A Comprehensive Survey on AI-based Methods for Patents

2024-04-02

arXiv.org

AI Summary

To address the fragmented and unsystematic application of NLP and multimodal AI in patent analysis, this study systematically reviews over 40 AI-driven patent analysis papers published between 2017 and 2023. We propose, for the first time, a two-dimensional classification framework integrating *patent lifecycle stages* and *AI technical characteristics*. Furthermore, we unify and comprehensively survey deep learning, NLP, computer vision, graph neural networks, and multimodal fusion methods applied to both patent text (e.g., claims, descriptions) and images (e.g., drawings), covering core tasks including classification, retrieval, and value prediction. The resulting interdisciplinary, reusable knowledge framework serves as an authoritative reference for AI researchers, patent examiners, and developers of intelligent patent systems—enhancing analytical efficiency and accelerating technological opportunity discovery.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Surveying NLP and Multimodal AI for patent analysis tasks
• Enhancing patent classification and retrieval using PLMs and LLMs
• Providing a taxonomy for patent lifecycle tasks and methods

Innovation

• Uses Pretrained Language Models (PLMs)
• Applies multimodal AI techniques
• Introduces novel taxonomy for tasks