Abstract Big data is increasingly available in all areas of manufacturing and operations, which presents an opportunity for better decision making and discovery of the next generation of innovative technologies. Recently, there have been substantial developments in the field of patent analytics, which describes the science of analysing large amounts of patent information to discover trends. We define Intellectual Property Analytics (IPA) as the data science of analysing large amount of IP information, to discover relationships, trends and patterns for decision making. In this paper, we contribute to the ongoing discussion on the use of intellectual property analytics methods, i.e artificial intelligence methods, machine learning and deep learning approaches, to analyse intellectual property data. This literature review follows a narrative approach with search strategy, where we present the state-of-the-art in intellectual property analytics by reviewing 57 recent articles. The bibliographic information of the articles are analysed, followed by a discussion of the articles divided in four main categories: knowledge management, technology management, economic value, and extraction and effective management of information. We hope research scholars and industrial users, may find this review helpful when searching for the latest research efforts pertaining to intellectual property analytics.

中文翻译：

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知识产权分析 (IPA) 的最新技术：关于人工智能、机器学习和用于分析知识产权 (IP) 数据的深度学习方法的文献综述

摘要 大数据在制造和运营的所有领域越来越可用，这为更好地决策和发现下一代创新技术提供了机会。最近，专利分析领域取得了重大进展，该领域描述了分析大量专利信息以发现趋势的科学。我们将知识产权分析 (IPA) 定义为分析大量知识产权信息的数据科学，以发现决策制定的关系、趋势和模式。在本文中，我们对使用知识产权分析方法（即人工智能方法、机器学习和深度学习方法）来分析知识产权数据的持续讨论做出了贡献。本文献综述采用带有搜索策略的叙述方法，我们通过回顾最近的 57 篇文章来展示知识产权分析的最新技术。对文章的书目信息进行分析，然后对文章的讨论分为四大类：知识管理、技术管理、经济价值以及信息的提取和有效管理。我们希望研究学者和工业用户在搜索与知识产权分析相关的最新研究成果时会发现这篇评论很有帮助。随后对文章的讨论分为四大类：知识管理、技术管理、经济价值以及信息的提取和有效管理。我们希望研究学者和工业用户在搜索与知识产权分析相关的最新研究成果时会发现这篇评论很有帮助。随后对文章的讨论分为四大类：知识管理、技术管理、经济价值以及信息的提取和有效管理。我们希望研究学者和工业用户在搜索与知识产权分析相关的最新研究成果时会发现这篇评论很有帮助。