US 11,699,437 B2
System and method for quantifying meeting effectiveness using natural language processing
Christopher Camenares, Falls Church, VA (US); Jason Trost, Atlanta, GA (US); Patrick Sofo, Arlington, VA (US); Joseph Boayue, Reston, VA (US); Geeta Shyamala, Herndon, VA (US); Ana Cruz, Arlington, VA (US); Nahid Farhady Ghalaty, Fairfax, VA (US); and Vincent Pham, Champaign, IL (US)
Assigned to Capital One Services, LLC, McLean, VA (US)
Filed by Capital One Services, LLC, McLean, VA (US)
Filed on Jul. 10, 2020, as Appl. No. 16/925,903.
Prior Publication US 2022/0013114 A1, Jan. 13, 2022
Int. Cl. G10L 15/08 (2006.01); G10L 15/183 (2013.01); G10L 15/18 (2013.01); G10L 17/00 (2013.01); G10L 17/02 (2013.01); G10L 17/04 (2013.01); G10L 15/22 (2006.01); G06N 20/00 (2019.01); G06F 3/16 (2006.01)
CPC G10L 15/22 (2013.01) [G06F 3/167 (2013.01); G06N 20/00 (2019.01); G10L 15/18 (2013.01); G10L 2015/223 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A method comprising:
generating a user profile for a user based on transcripts of a plurality of meetings in which the user participated;
receiving additional meeting data for a new meeting in which the first user participated;
processing, using a machine learning process, a transcript of the additional meeting data into transcript segments and a set of tags associated with the transcript segments, wherein the set of tags is determined by tagging each respective segment of the transcript segments based on a respective context of the respective segment;
generating a meeting effectiveness score based on the set of tags and the user profile by comparing the set of tags to the user profile;
updating, based on the meeting effectiveness score and common patterns within the transcript segments, the machine learning process to improve an ability of the machine learning process to generate the transcript segments; and
storing values of the updated machine learning process in a memory.