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.
{"title":"Patentability of AI and global harmonization: An analysis of the current guidelines in Brazil and the IP5 offices","authors":"Tarso Mesquita Machado , Eduardo Winter , 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}
Pub Date : 2025-09-19DOI: 10.1016/j.wpi.2025.102397
Susan Bates
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 & Certification; and Legal & 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.
{"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 & Certification; and Legal & 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}
Pub Date : 2025-09-04DOI: 10.1016/j.wpi.2025.102388
Wenting Jin , Ji Huang
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&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&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&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&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.
{"title":"Innovation trends and evolutionary paths of electrocatalytic hydrogen evolution reaction technology: A global patent analysis","authors":"Wenting Jin , 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&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&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&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&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}
Pub Date : 2025-08-27DOI: 10.1016/j.wpi.2025.102387
Aleksei L. Kalinichenko, Kelvin W. Willoughby
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.
{"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, 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}
Pub Date : 2025-08-16DOI: 10.1016/j.wpi.2025.102386
Pietro Carlos Gonçalves Conceição , Natália Hlavnicka Miranda , Luiggi Cavalcanti Pessôa , Denilson de Jesus Assis , Jamille Santos Santana , Paulo Vitor França Lemos , Jania Betania Alves da Silva , Lucas Guimarães Cardoso , Karina Teixeira Magalhães-Guedes , Leonardo Moreira de Assunção , Carolina Oliveira de Souza
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&D investment to advance PHA-based packaging technologies toward large-scale adoption.
{"title":"Advancing PHA-Based packaging: A patent and scientific landscape analysis for sustainable innovation","authors":"Pietro Carlos Gonçalves Conceição , Natália Hlavnicka Miranda , Luiggi Cavalcanti Pessôa , Denilson de Jesus Assis , Jamille Santos Santana , Paulo Vitor França Lemos , Jania Betania Alves da Silva , Lucas Guimarães Cardoso , Karina Teixeira Magalhães-Guedes , Leonardo Moreira de Assunção , 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&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}
Pub Date : 2025-08-16DOI: 10.1016/j.wpi.2025.102385
Gülfem Özmen , Jussi Heikkilä , Matti Karvonen , Ville Ojanen
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.
{"title":"Digital marketing of standard essential patent licensing programs","authors":"Gülfem Özmen , Jussi Heikkilä , Matti Karvonen , 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}
Pub Date : 2025-08-13DOI: 10.1016/j.wpi.2025.102383
Tzu-Yu Lin , Li-Chieh Chou
Purpose
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.
Design/methodology/approach
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.
Findings
The results reveal exponential growth in AI-powered patent analysis research since the mid-2010s, with Technological Forecasting and Social Change (TFSC), Scientometrics, and World Patent Information (WPI) 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.
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.
Originality
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.
本研究旨在通过构建一个综合矩阵，将不同的人工智能技术与其相应的分析任务结合起来，系统地综合人工智能（AI）在专利分析中的实际应用。“人工智能技术和分析任务”矩阵提供了一个结构化框架，用于理解如何在专利分析领域内跨不同功能目标部署各种人工智能方法。本研究结合文献计量分析、基于bert的主题建模和文献综述，探索人工智能在专利分析中的应用。使用针对人工智能技术和专利分析任务的双焦点搜索策略从Web of Science核心馆藏中检索数据。明确区分了使用专利数据分析人工智能趋势的研究，只保留了那些将人工智能方法应用于专利分析的研究。根据这些策略，选择了718份相关出版物作为分析的基础。结果显示，自2010年代中期以来，人工智能驱动的专利分析研究呈指数级增长，其中技术预测与社会变革（TFSC）、科学计量学（Scientometrics）和世界专利信息（WPI）被确定为领先的出版平台。地理分析表明，中国和韩国的研究产出和机构参与都在迅速增加，而美国则保持着基础地位，但时间并不长。利用主题建模技术，本研究确定了11个主要的主题集群，涵盖了新兴知识发现、技术预测和机会识别等任务。这些被整合到“人工智能技术和分析任务”矩阵中，该矩阵系统地映射了人工智能方法（如预训练语言模型、卷积神经网络、语义分析和主题建模）与其实际实现之间的关系。其中，专利分类和自然语言处理（NLP）成为最具影响力的应用，突显了人工智能在实现可扩展、数据驱动的方法来管理复杂专利信息方面的重要作用。本研究将多层文献检索策略、文献计量分析、基于bert的主题建模和人工智能技术-分析任务矩阵相结合，构建系统化、结构化的知识框架。这种综合方法不仅描述了专利分析中人工智能应用的跨学科演变，而且为未来的研究提供了战略指导，特别是在推进经验验证、为政策应用提供信息和促进这一新兴领域的全球包容性方面。
{"title":"A systematic review of artificial intelligence applications and methodological advances in patent analysis","authors":"Tzu-Yu Lin , 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}
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
{"title":"Scalable multi-label patent classification via iterative large language model-assisted active learning","authors":"Songquan Xiong, Shikun Chen, Jianwei He, Yangguang Liu, Junjie Mao, 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}
Pub Date : 2025-08-04DOI: 10.1016/j.wpi.2025.102381
Shunsuke Sakaoka, Shingo Kano
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.
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