The strategic use of patents and its implications for enterprise and competition policies

This report was commissioned as a study into the strategic use of patents. In the course of its case investigations and legislative reviews the European Commission became aware of changes in the use of intellectual property, in particular the use of patents. It was noted that firms’ uses of intellectual property are becoming increasingly strategic. This raised concerns about the implications of firms’ patenting behaviour for enterprise and competition policy. The following report contains a comprehensive review of patenting behaviour, the extent to which patenting is becoming more strategic and the implications this has for competition and enterprise policies

Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one

The strategic use of patents and its implications for enterprise and competition policies

This report was commissioned as a study into the strategic use of patents. In the course of its case investigations and legislative reviews the European Commission became aware of changes in the use of intellectual property, in particular the use of patents. It was noted that firms’ uses of intellectual property are becoming increasingly strategic. This raised concerns about the implications of firms’ patenting behaviour for enterprise and competition policy. The following report contains a comprehensive review of patenting behaviour, the extent to which patenting is becoming more strategic and the implications this has for competition and enterprise policies

Patent Trespass and the Royalty Gap: Exploring the Nature and Impact of Patent Holdout

Patent Trespass and the Royalty Gap: Exploring the Nature and Impact of Patent Holdou

Exploring the Boundaries of Patent Commercialization Models via Litigation

This thesis explores direct patent commercialization via patent assertion, particularly patent infringement litigation, a complex nonmarket activity whose successful undertaking requires knowledge, creativity, and financial resources, as well as a colorable infringement case. Despite these complexities, firms have increasingly employed patents as competitive tools via patent assertions, particularly in the United States. This thesis explores the business models that have been created to facilitate the direct monetization of patents. Since secrecy underpins the patent assertion strategies studied, the thesis is based on rich and enhanced secondary data. In particular, a data chaining technique has been developed to assemble relevant but disparate data into a larger coherent data set that is amenable to combination and pairing with other forms of relevant public data. This research has discovered that one particularly successful business model that employs a leveraging strategy, known as the non-practicing entity (“NPE”), has itself spawned at least two other business models, the highly capitalized “patent mass aggregator” and the “patent privateer.” The patent privateer, newly discovered in this research, is particularly interesting because it provides a way for firms to employ patents to attack competitors by forming specialized NPEs in a manner that essentially expands the boundaries of the firm. This research has also examined plaintiff firm management processes during litigations brought under leveraging and proprietary strategies, the two patent litigation strategies in which firms affirmatively initiate infringement litigations. In particular, this research investigates the commercial contexts that drive patent assertion strategies to explore the effective limits of the patent right in a litigation context. The investigation concludes that a variety of robust business models and management processes may be quite successful in extracting value from patents in the US

Automated Patent Categorization and Guided Patent Search using IPC as Inspired by MeSH and PubMed

