Artificial intelligence for meeting management
A method, computer system, and a computer program product for automated management of a meeting are provided. A first computer may receive a meeting agenda for a first meeting. The first computer may access live content from the first meeting. The first computer may generate a meeting suggestion based on the meeting agenda and on the accessed live content. The first computer may transmit the meeting suggestion to a second computer.
1. A computer-implemented method for automated management of a meeting, the method comprising:
receiving, via a first computer, a meeting agenda for a first meeting that is occurring at least in part as a virtual meeting;
accessing, via the first computer, live content from the first meeting, wherein the live content includes at least one of audio and text transmitted for the virtual meeting;
generating, via the first computer, an avatar and a recommended interaction frequency for a user to interact with the first meeting, wherein the generating is based on the meeting agenda and on the accessed live content and comprises inputting the meeting agenda and the accessed live content into a machine learning model and, in response to the inputting, the machine learning model outputting the recommended interaction frequency, wherein the machine learning model performs natural language processing on the at least one of the audio and text to produce the recommended interaction frequency; and
presenting, via a display screen of the first computer, the avatar providing the recommended interaction frequency, the recommended interaction frequency comprising an indication of a specified time interval in which the user should provide, via the first computer, an interaction with the first meeting.
2. The method of claim 1 , wherein the machine learning model further outputs a summary of a first part of the first meeting in response to the inputting, and wherein the summary is presented via the avatar on the display screen of the first computer.
3. The method of claim 1 , wherein the machine learning model analyzes the accessed live content to determine meeting participation in the first meeting, and wherein the recommended frequency interaction is based on the determined meeting participation.
4. The method of claim 1 , wherein the user is a late-arriving party to the first meeting.
5. The method of claim 1 , wherein in response to the inputting the machine learning model further outputs a meeting suggestion that relates to a second meeting that is subsequent to the first meeting.
6. The method of claim 5 , wherein the meeting suggestion comprises a missed agenda point that was not discussed in the first meeting.
7. The method of claim 6 , wherein the missed agenda point is presented via the display screen in response to a similarity score of the second meeting with respect to the first meeting exceeding a pre-determined threshold.
8. The method of claim 5 , wherein the meeting suggestion comprises one or more preparation tasks to perform in advance of the second meeting.
9. The method of claim 8 , wherein a timing of a presentation on the display screen of the one or more preparation tasks is based on an estimated time amount needed to complete the one or more preparation tasks.
10. The method of claim 8 , wherein a timing of a presentation on the display screen of the one or more preparation tasks is based on a user profile setting.
11. The method of claim 5 , wherein the meeting suggestion relates to an action item that was established in the first meeting.
12. The method of claim 1 , wherein in response to the inputting the machine learning model further outputs a missed agenda point that has not yet been discussed in the first meeting, and the missed agenda point is presented via the display screen before an end of the first meeting.
13. The method of claim 1 , further comprising performing, via the first computer, an automatic retrieval of digital content, wherein the digital content is selected based on the meeting agenda and on the accessed live content.
14. The method of claim 1 , wherein the machine learning model further performs natural language processing on the meeting agenda in order to generate the recommended interaction frequency.
15. A computer system for automated management of a meeting, the computer system comprising:
one or more processors, one or more computer-readable memories, a display screen, and program instructions stored on at least one of the one or more computer-readable memories for execution by at least one of the one or more processors to cause the computer system to:
receive a meeting agenda for a first meeting that is occurring at least in part as a virtual meeting;
access live content from the first meeting, wherein the live content includes at least one of audio and text transmitted for the virtual meeting;
generate an avatar and a recommended interaction frequency for a user to interact with the first meeting, wherein the generating is based on the meeting agenda and on the accessed live content and comprises inputting the meeting agenda and the accessed live content into a machine learning model and, in response to the inputting, the machine learning model outputting the recommended interaction frequency, wherein the machine learning model performs natural language processing on the at least one of the audio and text to produce the recommended interaction frequency; and
present, via the display screen, the avatar providing the recommended interaction frequency, the recommended interaction frequency comprising an indication of a specified time interval in which the user should provide, via the computer system, an interaction with the first meeting.
16. A computer program product for automated management of a meeting, the computer program product comprising a computer-readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to:
receive a meeting agenda for a first meeting that is occurring at least in part as a virtual meeting;
access live content from the first meeting, wherein the live content includes at least one of audio and text transmitted for the virtual meeting;
generate an avatar and a recommended interaction frequency for a user to interact with the first meeting, wherein the generating is based on the meeting agenda and on the accessed live content and comprises inputting the meeting agenda and the accessed live content into a machine learning model and, in response to the inputting, the machine learning model outputting the recommended interaction frequency, wherein the machine learning model performs natural language processing on the at least one of the audio and text to produce the recommended interaction frequency; and
present, via a display screen of the computer, the avatar providing the recommended interaction frequency, the recommended interaction frequency comprising an indication of a specified time interval in which the user should provide, via the first computer, an interaction with the first meeting.
17. The method of claim 1 , wherein the avatar is generated according to a theme that matches one or more meeting points as indicated in at least one of the meeting agenda and the live content.
18. The method of claim 1 , wherein the avatar includes a persona that copies a real-life persona of a presenter of the first meeting.
19. The method of claim 18 , wherein the persona comprises a cartoon form that mimics captured one or more images of the presenter.
20. The method of claim 1 , wherein the avatar presents the recommended interaction frequency via at least one of:
audio that is played via a speaker of the first computer to correspond to a visual image of the avatar speaking; and
text that is displayed in a text bubble as a visual image on the display screen.