Pub Date : 2025-03-24DOI: 10.1016/j.wpi.2025.102351
Susan Bates (Independent Researcher)
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-February 2025. Key resources include Scopus, Digital Commons, publishers' RSS feeds, and serendipity! This article gives a selection of interesting references to whet your appetite - the full list of references can be found in the companion datafile.
{"title":"Literature listing","authors":"Susan Bates (Independent Researcher)","doi":"10.1016/j.wpi.2025.102351","DOIUrl":"10.1016/j.wpi.2025.102351","url":null,"abstract":"<div><div>Welcome to the latest quarterly Literature Listing intended as a current awareness service for readers indicating newly published books, journal, and conference articles on IP management; Information Retrieval Techniques; Patent Landscapes; Education & Certification; and Legal & Intellectual Property Office Matters. The current Literature Listing was compiled mid-February 2025. Key resources include Scopus, Digital Commons, publishers' RSS feeds, and serendipity! This article gives a selection of interesting references to whet your appetite - the full list of references can be found in the companion datafile.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":"Article 102351"},"PeriodicalIF":2.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143681930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.1016/j.wpi.2024.102332
Amy J.C. Trappey , Shao-Chien Chou , Gi-Kuen J. Li
As unmanned aerial vehicle (UAV), also called “drone”, swiftly advances with innovative functions and applications, the surge in patent applications has profoundly reshaped the intellectual property (IP) landscape in the UAV industry, leading to a growing number of litigations. This study is structured in two phases, aiming to develop an intelligent approach to analyzing the trend and evolution of patent litigations. The first phase involves macro- and micro-patent analyses of the related technology domain. Macro patent analysis elucidates the fundamental patent information in the drone industry, while micro patent analysis leverages the technology function matrix (TFM) to identify R&D hotspots and potentials. The second phase involves litigation (judgement) mining based on large language model (LLM). Beginning with the construction of a knowledge ontology, the domain infringement landscape can be detected through TFMs. A comparative analysis of the two-phase TFMs (i.e., both TFMs of patent and infringement allocations) is then conducted to pinpoint the key legal actions and the relevant technology. To drill deeper in infringement mining, dynamic topic modeling (DTM) is applied to analyze trends and dynamics in drone controller technology over time. This study aims to strengthen IP protection by developing an intelligent litigation mining approach that adopts large language model (LLM) and uses UAV/drone litigation studies as examples to show how the approach being applied in the industry.
{"title":"Patent litigation mining using a large language model—Taking unmanned aerial vehicle development as the case domain","authors":"Amy J.C. Trappey , Shao-Chien Chou , Gi-Kuen J. Li","doi":"10.1016/j.wpi.2024.102332","DOIUrl":"10.1016/j.wpi.2024.102332","url":null,"abstract":"<div><div>As unmanned aerial vehicle (UAV), also called “drone”, swiftly advances with innovative functions and applications, the surge in patent applications has profoundly reshaped the intellectual property (IP) landscape in the UAV industry, leading to a growing number of litigations. This study is structured in two phases, aiming to develop an intelligent approach to analyzing the trend and evolution of patent litigations. The first phase involves macro- and micro-patent analyses of the related technology domain. Macro patent analysis elucidates the fundamental patent information in the drone industry, while micro patent analysis leverages the technology function matrix (TFM) to identify R&D hotspots and potentials. The second phase involves litigation (judgement) mining based on large language model (LLM). Beginning with the construction of a knowledge ontology, the domain infringement landscape can be detected through TFMs. A comparative analysis of the two-phase TFMs (i.e., both TFMs of patent and infringement allocations) is then conducted to pinpoint the key legal actions and the relevant technology. To drill deeper in infringement mining, dynamic topic modeling (DTM) is applied to analyze trends and dynamics in drone controller technology over time. This study aims to strengthen IP protection by developing an intelligent litigation mining approach that adopts large language model (LLM) and uses UAV/drone litigation studies as examples to show how the approach being applied in the industry.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"80 ","pages":"Article 102332"},"PeriodicalIF":2.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study presents a multi-stage fine-tuning approach using DeBERTa for advanced patent analysis and landscaping on SDGs and decarbonization technologies. The method incorporates FI subclass estimation with the significant improved accuracy on extracting relevant technologies from patent documents. The model outperformed previous BERT-based approaches in various tasks and was applied to analyze Japanese and PCT international patent applications. Key findings include the continued leading R&D by Japanese companies in SDGs/decarbonization area and the rapid emergence of Chinese firms. The study also introduced the "Japio-Decarbonization Patent Index" which can identify companies filing highly decarbonization-oriented patents. This research demonstrates the effectiveness of advanced NLP techniques in patent analysis, providing valuable insights for innovation promotion and technology trend prediction in sustainable development.
