Patent Data Research Articles

The Duality of Technological Innovation and Dynamic Capabilities: The Micro-Foundation of China's Construction Machinery Industry's Rise up the Global Value Chain

In the context of global value chain reconstruction, Chinese construction machinery enterprises are facing the dual challenges of technological innovation and global competitiveness improvement. Based on organizational learning theory and dynamic capability perspective, this study constructs a theoretical framework of dual integration of technological innovation (exploratory innovation and pioneering innovation) and dynamic capability, and explores its impact mechanism on the global value chain upgrading of Chinese construction machinery enterprises. The study adopts a mixed research method, combining large-scale patent data analysis and in-depth case study, and constructs a longitudinal data set of 146 Chinese construction machinery enterprises from 2005 to 2022. By using structural equation modeling, dynamic panel regression and machine learning algorithms, this study reveals the following main conclusions: (1) The relationship between technological innovation duality and the global value chain position of enterprises is inverted U-shaped, and there is an optimal equilibrium point; (2) Dynamic capabilities partially mediate the relationship between technological innovation duality and global value chain climbing, among which absorptive capacity and reconstruction capacity are particularly critical; (3) The degree of internationalization of enterprises positively moderates the impact of technological innovation duality on dynamic capabilities; (4) The quality of the institutional environment moderates the relationship between technological innovation duality and dynamic capabilities, and a high-quality institutional environment strengthens the positive relationship between the two. The theoretical contributions of this study are: (1) it expands the global value chain theory and incorporates the duality of technological innovation into the analytical framework; (2) it reveals the mediating mechanism of dynamic capability theory in innovation and value chain climbing, enriching the dynamic capability theory; (3) it provides new empirical evidence for organizational learning theory and clarifies the dynamic process of exploration and utilization equilibrium. At the practical level, the research results provide guidance for engineering machinery companies to formulate innovation strategies and enhance global competitiveness, and also provide a basis for the government to formulate industrial upgrading policies. Future research can further explore the long-term evolution path of technological innovation duality and its differences in cross-cultural contexts.

Tracing scientific and technological development in genetically modified crops.

Genetically Modified (GM) Organisms have been used in various domains since their introduction in the 1980s. According to ISAAA data, the use of GM crops in agriculture has also increased significantly in the past 30years. However, even after 3 decades of commercialisation, GM crops are still surrounded with controversies with different countries adopting varying approaches to their introduction in the consumer markets, owing to different stances of various stakeholders. Motivated by this multitude of opinions, and absence of knowledge mapping, this study has undertaken scientometric analysis of the publication (Web of Science) and patent (Lens.org) data about genetically modified technology use in agriculture to explore the changing knowledge patterns and technological advancements in the area. It explores both scientific and technological perspectives regarding the use of Genetically Modified Crops, by using publication as well as patent data. The findings of this study highlight the major domains of research, technology development, and leading actors in the ecosystem. These findings can be helpful in taking effective policy decisions, and furthering the research activities. It presents a composite picture using both publications and patent data. Further, it will be of utility to explore the other technologies which are replacing GM technology in agriculture in future studies.

Analysis of Innovation Trends in Energy Storage Safety Technology based on Patent Data

Against the backdrop of energy transition and the large-scale development of renewable energy, the significance of energy storage technology has become increasingly prominent. Based on the data of invention patents, this paper analyzes the innovation situation of global energy storage safety technology, providing a reference basis for future patent research and industrial planning in the field of energy storage safety. Through the global and Chinese patent application volumes, the research comparatively analyzes the application situations of the main source countries and main applicants of patents, and analyzes the innovation situation of energy storage safety technology by combining research methods such as technical structure comparison and value degree comparison. The study finds that global energy storage safety innovation is in a relatively active period. China takes the leading position in the total amount of patent applications, but the proportion of patents with high value is low, and it still lags behind South Korea and the United States. Currently, the main research directions in this field worldwide mainly include battery monitoring and status monitoring, battery protection, fire detection and alarm, fire extinguishing systems, and battery pack structure design.

A transfer learning based patent transfer prediction model: Discussion about the condition of China and U.S.

