Determining Sentencing Recommendations and Patentability Using a Machine Learning Trained Expert System
Determining Sentencing Recommendations and Patentability Using a Machine Learning Trained Expert System
复制标题
使用经过机器学习训练的专家系统确定量刑建议和专利性
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
J. Straub
中科院分区：
文献类型：
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作者：
Logan Brown;Reid Pezewski;J. Straub
本文介绍了两个使用机器学习专家系统(MLES)的研究。其中一个重点是根据联邦量刑准则和罪犯特征向美国联邦法官提供关于一致的联邦刑事量刑的建议的制度。另一项研究旨在开发一种系统，有望帮助美国专利商标局自动化其专利性评估过程。这两项研究都使用经过机器学习训练的规则-事实专家系统网络来接受用于训练和展示的输入变量，并输出代表系统建议(例如，句子长度或专利性评估)的缩放变量。本文介绍并比较了为这些项目开发的规则-事实网络。它解释了这两个网络所使用的结构的基本决策过程，以及所需和执行的数据的预处理。并通过对两种系统的比较，讨论了不同的方法如何适用于MLES系统。
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.
影响因子：
5.4
作者：
Zhaoxian Wu;Qing Ling;Tianyi Chen;G. Giannakis
通讯作者：
Zhaoxian Wu;Qing Ling;Tianyi Chen;G. Giannakis