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

• URL: http://arxiv.org/abs/2108.04088v1
• Date: Thu, 5 Aug 2021 16:21:29 GMT
• Title: Determining Sentencing Recommendations and Patentability Using a Machine Learning Trained Expert System
• Authors: Logan Brown, Reid Pezewski, Jeremy Straub
• Abstract summary: This paper presents two studies that use a machine learning expert system (MLES) One study focuses on a system to advise to U.S. federal judges for regarding consistent federal criminal sentencing. The other study aims to develop a system that could assist the U.S. Patent and Trademark Office automate their patentability assessment process.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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