World Patent Information最新文献

{"title":"Patenting telemedicine tools: A cross-country analysis of technologies related to remote patient monitoring","authors":"Gazala Parveen,&nbsp;Padmavati Manchikanti","doi":"10.1016/j.wpi.2025.102361","DOIUrl":"10.1016/j.wpi.2025.102361","url":null,"abstract":"<div><div>Telemedicine has been practised since digital technology emerged in the mid-to-late 20th century. It evolved with technological advancements such as satellite communication in the 1960s, the internet in the 1990s and mobile health applications in the 2000s. Today, telemedicine forms a sub-set of digital health. In telemedicine, healthcare professionals provide medical services through information and communication technologies. The patenting of telemedicine tools is quite active, encompassing advancements in software applications, medical devices, and integrated systems for remote diagnosis, monitoring, and treatment. Effective protection of intellectual property for telemedicine tools relies on organised patent management and precise claim drafting. A study on the filing trends of the patents related to telemedicine tools and patent prosecution will give a better understanding of issues related to the patenting of such technologies. It highlights new developments that are expanding the scope of patent claims, particularly the increasing integration of telemedicine and software-enabled medical devices.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":"Article 102361"},"PeriodicalIF":2.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expanding the concept of drug lifecycle management to chimeric antigen receptor T-cell products through product-patent linkage analysis","authors":"Yasuaki Kawai,&nbsp;Shingo Kano","doi":"10.1016/j.wpi.2025.102357","DOIUrl":"10.1016/j.wpi.2025.102357","url":null,"abstract":"<div><div>Chimeric antigen receptor T (CAR-T) cell therapies have been actively developed, and five CAR-T products have been commercialized in Japan. Due to the ongoing development of CAR-T cell therapies, including next-generation variants, the patent landscape is expected to become increasingly complex. Therefore, understanding patent strategies for each CAR-T product is essential.</div><div>In the pharmaceutical industry, lifecycle management (LCM) centered on regulatory and patent protection has been implemented to maximize product value. While studies have reported CAR-T patents through patent landscape analysis to gain insights into the overall CAR-T technology, there is a lack of research on product-related patents for CAR-T products. As a result, the foundational knowledge regarding the LCM of CAR-T products remains unclear.</div><div>Therefore, we identified product-patent linkages for CAR-T products in the Japanese market by combining patent term extension (PTE) data with publicly available data and assessed the applicability of drug LCM to CAR-T products. Our identification of precise product-patent linkages revealed that all CAR-T products met the criteria for drug LCM. This study suggests that LCM activities can be implemented for CAR-T products and that the concept of drug LCM can be expanded to CAR-T products.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do large language models understand patents? Enhancing patent classification through AI-generated summaries","authors":"Naoya Yoshikawa ,&nbsp;Ralf Krestel","doi":"10.1016/j.wpi.2025.102353","DOIUrl":"10.1016/j.wpi.2025.102353","url":null,"abstract":"<div><div>Patent classification plays a crucial role in intellectual property management, but remains a challenging task due to the complexity of patent documents. This study explores a novel approach to enhance automatic patent classification by leveraging summaries generated by large language models (LLMs). Our approach involves using the GPT-3.5-turbo model to create concise summaries from different sections of patent texts, which are then used to fine-tune the RoBERTa and XLM-RoBERTa models for classification tasks. We conducted experiments on English and Japanese patent documents using two datasets: the well-established USPTO-70k and the newly developed JPO-70k, that we specifically created for this study.</div><div>Our findings show that models trained on AI-generated summaries – particularly those derived from patent claims or detailed descriptions – outperform models trained on original abstracts in both subclass-level multi-label classification and subgroup-level single-label classification. In particular, using detailed description summaries improved the micro-average F1 score for subclass-level classification by 2.9 points on the USPTO-70k and 3.0 points on the JPO-70k, compared to using original abstracts.</div><div>These results indicate that LLM-generated summaries effectively capture information relevant to patent classification from various sections of patent texts, offering a promising approach to enhance the accuracy and efficiency of patent classification across different languages.