Pub Date : 2026-01-28DOI: 10.1016/j.wpi.2026.102433
Renukswamy Chikkamath , Linda Andersson , Markus Endres
Semantic search with embedding models offers an alternative to traditional keyword-based patent retrieval but often struggles with computational cost and efficiency in real-time scenarios compared to methods like BM25. Meanwhile, the rapid advancement of language models raises questions about the necessity of domain-specific models versus the viability of general-purpose ones. This work presents a comprehensive evaluation of embedding-based patent search using the CLEF-IP 2011 dataset. We assess 10 configurations employing language models as retrievers, re-rankers, or hybrids, across 9 models, both patent-specific and general-purpose, tested in 105 experimental setups. Our best configurations deliver a 14.81% absolute MAP improvement over state-of-the-art baselines and outperform patent-specific embeddings by at least 28.95% in MAP. We further show that embedding quantization enables large-scale patent search with up to 30×faster retrieval and 32×lower memory usage. These results provide practical guidance for integrating embedding models into patent prior art search while addressing performance and scalability constraints.
{"title":"Rethinking patent retrieval with language models: Toward scalable and efficient search","authors":"Renukswamy Chikkamath , Linda Andersson , Markus Endres","doi":"10.1016/j.wpi.2026.102433","DOIUrl":"10.1016/j.wpi.2026.102433","url":null,"abstract":"<div><div>Semantic search with embedding models offers an alternative to traditional keyword-based patent retrieval but often struggles with computational cost and efficiency in real-time scenarios compared to methods like BM25. Meanwhile, the rapid advancement of language models raises questions about the necessity of domain-specific models versus the viability of general-purpose ones. This work presents a comprehensive evaluation of embedding-based patent search using the CLEF-IP 2011 dataset. We assess 10 configurations employing language models as retrievers, re-rankers, or hybrids, across 9 models, both patent-specific and general-purpose, tested in 105 experimental setups. Our best configurations deliver a 14.81% absolute MAP improvement over state-of-the-art baselines and outperform patent-specific embeddings by at least 28.95% in MAP. We further show that embedding quantization enables large-scale patent search with up to 30×faster retrieval and 32×lower memory usage. These results provide practical guidance for integrating embedding models into patent prior art search while addressing performance and scalability constraints.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"84 ","pages":"Article 102433"},"PeriodicalIF":1.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077610","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 : 2026-01-28DOI: 10.1016/j.wpi.2026.102430
Deokjin Choi , Boeun Park
Large language models (LLMs) show promise in document-level comparison but often lack transparency and consistency in judgment. These limitations hinder their use in high-stakes tasks such as patent evaluation, where reliable and explainable comparisons are essential. To address this gap, we propose a structured prompting framework that guides LLMs to compare patents across three evaluative dimensions: Functional Purpose (FP), Technical Uniqueness (TU), and Strategic Value (SV). Prompt refinement improves fairness, stability, and interpretability, but its effects vary across technical domains. Together, these findings position structured comparative judgment as a viable and auditable paradigm for deploying LLMs in high-stakes patent evaluation.
{"title":"Structured LLM-based patent comparison across three evaluation dimensions","authors":"Deokjin Choi , Boeun Park","doi":"10.1016/j.wpi.2026.102430","DOIUrl":"10.1016/j.wpi.2026.102430","url":null,"abstract":"<div><div>Large language models (LLMs) show promise in document-level comparison but often lack transparency and consistency in judgment. These limitations hinder their use in high-stakes tasks such as patent evaluation, where reliable and explainable comparisons are essential. To address this gap, we propose a structured prompting framework that guides LLMs to compare patents across three evaluative dimensions: Functional Purpose (FP), Technical Uniqueness (TU), and Strategic Value (SV). Prompt refinement improves fairness, stability, and interpretability, but its effects vary across technical domains. Together, these findings position structured comparative judgment as a viable and auditable paradigm for deploying LLMs in high-stakes patent evaluation.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"84 ","pages":"Article 102430"},"PeriodicalIF":1.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077609","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 : 2026-01-28DOI: 10.1016/j.wpi.2026.102431
Tom Willeke , Jörn Block , Darius Lambrecht
Although trademarks are the most widely used intellectual property right (IPR) they remain underrepresented in empirical research compared to other IPRs like patents. While patents capture mainly technological innovation, trademarks are broader and reflect firm strategy, brand positioning, and non-technological innovation. Despite their importance in practice, challenges in data accessibility and preprocessing have limited their application and investigation in (empirical) research. This review examines available trademark data sources, assesses their usability, and discusses key challenges in data integration. It further provides a structured overview of trademark-based measures for studying innovation, product strategy and economic development. We derive a research agenda of opportunities enabled by improved data accessibility and methodological advancements. Our study highlights the potential of trademarks as a data source, empirical measure, and research tool. We provide researchers with methodological guidance to facilitate the broader adoption of trademark data in business and economic studies.
