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The purpose of this paper is to highlight the use of big data through patentometric insights for R&D decision-making.

This study assesses the inventive activity through ‘big data’ patents, registered by inventors worldwide, using WIPO Patentscope database. The objective is to use the insights from patentometrics for R&D decision-making. The data from WIPO PatentScope (https://patentscope.wipo.int/search/en/search.jsf) was searched for current patent scenario in area of ‘big data’. The data was further organized and cleaned using the Google ‘OpenRefine’. Data was pre-processed to remove all null values. Cleaned data was analyzed using programming language ‘R’, MS Excel (charts and Pivot tables) and free data visualization tool called ‘Tableau Public’, to get insights for R&D decision-making.

The key insights included trends (patents with years of publication), top technologies trending the current space, top organizations leading in these technologies and the top inventors who are publishing patents in these technologies through leading organizations were drawn. Details in Section 5 in the paper.

Global patent data is multi-lingual and spreads across a set of multiple databases. Domain experts may be required to assess, identify and extract the relevant information for analysis and visualization of multi-lingual distributed data sets. Government organizations generally have multi-dimensional goals that may be more toward societal benefits. On the other hand, the commercial companies are more focused on profit. Therefore, the performance management process has to be really effective because it is critical for getting value in the government sector.

Insights from patent analytics serve as the important input to R&D managers as well as policymakers to assess the global needs to plan the national orientation according to the global market. This will help further for R&D projects prioritization, planning, budget allocations, human capital planning and other gamut of R&D management and decision-making.

Facilitation for R&D institutions (government as well as private) to formulate the research strategy for the domains or research areas to delve into. R&D decisions will be completely data-driven making them more accurate, reliable, valid and informed. These insights are very relevant for policymakers as well to facilitate the need assessment to determine the National priorities, make improvements in meeting societal country-level challenges during the resource allocation at top and subsequently at all other levels.

Data analytics of global patents in “big data” till 2019 to get insights to facilitate R&D decision-making.

Patents as one of the important components of intellectual capital are emerging as a new source for mining insights on open innovation (OI) practice of the organizations. Their role in value creation through collaboration and the inter-firm differences is yet to be explored in depth.

To achieve the aim, survey data is analyzed to rank OI practices (collaboration) of the firms, while patent data are analyzed to carry out descriptive and bivariate analysis to study the inter-firm differences in collaboration.

The survey findings highlight mergers and acquisitions (M&A) and patent pooling as the top two preferred modes of OI, while from patent data M&A has emerged as a predominant OI practice for mainly nonresident firms. At the firm level characteristics, out of firm age, number of granted patents and firm size, firm age has been found to be somewhat significant in few cases of OI practices.

It provides an alternative source, in this case patent data to study open innovation capabilities of firms in India. There is contribution to the patent value theory from profit motive to deriving strategic decisions on collaboration.

The managerial implications of this study lie in realizing granted patents as important business tools for seeking collaboration, tracing competitive intelligence and the geography of innovation of the firms' competitors.

The dataset of granted patents at the Indian Patent office (2005–2017), the sample of pharmaceutical firms drawn from this list of patents, patent data– based OI insights and the use of multiple imputation technique to missing data for meaningful insights are some of the unique aspects of this paper.

The purpose of this paper is to evaluate innovations in intellectual property rights (IPR) databases, techniques and software tools, with an emphasis on selected new developments and their contribution towards achieving advantages for IPR management (IPRM) and wider social benefits. Several industry buzzwords are addressed, such as IPR-linked open data (IPR LOD) databases, blockchain and IPR-related techniques, acknowledged for their contribution in moving towards artificial intelligence (AI) in IPRM.

The evaluation, following an original framework developed by the authors, is based on a literature review, web analysis and interviews carried out with some of the top experts from IPR-savvy multinational companies.

The paper presents the patent databases landscape, classifying patent offices according to the format of data provided and depicting the state-of-art in the IPR LOD. An examination of existing IPR tools shows that they are not yet fully developed, with limited usability for IPRM. After reviewing the techniques, it is clear that the current state-of-the-art is insufficient to fully address AI in IPR. Uses of blockchain in IPR show that they are yet to be fully exploited on a larger scale.

A critical analysis of IPR tools, techniques and blockchain allows for the state-of-art to be assessed, and for their current and potential value with regard to the development of the economy and wider society to be considered. The paper also provides a novel classification of patent offices and an original IPR-linked open data landscape.

