World Patent Information最新文献

{"title":"Digital marketing of standard essential patent licensing programs","authors":"Gülfem Özmen ,&nbsp;Jussi Heikkilä ,&nbsp;Matti Karvonen ,&nbsp;Ville Ojanen","doi":"10.1016/j.wpi.2025.102385","DOIUrl":"10.1016/j.wpi.2025.102385","url":null,"abstract":"<div><div>We present empirical evidence on the digital marketing choices of standard essential patent licensing programs on patent pool and licensor websites. We highlight the importance of dynamic learning in licensing negotiation events and strategic information revelation in the presence of asymmetric information. We document licensing schemes and licensed units adopted in patent licensing programs. We analyze the marketing strategies of licensing programs using applicable elements of the Marketing Mix framework. We observe significant variation in publicly available information across licensing programs. This suggests that licensors face trade-offs in deciding what information is revealed and anchored in pre-negotiations, as part of licensing program marketing, and during confidential licensing negotiations. Future studies could analyze how generative artificial intelligence (AI) systems may promote marketing and transparency of patent licensing programs.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"82 ","pages":"Article 102385"},"PeriodicalIF":1.9,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144851775","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":"A systematic review of artificial intelligence applications and methodological advances in patent analysis","authors":"Tzu-Yu Lin ,&nbsp;Li-Chieh Chou","doi":"10.1016/j.wpi.2025.102383","DOIUrl":"10.1016/j.wpi.2025.102383","url":null,"abstract":"<div><h3>Purpose</h3><div>This study aims to systematically synthesize the practical applications of artificial intelligence (AI) in patent analysis by constructing a comprehensive matrix that aligns distinct AI techniques with their corresponding analytical tasks. The “AI Technique and Analytical Task” matrix provides a structured framework for understanding how various AI approaches are deployed across different functional objectives within the patent analysis domain.</div></div><div><h3>Design/methodology/approach</h3><div>This study integrates bibliometric analysis, BERT-based topic modeling, and literature review to explore AI applications in patent analysis. Data were retrieved from the Web of Science Core Collection using a dual-focus search strategy targeting AI techniques and patent analysis tasks. A clear distinction was made to exclude studies analyzing AI trends using patent data, retaining only those applying AI methods to patent analytics. With these strategies, 718 relevant publications were selected as the basis for analysis.</div></div><div><h3>Findings</h3><div>The results reveal exponential growth in AI-powered patent analysis research since the mid-2010s, with <em>Technological Forecasting and Social Change (TFSC)</em>, <em>Scientometrics</em>, and <em>World Patent Information (WPI)</em> identified as the leading publication platforms. Geographical analysis shows that China and South Korea have rapidly increased their research output and institutional engagement, while the U.S. maintains a foundational yet less recent presence.</div><div>With topic modeling technique, this study identified eleven major thematic clusters, spanning tasks such as emerging knowledge discovery, technology forecasting, and opportunity identification. These were integrated into “AI Technique and Analytical Task” matrix, which systematically maps the relationships between AI methods (such as pretrained language models, convolutional neural networks, semantic analysis, and topic modeling) and their practical implementations. Among these, patent classification and nature language processing (NLP) emerged as the most impactful applications, underscoring AI's vital role in enabling scalable, data-driven approaches to managing complex patent information.</div></div><div><h3>Originality</h3><div>This study presents a novel integration of multi-layered literature retrieval strategies, bibliometric analysis, BERT-based topic modeling, and an AI technique-to-analytical task matrix to construct a systematic and structured knowledge framework. This integrative approach not only delineates the interdisciplinary evolution of AI applications in patent analysis but also provides strategic guidance for future research, particularly in advancing empirical validation, informing policy applications, and promoting global inclusivity in this emerging field.