Determining Sentencing Recommendations and Patentability Using a Machine Learning Trained Expert System

TL;DR

This paper introduces two machine learning expert systems designed to assist U.S. federal judges with sentencing decisions and the Patent Office with patentability assessments, demonstrating their structure, data processing, and comparative methods.

Contribution

It develops and compares two rule-fact expert systems for legal and patent decision support, illustrating their design, data handling, and application differences.

Findings

The systems provide consistent recommendations based on input variables.

Comparison reveals different methodological approaches for each application.

Pre-processing steps are crucial for system accuracy and reliability.

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.