Innovative activities of activision blizzard: A patent network analysis

Patent network based conjoint analysis for wearable device

Patent network analysis, an advanced method of patent analysis, is a useful tool for technology management. This method visually displays all the relationships among the patents and enables the analysts to intuitively comprehend the overview of a set of patents in the field of the technology being studied. Although patent network analysis possesses relative advantages different from traditional methods of patent analysis, it is subject to several crucial limitations. To overcome the drawbacks of the current method, this study proposes a novel patent analysis method, called the intelligent patent network analysis method, to make a visual network with great precision. Based on artificial intelligence techniques, the proposed method provides an automated procedure for searching patent documents, extracting patent keywords, and determining the weight of each patent keyword in order to generate a sophisticated visualization of the patent network. This study proposes a detailed procedure for generating an intelligent patent network that is helpful for improving the efficiency and quality of patent analysis. Furthermore, patents in the field of Carbon Nanotube Backlight Unit (CNT-BLU) were analyzed to verify the utility of the proposed method.

As a consequence of the convergence between manufacturing technology and the foundation technologies of Industry 4.0, it is becoming more important for firms to formulate an innovation strategy for their technological capabilities. In this context, the present study measures firm‐level technology convergence (TC) capability using patent network analysis. A firm’s TC capabilities are measured using three centrality indices pertaining to a patent network, which is constructed based on the relationship between patents and their international patent classification. For the empirical analysis, panel regression is conducted to observe the effect of TC capabilities on innovation for the top 30 firms in four manufacturing industries. We find that the TC degree positively influences the firms’ overall innovation, namely their total number of patents, and negatively influences their convergent innovation, calculated as the ratio between the number of TC patents and the total number of patents, while the effect of TC betweenness is the opposite. These findings imply that while concentrating on similar technologies may promote quick technology application, it could hamper the enhancement of a TC’s potential. To promote TC, a firm should thus develop technologies more likely to be involved in TC.

Because of the remarkable developments in robotics in recent years, technological convergence has been active in this area. We focused on finding patterns of convergence within robot technology using network analysis of patents in both the USPTO and KIPO. To identify the variables that affect convergence, we used quadratic assignment procedures (QAP). From our analysis, we observed the patent network ecology related to convergence and found technologies that have great potential to converge with other robotics technologies. The results of our study are expected to contribute to setting up convergence based R&D policies for robotics, which can lead new innovation.

Patent analysis is a useful tool used to analyze patent information for technology management. Biofuels have been recognized as a new sustainable energy in the real world. Owing to the urgent demands for green energy technology, exploring the technological trends and evolution of biofuels has become an important issue. Among approaches of patent analysis, patent network analysis is popular for technology analysis. However, this approach is subject to several limitations. Therefore, this study proposes a weighted keyword-based patent network (WKPN) approach, which combines Delphi technique, analytic hierarchy process, and network analysis, to overcome the limitations in order to identify the technological trends and evolution of biofuels. The procedure for generating a WKPN includes extraction of patent keywords, calculation of weighted value for each keyword, establishment of similarity matrix, and construction of precise network. Furthermore, quantitative indexes are suggested to analyze technological implications from the WKPN. The results indicate that the development tendency of biofuels can be divided by conventional technology and green biorefinery technology based on biomass feedstocks. The technological evolution of biofuel production follows the sequence of liquid, gas and solid biofuels. This fact demonstrates that the critical factor in the development of biofuels is to find new waste materials as energy sources with lower energy consumption or larger energy output. These insights are beneficial to assist researchers in grasping and managing the future development in biofuels. The fruitful approach is not only capable of promoting the efficiency and effectiveness of patent analysis, but also identifying the development trends and evolution in the emerging field of energy technology.

This paper examines a question of long standing: How can one obtain operational indexes of inventive and innovative activity and technical change? Specifically, for a sample of fifty-seven pharmaceutical manufacturing firms, we attempt to determine how well a simple count of invention patents serves as a surrogate for two alternative measures of technical change: the number of research and development personnel employed, reflecting inputs into the innovative process, and the value of new product sales, which reflects outputs of the process.