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"82 ","pages":"Article 102383"},"PeriodicalIF":1.9,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827863","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":"Scalable multi-label patent classification via iterative large language model-assisted active learning","authors":"Songquan Xiong,&nbsp;Shikun Chen,&nbsp;Jianwei He,&nbsp;Yangguang Liu,&nbsp;Junjie Mao,&nbsp;Chao Liu","doi":"10.1016/j.wpi.2025.102380","DOIUrl":"10.1016/j.wpi.2025.102380","url":null,"abstract":"<div><div>Patent classification faces increasingly complex challenges due to the exponential growth in volume and technical sophistication of global patent databases. A substantial proportion of patents inherently belong to multiple technological categories simultaneously, rendering classification particularly challenging for both manual and automated systems. Current approaches struggle with computational scalability, prohibitive annotation costs, and the accurate identification of overlapping technical concepts across interdisciplinary innovations. This study presents a novel iterative framework that combines the advanced text comprehension capabilities of Large Language Models (LLMs) with the sample-efficient principles of active learning (AL) for scalable multi-label patent classification. We evaluated our approach using drone-related technologies extracted from a comprehensive dataset of 100,000 patents, focusing on ten key technological component categories. Our LLM-assisted active learning methodology achieved Macro-F1 and Micro-F1 scores of 0.85 and 0.88, respectively, demonstrating a 15% improvement in Macro-F1 compared to established baseline methods. Our approach reduced the required manual annotation effort by approximately 60% while maintaining comparable classification performance. These empirical findings demonstrate the potential for transforming large-scale patent analysis workflows and improving the efficiency of intellectual property management systems</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"82 ","pages":"Article 102380"},"PeriodicalIF":1.9,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780997","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":"Quantitative insights on artificial intelligence in the pharmaceutical industry: A patent-basis analysis of technological trends and key players","authors":"Shunsuke Sakaoka,&nbsp;Shingo Kano","doi":"10.1016/j.wpi.2025.102381","DOIUrl":"10.1016/j.wpi.2025.102381","url":null,"abstract":"<div><div>The application of artificial intelligence (AI) in the pharmaceutical industry has rapidly expanded in recent years. To quantitatively assess this emerging trend, previous studies have conducted bibliometric analyses using publication databases to elucidate leading affiliations, research themes, and the contributions and research focuses of key players, including mega pharma, big IT firms, AI startups, and academic institutions. However, quantitative analyses that leverage patent databases to capture these significant shifts and assess the contributions of the key players are scarce. This study investigated technological trends in AI applications within the pharmaceutical industry and clarify the roles and strategic focuses of key players through patent analysis. A total of 1365 AI-related pharmaceutical patent applications in the United States between 2000 and 2023 were identified. Through a detailed analysis of patents, we revealed that AI is strategically employed across critical business domains, including drug discovery, drug development, diagnosis, manufacturing, marketing, and healthcare. Moreover, the study unveiled the specific business areas where each key player has focused their AI-driven initiatives and historical contributions to AI applications in the pharmaceutical industry. The findings underscore both patent and literature analyses are essential to comprehensively assess AI applications and contributions of key players in the pharmaceutical industry.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"82 ","pages":"Article 102381"},"PeriodicalIF":1.9,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144766933","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 landscape and innovation trajectories of mRNA vaccine technologies","authors":"Kyungdae Oh ,&nbsp;Youngbo Choi ,&nbsp;Surin Hong","doi":"10.1016/j.wpi.2025.102382","DOIUrl":"10.1016/j.wpi.2025.102382","url":null,"abstract":"<div><div>This study presents a comprehensive patent-based analysis of mRNA vaccine technologies, tracing their progression from experimental tools to scalable biomedical platforms after the COVID-19 pandemic. Leveraging Cooperative Patent Classification (CPC) codes and 25 years of global filings (2001–2025), we built a functional technology tree and mapped innovation across delivery systems, structural design, adjuvants and immune modulation, and Good Manufacturing Practice (GMP)-compliant manufacturing. Lipid nanoparticle-mediated delivery dominates recent applications, underscoring industry priorities in efficacy and scale. Growth curves signal entry into technological maturity, accompanied by wider participation from pharmaceutical firms, academia, and public institutes. Strategic profiling reveals contrasting R&amp;D strategies: ModernaTX and Translate Bio pursue vertically integrated platforms; CureVac emphasizes antigen design and RNA stability; MIT focuses on delivery technologies with broad cross-domain reach. These patterns indicate that mRNA vaccines are becoming foundational infrastructure for precision medicine, oncology, and next-generation immunotherapies. Future competition is poised to intensify around delivery innovation, RNA stabilization, immune modulation, and robust GMP production. Our findings illuminate evolving intellectual-property strategies and highlight platform integration, manufacturing optimization, and cross-sector collaboration as key drivers of innovation in the global mRNA vaccine ecosystem.