INTELLIGENT PREDICTION OF MEETING AVAILABILITY

1 . A method for predicting meeting availability for a user, comprising:

receiving:

a calendar associated with a user of a communication platform, comprising a schedule of one or more scheduled future meetings on the communication platform,

information associated with a plurality of past requested meetings on the communication platform, and

a user behavioral profile associated with the user, comprising a plurality of user behaviors associated with the past requested meetings;

receiving a meeting request for the user;

determining an earliest available time slot for the requested meeting;

determining that one or more of the scheduled future meetings are scheduled earlier than the earliest available time slot for the future meeting;

for each of the one or more scheduled future meetings which are earlier than the earliest available meeting time slot, deploying an artificial intelligence (AI) model to:

identify one or more similar past meeting requests based on the scheduled future meeting exceeding a similarity threshold with respect to one or more past requested meetings,

analyze the user behavioral profile with respect to the one or more similar past meeting requests, and

based on the analysis of the user behavioral profile, generate an availability prediction score for the scheduled future meeting, the availability prediction score indicating a likeliness of the user being available for the requested meeting during the time slot for the scheduled future meeting; and

providing one or more predicted available times for the user to attend the requested meeting based on the availability prediction scores for the one or more scheduled future meetings.

2 . The method of claim 1 , wherein one or more user behaviors with respect to the predicted available times are fed back into the AI model to improve generation of future availability prediction scores.

3 . The method of claim 1 , further comprising:

receiving notification that the user declined the one or more predicted available times for the meeting; and

sending, to the user, a new proposed time and an option to accept or decline the new proposed time.

4 . The method of claim 1 , wherein at least a subset of the user behaviors associated with past requested meetings relates to accepting or declining the past requested meetings.

5 . The method of claim 1 , wherein at least a subset of the user behaviors associated with past requested meetings relates to attending or not attending the past requested meetings that have been accepted.

6 . The method of claim 1 , wherein at least a subset of the user behaviors associated with past requested meetings relates to the user participating or not participating in the past requested meetings that were attended by the user.

7 . The method of claim 6 , wherein participating comprises one or more of: contributing vocally to the meeting, broadcasting a video feed, screen sharing, document collaboration, and textual messaging within the meeting interface.

8 . The method of claim 1 , wherein at least a subset of the user behaviors associated with past requested meetings relates to user engagement within the past requested meetings that were attended by the user.

9 . The method of claim 8 , wherein user engagement comprises one or more of: visual engagement via eye tracking, whether the meeting is within an active window of the user environment, and percentage of the meeting attended.

10 . The method of claim 1 , wherein at least a subset of the user behaviors associated with past requested meetings relates to likelihood the user will attend a meeting with one or more specific additional users, wherein the one or more specific additional users are users within a particular hierarchy or subhierarchy of an organization.

11 . The method of claim 1 , wherein the one or more predicted available times are provided in one of: descending order of predicted availability score; or chronological order wherein a rating can be assigned to each of the predicted available times.

12 . The method of claim 1 , wherein the AI model is trained on at least one or more datasets comprising user behavioral profiles for users of the communication platform with respect to past requested meetings.

13 . The method of claim 1 , wherein the AI model is trained based on whether predicted available times that were selected for requested meetings were times in which users actually attended the requested meetings.

14 . The method of claim 1 , further comprising:

providing, to the user, a decision digest comprising one or more decisions for scheduling requested meetings based on predicted available times for the requested meetings.

15 . The method of claim 14 , further comprising:

determining that an option to inform a host of a previously scheduled meeting about a decision related to the previously scheduled meeting has been enabled; and

providing notification to the host about the decision related to the previously scheduled meeting.

16 . The method of claim 1 , wherein receiving the meeting request for the user comprises:

receiving input from an additional user of the communication platform within a scheduling interface, the input relating to scheduling the requested meeting within the calendar of at least the user.

17 . The method of claim 1 , wherein receiving the meeting request for the user comprises:

receiving a message for the user related to a requested meeting;

sending, to the user, the message and an option to accept or decline the requested meeting; and

receiving, from the user, an acceptance of the requested meeting.

18 . The method of claim 1 , wherein determining that the future meeting meets or exceeds a similarity threshold comprises determining that the future meeting is a recurring meeting that has been requested for the user at least once before.

19 . A communication system comprising one or more processors configured to perform the operations of:

receiving:

a calendar associated with a user of a communication platform, comprising a schedule of one or more scheduled future meetings on the communication platform,

information associated with a plurality of past requested meetings on the communication platform, and

a user behavioral profile associated with the user, comprising a plurality of user behaviors associated with the past requested meetings;

receiving a meeting request for the user;

determining an earliest available time slot for the requested meeting;

determining that one or more of the scheduled future meetings are scheduled earlier than the earliest available time slot for the future meeting;

for each of the one or more scheduled future meetings which are earlier than the earliest available meeting time slot, deploying an artificial intelligence (AI) model to:

identify one or more similar past meeting requests based on the scheduled future meeting exceeding a similarity threshold with respect to one or more past requested meetings,

analyze the user behavioral profile with respect to the one or more similar past meeting requests, and

based on the analysis of the user behavioral profile, generate an availability prediction score for the scheduled future meeting, the availability prediction score indicating a likeliness of the user being available for the requested meeting during the time slot for the scheduled future meeting; and

providing one or more predicted available times for the user to attend the requested meeting based on the availability prediction scores for the one or more scheduled future meetings.

20 . A non-transitory computer-readable medium containing instructions for predicting meeting availability for a user, comprising:

instructions for receiving:

a calendar associated with a user of a communication platform, comprising a schedule of one or more scheduled future meetings on the communication platform,

information associated with a plurality of past requested meetings on the communication platform, and

a user behavioral profile associated with the user, comprising a plurality of user behaviors associated with the past requested meetings;

instructions for receiving a meeting request for the user;

instructions for determining an earliest available time slot for the requested meeting;

instructions for determining that one or more of the scheduled future meetings are scheduled earlier than the earliest available time slot for the future meeting;

for each of the one or more scheduled future meetings which are earlier than the earliest available meeting time slot, instructions for deploying an artificial intelligence (AI) model to:

identify one or more similar past meeting requests based on the scheduled future meeting exceeding a similarity threshold with respect to one or more past requested meetings,

analyze the user behavioral profile with respect to the one or more similar past meeting requests, and

based on the analysis of the user behavioral profile, generate an availability prediction score for the scheduled future meeting, the availability prediction score indicating a likeliness of the user being available for the requested meeting during the time slot for the scheduled future meeting; and

instructions for providing one or more predicted available times for the user to attend the requested meeting based on the availability prediction scores for the one or more scheduled future meetings.