Patent Databases Research Articles

Analysis of the Technological Convergence in Smart Textiles

Convergence between emerging technologies and traditional industries has become a crucial strategy for enhancing a technology’s competitiveness. Technical convergence (TC) for smart textiles aims to reveal the convergence of emerging technologies with textile technologies, including the field, structure, and critical technologies of the TC. For the empirical analysis, the technology life cycle (TLC) and network analysis method are utilized to observe the TC of 15,125 patent data for textiles from the Derwent Patent Database. The results indicate the following: (1) after 2021, the TC of smart textiles matured, with the number of patents reaching a peak in 2030. (2) Emerging technologies and textile technologies are inextricably linked. In addition to textile technologies, the primary technical fields involved in smart textiles are electronic engineering, tools design, chemical engineering, and mechanical engineering. Electronic engineering is the most common of these fields, accounting for 29.11%. (3) From a structural perspective, the density, breadth, and depth of the TC continues to expand. (4) Measurement, computer technology, and audio technology will be always essential to the TC, whereas electrical machinery, instrumentation, energy technology, other specialized technologies, and chemical engineering have tremendous growth potential. The findings above have substantial implications for the phenomenon of the TCs that have emerged in emerging technology and traditional industry fields. They can also aid the government in formulating policies that promote the transformation and growth of related industries.

Temperature change and industrial green innovation: Cost increasing or responsibility forcing?

This paper is to test the relationship between temperature change and industrial green innovation by combing data of China's average temperature change in cities with green patent database of listed companies.First, the results find that rising annual average temperature can inhibit green innovation. For every 1 °C increases in summer, green innovation reduces by 3.2%, and for every 1 °C increases in winter, green innovation increases by 1.9%. Second, rising summer temperature inhibits green innovation by crowding out research and development (R&D) funds and draining labor. While, rising temperature increases green innovation by increasing environmental responsibility and green venture capital. Third, differences in green R&D difficulty, process and objectives were also compared, temperature change has a stronger inhibition on green process innovation than green product innovation. And rising temperature inhibits green exploratory innovation and promotes green developmental innovation. Fourth, rising temperature increases green innovation in polluting firms as temperature is higher than 21 °C. And it inhibits green innovation in clean firms as temperature is higher than 27 °C. This paper can provide policy implications on climate regulation and promoting green innovation.

Innovations for Reducing Methane Emissions in Livestock toward a Sustainable System: Analysis of Feed Additive Patents in Ruminants.

Simple SummaryThe mitigation of the environmental impact of animal production is a global objective, and innovation can provide new strategies and technologies to support the transition toward a more sustainable livestock system. Using patent data analysis to identify innovation dynamics, we explored the sector of feed additives to reduce methane emissions in ruminants. We found that this innovation sector is recent and rapidly expanding, with the European Union representing the center of innovation. The most promising inventions are related to the use of beneficial microorganisms (probiotics) and plant-based extracts.An important challenge for livestock systems is the mitigation of environmental impacts while ensuring food security, and feed additives are considered as one of the most promising mitigation strategies. This study analyzed the innovation landscape of feed additives to reduce methane emissions in ruminants. The analysis is based on patent data to evaluate the development, scientific importance, and market-level impact of the innovations in this field. The results reveal that the EU is on the innovation frontier, with substantial and quality patent production. The innovation field is dominated by private players, characterized by high specificity in the R&D pipeline. Additives derived from plant or botanical extracts, together with 3-nitrooxypropanol (3-NOP), represent the emerging innovations, indicating a clear orientation toward more sustainable livestock systems. Despite the regulatory and semantic limitations related to the use of patent databases, data reveal a growing innovation activity at global level, which could lead to macroeconomic benefits for the entire livestock sector.

Re evaluating Indias Third Mission through Top Ranked Universities & Technological Institutes

The present study explores the patenting activities of the faculty staff of the top 20 Indian academic and engineering institutes during 2011-2020 by excavating the Indian patent office database. Of the total 963 granted patents, the highest patents were published in 2016, and the academician of IIT Madras invented 167 patents followed by IIT Mumbai with 156 patents during this period. Most of the innovation takes place in the macro-field like chemical engineering, and micro-fields like nano-technology, nano-materials, and nano-complex. Collaboration with inventors of the same academic institution is found as the best choice of the inventors, only a meagre portion of patents was published in collaboration with of industries and organisations around the globe. The tag ‘highest number of patents inventor’ goes to Padma Shri Prof. Thalappil Pradeep of IIT Madras who has contributed 19 Indian patents in the last 10 years.

