  We consider a communication system in which the destination receives status
updates from an information source that observes a physical process. The
transmitter performs semantics-empowered filtering as a means to send only the
most "important" samples to the receiver in a timely manner. As a first step,
we explore a simple policy where the transmitter selects to encode only a
fraction of the least frequent realizations of the observed random phenomenon,
treating the remaining ones as not informative. For this timely source coding
problem, we derive the optimal codeword lengths in the sense of maximizing a
semantics-aware utility function and minimizing a quadratic average length
cost. Our numerical results show the optimal number of updates to transmit for
different arrival rates and encoding costs and corroborate that semantic
filtering results in higher performance in terms of timely delivery of
important updates.