{"title":"Multi-stage fine-tuning of patent domain-specific DeBERTa for advanced patent landscape on SDGs/Decarbonization","authors":"Yoshiaki Maehara , Yukimasa Shiozawa , Yoshiyuki Osabe","doi":"10.1016/j.wpi.2025.102343","DOIUrl":"10.1016/j.wpi.2025.102343","url":null,"abstract":"<div><div>This study presents a multi-stage fine-tuning approach using DeBERTa for advanced patent analysis and landscaping on SDGs and decarbonization technologies. The method incorporates FI subclass estimation with the significant improved accuracy on extracting relevant technologies from patent documents. The model outperformed previous BERT-based approaches in various tasks and was applied to analyze Japanese and PCT international patent applications. Key findings include the continued leading R&D by Japanese companies in SDGs/decarbonization area and the rapid emergence of Chinese firms. The study also introduced the \"Japio-Decarbonization Patent Index\" which can identify companies filing highly decarbonization-oriented patents. This research demonstrates the effectiveness of advanced NLP techniques in patent analysis, providing valuable insights for innovation promotion and technology trend prediction in sustainable development.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":"Article 102343"},"PeriodicalIF":2.2,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-11DOI: 10.1016/j.wpi.2025.102341
Luong Vu Bui
Generative AI and Large Language Models (LLMs) are transforming patent law by automating complex tasks that traditionally demand significant legal and technical expertise. This paper examines AI-assisted systems designed to enhance patent drafting, prior art searches, and multimodal intellectual property (IP) protection. Human-in-the-Loop (HITL) frameworks play a crucial role in ensuring that AI-generated outputs remain accurate, legally compliant, and ethically sound, augmenting human expertise rather than replacing it.
We evaluate the applicability of LLMs such as GPT-4, Claude, and Gemini for patent-related tasks, highlighting their advantages and limitations. The study also explores critical challenges, including GDPR compliance, issues of interpretability, and the impact of outdated training data. Furthermore, strategies to mitigate AI-generated “hallucinations” and optimize prompt engineering for patent-specific applications are discussed. A comparative analysis of industry-leading platforms like Google Patents, PatSnap, and LexisNexis illustrates how AI tools are being integrated into patent workflows.
The paper provides both theoretical insights and practical recommendations for integrating AI into legal systems. By addressing the technical and ethical implications of AI-generated inventions, the study underscores the importance of transparency, accountability, and robust human oversight. This research aims to guide the seamless integration of AI technologies into patent law, promoting efficiency, accuracy, and compliance in an increasingly complex innovation landscape.
