US 12,217,013 B2
Machine-learning based transcript summarization
Rajesh Sabapathy, Haryana (IN); Chirag Mittal, Haryana (IN); Gourav Awasthi, Haryana (IN); and Aditya Teja Josyula, Collierville, TN (US)
Assigned to UnitedHealth Group Incorporated, Minnetonka, MN (US)
Filed by UnitedHealth Group Incorporated, Minnetonka, MN (US)
Filed on Oct. 3, 2022, as Appl. No. 17/937,616.
Claims priority of provisional application 63/366,797, filed on Jun. 22, 2022.
Prior Publication US 2023/0419051 A1, Dec. 28, 2023
Int. Cl. G06F 40/56 (2020.01); G06F 40/247 (2020.01); G06F 40/30 (2020.01); G06F 40/35 (2020.01); G06N 5/022 (2023.01)
CPC G06F 40/56 (2020.01) [G06F 40/247 (2020.01); G06F 40/30 (2020.01); G06F 40/35 (2020.01); G06N 5/022 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for automatically generating a summary of a multi-party interaction using natural language processing, the computer-implemented method comprising:
receiving a multi-party interaction transcript data object comprising a plurality of interaction utterances from at least two participants of the multi-party interaction transcript data object, wherein an interaction utterance comprises one or more sequential sentences associated with one of the at least two participants of the multi-party interaction transcript data object;
identifying, using an extractive summarization model, a key sentence of the multi-party interaction transcript data object;
identifying, from the multi-party interaction transcript data object, a particular interaction utterance of the plurality of interaction utterances that corresponds to the key sentence;
generating a contextual summary for the multi-party interaction transcript data object based at least in part on the particular interaction utterance; and
generating, using a machine-learning based speech converter model, a reported contextual summary based at least in part on the contextual summary, wherein the reported contextual summary comprises the contextual summary from a perspective of a particular participant.