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. Big data is increasingly available in all areas of manufacturing and operations [1] . Data as such presents value for enabling a competitive data-driven economy, which is at the heart of the Internet of things and Industry 4.0 [2, 3] . Increased data availability presents an opportunity for better decision making and strategy development [4] , to introduce the next generation of innovative and disruptive technologies [5] . Over the last two decades, there have been substantial developments in the field of patent analytics. Patent analytics describes the science of analysing large amounts of intellectual property information, in relation to other data sources, to discover relationships and trends [6] [7] [8] [9] . With the digitization of patent data, the world's largest repository of technical information has become accessible for rapidly decreasing costs. While patent data has long been considered the world's largest repository of technological information, and only with its digitization since the BACON project in 1984 [10] and numerous gradual and cumulative improvements of tbe data quality and analytical techniques over the last decades, patent data has become increasingly accessible to and useful for a non-specialist audience [11] . With the rise of artificial intelligence (AI), and the increase in the usage of methods such as machine learning (ML) and deep learning (DL), a number of these have been applied to analyse IP data [6, [12] [13] [14] . In a recent study, we have used the technology roadmapping approach [15] to explore the future of patent analytics [16, 17] . We identify 11 priority technologies, such as artificial intelligence and artificial neural networks, that industry experts believe to be important to be adopted at a higher rate in the patent analytics domain [12] . While other domains have adopted such technologies already widely, the patent analytics domain seems to be catching up. We identify the need of adoption of these computer science techniques, to complement decision processes and provide decision support [11, 12, 18] . This is very much in line with the propositions by Refs. [19] [20] [21] [22] . In this paper, we contribute to the ongoing discussion on the use of machine learning and deep learning approaches to analyse intellectual property data [12] , by presenting the outcomes of a literature review on the state-of-the-art on intellectual property analytics. T techniques, such as machine learning and deep learning, to analyse intellectual property data. We create a taxonomy on the segmentation of the different approaches and methods to analyse the data, which builds on the work by Refs. [6, 17, 18] . The literature review is organized as follows. Section 2 defines intellectual property analytics. Section 3 presents the methodology adopted, followed by section 4, which presents the background and significance of the patent analysis process. Section 5 presents the bibliographic analysis results, followed by section 6, which discusses the reviewed papers. Section 7 concludes the literature review and highlights some future research directions. Intellectual Property Analytics (IPA) is the data science of analysing large amount of intellectual property information, to discover relationships, trends and patterns in the data for decisiong making. It is a multidisciplinary approach that makes use of mathematics, statistics, computer programming, and operations research to gain valuable knowledge from data, to support decision making rooted in the business context. We make use of this definition, as there is no widely accepted definition of IPA; however, this is very much in line with the definition of Patinformatics [8, 9] . It is important to understand the process of analyzing patent data when discussing patent analytics and intellectual property analytics. Trippe [14] presents a WIPO guide, which identifies and explains a large number of concepts on patent analysis and the methodology on how to run the different types of analysis on patent data. With the recent advancements of artificial intelligence, there has been a positive amount of activity around the different methodologies involved that could be applied to intellectual property data [11, 12] . Most of the literature makes use of the process as defined by Moehrle et al. [8] in a business context, and consists of three main stages: the preprocessing stage, the processing stage and the post processing stage. In the pre-processing stage, the data are collected, after information extraction, cleaned and well prepared, with the purpose in providing these data in high quality, correctness and completeness. In the processing stage, the analysis of the data extracted in the pre-processing stage takes place using different methods to classify, cluster, and identify meaningful insights from the information. In the post processing stage, also known as discovered knowledge, the results and information from the processing stage are visualized and evaluated to support strategic decision making. In this literature review, we give particular focus on the processing and postprocessing stage, and the specific algorithms used to analyse the data. This is similar to Abbas et al. [6], who presents a generic patent analysis work-flow, with the distinction that every analysis made has a specific purpose. Raturi et al. [23] argues that this process is a complementary process to the innovation cycle, and that the analysis of intellectual property data has many application in many fields. Bonino et al. [24] link the