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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 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

World Patent Information

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

Andre Herzberg

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.

在电信技术领域，标准必要专利（sep）的识别在知识产权管理中起着至关重要的作用。该技术受主要基于sep内容的技术标准的监管。这些专利被其所有者宣布为标准必需专利，因为它们包含技术标准的元素。申报过程为申报过多和申报不足留下了空间，这给组织带来了风险。本文重点讨论了如何使用生成式人工智能来评估5G技术专利的标准必要性。为此，使用不同的提示来评估标准的重要性，这些提示带有两个变体中的四个大型语言模型（llm）。在第一种变体中，LLM结果是由一个相当简单的提示生成的，并与基于无监督和有监督机器学习的方法进行比较。结果表明，大型llm能够评估标准必要性。在第二个变体中，选择性能最好的LLM，并将提示扩展为包含技术标准的选定部分。虽然评估结果基本保持不变，但法学硕士现在能够解释专利在哪些细节上是标准的一部分。这对专利评估、许可和诉讼策略有几个影响。

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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 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

World Patent Information

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

Mimmi Hanson

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.

近几十年来，专利在全球经济中变得越来越重要。今天，专利被用于各种各样的目的，远远超出了最初防止新发明被模仿进入社会的作用。在为更可持续的社会制定解决方案的工作中，专利具有促进必要的合作和技术共享的潜力。为了补充关于将专利用于这些目的的许多研究，本文考察了实施专利战略的组织内部实践。引入边界对象的概念来分析专利工作及其在不同站点之间的协调。本文对文献有三个贡献。首先，战略工作是专利工作的必要组成部分，但不是充分组成部分；战略的实施依赖于组织中所有的专利工作。其次，在组织专利工作时，重点应该是确定相关的专家群体，而不仅仅是组织单位。第三，“专利”作为边界对象可以协调围绕专利的话语，从而产生从事专利工作的动机和承诺。然而，它还不够强大，不足以协调工作本身；这是由在节点之间传输的与专利相关的工件来协调的。

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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 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

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

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.

专利是通过技术概念系统（如专利合作分类）组织起来的。分类信息对专利专业人员来说非常宝贵，尤其是在专利检索方面。事实证明，语言模型在自然语言处理任务（包括文档分类）中非常有用。一般来说，对某一领域进行预训练对于优化下游性能至关重要。目前，还没有针对序列长度超过 512 的专利进行预训练的模型。我们对序列长度为 1024 的 RoBERTa 模型进行了预训练，将完全覆盖的权利要求部分从 12% 增加到 53%。该模型采用 "基础 "配置，与专利领域的 "大型 "模型相比，自由参数减少了三倍。我们在 CPC 的分类任务中对模型进行了微调，直至叶级。我们的标记符号生成器生成的序列比一般的英语 RoBERTa 标记符号生成器平均短 5%，最多可短 10%。使用我们预先训练好的 "基本 "大小模型，我们的分类性能比一般英语模型更好，可与预先训练好的专利 "大 "模型相媲美。在最细的 CPC 粒度上，88% 的测试文档在前 10 项预测中至少有一个地面实况符号。我们的 CPC 预测模型和数据集均可公开访问。利用所述程序，我们可以定期重复预训练和微调，以应对漂移效应。

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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 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

World Patent Information

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

Li Chen , Liang Gao , Sibei Sheng

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.

在科技革命深入发展、规范不断演变的大背景下，推动创新驱动发展成为实现国民经济高质量增长的关键。这种发展对于加强区域知识产权制度的协调发展至关重要。本文通过对中国30个省区市数据的分析，构建了包含知识产权创造、利用、保护和服务四个子系统的区域知识产权协同效应评价指标体系。采用熵值法对2009 - 2022年各子系统的组织水平进行了评价，并对区域知识产权制度的协调发展程度进行了评价。此外，本研究通过变异系数、基尼系数和泰尔指数考察了区域间差异。研究发现，中国区域知识产权制度的协同效应存在增长波动和显著的区域差异。低水平的协同会阻碍区域知识产权能力的增强，降低知识产权产出的效率。区域间协同水平差异是相关网络互联互通与合作的主要障碍。

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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 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

World Patent Information

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

Gazala Parveen, Padmavati Manchikanti

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.