In the same patent classification, there are significant differences in the patent transfer quantity for different countries. For the countries with small patent transfer quantities, the performance of the patent transfer prediction is worse than that of high-transfer countries. To improve the performance of the patent prediction model learned for the low-transfer countries, we propose a transfer learning based patent transfer prediction model, for which the high-transfer countries are the source domain and the low-transfer countries are the target domain. In the model, the textual feature of each patent text is embedded using BERT-CNN with the parameters fine-tuned on the target domain from the source domain. The textual features are optimized to minimize the domain loss of Maximum Mean Discrepancy between source and target domain. What is more, we extract graph feature vectors by embedding the patent node in a three-layer heterogeneous network which consists of patent nodes, International Patent Classification nodes, consumer nodes, consumer location nodes and their relations. Finally, the patent text vectors, structural feature vectors, and graph feature vectors are spliced together to predict target domain patents transfer or not. The performance of the model is verified by some comparative experiments. The experimental results show that the proposed model outperforms significantly the baseline models in the values of Precision, Recall, and F1 of patent data improve by 6.47%, 6.54%, and 6.58% on average, respectively.

New energy technology innovation and energy poverty alleviation in China

Energy poverty has received attention, but in-depth research on the relationship between new energy technology innovation and energy poverty is lacking. Using data from 30 Chinese provinces spanning 2006 to 2020, we examine the impact of new energy technology innovation on energy poverty in China. We collect provincial-level patent data and energy poverty evaluation indicators, categorizing them into four subsystems: energy use level, energy capacity, energy use structure, and energy investment management. Findings indicates: (1) New energy technology innovation significantly alleviates energy poverty in China. This is primarily attributed to improvements in energy supply and residents' income. (2) The effectiveness of new energy technology innovations in alleviating energy poverty is enhanced by provincial transportation development and technology market activity but diminished by urbanization. (3) New energy technology innovations lead to notable increases in energy use level and energy capacity, a decrease in energy use structure, and no significant impact on energy investment management. (4) Innovations from enterprises, university and research institutes significantly contribute to alleviating China's energy poverty. However, innovations from industry-university-research collaborations and other innovators do not significantly affect China's energy poverty. Our findings provide valuable insights for theoretical research and energy poverty policy design.

Revolutionizing Skin Cancer Treatment: The Rise of PD-1/PDL-1 and CTLA-4 as Key Therapeutic Targets.

Skin cancer is a significant health concern, affecting millions of individuals globally on an annual basis. According to data from the World Health Organization, it stands as the most prevalent form of cancer within the white population. Current treatments for skin cancer typically involve a combination of chemotherapy, radiation therapy, and surgery. However, these methods often come with drawbacks, such as side effects and potential scarring. Therefore, there is a growing need for alternative treatments that can offer effective results with fewer adverse effects, driving ongoing research in skin cancer therapy. The advancement of immune checkpoint inhibitors has been facilitated by a more profound comprehension of the interplay between tumors and the immune system, along with the regulatory mechanisms governing T-cells. As cancer treatment continues to evolve, immunotherapy is emerging as a powerful strategy, leading to a growing interest in the role of immunological checkpoints in skin cancer. Various types of immune checkpoints and their expression, including PD-1, PDL-1, CTLA-4, lymphocyte activation gene 3, and B7-H3, along with their blockers and monoclonal antibodies, have been established for various cancers. PD-1, PDL-1, and CTLA-4 are crucial immune system regulators, acting as brakes to prevent T-- cell overactivation and potential autoimmunity. However, tumors can exploit these checkpoints to evade immune detection. Inhibiting these immune checkpoints can enhance the body's ability to recognize and attack cancer cells. This review focuses on the characteristics of PD-1, PDL-1, and CTLA-4 immune checkpoints, their mechanism of action, and their role in skin cancer. Additionally, it summarizes the ongoing clinical trials sponsored or conducted by various pharmaceutical companies and provides insights into the latest patent data.