patent life cycle to the patent related information sources and the different tasks along the patent analytics tasks. They argue that a patent analytics process is a purpose-driven process, which consists of search tasks (patent ability, validity, infringement, portfolio survey, technology survey), analysis tasks (micro and macro assessment of business value, technical assessment and technology suggestions), and monitoring tasks (early sign monitoring, technology monitoring, portfolio monitoring, single patent monitoring). Similarly, Baglieri and Cesaroni [7] argue that patent analysis is a form of patent intelligence to support decision making. They argue that there are two meanings to the term of patent analysis, the process that considers all of the above, and the actual analysis of the patent data. They use the research by Bonino et al. [24] to define the three patent analysis tasks, patent searching, patent analysing and patent monitoring, and link value of information from these to the open innovation funnel. The paper aims to summarise the existing work, especially when it comes to the application of artificial intelligence methods, such as machine learning, artificial neural networks and deep learning, in the intellectual property domain [6, 12] . To carry out the literature review, the narrative and scoping literature review approaches have been adopted [25, 26] , and a research search strategy has been developed [27, 28] . The detailed search strategy is found in Appendix A. Fig. 1 shows the process flow for the comprehensive literature review.  L. Aristodemou, F. Tietze World Patent Information 55 (2018) 37-51 The articles on intellectual property analytics and patent analytics were identified from the Scopus database to find the most relevant published articles or in press articles. We search within the tittle, abstract and key words for various terms such as "patent", "patent data", "patent analysis" and "intellectual property data". The search is then narrowed to documents that also contain either in the tittle or the abstract or in the key words, the terms "machine learning", "machine learning models", "neural networks", "deep learning" and "artificial intelligence". In order to focus on recent literature, the search is limited to articles published after the year 2000, and to the fields of business, computer science, engineering, social science and mathematics. The search, effective the 8th January 2018, retrieved 90 documents. After manual screening, e.g. removing duplicate or irrelevant articles, 57 articles remained, which form the core of this review. The purpose of presenting the research articles in detail is to provide the readers with the latest research on the use of artificial intelligence methods, such as machine learning and deep learning, in the intellectual property domain, which analyse patent data. Results are presented in section 5, with the first level focusing on the bibliographic information, followed by the discussion of the methods in section 6 and the emerging themes of application. The first level of the analysis of the reviewed papers focuses on the analysis of the bibliographic information from the 57 articles (n 1 ). The detailed review of the aim, data, pre-processing and algorithms of each article is found in Appendix B. The number of articles have increased over the last few year, reaching a peak of 12 articles published in 2017. Fig. 2 shows the number of papers per year since the year 2000. There is an upward trend with the number of publications in recent years indicating an increasing interest in this particular field. This is further enhance by Fig. 3 , which shows the cumulative number of citations received per article per year, rising with an upward trend and reaching the peak of 153 citations in the year 2017. Fig. 4 illustrates the percentage of articles by subject area. The majority of articles are concentrate in the subject area of computer science (29%), followed by social sciences (14%), business, management and accounting (12%), and engineering (8%). Table 1 shows the top 10 affiliations of the article authors. It is evident from the information that Asia is the leading continent in the application of machine learning techniques to patent data. This is also supported by Table 2 , where the top 3 countries are Taiwan, South Korea and China, with 18, 12 and 8 articles respectively. However, from Table 2 we can see that contributions are also made by US scholars (8% of the total articles) despite no US affiliation appearing in Table 1 . European countries also are shown to have a strong influence in this domain, with countries like Germany, Serbia, accounting for the majority of articles, followed by Spain and Belgium. Moreover, Table 3 reveals the top 10 journals in which relevant articles are published. The top two journals, which account for 30% of the articles (15% each) are Technological Forecasting and Social Change and Scientrometrics. These are followed by Expert Systems With Applications with 7%, and World Patent Information and Sustainability Switzerland with 5% each. The top 10 journals account for a total of 33 articles. This indicates that published articles in this field are fragmented in a total of 33 journals. In both Tables 1 and 2 , any article with one or more afflication from different countries is multi-counted. For example, if an article has 3 different affiliations from 2 different countries, is counted 3 times in Tables 1 and 2 times in  Table 2 . Table 4 shows the top 10 cited articles, their citations and the citation frequency. The most cited article is Klinger et al. Note: Articles with one or more affiliations are multi-counted. Note: Articles with one or more countries are multi-counted.  