Abstract: In accordance with an aspect, there is provided a system and method for electronic communications that involves machine learning and natural language processing for contact relationship discovery, ranking and retrieval. In accordance with an aspect, there is provided a system and device that discovers contacts related to user search terms, quantitatively measures and ranks the strength between contact relationships according to a score calculated by natural language processing and machine learning. The system generates visual effects to present recommendations to the user with multiple alternate route options to establish relationships between contacts.

Abstract: In accordance with an aspect, there is provided a system and method for electronic communications that involves machine learning and natural language processing for contact relationship discovery, ranking and retrieval. In accordance with an aspect, there is provided a system and device that discovers contacts related to user search terms, quantitatively measures and ranks the strength between contact relationships according to a score calculated by natural language processing and machine learning. The system generates visual effects to present recommendations to the user with multiple alternate route options to establish relationships between contacts.

Abstract: An example method is illustrated as including receiving a first content set, the first content set 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 set 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.

Abstract: An example method is illustrated as including receiving a first content set, the first content set 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 set 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.