World Patent Information最新文献

Patent statistics have been widely used to assess the competitive position in various technological domains, both at the level of companies and countries. In the literature, there is a gap related to studies that highlight the importance of patents as a strategic instrument in the context of industry 4.0 technologies. In this context, the present article seeks to fill this gap by presenting a conceptual and empirical framework based on a systematic literature review (SLR) to analyze the relationship between patents and industry 4.0. For this purpose, 958 studies published in journals listed in the Web of Science and Scopus databases were selected. Patent analysis refers to examining various features of technological progress and innovation activities that characterize a given industrial or technological domain. This relationship points to the importance of patenting, as the literature review results showed an increase in patenting activity related to Industry 4.0. This study reinforces that patent analysis refers to the examination of various characteristics of technological progress and that innovation activities characterize a particular industrial or technological domain. This article also contributes to the patents and industry 4.0 fields by presenting the state of the art of this relationship and directing future research.

Semiconductors are among the most important core technologies contributing to the Fourth Industrial Revolution. The United States, Taiwan, and China have been investing heavily in semiconductor research and development. To achieve international competitiveness in the semiconductor industry, Korea needs to establish a research and development (R&D) roadmap for small- and medium-sized enterprises (SMEs). Our study identified trends in the semiconductor industry by analyzing the characteristics of core technologies based on patents that disclose technologies instead of holding exclusive ownership. Specifically, we analyzed registered patents concerned with artificial intelligence and machine learning pertaining to the semiconductor industry, which are attracting considerable attention. Using the Korea Intellectual Property Rights Information Service database, we identified 3569 patent specifications related to AI technology and the semiconductor industry. The text mining and network analysis results indicated that the application of deep neural networks is the most important and affects various aspects of R&D. Particularly, AI technology is actively studied for monitoring manufacturing and etch processes. Additionally, technology convergence among virtual reality, visualization, smart factories, and etching technology was identified. The analysis results identify promising technologies related to semiconductors and provide insights that would enable SMEs in the Korean semiconductor industry to establish a technology roadmap.

This paper evaluates domestic and foreign patent portfolios owned by ten top competitors in Japan on the optical lenses of digital cameras. The objective is to establish an efficient and simple tool to provide an enterprise with the ability to investigate its competing position in order to identify its favorable research and development directions and enhance its patent application strategy.

The method is to use three quantitative indicators defined by patent classifications, especially the Japanese patent classification F-term. The indicators represent the technical value of a single invention, between multiple inventions, and all inventions, respectively. Comparison of all aspects of values between Japanese and US patent portfolios are presented on two-dimensional maps.

The evaluations show the performance of competitors and obtain the advisements of patent application strategy for an individual company. The results show that Jaccard index based on F-term is a very good tool for calculating the similarity between patents to obtain innovativeness. All indicators show that the thinking of Japanese competitors in R&D and patent filing strategies have high similarity, and there is currently no breakthrough between all competitors. The multi-viewpoint F-term system has resulted in Japanese companies having a large number of patents in a relatively closed environment.

The Atlantic Forest is the Brazilian biome with the greatest variety of plant species. Despite the potential of resources that can be used in the development of products and technologies, several species are imminently threatened with extinction. This study analyzes the technologies developed with native species of the Atlantic Forest, and their holders, using patent analysis. It also investigates methodological restrictions and gaps in information sources to relate the use of genetic resources to patented technologies, and also relates the use of these resources to the production and commercialization of products. We found that 92% of patents with native flora of the Atlantic Forest were developed and filled outside Brazil. The patents are mainly in the areas of agriculture livestock, medical or cosmetic preparations, food and beverages, and water, sewage, or waste treatment. The procedures for examining applications for patents, and control procedures for accessing biodiversity have limitations in identifying and monitoring the origin of genetic resources. The lack of transparent access to biodiversity results in the patent analysis as one of the main strategies used to evaluate the misappropriation of traditional knowledge associated with genetic resources, since patents have descriptive details of inventions and worldwide publicity.

Twitter accounts can be a relevant (in addition to websites) source of information, particularly for studying the public profile of an institution. In this study, a content analysis of the @epoorg account was performed. The description of the EPO on social networks has never been researched thus far. Using the Tractor software, 19,565 tweets were downloaded and uploaded to the Graphext platform, which uses machine learning to perform topic analysis. Twenty main clusters were identified, which, once organized, engendered the following four main content groups: awards, training, events, and thematic. The thematic contents that the EPO addresses include industry 4.0, cancer, COVID-19, battery innovation, clean energy, climate change. The content is balanced: no country stands out.

Intellectual property work covers a wide range of areas. In particular, prior art literature searching in the patent field requires finding documents that can be used to determine novelty and inventive steps from a vast amount of past literature. Concerning this search practice, research and development of a drawing search technology that directly searches drawings, and essential information about inventions, has long been desired. However, patent drawings are usually described as black-and-white abstract drawings, except in some countries, and their modal characteristics are very different from those of natural images, so they have yet to be explored. This study achieved higher accuracy than the previous ones by introducing InfoNCE and ArcFace in the DeepPatent (Kucer et al., 2022) dataset instead of the conventional Triplet. In addition, we developed an application that enables users to search for patent drawings using any images. Our architecture can be applied to patent drawings and many other modal-like drawings, such as mechanical drawings, design patents, trademarks, diagrams, and sketches.

In this paper, approximately 2,326,000 published patent applications applied between 2010 and 2019 by Japanese applicants in Japan were collected. 5 prefectures where patent applications much increased between 2010 and 2019 were identified, and their characteristics were analyzed. It was observed that they could be classified according to the diversity of the applicants and the IPC sections that contributed to the increase in patent applications. Based on these findings, this paper proposes a new model to classify regional patent application capability maturity. This model is useful for national and prefectural policy-makers to know the recommended policy to improve the maturity level of the target region and to assess the effectiveness of the implemented policy from the viewpoint of increases in patent applications.

Mapping the technology landscape is crucial for market actors to take informed investment decisions. However, given the large amount of data on the Web and its subsequent information overload, manually retrieving information is a seemingly ineffective and incomplete approach. In this work, we propose an end-to-end recommendation based retrieval approach to support automatic retrieval of technologies and their associated companies from raw Web data. This is a two-task setup involving (i) technology classification of entities extracted from company corpus, and (ii) technology and company retrieval based on classified technologies. Our proposed framework approaches the first task by leveraging DistilBERT which is a state-of-the-art language model. For the retrieval task, we introduce a recommendation-based retrieval technique to simultaneously support retrieving related companies, technologies related to a specific company and companies relevant to a technology. To evaluate these tasks, we also construct a data set that includes company documents and entities extracted from these documents together with company categories and technology labels. Experiments show that our approach is able to return 4 times more relevant companies while outperforming traditional retrieval baseline in retrieving technologies.

Both public policy and business management are increasingly interested in how to manage trade secrets. One of the driving forces is the growing significance of data as an asset, as ‘oil of the 21st century'. Trade secrets are often seen as the major Intellectual Property (IP) tool for protecting data. There is also the understanding that the need to share data is increasing to allow for new types of innovation. This paper seeks to understand how data sharing practices and the use of trade secrets are evolving in the agricultural industries. Using explorative empirical data from four in-depth case studies, the paper develops a framework for data sharing practices, value sharing, and trade secrets use. We find that current data sharing practices pool around two scenarios, where data is not shared or shared only with limited partners (hence closed) and there are differences whether value created from the data is shared. We conclude that a nuanced view on the use of trade secrets in data sharing is mandated for both IP/data managers and scholars analysing the topic.