There are several analytic methods in the literature that have been used with intellectual property data, and specifically patent data [6, 14] . Abbas et al. [6] provide a comprehensive literature review on the patent analytics techniques, where they distinguish between text mining and visualization approaches and the applicability to structured and unstructured data [24] . In this literature review, we specifically review articles that use intellectual property analytics methods, i.e artificial intelligence methods and approaches, to analyse intellectual property data and more specific patent data. Table 5 presents a summary of the intellectual property analytics methods, identified in the literature. It is evident that the majority of articles are concentrated around artificial neural networks (ANN) and the use of the back propagation learning method, followed by support vector machine (SVM), as well as conditional random fields (CRF). Moreover, the majority of articles focus on classification methods, with some combining both clustering and classification methods. From the review, four categories emerge in which the use of artificial intelligence methods with intellectual property data is implemented: the first category is knowledge managmenet, with a number of scholars focusing on patent evaluation and patent quality classification (e.g Refs. [30, 31, 60] . The second category is technology management, which includes technological patentability, R&D planning within organisations, technology intelligence including monitoring technological changes, identification and forecasting of emerging technologies (e.g. Ref. [32, 35, 46] . The third category is the economic value of intellectual property(in this case patents), and its impact in other areas for example law (e.g. Refs. [41, 57, 62] . The fourth category is a hybrid category, which includes extraction of information and effective management of the information. This concentrates on extraction of features from patents, such as chemical formulae and figures, or the effective classification of patents in technological areas (e.g Refs. [29, 65, 75] . The boundaries between these categories are permeable, if not overlapping, as the categories are inter-related and one article could be part of more than one category. Further details on these categories are provided in the following subsections, with the focus being on AI, ML, and DL publications. In each category, the articles are reviewed by order of citations, i.e. starting with the article that has the highest number of citations, and then grouped into themes. Knowledge management is important for firms, as it supports both the internal and external organisations, and improves their ability to solve problems better, adapt, and evolve to meet changing business requirements [78] . To support explicit knowledge management, Trappey et al. [30] develop a document classification and search methodology based on neural network technology that helps companies manage patent documents more effectively. In an overlapping area with innovation management, Trappey et al. [31] help firms to evaluate intellectual property rights and the quality of patent documents for innovative product development and discovery of state-of-the-art technology trends. Using patent transactions with the back propagation neural network, the classify patents according to their quality, with an accuracy of 85%. Moreover, Trappey et al. [49] propose a knowledge management approach using ontology-based artificial neural network algorithm to automatically classify and search knowledge documents, stimulating new product development innovation for effective collaborative management [42] . The focus on patent quality is also evident in the work by Wu et al. [60] , who develop an automatic patent quality analysis and qualification system, which is based on a combination of self-organising maps, kernel principal component analysis and support vector machine. To improve the incorporation of prior knowledge in incremental innovation, Lu et al. [74] use a hybrid min-max modular (M3) and support vector machine classifier to improve learning performance on Japanese patents. In Hido et al. [69] assess the quality of patent applications, with a combined machine learning and text mining approach, which computes a patentability score. The patentability score gives the likelihood that a patent application is approved by a patent office. Technology management is a set of management disciplines that allows organisations to manage their technologies to create competitive advantage [79] . Identification of technological trends is important to decision makers for R&D management. Thorleuchter et al. [32] propose a methodology to make the technology impact more transparent, which is based on a quantitive cross impact analysis. This method shows current technology impact and trends from the R&D of an organization's competitors, comparing these to the technology impact and impact trends from the organization's own R&D, and estimating the impact across technologies. Similarly, Jun et al. [33] use a hollistic approach to analyse published articles, papers and patents on developing technologies, to identify scientific and technological trends. Suominen et al. [56] discuss the benefits and constraints of machine learning approaches in industry level patent analysis. They also propose a classification using full-text descriptions with Latent Dirichlet Allocation, to