Analysis Of Patent Data Research Articles

Novel mixed-encoding for forecasting patent grant duration

In an age when data is regarded as the most essential commodity, organizations are racing to use it for better decision making. The quality of the patent portfolio is an important indicator of technological innovation in an organization and its analysis can reveal several indicators linked to the growth of a company. The advancement of machine learning along with the access to large amounts of patent data has led to a paradigm shift from traditional patent data analysis methodologies to novel approaches. A lot of research has been done in this direction for analysing data on patent citations, patent text, IPC class etc. However, much less has been explored regarding the forecast of patent grant duration and its significance for decision making with an even lower focus on data collected from developing countries. This work is built upon our existing study on patent grant duration prediction by devising a novel methodology of encoding the data using a combination of augmented one-hot encoding and label-encoding. Thereafter, methodologies such as Outlier Detection have been applied to this data to yield an improved result vis-à-vis our baseline results. In addition, we identify some of the important factors which impact the decision on grant duration of patent applications using the raw data from the Indian Patent Office.

Modes of collaboration in open innovation practice of pharmaceutical firms in India: the analysis of survey and patent data

PurposePatents as one of the important components of intellectual capital are emerging as a new source for mining insights on open innovation (OI) practice of the organizations. Their role in value creation through collaboration and the inter-firm differences is yet to be explored in depth.Design/methodology/approachTo achieve the aim, survey data is analyzed to rank OI practices (collaboration) of the firms, while patent data are analyzed to carry out descriptive and bivariate analysis to study the inter-firm differences in collaboration.FindingsThe survey findings highlight mergers and acquisitions (M&A) and patent pooling as the top two preferred modes of OI, while from patent data M&A has emerged as a predominant OI practice for mainly nonresident firms. At the firm level characteristics, out of firm age, number of granted patents and firm size, firm age has been found to be somewhat significant in few cases of OI practices.Research limitations/implicationsIt provides an alternative source, in this case patent data to study open innovation capabilities of firms in India. There is contribution to the patent value theory from profit motive to deriving strategic decisions on collaboration.Practical implicationsThe managerial implications of this study lie in realizing granted patents as important business tools for seeking collaboration, tracing competitive intelligence and the geography of innovation of the firms' competitors.Originality/valueThe dataset of granted patents at the Indian Patent office (2005–2017), the sample of pharmaceutical firms drawn from this list of patents, patent data– based OI insights and the use of multiple imputation technique to missing data for meaningful insights are some of the unique aspects of this paper.

A Study On the Healthcare Technology Trends through Patent Data Analysis

A Study On the Healthcare Technology Trends through Patent Data Analysis

A patent-based method for monitoring the development of technological innovations based on knowledge diffusion

PurposeThe purpose of this paper is to investigate the diffusion patterns of knowledge in inspiring technological innovations and to enable monitoring development trends of technological innovations based on patent data analysis, thus, to manage knowledge wisely to innovate.Design/methodology/approachThe notion of knowledge innovation potential (KIP) is proposed to measure the innovativeness of knowledge by the cumulative number of patents originated from its inspiration. KIP calculating formula is regressed in forms of two specific diffusion models by conducting a series of empirical studies with the patent-based indicators involving forward and backward citation numbers to reveal knowledge managing strategies regarding innovative activities.FindingsTwo specific diffusion models for regressing KIP formula are compared by empirical studies with the result indicating the Gompertz model has higher accuracy than the Logistic model to describe the developing curve of technological innovations. Moreover, the analysis of patent-based indicators over diffusion stages also revealed that patents applied at earlier diffusion stages normally has higher forward citation numbers indicating higher innovativeness meanwhile the patents applied at the latter stages usually requiring more knowledge inflows observed by their larger non-patent citation and backward citation amounts.Originality/valueAlthough there is a large body of literature concerning knowledge-based technological innovation, there still room for discussing the mechanism of how knowledge diffuses and inspired knowledge. To the best of authors' knowledge, this study is the first attempt to quantitate the innovativeness of knowledge in technological innovation from the knowledge diffusion perspective with findings to support rational knowledge management related to innovation activities.

