World Patent Information最新文献

Microalgae are rich sources of biomolecules, especially proteins and peptides, with bioactive properties, such as antihypertensive action that act through the inhibition of the angiotensin-converting enzyme (ACE). Innovation in production methods of these peptides is crucial to make them more efficient and accessible. The aim of this work aims to review patents about antihypertensive peptides from microalgae. The search was conducted on three electronic databases between 2010 and 2023. The search covered 2248 patents, which only 6 met the inclusion criteria. All patents were filed on the Asian continent, particularly in the China and Korea. Spirulina, Chlorella and Nannochloropsis oculate were more reported, being used in the production of medicines and/or pharmaceutical compositions. The majority of patents filed show obtention methods and alternatives for producing peptides, containing 3 to 7 amino acid sequences. The highest concentration of patents was about medical treatments or examinations (A61) followed by food or methods or food products (A23) and involving chemical compounds or extraction methods for pharmaceutical products (C07). Thus, this study provided a comprehensive view of technological innovations related to methods of producing peptides from microalgae, contributing to advances in cardiovascular health and the development of new pharmaceutical bioproducts.

The years 2023–2024 bring up some significant anniversaries for a range of historical events in the development of modern intellectual property law. Many of them have implications for how patents are obtained and recorded in the public domain. The original documents are cited for further reading.

This study employs data-driven analysis to examine the patent filing statistics in the offshore wind energy sector, offering valuable insights for policymakers. The study uses data from a newly constructed patent dataset (available online for complete download) covering twelve aspects of offshore wind technology, including foundations, tower designs, transmission systems, blades, rotors, and submarine cables. The creation of the dataset is the result of the collaborative efforts of technology specialists, patent analysts, and expert patent examiners in the field of wind technology, ensuring data quality and extensive coverage of the latest offshore wind energy patents. The analysis shows a surge in global patent filings from 2006 to 2012, followed by a period of stagnation until 2017, when patent activity experienced a resurgence. Europe, Asia, and the USA emerge as prominent players, with Germany, Denmark, China, and Japan leading the charge, indicative of a global offshore wind market. Areas for further development include optimising floating foundations, improving tower and blade designs, recycling wind blades, addressing rare earth material impacts and prioritising energy storage and green hydrogen production for power system balance and decarbonisation.

This research aims to analyze the contribution of univariate and relational citation analysis methods, applied to patents, for the identification and characterization of scientific-technological domains, in documents indexed in the Derwent Innovation Index database. The adopted method was patentometrics associated with bibliometrics, using joint analysis of the relational bibliometric citation methods: co-citation and bibliographic coupling. The corpus of the study is composed of 144 patent families. Through the bibliographic coupling, 5 theme clusters and researchers with well-defined thematic domains were observed. Employing co-citation, 23 clusters were identified, characterizing the epistemic domains related to technological currents in which stem cell inventors operate. Such results allowed us to prospect the scientific-technological scenario in this theme, which can illustrate some institutions’ innovation potentials and explain who the actors at the forefront of such research are. It is proposed that applying this methodology allies to topic modeling techniques.

Technology convergence is considered one of the cornerstones of technological innovation as a phenomenon emerging at the intersection of two previously unrelated fields of technology. The new technological system is a new combination of knowledge types, technology components and intersections. For this matter, analyzing patents is an essential part for strategic decision making. However, the manual analysis of large amounts of patent semantics is often time-consuming, extensive, and difficult even for experts. To enhance manual patent analyses, new machine learning-based techniques are gaining increasing interest. This study aims to enrich this methodological research by developing and evaluating an unsupervised text-mining approach to automatically cluster patents of two knowledge types into four technology components. To this end, this study presents a five-step method including the comparison between different algorithms and design choices. This method is applied to printed electronics-relevant patents extracted from the Derwent World Patent Index and enables to draw recommendations for automated patent analyses. The findings show different significances for types of components: while components of the specialized knowledge type could be predicted with significance, components of the design knowledge types could not provide significant results.

Technological innovation can play a major role in developing a sustainable mobility system. To generate a state-of-the-art of sustainable mobility innovations, and understand its future trajectory we use the systemic perspective of the sustainable mobility paradigm Avoid-Shift-Improve to present a patent landscape of the entire land-transportation system. The descriptive part of our study covers 425,885 granted patent families between 1970 and 2016 across 75 patent jurisdictions. In the predictive part, we use this dataset to forecast sustainable mobility innovations till 2030. We identify the USA, and China are the leading knowledge importers in sustainable mobility innovations, whereas Japan and Germany are the leading knowledge exporters. Overall, our analysis suggests that there is a visible shift in sustainable mobility innovation towards a low emission mobility future that is more connected and electric than before. However, the combined effects of continued growth in innovation for efficiency gain in GHG emitting mobility technologies, a high level of uncertainty in future innovation trajectory in vehicle charging and hydrogen technology, and a low level of innovation activities in mass and non-motorized transportation technologies can imperil a faster transition to a zero-emission future of mobility.

Biodegradable polymers (BDPs), due to their degradability, have great merits for applications in the environmental fields compared to conventional polymers. Furthermore, they are recently receiving renewed attention with the rise of globally issued environmental problems caused by plastic wastes. However, BDPs are still produced and utilized with limited amounts and applications in comparison to conventional plastics. To help expand the application area of BDPs, we mainly focus on the emerging and exotic technologies of BDPs by analyzing International Patent Classification (IPC) co-classification networks constructed from 2,862 patents on BDPs filed with the US Patent and Trademark Office from 2006 to 2022. As a result, by detecting both recently appearing and isolated IPCs, new and relatively unknown application areas of BDPs such as animal trap, polishing composition, rope lubricant, underground structure, ammunition, digital data carrier are identified. Our IPC analysis methodology studied in the field of BDPs would be also useful for researchers and entrepreneurs searching new and emerging applications in various technology areas.

This study proposes a novel method to forecast the emerging patenting area with Main Path Analysis and Word Cloud Analysis. To test the methods, we used Wind Power Energy as an example to illustrate the method's usefulness. Firstly, we used “wind power” and “wind energy” to collect 40,827 related journal papers in Scopus and 72,979 related patents in Derwent Innovation databases. Main Path Analysis was conducted to explore the development trajectory. The results of the Main Path Analysis for the papers and patents were visualized with Pajek software. Secondly, we used VOSviewer to extract the technological areas (i.e., keywords) of the collected academic papers and patents. Then, we calculated the average time lag between the first paper published and the first patent filed for each technological area (keyword). Finally, we forecasted the trend of patenting for wind power energy based on the average lag time and academic research themes in recent years.

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.