Determining Sentencing Recommendations and Patentability Using a Machine Learning Trained Expert System
This work addresses the need for consistent and automated decision support in legal and patent domains, though it appears incremental as it applies an existing MLES framework to new tasks.
The paper tackles the problem of automating decision-making in federal criminal sentencing and patentability assessment by developing a machine learning expert system (MLES) that outputs scaled recommendations, achieving functional systems for both applications.
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.