Fair Isaac Corporation

Girish Kunjur

John R Ripley

Computer-implemented systems and methods for efficiently searching large data volumes for one or more items with a definable degree of similarity. The systems and methods may include functionality directed to selecting at least one token from the one or more tokens in a target item, the token including an identifiable character string defining, fully or partially, at least one of a name, an address, an entity or other identifier associated with the target item; extracting a character from the identifiable character string after the character string is standardized to a known common version of the character string; responsive to a character distribution lookup, determining that the extracted character corresponds to a first shard from among a plurality of discrete shards; and grouping the item into the first shard, the character distribution lookup being adjustable overtime to provide for a balanced distribution of items across the plurality of discrete shards.

Similarity sharding

Computer-implemented methods, systems and products for character string frequency analysis. The method includes a set of operations or steps, including parsing a plurality of character strings into one or more tokens, categorizing the one or more tokens into one or more token frequency categories, and generating a first similarity score between one or more pairs of character strings of the plurality of character strings. The method further includes calculating one or more degrees of commonality or rarity of the plurality of character strings based on the categorizing, generating one or more penalties for token pairs of the one or more pairs of character strings associated with the first similarity score based on the one or more degrees of commonality or rarity and the categorizing, and generating a second similarity score based the first similarity score and the one or more penalties.

Entity resolution based on character string frequency analysis

In one aspect, a method for similarity sharding of datatype items is provided. The method includes a set of operations or steps, including parsing a datatype item into one or more tokens, extracting at least one selected token from the parsed datatype item, the at least one selected token comprising a character string including one or more characters. The method further includes standardizing the character string of the at least one selected token, extracting a first character from the one or more characters included in the at least one standardized selected token, and assigning the datatype item to a select shard of a plurality of shards via character distribution lookup based on the extracted first character.

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Houston, TX, United States of America

Girish Kunjur