Patent contains vast information on developed technologies because of the patent system. So, it is important to analyze patent data for understanding technologies. Most previous studies on patent analysis were focused on the technology itself. Their research results lacked the consideration of products. But the patent analysis based on products is crucial for company because a company grows by sales of competitive products. The purpose of this paper is to propose a novel methodology of patent analysis for product-based technology. This study contributes to the product development strategy of a company.

The primary goal for developing technology is to release a new product. So it is important to analyze the technology based on the product. In this study, the authors analyze Apple’s technologies based in iPod, iPhone, and iPad. In addition, the authors propose a new methodology to analyze product-based technology. The authors call this an integrated social network mining (ISNM). In the ISNM, the authors carry out a social network analysis (SNA) according to each product of Apple, and integrate all SNA results of iPod, iPhone, and iPad using the technological keywords.

In this case study, the authors analyze Apple’s technologies according to Apple’s innovative products, such as the iPod, iPhone, and iPad. From the ISNM results of Apple’s technology, the authors can find which technological detail is more important in overall structure of Apple’s technologies.

This study contributes to the management of technology including new product development, technological innovation, and research and development planning. To know the technological relationship between whole technologies based on products can be the source of intensification of technological competitiveness.

Most of studies on technology analysis were focused on patent technology itself. Though one of their research goals was to develop new product, they had their limits considering the products because they did not use the technology information in the technology analysis. The originality of this research is to use the product information in technology analysis using the proposed ISNM.

The purpose of this paper is to present a comprehensive picture of the innovative efforts undertaken over time to develop the digital technologies for managing the interface between supply chain management and marketing processes and the role they play in sustaining supply chain management-marketing (SCM-M) integration from an information processing point of view.

Patent analysis and actual examples are used to carry out this study. In detail, first, the authors identify the subset of enabling technologies pertaining to the fourth industrial revolution (Industry 4.0) that can be considered the most relevant for effective SCM-M integration (i.e. Industrial Internet of Things, Cloud computing, Big Data analytics and customer profiling, Cyber security). Second, the authors carry out a patent analysis aimed at providing a comprehensive overview of the patenting activity trends characterizing the set of digital technologies under investigation, hence highlighting their innovation dynamics and applications.

This research provides insightful information about which digital technologies may enable the SCM-M integration. Specifically, the authors highlight the role those solutions play in terms of information acquisition, storage and elaboration for SCM-M integration by relying on illustrative actual examples. Moreover, the authors present the organisations more involved in the development of digital technologies for SCM-M integration over time and offer an examination of their technological impact in terms of influence on subsequent technological developments.

So far, much has been said about why marketing and supply chain management functions should be integrated. However, a clear picture of the digital technologies that might be adopted to achieve this objective has yet to be revealed. Thus, the paper contributes to the literature on SCM-M integration and Industry 4.0 by highlighting the enabling technologies for the Industry 4.0 that may particularly serve for managing the SCM-M interface from an information processing perspective.

Patent offices and other stakeholders in the patent domain need to classify patent applications according to a standardized classification scheme. The purpose of this paper is to examine the novelty of an application it can then be compared to previously granted patents in the same class. Automatic classification would be highly beneficial, because of the large volume of patents and the domain-specific knowledge needed to accomplish this costly manual task. However, a challenge for the automation is patent-specific language use, such as special vocabulary and phrases.

To account for this language use, the authors present domain-specific pre-trained word embeddings for the patent domain. The authors train the model on a very large data set of more than 5m patents and evaluate it at the task of patent classification. To this end, the authors propose a deep learning approach based on gated recurrent units for automatic patent classification built on the trained word embeddings.

Experiments on a standardized evaluation data set show that the approach increases average precision for patent classification by 17 percent compared to state-of-the-art approaches. In this paper, the authors further investigate the model’s strengths and weaknesses. An extensive error analysis reveals that the learned embeddings indeed mirror patent-specific language use. The imbalanced training data and underrepresented classes are the most difficult remaining challenge.

The proposed approach fulfills the need for domain-specific word embeddings for downstream tasks in the patent domain, such as patent classification or patent analysis.

The purpose of this paper is to propose an objective method for technology forecasting (TF). For the construction of the proposed model, the paper aims to consider new approaches to patent mapping and clustering. In addition, the paper aims to introduce a matrix map and K‐medoids clustering based on support vector clustering (KM‐SVC) for vacant TF.