{"title":"Using trademark data in research","authors":"Tom Willeke , Jörn Block , Darius Lambrecht","doi":"10.1016/j.wpi.2026.102431","DOIUrl":"10.1016/j.wpi.2026.102431","url":null,"abstract":"<div><div>Although trademarks are the most widely used intellectual property right (IPR) they remain underrepresented in empirical research compared to other IPRs like patents. While patents capture mainly technological innovation, trademarks are broader and reflect firm strategy, brand positioning, and non-technological innovation. Despite their importance in practice, challenges in data accessibility and preprocessing have limited their application and investigation in (empirical) research. This review examines available trademark data sources, assesses their usability, and discusses key challenges in data integration. It further provides a structured overview of trademark-based measures for studying innovation, product strategy and economic development. We derive a research agenda of opportunities enabled by improved data accessibility and methodological advancements. Our study highlights the potential of trademarks as a data source, empirical measure, and research tool. We provide researchers with methodological guidance to facilitate the broader adoption of trademark data in business and economic studies.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"84 ","pages":"Article 102431"},"PeriodicalIF":1.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077611","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 : 2026-01-23DOI: 10.1016/j.wpi.2026.102429
Elisa J. Choi, Gyoo Gun Lim
Compact yet powerful, patent titles embed signals that uncover emerging technological trends. This study introduces a lightweight, syntax-aware method for keyword extraction that identifies functionally meaningful trigrams by leveraging high-frequency prepositions (such as for, on, and using) as structural anchors. Unlike conventional approaches that disregard such function words, the proposed method treats them as semantic pivots, or anchor points in the sentence structure, to capture context-specific expressions, especially in short texts such as patent titles. Applied to 21,100 AI patent titles (2014–2024), the method outperformed six baselines in terms of semantic cohesion (PMI = 11.47), and runtime efficiency, while also demonstrating external validity through alignment with official CPC classification trends (r = 0.73). These results demonstrate the effectiveness of syntactic cues for metadata-level text analysis and highlight the method's practical utility for innovation tracking, patent analytics, and early-stage technology scouting. The study also contributes to the broader discourse on function-oriented innovation by offering a scalable tool for identifying evolving functional expressions in patent corpora.
