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Smart manufacturing can lead to disruptive changes in production technologies and business models in the manufacturing industry. This paper aims to identify technological topics in smart manufacturing by using patent data, investigating technological trends and exploring potential opportunities.

The latent Dirichlet allocation (LDA) topic modeling technique was used to extract latent technological topics, and the generalized linear mixed model (GLMM) was used to analyze the relative emergence levels of the topics. Topic value and topic competitive analyses were developed to evaluate each topic's potential value and identify technological positions of competing firms, respectively.

A total of 14 topics were extracted from the collected patent data and several fast growth and high-value topics were identified, such as smart connection, cyber-physical systems (CPSs), manufacturing data analytics and powder bed fusion additive manufacturing. Several leading firms apply broad R&D emphasis across a variety of technological topics, while others focus on a few technological topics.

The developed methodology can help firms identify important technological topics in smart manufacturing for making their R&D investment decisions. Firms can select appropriate technology strategies depending on the topic's emergence position in the topic strategy matrix.

Previous research studies have not analyzed the maturity levels of technological topics. The topic-based patent analytics approach can complement previous studies. In addition, this study provides a multi-valuation framework for exploring technological opportunities, thus providing valuable information that supports a more robust understanding of the technology landscape of smart manufacturing.

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.

This study aims to investigate the effects of open innovation (OI) and big data analytics (BDA) on reflective knowledge exchange (RKE) within the context of complex collaborative networks. Specifically, it considers the relationships between sourcing knowledge from an external environment, transferring knowledge to an external environment and adopting solutions that are useful to appropriate returns from innovation.

This study analyzes the connection between the number of patent applications and the amount of OI, as well as the association between the number of patent applications and the use of BDA. Data from firms in the 27 European Union countries were retrieved from the Eurostat database for the period 2014–2019 and were investigated using an ordinary least squares regression analysis.

Because of its twofold lens based on both knowledge management and OI, this study sheds light on OI collaboration modes and highlights the crucial role they could play in innovation. In particular, the results suggest that OI collaboration modes have a strong effect on innovation performance, stimulating the search for RKE.

This study furthers a deeper understanding of RKE, which is shown to be an important mechanism that incentivizes firms to increase their efforts in the innovation process. Further, RKE supports firms in taking full advantage of the innovative knowledge they generate within their inter-organizational network.

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.

Using the large database of patent, the purpose of this paper is to structure a technology convergence network using various patent network analysis for integrating different results according to network characteristics.

The patent co-class analysis and the patent citation analysis are applied to discover core safety fields and technology, respectively. In specific, three types of network analysis, which are centrality analysis, association rule mining analysis and brokerage network analysis, are applied to measure the individual, synergy and group intensity.

The core safety fields derived from three types of network analysis used by different nature of data algorithms are compared with each other to understand distinctive meaning of cores of patent class such as medical safety, working safety and vehicle safety, differentiating network structure. Also, to be specific, the authors find the detailed technology contained in the core patent class using patent citation network analysis.

The results provide meaningful implications to various stakeholders in organization: safety management, safety engineering and safety policy. The multiple patent network enables safety manager to identify core safety convergence fields and safety engineers to develop new safety technology. Also, in the view of technology convergence, the strategy of safety policy can be expanded to collaboration and open innovation.

This is the initial study on applying various network analysis algorithms based on patent data (class and citation) for safety management. Through comparison among network analysis techniques, the different results are identified and the collective decision making on finding core of safety technology convergence is supported. The decision maker can obtain the various perspectives of tracing technology convergence.

This study aims to propose a visualized model of hot technology evolution to describe its development.

The basic concept is to divide a technological field into a timeline consisting of several patent clusters. Hot technology trajectories are then explored using their continuity, as well as the point in time at which they occur.

Patents in orthopaedics between 1999 and 2014 have been chosen as the research subjects and the field is divided into several hot technology trajectories. A further step is taken by interpreting high-frequency key terms. Three categories – spine-related materials, bone repairing materials and bone plates – have been identified.

The trajectories presented by evolving diagrams allow readers to understand the evolution of hot technology and help analysts to plan layout and strategies to remain competitive.

