Legaltech and Lawtech: Global Perspectives, Challenges, and Opportunities

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

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.

A Survey of Research on Data Analytics-Based Legal Tech

Data analytics provides important tools and methods for processing the data generated during legal services. This paper aims to provide a systematic survey of the research papers on the application of quantitative data analytics algorithms in the legal domain. To this end, relevant research papers were collected and used to analyze topics and trends of research on data analytics-based Legal Tech. The key findings of this paper are as follows. Firstly, the number of research papers about Legal Tech has increased dramatically recently. Secondly, the application of supervised learning techniques to legal judgment data is a very popular approach in this research area. Thirdly, preprocessing legal documents is a very important procedure as many legal documents exist in text form. Fourthly, artificial neural networks and their variations are widely used in research on data analytics-based Legal Tech. Fifthly, data analytics-based Legal Tech is a multidisciplinary research topic related to computer science and social science, etc.

Laws and Emerging Technologies

New technologies and so-called communication and information technologies are transforming our society, the way in which we relate to each other, and the way we understand the world. By a wider extension, they are also influencing the world of law. That is why technologies will have a huge impact on society in the coming years and will bring new challenges and legal challenges to the legal sector worldwide. On the other hand, the new communications era also brings many new legal issues such as those derived from e-commerce and payment services, intellectual property, or the problems derived from the use of new technologies by young people. This will undoubtedly affect the development, evolution, and understanding of law. This Special Issue has become this window into the new challenges of law in relation to new technologies.