  The Proposal Auto-Categorizer and Manager (PACMan) tool was written to
respond to concerns on subjective flaws and potential biases in some aspects of
the proposal review process for time allocation for the {\it Hubble Space
Telescope} (HST), and to partially alleviate some of the anticipated additional
workload from the {\it James Webb Space Telescope} (JWST) proposal review.
PACMan is essentially a mixed-method Naive Bayesian spam filtering routine,
with multiple pools representing scientific categories, that utilizes the
Robinson method for combining token (or word) probabilities. PACMan was trained
to make similar programmatic decisions in science category sorting, panelist
selection, and proposal-to-panelists assignments to those made by individuals
and committees in the Science Policies Group (SPG) at Space Telescope Science
Institute. Based on training from the previous cycle's proposals, PACMan made
the same science category assignments for proposals in Cycle 24 as did the SPG,
an average of 87\% of the time. Tests for similar science categorizations,
based on training using proposals from additional cycles, show that this
accuracy can be further improved, to the $>95\%$ level. This tool will be used
to augment or replace key functions in the TAC review processes in future HST
and JWST cycles.