{"title":"Advancing patent law with generative AI: Human-in-the-loop systems for AI-assisted drafting, prior art search, and multimodal IP protection","authors":"Luong Vu Bui","doi":"10.1016/j.wpi.2025.102341","DOIUrl":"10.1016/j.wpi.2025.102341","url":null,"abstract":"<div><div>Generative AI and Large Language Models (LLMs) are transforming patent law by automating complex tasks that traditionally demand significant legal and technical expertise. This paper examines AI-assisted systems designed to enhance patent drafting, prior art searches, and multimodal intellectual property (IP) protection. Human-in-the-Loop (HITL) frameworks play a crucial role in ensuring that AI-generated outputs remain accurate, legally compliant, and ethically sound, augmenting human expertise rather than replacing it.</div><div>We evaluate the applicability of LLMs such as GPT-4, Claude, and Gemini for patent-related tasks, highlighting their advantages and limitations. The study also explores critical challenges, including GDPR compliance, issues of interpretability, and the impact of outdated training data. Furthermore, strategies to mitigate AI-generated “hallucinations” and optimize prompt engineering for patent-specific applications are discussed. A comparative analysis of industry-leading platforms like Google Patents, PatSnap, and LexisNexis illustrates how AI tools are being integrated into patent workflows.</div><div>The paper provides both theoretical insights and practical recommendations for integrating AI into legal systems. By addressing the technical and ethical implications of AI-generated inventions, the study underscores the importance of transparency, accountability, and robust human oversight. This research aims to guide the seamless integration of AI technologies into patent law, promoting efficiency, accuracy, and compliance in an increasingly complex innovation landscape.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"80 ","pages":"Article 102341"},"PeriodicalIF":2.2,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378445","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 : 2025-01-22DOI: 10.1016/j.wpi.2025.102334
Jyosna Devi Ravula , Ramakrishna Nirogi , Manthan D. Janodia
The pharmaceutical industry faces unique challenges in patent prosecution due to the broad scope of innovations that patents cover. Navigating the patent prosecution process is arduous due to various factors, including its inherent complexity and the potential for patentability rejections. The study developed a new dataset containing the Orange Book listed patents based on USFDA calendar year 2020 approvals. It aimed to analyze the patent prosecution process, specifically examining the severity of office action rejections through the patent rejection severity scale and their impact on prosecution timelines. Out of 437 patents, the claim scope significantly changed for 322 (65 primary and 257 secondary) during prosecution. The results indicated that the obviousness rejections occurred more frequently for secondary patents, with a rate of 72.76 % (187/257), compared to primary patents with 33.85 % (22/65). Further analysis revealed a strong association between the severity of office action rejections and the number of office actions (p-value of 0.0036) and prosecution time (p-value of 0.0008) for secondary patents. The study also emphasized the necessity of implementing various strategies and the use of experimental data to mitigate patentability rejections and circumvent prosecution delays. This information could provide practical insights to patent practitioners dealing with rejections.
{"title":"Analysis of patentability factors and its impact on claim protection and prosecution timelines for pharmaceutical patents","authors":"Jyosna Devi Ravula , Ramakrishna Nirogi , Manthan D. Janodia","doi":"10.1016/j.wpi.2025.102334","DOIUrl":"10.1016/j.wpi.2025.102334","url":null,"abstract":"<div><div>The pharmaceutical industry faces unique challenges in patent prosecution due to the broad scope of innovations that patents cover. Navigating the patent prosecution process is arduous due to various factors, including its inherent complexity and the potential for patentability rejections. The study developed a new dataset containing the Orange Book listed patents based on USFDA calendar year 2020 approvals. It aimed to analyze the patent prosecution process, specifically examining the severity of office action rejections through the patent rejection severity scale and their impact on prosecution timelines. Out of 437 patents, the claim scope significantly changed for 322 (65 primary and 257 secondary) during prosecution. The results indicated that the obviousness rejections occurred more frequently for secondary patents, with a rate of 72.76 % (187/257), compared to primary patents with 33.85 % (22/65). Further analysis revealed a strong association between the severity of office action rejections and the number of office actions (p-value of 0.0036) and prosecution time (p-value of 0.0008) for secondary patents. The study also emphasized the necessity of implementing various strategies and the use of experimental data to mitigate patentability rejections and circumvent prosecution delays. This information could provide practical insights to patent practitioners dealing with rejections.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"80 ","pages":"Article 102334"},"PeriodicalIF":2.2,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105425","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 : 2025-01-21DOI: 10.1016/j.wpi.2025.102339
Feng-Chi Chen , Chia-Lin Pan , AIPlux Development Team
Automatic patent claim generation is an emerging application of large language models (LLMs). However, the performances of general-purpose LLMs in this regard remain unclear. Here we empirically evaluate the effectiveness of four different LLMs (two from the LLaMA-2 family and two from the Mistral family) in generating biomedical patent claims. This allows comparisons between LLMs with different sizes and architectures. We show that these open-source LLMs fail to produce correctly styled patent claims despite their reported strengths in natural language tasks. Nevertheless, given selected training data and adequate fine-tuning, even relatively small LLMs can yield high-quality, correctly styled patent claims. Notably, one limitation of LLMs is that they lack the creativity and insights of human drafters. For such a professional task as claim drafting, LLMs should be considered as a digital assistant that requires human oversight.
