World Patent Information最新文献

{"title":"Towards a new paradigm for patent experimentation: WPI+","authors":"Michail Salampasis ,&nbsp;Eleni Kamateri ,&nbsp;Vasileios Stamatis ,&nbsp;Mihai Lupu ,&nbsp;Allan Hanbury ,&nbsp;Florina Piroi","doi":"10.1016/j.wpi.2025.102389","DOIUrl":"10.1016/j.wpi.2025.102389","url":null,"abstract":"<div><div>We enhance the WPI patent research collection, which is publicly accessible and free of charge, to facilitate more comparable, transparent, and reproducible experiments. This is accomplished through what we call “soft standardization” advocating the adoption of consistent methods in using the test collection. We offer data statistics, predefined collection subsets, ground-truth data for additional tasks, and open-source tools for using the collection, all on a public GitHub repository. These resources not only relieve researchers from performing essential collection analysis tasks but also implicitly guide them toward sound methods for conducting experiments with the collection. Our initiative is primarily motivated by the goal of enhancing comparability and reproducibility of patent research. This is achieved through the development of a carefully designed resource that will be continuously expanded and maintained. Our work is also driven by the observation that highly integrated Information Retrieval experiment platforms for large scale evaluation are not widely adopted by researchers. We provide examples of how the WPI+ resource/collection can be used for research on multiple patent specific tasks, including prior-art search, patent classification, and summarization. Overall, our work shows that the traditional concept of a test collection—limited to just a corpus, topics, and relevance assessments—can be broadened to support more efficient and reliable scientific experimentation.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"83 ","pages":"Article 102389"},"PeriodicalIF":1.9,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227418","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":"Patentability of AI and global harmonization: An analysis of the current guidelines in Brazil and the IP5 offices","authors":"Tarso Mesquita Machado ,&nbsp;Eduardo Winter ,&nbsp;Hernane Borges de Barros Pereira","doi":"10.1016/j.wpi.2025.102400","DOIUrl":"10.1016/j.wpi.2025.102400","url":null,"abstract":"<div><div>Innovations related to artificial intelligence can impact many technological fields and several sectors from industry, including patent offices and the patent system. Major patent offices have been updating their examination guidelines to address the particularities of AI inventions, providing legal certainty and predictability to the players in the patent system. The present study has explored the responses of the IP5 patent offices and Brazil's INPI to these challenges, revealing key areas of harmonization, divergence, and areas requiring further development. The analysis shows that, while the IP5 offices have taken steps to adapt their patent guidelines to account for the unique features of AI technologies, Brazil's INPI lags behind in terms of clarity and specificity. This works also analyses the level of harmonization among the IP5, where we conclude that significant differences remain between their approaches, especially in the case of the USPTO, which continues to rely heavily on judicial interpretations. Brazil's INPI must continue to evolve its guidelines, and there is an opportunity to observe the behavior of the IP5 to incorporate best examination practices in Brazil. By aligning with international best practices and offering clear, detailed guidance, patent offices can provide the legal certainty necessary to foster sustained investment in AI, ensuring that these transformative technologies benefit both inventors and society at large.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"83 ","pages":"Article 102400"},"PeriodicalIF":1.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158505","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","doi":"10.1016/j.wpi.2025.102397","DOIUrl":"10.1016/j.wpi.2025.102397","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-August-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":"83 ","pages":"Article 102397"},"PeriodicalIF":1.9,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145106777","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":"Innovation trends and evolutionary paths of electrocatalytic hydrogen evolution reaction technology: A global patent analysis","authors":"Wenting Jin ,&nbsp;Ji Huang","doi":"10.1016/j.wpi.2025.102388","DOIUrl":"10.1016/j.wpi.2025.102388","url":null,"abstract":"<div><div>Electrocatalytic hydrogen evolution reaction has emerged as a key driver of technological innovation and industrial advancement in the hydrogen energy sector. By conducting statistical analysis on patent information in this technology field, we can effectively grasp the trends and directions of technological research and development (R&amp;D), thereby providing a critical basis for scientific policy making and industrial deployment strategies in related fields. Based on search results from the IncoPat database, this study integrates text mining with KeyBERT algorithm, CiteSpace visualization analytics, and Logistic model to conduct a comprehensive investigation from multiple dimensions including patent quantity and quality, R&amp;D hotspots and frontiers, as well as technology lifecycle. The results indicate that: (1) The patented technologies in this field predominantly originate from core innovation clusters in China, Japan, the United States, and South Korea. China maintains an