The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jul. 11, 2023

Filed:

Jul. 10, 2020
Applicant:

Capital One Services, Llc, McLean, VA (US);

Inventors:

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);

Vincent Pham, Champaign, IL (US);

Assignee:

Capital One Services, LLC, McLean, VA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
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);
U.S. Cl.
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);
Abstract

Systems, methods, and computer-readable storage media for quantifying meeting effectiveness for an individual. A system configured as disclosed herein uses data from multiple meetings in which a user participated to create a user profile for the user. The system then receives data related to a new meeting in which the user participated, processes the new meeting data into segments using natural language processing, tags the resulting segments based on contexts, and compares the tagged segments to the user profile to generate a meeting effectiveness score for the new meeting which is specific to the user. The system can use machine learning to iteratively improve an ability of the system to generate the tagged segments using historical meeting data and updating that historical meeting data with each iteration of scoring a meeting's effectiveness.


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