Fuel-Cell Electric Vehicles: Plotting a Scientific and Technological Knowledge Map

The fuel-cell electric vehicle (FCEV) has been defined as a promising way to avoid road transport greenhouse emissions, but nowadays, they are not commercially available. However, few studies have attempted to monitor the global scientific research and technological profile of FCEVs. For this reason, scientific research and technological development in the field of FCEV from 1999 to 2019 have been researched using bibliometric and patent data analysis, including network analysis. Based on reports, the current status indicates that FCEV research topics have reached maturity. In addition, the analysis reveals other important findings: (1) The USA is the most productive in science and patent jurisdiction; (2) both Chinese universities and their authors are the most productive in science; however, technological development is led by Japanese car manufacturers; (3) in scientific research, collaboration is located within the tri-polar world (North America–Europe–Asia-Pacific); nonetheless, technological development is isolated to collaborations between companies of the same automotive group; (4) science is currently directing its efforts towards hydrogen production and storage, energy management systems related to battery and hydrogen energy, Life Cycle Assessment, and greenhouse gas (GHG) emissions. The technological development focuses on technologies related to electrically propelled vehicles; (5) the International Journal of Hydrogen Energy and SAE Technical Papers are the two most important sources of knowledge diffusion. This study concludes by outlining the knowledge map and directions for further research.

Analysis of patent data of Panax notoginseng and enlightenment of industrial development

In order to find the trends in Panax notoginseng industries of China, a combination of data analysis and empirical analysis was applied to analyze the application of global P. notoginseng patents, the innovation field, the distribution of patent assets, the patent citation network, the distribution of enterprises, the talent team, the competition and cooperation situation based on the financial big data platform of intellectual property industry. From the perspective of industrial technology hotspots, the clinical application of P. notoginseng is becoming more and more widespread, and its clinical treatment field is further expanding. From the perspective of industrial fields, the P. notoginseng industry has gradually expanded from the traditional Chinese medicine field to food, cosmetics, beverages, tea and other daily consumer goods. From the perspective of patent quality, the average maintenance life of P. notoginseng patents of China is lower than the global level, and the value and quality of patents need to be further improved. And the quality of P. notoginseng of China related patent applications has gradually improved in recent years. We analyzed the technology, capital, and talent issues of P. notoginseng industry of China, and summarized and forecasted the status and trends of P. notoginseng industry development of China. It is considered that scientific and technological innovation has become the core engine for the development of the P. notoginseng industry and intellectual property, especially patent protection, escorted the sustainable development of the P. notoginseng industry. Based on the above analysis, several suggestions were put forward to promote the high-quality development of the P. notoginseng industry. It should strengthen scientific and technological support, especially the need for a large number of high-quality scientific and technological output, expand multi-party cooperation, build a high-level technological innovation platform for the P. notoginseng industry, guide long-term capital to continuously and stably invest, and help the P. notoginseng industry to revitalize.

Patent Data Analysis of Artificial Intelligence Using Bayesian Interval Estimation

Technology analysis is one of the important tasks in technology and industrial management. Much information about technology is contained in the patent documents. So, patent data analysis is required for technology analysis. The existing patent analyses relied on the quantitative analysis of the collected patent documents. However, in the technology analysis, expert prior knowledge should also be considered. In this paper, we study the patent analysis method using Bayesian inference which considers prior experience of experts and likelihood function of patent data at the same time. For keyword data analysis, we use Bayesian predictive interval estimation with count data distributions such as Poisson. Using the proposed models, we forecast the future trends of technological keywords of artificial intelligence (AI) in order to know the future technology of AI. We perform a case study to provide how the proposed method can be applied to real areas. In this paper, we retrieve the patent documents related to AI technology, and analyze them to find the technological trend of AI. From the results of AI technology case study, we can find which technological keywords are more important or critical in the entire structure of AI industry. The existing methods for patent keyword analysis were depended on the collected patent documents at present. But, in technology analysis, the prior knowledge by domain experts is as important as the collected patent documents. So, we propose a method based on Bayesian inference for technology analysis using the patent documents. Our method considers the patent data analysis with the prior knowledge from domain experts.

Qualitative and quantitative analysis of patent data in nanomedicine for bridging the gap between research activities and practical applications

Nanomedicines have made a great breakthrough in the diagnosis, treatment and prevention of disease, but there are still significant challenges to make a leap from the scientific discovery to the practical and clinical success. Herein, we contribute to establish effective strategies that can bridge the gaps by an integrated and balanced analysis of patent literatures on the nanomedicine. Since most of successful commercialization of technologies stems from patents, it is important to comprehensively analyze the patent information. Unfortunately, many previous studies of patent analyses for technological trends and strategies are dependent on only quantitative analyses using structured data such as keyword networks and indicators, thus, these studies are difficult to offer detailed technological key components and trends on scientific perspectives. To address these problems, the present study performs qualitative and quantitative analysis using both structured and unstructured patent data, and provides evolving detailed trends in nanomedicine fields from a scientific point of view. Furthermore, upcoming advances in strategies regarding clinical needs, commercial events and strategies of potential leading groups in nanomedicine could be catalogued. Our analytical method and the results of several meaningful patterns and trends were applied to forecast the future practical issue of nanomedicines.