Exploring Electric Vehicle Patent Trends through Technology Life Cycle and Social Network Analysis

In response to environmental and energy challenges, electric vehicles (EVs) have re-emerged as a viable alternative to internal combustion engines. However, existing research lacks a comprehensive analysis of the technology life cycle of EVs in both global and South Korean contexts and offers limited strategic guidance. This study introduces a novel approach to address these gaps by integrating the S-curve model with social network analysis (SNA), time series analysis, and core applicant layouts. The study specifically utilizes the logistic curve to model technology growth. It applies SNA methods, including International Patent Classification (IPC) co-occurrence analysis and the betweenness centrality metric, to identify the stages of technological development and sustainable research directions for EVs. By analyzing patent data from 2004 to 2023, the study reveals that EV technologies have reached the saturation phase globally and in South Korea, with South Korea maintaining a two-year technological advantage. The research identifies sustainable research directions, including fast charging technology and charging infrastructure, battery monitoring and management, and artificial intelligence (AI) applications. Additionally, the study also determined the sustainability of these research directions by examining the sustainability challenges faced by EVs. These insights offer a clear view of EV technology trends and future directions, guiding stakeholders.

Identify the digitalization technology opportunity of low-carbon energy technologies: Using the patent data and collaborative filtering.

The digitalization of low-carbon energy technologies (LCET) provides important technical support for the transition to a greener energy system. Digitalization addresses the phenomenon of the growing application of information and communications technologies (ICT) across the economy, which is regarded as the technology convergence between ICT and other technologies. Scholars have revealed the signs that LCET and ICT are becoming increasingly interlinked, which raises the challenges for predicting and identifying the technology opportunities for innovations in the converged technology area. To address the challenges, this paper proposes a collaborative filtering approach to identify the digitalization technology opportunity of low-carbon energy technologies using patent classification and patent citation information. We applied the proposed collaborative filtering approach using a large LCET patent dataset derived from the United States Patent and Trademark Office (USPTO). The results indicate that the proposed method can effectively identify digitalization technology opportunities of LCET, and the current LCET digitalization technology opportunities identified based on this approach are mainly concentrated in the Energy storage field. The advantages of the proposed approach are that its underlying data are more readily available and its technical complexity is relatively lower, and thus, more replicable for other technology fields.

A framework armed with node dynamics for predicting technology convergence

In the rapidly evolving landscape of industrial and societal progress, technology convergence plays a pivotal role. This dynamic process is usually characterized by the emergence of new nodes and new links. With the long-term and recent interests in predicting technology convergence, link prediction has become the primary approach on the basis of large-scale patent data. Though, the problem of node dynamics is still not addressed in the literature. For this purpose, this paper presents a technology convergence prediction framework with three core modules as follows. (1) A candidate node set is introduced during the network construction phase, mimicking the generation of newly-emerging nodes. (2) An inductive graph representation learning approach is deployed to generate feature vectors for newly-emerging nodes as well as existing ones. (3) The evaluation criteria are revised to shift from the predictable range to the actual predicted range, which can provide a more realistic assessment of predictive performance. Finally, experimental results on the domain of cancer drug development validate the feasibility and effectiveness of our framework in capturing the dynamics of technology convergence, especially concerning the relationships of newly emerged nodes and links. This study provides valuable insights into technology convergence dynamics and points to future research and applications.

Revealing Chemical Trends: Insights from Data-Driven Visualization and Patent Analysis in Exposomics Research.

Understanding historical chemical usage is crucial for assessing current and past impacts on human health and the environment and for informing future regulatory decisions. However, past monitoring data are often limited in scope and number of chemicals, while suitable sample types are not always available for remeasurement. Data-driven cheminformatics methods for patent and literature data offer several opportunities to fill this gap. The chemical stripes were developed as an interactive, open source tool for visualizing patent and literature trends over time, inspired by the global warming and biodiversity stripes. This paper details the underlying code and data sets behind the visualization, with a major focus on the patent data sourced from PubChem, including patent origins, uses, and countries. Overall trends and specific examples are investigated in greater detail to explore both the promise and caveats that such data offer in assessing the trends and patterns of chemical patents over time and across different geographic regions. Despite a number of potential artifacts associated with patent data extraction, the integration of cheminformatics, statistical analysis, and data visualization tools can help generate valuable insights that can both illuminate the chemical past and potentially serve toward an early warning system for the future.

