Multiclass Legal Judgment Outcome Prediction for Consumer Lawsuits using XGBoost and TPE

Santos, Pedro; Henrique, Fernando; Garcia, Venicius; Ferreira, Victor; Neto, Antonino Calisto dos Santos; Souza, Johnatan Carvalho; Manfredini, Caio; França, João Vitor Ferreira; Boaro, Jose M. C.; Bráz, Geraldo; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de; Oliveira, Milton S. L. de; Alves, Erika W. B. A. L.; Moraes, Renato U. Moreira e; Neto, Jose S. Sobral

doi:10.1109/smc42975.2020.9283094

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Cited by 2 publications

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Legal Judgment Prediction: A Systematic Literature Review

Dharma,

Widyawan,

Pratama

2023

2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA)

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Legal Judgment Prediction: A Systematic Literature Review

Dharma,

Widyawan,

Pratama

2023

2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation (ICAMIMIA)

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Predicting Institution Outcomes for Inter Partes Review (IPR) Proceedings at the United States Patent Trial & Appeal Board by Deep Learning of Patent Owner Preliminary Response Briefs

Sokhansanj

Rosen

2022

Applied Sciences

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A key challenge for artificial intelligence in the legal field is to determine from the text of a party’s litigation brief whether, and why, it will succeed or fail. This paper shows a proof-of-concept test case from the United States: predicting outcomes of post-grant inter partes review (IPR) proceedings for invalidating patents. The objectives are to compare decision-tree and deep learning methods, validate interpretability methods, and demonstrate outcome prediction based on party briefs. Specifically, this study compares and validates two distinct approaches: (1) representing documents with term frequency inverse document frequency (TF-IDF), training XGBoost gradient-boosted decision-tree models, and using SHAP for interpretation. (2) Deep learning of document text in context, using convolutional neural networks (CNN) with attention, and comparing LIME and attention visualization for interpretability. The methods are validated on the task of automatically determining case outcomes from unstructured written decision opinions, and then used to predict trial institution or denial based on the patent owner’s preliminary response brief. The results show how interpretable deep learning architecture classifies successful/unsuccessful response briefs on temporally separated training and test sets. More accurate prediction remains challenging, likely due to the fact-specific, technical nature of patent cases and changes in applicable law and jurisprudence over time.

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Multiclass Legal Judgment Outcome Prediction for Consumer Lawsuits using XGBoost and TPE

Cited by 2 publications

References 20 publications

Legal Judgment Prediction: A Systematic Literature Review

Legal Judgment Prediction: A Systematic Literature Review

Predicting Institution Outcomes for Inter Partes Review (IPR) Proceedings at the United States Patent Trial & Appeal Board by Deep Learning of Patent Owner Preliminary Response Briefs

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