Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law最新文献_第4页

Performance improvement on legal model checking 法律模型检查的绩效改进

Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law Pub Date : 2017-06-12 DOI: 10.1145/3086512.3086518

Carlos Faciano, S. Mera, F. Schapachnik, Ana Haydée Di Iorio, Bibiana Luz Clara, Verónica Uriarte, María Fernanda Giaccaglia, María Belén Ruffa, Cristian Marcos

引用次数: 2

A sequence approach to case outcome detection 病例结果检测的序列方法

Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law Pub Date : 2017-06-12 DOI: 10.1145/3086512.3086534

Tom Vacek, Frank Schilder

引用次数: 8

The genealogy of ideology: predicting agreement and persuasive memes in the U.S. courts of appeals 意识形态谱系:预测美国上诉法院的一致性和说服性模因

Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law Pub Date : 2017-06-12 DOI: 10.1145/3086512.3086544

Shiva Verma, Adithya Parthasarathy, Daniel L. Chen

引用次数: 1

LexrideLaw: an argument based legal search engine LexrideLaw:一个基于争论的法律搜索引擎

Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law Pub Date : 2017-06-12 DOI: 10.1145/3086512.3086548

Matthew Gifford

引用次数: 10

Early predictability of asylum court decisions 庇护法庭判决的早期可预测性

Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law Pub Date : 2017-01-26 DOI: 10.1145/3086512.3086537

Matthew Dunn, Levent Sagun, Hale Sirin, Daniel Chen

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引用次数: 26

Predicting litigation likelihood and time to litigation for patents 预测专利诉讼的可能性和时间

Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law Pub Date : 2016-03-23 DOI: 10.1145/3086512.3086545

Papis Wongchaisuwat, D. Klabjan, John O. McGinnis

{"title":"Predicting litigation likelihood and time to litigation for patents","authors":"Papis Wongchaisuwat, D. Klabjan, John O. McGinnis","doi":"10.1145/3086512.3086545","DOIUrl":"https://doi.org/10.1145/3086512.3086545","url":null,"abstract":"An ability to forecast the likelihood of a patent litigation1 and time-to-litigation benefits companies in many aspects, such as in patent portfolio management, and strategic planning. Thus, we develop predictive models for estimating the likelihood of litigation for patents and the expected time to litigation. Our work focuses on improving the state-of-the-art by relying on a different set of features and employing more sophisticated algorithms with realistic data. Specifically, we consider potential factors influencing a patent to be litigated in the model. These features, collected at the issue date of the patent and thus prior to the actual litigation, include textual features, patent's general information as well as financial information of patent's assignee. Our proposed models are a combination of a clustering approach coupled with an ensemble classification method. With a very low litigation rate of 1 to 2 percent, the results from the models show promising predictability. Financial information and features related to referencing are important indicators to distinguish between litigated and non-litigated patents","PeriodicalId":425187,"journal":{"name":"Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law","volume":"8 S1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113959624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16