A Comprehensive Review on Co-Crystals: Transforming Drug Delivery with Enhanced Solubility and Bioavailability

Poor solubility and bioavailability of various drug compounds are the biggest challenges faced by researchers and industrialists, hindering their therapeutic efficacy. Researchers have developed a versatile approach to enhance the solubility and bioavailability of the drug i.e., co-crystallization. Pharmaceutical co-crystals are solid, crystalline materials consisting of API and co-formers that have supramolecular chemistry with one another. Co-crystallization helps in enhancing a drug’s physico-chemical properties, such as bioavailability, solubility and dissolution, preserving its therapeutic effect. The API and co-former in co-crystals are bound to each other via hydrogen bonding, π-stacking, and Van der Waals forces. Several methods to prepare co-crystals, such as solvent evaporation method, grinding method, cooling crystallization method, etc, and various research reports, including all the methods of preparation are discussed in this review article. Conventional marketed products and patents on co-crystals are also included. Data has been gathered, and relevant literature reports have been examined utilizing a variety of search engines, including Google Scholar, ScienceDirect, Pubmed, and Google patents. After reviewing the literature, the researchers found that the cocrystallization method is one the simplest method to enhance drug bioavailability and solubility. Moreover, it enhances the pharmacokinetics parameters, pharmacodynamics properties, and melting point of the drug. In this review article, the researchers have compiled the recent literature reports on enhanced drug solubility via co-crystallization method. The researchers concluded that this review article can help other researchers by providing them with recent literature on this article and can compare the various methods of enhancing drug solubility and bioavailability. It also consists of compiled data of patents and marketed formulations prepared by the co-crystallization technique. Thus, co-crystallization could be established as a versatile approach for enhancing drug solubility and bioavailability.

Inventor’s ego network change and invention impact: The moderating role of knowledge networks

Prior research on social networks and innovation demonstrated that ego networks contribute to innovation. However, how changes in inventors’ ego network composition affect their invention impact remains unclear. From the perspective of knowledge recombination, we investigate the aforementioned relationship and further explore whether it varies with the combinatorial opportunities, and combinatorial potential of new knowledge elements acquired by the focal inventor and his/her knowledge diversity. We validated our hypotheses using patent data from Huawei filed in China from 2004 to 2013. Our findings suggest that an inventor’s ego network change has a positive effect on the inventor’s invention impact. Furthermore, the combinatorial potential of new knowledge elements weakens the aforementioned relationship, whereas the combinatorial opportunities of new knowledge elements and the focal inventor’s knowledge diversity strengthen it.

Characteristics and influencing factors of the industry-university-research collaborative innovation network in China's new energy vehicle industry

Industry-university-research collaboration is the primary channel of industrial innovation, and China is a common context for research on innovation in the new energy vehicle industry. This study analyzes the industry-university-research collaborative innovation network in China's new energy vehicle industry using various software programs, including Gephi and ArcGIS, based on collaborative invention patent data related to the new energy vehicle industry provided by the global patent database incoPat. According to the findings of this study, the highest number of patents obtained through industry-university-research collaboration was observed in new energy vehicle device and accessory manufacturing, whereas the number of new entrants was significantly higher than the number of incumbents albeit in a downward trend, suggesting that the creation of new collaborative relationships among new entrants has always been the main phenomenon in the process of building an industry-university-research innovation system in the new energy vehicle industry. As private firms have long been playing an integral role in industry-university-research collaboration in the new energy vehicle industry, the number of private firms in the network exhibited an upward trend annually, while the dual heterogeneous connection mode was the primary connection mode in industry-university-research collaboration. There were some differences between the spatial characteristics of intraprovincial and interprovincial collaboration in the industry-university-research collaborative network in China's new energy vehicle industry. The analysis results obtained using GeoDetector revealed that the influence of economic base, level of openness to the outside world, government dominance, market dominance, regional science and technology finance, and entrepreneurial spirit varied from one stage to another.