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"82 ","pages":"Article 102382"},"PeriodicalIF":1.9,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772234","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":"The patent landscape of Nanomedicine: Opportunities and obstacles","authors":"Amrita Singh ,&nbsp;Sarita Rana ,&nbsp;Mansi Trivedi ,&nbsp;Abhay Kant Upadhyay ,&nbsp;Omar Awad Alsaidan ,&nbsp;Waleed H. Almalki ,&nbsp;Salem Salman Almujri ,&nbsp;Ankit Sahoo ,&nbsp;Mahfoozur Rahman","doi":"10.1016/j.wpi.2025.102379","DOIUrl":"10.1016/j.wpi.2025.102379","url":null,"abstract":"<div><h3>Objective</h3><div>The objective of this review is to discover systemic barriers and strategic possibilities that impact innovation, commercialization, and regulatory alignment by critically analyzing the patents of nanomedicine worldwide. The translational trajectory of nanomedicine is examined in relation to intellectual property (IP) frameworks, regulatory heterogeneity, and claim complexity.</div></div><div><h3>Results</h3><div>According to the report, the patent landscape is crowded and fragmented, with overlapping material and method claims delaying market entrance and driving up licensing costs by as much as 50 %. The intricacy of patent claims and disparate jurisdictional categorization schemes lead to protracted examination backlogs, which sometimes surpass 30 months. Timelines for patent drafting and clearance are complicated by regulatory misalignment, especially between the FDA, EMA, and other authorities. Early freedom-to-operate analyses, cross-licensing, and involvement in ISO/ASTM working groups are examples of strategic interventions that can reduce the number of transactions by 50 % and accelerate the time-to-market ratio by approximately 20 %. Reforms at the policy level, such as shared-IP incentives, specialist examiner tracks, and harmonized definitions (e.g., nanoparticle sizes of 1–100 nm), might reduce licensing costs by approximately 30 %. Patent landscape analysis in medicine and health care has transformed into a multidimensional, AI-powered endeavor integrating semantic extraction, graph theory, and economic modeling to reveal innovation hubs and strategic opportunities across diverse subfields. Clinical translation is facilitated, and public trust is increased when ethical, legal, and social factors are included throughout the R&amp;D lifecycle. A robust and cohesive IP environment is necessary to realize the full potential of nanomedicine. This comprises an integrated regulatory-patent strategy, simplified patent office processes, enhanced examiner knowledge, and uniform definitions. In addition to increasing the quality and enforcement of patents, these reforms will increase competition, draw in investment, and hasten the introduction of nanomedicine advances to patients, which will ultimately lower healthcare costs and improve patient outcomes. To fully realize the therapeutic and financial promise of nanomedicine, this review discusses that standardizing patent definitions, expediting assessment procedures, and incorporating ethical frameworks are crucial. Innovation will be accelerated, prices will decrease, and patient access to cutting-edge treatments will be enhanced by a robust, internationally coordinated IP system.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"82 ","pages":"Article 102379"},"PeriodicalIF":1.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750302","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":"Patenting trends in biodegradable implant: An analysis","authors":"Moumita Sarkar ,&nbsp;Tapas Kumar Bandyopadhyay","doi":"10.1016/j.wpi.2025.102378","DOIUrl":"10.1016/j.wpi.2025.102378","url":null,"abstract":"<div><div>Biodegradable metal implants are a significant innovation in medical science, offering temporary mechanical support and gradually dissolving within the body, thus avoiding the need for a second surgery. This study presents a comprehensive patent landscape analysis of metallic biodegradable implants: examining key trends, technological advancements, and major patent holders. Analysing 2114 global patent filings, the study identifies leading advancements in composition, manufacturing techniques, surface modifications, and clinical applications. There is clear information about the legal status of approximately 65 % of the total patent records, including granted patents that are still in force and those that have lapsed or discontinued due to non-renewal or other reasons. Specifically for India, a total of 177 individual patent application records involving filings in Indian jurisdiction resulted from a targeted screening procedure, with 99.4 % remaining active and 0.6 % marked as expired. However, biodegradable implant patent filings in India remain below expectations. This is attributed to the stringent application of Sections 3(i), 3(d), 3(f), and 3(e) of The Patent Act, 1970, that aim to minimize social costs in medical technology. These findings offer valuable insights for researchers, policymakers, and industry stakeholders navigating India's evolving patent landscape and future commercialization of metallic biodegradable implants.