Risk Capital and Emerging Technologies: Innovation and Investment Patterns Based on Artificial Intelligence Patent Data Analysis

The promise of artificial intelligence (AI) to drive economic growth and improve quality of life has ushered in a new AI arms race. Investments of risk capital fuel this emerging technology. We examine the role that venture capital (VC) and corporate investments of risk capital play in the emergence of AI-related technologies. Drawing upon a dataset of 29,954 U.S. patents from 1970 to 2018, including 1484 U.S. patents granted to 224 VC-backed start-ups, we identify AI-related innovation and investment characteristics. Furthermore, we develop a new measure of knowledge coupling at the firm-level and use this to explore how knowledge coupling influences VC risk capital decisions in emerging AI technologies. Our findings show that knowledge coupling is a better predictor of VC investment in emerging technologies than the breadth of a patent’s technological domains. Furthermore, our results show that there are differences in knowledge coupling between private start-ups and public corporations. These findings enhance our understanding of what types of AI innovations are more likely to be selected by VCs and have important implications for our understanding of how risk capital induces the emergence of new technologies.

Review of existing designs of drill-and-injection anchor piles

Introduction. The paper examines the main types of anchor piles used in geotechnical construction are examined in terms of their design variants, the scope of their application, advantages and disadvantages, designs and types of drill bits depending on the type of soil foundation. The aim of the study is the search for and analysis of existing domestic and foreign designs of drill-and-injection anchor piles used for strengthening existing foundations and bases, for dowel reinforcing landslide slopes and different installations such as caissons, retaining walls, masts, towers and pillars bearing pull out loads as well as discovering new, previously little-known anchor designs. The objectives of the study are to perform a patent search and a literary review of existing designs of drill-and-injection anchor piles.
 Materials and methods. Systematization, structural, comparative and contrastive analyses. The paper also utilized the theoretical generalization of the data obtained in the detailed analysis of literature and patent data containing information about the previously developed and applied designs of anchor piles were used in the search and selection of relevant literature.
 Results. The application of the anchor piles is of great importance in modern foreign and domestic geotechnical practice. The need for their application is connected with ensuring the safety of construction, reducing the period of foundation work execution, and the economic efficiency of approved engineering solutions.
 Conclusions. It was found that despite a large number of anchor pile types, all of them, as a rule, are made the using technology of the company Bauer. It is revealed that commonly the anchor design consists of three main parts: a head (supporting element), a rod (element transferring the load from the anchor head to its root) and an anchor root (element transferring the load from reinforced structure to ground and foundation soil).

Patent Keyword Analysis using Time Series and Copula Models

The technological keywords extracted from patent documents have much information about a developed technology. We can understand the technological structure of a product by examining the results of patent analysis. So far, much research has been done on patent data analysis. The technological keywords of patent documents contain representative information on the developed technology. As such, the patent keyword is one of the most important factors in patent data analysis. In this paper, we propose a patent data analysis model combining a integer valued time series model and copula direction dependence for integer valued patent keyword analysis over time. Most patent keywords are frequency values and keywords often change over time. However, the existing patent keywords analysis works do not account for two major factors: integer value and time. For modeling integer valued keyword data with time factor, we use a copula directional dependence model based on marginal regression with a beta logit function and integer valued generalized autoregressive conditional heteroskedasticity model. Using the proposed model, we find technological trends and relations in the target technological domain. To illustrate the performance and implication of our paper, we carry out experiments using the patent documents applied and registered by Apple company. This study contributes to the effective planning for the research and development of technologies by utilizing the evolution of technology over time.