The emergence of new regional technological specialisations: exploring the role of organisations and their technological network structure

ABSTRACT Technological collaborations are crucial for shaping the emergence of new regional technological specialisations. This article explores the relationship between technological networks formed between organisations, within and across regions, and the ability of these regions to branch into new technological fields. To identify the organisations’ collaboration structure, we exploit patent co-applicant data and perform a network analysis of the organisations’ collaborations for 245 European NUTS-II regions from 1990 to 2016. Panel regression analyses reveal that (1) a more locally oriented and less dense region-internal network, (2) a more diverse set of region-external linkages, and (3) a higher share of single applicants in a region are positively associated with the emergence of new technological specialisations. These findings highlight the importance of maintaining a balance between the organisations’ inward and outward orientation to acquire new technological specialisations. Regions must leverage both internal technological capabilities and external networks to foster processes of technological renewal.

A novel approach to enterprise technical collaboration: Recommending R&D partners through technological similarity and complementarity

Choosing the right partner is a key factor in the success of enterprise R&D cooperation, directly affecting innovation outcomes and market competitiveness. Technical similarity provides a common language and foundational understanding between enterprises, while technical complementarity offers opportunities for knowledge exchange and innovation. However, no previous research has effectively integrated these two features within a collaborator recommendation framework. This study aims to explore a method that combines technological similarity and complementarity for collaborator recommendations. We introduced the Technological Similarity and Complementarity Enhanced Collaborator Recommendation (TSCE-CR) model, which constructs a heterogeneous corporate collaboration network and designs a tailored loss function. This model effectively integrates features of technological similarity and complementarity, enabling the neural network to capture and elucidate the nonlinear and multidimensional relationships in corporate collaborations. Experimental validation on patent data in the field of artificial intelligence demonstrated that our TSCE-CR model excels in identifying potential collaborators, effectively confirming the critical role of technological complementarity in R&D collaboration. This research provides a flexible framework for future studies on collaborator recommendations and offers reliable decision-making support for enterprises in selecting R&D partners.

A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems

The increasing density of urban populations has spurred interest in utilizing underground space. Underground logistics systems (ULS) are gaining traction due to their effective utilization of this space to enhance urban spatial efficiency. However, research on technological advancements in related fields remains limited. To address this gap, we applied a data-driven approach using patent data related to the ULS to develop a technology roadmap for the field. We employed Latent Dirichlet Allocation (LDA), a machine learning-based topic modeling technique, to categorize and identify six specific technology areas within the ULS domain. Subsequently, we conducted portfolio analytics to pinpoint technology areas with high technological value and to identify the major patent applicants in these areas. Finally, we assessed the technology market potential by mapping the technology life cycle for the identified high-value areas. Among the six technology areas identified, Topic 1 (Underground Material Handling System) and Topic 4 (Underground Transportation System) showed significant patent activity from companies and research institutions in China, the United States, South Korea, and Germany compared to other countries. These areas have the top 10 patent applicants, accounting for 20.8% and 13.6% of all patent applications, respectively. Additionally, technology life cycle analytics revealed a growth trajectory for these identified areas, indicating their rapid expansion and high innovation potential. This study provides a data-driven methodology to develop a technology roadmap that offers valuable insights for researchers, engineers, and policymakers in the ULS industry and supports informed decision-making regarding the field’s future direction.

Renewable energy policy performance and technological innovation in Africa: A Bayesian estimation

This paper examines the effect of renewable energy policy performance on innovation activity in the renewable energy sector in a sample of thirty-three African countries during the period 2010–2021. The policy performance we use covers carbon pricing, incentives for grid-connected and distributed renewable energy generation, fiscal incentives for renewable energy, legal framework for renewable energy, off-take risk, network connection and pricing, and policy design attributes. Using patent data and renewable policy performance to fit a Bayesian negative binomial, our results generally show that renewable energy policy in the region supports technological innovation in the sector. Policy initiatives focused on market-based incentives (auctions and tariffs), renewable portfolio standards, risk mitigation, network connection and pricing are largely responsible for a strong and statistically significant relationship between renewable energy policy and innovation activity. We also find evidence that intellectual property rights, renewable energy capacity, electricity prices, affordability of renewable energy, and openness to trade proxies are important drivers of innovation activity in the renewable energy sector. Our analysis finds no evidence that research and development is a significant determinant of renewable energy innovation in African countries. Our results serve to inform policymakers on the areas in which specific policy actions are most effective at promoting innovation in the renewable energy sector.