Patent Data for Engineering Design: A Critical Review and Future Directions

Abstract Patent data have long been used for engineering design research because of its large and expanding size and widely varying massive amount of design information contained in patents. Recent advances in artificial intelligence and data science present unprecedented opportunities to develop data-driven design methods and tools, as well as advance design science, using the patent database. Herein, we survey and categorize the patent-for-design literature based on its contributions to design theories, methods, tools, and strategies, as well as the types of patent data and data-driven methods used in respective studies. Our review highlights promising future research directions in patent data-driven design research and practice.

Applying Natural Language Processing and TRIZ Evolutionary Trends to Patent Recommendations for Product Design

Traditional TRIZ theory provides methods and processes for systematic analysis on engineering problems, which can improve the efficiency of solving problems. However, the effect of solving problems is not necessarily guaranteed, and depends on the user’s profession and experience. Therefore, this study proposes a methodology to apply evolutionary benefits in the 37 trend lines developed by TRIZ researchers to assist in intelligently screening relevant patents applicable to the content of the product design. In such a way, the efficiency of problem solving and product design quality may be improved more effectively. First, the patent database is used as the training dataset, words and sentences in the patent documents are analyzed through natural language processing to obtain keywords that may be related to evolutionary benefits. Using word vectors trained by Doc2vec, the semantic similarity can be calculated to obtain the similarity relationship between patent text and evolutionary benefit. Secondly, the goals of the product development project may make be related to the evolutionary benefits, and then applicable patent recommendations can be provided. The proposed methodology may achieve the purpose of intelligent design assistance to enhance the product development process and problem-solving.

Identifying Trends and Quantifying Growth for Technological Innovation in Knee Arthroplasty: An Analysis of a Patent Database (1990 to 2020).

Technological innovation is the key for surgical progress in knee arthroplasty and improvement in patient outcomes. Exploring patented technologies can help elucidate trends and growth for numerous innovative technologies. However, patent databases, which contain millions of patents, remain underused in arthroplasty research. Therefore, the present study aimed to: (1) quantify patent activity; (2) group patents related to similar technologies into well-defined clusters; and (3) compare growth between technologies in the field of knee arthroplasty over a 30-year period. An open-source international patent database was queried from January 1990 to January 2020 for all patents related to knee arthroplasty A search strategy identified 70,154 patents, of which 24,425 were unique and included analysis. Patents were grouped into 14 independent technology clusters using Cooperative Patent Classification (CPC) codes. Patent activity was normalized via a validated formula adjusting for exponential growth. Compound annual growth rates (CAGR) were calculated (5-year, 10-year, and 30-year CAGR) and compared for each cluster. Overall yearly patent activity increased by 2,023%, from 104 patents in 1990 to 2,208 patents in 2020. The largest technology clusters were "drugs" (n = 5,347; 23.8%), "components" (n = 4,343; 19.0%), "instruments" (n = 3,130; 13.7%), and "materials" (n = 2,378; 10.4%). The fastest growing technologies with their 5-year CAGR were: "user interfaces for surgical systems" (58.1%); "robotics" (28.6%); "modularity" (21.1%); "navigation" (15.7%); and "computer modeling" (12.5%). Since 1990, overall patent growth rate has been greatest for "computer modeling" (8.4%), "robotics" (8.0%), "navigation" (7.9%), and "patient-specific instrumentation" (6.4%). Most patents in knee arthroplasty for the last 30 years have focused on drugs, components, instruments, and materials. Recent exponential growth was mainly observed for user interfaces for surgical systems, robotics, modularity, navigation, and computer-assisted technologies. Innovation theory would suggest that these rapidly growing technologies are experiencing high innovation output, increased resource investments, growing adoption by providers, and significant clinical impact. Periodic monitoring of technological innovation via patent databases can be useful to establish trends and future directions in the field of knee arthroplasty.

