Finding frequencies in a noisy, "uneven" dataset
Just to point you in one possible direction: you could treat this problem as one of probabilistic mixture modeling.

Imagine that each person's drink ordering is governed by a probability distribution. That distribution may be characterized by the time since their last drink order.  As time passes, the probability that they will order another drink increases until eventually they order another drink and the time resets.

One possible model for the time between drink orders for a single person is the exponential distribution.  If you consider many people ordering, the resultant times would likely be a mixture of exponentials. The problem then comes down to fitting a mixture of exponentials to the table's drink data.  You would likely have to supply some additional prior data as well to get a meaningful model (otherwise it's difficult to tell the difference between 5 people ordering 2 drinks a piece or 1 person ordering 10 drinks).