; and wherein assigning a weighted value determining the relevancy score includes applying a formula of P n =Σf i p i /n, where: n is the total number of known patterns in the system; i is 0 to n; p i is 1 if the bit pattern or co-related patterns exists; and f is the fuzziness of a pattern towards another pattern, and wherein determining a relevancy score includes performing a union function on the weighted values.