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
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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DOI：
10.3390/app12073656
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
B. Sokhansanj;G. Rosen
中科院分区：
文献类型：
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作者：
B. Sokhansanj;G. Rosen
人工智能在法律领域的一个关键挑战是从当事人的诉讼摘要文本中确定它是否以及为什么会成功或失败。本文展示了一个来自美国的概念验证测试案例：预测专利授权后各方间审查(IPR)程序的结果。其目标是比较决策树和深度学习方法，验证可解释性方法，并演示基于缔约方简报的结果预测。具体地说，本研究比较和验证了两种不同的方法：(1)用词频倒置文档频率(TF-IDF)表示文档，训练XGBoost梯度增强决策树模型，以及使用Shap进行解释。(2)在上下文中对文档文本进行深度学习，使用具有注意力的卷积神经网络(CNN)，并比较LIME和注意力可视化在可解释性方面的差异。该方法在从非结构化书面裁决意见中自动确定案件结果的任务中得到验证，然后根据专利权人的初步回应简报预测审判机构或驳回。结果表明，可解释深度学习结构如何在时间上分离的训练和测试集上对成功/不成功的回答摘要进行分类。更准确的预测仍然具有挑战性，这可能是由于专利案件的具体事实、技术性质以及适用法律和判例随着时间的推移而发生的变化。
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.