Abstract: A processing system may receive an input for scheduling an in-person meeting between meeting participants. The input may include an indication of the meeting participants. The processing system may use a machine learning model to predict preferences of one or more of the meeting participants for attending the physical meeting. The preferences may include a physical location and an availability. The machine learning model may be trained using historical information including a past physical location and a past availability of the one or more meeting participants. The processing system may determine scheduling information for the in-person meeting based on the input and the preferences. The scheduling information may include a time, a date, and a physical location for the in-person meeting. The processing system may transmit the scheduling information to a meeting participant.

Abstract: A recommended workspace at a premises is determined for a user of a software platform requesting a workspace reservation based on learned preferences specific to the user. The workspace reservation request is initiated via an interaction by the user with one or more graphical user interfaces displaying information associated with workspaces at the premises. Scores for each of multiple candidate workspaces identified for the user from amongst available ones of the workspaces are determined based on weights defined according to learned personnel preferences of the user and learned workspace preferences of the user. A recommended workspace is determined for the user as a candidate workspace of the multiple candidate workspaces corresponding to a highest one of the scores. An indication of the recommended workspace is then output for display within the one or more graphical user interfaces in response to the workspace reservation request.