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The internet of things (IoT) technology has garnered significant attention in recent years due to its wide-ranging applications. IoT, with its high connectivity capabilities, integrates various industrial, domestic, and agricultural devices into a smart and remotely controllable software and hardware platform. The field of IoT technology is expansive and encompasses a multitude of sub-technologies. Identifying core technologies in this domain is crucial for guiding research and development efforts by companies. Given the interrelation of these core technologies and their combination with recent decision-making approaches, network-based strategies have recently received special attention. The developed methods are based on static conditions and the assumption of stability, while in emerging technologies like IoT, the pace of changes over time is high. This leads to changes in the importance of technologies under various scenarios.

In this study, in order to analyze the extracted patent data, association rule mining (ARM) algorithms were applied to identify the relationships between technologies and social network analysis was used to analyze the relationships between technologies and estimate their initial weights. Finally, fuzzy cognitive map (FCM) were used to estimate the final weights of technologies and rank them. The fcm approach allows for simultaneous modeling of both static and dynamic states of the system and, on the other hand, by calculating under various scenarios, suggests a core technology that is sustainable.

The research results show that digital information transmission technologies, digital or electrical data processing, and wireless communication networks are the most important sub-technologies of Internet of things.

This paper scrutinizes the act of patent classification as it is performed by specialists, namely patent examiners, and currently supported by automated systems in patent offices for assigning classification codes to patent application documents. It collectively discusses aspects of the patent classification operation, some of them not very visible, which are not commonly encountered in other document and text classification tasks. The advent of Deep Learning (DL) and, especially, Large Language Models (LLMs) offer a new perspective on the development of automated systems addressing these inherent aspects of patent classification. Towards this direction, the paper analyses how these technologies can address the patent classification problems and concludes with a discussion of potential challenges and benefits that the application of Artificial Intelligence (AI) technologies may bring to the task of patent classification.

Among the main zoonoses with the highest morbidity burden is leishmaniasis. The parasite of the genus Leishmania is its etiological agent. There are three forms of the disease: cutaneous, mucocutaneous and visceral. Pentavalent antimonial drugs are used for leishmaniasis treatment, however toxic side effects and parasite resistance have been detected. Intellectual Property Rights (IPRs) protect inventions that promote benefits to society, and patents are particularly notable for preserving advancements in biotechnology. To introduce innovative technologies, it is necessary to analyze the elements and processes involved in the development of products that may be protected by IPRs. This process consists of product deconstruction into its elements for an intellectual property analysis to contemplate commercial viability, and to determine strategies to avoid infringing third party rights (Freedom-to-Operate approach). In this project, a sample of patents protecting treatments directed against leishmaniasis were evaluated in order to know their characteristics, and to establish useful elements for Freedom-to-operate strategies. Open access patent databases were used (The Lens, Google patents, Patentscope, among others), and relevant characteristics such as claims, owners, technology protection region, or year of application for protection were determined. The sample obtained includes patents from 1967 to 2023. The majority of patents were found to be located in countries such as Brazil or the United States, and described developments focused on compounds derived from chemical synthesis, polypeptides, extracts, antigens, or structural components of the parasite. There are several patents that have already expired and whose information can be freely used. The Bolar exemption would allow the use of information from these patents for regulatory compliance purposes. This study provides an overview of what has been patented for leishmaniasis treatment, and information on Freedom-to-operate focus.

Welcome to the latest quarterly Literature Listing intended as a current awareness service for readers indicating newly published books, journal, and conference articles on IP management; Information Retrieval Techniques; Patent Landscapes; Education & Certification; and Legal & Intellectual Property Office Matters. The current Literature Listing was compiled end-May 2024. Key resources include Scopus, Digital Commons, publishers' RSS feeds, and serendipity! This article gives a selection of interesting references to whet your appetite - the full list of references can be found in the companion datafile.

Novelty is considered a conditio sine qua non for the grant of a patent by most relevant patent authorities and in U.S. patent law defined by 35 U S C. §102. Previous attempts to operationalize patent novelty have been mostly based on theoretical principles, such as recombination theory, and have not estimated novelty according to data that officially determines whether sufficient novelty is present in an application. To overcome this gap, this study analyzes whether established measures of patent novelty are capable of predicting the rejection of an application based on lack of novelty. Furthermore, this study applies a combination of sophisticated unsupervised and supervised machine learning techniques to patent data and provides the possibility to estimate statutory novelty practiced by the USPTO by a modeled indicator. Measuring such statutory novelty would give applicants a tremendous competitive advantage to pursue patent strategies offensively and/or defensively. For example, the indicator allows companies – particularly small and medium-sized companies with limited resources – to assess the novelty and therefore the patentability of one’s own invention. Large companies, most of which are more likely to pursue an offensive patent strategy, can use the indicator to measure the novelty of numerous published third-party patents and challenge their validity.

