  This paper presents two studies that use a machine learning expert system
(MLES). One focuses on a system to advise to United States federal judges for
regarding consistent federal criminal sentencing, based on both the federal
sentencing guidelines and offender characteristics. The other study aims to
develop a system that could prospectively assist the U.S. Patent and Trademark
Office automate their patentability assessment process. Both studies use a
machine learning-trained rule-fact expert system network to accept input
variables for training and presentation and output a scaled variable that
represents the system recommendation (e.g., the sentence length or the
patentability assessment). This paper presents and compares the rule-fact
networks that have been developed for these projects. It explains the
decision-making process underlying the structures used for both networks and
the pre-processing of data that was needed and performed. It also, through
comparing the two systems, discusses how different methods can be used with the
MLES system.
