Topic relevance detection in video conferencing
A conference system automatically detects a topic in a discussion between two or more participants in a conference based on a transcription of an audio component of the conference. The conference system determines that the discussion is a side conversation based on a determination that the topic is not related to any discussion points of the conference. The conference system determines which participants are related to the side conversation and schedules a future conference between these participants. The conference system generates one or more discussion points for the future conference based on the topic.
1. A method comprising:
detecting a phrase based on a real-time transcription and discussion points of a conference;
determining a topic based on keywords within a neighboring word range of the phrase;
determining that the topic of the conference is unrelated to the discussion points of the conference by processing the real-time transcription using a machine learning (ML) model trained for contextual awareness;
transmitting a prompt to participants related to the topic to confirm that the topic is unrelated to the discussion points;
generating a future discussion point based on the topic responsive to receiving one or more confirmation responses; and
adding a future conference including the future discussion point to respective calendars of the participants related to the topic.
2. The method of claim 1 , wherein the ML model is configured to detect that a further discussion is to be left for a later time.
3. The method of claim 1 , wherein determining the topic is based on a keyword that references one or more subjects.
4. The method of claim 3 , wherein the one or more subjects is based on a plan for the conference.
5. The method of claim 1 , wherein the respective calendars include an audio portion of the conference.
6. The method of claim 1 , wherein determining the topic includes performing a semantic analysis on the real-time transcription.
7. The method of claim 1 , wherein determining the topic includes performing a semantic analysis on the real-time transcription when a threshold is met for a duration of time that a keyword or phrase is not detected.
8. The method of claim 1 , wherein the respective calendars include a video portion of the conference associated with the future discussion point.
9. A system comprising:
a server configured to:
detect a phrase based on a real-time transcription and discussion points of a conference;
determine a topic based on keywords within a neighboring word range of the phrase;
process the real-time transcription using a machine learning (ML) model trained for contextual awareness to determine that the topic of the conference is unrelated to the discussion points of the conference;
transmit a prompt to participants related to the topic to confirm that the topic is unrelated to the discussion points;
generate a future discussion point based on the topic responsive to receiving one or more confirmation responses; and
add a future conference that includes the future discussion point to respective calendars of the participants related to the topic.
10. The system of claim 9 , wherein the ML model is configured to detect that a further discussion of the topic is to be left for a later time.
11. The system of claim 9 , wherein the server is configured to determine the topic based on a keyword that references one or more subjects.
12. The system of claim 11 , wherein the one or more subjects is based on a plan for the conference.
13. The system of claim 11 , wherein the one or more subjects is learned from a previous conference plan.
14. The system of claim 9 , wherein the server is configured to perform a semantic analysis on the real-time transcription to determine the topic.
15. A non-transitory computer-readable medium comprising instructions stored on a memory, that when executed by a processor, cause the processor to:
detect a phrase based on a real-time transcription and discussion points of a conference;
determine a topic based on keywords within a neighboring word range of the phrase;
process the real-time transcription using a machine learning (ML) model trained for contextual awareness to determine that the topic of the conference is unrelated to the discussion points of the conference;
transmit a prompt to participants related to the topic to confirm that the topic is unrelated to the discussion points;
generate a future discussion point based on the topic responsive to receiving one or more confirmation responses; and
add a future conference that includes the future discussion point to respective calendars of the participants related to the topic.
16. The non-transitory computer-readable medium of claim 15 , wherein the ML model is configured to detect that a further discussion of the topic is to be left for a later time.
17. The non-transitory computer-readable medium of claim 15 , wherein the instructions, when executed by the processor, cause the processor to determine the topic based on a keyword that references at least one subject.
18. The non-transitory computer-readable medium of claim 17 , wherein the at least one subject is based on a plan for the conference.
19. The non-transitory computer-readable medium of claim 17 , wherein the at least one subject is learned from a previous conference plan.
20. The non-transitory computer-readable medium of claim 15 , wherein the respective calendars include an audio portion of the conference.