World Patent Information最新文献_第3页

Special issue on applications of Generative AI and Large Language Models in the patent domain 专利领域的生成式人工智能和大型语言模型应用特刊

IF 2.2

World Patent Information Pub Date : 2025-06-24 DOI: 10.1016/j.wpi.2025.102365

Tony Trippe (Associate Editor), Jieh-Sheng Jason Lee (Assistant Professor)

引用次数: 0

Literature listing 文献清单

IF 2.2

World Patent Information Pub Date : 2025-06-20 DOI: 10.1016/j.wpi.2025.102374

Susan Bates (Independent Researcher)

引用次数: 0

Evaluating the effectiveness of ranking-based patent search engines for identifying relevant prior art: A comparative study in the area of chemistry 评估基于排名的专利搜索引擎识别相关现有技术的有效性：化学领域的比较研究

IF 2.2

World Patent Information Pub Date : 2025-06-05 DOI: 10.1016/j.wpi.2025.102358

David Rees, Manuel Wirz

引用次数: 0

A comprehensive framework for patent value indicators: Machine learning-driven value prediction in renewable energy patents 专利价值指标的综合框架：机器学习驱动的可再生能源专利价值预测

IF 2.2

World Patent Information Pub Date : 2025-06-01 DOI: 10.1016/j.wpi.2025.102364

Nil Girgin Kalıp , F. Tunç Bozbura , Yusef Atteyih

{"title":"A comprehensive framework for patent value indicators: Machine learning-driven value prediction in renewable energy patents","authors":"Nil Girgin Kalıp , F. Tunç Bozbura , Yusef Atteyih","doi":"10.1016/j.wpi.2025.102364","DOIUrl":"10.1016/j.wpi.2025.102364","url":null,"abstract":"<div><div>This study proposes a machine learning (ML) framework for predicting the value of renewable energy (RE) patents using a structured set of bibliometric and inventor-related indicators. Motivated by the strategic importance of clean technologies, a structured dataset of renewable energy patents is constructed using a technology-specific classification approach. In addition to widely used features, the study introduces and empirically tests three underexplored value indicators: first forward citation speed, lead inventor experience, and inventor team experience. Six ML algorithms; Naïve Bayes, Logistic Regression, Random Forest (RF), Extreme Gradient Boosting, Support Vector Machines, and Artificial Neural Networks (ANN), are implemented under five comparative strategy settings to evaluate classification performance. Results demonstrate that models trained with a comprehensive set of indicators significantly outperform those relying on limited features, with RF and ANN showing the strongest accuracy. SHapley Additive exPlanations are applied to interpret model behavior and quantify the influence of all indicators. While established citation-based measures retain importance, the newly introduced indicators provide consistent and meaningful contributions to model performance. The proposed framework advances patent valuation research by demonstrating how interpretable, data-driven methods can improve the prediction of valuable patents in policy-intensive technology sectors. These findings offer practical insights for technology managers, policy-makers, and innovation strategists seeking to enhance portfolio analysis, funding prioritization, and strategic decision-making in the context of green innovation.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":"Article 102364"},"PeriodicalIF":2.2,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205148","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}

引用次数: 0

Improved multi-label hierarchical patent classification using LLMs 基于llm的改进多标签分层专利分类

IF 2.2

World Patent Information Pub Date : 2025-05-09 DOI: 10.1016/j.wpi.2025.102356

Bardia Rafieian, Pere-Pau Vázquez

{"title":"Improved multi-label hierarchical patent classification using LLMs","authors":"Bardia Rafieian, Pere-Pau Vázquez","doi":"10.1016/j.wpi.2025.102356","DOIUrl":"10.1016/j.wpi.2025.102356","url":null,"abstract":"<div><div>Classifying multi-label documents has always been a challenging task, especially when the labels follow a hierarchical structure. This complexity increases the difficulty of accurately predicting multiple interrelated labels, often limiting the overall classification performance. To address these challenges and improve the accuracy of hierarchical multi-label classification systems, we introduce a novel pipeline leveraging large language models (LLMs). By incorporating quantization techniques and optimizing weight updates through smaller matrices, we improve computational efficiency and scalability. Our approach demonstrates an improvement in accuracy compared to current state-of-the-art models. In this work, we apply this pipeline to patent documents, focusing on the multi-label hierarchical text classification problem using a transformer-based architecture. The hierarchical structure of the CPC (Cooperative Patent Classification) labels is preserved through a graph-based taxonomy, which enables more effective processing of patent categories. Our model is trained and evaluated on the USPTO-70k dataset, and we achieve substantial improvements across various metrics, including precision, recall, F1-score, and AUC.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":"Article 102356"},"PeriodicalIF":2.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143927404","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}

