1,921 research outputs found

    The rise of China's technological power: the perspective from frontier technologies

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    We use patent data to study the contribution of the US, Europe, China and Japan to frontier technology using automated patent landscaping. We find that China's contribution to frontier technology has become quantitatively similar to the US in the late 2010s while overcoming the European and Japanese contributions respectively. Although China still exhibits the stigmas of a catching up economy, these stigmas are on the downside. The quality of frontier technology patents published at the Chinese Patent Office has leveled up to the quality of patents published at the European and Japanese patent offices. At the same time, frontier technology patenting at the Chinese Patent Office seems to have been increasingly supported by domestic patentees, suggesting the build up of domestic capabilities

    PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT

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    This study provides an efficient approach for using text data to calculate patent-to-patent (p2p) technological similarity, and presents a hybrid framework for leveraging the resulting p2p similarity for applications such as semantic search and automated patent classification. We create embeddings using Sentence-BERT (SBERT) based on patent claims. We leverage SBERTs efficiency in creating embedding distance measures to map p2p similarity in large sets of patent data. We deploy our framework for classification with a simple Nearest Neighbors (KNN) model that predicts Cooperative Patent Classification (CPC) of a patent based on the class assignment of the K patents with the highest p2p similarity. We thereby validate that the p2p similarity captures their technological features in terms of CPC overlap, and at the same demonstrate the usefulness of this approach for automatic patent classification based on text data. Furthermore, the presented classification framework is simple and the results easy to interpret and evaluate by end-users. In the out-of-sample model validation, we are able to perform a multi-label prediction of all assigned CPC classes on the subclass (663) level on 1,492,294 patents with an accuracy of 54% and F1 score > 66%, which suggests that our model outperforms the current state-of-the-art in text-based multi-label and multi-class patent classification. We furthermore discuss the applicability of the presented framework for semantic IP search, patent landscaping, and technology intelligence. We finally point towards a future research agenda for leveraging multi-source patent embeddings, their appropriateness across applications, as well as to improve and validate patent embeddings by creating domain-expert curated Semantic Textual Similarity (STS) benchmark datasets.Comment: 18 pages, 7 figures and 4 Table

    Text Mining-Based Patent Analysis of Blockchain Technology Applications

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    This study focuses on the emergence of blockchain-related technologies regarding patenting activity. Blockchain technology has gained the attention of the general public. It has intensified in recent years, making it a fascinating subject of study for a patent analysis to scrutinize the evolution of this technology. However, research using the patent landscape to study the evolution of blockchain technologies is scarce. This article follows a unique methodology and comprehensive search strategy based on patent mapping and text mining to identify and categorize Blockchain patent documents extracted from the United States Patent and Trademark Office and the World Intellectual Property Organization databases. This methodology and dataset can be used for patent landscaping exercises or bibliometric analysis

    Patent\u27s New Salience

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    The vast majority of patents do not matter. They are almost never enforced or licensed and, in consequence, are almost always ignored. This is a well-accepted feature of the patent system and has a tremendous impact on patent policy. In particular, while there are many aspects of patent law that are potentially troubling—including grants of unmerited patents, high transaction costs in obtaining necessary patent licenses, and patents’ potential to block innovation and hinder economic growth—these problems may be insignificant in practice because patents are under-enforced and routinely infringed without consequence. This Article argues that technological developments are greatly increasing the salience of patents by making patents easier and cheaper to find and enforce. These developments—including private platforms’ adjudication systems and AI-driven patent analytics—profoundly impact how the patent system functions and upend the system’s present dependence on under-enforcement and ignorance. Where most patents could previously be safely disregarded, formerly forgotten patents now matter. This Article makes four contributions to the literature. First, this Article explores the technology that is rendering patents newly salient and explains how this alters basic assumptions underlying the patent system. Second, this Article demonstrates that although new technology is increasing the number of patents that can be reviewed and enforced, this transformation sometimes decreases the depth of patent analysis. Because it is difficult to draw conclusions about patent scope or validity without in-depth analysis, this omission means that technological review of patents may give patents unmerited influence. Third, this Article shows a sharp divergence between public policy goals and private use of patents. For several decades, the courts and Congress have been reforming patent policy to decrease the impact of patents to alleviate concerns that patent owners hinder innovation by others. This Article demonstrates, in clear contrast to this goal, an increase in patent salience that is due exclusively to the use of private platforms and technologies. Further, the use of private platforms to find, analyze, and enforce patents creates the risk that choices made by companies and software developers will displace substantive patent law. Finally, this Article suggests policy reform, including ways to improve technology and patents and adjusted approaches to patent doctrine and theory

    Three real-world datasets and neural computational models for classification tasks in patent landscaping

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    Patent Landscaping, one of the central tasks of intellectual property management, includes selecting and grouping patents according to user-defined technical or application-oriented criteria. While recent transformer-based models have been shown to be effective for classifying patents into taxonomies such as CPC or IPC, there is yet little research on how to support real-world Patent Landscape Studies (PLSs) using natural language processing methods. With this paper, we release three labeled datasets for PLS-oriented classification tasks covering two diverse domains. We provide a qualitative analysis and report detailed corpus statistics.Most research on neural models for patents has been restricted to leveraging titles and abstracts. We compare strong neural and non-neural baselines, proposing a novel model that takes into account textual information from the patents’ full texts as well as embeddings created based on the patents’ CPC labels. We find that for PLS-oriented classification tasks, going beyond title and abstract is crucial, CPC labels are an effective source of information, and combining all features yields the best results

    Industrial challenges in patent management and crowdsourcing patent landscapes for engineering design innovation

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    Innovation is critical to sustain in prevailing competitive business environments. Industries need effective innovation strategies in-practice to develop and deliver novel products and services swiftly. In order to implement innovation strategies effectively, industries need innovation capacity in engineering design supported with intellectual assets. However, there are many issues that prevent streamlining these processes. The objectives of this research are to explicit the issues related to industrial patents (one of the important resources in intellectual assets) generation and management processes, and propose cost-effective crowdsourcing approach as a tool for patent landscaping activities. Interviews with patent attorneys and intellectual audit specialists reveal that most industries have ineffective intellectual property strategy; engineers do little patent searching, face challenges to identify novel product features, and often find difficulties to interpret patent information. The initial experiments of using the crowdsourcing approach for patent clustering activity reveal that general crowd workers (not knowing much about patents) were able to identify one third of expert clustered schema for much lesser cost. Further research work to strengthen the usefulness of the crowdsourcing approach for patent landscaping related activities is discussed

    Thirty years of artificial intelligence and law : the third decade

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