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":"Article 102353"},"PeriodicalIF":2.2,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Generative Artificial Intelligence techniques into technology function matrix analysis","authors":"Huei-Yu Wang ,&nbsp;Shu-Hao Chang ,&nbsp;Chia-Yi Chuang","doi":"10.1016/j.wpi.2025.102352","DOIUrl":"10.1016/j.wpi.2025.102352","url":null,"abstract":"<div><div>This study proposes a novel method for automating the construction of technology-function matrices using generative artificial intelligence (GAI), specifically focusing on quantum technologies. By leveraging GAI to analyze International Patent Classification (IPC) definitions and benchmark reports, we developed a system that rapidly generates technology-function matrices, significantly reducing the time required for manual analysis. The method was applied to 2,399 quantum technology patents from 2023 to March 2024, covering four key areas: secure communications, computing, quantum simulators, and sensors. This approach not only aids government agencies in identifying new technological opportunities but also facilitates the industrialization of potential technologies. By combining GAI with established analytical frameworks, this study contributes to both the theoretical understanding and practical application of patent analysis in emerging fields.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":"Article 102352"},"PeriodicalIF":2.2,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Knowledge flows in technology-intensive publicly listed company - Evidence from Chinese patent citation data","authors":"Shi Chen ,&nbsp;Yifa Wang","doi":"10.1016/j.wpi.2025.102354","DOIUrl":"10.1016/j.wpi.2025.102354","url":null,"abstract":"<div><div>This empirical study utilizes patent citations from technology-intensive publicly listed companies in China between 2000 and 2019 to analyze the current state of knowledge flow within and among these companies. While capital markets are crucial for innovation, the extent to which publicly listed firms facilitate knowledge flow remains unclear. This research delineates the circulation of technological knowledge both intra- and inter-company, across similar and disparate industries, and between listed companies and non-listed innovative entities. The findings indicate a pronounced tendency among technology-intensive listed companies to cite their patents extensively. Self-citations comprise nearly 60 % of total citations, suggesting that technological knowledge primarily circulates within individual companies. Furthermore, the exchange of technological knowledge among different listed companies within the same industry is notably sparse, with only a fractional increase in the frequency of knowledge flows within the industry compared to across industry boundaries. Predominantly, the technological knowledge that technology-intensive listed companies acquire from non-listed innovative entities stems from domestic unlisted companies, with foreign entities and universities contributing to a lesser extent. When examining the spillover of technological knowledge to non-listed innovative entities, it is observed that other non-listed companies predominantly absorb such knowledge, with universities and individual innovators receiving lesser proportions. Finally, this study is significant as it provides empirical evidence on the flow of technological knowledge within and between publicly listed technology-intensive companies in China, revealing the dominance of self-citations and limited cross-company knowledge exchange. By analyzing patent citation data, this research provides valuable insights into the interactions between listed companies and non-listed innovative entities. The findings highlight the significant role of non-listed firms, universities, and foreign entities in shaping technological development. Strengthening these connections can further foster innovation and en hance knowledge diffusion across sectors.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":"Article 102354"},"PeriodicalIF":2.2,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progress in patent technologies for methane catalytic combustion catalysts research","authors":"Bo Yuan ,&nbsp;Tao Zhu ,&nbsp;Meidan Wang ,&nbsp;Xueli Zhang ,&nbsp;Chen Li ,&nbsp;Xinyue Zhang ,&nbsp;Xudong Xu ,&nbsp;Qian Sun","doi":"10.1016/j.wpi.2025.102355","DOIUrl":"10.1016/j.wpi.2025.102355","url":null,"abstract":"<div><div>Catalytic combustion is an important approach to the effective utilization of methane, with the key being the search for efficient catalysts to maximize catalytic activity and resistance to poisoning. This work is based on the IncoPat commercial global patent database, combined with search results from the China Patent Abstracts database and the Derwent World Patents Index database, employing patent analysis methods to conduct a comprehensive analysis of the development trends in the global methane catalytic combustion catalyst materials technology field. By analyzing the trends in patent application and patent family numbers, the distribution of applicant countries/organizations, and leading applicants, this study summarizes the classification, preparation methods, and other technical key points of methane catalytic combustion catalyst materials, clarifying