引用次数: 0

Assessing the standard-essentiality of 5G technology patents by means of generative artificial intelligence 利用生成式人工智能评估5G技术专利的标准必要性

IF 2.2

World Patent Information Pub Date : 2025-05-09 DOI: 10.1016/j.wpi.2025.102363

Andre Herzberg

{"title":"Assessing the standard-essentiality of 5G technology patents by means of generative artificial intelligence","authors":"Andre Herzberg","doi":"10.1016/j.wpi.2025.102363","DOIUrl":"10.1016/j.wpi.2025.102363","url":null,"abstract":"<div><div>In telecommunication technology, identifying standard-essential patents (SEPs) plays a crucial role in the management of intellectual property. This technology is regulated by technical standards that are largely based on the content of SEPs. These patents are declared standard-essential by their owners because they contain elements of a technical standard. The declaration process leaves room for over- and under-declaration, which entails risks for organizations. This paper focuses on the question of how generative artificial intelligence can be used to assess the standard-essentiality of 5G technology patents. For this purpose, the standard-essentiality is assessed using different prompts with four Large Language Models (LLMs) in two variants. In the first variant, the LLM results are generated by a rather simple prompt and compared with an approach based on unsupervised and supervised machine learning. The result shows that large LLMs are capable of assessing the standard-essentiality. In the second variant, the best-performing LLM is selected and the prompt is expanded to include selected parts of a technical standard. While the assessment results remain largely the same, the LLM is now able to explain in which detail a patent is part of a standard. This has several implications for patent evaluation, licensing and litigation strategies.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":"Article 102363"},"PeriodicalIF":2.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922957","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}

引用次数: 0

How the magic happens: Patenting work at a multinational manufacturing company 奇迹是如何发生的：在一家跨国制造公司申请专利

IF 2.2

World Patent Information Pub Date : 2025-04-29 DOI: 10.1016/j.wpi.2025.102362

Mimmi Hanson

{"title":"How the magic happens: Patenting work at a multinational manufacturing company","authors":"Mimmi Hanson","doi":"10.1016/j.wpi.2025.102362","DOIUrl":"10.1016/j.wpi.2025.102362","url":null,"abstract":"<div><div>Patents have become increasingly important in the global economy in recent decades. Today, patents are used for various purposes, far beyond the initial role of preventing imitation of new inventions introduced into society. In the work to develop solutions for more sustainable societies, patents have the potential to facilitate the necessary collaboration and sharing of technology. To complement the many studies on using patents for these purposes, this paper examines the intra-organisational practices with which patent strategies are implemented. The concept of boundary objects is introduced to analyse patenting work and its coordination across different sites. The paper makes three contributions to the literature. First, strategy work is a necessary but not a sufficient part of patenting work; implementing a strategy relies on all patenting work in the organisation. Second, In the organisation of patenting work, the focus should be on identifying relevant communities of experts and not simply organisational units. Third, \"patents\" as boundary objects can coordinate discourse around patents, which creates motivation and commitment to engage in patenting work. It is, however, not strong enough to coordinate the work itself; this is instead coordinated by patent-related artefacts that travel between the nodes.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":"Article 102362"},"PeriodicalIF":2.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143881677","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}

引用次数: 0

Encoder models at the European Patent Office: Pre-training and use cases 欧洲专利局编码器模型：预训练和用例