Epigenetics is a biomedical novelty in drug design and disease control whose mechanisms play a significant role in transferring environmental signals to determine patterns of gene expression. Systematic identification of the main trends in epigenetics patenting activity provides insights into fundamental building blocks of this research field and policy guidance to funding agencies. The review aims at providing a comprehensive overview of the research and development trend in epigenetics by mapping the knowledge structure in patent landscape. Citation-based patent network analysis was performed to visualize the technological landscape. We focus on identifying the structure of the knowledge networks to study the technological trajectories. Patents that play an integral part in the dissemination and bridging of the technical knowledge are located and ranked. The latent topics in patent documents are highlighted by means of a topic modeling technique. Visualization of the patent network results in four main clusters. The first two clusters deal with the inhibition of histone deacetylase (HDAC). The third cluster covers inventions related to DNA methylation, which represents an epigenetic signaling tool that cells use to control gene expression. The fourth cluster encompasses computing systems and data mining techniques for identifying combinations of genetic and epigenetic attributes related to health and lifestyle improvements. We are in the growth period of gathering knowledge on various mechanisms of epigenetic regulation. There is enormous potential for improving healthcare through better understanding of the interrelationships between epigenetic control of gene expression and compounds that trigger these modifications.

The number of design patents has grown significantly in the last 140 years. However, a data-driven approach for design patents has been overlooked and underutilised in the design management and innovation research communities. Through the prism of a patent professional, data analyst and designer, this photo essay demystifies the complexity of design patent data and sheds light on the underlying value of design as it features among a range of diverse innovation activities. Patent network analysis and visualisation techniques enable the building of a series of patent citation maps and co-inventor networks. Cases from renowned companies—Apple, Dyson, Samsung, and LG electronics— reveal different shapes of innovation activities, focusing on product diversification strategies, collaboration patterns and design-technology cross-pollination flows.

This study attempts to explore Sino-South Korea collaborations in science and technology, focusing on the networks of university–industry–government (UIG) relations embedded in co-authored papers and co-assigned patents at the institutional and global levels. Patents and scientific publications are examined over the period 2004–2014 to capture recent trends and patterns of collaborative innovation activities among UIG actors in China and South Korea. The analysis has been carried out from a triple helix perspective, investigating the structure and dynamics of the Sino-Korea scientific and technological collaborations. The result reveals an imbalance between science and technology in knowledge dynamics and the different extent and intensity of collaborative efforts of the two countries. Based on comparative analysis of co-authorship and co-inventorship networks, the scale and density of scientific collaboration networks appear much higher than those of the technological counterpart. In fact, the technological collaboration between China and Korea is not common in patents and its network needs to be further strengthened. Implications for both China and Korea of future collaborative relationships are discussed.

Detecting promising technology groups for recombination holds the promise of great value for R&D managers and technology policymakers, especially if the technologies in question can be detected before they have been combined. However, predicting the future is always easier said than done. In this regard, Arthur’s theory (The nature of technology: what it is and how it evolves, Free Press, New York, 2009) on the nature of technologies and how science evolves, coupled with Kuhn’s theory of scientific revolutions (Kuhn in The structure of scientific revolutions, 1st edn, University of Chicago Press, Chicago, p 3, 1962), may serve as the basis of a shrewd methodological framework for forecasting recombinative innovation. These theories help us to set out quantifiable criteria and decomposable steps to identify research patterns at each stage of a scientific revolution. The first step in the framework is to construct a conceptual model of the target technology domain, which helps to refine a reasonable search strategy. With the model built, the landscape of a field—its communities, its technologies, and their interactions—is fleshed out through community detection and network analysis based on a set of quantifiable criteria. The aim is to map normal patterns of research in the domain under study so as to highlight which technologies might contribute to a structural deepening of technological recombinations. Probability analysis helps to detect and group candidate technologies for possible recombination and further manual analysis by experts. To demonstrate how the framework works in practice, we conducted an empirical study on AI research in China. We explored the development potential of recombinative technologies by zooming in on the top patent assignees in the field and their innovations. In conjunction with expert analysis, the results reveal the cooperative and competitive relationships among these technology holders and opportunities for future innovation through technological recombinations.

