Exploring Technology Influencers from Patent Data Using Association Rule Mining and Social Network Analysis

The role of design engineers: Evidence from intra-firm knowledge and collaboration networks

Successful new product development requires the integration of design and engineering, bridging the gap between technological feasibility and user-centric considerations. However, direct collaboration between designers and engineers with heterogeneous knowledge presents challenges. In this context, the role of design engineers-professionals skilled in both design and engineering-becomes pivotal. This study categorizes inventors into three primary groups: engineers, designers, and design engineers based on the type of patent applications they hold and investigates their differences in knowledge portfolios and collaboration patterns. The study relies on patent data for 4,665 US publicly-traded firms from 1980 to 2015 from the PATSTAT database, and constructs two networks for each firm period: a social network of inventors and a knowledge network of knowledge elements. Findings show that design engineers are highly connected within the social network but have disconnected knowledge in the knowledge network in comparison to engineers. While design engineers may not be the primary drivers of firms' technological innovations, they facilitate interdisciplinary communication and decision-making, fostering a design-technology integrated new product development environment. This research has practical implications for firms seeking to optimize their innovation processes by creating interdisciplinary teams that harness the complementary strengths of engineers and design engineers.

Patent Analysis of the Development of Technologies Applied to the Combustion Process

The use of combustion in industrial activity is of paramount importance for economic and social development. However, combustion reactions are the main sources of atmospheric pollutant emissions. Given this reality, it is necessary to study new combustion techniques, such as the application of oxygen in the process, in order to increase the efficiency and productivity of the burning process and energy production. In addition, studies have reported the use of acoustic excitation, a low-investment technique that can promote higher rates of heat and mass transfer. Thus, the goal of this study was to bring data on the current scenario related to the application of these two technologies to the combustion process where, through the reported results, they can be used as a guide for companies’ decisions about new technologies and global trends to be identified. For this, a technological prospection was carried out which focused on patents to investigate the use of oxygen-enhanced combustion and acoustic excitation coupled to the combustion process; a total of 88 documents were found. Few documents applied acoustic excitation for process improvement, indicating that its use is recent; however, according to the literature, it is a promising field to be explored. Siemens AG was the main depositor, and ten primary inventors were identified. Germany and the United States were the countries with the highest number of filings. In the prospected documents, it was possible to identify that there is a need for the further investigation of the joint use of both techniques. These investigations may lead to the development of processes and devices that can provide economic and environmental gains for the energy industry.

Technology Originality and Convergence Analysis in the Wind Power Field Using Patents

Currently, the world is moving rapidly toward carbon neutrality, and renewable energy technology is very important in carbon neutrality. Among various renewable energy technologies, wind power is attracting much attention due to its sustainability, resource abundance, and high applicability. This study analyzed wind power patents from 2010 to 2021 to derive current global originality and convergence information. For IP5 countries, the growth stages of technology and the patent convergence networks were inferred by time series analysis and an association rule mining algorithm, respectively. The results showed that, during the analysis period, about 5000 patents were applied on annual average, and it was analyzed that China held the most patents at 48%, followed by the US, Europe, Korea, and Japan. According to the technology convergence network of patent codes from all the IP5 countries, patents related to ‘wind turbines with rotation axes in the wind direction’ occupied a very central position, and ‘power conversion electric or electronic aspects’ and ‘integration of patents related to renewable energy sources in buildings with wind power’ were found to show high connection strength. By country, it was analyzed that the US and China showed high patent competitiveness onshore and possessed many ‘independent power conversion cluster’ technologies. The research hypothesis was that technology development trends can be analyzed and the characteristics of each country can be understood through patent analysis in the wind power field. This hypothesis was analyzed through various patent analysis techniques, and this paper has novelty in that it presents the global megatrend in the wind power field through patent analysis and quantitatively presents the current status of technology development in five major countries.

Patent data access control and protection using blockchain technology

The purposes are to develop the patent data profoundly, control the data access process effectively, and protect the patent information and content. The traditional patent review systems are analyzed. For the present patent data security and privacy protection technologies and algorithms, the patent information data are stored on different block nodes after data fragmentation using blockchain technology. Then the data are shared using the data encryption algorism. In this way, data access control can be restricted to particular users. Finally, a patent data protection scheme based on privacy protection is proposed. The security of the scheme and the model performance are verified through simulation experiments. The time required to encrypt 10 MB files with 64-bit and 128-bit data is 35 ms and 105 ms, respectively. The proposed re-encryption algorithm only needs 1 s to decrypt 64 KB data, and only 1% of the data needs asymmetric encryption. This greatly reduces the computational overhead of encryption. Results demonstrate that the system can effectively control the access methods of users, efficiently protect the personal privacy and patent content of patent applicants, and reduce the patent office cloud computing overhead using the local resources of branches. The distributed storage methods can reduce the cloud system interaction of the patent office, thereby greatly improving the speed of encryption and ensuring data security. Compared with the state of the art methods, the proposed patent data access and protection system based on blockchain technology have greater advantages in data security and model performance. The research results can provide a research foundation and practical value for the protection and review systems of patent data.

