Determining Sentencing Recommendations and Patentability Using a Machine Learning Trained Expert System
Abstract
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.
Cite
@article{arxiv.2108.04088,
title = {Determining Sentencing Recommendations and Patentability Using a Machine Learning Trained Expert System},
author = {Logan Brown and Reid Pezewski and Jeremy Straub},
journal= {arXiv preprint arXiv:2108.04088},
year = {2021}
}