Patent Claim Structure Recognition

In patents, the claims section is the most relevant part. It is written in a legal jargon, containing independent and dependent claims, forming a hierarchy. We present our work aimed at automatically identifying that hierarchy within complete patent claim texts. Beginning with a short introduction into patent claims and typical use cases for searching in claims, we proceed to show results from a preliminary context analysis with English claims from the European Patents Fulltext (EPFULL) database. We point out some possibilities with which claim dependency is indicated in the text and show a way of identifying them. Additionally, we describe several of the problems encountered, in particular problems resulting from noisy data. Finally, we show results from our internal evaluations, in which accuracies greater than 93% were measured. We also indicate areas of further research

Natural Language Processing for Technology Foresight Summarization and Simplification: the case of patents

Technology foresight aims to anticipate possible developments, understand trends, and identify technologies of high impact. To this end, monitoring emerging technologies is crucial. Patents -- the legal documents that protect novel inventions -- can be a valuable source for technology monitoring. Millions of patent applications are filed yearly, with 3.4 million applications in 2021 only. Patent documents are primarily textual documents and disclose innovative and potentially valuable inventions. However, their processing is currently underresearched. This is due to several reasons, including the high document complexity: patents are very lengthy and are written in an extremely hard-to-read language, which is a mix of technical and legal jargon. This thesis explores how Natural Language Processing -- the discipline that enables machines to process human language automatically -- can aid patent processing. Specifically, we focus on two tasks: patent summarization (i.e., we try to reduce the document length while preserving its core content) and patent simplification (i.e., we try to reduce the document's linguistic complexity while preserving its original core meaning). We found that older patent summarization approaches were not compared on shared benchmarks (making thus it hard to draw conclusions), and even the most recent abstractive dataset presents important issues that might make comparisons meaningless. We try to fill both gaps: we first document the issues related to the BigPatent dataset and then benchmark extractive, abstraction, and hybrid approaches in the patent domain. We also explore transferring summarization methods from the scientific paper domain with limited success. For the automatic text simplification task, we noticed a lack of simplified text and parallel corpora. We fill this gap by defining a method to generate a silver standard for patent simplification automatically. Lay human judges evaluated the simplified sentences in the corpus as grammatical, adequate, and simpler, and we show that it can be used to train a state-of-the-art simplification model. This thesis describes the first steps toward Natural Language Processing-aided patent summarization and simplification. We hope it will encourage more research on the topic, opening doors for a productive dialog between NLP researchers and domain experts.Technology foresight aims to anticipate possible developments, understand trends, and identify technologies of high impact. To this end, monitoring emerging technologies is crucial. Patents -- the legal documents that protect novel inventions -- can be a valuable source for technology monitoring. Millions of patent applications are filed yearly, with 3.4 million applications in 2021 only. Patent documents are primarily textual documents and disclose innovative and potentially valuable inventions. However, their processing is currently underresearched. This is due to several reasons, including the high document complexity: patents are very lengthy and are written in an extremely hard-to-read language, which is a mix of technical and legal jargon. This thesis explores how Natural Language Processing -- the discipline that enables machines to process human language automatically -- can aid patent processing. Specifically, we focus on two tasks: patent summarization (i.e., we try to reduce the document length while preserving its core content) and patent simplification (i.e., we try to reduce the document's linguistic complexity while preserving its original core meaning). We found that older patent summarization approaches were not compared on shared benchmarks (making thus it hard to draw conclusions), and even the most recent abstractive dataset presents important issues that might make comparisons meaningless. We try to fill both gaps: we first document the issues related to the BigPatent dataset and then benchmark extractive, abstraction, and hybrid approaches in the patent domain. We also explore transferring summarization methods from the scientific paper domain with limited success. For the automatic text simplification task, we noticed a lack of simplified text and parallel corpora. We fill this gap by defining a method to generate a silver standard for patent simplification automatically. Lay human judges evaluated the simplified sentences in the corpus as grammatical, adequate, and simpler, and we show that it can be used to train a state-of-the-art simplification model. This thesis describes the first steps toward Natural Language Processing-aided patent summarization and simplification. We hope it will encourage more research on the topic, opening doors for a productive dialog between NLP researchers and domain experts