create an overall view of patenting within the industry. In this way, they are able to identify technology trends and forecast future trends. Moreover, the visual impact of the evolution of technological trends can be very useful. Sung et al. [80] use a growing cell structure neural network, paired with the Girvan-Newman algorithm, to construct a map that visualises technological evolution and shows the developmental trend in a technological field. In addition, technology life cycle analysis is important for firm related investment strategies, as technological trend monitoring. Lee et al. [72] propose a stochastic technology life cycle analysis that uses multiple patent indicators to examine a technology's progression through its life cycle. The authors employ a hidden Markov model to estimate the probability of a technology being at a certain stage of its life cycle and identify patterns. Govindarajan et al. [71] propose a topic modelling approach, based on latent Dirichlet allocationp (LDA) algorithm to construct a domain ontology and identify technical and functional development trends for Industry 4.0. Closely related to the identification of technological trends, is the ability to forecast technological innovation. To study the technological innovation and forecasting of Apple, Jun and Park [35] firstly build a statistical models using the time series regression and multiple linear regression methods to create a technology map, followed by clustering to find Apple's vacant technology domains. They then use social network analysis to search for technologies central to Apple's future. Having a new technology opportunity is a significant variable that can lead to dominance in a competitive market. In that context, accurately understanding the state of development of technology convergence and forecasting promising technology convergence can determine the success of a firm. Kim and Lee [44] propose a forecasting methodology for multi-technology convergence, based on a patent-citation analysis, a dependency-structure matrix, and a neural-network analysis. The methodology enables planning for technology development of future technology combinations. Forecasting the number of patent applications is also an important factor to see the development of a technological field, where Zhang et al. [76] propose a support vector machine approach of doing that, overcoming the sparsity problem mainly found in patents. Jun [81] build a combined clustering method using dimension reduction and K-means clustering, which is based on support vector clustering and Silhouette measure, to identify clusters for technology forecasting by patent analysis.  Economic development can be achieved through science and technology. By applying computational intelligence methodologies, such as artificial neural networks, economic development estimation can be determined based on different science and technology factors [43, 47, 85] . Markovic et al. [55]d evelop and apply the extreme learning machine (ELM) to forecast the gross domestic product (GDP) growth rate. Moreover, Lee et al. [72] propose a quantitative corporate performance prediction model that applies the support vector regression (SVR) algorithm to predict corporate performance, from both financial and technical data. Lee et al. [57] construct a deep neural networkbased corporate performance prediction model that uses a company's financial and patent indicators as predictors. The model includes an unsupervised learning phase, which uses a restricted Boltzmann machine, and a fine-tuning phase, with a backpropagation algorithm. Furthermore, in a group of studies in the US pharmaceutical industry, a number of scholars apply artificial neural networks to explore the influences of the quantitative and qualitative patent indicators upon corporate market value, showing that US pharmaceutical companies should not concentrate most of their R&D resources on one particular technological field, but create wider technological capabilities to avoid missing new technological opportunities [36, 39] . In a similar way, Chen and Chang [40] explore the nonlinear effects of firm size, profitability, and employee productivity on patent citations, whereas Chen and Chang [41] explore the relationship between the Herfindahl-Hirschmann Index (HHI) of patents and the relative patent position in the most important technological field of the firm [51] . Bass and Kurgan [62] analyse nanotechnology patents with a number of different classification algorithms, to identify the impact of different factors on patent value, determing which of these differentiate between the top-performing and the remaining nanotechnology patents. Lai and Che [54] focus their research on patent law and proposed a valuation model, based on an extension neural network, for the monetary legal value of patents, which uses the damage award of a patent infringement lawsuit as the legal value of a patent [53]. Extraction of information and effective management of information are fundamental systems of any firm's management system. Research in this area has mainly focused on three theme: (1) the extraction and chemical name recognition, (2) the extraction and identification of figures, and (3) the effective management of collective information.
In the patent field, Aristodemou et al. REF reviewed 57 recent articles on the use of artificial intelligence methods, machine learning and deep learning approaches for analyzing intellectual property data.
The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data
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