Tracing the technological development trajectory in post-lithium-ion battery technologies: A patent-based approach

Abstract With the ever-growing energy demands, new battery chemistries beyond lithium ion technology are required to deal with an increased power consumption and promote the vehicle electrification. However, there are still challenges in understanding the cognitive structure of evolving R&D processes. The study of the patent landscape can provide a complementary perspective on the trajectories of technological change and facilitate interdisciplinary discussions that help make future technology planning more rationally. In this study, we investigate the technological development trajectory of post-lithium-ion battery technologies based on the analysis of patent data. A data-driven approach, which comprises of patent co-classification analysis, application of link prediction algorithm and text mining technique, is used to highlight the recent research progress on selected post-lithium-ion battery technologies. These are lithium air, lithium sulfur and sodium-ion batteries. A lithium air battery is composed of a lithium metal anode, a porous carbon cathode with high catalytic activities towards electrochemical oxygen reactions, and either an aqueous or non-aqueous electrolyte. Similarly, a lithium sulfur battery consists of a lithium metal anode, a sulfur composite cathode, and an organic electrolyte. Sodium-ion batteries share the same architecture as the lithium ion-batteries, but are normally based on carbonaceous anode materials, layered transition metal oxides cathode demonstrating a minimal structural change upon intercalation, and aqueous electrolytes. The results indicate that the number of patents related to post-lithium-ion battery technologies has noticeably increased since 2008. A few East Asian countries and USA dominate the underlying patent landscape. The co-classification analysis showed that not only the amount of interacting knowledge areas but also the cross-linking with adjacent technological knowledge areas has increased. Moreover, the knowledge areas related to the electric vehicles and polymer applications are predicted to gain more relevance in the future. According to the text mining results, the most common application areas comprise vehicles, devices, energy storage, computers and phones. The main innovations of this study are as follows: First, it identified the current research trends and prospects for the post-lithium-ion battery technologies based on the insights gained from objective data, assisting R&D planners in determining their further directions for research and development. The exploration of the technological development trajectory of post-lithium-ion battery could serve as a reference for relevant researchers to better understand the dynamics and make strategic adjustments, which have profound significance for the progress of clean energy and green chemistry development. Second, it discovered the dynamics of interacting knowledge areas, which indicate an increased tendency of technology convergence. In case of lithium air and lithium sulfur batteries, notable interactions between knowledge areas pertaining to the core battery components (cell configurations and electrolytes) and application-driven knowledge fields (electric vehicles and phones) were observed. The intensity of these interactions was rather weak in case of sodium-ion battery technology. Accordingly, researchers need to seek for new knowledge outside of their traditional knowledge boundaries to accelerate the commercial breakthrough of post-lithium-ion batteries. In particular, knowledge areas related to electrical vehicles have gained in importance in all of the examined battery technologies, reflecting the drive to produce high-energy battery systems. Third, it predicted the knowledge areas that could gain more relevance in the future research by comparing the predictive power of different machine learning algorithms. This forward-looking approach can add a new empirical perspective to the discussion of sustainable technology development. Fourth, in contrast to common assumption, it showed that SIBs could also power up portable applications like mobile devices or electric vehicles.

Change of Data-Driven Drug Design Trends Through Patent Analysis

The pharmaceutical industry is one of the most research and development (R&D)-intensive industries. This industry has tried many strategies to overcome the limitations of a business model that had a high return and high risk. In recent years, the fourth industrial revolution has affected many industries, causing them to update their traditional production and business strategies to a “data science-based” approach. This data science methodology, based on the largely increased size of the data environment, has actively changed the pharmaceutical industry. Therefore, this study aimed to identify specific characteristics of data science innovation in the pharmaceutical industry through the analysis of patent data from the triadic patent databases from the United States, Japan, and Europe.

Research on Development of Fluoride Pollution Control Technology in China Based on Patent Document Analysis

In order to clarify the technical layout and development trend of fluorine pollution control technology in China, this paper analyzes the patent applications related to domestic fluorine pollution control from 1985 based on the SooPAT patent search database. It also reveals the overall development trend of patent technology in this field. This paper concretely analyzes the annual changes trend of the patent, the legal status of the patent, the focus of technology research and layout, patent-related subjects. Finally, this paper summarizes the fluorine pollution control technology on the base of patent data analysis.

Analysis of technological knowledge flows in the Basque Country

<p>Knowledge flow of technology is important for continuous growth and extension of science. Patent data analysis has facilitated this knowledge acquisition. The available patent information crosses borders, corresponds and interacts with new inventions to give new strength and dimension to the technology. Therefore, the patent citation information functions as a key indicator of the knowledge flow providing relevant information. It can be identified to which extent a region is a relevant technological knowledge generator to other regions. As an illustrative case, we present a study to determine the role played by the Basque Country region as a generator of technological innovation during the period 1991-2011.</p>

A method for the detection and characterization of technology fronts: Analysis of the dynamics of technological change in 3D printing technology.