The patent domain is a very important source of scientific information that is currently not used to its full potential. Searching for relevant patents is a complex task because the number of existing patents is very high and grows quickly, patent text is extremely complicated, and standard vocabulary is not used consistently or doesn’t even exist. As a consequence, pure keyword searches often fail to return satisfying results in the patent domain. Major companies employ patent professionals who are able to search patents effectively, but even they have to invest a lot of time and effort into their search. Academic scientists on the other hand do not have access to such resources and therefore often do not search patents at all, but they risk missing up-to-date information that will not be published in scientific publications until much later, if it is published at all. Document search on PubMed, the pre-eminent database for biomedical literature, relies on the annotation of its documents with relevant terms from the Medical Subject Headings ontology (MeSH) for improving recall through query expansion. Similarly, professional patent searches expand beyond keywords by including class codes from various patent classification systems. However, classification-based searches can only be performed effectively if the user has very detailed knowledge of the system, which is usually not the case for academic scientists. Consequently, we investigated methods to automatically identify relevant classes that can then be suggested to the user to expand their query. Since every patent is assigned at least one class code, it should be possible for these assignments to be used in a similar way as the MeSH annotations in PubMed. In order to develop a system for this task, it is necessary to have a good understanding of the properties of both classification systems. In order to gain such knowledge, we perform an in-depth comparative analysis of MeSH and the main patent classification system, the International Patent Classification (IPC). We investigate the hierarchical structures as well as the properties of the terms/classes respectively, and we compare the assignment of IPC codes to patents with the annotation of PubMed documents with MeSH terms. Our analysis shows that the hierarchies are structurally similar, but terms and annotations differ significantly. The most important differences concern the considerably higher complexity of the IPC class definitions compared to MeSH terms and the far lower number of class assignments to the average patent compared to the number of MeSH terms assigned to PubMed documents. As a result of these differences, problems are caused both for unexperienced patent searchers and professionals. On the one hand, the complex term system makes it very difficult for members of the former group to find any IPC classes that are relevant for their search task. On the other hand, the low number of IPC classes per patent points to incomplete class assignments by the patent office, therefore limiting the recall of the classification-based searches that are frequently performed by the latter group. We approach these problems from two directions: First, by automatically assigning additional patent classes to make up for the missing assignments, and second, by automatically retrieving relevant keywords and classes that are proposed to the user so they can expand their initial search. For the automated assignment of additional patent classes, we adapt an approach to the patent domain that was successfully used for the assignment of MeSH terms to PubMed abstracts. Each document is assigned a set of IPC classes by a large set of binary Maximum-Entropy classifiers. Our evaluation shows good performance by individual classifiers (precision/recall between 0:84 and 0:90), making the retrieval of additional relevant documents for specific IPC classes feasible. The assignment of additional classes to specific documents is more problematic, since the precision of our classifiers is not high enough to avoid false positives. However, we propose filtering methods that can help solve this problem. For the guided patent search, we demonstrate various methods to expand a user’s initial query. Our methods use both keywords and class codes that the user enters to retrieve additional relevant keywords and classes that are then suggested to the user. These additional query components are extracted from different sources such as patent text, IPC definitions, external vocabularies and co-occurrence data. The suggested expansions can help unexperienced users refine their queries with relevant IPC classes, and professionals can compose their complete query faster and more easily. We also present GoPatents, a patent retrieval prototype that incorporates some of our proposals and makes faceted browsing of a patent corpus possible

Privacy-aware Security Applications in the Era of Internet of Things

In this dissertation, we introduce several novel privacy-aware security applications. We split these contributions into three main categories: First, to strengthen the current authentication mechanisms, we designed two novel privacy-aware alternative complementary authentication mechanisms, Continuous Authentication (CA) and Multi-factor Authentication (MFA). Our first system is Wearable-assisted Continuous Authentication (WACA), where we used the sensor data collected from a wrist-worn device to authenticate users continuously. Then, we improved WACA by integrating a noise-tolerant template matching technique called NTT-Sec to make it privacy-aware as the collected data can be sensitive. We also designed a novel, lightweight, Privacy-aware Continuous Authentication (PACA) protocol. PACA is easily applicable to other biometric authentication mechanisms when feature vectors are represented as fixed-length real-valued vectors. In addition to CA, we also introduced a privacy-aware multi-factor authentication method, called PINTA. In PINTA, we used fuzzy hashing and homomorphic encryption mechanisms to protect the users\u27 sensitive profiles while providing privacy-preserving authentication. For the second privacy-aware contribution, we designed a multi-stage privacy attack to smart home users using the wireless network traffic generated during the communication of the devices. The attack works even on the encrypted data as it is only using the metadata of the network traffic. Moreover, we also designed a novel solution based on the generation of spoofed traffic. Finally, we introduced two privacy-aware secure data exchange mechanisms, which allow sharing the data between multiple parties (e.g., companies, hospitals) while preserving the privacy of the individual in the dataset. These mechanisms were realized with the combination of Secure Multiparty Computation (SMC) and Differential Privacy (DP) techniques. In addition, we designed a policy language, called Curie Policy Language (CPL), to handle the conflicting relationships among parties. The novel methods, attacks, and countermeasures in this dissertation were verified with theoretical analysis and extensive experiments with real devices and users. We believe that the research in this dissertation has far-reaching implications on privacy-aware alternative complementary authentication methods, smart home user privacy research, as well as the privacy-aware and secure data exchange methods