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Found 2 papers, 2 papers with code
Patent Sentiment Analysis to Highlight Patent Paragraphs
The contributions of this work are: i) we developed a multi-class, novel dataset of size 150k samples by traversing USPTO patents over a decade, ii) articulated statistics and distributions of data using imperative exploratory data analysis, iii) baseline Machine Learning models are developed to utilize the dataset to address patent paragraph highlighting task, iv) dataset and codes relating to this task are open-sourced through a dedicated GIT web page: https://github. com/Renuk9390/Patent_Sentiment_Analysis and v) future path to extend this work using Deep Learning and domain specific pre-trained language models to develop a tool to highlight is provided.
PatentMatch: A Dataset for Matching Patent Claims & Prior Art
For these reasons, we address the computer-assisted search for prior art by creating a training dataset for supervised machine learning called PatentMatch.