{"title":"Evaluating application of large language models to biomedical patent claim generation","authors":"Feng-Chi Chen , Chia-Lin Pan , AIPlux Development Team","doi":"10.1016/j.wpi.2025.102339","DOIUrl":"10.1016/j.wpi.2025.102339","url":null,"abstract":"<div><div>Automatic patent claim generation is an emerging application of large language models (LLMs). However, the performances of general-purpose LLMs in this regard remain unclear. Here we empirically evaluate the effectiveness of four different LLMs (two from the LLaMA-2 family and two from the Mistral family) in generating biomedical patent claims. This allows comparisons between LLMs with different sizes and architectures. We show that these open-source LLMs fail to produce correctly styled patent claims despite their reported strengths in natural language tasks. Nevertheless, given selected training data and adequate fine-tuning, even relatively small LLMs can yield high-quality, correctly styled patent claims. Notably, one limitation of LLMs is that they lack the creativity and insights of human drafters. For such a professional task as claim drafting, LLMs should be considered as a digital assistant that requires human oversight.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"80 ","pages":"Article 102339"},"PeriodicalIF":2.2,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105424","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 : 2025-01-21DOI: 10.1016/j.wpi.2025.102340
José Alberto Solis-Navarrete
The global pandemic caused by COVID-19 has devastated health, the economy, and overall well-being. However, scientific research and technological advancements have played a crucial role in reducing vulnerability to the disease, mainly through the development and distribution of vaccines. This paper explores potential innovations arising from the pandemic, focusing on patent analysis related to COVID-19, particularly from non-profit and public institutions. This research has identified over seven thousand patents granted for medical preparations, vaccine development compounds, and diagnostic, preventative, and treatment instruments, with the United States, China, and India leading in their production. Non-profit and public institutions in Brazil, France, China, and South Korea have also contributed to developing potential innovations. The study highlights the need for a comprehensive global and regional analysis of patents related to COVID-19, regardless of their type, sector of origin, or application. This research can guide academia and public policy in fostering innovation for present and future health challenges by exploring the role and potential of public and non-profit institutions in generating social innovations.
{"title":"Innovations in response to the COVID-19 pandemic: The role of non-profit and public institutions","authors":"José Alberto Solis-Navarrete","doi":"10.1016/j.wpi.2025.102340","DOIUrl":"10.1016/j.wpi.2025.102340","url":null,"abstract":"<div><div>The global pandemic caused by COVID-19 has devastated health, the economy, and overall well-being. However, scientific research and technological advancements have played a crucial role in reducing vulnerability to the disease, mainly through the development and distribution of vaccines. This paper explores potential innovations arising from the pandemic, focusing on patent analysis related to COVID-19, particularly from non-profit and public institutions. This research has identified over seven thousand patents granted for medical preparations, vaccine development compounds, and diagnostic, preventative, and treatment instruments, with the United States, China, and India leading in their production. Non-profit and public institutions in Brazil, France, China, and South Korea have also contributed to developing potential innovations. The study highlights the need for a comprehensive global and regional analysis of patents related to COVID-19, regardless of their type, sector of origin, or application. This research can guide academia and public policy in fostering innovation for present and future health challenges by exploring the role and potential of public and non-profit institutions in generating social innovations.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"80 ","pages":"Article 102340"},"PeriodicalIF":2.2,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-20DOI: 10.1016/j.wpi.2025.102335
Kuo-tsan Liu, Wenchuan Chang
The quality of patent application claims determines a patent's scope of protection and legal value. This study defines a new indicator based on claims structure to measure claim coverage, evaluate individual patents, and compare the strengths and weaknesses of competitors' patent portfolios in additive manufacturing technology. The new indicator's credibility is verified by comparing literature indicators.
Claim drafting is highly influenced by the official fees and legal regulations of national patent offices. Seven main additive manufacturing companies were selected to investigate their claim drafting strategies when applying for European patents while claiming priority in the United States. The indicators are shown on 2D maps of individual patents and patent portfolios. The ATT indicator is defined by independent claims representing attack strength, the DEF indicator is defined by dependent claims representing defense ability, and the TOT indicator is the sum of both. These indicators also represent the technical value of the patented invention, because more claims can be drafted for an invention with a wider range of technologies or more embodiments. 3D Systems and Stratasys make good use of domestic claim regulations to obtain higher patent values in the United States and Europe while minimizing fees.