the latest developments in global methane catalytic combustion catalyst materials technology. This provides technical references for companies producing methane combustion catalytic materials in terms of product development and patent strategy layout, and supports the rapid development of the global methane combustion catalytic materials industry. The research findings indicate that global methane catalytic combustion catalyst technology is in a phase of rapid development, with research and applications in this field accelerating globally among countries and organizations, displaying intense technological competition and cooperation trends. Future research will focus on enhancing the activity, stability, and resistance to poisoning of catalysts, to aid in methane reduction and provide technical support for achieving global climate goals.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":"Article 102355"},"PeriodicalIF":2.2,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Literature listing","authors":"Susan Bates (Independent Researcher)","doi":"10.1016/j.wpi.2025.102351","DOIUrl":"10.1016/j.wpi.2025.102351","url":null,"abstract":"<div><div>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 &amp; Certification; and Legal &amp; Intellectual Property Office Matters. The current Literature Listing was compiled mid-February 2025. 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.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":"Article 102351"},"PeriodicalIF":2.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patent litigation mining using a large language model—Taking unmanned aerial vehicle development as the case domain","authors":"Amy J.C. Trappey ,&nbsp;Shao-Chien Chou ,&nbsp;Gi-Kuen J. Li","doi":"10.1016/j.wpi.2024.102332","DOIUrl":"10.1016/j.wpi.2024.102332","url":null,"abstract":"<div><div>As unmanned aerial vehicle (UAV), also called “drone”, swiftly advances with innovative functions and applications, the surge in patent applications has profoundly reshaped the intellectual property (IP) landscape in the UAV industry, leading to a growing number of litigations. This study is structured in two phases, aiming to develop an intelligent approach to analyzing the trend and evolution of patent litigations. The first phase involves macro- and micro-patent analyses of the related technology domain. Macro patent analysis elucidates the fundamental patent information in the drone industry, while micro patent analysis leverages the technology function matrix (TFM) to identify R&amp;D hotspots and potentials. The second phase involves litigation (judgement) mining based on large language model (LLM). Beginning with the construction of a knowledge ontology, the domain infringement landscape can be detected through TFMs. A comparative analysis of the two-phase TFMs (i.e., both TFMs of patent and infringement allocations) is then conducted to pinpoint the key legal actions and the relevant technology. To drill deeper in infringement mining, dynamic topic modeling (DTM) is applied to analyze trends and dynamics in drone controller technology over time. This study aims to strengthen IP protection by developing an intelligent litigation mining approach that adopts large language model (LLM) and uses UAV/drone litigation studies as examples to show how the approach being applied in the industry.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"80 ","pages":"Article 102332"},"PeriodicalIF":2.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"Massimo Barbieri","doi":"10.1016/j.wpi.2025.102342","DOIUrl":"10.1016/j.wpi.2025.102342","url":null,"abstract":"","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"80 ","pages":"Article 102342"},"PeriodicalIF":2.2,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-stage fine-tuning of patent domain-specific DeBERTa for advanced patent landscape on SDGs/Decarbonization","authors":"Yoshiaki Maehara ,&nbsp;Yukimasa Shiozawa ,&nbsp;Yoshiyuki Osabe","doi":"10.1016/j.wpi.2025.102343","DOIUrl":"10.1016/j.wpi.2025.102343","url":null,"abstract":"<div><div>This study presents a multi-stage fine-tuning approach using DeBERTa for advanced patent analysis and landscaping on SDGs and decarbonization technologies. The method incorporates FI subclass estimation with the significant improved accuracy on extracting relevant technologies from patent documents. The model outperformed previous BERT-based approaches in various tasks and was applied to analyze Japanese and PCT international patent applications. Key findings include the continued leading R&amp;D by Japanese companies in SDGs/decarbonization area and the rapid emergence of Chinese firms. The study also introduced the \"Japio-Decarbonization Patent Index\" which can identify companies filing highly decarbonization-oriented patents. This research demonstrates the effectiveness of advanced NLP techniques in patent analysis, providing valuable insights for innovation promotion and technology trend prediction in sustainable development.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":"Article 102343"},"PeriodicalIF":2.2,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}