自20世纪中后期数字技术出现以来，远程医疗一直在实施。随着技术的进步，如20世纪60年代的卫星通信、90年代的互联网和21世纪初的移动医疗应用，它不断发展。今天，远程医疗形成了数字健康的一个子集。在远程医疗中，保健专业人员通过信息和通信技术提供医疗服务。远程医疗工具的专利申请非常活跃，包括软件应用、医疗设备和用于远程诊断、监测和治疗的集成系统方面的进步。远程医疗工具知识产权的有效保护依赖于有组织的专利管理和精确的权利要求起草。对与远程医疗工具和专利申请相关的专利申请趋势的研究将有助于更好地理解与此类技术的专利申请相关的问题。它强调了正在扩大专利权利要求范围的新发展，特别是远程医疗和软件支持的医疗设备日益一体化。

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引用次数: 0

Expanding the concept of drug lifecycle management to chimeric antigen receptor T-cell products through product-patent linkage analysis 通过产品-专利连锁分析，将药物生命周期管理的概念扩展到嵌合抗原受体t细胞产品

IF 2.2 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

World Patent Information

Pub Date : 2025-04-09 DOI: 10.1016/j.wpi.2025.102357

Yasuaki Kawai, Shingo Kano

Chimeric antigen receptor T (CAR-T) cell therapies have been actively developed, and five CAR-T products have been commercialized in Japan. Due to the ongoing development of CAR-T cell therapies, including next-generation variants, the patent landscape is expected to become increasingly complex. Therefore, understanding patent strategies for each CAR-T product is essential.

In the pharmaceutical industry, lifecycle management (LCM) centered on regulatory and patent protection has been implemented to maximize product value. While studies have reported CAR-T patents through patent landscape analysis to gain insights into the overall CAR-T technology, there is a lack of research on product-related patents for CAR-T products. As a result, the foundational knowledge regarding the LCM of CAR-T products remains unclear.

Therefore, we identified product-patent linkages for CAR-T products in the Japanese market by combining patent term extension (PTE) data with publicly available data and assessed the applicability of drug LCM to CAR-T products. Our identification of precise product-patent linkages revealed that all CAR-T products met the criteria for drug LCM. This study suggests that LCM activities can be implemented for CAR-T products and that the concept of drug LCM can be expanded to CAR-T products.

嵌合抗原受体T （CAR-T）细胞疗法积极发展，在日本已经有5种CAR-T产品商业化。由于CAR-T细胞疗法（包括下一代变体）的持续发展，预计专利前景将变得越来越复杂。因此，了解每个CAR-T产品的专利策略至关重要。在制药行业，以法规和专利保护为中心的生命周期管理（LCM）已经实施，以最大化产品价值。虽然有研究通过专利景观分析来报道CAR-T专利，以了解整体CAR-T技术，但缺乏对CAR-T产品相关专利的研究。因此，关于CAR-T产品LCM的基础知识仍然不清楚。因此，我们通过将专利期限延长（PTE）数据与公开数据相结合，确定了日本市场CAR-T产品的产品-专利联系，并评估了药物LCM对CAR-T产品的适用性。我们对产品-专利联系的精确鉴定表明，所有CAR-T产品都符合药物LCM的标准。本研究表明，LCM活性可用于CAR-T产品，药物LCM的概念可扩展到CAR-T产品。

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引用次数: 0

Do large language models understand patents? Enhancing patent classification through AI-generated summaries 大型语言模型能理解专利吗？通过人工智能生成摘要加强专利分类

IF 2.2 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

World Patent Information

Pub Date : 2025-04-05 DOI: 10.1016/j.wpi.2025.102353

Naoya Yoshikawa , Ralf Krestel

Patent classification plays a crucial role in intellectual property management, but remains a challenging task due to the complexity of patent documents. This study explores a novel approach to enhance automatic patent classification by leveraging summaries generated by large language models (LLMs). Our approach involves using the GPT-3.5-turbo model to create concise summaries from different sections of patent texts, which are then used to fine-tune the RoBERTa and XLM-RoBERTa models for classification tasks. We conducted experiments on English and Japanese patent documents using two datasets: the well-established USPTO-70k and the newly developed JPO-70k, that we specifically created for this study.

Our findings show that models trained on AI-generated summaries – particularly those derived from patent claims or detailed descriptions – outperform models trained on original abstracts in both subclass-level multi-label classification and subgroup-level single-label classification. In particular, using detailed description summaries improved the micro-average F1 score for subclass-level classification by 2.9 points on the USPTO-70k and 3.0 points on the JPO-70k, compared to using original abstracts.

These results indicate that LLM-generated summaries effectively capture information relevant to patent classification from various sections of patent texts, offering a promising approach to enhance the accuracy and efficiency of patent classification across different languages.

