IP Library Granted Patent US 11,546,182
Granted Patent B2
US 11,546,182 · App. 16/934,776 · Granted Jan 3, 2023

Methods and systems for managing meeting notes

Inventors: Alexey Krikunov (Saint-Petersburg, RU); Ivan Chirva (Saint-Petersburg, RU); Danil Bliznyuk (Saint-Petersburg, RU); Alexander Bogatko (Saint-Petersburg, RU); Vlad Vendrow (Reno, NV); Christopher Van Rensburg (Portland, OR)
Assignee: RingCentral, Inc.
H04L12/1831G06V40/176G06V40/20H04L12/1818H04L12/1822
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Quick Facts
Patent No.
US 11,546,182
App. No.
16/934,776
Granted
Jan 3, 2023
Kind
B2
Abstract

The present disclosure provides systems and methods for managing meeting notes. In accordance with some embodiments, a method is provided that includes receiving nonverbal cue information associated with one or more meeting participants, determining an engagement level for each of the one or more meeting participants based on emotional information associated with the nonverbal cue information, composing meeting notes based on the determined engagement level for each of the one or more meeting participants, and storing the meeting notes.

Claims (54)

1. A method for managing meeting notes comprising:

receiving, by a processor, nonverbal cue information associated with one or more meeting participants via a data capturing device;

determining, by the processor, a reaction classifier for each of the one or more meeting participants associated with the nonverbal cue information;

determining, by the processor, a reaction classifier intensity associated with the reaction classifier determined for the one or more meeting participants, the reaction classifier intensity characterized by a numerical amplitude;

selecting, by the processor, a first reaction classifier intensity corresponding to a first reaction classifier for a first meeting participant, the first reaction classifier intensity characterized by a numerical amplitude;

determining, by the processor, a first time-dependent change of the first reaction classifier intensity, wherein the first time-dependent change is represented by a first slope indicating a ratio of a change of the first reaction classifier intensity to a change of time;

selecting, by the processor, a second reaction classifier intensity corresponding to a second reaction classifier for a second meeting participant, the second reaction classifier intensity characterized by a numerical amplitude;

determining, by the processor, a second time-dependent change of the second reaction classifier intensity, wherein the second time-dependent change is represented by a second slope indicating a ratio of a change of the second reaction classifier intensity to a change of time;

determining, by the processor, a correlation between the first slope and the second slope;

adjusting, by the processor, a meeting parameter based on the correlation;

composing, by the processor, meeting notes based on the determined reaction classifier intensity for the one or more meeting participants and based on the determined correlation; and

storing the meeting notes in a database.

2. The method of claim 1 , wherein determining the reaction classifier intensity for the one or more meeting participants is further based on a machine learning algorithm.

3. The method of claim 1 , wherein the nonverbal cue information is based on at least one of vocal tonality information or vocal intensity information.

4. The method of claim 1 , wherein the managing meeting notes is further based on a transcript of a meeting.

5. The method of claim 4 , further comprising determining one or more important parts of a meeting for each of the one or more meeting participants based on the determined reaction classifier and the reaction classifier intensity for at least one of the one or more meeting participants and wherein managing meeting notes comprises emphasizing parts of the transcript corresponding to the one or more important parts of the meeting.

6. The method of claim 5 , wherein emphasizing parts of the transcript corresponding to the one or more important parts of the meeting comprises one of arranging the parts of the transcript corresponding to the one or more important parts of the meeting at the top of the meeting notes, bolding the parts of the transcript corresponding to the one or more important parts of the meeting in the meeting notes, or underlining the parts of the transcript corresponding to the one or more important parts of the meeting in the meeting notes.

7. The method of claim 4 , further comprising:

determining an aggregate reaction classifier intensity for the meeting based on the reaction classifier for the at least one of the one or more meeting participants;

determining one or more important parts of the meeting based on the determined aggregate reaction classifier intensity, and

wherein managing the meeting notes comprises emphasizing parts of the transcript corresponding with the one or more important parts of the meeting.

8. The method of claim 7 , wherein determining the aggregate reaction classifier intensity for the meeting is further based on a machine learning algorithm.