Semantic Exploration of Text Documents with Multi-Faceted Metadata Employing Word Embeddings: The Patent Landscaping Use Case

Die Menge der Veröentlichungen, die den wissenschaftlichen Fortschritt dokumentieren, wächst kontinuierlich. Dies erfordert die Entwicklung der technologischen Hilfsmittel für eine eziente Analyse dieser Werke. Solche Dokumente kennzeichnen sich nicht nur durch ihren textuellen Inhalt, sondern auch durch eine Menge von Metadaten-Attributen verschiedenster Art, unter anderem Beziehungen zwischen den Dokumenten. Diese Komplexität macht die Entwicklung eines Visualisierungsansatzes, der eine Untersuchung der schriftlichen Werke unterstützt, zu einer notwendigen und anspruchsvollen Aufgabe. Patente sind beispielhaft für das beschriebene Problem, weil sie in großen Mengen von Firmen untersucht werden, die sich Wettbewerbsvorteile verschaffen oder eigene Forschung und Entwicklung steuern wollen. Vorgeschlagen wird ein Ansatz für eine explorative Visualisierung, der auf Metadaten und semantischen Embeddings von Patentinhalten basiert ist. Wortembeddings aus einem vortrainierten Word2vec-Modell werden genutzt, um Ähnlichkeiten zwischen Dokumenten zu bestimmen. Darüber hinaus helfen hierarchische Clusteringmethoden dabei, mehrere semantische Detaillierungsgrade durch extrahierte relevante Stichworte anzubieten. Derzeit dürfte der vorliegende Visualisierungsansatz der erste sein, der semantische Embeddings mit einem hierarchischen Clustering verbindet und dabei diverse Interaktionstypen basierend auf Metadaten-Attributen unterstützt. Der vorgestellte Ansatz nimmt Nutzerinteraktionstechniken wie Brushing and Linking, Focus plus Kontext, Details-on-Demand und Semantic Zoom in Anspruch. Dadurch wird ermöglicht, Zusammenhänge zu entdecken, die aus dem Zusammenspiel von 1) Verteilungen der Metadatenwerten und 2) Positionen im semantischen Raum entstehen. Das Visualisierungskonzept wurde durch Benutzerinterviews geprägt und durch eine Think-Aloud-Studie mit Patentenexperten evaluiert. Während der Evaluation wurde der vorgestellte Ansatz mit einem Baseline-Ansatz verglichen, der auf TF-IDF-Vektoren basiert. Die Benutzbarkeitsstudie ergab, dass die Visualisierungsmetaphern und die Interaktionstechniken angemessen gewählt wurden. Darüber hinaus zeigte sie, dass die Benutzerschnittstelle eine deutlich größere Rolle bei den Eindrücken der Probanden gespielt hat als die Art und Weise, wie die Patente platziert und geclustert waren. Tatsächlich haben beide Ansätze sehr ähnliche extrahierte Clusterstichworte ergeben. Dennoch wurden bei dem semantischen Ansatz die Cluster intuitiver platziert und deutlicher abgetrennt. Das vorgeschlagene Visualisierungslayout sowie die Interaktionstechniken und semantischen Methoden können auch auf andere Arten von schriftlichen Werken erweitert werden, z. B. auf wissenschaftliche Publikationen. Andere Embeddingmethoden wie Paragraph2vec [61] oder BERT [32] können zudem verwendet werden, um kontextuelle Abhängigkeiten im Text über die Wortebene hinaus auszunutzen

Text mining techniques for patent analysis.