{"title":"Syntactic anchoring for artificial intelligence patent insight: A lightweight framework for keyword extraction","authors":"Elisa J. Choi, Gyoo Gun Lim","doi":"10.1016/j.wpi.2026.102429","DOIUrl":"10.1016/j.wpi.2026.102429","url":null,"abstract":"<div><div>Compact yet powerful, patent titles embed signals that uncover emerging technological trends. This study introduces a lightweight, syntax-aware method for keyword extraction that identifies functionally meaningful trigrams by leveraging high-frequency prepositions (such as <em>for</em>, <em>on</em>, and <em>using</em>) as structural anchors. Unlike conventional approaches that disregard such function words, the proposed method treats them as semantic pivots, or anchor points in the sentence structure, to capture context-specific expressions, especially in short texts such as patent titles. Applied to 21,100 AI patent titles (2014–2024), the method outperformed six baselines in terms of semantic cohesion (PMI = 11.47), and runtime efficiency, while also demonstrating external validity through alignment with official CPC classification trends (<em>r</em> = 0.73). These results demonstrate the effectiveness of syntactic cues for metadata-level text analysis and highlight the method's practical utility for innovation tracking, patent analytics, and early-stage technology scouting. The study also contributes to the broader discourse on function-oriented innovation by offering a scalable tool for identifying evolving functional expressions in patent corpora.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"84 ","pages":"Article 102429"},"PeriodicalIF":1.9,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022549","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 : 2026-01-17DOI: 10.1016/j.wpi.2026.102427
Nan Wang , Ziyi Wan , Hongyu Zhao , Chang Wang , Yake Wang
Patent text similarity is critical for semantic search, due diligence, infringement detection, and litigation. As global patent filings increase, conventional keyword-, citation-, and classification-based methods fail to capture the contextual and functional semantics of patent documents. Function–Object–Property (FOP) representations offer a promising alternative, but progress is limited by the scarcity of specialized Semantic Text Similarity (STS) datasets and by insufficient evaluations. We propose the FOPNet, a comprehensive framework that decomposes patent text into F–O–P triples, links them to a patent knowledge graph, and produces FOP embeddings augmented by clustering-based weighting and learned re-ranking. We constructed two STS benchmarks from USPTO examination decisions and PTAB appeals: a binary (2-point) similarity set and a ranked retrieval set — the first openly available benchmarks of this type. Experimental evaluations show that the proposed FOP-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, while a knowledge-based similarity threshold of 0.4–0.6 maximized distinction between similar and dissimilar patents. Ablation analysis identified the optimal configuration as combining FOP embeddings derived from pre-trained patent vectors with clustering-based weighting, similarity thresholds, and semantic knowledge extensions. This configuration reduced the average ranking position of relevant patents from 5.7 to 2.7 and achieved top-3 retrieval in all test cases.
{"title":"FOPNet:A comprehensive functional semantic knowledge graph for deep technical analysis in patents","authors":"Nan Wang , Ziyi Wan , Hongyu Zhao , Chang Wang , Yake Wang","doi":"10.1016/j.wpi.2026.102427","DOIUrl":"10.1016/j.wpi.2026.102427","url":null,"abstract":"<div><div>Patent text similarity is critical for semantic search, due diligence, infringement detection, and litigation. As global patent filings increase, conventional keyword-, citation-, and classification-based methods fail to capture the contextual and functional semantics of patent documents. Function–Object–Property (FOP) representations offer a promising alternative, but progress is limited by the scarcity of specialized Semantic Text Similarity (STS) datasets and by insufficient evaluations. We propose the FOPNet, a comprehensive framework that decomposes patent text into F–O–P triples, links them to a patent knowledge graph, and produces FOP embeddings augmented by clustering-based weighting and learned re-ranking. We constructed two STS benchmarks from USPTO examination decisions and PTAB appeals: a binary (2-point) similarity set and a ranked retrieval set — the first openly available benchmarks of this type. Experimental evaluations show that the proposed FOP-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, while a knowledge-based similarity threshold of 0.4–0.6 maximized distinction between similar and dissimilar patents. Ablation analysis identified the optimal configuration as combining FOP embeddings derived from pre-trained patent vectors with clustering-based weighting, similarity thresholds, and semantic knowledge extensions. This configuration reduced the average ranking position of relevant patents from 5.7 to 2.7 and achieved top-3 retrieval in all test cases.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"84 ","pages":"Article 102427"},"PeriodicalIF":1.9,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976657","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 : 2026-01-14DOI: 10.1016/j.wpi.2026.102426
Nathan Monnet , Loïc Maréchal
We introduce a novel approach to text classification by combining doc2vec embeddings with advanced clustering techniques to improve the analysis of specialized, high-dimensional textual data. We integrate unsupervised methods such as Louvain, K-means, and Spectral clustering with doc2vec to enhance the detection of semantic patterns across a large corpus. As a case study, we apply this methodology to cybersecurity risk analysis using the MITRE ATT&CK framework to structure and reduce the dimensionality of cyberattack tactics. Louvain clustering proved the most effective among the tested methods, achieving the best balance between cluster coherence and computational efficiency. Our approach identifies four “super tactics”, demonstrating how clustering improves thematic coherence and risk attribution. The results validate the utility of combining doc2vec with clustering, particularly Louvain, for enhancing topic modelling and text classification.