unequivocal dominance in the volume of technological outputs and has made effective strides in catching up with developed countries in terms of patent quality. However, the industrial application of Chinese patents may encounter certain difficulties. In contrast, the technological innovations of the United States and Japan maintain comparative advantages in terms of global influence and market presence. (2) The R&amp;D hotspots in this field are concentrated primarily on topics such as precious metal-based catalysts and transition metal-based catalysts. (3) The evolutionary trajectory of this technology can be delineated into three distinct phases, with each phase featuring distinct R&amp;D focuses and mainstream paths. (4) The technology is currently in a rapid growth phase, with forecasts suggesting it will enter the technological maturity stage by 2026 and the decline stage by 2036.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"83 ","pages":"Article 102388"},"PeriodicalIF":1.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997631","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 effective use of artificial intelligence in patent searches: A case study in using AI-based classifiers to identify AI inventions","authors":"Aleksei L. Kalinichenko,&nbsp;Kelvin W. Willoughby","doi":"10.1016/j.wpi.2025.102387","DOIUrl":"10.1016/j.wpi.2025.102387","url":null,"abstract":"<div><div>This study proposes a new patent search methodology for enhancing the quality and utility of patent research. The methodology focuses on techniques for effectively searching large patent datasets using artificial intelligence (AI) based classifiers to generate robust and reproducible results for subsequent statistical analysis. An extensive literature review revealed that salient approaches to patent searching fail to provide transparent, accurate and reproducible results, thereby hindering validation as well as evoking the need for manual post-processing and subjective judgments. Our proposed methodology, to enable precise, reliable and reproducible AI-enabled search queries, involves employing a novel terminological framework and formulating search regulations based on a formal definition of the technological subject matter of interest. We tested the methodology by applying it to patent searches in the field of AI technologies. In other words, we employed AI to facilitate our development of an operational technical definition of AI for patent searches. The primary results of our research are: (1) an automated patent search technique utilizing a learning algorithm guided by a formal definition of the search area; and (2) a novel terminological framework tailored for patent searches in the AI technology domain. Our approach offers enhanced transparency, reproducibility, and reliability in patent research, with applicability to both AI and other fields of technology.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"82 ","pages":"Article 102387"},"PeriodicalIF":1.9,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144903091","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":"George J.H. Huang","doi":"10.1016/j.wpi.2025.102384","DOIUrl":"10.1016/j.wpi.2025.102384","url":null,"abstract":"","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"82 ","pages":"Article 102384"},"PeriodicalIF":1.9,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865219","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":"Advancing PHA-Based packaging: A patent and scientific landscape analysis for sustainable innovation","authors":"Pietro Carlos Gonçalves Conceição ,&nbsp;Natália Hlavnicka Miranda ,&nbsp;Luiggi Cavalcanti Pessôa ,&nbsp;Denilson de Jesus Assis ,&nbsp;Jamille Santos Santana ,&nbsp;Paulo Vitor França Lemos ,&nbsp;Jania Betania Alves da Silva ,&nbsp;Lucas Guimarães Cardoso ,&nbsp;Karina Teixeira Magalhães-Guedes ,&nbsp;Leonardo Moreira de Assunção ,&nbsp;Carolina Oliveira de Souza","doi":"10.1016/j.wpi.2025.102386","DOIUrl":"10.1016/j.wpi.2025.102386","url":null,"abstract":"<div><div>Polyhydroxyalkanoates (PHAs) have emerged as promising biodegradable alternatives to petrochemical plastics for packaging applications, aligning with global sustainability goals. This study presents a comprehensive analysis of the scientific and technological landscape of PHA-based packaging from 1992 to 2024, combining a systematic review of 4176 scientific articles with a patent landscape analysis of 3328 patent families. The results reveal a robust increase in academic research since 2015, focusing on enhancing mechanical, barrier, and antimicrobial properties through nanomaterials and bioactive additives. Patent trends show a technological evolution from basic PHA formulations to multifunctional, smart packaging systems. The food sector dominates application areas, while blending PHAs with polymers such as PLA, starch, and PBAT remains a key strategy to improve performance and reduce costs. China leads in patent filings, driven by strong regulatory support, whereas Japan and the U.S. contribute significantly through industrial innovation. Despite the high potential of PHAs, challenges such as production cost, thermal sensitivity, and limited university-industry partnerships persist. The findings highlight the need for intensified collaboration and continued R&amp;D investment to advance PHA-based packaging technologies toward large-scale adoption.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"82 ","pages":"Article 102386"},"PeriodicalIF":1.9,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144851774","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":"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}