The examination of resource tax reform facilitating firms’ green innovation in resource-related industry in China

The impact of resource tax reform on green innovation in resource related industry at micro-firm level is still unknown. By matching data from resource-associated firms in China's A-share market with green patent database from State Intellectual Property Office from 2000 to 2017, and applying Generalized Method of Moments (GMM) and difference-in-difference (DID), the research results demonstrate that resource tax reform significantly facilitates firms' green innovation in resource industry and executive compensation intensifies the promoting effect. Further study reveals enterprise profitability works as an intermediary enhancing effect. Our findings on some degree coincide the literature like emissions trading scheme promoting green innovation, which emphasizes ex post or from emission consequences. In contrast, this paper focuses on levying resource tax that is limited to mineral resource-related firms and at the source of resource exploitation, and treats micro-firms as the main body of research unlike existing literature at macro level. More importantly, this paper combines GMM-DID models to solve endogeneity problem. Furthermore, this paper explores the mediating and moderating effects by corporate profitability and executive compensation, which have been neglected by the literature.

Artificial intelligence and unemployment:An international evidence

This paper examines the possible effect of artificial intelligence (AI) on unemployment using a broad database of AI-related patents in 40 developed and developing markets from 2000 to 2019. The study employs a panel smooth transition regression (PSTR) model to analyse the relationship between artificial intelligence and unemployment under various inflation levels. The study contributes to the existing literature with several findings. First, results from our analysis confirm the non-linear relationship between artificial intelligence and unemployment depending on the threshold of inflation. In general, artificial intelligence increases unemployment until a certain inflation threshold is attained, and then the effect reduces afterwards. Second, the smooth mechanism employed in this analysis can capture individual estimates varying amongst countries over time.

Structural characteristics of China's COVID-19 patent cooperation network at the province, city, and patent applicant levels.

The Chinese Plan has provided an important model for the global fight against COVID-19 since its outbreak. The present study describes the structural characteristics of China's COVID-19 patent cooperation network at the province, city, and applicant levels by using social network analysis based on data from the Incopat global patent database since 2020, which helps to clarify the current technology accumulation in this field in China, and provide patent information support for the scientific efforts to fight against COVID-19. The findings are as follows: First, the inter-regional cooperation level in China's COVID-19 patent cooperation network shows a decreasing trend from eastern to central to western regions. At the inter-applicant cooperation level, kinship-based cooperation is the strongest, business-based cooperation has the widest scope, while proximity-based cooperation exists throughout these two main models of cooperation. Second, coastal provinces and cities occupy a core position in the network, and play an important role in utilizing structural holes and bridging. Patent applicants with high centrality are mostly firms. Research institutes and universities mainly play the role of bridges. Third and lastly, there is no large number of cliques at the province and city levels. However, there is a tendency for cliques to develop at the applicant level. Hence, actions are needed to prevent the development of information barriers.

Towards expert–machine collaborations for technology valuation: An interpretable machine learning approach

Although technology valuation has benefited considerably from recent advances in machine learning technology, the results of prior studies in this field are of limited use in practice because they rely solely on black box models whose internal mechanisms are hidden. We develop an analytical framework for successful expert–machine collaborations for technology valuation using interpretable machine learning that makes a model's behaviors and predictions understandable to humans. First, a technological characteristics–economic value matrix is constructed using patent and technology transaction databases. Second, machine learning models are developed to examine the nonlinear and complex relationships between the technological characteristics and economic value of technologies. Third, the performance of the machine learning models is assessed using quantitative metrics. Finally, the SHapley Additive exPlanation method is applied to the best-performing model to explain which technological characteristics influence the economic value of technologies. By these means, we investigate the importance of the features of technological characteristics (and their interactions) in technology valuation and offer theoretical and practical implications of the analysis results. A case study of the technologies registered in the Office of Technology Licensing at Stanford University confirms that our framework is a useful complementary tool for technology valuation.

Can green public procurement change energy efficiency? Evidence from a quasi-natural experiment in China

Green public procurement (GPP) is an important way to achieve environmentally friendly products, but there is no evidence on whether it can improve the efficiency of corresponding firms. Accordingly, this study constructs a difference-in-differences model using the first Chinese green procurement list enacted as a quasi-natural experiment. We constructed a large sample of firm-level data by cleaning and matching China's industrial, emissions, and patent databases. The baseline model finds that GPP significantly improves the energy efficiency of regulated firms, and further, the robustness of the regression results is verified by the substitution of explained variables, the exclusion of other contemporaneous policies, the reduction of self-selection bias, and a placebo test. The mechanism test confirms that GPP encourages regulated firms to optimize energy consumption structure, and promotes them to improve production processes and technology. The heterogeneity test show that GPP improves the energy efficiency of firms in the eastern and central regions, with stronger promotional utility among state-owned enterprises and large firms. Finally, based on input-output correlation data, this study explores the spillover effects of the policy in the production network, where the implementation of green procurement can improve the energy efficiency of key upstream industries.

