DETERMINING VIDEO CALL EFFECTIVENESS SCORES FOR ACCOMPLISHING TARGET GOALS
The present disclosure is directed toward systems, methods, and non-transitory computer readable media for generating and providing intelligent insights for video calls and other virtual meetings. In some embodiments, the disclosed systems analyze stored meeting data from past video calls and other virtual meetings to generate intelligent insights for an upcoming video call. The disclosed systems also generate and provide intelligent insights or coaching tools for ongoing video calls. As part of the intelligent coaching tools for ongoing video calls, the disclosed systems can generate predictions for accomplishing target goals for the video calls. Further, the disclosed systems can generate intelligent insights or coaching tools after video calls take place.
1 . A method comprising:
extracting, from an agenda for a video call of a user account within a content management system, a target goal embedding that encodes a target goal for the video call;
generating, from video call data captured from multiple video streams of participating client devices during the video call, a plurality of topic discussion embeddings that encode topics discussed during the video call; and
generating, utilizing a video call prediction model to process the plurality of topic discussion embeddings and the target goal embedding, a video call effectiveness score indicating a probability of accomplishing the target goal during the video call.
2 . The method of claim 1 , wherein extracting the target goal embedding comprises utilizing the video call prediction model to generate a vector representation of the agenda from a plurality of meeting items included in the agenda.
3 . The method of claim 1 , wherein generating the plurality of topic discussion embeddings comprises:
utilizing the video call prediction model to extract a first vector representation of a first topic discussed at a first timestamp during the video call; and
utilizing the video call prediction model to extract a second vector representation of a second topic discussed at a second timestamp during the video call.
4 . The method of claim 1 , wherein generating the video call effectiveness score comprises comparing the target goal embedding with the plurality of topic discussion embeddings to determine a probability of accomplishing the target goal during the video call.
5 . The method of claim 1 , further comprising:
determining, from the video call data captured from the multiple video streams, a plurality of user accounts attending the video call; and
generating the video call effectiveness score based on the plurality of user accounts attending the video call.
6 . The method of claim 1 , further comprising:
detecting a discussion dissonance by determining that fewer than a threshold number of topic discussion embeddings are within a threshold distance of each other within embedding space; and
generating the video call effectiveness score based on the discussion dissonance.
7 . The method of claim 1 , further comprising generating, for display during the video call on a client device associated with the user account, a notification comprising a prompt for improving the video call effectiveness score.
8 . A system comprising:
at least one processor; and
a non-transitory computer readable medium comprising instructions that, when executed by the at least one processor, cause the system to:
extract, from an agenda for a video call of a user account within a content management system, a target goal embedding that encodes a target goal for the video call;
generate, from video call data captured from multiple video streams of participating client devices during the video call, a plurality of topic discussion embeddings that encode topics discussed during the video call; and
generate, utilizing a video call prediction model to compare the plurality of topic discussion embeddings and the target goal embedding, a video call effectiveness score indicating a probability of accomplishing the target goal during the video call.
9 . The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to:
generate agenda item embeddings from items in the agenda utilizing the video call prediction model;
generate the plurality of topic discussion embeddings from the video call data in real time as topics are discussed during the video call; and
compare the plurality of topic discussion embeddings with the agenda item embeddings to predict accomplishment of the items in the agenda.
10 . The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to utilize the video call prediction model to:
classify, based on the agenda, the video call into a video call category from among a set of video call categories comprising a decision category, an information gathering category, and an information sharing category; and
generate the video call effectiveness score based on determining, from the video call data captured from the multiple video streams of the video call, one or more category parameters met for the video call category.
11 . The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to:
generate the video call effectiveness score at a first timestamp of the video call by comparing the target goal embedding with the plurality of topic discussion embeddings to determine a probability of accomplishing the target goal during the video call; and
update the video call effectiveness score at a second timestamp of the video call based on comparing the target goal embedding with new topic discussion embeddings for new topic discussions after the first timestamp of the video call.
12 . The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to:
determine a dissonance score by determining distances between the plurality of topic discussion embeddings within an embedding space; and
generating the video call effectiveness score based on the dissonance score.
13 . The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to generate the video call effectiveness score by:
determining a trajectory for the video call based on a number of agenda items accomplished over an elapsed time within the video call; and
determining a predicted percentage of the target goal that will be accomplished by termination of the video call based on the trajectory.
14 . The system of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the system to generate, for display during the video call on a client device associated with the user account, a notification comprising instructions for improving the video call effectiveness score.
15 . A non-transitory computer readable medium comprising instructions that, when executed by at least one processor, cause the at least one processor to:
extract, from an agenda for a video call of a user account within a content management system, a target goal embedding that encodes a target goal for the video call;
generate, from video call data captured from multiple video streams of participating client devices during the video call, a plurality of topic discussion embeddings that encode topics discussed during the video call; and
generate, by comparing the plurality of topic discussion embeddings and the target goal embedding utilizing a video call prediction model, a video call effectiveness score indicating a probability of accomplishing the target goal during the video call.
16 . The non-transitory computer readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the at least one processor to generate the video call effectiveness score by:
determining a trajectory for the video call based on a number of agenda items accomplished up to a timestamp within the video call; and
determining a predicted percentage of agenda items that will be accomplished by a scheduled end of the video call based on the trajectory.
17 . The non-transitory computer readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the at least one processor to generate, for display during the video call on a client device associated with the user account, a notification comprising instructions for improving the video call effectiveness score.
18 . The non-transitory computer readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the at least one processor to:
generate the video call effectiveness score at a first timestamp of the video call by comparing the target goal embedding with the plurality of topic discussion embeddings to determine a probability of accomplishing the target goal during the video call; and
update the video call effectiveness score at a second timestamp of the video call based on comparing the target goal embedding with new topic discussion embeddings for new topic discussions after the first timestamp of the video call.
19 . The non-transitory computer readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the at least one processor to:
determine, from the video call data captured from the multiple video streams, a plurality of user accounts attending the video call; and
generate the video call effectiveness score based on the plurality of user accounts attending the video call.
20 . The non-transitory computer readable medium of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the at least one processor to:
determine a dissonance score by determining distances between the plurality of topic discussion embeddings within an embedding space; and
generate the video call effectiveness score based on the dissonance score.