Patent search presents unique challenges due to the intricate structure and specialized terminology embedded in patent documents. While neural models have been successfully applied in various information retrieval (IR) tasks, these inherent complexities have hindered their effectiveness in patent search. To address these challenges, we propose a novel re-ranking architecture that effectively handles long, structured patent documents and leverages AI models to interpolate lexical and semantic signals of relevance. Additionally, the architecture incorporates query-specific weights for the final re-ranking process. To address partial relevance between patent sections our method effectively models the relevance relationships between different sections of patent documents. We calculate lexical and semantic signals of relevance from each document section and feed them as input features to AI models that estimate a combined relevance score. Finally, we compute query-specific weights to determine the relative contributions of lexical and semantic relevance for the final re-ranking. Extensive experiments on the CLEF-IP dataset demonstrate that our method outperforms several baselines, achieving substantial and statistically significant improvements in retrieval performance. We further assess the adaptability of our method using the MSMARCO dataset, where it exhibits limited performance, indicating its suitability for domain-specific patent research.

While zero-trust (ZT) technologies cover many aspects of trust in information security (TinIS) critical to Federal enterprises, more research is required to effectively identify their benefits at scale. In this sense, an appropriate valuation of ZT intellectual property (IP) uncovers future challenges with ZT principles to both scholars and Federal enterprises. Therefore, this research attempts to understand the marginal effects of having ZT IP on Federal enterprise governance in terms of return on government effectiveness, regulatory quality, and e-participation. We collected objective data from Derwent Innovation (Clarivate Analytics) and the World Intellectual Property Organization (WIPO IP Statistics) to test the interplay of the 1297 ZT strategic patent with Federal enterprise governance. Through panel regression models using fixed effects, we conduct Heckman correction model analyses and the generalized moment method (GMM) to alleviate potential sampling biases and endogeneity. The findings suggest that ZT strategic patent is positively related to both government effectiveness and regulatory quality but without any effect on e-participation. Furthermore, we find that diverse technological IP portfolios in ZT technologies do not strengthen the effects of ZT strategic patent on Federal enterprises.

Patents contribute to economic development; therefore, estimations of patent value are significant in measuring their impact on the economy. With this in mind, this article discusses estimations of patent value with the renewal model developed by several economists. Targeted patents are those issued in the United States and Japan from 1990 to 2010. In addition, the study examines patent values for several technology fields and considers which technology field should be the focus for the United States and Japan. Moreover, this article compares estimations based on the renewal model versus those based on other data such as sales, trade prices, or trials and analyzes how close this model's estimations are to the real values. This article's results demonstrate that the renewal model is useful for evaluating patents overall and analyzing technological trends from a patent perspective.

This review analyzed 471 journal articles focusing on Intellectual Property Rights (IPR) in the agri-food sector. Using bibliometric analysis and systematic literature review techniques, it aimed to uncover key debates and insights. It examined relevant bibliometric data by employing tools like HistCite 12.03.17, VOSviewer 1.6.19, and CiteNetExplorer 1.0.0. Furthermore, using content analysis, the literature was classified and discussed under six main clusters: IPR's impact on technology and innovation, IPR's influence on seeds, IPR's role in sustainable development, intersections between IPR and research and development, discussions on geographical indications (GIs), and IPR management and policy. These clusters provided diverse perspectives on IPR's role in the agri-food sector, ranging from fostering innovation and sustainability to concerns about power concentration, farmer rights, environmental impacts, and socioeconomic outcomes. GIs were unanimously recognized for safeguarding product reputation tied to specific regions. The review summarized the intricate relationship between IPR and research and development and tools for effective IPR management and policy considerations. Overall, the IPR in agri-food sector literature offers diverse, sometimes conflicting views with limited empirical research. As a result, further comprehensive empirical investigations are required to understand the subject and better inform policymakers.

Cyber security is one of the topics that gain importance today. It is necessary to determine the basic components, basic dynamics, and main actors of the Cyber security issue, which is obvious that it will have an impact in many areas from social, social, economic, environmental, and political aspects, as a hot research topic. When the subject literature is examined, it has become a trend-forming research subject followed by institutions and organizations that produce R&D policy, starting from the level of governments. In this study, cybersecurity research is examined in the context of 5 basic cyber security functions specified in the cyber security standard (CSF) defined by the National Institute of Standards and Technology (NIST). It is aimed to determine the research topics emerging in the international literature, to identify the most productive countries, to determine the rankings created by these countries according to their functions, to determine the research clusters and research focuses. In the study, several quantitative methods were used, especially scientometrics, social network analysis (SNA) line theory and structural hole analysis. Statistical tests (Log-Likelihood Ratio) were used to reveal the prominent areas, and the text mining method was also used. we first defined a workflow according to the “Identify”, “Protect”, “Detect”, “Respond” and “Recover” setups, and conducted an online search on the Web of Science (WoS) to access the information on the publications on the relevant topics It is seen that actors, institutions and research create different densities according to various geographical regions in the 5 functions defined within the framework of cybersecurity. It is possible to say that infiltration detection, the internet of things and the concept of artificial intelligence are among the other prominent research focuses, although it is seen that smart grids are among the most prominent research topics. In the first clustering analysis we performed, we can say that 17 clusters are formed, especially when we look under the definition function. The largest of these clusters has 32 data points, so-called "decision making models".