World Patent Information最新文献

{"title":"Generative AI-based intelligent patent summarization for intellectual property knowledge communication and cooperation","authors":"Amy J.C. Trappey ,&nbsp;Yuga Y.C. Lin ,&nbsp;Chun-Yi Wu","doi":"10.1016/j.wpi.2025.102410","DOIUrl":"10.1016/j.wpi.2025.102410","url":null,"abstract":"<div><div>The substantial increase in patent applications has created notable challenges in retrieving, analyzing, and managing patent data. According to the World Intellectual Property Indicators (WIPI) report published by the World Intellectual Property Organization (WIPO) in 2024, the total number of patent applications worldwide surpassed 3.55 million in 2023. Traditional manual methods for extracting and interpreting key patented knowledge are usually time-consuming, expensive, subjective, and lack validation. To address the rise in patent filings and the growing need for effective patent knowledge management, we have developed an intelligent patent summarization system that utilizes large language model (LLM) technology to enhance the understanding and usability of patent documents. This research uses patents related to advanced vehicle-to-everything (V2X) technologies as case studies. Through empirical analysis, we show that the system can automatically condense large amounts of patent documents into concise and meaningful summaries. This intelligent patent summarization system runs efficiently on consumer-grade computers. Experimental results indicate that its semantic structure achieves nearly 90 % similarity compared to patents written by domain experts. This research aims to enhance the efficiency, accuracy, and accessibility of patent document processing, thereby significantly advancing the practical application of this technology.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"83 ","pages":"Article 102410"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528012","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":"EigenPatent: A novel eigenvector-based ranking method for enhanced patent similarity detection","authors":"Ioannis Pontikis,&nbsp;Chen Li,&nbsp;Dimitrios Chrysostomou","doi":"10.1016/j.wpi.2025.102411","DOIUrl":"10.1016/j.wpi.2025.102411","url":null,"abstract":"<div><div>Patent retrieval often faces unique challenges due to the complex structure and nature of technical documents. Traditional similarity measures often fail to capture the nuanced semantic relationships between inventions, marking it difficult for non-experts to retrieve relevant prior art. This study introduces a novel eigenvector-based ranking methodology for patent similarity that significantly outperforms traditional embedding approaches. We integrate covariance-matrix analysis with hyperplane projections, to capture both semantic and structural relationships between technical documents. Experiments within the F03D patent subclass demonstrate our approach achieves a similarity score of 83.37, substantially outperforming Word2Vec (23.3), ELMo (21.1), and SimCSE (44.8). This work addresses critical challenges in patent retrieval while introducing innovations applicable to broader technical document similarity tasks, enabling non-experts to efficiently identify relevant prior work without specialized knowledge of patent systems.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"83 ","pages":"Article 102411"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528013","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":"Modularizing patent knowledge for enhanced technological impact","authors":"L. Siddharth","doi":"10.1016/j.wpi.2025.102407","DOIUrl":"10.1016/j.wpi.2025.102407","url":null,"abstract":"<div><div>Modularity is a core design principle that enables technological artefacts to remain adaptable and evolvable, while creating value, maintaining technical robustness, and protecting intellectual property. Despite the perceived advantages, modularity has mostly been theoretically argued without sufficient empirical support—primarily due to the lack of large-scale datasets of structured representations of the knowledge of technological artefacts. In this study, we leverage a recently populated dataset of over 33,800 patent knowledge graphs built by recurrently extracting facts (head entity-relationship-tail entity) from patent sentences. Considering these knowledge graphs as explicit representations of patent knowledge, we investigate the influence of modularity upon the technological impact of patents, controlling for structural and semantic variables of patent knowledge graphs. We find a consistent positive influence of modularity on technological impact—quantified by short-term (5 years) and long-term (10 years) forward citation scores. Empirically substantiating the influence of modularity argued in design theories, we develop a predictive framework combining Graph Neural Networks (GNNs) and regression models to estimate citation scores from patent knowledge graphs. Using this framework, we showcase how modifications to patent knowledge—either through re-design or re-representation—can enhance the citation scores increasingly over 5- to 10-year periods—particularly for under- or un-cited patents.