A Comprehensive Survey on AI-based Methods for Patents

• URL: http://arxiv.org/abs/2404.08668v2
• Date: Tue, 18 Jun 2024 04:58:56 GMT
• Title: A Comprehensive Survey on AI-based Methods for Patents
• Authors: Homaira Huda Shomee, Zhu Wang, Sathya N. Ravi, Sourav Medya,
• Abstract summary: AI-based tools present opportunities to streamline and enhance important tasks in the patent cycle. This interdisciplinary survey aims to serve as a resource for researchers and practitioners working at the intersection of AI and patent analysis.
• Score: 14.090575139188422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in Artificial Intelligence (AI) and machine learning have demonstrated transformative capabilities across diverse domains. This progress extends to the field of patent analysis and innovation, where AI-based tools present opportunities to streamline and enhance important tasks in the patent cycle such as classification, retrieval, and valuation prediction. This not only accelerates the efficiency of patent researchers and applicants but also opens new avenues for technological innovation and discovery. Our survey provides a comprehensive summary of recent AI tools in patent analysis from more than 40 papers from 26 venues between 2017 and 2023. Unlike existing surveys, we include methods that work for patent image and text data. Furthermore, we introduce a novel taxonomy for the categorization based on the tasks in the patent life cycle as well as the specifics of the AI methods. This interdisciplinary survey aims to serve as a resource for researchers and practitioners who are working at the intersection of AI and patent analysis as well as the patent offices that are aiming to build efficient patent systems.

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