US 12,243,536 B2
Automatically recognizing and surfacing important moments in multi-party conversations
Krishnamohan Reddy Nareddy, Bellevue, WA (US); Abhishek Abhishek, Sammamish, WA (US); Rohit Ganpat Mane, Seattle, WA (US); and Rajiv Garg, Seattle, WA (US)
Assigned to Outreach Corporation, Seattle, WA (US)
Filed by Outreach Corporation, Seattle, WA (US)
Filed on Aug. 12, 2023, as Appl. No. 18/233,303.
Application 18/233,303 is a continuation of application No. 17/179,125, filed on Feb. 18, 2021, granted, now 11,763,823.
Claims priority of provisional application 62/987,525, filed on Mar. 10, 2020.
Prior Publication US 2023/0386477 A1, Nov. 30, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 16/9535 (2019.01); G06F 16/334 (2025.01); G06N 20/00 (2019.01); G10L 17/14 (2013.01)
CPC G10L 17/14 (2013.01) [G06F 16/3344 (2019.01); G06N 20/00 (2019.01); G06F 16/9535 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A non-transitory computer-readable medium comprising instructions encoded thereon to identify a moment in a transcript, the instructions when executed by at least one processor causing the at least one processor to:
receive a transcription of a conversation, the conversation being ongoing between a plurality of participants and the transcription received as the conversation continues;
identify each participant of the plurality of participants;
access a plurality of machine learning models, each machine learning model selected for a corresponding participant based on a respective profile of the corresponding participant;
apply, as input to each machine learning model of the plurality of machine learning models, the transcription on an ongoing basis as the conversation continues;
receive, as output from each respective machine learning model, a respective portion of the transcription having relevance to its respective participant; and
generate for display, to each respective participant, on an ongoing basis as the conversation from which the transcription was received continues, respective information pertaining to the respective portion, each respective information tailored to each respective participant based on the respective portion output by the respective machine learning model.