使用机器学习训练的专家系统确定量刑建议和专利性
Determining Sentencing Recommendations and Patentability Using a Machine Learning Trained Expert System
摘要：
本文介绍了两项研究，使用机器学习专家系统（MLES）。其中一个重点是向美国联邦法官提供咨询意见的制度，以便根据联邦判刑准则和罪犯的特点，作出一致的联邦刑事判刑。另一项研究旨在开发一个系统，可以前瞻性地帮助美国专利商标局自动化他们的专利性评估过程。这两项研究都使用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呈现的输入变量，并输出表示系统推荐的缩放变量（例如，（一）专利申请的期限（专利评估）。本文介绍并比较了这些项目已经开发的规则-事实网络。它解释了用于两个网络的结构的决策过程以及所需和执行的数据预处理。通过对两种系统的比较，讨论了如何将不同的方法应用于MLES系统。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