IF 2.2

World Patent Information Pub Date : 2025-04-25 DOI: 10.1016/j.wpi.2025.102360

Volker D. Hähnke, Arnaud Wéry, Matthias Wirth, Alexander Klenner-Bajaja

{"title":"Encoder models at the European Patent Office: Pre-training and use cases","authors":"Volker D. Hähnke, Arnaud Wéry, Matthias Wirth, Alexander Klenner-Bajaja","doi":"10.1016/j.wpi.2025.102360","DOIUrl":"10.1016/j.wpi.2025.102360","url":null,"abstract":"<div><div>Patents are organized using systems of technical concepts like the Cooperative Patent Classification. Classification information is extremely valuable for patent professionals, particularly for patent search. Language models have proven useful in Natural Language Processing tasks, including document classification. Generally, pre-training on a domain is essential for optimal downstream performance. Currently, there are no models pre-trained on patents with sequence length above 512. We pre-trained a RoBERTa model with sequence length 1024, increasing the fully covered claims sections from 12% to 53%. It has a ‘base’ configuration, reducing free parameters compared to ‘large’ models in the patent domain three-fold. We fine-tuned the model on classification tasks in the CPC, up to leaf level. Our tokenizer produces sequences on average 5% and up to 10% shorter than the general English RoBERTa tokenizer. With our pre-trained ‘base’ size model, we reach classification performance better than general English models, comparable to ‘large’ models pre-trained on patents. On the finest CPC granularity, 88% of test documents have at least one ground truth symbol in the top 10 predictions. Our CPC prediction models and data sets are publicly accessible. With the described procedures, we can periodically repeat pre-training and fine-tuning to cope with drift effects.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":"Article 102360"},"PeriodicalIF":2.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868351","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}

引用次数: 0

Dynamics of regional intellectual property systems in China: A spatiotemporal synergy analysis 中国区域知识产权制度动态：时空协同分析

IF 2.2

World Patent Information Pub Date : 2025-04-19 DOI: 10.1016/j.wpi.2025.102359

Li Chen , Liang Gao , Sibei Sheng

{"title":"Dynamics of regional intellectual property systems in China: A spatiotemporal synergy analysis","authors":"Li Chen , Liang Gao , Sibei Sheng","doi":"10.1016/j.wpi.2025.102359","DOIUrl":"10.1016/j.wpi.2025.102359","url":null,"abstract":"<div><div>In the context of a burgeoning scientific and technological revolution and evolving norms, the push towards innovation-driven development has become crucial for achieving high-quality national growth. Such development is essential for enhancing the coordinated evolution of regional intellectual property (IP) systems. This study analyses data from 30 provincial units in China to develop an evaluation index system for regional IP synergy, encompassing the following four subsystems: IP creation, utilization, protection, and service. Using the entropy method, this study assesses the organizational level within each subsystem from 2009 to 2022 and evaluates the degree of coordinated development of regional IP systems. Furthermore, this work examines interregional disparities through the coefficient of variation, the Gini coefficient, and the Theil index. The findings reveal that the synergy of China's regional IP systems is characterized by growth fluctuations and significant regional disparities. Low levels of synergy can impede the enhancement of regional IP capabilities and reduce the efficiency of IP output. Disparities in synergy levels among regions are the main obstacles to connectivity and cooperation within the relevant network.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":"Article 102359"},"PeriodicalIF":2.2,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850147","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}

引用次数: 0

Patenting telemedicine tools: A cross-country analysis of technologies related to remote patient monitoring 为远程医疗工具申请专利：对远程患者监测相关技术的跨国分析

IF 2.2

World Patent Information Pub Date : 2025-04-14 DOI: 10.1016/j.wpi.2025.102361

Gazala Parveen, Padmavati Manchikanti

{"title":"Patenting telemedicine tools: A cross-country analysis of technologies related to remote patient monitoring","authors":"Gazala Parveen, Padmavati Manchikanti","doi":"10.1016/j.wpi.2025.102361","DOIUrl":"10.1016/j.wpi.2025.102361","url":null,"abstract":"<div><div>Telemedicine has been practised since digital technology emerged in the mid-to-late 20th century. It evolved with technological advancements such as satellite communication in the 1960s, the internet in the 1990s and mobile health applications in the 2000s. Today, telemedicine forms a sub-set of digital health. In telemedicine, healthcare professionals provide medical services through information and communication technologies. The patenting of telemedicine tools is quite active, encompassing advancements in software applications, medical devices, and integrated systems for remote diagnosis, monitoring, and treatment. Effective protection of intellectual property for telemedicine tools relies on organised patent management and precise claim drafting. A study on the filing trends of the patents related to telemedicine tools and patent prosecution will give a better understanding of issues related to the patenting of such technologies. It highlights new developments that are expanding the scope of patent claims, particularly the increasing integration of telemedicine and software-enabled medical devices.</div></div>","PeriodicalId":51794,"journal":{"name":"World Patent Information","volume":"81 ","pages":"Article 102361"},"PeriodicalIF":2.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826182","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}

引用次数: 0