Abstract Patent documents contain important research results. However, they are lengthy and rich in technical terminology such that it takes a lot of human efforts for analyses. Automatic tools for assisting patent engineers or decision makers in patent analysis are in great demand. This paper describes a series of text mining techniques that conforms to the analytical process used by patent analysts. These techniques include text segmentation, summary extraction, feature selection, term association, cluster generation, topic identification, and information mapping. The issues of efficiency and effectiveness are considered in the design of these techniques. Some important features of the proposed methodology include a rigorous approach to verify the usefulness of segment extracts as the document surrogates, a corpus-and dictionary-free algorithm for keyphrase extraction, an efficient co-word analysis method that can be applied to large volume of patents, and an automatic procedure to create generic cluster titles for ease of result interpretation. Evaluation of these techniques was conducted. The results confirm that the machine-generated summaries do preserve more important content words than some other sections for classification. To demonstrate the feasibility, the proposed methodology was applied to a realworld patent set for domain analysis and mapping, which shows that our approach is more effective than existing classification systems. The attempt in this paper to automate the whole process not only helps create final patent maps for topic analyses, but also facilitates or improves other patent analysis tasks such as patent classification, organization, knowledge sharing, and prior art searches

Hard-Hearted Scrolls: A Noninvasive Method for Reading the Herculaneum Papyri

The Herculaneum scrolls were buried and carbonized by the eruption of Mount Vesuvius in A.D. 79 and represent the only classical library discovered in situ. Charred by the heat of the eruption, the scrolls are extremely fragile. Since their discovery two centuries ago, some scrolls have been physically opened, leading to some textual recovery but also widespread damage. Many other scrolls remain in rolled form, with unknown contents. More recently, various noninvasive methods have been attempted to reveal the hidden contents of these scrolls using advanced imaging. Unfortunately, their complex internal structure and lack of clear ink contrast has prevented these efforts from successfully revealing their contents. This work presents a machine learning-based method to reveal the hidden contents of the Herculaneum scrolls, trained using a novel geometric framework linking 3D X-ray CT images with 2D surface imagery of scroll fragments. The method is verified against known ground truth using scroll fragments with exposed text. Some results are also presented of hidden characters revealed using this method, the first to be revealed noninvasively from this collection. Extensions to the method, generalizing the machine learning component to other multimodal transformations, are presented. These are capable not only of revealing the hidden ink, but also of generating rendered images of scroll interiors as if they were photographed in color prior to their damage two thousand years ago. The application of these methods to other domains is discussed, and an additional chapter discusses the Vesuvius Challenge, a $1,000,000+ open research contest based on the dataset built as a part of this work

Applying text timing in corporate spin-off disclosure statement analysis: understanding the main concerns and recommendation of appropriate term weights

Text mining helps in extracting knowledge and useful information from unstructured data. It detects and extracts information from mountains of documents and allowing in selecting data related to a particular data. In this study, text mining is applied to the 10-12b filings done by the companies during Corporate Spin-off. The main purposes are (1) To investigate potential and/or major concerns found from these financial statements filed for corporate spin-off and (2) To identify appropriate methods in text mining which can be used to reveal these major concerns. 10-12b filings from thirty-four companies were taken and only the Risk Factors category was taken for analysis. Term weights such as Entropy, IDF, GF-IDF, Normal and None were applied on the input data and out of them Entropy and GF-IDF were found to be the appropriate term weights which provided acceptable results. These accepted term weights gave the results which was acceptable to human expert\u27s expectations. The document distribution from these term weights created a pattern which reflected the mood or focus of the input documents. In addition to the analysis, this study also provides a pilot study for future work in predictive text mining for the analysis of similar financial documents. For example, the descriptive terms found from this study provide a set of start word list which eliminates the try and error method of framing an initial start list --Abstract, page iii