专利分类在知识产权管理中起着至关重要的作用，但由于专利文献的复杂性，专利分类仍然是一项具有挑战性的任务。本研究探索了一种利用大型语言模型（llm）生成的摘要来增强自动专利分类的新方法。我们的方法包括使用gpt -3.5 turbo模型从专利文本的不同部分创建简洁的摘要，然后使用这些摘要对RoBERTa和XLM-RoBERTa模型进行微调，以完成分类任务。我们使用两个数据集对英语和日语专利文件进行了实验：完善的USPTO-70k和新开发的JPO-70k，这是我们专门为这项研究创建的。我们的研究结果表明，在人工智能生成的摘要上训练的模型-特别是那些来自专利权利要求或详细描述的模型-在子类级多标签分类和子组级单标签分类中都优于原始摘要训练的模型。特别是，与使用原始摘要相比，使用详细的描述摘要将USPTO-70k的子类级分类的微平均F1分数提高了2.9分，JPO-70k的微平均F1分数提高了3.0分。这些结果表明，llm生成的摘要可以有效地从专利文本的各个部分中捕获与专利分类相关的信息，为提高不同语言专利分类的准确性和效率提供了一种有希望的方法。

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引用次数: 0

Integrating Generative Artificial Intelligence techniques into technology function matrix analysis 将生成式人工智能技术与技术功能矩阵分析相结合

IF 2.2 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

World Patent Information

Pub Date : 2025-04-03 DOI: 10.1016/j.wpi.2025.102352

Huei-Yu Wang , Shu-Hao Chang , Chia-Yi Chuang

This study proposes a novel method for automating the construction of technology-function matrices using generative artificial intelligence (GAI), specifically focusing on quantum technologies. By leveraging GAI to analyze International Patent Classification (IPC) definitions and benchmark reports, we developed a system that rapidly generates technology-function matrices, significantly reducing the time required for manual analysis. The method was applied to 2,399 quantum technology patents from 2023 to March 2024, covering four key areas: secure communications, computing, quantum simulators, and sensors. This approach not only aids government agencies in identifying new technological opportunities but also facilitates the industrialization of potential technologies. By combining GAI with established analytical frameworks, this study contributes to both the theoretical understanding and practical application of patent analysis in emerging fields.

本研究提出了一种使用生成式人工智能（GAI）自动化构建技术-功能矩阵的新方法，特别关注量子技术。通过利用GAI分析国际专利分类（IPC）定义和基准报告，我们开发了一个快速生成技术功能矩阵的系统，大大减少了手工分析所需的时间。从2023年到2024年3月，该方法被应用于2399项量子技术专利，涵盖了四个关键领域：安全通信、计算、量子模拟器和传感器。这种方法不仅有助于政府机构确定新的技术机会，而且有助于潜在技术的工业化。通过将GAI与已有的分析框架相结合，本研究有助于对新兴领域专利分析的理论认识和实际应用。

引用次数: 0

Knowledge flows in technology-intensive publicly listed company - Evidence from Chinese patent citation data 技术密集型上市公司的知识流动——来自中国专利引文数据的证据

IF 2.2 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

World Patent Information

Pub Date : 2025-03-29 DOI: 10.1016/j.wpi.2025.102354

Shi Chen , Yifa Wang

This empirical study utilizes patent citations from technology-intensive publicly listed companies in China between 2000 and 2019 to analyze the current state of knowledge flow within and among these companies. While capital markets are crucial for innovation, the extent to which publicly listed firms facilitate knowledge flow remains unclear. This research delineates the circulation of technological knowledge both intra- and inter-company, across similar and disparate industries, and between listed companies and non-listed innovative entities. The findings indicate a pronounced tendency among technology-intensive listed companies to cite their patents extensively. Self-citations comprise nearly 60 % of total citations, suggesting that technological knowledge primarily circulates within individual companies. Furthermore, the exchange of technological knowledge among different listed companies within the same industry is notably sparse, with only a fractional increase in the frequency of knowledge flows within the industry compared to across industry boundaries. Predominantly, the technological knowledge that technology-intensive listed companies acquire from non-listed innovative entities stems from domestic unlisted companies, with foreign entities and universities contributing to a lesser extent. When examining the spillover of technological knowledge to non-listed innovative entities, it is observed that other non-listed companies predominantly absorb such knowledge, with universities and individual innovators receiving lesser proportions. Finally, this study is significant as it provides empirical evidence on the flow of technological knowledge within and between publicly listed technology-intensive companies in China, revealing the dominance of self-citations and limited cross-company knowledge exchange. By analyzing patent citation data, this research provides valuable insights into the interactions between listed companies and non-listed innovative entities. The findings highlight the significant role of non-listed firms, universities, and foreign entities in shaping technological development. Strengthening these connections can further foster innovation and en hance knowledge diffusion across sectors.