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"83 ","pages":"Article 102407"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362679","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":"AI-assisted patent drafting tools: A patent landscape & future prospectives","authors":"Narender Yadav ,&nbsp;Mukesh Kumar Kumawat ,&nbsp;Gufran Ajmal ,&nbsp;Khalid Bashir Mir","doi":"10.1016/j.wpi.2025.102402","DOIUrl":"10.1016/j.wpi.2025.102402","url":null,"abstract":"<div><div>As of early 2020, the global patent landscape for patent drafting tools has undergone significant transformation, driven by technological advancements, increased automation, and the integration of artificial intelligence. This patent landscape analysis examines global patent filings on drafting tools using Lens.org and Scifinder to identify innovation and strategic opportunities amidst the increasing complexity and volume of worldwide patent filings. It highlights key trends, innovative features, claim types, leading inventors and applicants, jurisdictional distribution, and the technological characteristics of 122 relevant patent applications spanning 56 extended patent families, thereby providing a holistic view of ongoing innovations and research activities. Furthermore, whitespace analysis reveals technological gaps and underrepresented areas including infringement analysis tools, collaboration platforms, and automated patent drawing systems pointing to promising avenues for strategic innovation and market differentiation. In addition, this study reviews 41 patent drafting tools currently available worldwide, outlining their innovative features and associated patent applications. Overall, the study offer valuable insights for researchers, innovators, patent practitioners, and stakeholders, providing foundational knowledge and clear guidance for future developments and investments in the rapidly evolving field of patent drafting technologies.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"83 ","pages":"Article 102402"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267826","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":"New patent text similarity methods with a comprehensive understanding of SAO semantics","authors":"Nan Wang ,&nbsp;Ziyi Wan ,&nbsp;Hongyu Zhao ,&nbsp;Yingtong Hu","doi":"10.1016/j.wpi.2025.102403","DOIUrl":"10.1016/j.wpi.2025.102403","url":null,"abstract":"<div><div>Patent text similarity measurement is recognized as a critical component of semantic search, due diligence, infringement detection, and litigation in intellectual property management. With the continued growth in global patent filings, conventional keyword-, citation-, and classification-based approaches have been shown to inadequately capture the contextual semantics inherent in patent documents. Subject–Action–Object (SAO) structures provide a promising semantic representation; however, their effectiveness has been limited by the scarcity of specialized Semantic Text Similarity (STS) datasets and the lack of comprehensive evaluations. In this study, a novel and comprehensive framework for patent text similarity leveraging SAO semantics is proposed. Specialized patent STS datasets were constructed from USPTO examination decisions and PTAB appeal documents, comprising a 2-point scale similarity dataset and a ranking dataset for retrieval evaluation—the first openly available benchmarks of this kind. The framework integrates multiple SAO extraction techniques, novel weighting strategies including clustering-based methods, and a variety of similarity computation approaches ranging from lexical to deep learning models. Experimental evaluations show that the proposed SAO-based framework improves retrieval accuracy by 43 % over keyword-based baselines and by 26 % over standard document embedding methods. Vector-based similarity algorithms incorporating K-means clustering weights achieved a 32 % improvement over unweighted baselines. The vector-based similarity algorithm combined with K-means clustering weights improved by 32 % compared to the unweighted baseline, while the knowledge-based similarity threshold of 0.4–0.6 achieved the maximum distinction between similar and dissimilar patents. A systematic ablation analysis identified the optimal configuration as combining SAO embeddings derived from pre-trained patent vectors with clustering-based weighting, similarity thresholds, and semantic knowledge extensions. This configuration yielded superior performance in litigation support, infringement detection, and patent retrieval, reducing the average ranking position of relevant patents from 5.7 to 2.7 and achieving top-3 retrieval in all test cases.