{"title":"Clustering doc2vec output for topic-dimensionality reduction: A MITRE ATT&CK calibration","authors":"Nathan Monnet , Loïc Maréchal","doi":"10.1016/j.wpi.2026.102426","DOIUrl":"10.1016/j.wpi.2026.102426","url":null,"abstract":"<div><div>We introduce a novel approach to text classification by combining doc2vec embeddings with advanced clustering techniques to improve the analysis of specialized, high-dimensional textual data. We integrate unsupervised methods such as Louvain, K-means, and Spectral clustering with doc2vec to enhance the detection of semantic patterns across a large corpus. As a case study, we apply this methodology to cybersecurity risk analysis using the MITRE ATT&CK framework to structure and reduce the dimensionality of cyberattack tactics. Louvain clustering proved the most effective among the tested methods, achieving the best balance between cluster coherence and computational efficiency. Our approach identifies four “super tactics”, demonstrating how clustering improves thematic coherence and risk attribution. The results validate the utility of combining doc2vec with clustering, particularly Louvain, for enhancing topic modelling and text classification.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"84 ","pages":"Article 102426"},"PeriodicalIF":1.9,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977370","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 : 2026-01-02DOI: 10.1016/j.wpi.2025.102424
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-November 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.2025.102424","DOIUrl":"10.1016/j.wpi.2025.102424","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-November 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":"84 ","pages":"Article 102424"},"PeriodicalIF":1.9,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884446","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 : 2026-01-02DOI: 10.1016/j.wpi.2025.102425
Xiaoyu Zhang, Jing Shi, Lele Kang
Can open IP strategies promote innovation among competitors, thereby advancing the development of the technology field? This empirical question has remained a central topic of debate in the open innovation literature. To address this question, this study examines how open IP strategies adopted by leading firms affect technological advancement. The patent pledges by Tesla and Toyota serve as exogenous shocks, enabling an empirical analysis of the impact of open IP strategies on technological development in the Low Emission Vehicles (LEVs) industry. We utilized Difference-in-Differences (DID) models analyzing patent data from 2010 to 2019 to measure the effects on technological performance across firms. Our results indicate that open IP strategies significantly enhance technological output, including quantity, quality, and novelty, especially benefiting start-ups, and to a lesser extent, firms with rich knowledge bases. This study contributes to understanding the role of open innovation in fostering technological competition.
{"title":"The impact of the open IP strategies on technology development: Evidence from the low emission vehicles field","authors":"Xiaoyu Zhang, Jing Shi, Lele Kang","doi":"10.1016/j.wpi.2025.102425","DOIUrl":"10.1016/j.wpi.2025.102425","url":null,"abstract":"<div><div>Can open IP strategies promote innovation among competitors, thereby advancing the development of the technology field? This empirical question has remained a central topic of debate in the open innovation literature. To address this question, this study examines how open IP strategies adopted by leading firms affect technological advancement. The patent pledges by Tesla and Toyota serve as exogenous shocks, enabling an empirical analysis of the impact of open IP strategies on technological development in the Low Emission Vehicles (LEVs) industry. We utilized Difference-in-Differences (DID) models analyzing patent data from 2010 to 2019 to measure the effects on technological performance across firms. Our results indicate that open IP strategies significantly enhance technological output, including quantity, quality, and novelty, especially benefiting start-ups, and to a lesser extent, firms with rich knowledge bases. This study contributes to understanding the role of open innovation in fostering technological competition.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"84 ","pages":"Article 102425"},"PeriodicalIF":1.9,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884445","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 : 2025-12-20DOI: 10.1016/j.wpi.2025.102423
Milad Armani Dehghani , Mehmet Sahiner , Noptanit Chotisarn
Patents are critical indicators of innovation, especially in fast-evolving domains like Financial Technology (FinTech). However, accurately predicting patent grant outcomes with modern artificial intelligence techniques has remained challenging. This study addresses that gap by applying state-of-the-art machine learning (ML), including ensemble methods and deep learning models, to a dataset of 20,008 FinTech patent applications from 2000 to 2020. We demonstrate that our ML framework can forecast grant success with high precision (up to 89 %), revealing that patent quality and strategic filing choices, such as optimal IPC classes and jurisdictions, are key determinants of grant probability. The findings highlight practical implications for innovators and intellectual property managers, such as better resource allocation and informed patent strategy decisions. Overall, this work introduces a novel, AI-driven approach to patent analytics in FinTech, offering a forward-looking tool to enhance innovation management and strategic IP planning.