Identification of emerging risks in the food chain is essential if EFSA is to anticipate future needs in risk assessment, in relation to both data and methodology. The objectives and research proposed in the DEMETER project were specifically designed to support current (and future) EFSA procedures for emerging issue and risks identification by providing community resourcesto allow EFSA and EU Member State authorities to share data, knowledge and methods on emerging risks identification in a rapid and effective manner through a digital platform. To this end, an “Emerging Risk Knowledge Exchange Platform (ERKEP)” was developed as a prototype technical solution. Its design is based on a consultation on end‐users needs and the analysis of existing knowledge sharing solutions. ERKEP consists of three main components: 1) A content management system (CMS) providing the end‐user's “entry point” and Graphical User Interface (GUI) to ERKEP; 2) A web‐based data analytics platform (DAP) for sharing and executing data analytics workflows (DAWs), based on the KNIME Server infrastructure; 3) External web‐based services hosted by 3rd party service providers. Different DAWs were developed and added to the platform, these are: 1) Emerging risk identification system for the milk supply chain based on automated data retrieval; 2) NewsRadar; 3)Trending topics in news based on text mining and network analysis, and;4) Patent network analysis. Methodologies were identified to integrate social science information and data, into the emerging risk identification framework. Systematic reviews of the literature wereconducted in the areas of expert elicitation, citizen science, and behavioural science and a framework to incorporate data from Citizen Science into the EKREP platform was proposed. Finally, sustainability and maintenance of the project's outputs were conceptualized to enable use thereof beyond project DEMETER.

Effective patent management is essential for organizations to maintain their competitive advantage. The classification of patents is a critical part of patent management and industrial analysis. This study proposes a hybrid-patent-classification approach that combines a novel patent-network-based classification method with three conventional classification methods to analyze query patents and predict their classes. The novel patent network contains various types of nodes that represent different features extracted from patent documents. The nodes are connected based on the relationship metrics derived from the patent metadata. The proposed classification method predicts a query patent's class by analyzing all reachable nodes in the patent network and calculating their relevance to the query patent. It then classifies the query patent with a modified k-nearest neighbor classifier. To further improve the approach, we combine it with content-based, citation-based, and metadata-based classification methods to develop a hybrid-classification approach. We evaluate the performance of the hybrid approach on a test dataset of patent documents obtained from the U.S. Patent and Trademark Office, and compare its performance with that of the three conventional methods. The results demonstrate that the proposed patent-network-based approach yields more accurate class predictions than the patent network-based approach.

Empirical work on the causes and effects of inventive activity has had difficulty in finding measures that can indicate when and where changes in either inventive inputs or inventive output have occurred. The recent computerization of the U.S. Patent Office's data base may prove helpful in this context, but there is the problem that a priori we do not know the relationships between patent applications and economically meaningful measures of these inputs and outputs. To help solve this problem, this paper investigates the dynamic relationships among the number of successful patent applications of firms, a measure of the firm's investment in inventive activity (its R & D expenditures), and an indicator of its inventive output (the stock market value of the firm). (This abstract was borrowed from another version of this item.)

Globalization of technology: Network analysis of global patents and trademarks

In light of larger public policy debates over intellectual property and climate change, this article considers patent practice, law, and policy in respect of biofuels. This debate has significant implications for public policy discussions in respect of energy independence, food security, and climate change. The first section of the paper provides a network analysis of patents in respect of biofuels across the three generations. It provides empirical research in respect of patent subject matter, ownership, and strategy in respect of biofuels. The second section provides a case study of significant patent litigation over biofuels. There is an examination of the biofuels patent litigation between the Danish company Novozymes, and Danisco and DuPont. The third section examines flexibilities in respect of patent law and clean technologies in the context of the case study of biofuels. In particular, it explores the debate over substantive doctrinal matters in respect of biofuels – such as patentable subject matter, technology transfer, patent pools, compulsory licensing, and disclosure requirements. The conclusion explores the relevance of the debate over patent law and biofuels to the larger public policy discussions over energy independence, food security, and climate change.