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29 pages, 2329 KiB

Open AccessArticle

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

by Bahrad A. Sokhansanj and Gail L. Rosen

Appl. Sci. 2022, 12(7), 3656; https://doi.org/10.3390/app12073656 - 5 Apr 2022

Cited by 1 | Viewed by 2639

Abstract

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. Full article

(This article belongs to the Special Issue Development and Applications of AI on Legal Tech)

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16 pages, 480 KiB

Open AccessArticle

Building a Production-Ready Multi-Label Classifier for Legal Documents with Digital-Twin-Distiller

by Gergely Márk Csányi, Renátó Vági, Dániel Nagy, István Üveges, János Pál Vadász, Andrea Megyeri and Tamás Orosz

Appl. Sci. 2022, 12(3), 1470; https://doi.org/10.3390/app12031470 - 29 Jan 2022

Cited by 4 | Viewed by 3186

Abstract

One of the most time-consuming parts of an attorney’s job is finding similar legal cases. Categorization of legal documents by their subject matter can significantly increase the discoverability of digitalized court decisions. This is a multi-label classification problem, where each relatively long text can fit into more than one legal category. The proposed paper shows a solution where this multi-label classification problem is decomposed into more than a hundred binary classification problems. Several approaches have been tested, including different machine-learning and text-augmentation techniques to produce a practically applicable model. The proposed models and the methodologies were encapsulated and deployed as a digital-twin into a production environment. The performance of the created machine learning-based application reaches and could also improve the human-experts performance on this monotonous and labor-intensive task. It could increase the e-discoverability of the documents by about 50%. Full article

(This article belongs to the Special Issue Development and Applications of AI on Legal Tech)

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13 pages, 3993 KiB

Open AccessArticle

Evaluating Human versus Machine Learning Performance in a LegalTech Problem

by Tamás Orosz, Renátó Vági, Gergely Márk Csányi, Dániel Nagy, István Üveges, János Pál Vadász and Andrea Megyeri

Appl. Sci. 2022, 12(1), 297; https://doi.org/10.3390/app12010297 - 29 Dec 2021

Cited by 12 | Viewed by 2902

Abstract

Many machine learning-based document processing applications have been published in recent years. Applying these methodologies can reduce the cost of labor-intensive tasks and induce changes in the company’s structure. The artificial intelligence-based application can replace the application of trainees and free up the time of experts, which can increase innovation inside the company by letting them be involved in tasks with greater added value. However, the development cost of these methodologies can be high, and usually, it is not a straightforward task. This paper presents a survey result, where a machine learning-based legal text labeler competed with multiple people with different legal domain knowledge. The machine learning-based application used binary SVM-based classifiers to resolve the multi-label classification problem. The used methods were encapsulated and deployed as a digital twin into a production environment. The results show that machine learning algorithms can be effectively utilized for monotonous but domain knowledge- and attention-demanding tasks. The results also suggest that embracing the machine learning-based solution can increase discoverability and enrich the value of data. The test confirmed that the accuracy of a machine learning-based system matches up with the long-term accuracy of legal experts, which makes it applicable to automatize the working process. Full article

(This article belongs to the Special Issue Development and Applications of AI on Legal Tech)

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