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Name: RENU
Type: User
Name: RENU
Type: User
Model for EXPAAI - Explainable Patent Analysis using Artificial Intelligence
API for the EXPAAI AI Model
Patent Sentiment Analysis is a process of highlighting patent paragraphs (advantages, problems and neutral text) to ease examination process and also to assist patent attorneys in handling litigation and infringement cases during patent analysis.
Recent research in applying deep learning to the problem of prior-art searching has enabled development of easy-to-use prior-art search engines that accept natural language search queries and provide improved search performance. However, as opposed to the conventional keyword-based techniques where the results are readily explained by the presence of queried keywords, deep-learning based techniques act like a black box. As a result, it is difficult for users to understand how to articulate their information need as a high-quality search query to obtain optimal results. In this paper we share insights on this problem from extensive experimentation with PQAI, an open source deep learning based prior-art search engine. We study the effects of various query parameters such as verbosity, grammar, and specificity on the search results and show that ill-formed queries containing grammatical errors, non-essential content, and broad terminology adversely affect relevance of search results. We also develop and benchmark a number of machine learning models, viz. Grammatical Error Detection Model (GEDM), Query Specificity Model (QSM), and Query Verbosity Model (QVM), to identify and mitigate commonly encountered issues with ill-formed queries.
A python tool for reading, parsing and finding patent using the United States Patent and Trademark (USPTO) Bulk Data Storage System.
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