专利申请权利要求的质量决定了专利的保护范围和法律价值。本研究定义了一个基于权利要求结构的新指标，用于衡量权利要求的覆盖范围、评估单个专利以及比较竞争对手在增材制造技术领域专利组合的优劣。权利要求的撰写受国家专利局官方收费和法律规定的影响很大。我们选取了七家主要的增材制造公司，调查它们在申请欧洲专利时的权利要求撰写策略，以及在美国申请优先权时的权利要求撰写策略。这些指标显示在单个专利和专利组合的二维地图上。ATT 指标由代表攻击强度的独立权利要求定义，DEF 指标由代表防御能力的从属权利要求定义，而 TOT 指标则是两者的总和。这些指标也代表了专利发明的技术价值，因为一项发明的技术范围更广或实施方式更多，就可以起草更多的权利要求。3D Systems 和 Stratasys 充分利用国内权利要求法规，在美国和欧洲获得了更高的专利价值，同时将费用降到最低。
{"title":"A new indicator to evaluate the quality of patent claims of additive manufacturing applicants in the US and EP","authors":"Kuo-tsan Liu, Wenchuan Chang","doi":"10.1016/j.wpi.2025.102335","DOIUrl":"10.1016/j.wpi.2025.102335","url":null,"abstract":"<div><div>The quality of patent application claims determines a patent's scope of protection and legal value. This study defines a new indicator based on claims structure to measure claim coverage, evaluate individual patents, and compare the strengths and weaknesses of competitors' patent portfolios in additive manufacturing technology. The new indicator's credibility is verified by comparing literature indicators.</div><div>Claim drafting is highly influenced by the official fees and legal regulations of national patent offices. Seven main additive manufacturing companies were selected to investigate their claim drafting strategies when applying for European patents while claiming priority in the United States. The indicators are shown on 2D maps of individual patents and patent portfolios. The ATT indicator is defined by independent claims representing attack strength, the DEF indicator is defined by dependent claims representing defense ability, and the TOT indicator is the sum of both. These indicators also represent the technical value of the patented invention, because more claims can be drafted for an invention with a wider range of technologies or more embodiments. 3D Systems and Stratasys make good use of domestic claim regulations to obtain higher patent values in the United States and Europe while minimizing fees.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"80 ","pages":"Article 102335"},"PeriodicalIF":2.2,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180292","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 : 2025-01-17DOI: 10.1016/j.wpi.2025.102337
Marina Flamand , Vincent Frigant , Stéphane Miollan
Knowledge production activity is central within a technological innovation system. The number of patent applications is commonly used to evaluate this activity. However, it is subject to bias and inaccurate evaluations can occur. This article proposes a multi-criteria framework based on seven complementary patent indicators, taking into account the persistence, commitment, and coherence of knowledge production activities for a more comprehensive evaluation. We demonstrate the value of our proposal through a case study on hydrogen storage, comparing patent data since 2000 about three technological solutions: physical, chemical and adsorption technologies. Our framework clearly shows that physical hydrogen storage is the most advanced in terms of knowledge production, despite not having the highest number of patent applications.
{"title":"Knowledge production in technological innovation system: A comprehensive evaluation using a multi-criteria framework based on patent data—a case study on hydrogen storage","authors":"Marina Flamand , Vincent Frigant , Stéphane Miollan","doi":"10.1016/j.wpi.2025.102337","DOIUrl":"10.1016/j.wpi.2025.102337","url":null,"abstract":"<div><div>Knowledge production activity is central within a technological innovation system. The number of patent applications is commonly used to evaluate this activity. However, it is subject to bias and inaccurate evaluations can occur. This article proposes a multi-criteria framework based on seven complementary patent indicators, taking into account the persistence, commitment, and coherence of knowledge production activities for a more comprehensive evaluation. We demonstrate the value of our proposal through a case study on hydrogen storage, comparing patent data since 2000 about three technological solutions: physical, chemical and adsorption technologies. Our framework clearly shows that physical hydrogen storage is the most advanced in terms of knowledge production, despite not having the highest number of patent applications.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"80 ","pages":"Article 102337"},"PeriodicalIF":2.2,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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