An Integrated Framework for Patent Analysis and Mining

Patent documents are important intellectual resources of protecting interests of individuals, organizations and companies. These patent documents have great research values, beneficial to the industry, business, law, and policy-making communities. Patent mining aims at assisting patent analysts in investigating, processing, and analyzing patent documents, which has attracted increasing interest in academia and industry. However, despite recent advances in patent mining, several critical issues in current patent mining systems have not been well explored in previous studies. These issues include: 1) the query retrieval problem that assists patent analysts finding all relevant patent documents for a given patent application; 2) the patent documents comparative summarization problem that facilitates patent analysts in quickly reviewing any given patent documents pairs; and 3) the key patent documents discovery problem that helps patent analysts to quickly grasp the linkage between different technologies in order to better understand the technical trend from a collection of patent documents. This dissertation follows the stream of research that covers the aforementioned issues of existing patent analysis and mining systems. In this work, we delve into three interleaved aspects of patent mining techniques, including (1) PatSearch, a framework of automatically generating the search query from a given patent application and retrieving relevant patents to user; (2) PatCom, a framework for investigating the relationship in terms of commonality and difference between patent documents pairs, and (3) PatDom, a framework for integrating multiple types of patent information to identify important patents from a large volume of patent documents. In summary, the increasing amount and textual complexity of patent repository lead to a series of challenges that are not well addressed in the current generation systems. My work proposed reasonable solutions to these challenges and provided insights on how to address these challenges using a simple yet effective integrated patent mining framework

Road Infrastructure Challenges Faced by Automated Driving: A Review

Automated driving can no longer be referred to as hype or science fiction but rather a technology that has been gradually introduced to the market. The recent activities of regulatory bodies and the market penetration of automated driving systems (ADS) demonstrate that society is exhibiting increasing interest in this field and gradually accepting new methods of transport. Automated driving, however, does not depend solely on the advances of onboard sensor technology or artificial intelligence (AI). One of the essential factors in achieving trust and safety in automated driving is road infrastructure, which requires careful consideration. Historically, the development of road infrastructure has been guided by human perception, but today we are at a turning point at which this perspective is not sufficient. In this study, we review the limitations and advances made in the state of the art of automated driving technology with respect to road infrastructure in order to identify gaps that are essential for bridging the transition from human control to self-driving. The main findings of this study are grouped into the following five clusters, characterised according to challenges that must be faced in order to cope with future mobility: international harmonisation of traffic signs and road markings, revision of the maintenance of the road infrastructure, review of common design patterns, digitalisation of road networks, and interdisciplinarity. The main contribution of this study is the provision of a clear and concise overview of the interaction between road infrastructure and ADS as well as the support of international activities to define the requirements of road infrastructure for the successful deployment of ADS

Enhancing Key Digital Literacy Skills: Information Privacy, Information Security, and Copyright/Intellectual Property

Key Messages Background Knowledge and skills in the areas of information security, information privacy, and copyright/intellectual property rights and protection are of key importance for organizational and individual success in an evolving society and labour market in which information is a core resource. Organizations require skilled and knowledgeable professionals who understand risks and responsibilities related to the management of information privacy, information security, and copyright/intellectual property. Professionals with this expertise can assist organizations to ensure that they and their employees meet requirements for the privacy and security of information in their care and control, and in order to ensure that neither the organization nor its employees contravene copyright provisions in their use of information. Failure to meet any of these responsibilities can expose the organization to reputational harm, legal action and/or financial loss. Context Inadequate or inappropriate information management practices of individual employees are at the root of organizational vulnerabilities with respect to information privacy, information security, and information ownership issues. Users demonstrate inadequate skills and knowledge coupled with inappropriate practices in these areas, and similar gaps at the organizational level are also widely documented. National and international regulatory frameworks governing information privacy, information security, and copyright/intellectual property are complex and in constant flux, placing additional burden on organizations to keep abreast of relevant regulatory and legal responsibilities. Governance and risk management related to information privacy, security, and ownership are critical to many job categories, including the emerging areas of information and knowledge management. There is an increasing need for skilled and knowledgeable individuals to fill organizational roles related to information management, with particular growth in these areas within the past 10 years. Our analysis of current job postings in Ontario supports the demand for skills and knowledge in these areas. Key Competencies We have developed a set of key competencies across a range of areas that responds to these needs by providing a blueprint for the training of information managers prepared for leadership and strategic positions. These competencies are identified in the full report. Competency areas include: conceptual foundations risk assessment tools and techniques for threat responses communications contract negotiation and compliance evaluation and assessment human resources management organizational knowledge management planning; policy awareness and compliance policy development project managemen