Adversarial Training Methods for Deep Learning: A Systematic Review

Deep neural networks are exposed to the risk of adversarial attacks via the fast gradient sign method (FGSM), projected gradient descent (PGD) attacks, and other attack algorithms. Adversarial training is one of the methods used to defend against the threat of adversarial attacks. It is a training schema that utilizes an alternative objective function to provide model generalization for both adversarial data and clean data. In this systematic review, we focus particularly on adversarial training as a method of improving the defensive capacities and robustness of machine learning models. Specifically, we focus on adversarial sample accessibility through adversarial sample generation methods. The purpose of this systematic review is to survey state-of-the-art adversarial training and robust optimization methods to identify the research gaps within this field of applications. The literature search was conducted using Engineering Village (Engineering Village is an engineering literature search tool, which provides access to 14 engineering literature and patent databases), where we collected 238 related papers. The papers were filtered according to defined inclusion and exclusion criteria, and information was extracted from these papers according to a defined strategy. A total of 78 papers published between 2016 and 2021 were selected. Data were extracted and categorized using a defined strategy, and bar plots and comparison tables were used to show the data distribution. The findings of this review indicate that there are limitations to adversarial training methods and robust optimization. The most common problems are related to data generalization and overfitting.

The Effect of Environmental Policy Uncertainty on Enterprises' Pollution Emissions: Evidence from Chinese Industrial Enterprise.

In response to the global call for emission reduction, China has assumed international responsibility for energy conservation and emission reduction by enacting several environmental policies to save energy and reduce consumption. However, it is debatable whether the increased uncertainty in environmental policies negatively affects firms’ emission reduction. Few studies have examined this relationship based on micro-level data. Therefore, this study constructs a theoretical framework of environmental policy uncertainty affecting firms’ pollution emissions. Based on comprehensive data from the Chinese Industrial Enterprise Database, the Chinese Industrial Enterprise Pollution Emission Database, and the Chinese Patent Database from 2002 to 2014, we empirically analyzed the impact of environmental policy uncertainty on firms’ pollution emissions. The results show that (1) environmental policy uncertainty significantly aggravates the pollution emission intensity of industrial enterprises; (2) environmental policy uncertainty inhibits the improvement of enterprises’ innovation capacity, reduces their human capital stock and foreign investment, and aggravates their pollution emission; (3) environmental policy uncertainty has significant heterogeneity on enterprise pollution emissions, that is, environmental policy uncertainty has a greater impact on non-export enterprises, large enterprises, young enterprises, capital-intensive enterprises, state-owned enterprises, and enterprises in polluting industries and central regions. This study provides a useful reference for the improvement of environmental policy and the green transformation of enterprises.

Government R&D spending as a Driving Force of Technology Convergence

This paper investigates the impact of government R&D spending on promoting technology convergence. We test the hypotheses that a government funding program has a positive effect on technology convergence and the effects are heterogenous on different participants (i.e., academic, and industrial inventors). To investigate this, our empirical test applies the Advanced Sequencing Technology Program (ASTP) as one example. We develop a novel dataset by linking the ASTP grantee information with the PATSTAT patent database. Based on this, we create inventor-level characteristics to implement propensity score matching, selecting an appropriate control group of inventors who are comparable to those enrolled in the ASTP. We then employ DiD models to evaluate the impact of the program on the matched sample. The results confirm that the program is a driving force of technology convergence. The findings also indicate that the program is more influential to industry inventors than their academic counterparts. Besides, we conceptualize a ‘leverage effect’ of the program and show it can attract many external industrial inventors. The work contributes to better understanding the role of a government-funded program in encouraging convergence and provide implications for developing convergence-related R&D programs in the future.

Anti-cancer Research on Arnebiae radix-derived Naphthoquinone in Recent Five Years.

