This file was created by the TYPO3 extension bib --- Timezone: CEST Creation date: 2024-04-19 Creation time: 21-12-44 --- Number of references 4 article Jakobs_2023_3 European Intellectual Property Review 2023 7 45 7 371-375 It has long been recognized in Europe and elsewhere that standards-development organizations (SDOs) may adopt policies that require their participants to license patents essential to the SDO’s standards (standards-essential patents or SEPs) to manufacturers of standardized products (“implementers”) on a royalty-free (RF) basis. This requirement contrasts with SDO policies that permit SEP holders to charge implementers monetary patent royalties, sometimes on terms that are specified as “fair, reasonable and nondiscriminatory” (FRAND). As demonstrated by two decades of intensive litigation around the world, FRAND royalties have given rise to intractable disputes regarding the manner in which such royalties should be calculated and adjudicated. In contrast, standards distributed on an RF basis are comparatively free from litigation and the attendant transaction costs. Accordingly, numerous SDOs around the world have adopted RF licensing policies and many widely adopted standards, including Bluetooth, USB, IPv6, HTTP, HTML and XML, are distributed on an RF basis. This note briefly discusses the commercial considerations surrounding RF standards, the relationship between RF standards and open source software (OSS) and the SDO policy mechanisms – including “universal reciprocity” -- that enable RF licensing to succeed in the marketplace. 0142-0461 10.2139/ssrn.4235647 1 JorgeContreras RudiBekkers BradBiddle EnricoBonadio Michael A.Carrier BernardChao CharlesDuan RichardGilbert JoachimHenkel ErikHovenkamp MartinHusovec KaiJakobs Dong-hyuKim Mark A.Lemley Brian J.Love LukeMcDonagh Fiona M.Scott Morton JasonSchultz TimothySimcoe Jennifer M.Urban Joy YXiang incollection 2023_rueppel_crd-b2.ii 2023 2 8 221-246 The main objectives in production technology are quality assurance, cost reduction, and guaranteed process safety and stability. Digital shadows enable a more comprehensive understanding and monitoring of processes on shop floor level. Thus, process information becomes available between decision levels, and the aforementioned criteria regarding quality, cost, or safety can be included in control decisions for production processes. The contextual data for digital shadows typically arises from heterogeneous sources. At shop floor level, the proximity to the process requires usage of available data as well as domain knowledge. Data sources need to be selected, synchronized, and processed. Especially high-frequency data requires algorithms for intelligent distribution and efficient filtering of the main information using real-time devices and in-network computing. Real-time data is enriched by simulations, metadata from product planning, and information across the whole process chain. Well-established analytical and empirical models serve as the base for new hybrid, gray box approaches. These models are then applied to optimize production process control by maximizing the productivity under given quality and safety constraints. To store and reuse the developed models, ontologies are developed and a data lake infrastructure is utilized and constantly enlarged laying the basis for a World Wide Lab (WWL). Finally, closing the control loop requires efficient quality assessment, immediately after the process and directly on the machine. This chapter addresses works in a connected job shop to acquire data, identify and optimize models, and automate systems and their deployment in the Internet of Production (IoP). Process control; Model-based control; Data aggregation; Model identification; Model optimization internet-of-production https://www.comsys.rwth-aachen.de/fileadmin/papers/2023/2023-rueppel-iop-b2.i.pdf Springer Interdisciplinary Excellence Accelerator Series Internet of Production: Fundamentals, Applications and Proceedings 978-3-031-44496-8 10.1007/978-3-031-44497-5_7 1 Adrian KarlRüppel MuzafferAy BenediktBiernat IkeKunze MarkusLandwehr SamuelMann JanPennekamp PascalRabe Mark P.Sanders DominikScheurenberg SvenSchiller TiandongXi DirkAbel ThomasBergs ChristianBrecher UweReisgen Robert H.Schmitt KlausWehrle inproceedings 2020_pennekamp_parameter_exchange 2020 12 10 510-525 Nowadays, collaborations between industrial companies always go hand in hand with trust issues, i.e., exchanging valuable production data entails the risk of improper use of potentially sensitive information. Therefore, companies hesitate to offer their production data, e.g., process parameters that would allow other companies to establish new production lines faster, against a quid pro quo. Nevertheless, the expected benefits of industrial collaboration, data exchanges, and the utilization of external knowledge are significant. In this paper, we introduce our Bloom filter-based Parameter Exchange (BPE), which enables companies to exchange process parameters privacy-preservingly. We demonstrate the applicability of our platform based on two distinct real-world use cases: injection molding and machine tools. We show that BPE is both scalable and deployable for different needs to foster industrial collaborations. Thereby, we reward data-providing companies with payments while preserving their valuable data and reducing the risks of data leakage. secure industrial collaboration; Bloom filter; oblivious transfer; Internet of Production internet-of-production https://www.comsys.rwth-aachen.de/fileadmin/papers/2020/2020-pennekamp-parameter-exchange.pdf ACM Proceedings of the 36th Annual Computer Security Applications Conference (ACSAC '20), December 7-11, 2020, Austin, TX, USA Austin, TX, USA December 7-11, 2020 978-1-4503-8858-0/20/12 10.1145/3427228.3427248 1 JanPennekamp ErikBuchholz YannikLockner MarkusDahlmanns TiandongXi MarcelFey ChristianBrecher ChristianHopmann KlausWehrle inproceedings 2012-AIS-Gerst-vehicles 2012 6 8 Proc. of the Annual Conference of the Academy of Innovation and Entrepreneurship (AIS). MartinaGerst KaiJakobs GaoXudong