Machine learning techniques for determining predicted similarity scores for input sequences

Systems and methods for dynamically generating a predicted similarity score for a pair of input sequences. A predicted similarity score for a pair of input sequences is determined based at least in part on at least one of a token-level similarity probability score for the pair of input sequences, a...

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Bibliographic Details
Main Authors Ombase, Reshma S, Booher, Brad, Sverdlin, Edward, Dey, Subhodeep, Yadav, Raghvendra Kumar
Format Patent
LanguageEnglish
Published 02.04.2024
Subjects
CALCULATING
COMPUTING
COUNTING
PHYSICS
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Summary:Systems and methods for dynamically generating a predicted similarity score for a pair of input sequences. A predicted similarity score for a pair of input sequences is determined based at least in part on at least one of a token-level similarity probability score for the pair of input sequences, a target region match indication for the pair of input sequences, a fuzzy match score for the pair of input sequences, a character-level match score for the pair of input sequences, one or more similarity ratio occurrence indicators for the pair of input sequences, and a harmonic mean score of the fuzzy match score for the pair of input sequences and the token-level similarity probability score for the pair of input sequences.
Bibliography:Application Number: US202117560491