Intelligent meeting timeslot analysis and recommendation

A system comprising:

• one or more non-transitory machine-readable mediums configured to store instructions;
• and
• one or more processors configured to execute the instructions stored on the one or more non-transitory machine-readable mediums, wherein execution of the instructions causes the one or more processors to carry out a process comprising:
• providing a representational state transfer (REST) API interface to a calendaring service and a second API interface to a client application;
• continuously retrieving calendar details of one or more users from the calendaring service through the REST interface;
• generating from the calendar details a training dataset comprising a plurality of training samples, each training sample of the plurality of training samples comprising a plurality of relevant features from historical meeting information, the plurality of relevant features indicating an attendee of a historical meeting, an organizer of the historical meeting, a date of the historical meeting, a time of the historical meeting, and a subject category of the historical meeting, wherein the plurality of relevant features influence a prediction of an attendee meeting acceptance level;
• training a machine learning (ML) model to predict an attendee meeting acceptance level for an invitation to a meeting using the training dataset, wherein training the ML model includes inputting the plurality of training samples to the ML model;
• receiving via the second API interface a request to analyze a timeslot of a meeting;
• determining meeting details, wherein the meeting details include an organizer of the meeting, a date of the meeting, a time of the meeting, a subject of the meeting, and one or more attendees of the meeting;
• predicting, using the ML model, an attendee meeting acceptance level for each attendee of the one or more attendees, the attendee meeting acceptance level based on a likelihood of the one or more attendees joining the meeting;
• aggregating the attendee meeting acceptance levels for each attendee;
• determining from the aggregated attendee meeting acceptance levels an acceptance level of the meeting;
• generating status bar indicative of one or more aggregated acceptance levels of the meeting and the meeting acceptance level;
• providing a user-interface to the client device over the second API interface, the user-interface configured to display the status bar and the meeting acceptance level of the meeting in a response to the request to analyze the timeslot of the meeting;
• and
• pushing in real-time updates over the second API interface to the user-interface based on the continuously retrieved calendar details.

A non-transitory machine-readable medium encoding instructions that when executed by one or more processors cause a process to be carried out, the process including:

• providing a representational state transfer (REST) API interface to a calendaring service and a second API interface to a client application;
• continuously retrieving calendar details of one or more users from the calendaring service through the REST interface;
• generating from the calendar details a training dataset comprising a plurality of training samples, each training sample of the plurality of training samples comprising a plurality of relevant features from historical meeting information, the plurality of relevant features indicating an attendee of a historical meeting, an organizer of the historical meeting, a date of the historical meeting, a time of the historical meeting, and a subject category of the historical meeting, wherein the plurality of relevant features influence a prediction of an attendee meeting acceptance level;
• training a machine learning (ML) model to predict an attendee meeting acceptance level for an invitation to a meeting using the training dataset, wherein training the ML model includes inputting the plurality of training samples to the ML model;
• receiving via the second API interface a request to analyze a timeslot of a meeting;
• determining meeting details, wherein the meeting details include an organizer of the meeting, a date of the meeting, a time of the meeting, a subject of the meeting, and one or more attendees of the meeting;
• predicting, using the ML model, an attendee meeting acceptance level for each attendee of the one or more attendees, the attendee meeting acceptance level based on a likelihood of the one or more attendees joining the meeting;
• aggregating the attendee meeting acceptance levels for each attendee;
• determining from the aggregate attendee meeting acceptance levels an acceptance level of the meeting;
• generating a status bar indicative of one or more aggregated acceptance levels of the meeting and the meeting acceptance level;
• providing a user-interface to the client device over the second API interface, the user-interface configured to display the status bar and the meeting acceptance level of the meeting in a response to the request to analyze the timeslot of the meeting;
• and
• pushing in real-time updates over the second API interface to the user-interface based on the continuously retrieved calendar details.