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"82 ","pages":"Article 102378"},"PeriodicalIF":1.9,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738700","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":"Innovative data extraction in patent reviews with SciSpace","authors":"Paula dos Passos Menezes ,&nbsp;Alcimary Bispo Santos ,&nbsp;Maria Eduarda da Conceição Andrade ,&nbsp;José Adão Carvalho Nascimento Júnior ,&nbsp;Marcelo Cavalcante Duarte ,&nbsp;Shanmugan Saravanan ,&nbsp;Mairim Russo Serafini","doi":"10.1016/j.wpi.2025.102377","DOIUrl":"10.1016/j.wpi.2025.102377","url":null,"abstract":"<div><div>Patent review is a time-consuming process because it involves the complexity of the documents with technical language and unpatterned files. Automating steps like data extraction can make technological prospects faster and more efficient. Thus, this study aimed to compare the use of an Artificial Intelligence (AI) tool to automate the data extraction of patents. A traditional patent review was conducted in Espacenet and WIPO databases, using the test subject: AI tools for skin cancer detection. Two independent reviewers performed the patent screening in Excel traditionally, and the data extraction step was manually made both and using SciSpace. SciSpace was 13.2 times faster than the traditional way and correctly extracted 69.7 % of information. Although it is a promising tool to accelerate the patent review process, it is crucial to author review the final version to ensure its accuracy and the representation of its ideas.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"82 ","pages":"Article 102377"},"PeriodicalIF":2.2,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596276","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 and bibliometric analysis of the scientific landscape of the use of graphene-based biosensors and their prospects in digital medicine","authors":"Olena Litvinova ,&nbsp;Michel-Edwar Mickael ,&nbsp;Gernot Gerger ,&nbsp;Andy Wai Kan Yeung ,&nbsp;Ahmed Fatimi ,&nbsp;Hossam Haick ,&nbsp;Atanas G. Atanasov ,&nbsp;Harald Willschke","doi":"10.1016/j.wpi.2025.102376","DOIUrl":"10.1016/j.wpi.2025.102376","url":null,"abstract":"<div><div>This article presents a patent and bibliometric analysis of the scientific landscape of the use of graphene-based biosensors and their prospects in the field of digital medicine.</div><div>A study of patent activity in the field of graphene biosensors (2005–2025) showed an increase in interest, especially since 2015, with a peak in 2021, with China, the U.S., and the World Intellectual Property Organization as the most active offices. Web of Science revealed 16,551 articles, with active growth since 2010, focusing primarily on analytical chemistry, nanotechnology, and electrochemistry. China and India lead the way in research.</div><div>Bibliometric analysis using VOSviewer identified four key clusters reflecting the main research directions of graphene biosensors: medical diagnostics, integration with digital technologies, the use of metals and nanomaterials, and analytical methods in biosensor technologies. The distribution of keywords over time analyzed with VOSviewer and CiteSpace indicates increasing digitalization in graphene biosensor research from 2020, underscoring the growing interest in flexible, highly sensitive sensors and their integration with digital platforms for diagnosis and analysis.</div><div>Despite the high potential of graphene biosensors, there are challenges pointed out in the literature, such as scaling, biocompatibility, and cost issues, as well as the need to address safety and reliability concerns.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"82 ","pages":"Article 102376"},"PeriodicalIF":2.2,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144564039","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":"The global patent landscape of devices for psoriasis diagnosis and treatment","authors":"Chang Xu ,&nbsp;Tingrui Zhang ,&nbsp;Jie Zhang ,&nbsp;Fei Tan ,&nbsp;Zongguang Tai ,&nbsp;Zhongjian Chen","doi":"10.1016/j.wpi.2025.102375","DOIUrl":"10.1016/j.wpi.2025.102375","url":null,"abstract":"<div><div>Psoriasis, a chronic systemic inflammatory disease affecting over 125 million people globally (2–3 % prevalence) with substantial economic burden (annual treatment costs exceeding 15 billion and projected to reach 30 billion by 2027), drives demand for advanced diagnostic and therapeutic devices. This study aims to provide developers with current intelligence on technological advancements in psoriasis management, identifying active R&amp;D areas and aiding resource allocation. Patent data from global intellectual property offices and the PatSnap Wisdom Sprout database were analysed to track the maturity of diagnostic and treatment technologies from 1975 to 2023. The study reveals a focus on therapeutic devices, particularly phototherapy, with improvements in light sources, power supplies, heat management, safety, flexibility, cleanliness, and intelligent control. Diagnostic equipment development concentrates on image detection technologies, emphasizing cleanliness, adaptability, multifunctionality, smart device integration, and lightweight design. To address the evolving needs of psoriasis treatment, it is recommended to integrate beneficial technologies, enhancing interdisciplinary collaboration, and innovating to overcome technological challenges.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"82 ","pages":"Article 102375"},"PeriodicalIF":2.2,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549566","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}