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

arXiv: https://arxiv.org/abs/2108.04088 · HTML: https://mlsanity.com/paper/2108.04088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 44 references that are in the map.
- First posted on arXiv 5 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss](https://mlsanity.com/paper/2002.04486/) (2020)
- [Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks](https://mlsanity.com/paper/1912.12716/) (2019)
- [Adaptively Truncating Backpropagation Through Time to Control Gradient Bias](https://mlsanity.com/paper/1905.07473/) (2019)
- [Towards Query-Efficient Black-Box Adversary with Zeroth-Order Natural Gradient Descent](https://mlsanity.com/paper/2002.07891/) (2020)
- [Expert System Gradient Descent Style Training: Development of a Defensible Artificial Intelligence Technique](https://mlsanity.com/paper/2103.04314/) (2021)

## Abstract (arXiv)

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.