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"83 ","pages":"Article 102403"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267828","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":"Generating patent claims with semantic novelty","authors":"Jieh-Sheng Lee","doi":"10.1016/j.wpi.2025.102404","DOIUrl":"10.1016/j.wpi.2025.102404","url":null,"abstract":"<div><div>This manuscript represents an initial step to explore the potential of leveraging generative AI to drive AI-assisted inventions. From a technical standpoint, large language models are transforming industries and driving innovation, with the patent domain being no exception. From a domain perspective, the USPTO’s recent inventorship guidance highlights that AI-assisted inventions are not categorically unpatentable, opening an exciting new frontier for inventors, patent professionals, and computer scientists. The goal of this research is to generate patent claims that exhibit a higher degree of novelty. Central to this study is the investigation of whether reinforcement learning can facilitate the generation of patent claims with such novelty. In patent law, a patent is granted when it fulfills various legal requirements, such as novelty, nonobviousness, utility, written description, and subject matter eligibility. This manuscript focuses on addressing the novelty requirement by employing reinforcement learning to generate patent claim text with a higher degree of “semantic novelty.” The semantic novelty is regarded as inversely proportional to sentence similarity, which is measured by sentence embeddings. Semantic novelty serves as a computational metric to approximate the concept of novelty as understood in patent law. In pursuit of empirical investigation, this study seeks to generate dependent claims with a higher degree of novelty relative to the independent claims, and vice versa. While the experiments presented in this manuscript are preliminary and not comprehensive, they demonstrate the efficacy of reinforcement learning and the model’s capacity to generate novel ideas, underscoring the potential of this research direction for AI-assisted inventions in the future.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"83 ","pages":"Article 102404"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333156","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":"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":"2025-12-01","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":"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":"2025-12-01","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":"Technological and patent landscape of biosensors for leukemia detection: Evolution, maturity, and future prospects","authors":"María C. Otálora Trujillo,&nbsp;Rubén Camargo-Amado,&nbsp;Fiderman Machuca-Martínez","doi":"10.1016/j.wpi.2025.102405","DOIUrl":"10.1016/j.wpi.2025.102405","url":null,"abstract":"<div><div>The growing demand for rapid, sensitive, and accessible diagnostic tools for early leukemia detection has driven the advancement of biosensors, particularly those based on electrochemical principles. These devices stand out for their low cost, portability, and integration into point-of-care systems, making them attractive alternatives to conventional diagnostic methods. However, doubts remain about their technological maturity and long-term prospects.</div><div>This study evaluated the evolution and technological life cycle of biosensors for leukemia detection through an integrated bibliometric and patentometric analysis. A total of 1278 scientific articles indexed in Scopus and 4756 patent families retrieved from Orbit Intelligence between 2004 and 2024 were analyzed. Of these, 2332 granted and in-force patent families were considered for technological maturity and forecasting analyses. Bibliometric mapping was performed using VOSviewer, while patent classification followed International Patent Classification codes. Logistic S-curve models (Loglet Lab 4) and Yoon's parameters were applied to estimate maturity levels and future projections.</div><div>Results reveal sustained growth in publications and patents, indicating that biosensors entered an early maturity stage in 2025, with projected technological and market relevance until approximately 2050. Among detection techniques employed in electrochemical biosensors, cyclic voltammetry accounts for the largest projected number of publications and patents, while electrochemical impedance spectroscopy shows the longest technological longevity. Differential pulse voltammetry, although less represented, remains a complementary technique.</div><div>In conclusion, biosensors represent a consolidating technology with strong potential to transform leukemia diagnostics. Patentometric analysis provides strategic intelligence to guide innovation policies, strengthen intellectual property management, and support technology transfer in this developing area.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"83 ","pages":"Article 102405"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362681","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":"2025-12-01","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}