Computer support for meetings

An automated meeting-support system comprising:

• a processor;
• a network device operatively coupled to the processor;
• and
• computer memory operatively coupled to the processor and configured to hold program instructions executable by the processor, the program instructions comprising:
• instructions to train a machine-learning model on previous meeting minutes to recognize directives;
• instructions to access one or more sensory data streams providing digitized sensory data responsive to an activity of one or more invitees during a meeting, the one or more sensory data streams including at least one audio stream,
• instructions to subject the at least one audio stream to phonetic and situational computer modeling to recognize a sequence of words in the at least one audio stream and to assign each word of the sequence of words to an invitee,
• instructions to subject the sequence of words to semantic computer modeling by the machine-learning model to recognize a sequence of directives in the sequence of words,
• instructions to release, via the network device, one or more output data streams based on the sequence of directives, the one or more output data streams including one or more notifications comprising a list of directives recognized by the machine-learning model,
• instructions to receive an edited list of directives actually discussed during the meeting, and
• instructions to refine the machine-learning model based on the edited list of directives.

One or more memory devices having embodied thereon computer-executable instructions which, when executed by one or more processors, performs a method of providing support for a meeting of invitees, the method comprising:

• accessing one or more sensory data streams providing digitized sensory data responsive to an activity of one or more invitees during the meeting, the one or more sensory data streams including at least one audio stream;
• subjecting the at least one audio stream to phonetic and situational computer modeling to recognize a sequence of words in the at least one audio stream;
• accessing depth images obtained by a depth-imaging camera;
• analyzing the depth images to determine relative postures of the invitees;
• assigning each word of the sequence of words to an invitee based at least in part on the relative postures of the invitees to associate audio with individual invitees;
• subjecting the sequence of words to semantic computer modeling by a machine-learning model to recognize a sequence of directives in the sequence of words;
• releasing, via a network device, one or more output data streams based on the sequence of directives, the one or more output data streams including one or more notifications comprising a list of directives recognized by the machine-learning model;
• receiving an edited list of directives actually discussed during the meeting;
• and
• refining the machine-learning model based on the edited list of directives.