A Scientometric Assessment of Agri-Food Technology for Research Activity and Productivity

In accordance with the UN Sustainable Development Goals (SDGs), several SDGs target global food issues, including zero hunger (food security and sustainable agriculture), responsible consumption and production (food losses), climate action (greenhouse gas emissions from food waste), and partnerships for the goals (research collaboration). As such, it is vital to identify technology and market opportunities to support advanced development by exploring scientific and technological research on such SDGs. The significance of technological innovation and evaluations of activity, productivity, and collaboration aids and guides future research streams. Motivated by the growing severity of the global food waste crisis, this paper focuses on the case study of shelf-life extension technology for food and applies a scientometric analysis of patents based on text mining. VantagePoint was used to analyze 2516 patents issued between 2000 and 2020, with the aim of understanding the conceptual structure of knowledge and the social relationships among key players. The results indicate that the technology is experiencing a period of growth, and it can be clustered into five technology sectors. Across all technology clusters, China outperformed other countries in terms of the number of patents. Almost all of China’s patents applied for technology commercialization domestically, whereas other countries tended to apply for patents overseas to exploit opportunities. The findings have implications for both policymaking and strategic decision-making using a multi-layered network innovation system.

Technology Hotspot Tracking: Topic Discovery and Evolution of China’s Blockchain Patents Based on a Dynamic LDA Model

Tracking scientific and technological (S&T) research hotspots can help scholars to grasp the status of current research and develop regular patterns in the field over time. It contributes to the generation of new ideas and plays an important role in promoting the writing of scientific research projects and scientific papers. Patents are important S&T resources, which can reflect the development status of the field. In this paper, we use topic modeling, topic intensity, and evolutionary computing models to discover research hotspots and development trends in the field of blockchain patents. First, we propose a time-based dynamic latent Dirichlet allocation (TDLDA) modeling method based on a probabilistic graph model and knowledge representation learning for patent text mining. Second, we present a computational model, topic intensity (TI), that expresses the topic strength and evolution. Finally, the point-wise mutual information (PMI) value is used to evaluate topic quality. We obtain 20 hot topics through TDLDA experiments and rank them according to the strength calculation model. The topic evolution model is used to analyze the topic evolution trend from the perspectives of rising, falling, and stable. From the experiments we found that 8 topics showed an upward trend, 6 topics showed a downward trend, and 6 topics became stable or fluctuated. Compared with the baseline method, TDLDA can have the best effect when K is 40 or less. TDLDA is an effective topic model that can extract hot topics and evolution trends of blockchain patent texts, which helps researchers to more accurately grasp the research direction and improves the quality of project application and paper writing in the blockchain technology domain.

Analysis of Cultural Meme Characteristics for Big Data of Cultural Relics

The cultural meme is the smallest unit constituting a dynasty′s culture, which has the same inheritance and variability as biological genes. Here, based on the name of cultural relics, we extract cultural memes through semantic word segmentation, word frequency statistics, and the synonym merging method, and construct dynasty cultural meme vectors. We analyzed color, auxiliary, texture, shape, and overall networks of five types of model to construct the culture network, using the social network analysis method, and explored the clustering and degrees of centrality characteristics of cultural memes. We then analyzed the similarities and differences among cultures of the dynasties. The main conclusions are as follows: (1) Cultural memes represent different cultural characteristics of dynasties, and the inheritance and differences among dynasties’ cultures are closely related to their historical background. (2) Prevalence memes construct the cultural label of dynasties, which can roughly restore the cultural appearance of dynasties through fewer prevalence memes. (3) The use of community detection with a cultural meme network can determine the clustering of dynasties′ cultures, and the degree of centrality further reflects the main cultural characteristics presented by successive dynasties.

Social Media and E-mail Marketing Campaigns: Symmetry versus Convergence

Companies use social business intelligence (SBI) to identify and collect strategically significant information from a wide range of publicly available data sources, such as social media (SM). This study is an SBI-driven analysis of a company operating in the insurance sector. It underlines the contribution of SBI technology to sustainable profitability of a company by using an optimized marketing campaign on Facebook, in symmetry with a traditional e-mail campaign. Starting from a campaign on SM, the study identified a client portfolio, processed data, and applied a set of statistical methods, such as the index and the statistical significance (T-test), which later enabled the authors to validate research hypotheses (RH), and led to relevant business decisions. The study outlines the preferences of the selected group of companies for the manner in which they run a marketing campaign on SM in symmetry with an e-mail-run campaign. Although the study focused on the practical field of insurance, the suggested model can be used by any company of any industry proving that BI technologies is the nexus of collecting and interpreting results that are essential, globally applicable, and lead to sustainable development of companies operating in the age of globalization. The results of the study prove that symmetrical unfolding (time and opportunity symmetry) of SM marketing campaigns, and using email, could lead to better results compared to two separate marketing campaigns. Moreover, the outcomes of both campaigns showed convergence on SBI platforms, which led to higher efficiency of management of preferences of campaign beneficiaries in the insurance sector.