Technological Convergence of AI Across the Industrial Sectors

The AI market has been experiencing significant growth recently and is projected to thrive. Yet, there is still a lack of comprehensive studies integrating diverse industries and technologies in AI. Furthermore, AI-related patent analysis often examines AI technologies without considering their convergence with other sectors. Therefore, to fill this gap, this study aims to explore the technological convergence of AI using a network analysis approach with patent data in finance & management, healthcare, semiconductors, games, biotechnology, and transport. This study used an IPC-based convergence network methodology to define critical industrial areas and influential technologies with the four-digit IPC codes for the AI patent group from 2000 to 2019. Moreover, this study conducted a centrality analysis using Net-Miner software to identify hubs and connected nodes and analyze the comprehensive convergence status related to AI. According to the results, we defined hub nodes based on the degree and centralities and analyzed the centrality of six sectors in the AI convergence network. In addition, the technology classification of solid ties is analyzed based on IPC network analysis. Finally, this study attempts to deliver theoretical and empirical contributions to technological convergence, providing a comprehensive framework for understanding how different technologies can converge in AI with three categories: 1) learning and reasoning, 2) natural language processing, and 3) computer vision. This study suggests that companies operating within the industrial AI space should reflect the evolution of technology as revealed in the mainstream trends of sector-specific AI integration.

Terroir takes on technology: Geographical indications, agri-food innovation, and regional competitiveness in Europe

In the agri-food sector, firms and local systems can utilize both Geographical Indications and technological advancements as key strategic assets for growth in many European regions, but the combined contribution of Geographical Indications and innovation activities to the economy of European regions is still poorly investigated. This study aims to understand how Geographical Indications and agri-food innovation affect the competitiveness of the agriculture and food industry in European regions and how these strategies interact. To achieve this goal, a longitudinal and original dataset has been organized, including data related to Geographical Indications and agri-food patents from 265 European NUTS-2 regions between 1996 and 2014. The data for Geographical Indications and agri-food patents are collected from the eAmbrosia and OECD RegPat databases, respectively. The results show that Geographical Indications have a positive and significant impact on regional competitiveness, while the effect of agri-food innovations is controversial. The implications of these findings in terms of policy design are further discussed.

MIGP: Metapath Integrated Graph Prompt Neural Network

Graph neural networks (GNNs) leveraging metapaths have garnered extensive utilization. Nevertheless, the escalating parameters and data corpus within graph pre-training models incur mounting training costs. Consequently, GNN models encounter hurdles including diminished generalization capacity and compromised performance amidst small sample datasets. Drawing inspiration from the efficacy demonstrated by self-supervised learning methodologies in natural language processing, we embark on an exploration. We endeavor to imbue graph data with augmentable, learnable prompt vectors targeting node representation enhancement to foster superior adaptability to downstream tasks. This paper proposes a novel approach, the Metapath Integrated Graph Prompt Neural Network (MIGP), which leverages learnable prompt vectors to enhance node representations within a pretrained model framework. By leveraging learnable prompt vectors, MIGP aims to address the limitations posed by mall sample datasets and improve GNNs’ model generalization. In the pretraining stage, we split symmetric metapaths in heterogeneous graphs into short metapaths and explicitly propagate information along the metapaths to update node representations. In the prompt-tuning stage, the parameters of the pretrained model are fixed, a set of independent basis vectors is introduced, and an attention mechanism is employed to generate task-specific learnable prompt vectors for each node. Another notable contribution of our work is the introduction of three patent datasets, which is a pioneering application in related fields. We will make these three patent datasets publicly available to facilitate further research on large-scale patent data analysis. Through comprehensive experiments conducted on three patent datasets and three other public datasets, i.e., ACM, IMDB, and DBLP, we demonstrate the superior performance of the MIGP model in enhancing model applicability and performance across a variety of downstream datasets. The source code and datasets are available in the website.11https://github.com/hzw-ai/MIGP, with password: MIGPNN2024.