TF is an important research and development (R&D) policy issue for both companies and government. Vacant TF is one of the key technological planning methods for improving the competitive power of firms and governments. In general, a forecasting process is facilitated subjectively based on the researcher's knowledge, resulting in unstable TF performance. In this paper, the authors forecast the vacant technology areas in a given technology field by analyzing patent documents and employing the proposed matrix map and KM‐SVC to forecast vacant technology areas in the management of technology (MOT).

The paper examines the vacant technology areas for MOT patent documents from the USA, Europe, and China by comparing these countries in terms of technology trends in MOT and identifying the vacant technology areas by country. The matrix map provides broad vacant technology areas, whereas KM‐SVC provides more specific vacant technology areas. Thus, the paper identifies the vacant technology areas of a given technology field by using the results for both the matrix map and KM‐SVC.

The authors use patent documents as objective data to develop a model for vacant TF. The paper attempts to objectively forecast the vacant technology areas in a given technology field. To verify the performance of the matrix map and KM‐SVC, the authors conduct an experiment using patent documents related to MOT (the given technology field in this paper). The results suggest that the proposed forecasting model can be applied to diverse technology fields, including R&D management, technology marketing, and intellectual property management.

Most TF models are based on qualitative and subjective methods such as Delphi. That is, there are few objective models. In this regard, this paper proposes a quantitative and objective TF model that employs patent documents as objective data and a matrix map and KM‐SVC as quantitative methods.

Previous studies employing the behavioral theory of the firm have not explicitly taken the roles of decision makers and corporate governance into consideration. The purpose of this paper is to fill in this gap by integrating CEO overconfidence and discretion into the performance feedback mechanism.

Financial data were collected from 1,730 Chinese listed companies in the period 2011–2015. Firm-level patent application data were collected for 1988–2015 to measure firm patent application rhythm. Hypothesis testing relied on the fixed effect panel data model.

There is a positive relationship between performance discrepancy and a firm’s patent application rhythm. CEO overconfidence will weaken this positive relationship. The negative moderating effect of CEO overconfidence will be less pronounced when CEO discretion is high.

To the best of the authors’ knowledge, this work is the first empirical study that investigates the roles of CEO overconfidence and discretion in shaping the performance feedback mechanism.

This paper aims to forecast technological change for laptop batteries. The most promising technology to replace laptop batteries emerging today is micro fuel cells.

The authors use several sources of technical data like the Department of Energy Sandia National Laboratory Technical Library for exploring this topic further. Patents were searched for fuel cell and lithium battery development and to perform a technology cycle time analysis, identify countries filing patents, and discover what areas they are working on development.

Based on the analysis, fuel cells promise to be the technology that will replace laptop lithium batteries.

This paper attempts to draw a framework bringing different scientific data sources together for technology forecasting.

The keywords from patent documents contain a lot of information of technology. If we analyze the time series of keywords, we will be able to understand even more about technological evolution. The previous researches of time series processes in patent analysis were based on time series regression or the Box-Jenkins methodology. The methods dealt with continuous time series data. But the keyword time series data in patent analysis are not continuous, they are frequency integer values. So we need a new methodology for integer-valued time series model. The purpose of this paper is to propose modeling of integer-valued time series for patent analysis.

For modeling frequency data of keywords, the authors used integer-valued generalized autoregressive conditional heteroskedasticity model with Poisson and negative binomial distributions. Using the proposed models, the authors forecast the future trends of target keywords of Apple in order to know the future technology of Apple.

The authors carry out a case study to illustrate how the methodology can be applied to real problem. In this paper, the authors collect the patent documents issued by Apple, and analyze them to find the technological trend of Apple company. From the results of Apple case study, the authors can find which technological keywords are more important or critical in the entire structure of Apple’s technologies.

This paper contributes to the research and development planning for producing new products. The authors can develop and launch the innovative products to improve the technological competition of a company through complete understanding of the technological keyword trends.

The retrieved patent documents from the patent databases are not suitable for statistical analysis. So, the authors have to transform the documents into structured data suitable for statistics. In general, the structured data are a matrix consisting of patent (row) and keyword (column), and its element is an occurred frequency of a keyword in each patent. The data type is not continuous but discrete. However, in most researches, they were analyzed by statistical methods for continuous data. In this paper, the authors build a statistical model based on discrete data.