9. The method of claim 1 , further comprising:

determining a base-level reaction classifier intensity for the reaction classifier; and

determining a deviation of the reaction classifier intensity from the base-level reaction classifier intensity.

10. The method of claim 1 , further comprising:

determining that the first time-dependent change of the first reaction classifier intensity negatively affects the second time-dependent change of the second reaction classifier intensity; and

adjusting a meeting parameter related to the first reaction classifier intensity negatively.

11. A system for managing meeting notes based on emotions comprising a nonverbal information processing system configured to:

receive nonverbal cues for one or more meeting participants via a data capturing device;

generate nonverbal information based on the received nonverbal cues for one or more meeting participants;

determine a reaction classifier for each of the one or more meeting participants based on the received nonverbal information;

determine a reaction classifier intensity association with the reaction classifier determined for the one or more meeting participants, wherein the reaction classifier intensity is characterized by a numerical amplitude;

select a first reaction classifier intensity corresponding to a first reaction classifier for a first meeting participant, the first reaction classifier intensity characterized by a numerical amplitude;

determine a first time-dependent change of the first reaction classifier intensity, wherein the first time-dependent change is represented by a first slope indicating a ratio of a change of the first reaction classifier intensity to a change of time;

select a second reaction classifier intensity corresponding to a second reaction classifier for a second meeting participant, the second reaction classifier intensity characterized by a numerical amplitude;

determine a second time-dependent change of the second reaction classifier intensity, wherein the second time-dependent change is represented by a second slope indicating a ratio of a change of the second reaction classifier intensity to a change of time;

determine a correlation between the first slope and the second slope;

adjust a meeting parameter based on the correlation;

compose meeting notes based on the determined reaction classifier intensity for the one or more meeting participants and based on the determined correlation; and

store the meeting notes for subsequent review in a database.

12. The system of claim 11 , wherein the nonverbal information comprises a reaction classifier having an associated intensity level characterized by a numerical amplitude, the reaction classifier and the associated intensity level determined for each of the one or more meeting participants, wherein the associated intensity level is determined for different points in time for a meeting.

13. The system of claim 12 , wherein the reaction classifier is a classifier for a mental state of each of the one or more meeting participants.

14. The system of claim 11 , wherein determining the reaction classifier intensity for the one or more meeting participants is further based on a machine learning algorithm.

15. The system of claim 11 , wherein the nonverbal information is based on at least one of biometric information, posture information, facial expression information, ocular focus information, vocal tonality information, and vocal intensity information.

16. The system of claim 11 , wherein managing meeting notes is further based on a transcript of a meeting.

17. The system of claim 16 , wherein the nonverbal information processing system is further configured to determine one or more important parts of the meeting based on the determined reaction classifier intensity for at least one of the one or more meeting participants and wherein managing meeting notes comprises emphasizing parts of the transcript corresponding with the important part of the meeting.

18. The system of claim 16 , wherein the nonverbal information processing system is further configured to:

determine an aggregate reaction classifier intensity for the meeting based on the reaction classifier intensity for the at least one of the one or more meeting participants;

determine one or more important parts of a meeting based on the determined aggregate reaction classifier intensity; and

wherein managing meeting notes comprises emphasizing parts of the transcript corresponding with the important part of the meeting.

19. The system of claim 11 , wherein the nonverbal information processing system is further configured to:

determine that the first time-dependent change of the first reaction classifier intensity negatively affects the second time-dependent change of the second reaction classifier intensity; and

adjust a meeting parameter related to the first reaction classifier intensity negatively.

Assignments (2)
SECURITY INTEREST Recorded Feb 14, 2023
From: RINGCENTRAL, INC.
To: BANK OF AMERICA, N.A., AS COLLATERAL AGENT
Reel/Frame 062973/0194 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 21, 2020
From: KRIKUNOV, ALEXEY; CHIRVA, IVAN; BLIZNYUK, DANIL; BOGATKO, ALEXANDER; VENDROW, VLAD; VAN RENSBURG, CHRISTOPHER
To: RINGCENTRAL, INC.
Reel/Frame 053269/0470 →
Continuity (2)
Continuation PCTRU2020000189 · Mar 26, 2020
Related Publication 20210306173A1 · Sep 30, 2021