{"title":"From filing to grant: Predicting patent outcomes in FinTech using a predictive analytics perspective","authors":"Milad Armani Dehghani , Mehmet Sahiner , Noptanit Chotisarn","doi":"10.1016/j.wpi.2025.102423","DOIUrl":"10.1016/j.wpi.2025.102423","url":null,"abstract":"<div><div>Patents are critical indicators of innovation, especially in fast-evolving domains like Financial Technology (FinTech). However, accurately predicting patent grant outcomes with modern artificial intelligence techniques has remained challenging. This study addresses that gap by applying state-of-the-art machine learning (ML), including ensemble methods and deep learning models, to a dataset of 20,008 FinTech patent applications from 2000 to 2020. We demonstrate that our ML framework can forecast grant success with high precision (up to 89 %), revealing that patent quality and strategic filing choices, such as optimal IPC classes and jurisdictions, are key determinants of grant probability. The findings highlight practical implications for innovators and intellectual property managers, such as better resource allocation and informed patent strategy decisions. Overall, this work introduces a novel, AI-driven approach to patent analytics in FinTech, offering a forward-looking tool to enhance innovation management and strategic IP planning.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"84 ","pages":"Article 102423"},"PeriodicalIF":1.9,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145840464","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 : 2025-12-08DOI: 10.1016/j.wpi.2025.102419
Jeong-sang Eom , Dong-chan Kim , Ji-hun Han , Won-Gyu Bae
Offshore wind energy is emerging as a pivotal energy resource, and as turbine dimensions expand to meet growing power demands, structural requirements for support towers have intensified. This has led to the use of thicker steel plates, introducing challenges such as microstructural inhomogeneity from uneven cooling across plate thicknesses. To address these issues, we conducted a comprehensive patent analysis on heavy steel plate technologies to identify technological gaps and track innovation trends. We developed a classification framework to organize production methods aimed at enhancing mechanical properties. Additionally, we assessed average steel plate thicknesses across countries and companies, reflecting the trend towards larger turbines and towers. Patent impact and market potential were evaluated using the Cites Per Patent (CPP) and Patent Family Size (PFS) indices.
{"title":"Enhancing mechanical performance of thick steel plates for offshore wind structures: A classification and patent landscape study","authors":"Jeong-sang Eom , Dong-chan Kim , Ji-hun Han , Won-Gyu Bae","doi":"10.1016/j.wpi.2025.102419","DOIUrl":"10.1016/j.wpi.2025.102419","url":null,"abstract":"<div><div>Offshore wind energy is emerging as a pivotal energy resource, and as turbine dimensions expand to meet growing power demands, structural requirements for support towers have intensified. This has led to the use of thicker steel plates, introducing challenges such as microstructural inhomogeneity from uneven cooling across plate thicknesses. To address these issues, we conducted a comprehensive patent analysis on heavy steel plate technologies to identify technological gaps and track innovation trends. We developed a classification framework to organize production methods aimed at enhancing mechanical properties. Additionally, we assessed average steel plate thicknesses across countries and companies, reflecting the trend towards larger turbines and towers. Patent impact and market potential were evaluated using the Cites Per Patent (CPP) and Patent Family Size (PFS) indices.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"84 ","pages":"Article 102419"},"PeriodicalIF":1.9,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738647","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号