本实证研究利用 2000 年至 2019 年间中国技术密集型上市公司的专利引用情况，分析这些公司内部和之间的知识流动现状。虽然资本市场对创新至关重要，但上市公司在多大程度上促进了知识流动仍不清楚。本研究描述了技术知识在公司内部和公司之间、同类行业和不同行业之间以及上市公司和非上市创新实体之间的流通情况。研究结果表明，技术密集型上市公司有广泛引用其专利的明显趋势。自我引用占总引用量的近 60%，这表明技术知识主要在单个公司内部流通。此外，同一行业内不同上市公司之间的技术知识交流明显稀少，与跨行业相比，行业内的知识流动频率仅有零点几的增长。技术密集型上市公司从非上市创新实体获得的技术知识主要来自国内非上市公司，外国实体和大学的贡献较小。在研究技术知识向非上市创新实体的溢出时，发现其他非上市公司主要吸收这些知识，而大学和个人创新者获得的比例较小。最后，本研究的重要意义在于为中国技术密集型上市公司内部和之间的技术知识流动提供了实证证据，揭示了自我引用占主导地位和跨公司知识交流有限的问题。通过分析专利引用数据，本研究为上市公司与非上市创新实体之间的互动提供了有价值的见解。研究结果凸显了非上市公司、大学和外国实体在影响技术发展方面的重要作用。加强这些联系可以进一步促进创新，推动跨行业的知识传播。

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引用次数: 0

Progress in patent technologies for methane catalytic combustion catalysts research 甲烷催化燃烧催化剂专利技术研究进展

IF 2.2 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

World Patent Information

Pub Date : 2025-03-27 DOI: 10.1016/j.wpi.2025.102355

Bo Yuan , Tao Zhu , Meidan Wang , Xueli Zhang , Chen Li , Xinyue Zhang , Xudong Xu , Qian Sun

Catalytic combustion is an important approach to the effective utilization of methane, with the key being the search for efficient catalysts to maximize catalytic activity and resistance to poisoning. This work is based on the IncoPat commercial global patent database, combined with search results from the China Patent Abstracts database and the Derwent World Patents Index database, employing patent analysis methods to conduct a comprehensive analysis of the development trends in the global methane catalytic combustion catalyst materials technology field. By analyzing the trends in patent application and patent family numbers, the distribution of applicant countries/organizations, and leading applicants, this study summarizes the classification, preparation methods, and other technical key points of methane catalytic combustion catalyst materials, clarifying the latest developments in global methane catalytic combustion catalyst materials technology. This provides technical references for companies producing methane combustion catalytic materials in terms of product development and patent strategy layout, and supports the rapid development of the global methane combustion catalytic materials industry. The research findings indicate that global methane catalytic combustion catalyst technology is in a phase of rapid development, with research and applications in this field accelerating globally among countries and organizations, displaying intense technological competition and cooperation trends. Future research will focus on enhancing the activity, stability, and resistance to poisoning of catalysts, to aid in methane reduction and provide technical support for achieving global climate goals.

催化燃烧是有效利用甲烷的重要途径，关键在于寻找高效催化剂，最大限度地提高催化活性和抗中毒能力。本研究基于 IncoPat 全球商业专利数据库，结合中国专利文摘数据库和德文特世界专利索引数据库的检索结果，采用专利分析方法，对全球甲烷催化燃烧催化剂材料技术领域的发展趋势进行了全面分析。本研究通过对专利申请量、专利族数量变化趋势、申请人国别/机构分布、主要申请人等方面的分析，总结了甲烷催化燃烧催化剂材料的分类、制备方法等技术要点，阐明了全球甲烷催化燃烧催化剂材料技术的最新发展动态。这为甲烷燃烧催化材料生产企业在产品研发、专利战略布局等方面提供了技术参考，助力全球甲烷燃烧催化材料产业的快速发展。研究结果表明，全球甲烷催化燃烧催化剂技术正处于快速发展阶段，各国、各组织在该领域的研究和应用在全球范围内加速推进，呈现出激烈的技术竞争与合作趋势。未来的研究重点将放在提高催化剂的活性、稳定性和抗中毒能力上，以帮助甲烷减排，为实现全球气候目标提供技术支持。

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引用次数: 0