Patent clusters reflect the knowledge context of technology development. Previous studies have focused on only new technology evolution and have rarely explored the knowledge context of hot patents that have been frequently cited in recent years. Such patents often guide the development of technology.

The textile sector is one of the sectors where competition is intense and requires the production of high-value-added products. This study aims to conduct patent analysis to find the technology status, recent trends, applications and technological evaluations of protective textile technologies in practice.

More than 36,840 patent documents related to protective textile technologies are available for researchers, patent examiners and patent researchers. Patent analysis is conducted to report the technology status, recent trends and applications of protective textile technologies. This analysis provides insights into the possible future directions of protective textile technologies in practice. Additionally, association rule mining (ARM) is performed to find the hidden patterns among protective textile technologies.

The development of protective textile technologies is revealed by the technology evaluation in this study. In addition, the sub-technology classes affecting protective textile technologies are examined using the cooperative patent classification (CPC) codes of the patent documents. Technology status and recent trends of protective textile technologies are provided in detail. The results of this study show that (1) protective textile technologies are constantly being developed, (2) the working areas of medical protective textiles are increasing, (3) there are frequent studies on fabric structures for saving lives within the framework of human needs and (4) there are four technology classes, namely A41D, Y10T, B32B and A62B impacting the other technology classes related to textile technologies such as D10B, Y10T, F41H, A62D, D04H, Y10S and D10B.

To have a competitive advantage in the marketplace, evaluation of textile technologies is critical in developing “functionalized” and “technologized” textile products. In particular, evaluating technologies in developing protective textile products is extremely important to meet customer demands and present competitive products in the market. Examining these patents for technology developers, decision-makers and policymakers is an urgent and necessary job. However, studies examining the development of protective textile technologies with patent analysis are very limited in the literature. To fill this gap, technology status, recent trends and applications of protective textile technologies are reported based on patent analysis and ARM in this study.

The establishment of partnerships between companies, government and universities aims to enhance innovation and the technological development of institutions. The biotechnology sector has grown in recent years mainly driven by its cooperative business model. Compared to other countries, this sector is slowly advancing in Brazil, with delays in science, technology and innovation, especially in the private sector. This paper aims to examine, through social network analysis, the collaborative networks between institutions that filed patents in biotechnology – medicinal preparations from plants – whose inventions had Brazil as the priority country.

The study of technological cooperation using patent documents is a reliable approach as they serve as good indicators of the interactions between organizations that focus on innovation and development of new product. Social network analysis of cooperation networks helps to understand the connections between patent assignees, and how they establish relationships.

Results show that public universities are the institutions that most deposit patents, as well as those that co-operate the most, especially Universidade of Campinas. The study also reveals the critical role of Research Support Agencies in stimulating research and technological development, which result in new technologies.

The study applied the social network analysis to provide an overview of the interactions among Brazilian institutions with the purpose of helping in decision-making and inciting public policies to leverage the biotechnology sector.

The purposes of this paper are (1) to explore the overall development of AI technologies and applications that have been demonstrated to be fundamentally important in the healthcare industry, and their related commercialized products and (2) to identify technologies with promise as the basis of useful applications and profitable products in the AI-healthcare domain.

This study adopts a technology-driven technology roadmap approach, combined with natural language processing (NLP)-based patents analysis, to identify promising and potentially profitable existing AI technologies and products in the domain of AI healthcare.

Robotics technology exhibits huge potential in surgical and diagnostics applications. Intuitive Surgical Inc., manufacturer of the Da Vinci robotic system and Ion robotic lung-biopsy system, dominates the robotics-assisted surgical and diagnostic fields. Diagnostics and medical imaging are particularly active fields for the application of AI, not only for analysis of CT and MRI scans, but also for image archiving and communications.

This study is a pioneering attempt to clarify the interrelationships of particular promising technologies for application and related products in the AI-healthcare domain. Its findings provide critical information about the patent activities of key incumbent actors, and thus offer important insights into recent and current technological and product developments in the emergent AI-healthcare sector.