This paper presents a method for the identification of the “technology fronts”—core technological solutions—underlying a certain broad technology, and the characterization of their change dynamics. We propose an approach based on the Latent Dirichlet Allocation (LDA) model combined with patent data analysis and text mining techniques for the identification and dynamic characterization of the main fronts where actual technological solutions are put into practice. 3D printing technology has been selected to put our method into practice for its market emergence and multidisciplinarity. The results show two highly relevant and specialized fronts strongly related with mechanical design that evolve gradually, in our opinion acting as enabling technologies. On the other side, we detected three fronts undergoing significant changes, namely layer-by-layer multimaterial manufacturing, data processing and stereolithograpy techniques. Laser and electron-beam based technologies take shape in the latter years and show signs of becoming enabling technologies in the future. The technology fronts and data revealed by our method have been convincing to experts and coincident with many technology trends already pointed out in technical reports and scientific literature.

Patentes Farmacêuticas e Biofarmacêuticas: A Oportunidade Dos Países Farmaemergentes

Countries with emerging pharmaceutical market (pharmerging countries) has gained prominence in the international scenario because they are identified as the future responsible for the sustainable growth of the new medicines sale. In this work, a technological and industrial mapping was developed through the analysis of patent data extracted from the Derwent World Patent Index, as well as an association with R&D reports. Pharmerging countries were not represented by the industries that most invest in R&D, nor do they stand out in patent protection, poiting to low exploitation of their markets and inexpressive innovation indices. It is suggested that the pharmerging countries adopt public policies aimed at the innovation generation, especially in the biopharmaceutical segment that has proven to have much to be explored, in order to gain a competitive advantage

Academic Inventors and the Antecedents of Green Technologies. A Regional Analysis of Italian Patent Data

This work investigates the knowledge generation mechanisms underlying the creation of green technologies (GTs) in Italian NUTS 3 regions. We hypothesise that local knowledge spillovers from non-green technological areas positively influence the generation of inventions in green domains, since the latter occur as the outcome of a recombination process among a wide array of technological domains. In addition, we hypothesise that the involvement of academic inventors in patenting activity bears positive effects on the generation of GTs, because inventors are able to manage the recombination process across different, and not necessarily related, technological domains. We also investigate whether academic inventors mitigate the impact of local spillovers from non-green knowledge on the generation of GTs. The results suggest that local spillovers from non-green technological domains as well as the involvement of academic inventors in patenting activity bear positive direct effects on the generation of GTs. We also find that academic inventors compensate for local scarcity of spillovers from non-green technological areas.

Technological character, function type, and the longevity of standardized knowledge

ABSTRACTThis study seeks to determine the validity of the current de jure standards management system. The de jure standard is an important tool for innovation policy. However, its review interval has been fixed in the management system and maintained without the use of empirical analysis to guide its development. Therefore, this study (1) examines the factors that affect the longevity of the standards, and (2) outlines methods for improving how the de jure standards are managed. Results indicate that design and mark standards influence the longevity of standardized knowledge. This is notable, given that design for innovation is an emerging area of research that is commonly studied through the analysis of design patent data. Taken together, this study’s major findings are twofold. First, different technological categories have significantly different effects on longevity. Because the longevity of some technological sectors is naturally longer than others, there exists a need for a more flexible interval system. Second, the longevity of the mark and design standard is longer than the longevity of other types of standards. Both developing and developed countries utilize the de jure standard, so the policy implications of these findings are widely applicable.

A Comparison Study on the Vertical Integration and Horizontal Specialization of Chinese ICT Companies

It is said that the decision of vertical integration or horizontal specialization (the boundary of the firm) affects a firm’s profit and competitive advantage, especially in the ICT industry. This paper focused on top market share Chinese ICT companies in global smartphone industry, Xiaomi and Huawei, to examine how they design their boundary of operation systems in R&D. Patent data analysis is used to investigate Xiaomi’s and Huawei’s technology strategies and technical networks, also to compare their systems with each other. According to the results of analysis, we suppose that Xiaomi has rough R&D organization structures and hence has low level of integration, which close to horizontal specialization. On the other hand, Huawei has high weight on internal R&D, and explores technologies related to core technologies. Its R&D structures are more highly integrated than Xiaomi.