In recent years, many naphthoquinone compounds with anticancer activity have been identified in Arnebiae Radix, and some of them have the potential to be developed into anticancer drugs. This article aimed to provide a comprehensive overview of the anticancer effects of naphthoquinone compounds through a detailed review of literature and Chinese patents, and discuss their potential to be developed as anticancer drugs for clinical application. Research papers were collected through the databases of PubMed, Cnki and SciDirect using keyword searches "naphthoquinone compounds" and "anticancer". The keywords of "shikonin" and "shikonin derivatives" were also used in PubMed, Cnki and SciDirect databases to collect research articles. The Chinese patents were collected using the Cnki patent database. Naphthoquinone compounds have been found to possess anti-cancer activity, and their modes of action are associated with inducing apoptosis, inhibiting cancer cell proliferation, promoting autophagy in cancer cells, anti-cancer angiogenesis and inhibition of cell adhesion, invasion and metastasis, inhibiting glycolysis and inhibiting DNA topoisomerase activity. Most of the naphthoquinone compounds show effective anti-cancer activity in vitro. The structure modification of naphthoquinone aims to develop anti-cancer drugs with high efficacy and low toxicity.

Effective design knowledge abstraction from Chinese patents based on a meta-model of the patent design knowledge graph

Patent document is one of the most important sources of knowledge for engineering design. Design for patentability has become increasingly important for the success of product in the competitive market. However, language differences hinder the effective extraction of design knowledge from patent databases of different countries using existing patent analysis methods. This paper proposes a new approach to build the patent design knowledge graph to extract useful design information from Chinese patent texts by using natural language processing techniques. A meta-model of the patent design knowledge graph is built based on the language characteristics of Chinese. The proposed method is verified using randomly selected patent text for the effective extraction of Chinese patent design knowledge. The feasibility of the proposed method is further tested to support design of a new storage device for the patentability.

Intellectual Property Rights (IPRs) and its role in propelling the growth of the MSME sector

AbstractFor micro‐, small‐, and medium‐sized enterprises (MSMEs), the current economic scenario might be altering. The transformation of a country's socioeconomic standing, particularly in a developing country like India, is not a simple process. Micro, small, and medium‐sized firms are a varied and heterogeneous collection of enterprises with a wide range of ailments and constraints. In this respect, MSMEs should get involved in invention/innovation by doing continuous and productive research and development. Intellectual Property Rights (IPR), is one aspect that can help MSMEs avoid stumbling blocks and achieve global standards. IPR is a broad term that encompasses all aspects of a company and serves as a valuable corporate asset. Patent and trademark databases include a plethora of technological and commercial information that MSMEs may use to learn about recent technological advances, discover prospective partners, and learn about rivals' creative activities. For entrepreneurs all around the world, efficiently managing intellectual property and leveraging it to develop company strategies is becoming increasingly important. In the last 7 years, India has launched several programs such as Digital India, Start‐Up India, Make in India, and Stand‐up India, as well as the current Aathmanirbhar Bharat, which aims to achieve a $5 trillion economy and long‐term sustainability. The enterprise, via its vigilance and vitality, should demonstrate remarkable innovativeness and flexibility to withstand the recent economic downturn, and IPR will provide many chances for future growth in this respect. MSMEs' IP strategies should take a comprehensive approach to the various forms of IP assets. In this article, we have emphasized the impact and relevance of IPR as a competitive strategy for propelling the growth of MSMEs. The objective is to raise MSME knowledge of IPRs so that they may take steps to safeguard their ideas and business plans.

Patents, what are they good for? Academic chemistry researcher’s perceptions of patents and their importance

This paper asks the following questions: What are the patent reading and consulting habits of Brazilian chemistry researchers and how does this affect their approach to using/applying patents in their research? A total of 421 researchers were canvassed regarding their patent reading habits and only a minority (42.8%) consult patents. Of these, 92.2% consider patents as relevant sources of information, with 95% already benefiting from patents. However, researchers criticize the content and/or technical details in patents. The majority of canvassed researchers (78.3%) encourage students to use patent databases and/or deposit patents. However, most researchers prefer to publish articles, which provide greater visibility. Where appropriate, individual comments are included, providing insight into their opinions regarding patents.

The province-managing-county reform and green innovation of enterprises: evidence from the quasi-natural experiment.

Based on the database of green patents of China's A-share listed enterprises from 2001 to 2018, this paper identifies the impact of the province-managing-county (PMC) fiscal reform on the green innovation performance of enterprises by using staggered DID method. The results show that the PMC fiscal reform significantly promotes enterprises' green innovation performance, and this impact is not only reflected in the quantity of green patents but also in the quality. The mechanism test finds that the policy effect of PMC fiscal reform comes from the attraction of environmental investment and optimization of human capital structure. The PMC fiscal reform expands the financial autonomy of county governments, facilitating the compensation and allocation mechanism. Moreover, the paper incorporates the county-power-expansion (CPE) reform into the robustness test to exclude the interference of other policies in the same period.