US 11,948,378 B2
Machine learning techniques for determining predicted similarity scores for input sequences
Subhodeep Dey, Chandigarh (IN); Brad Booher, Baldwin, WI (US); Edward Sverdlin, Edina, MN (US); Reshma S. Ombase, Olathe, KS (US); and Raghvendra Kumar Yadav, Uttar Pradesh (IN)
Assigned to UnitedHealth Group Incorporated, Minnetonka, MN (US)
Filed by UnitedHealth Group Incorporated, Minnetonka, MN (US)
Filed on Dec. 23, 2021, as Appl. No. 17/560,491.
Prior Publication US 2023/0206666 A1, Jun. 29, 2023
Int. Cl. G06V 30/00 (2022.01); G06V 10/82 (2022.01); G06V 30/19 (2022.01)
CPC G06V 30/19093 (2022.01) [G06V 10/82 (2022.01); G06V 30/1912 (2022.01)] 16 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
generating, by one or more processors, a token-level similarity probability score for a pair of input sequences based at least in part on a first cross-token image representation for a first input sequence of the pair of input sequences and a second cross-token image representation for a second input sequence of the pair of input sequences, wherein the token-level similarity probability score is based at least in part on a comparison between a first dimensionally-reduced image representation for the first cross-token image representation and a second dimensionally-reduced image representation for the second cross-token image representation;
generating, by the one or more processors and a machine learning model, a predicted similarity score for the pair of input sequences based at least in part on the token-level similarity probability score; and
initiating, by the one or more processors, the performance of one or more prediction-based actions based at least in part on the predicted similarity score.