ITTC 2025

8. oktober 2025 l 8 October 2025 Koper, Slovenia IS 2025 18. Mednarodna konferenca o prenosu tehnologij 18th International Technology Transfer Conference Uredniki l Editors: Duško Odić, Terezija Poženel Kovačič, Robert Blatnik INFORMACIJSKA DRUZBA INFORMATION SOCIETY Zbornik 28. mednarodne multikonference Zvezek E Proceedings of the 28th International Multiconference Volume E ˇ Zbornik 28. mednarodne multikonference INFORMACIJSKA DRUŽBA – IS 2025 Zvezek E Proceedings of the 28th International Multiconference INFORMATION SOCIETY – IS 2025 Volume E 18. Mednarodna konferenca o prenosu tehnologij 18th International Technology Transfer Conference Uredniki / Editors Duško Odić, Terezija Poženel Kovačič, Robert Blatnik http://is.ijs.si 8. oktober 2025 / 8 October 2025 Koper, Slovenia Uredniki: Duško Odić Služba za projektno informatiko, organizacijo strokovnih dogodkov in konferenc, Institut "Jožef Stefan" Terezija Poženel Kovačič Služba za vsebinsko podporo projektom, prenos tehnologij in inovacije, Institut "Jožef Stefan" Robert Blatnik Služba za vsebinsko podporo projektom, prenos tehnologij in inovacije, Institut »Jožef Stefan«, Ljubljana Založnik: Institut »Jožef Stefan«, Ljubljana Priprava zbornika: Mitja Lasič, Vesna Lasič, Lana Zemljak Oblikovanje naslovnice: Vesna Lasič Dostop do e-publikacije: http://library.ijs.si/Stacks/Proceedings/InformationSociety Ljubljana, oktober 2025 Informacijska družba ISSN 2630-371X DOI: https://doi.org/10.70314/is.2025.ittc Kataložni zapis o publikaciji (CIP) pripravili v Narodni in univerzitetni knjižnici v Ljubljani COBISS.SI-ID 255459587 ISBN 978-961-264-324-9 (PDF) PREDGOVOR MULTIKONFERENCI INFORMACIJSKA DRUŽBA 2025 28. mednarodna multikonferenca Informacijska družba se odvija v času izjemne rasti umetne inteligence, njenih aplikacij in vplivov na človeštvo. Vsako leto vstopamo v novo dobo, v kateri generativna umetna inteligenca ter drugi inovativni pristopi oblikujejo poti k superinteligenci in singularnosti, ki bosta krojili prihodnost človeške civilizacije. Naša konferenca je tako hkrati tradicionalna znanstvena in akademsko odprta, pa tudi inkubator novih, pogumnih idej in pogledov. Letošnja konferenca poleg umetne inteligence vključuje tudi razprave o perečih temah današnjega časa: ohranjanje okolja, demografski izzivi, zdravstvo in preobrazba družbenih struktur. Razvoj UI ponuja rešitve za številne sodobne izzive, kar poudarja pomen sodelovanja med raziskovalci, strokovnjaki in odločevalci pri oblikovanju trajnostnih strategij. Zavedamo se, da živimo v obdobju velikih sprememb, kjer je ključno, da z inovativnimi pristopi in poglobljenim znanjem ustvarimo informacijsko družbo, ki bo varna, vključujoča in trajnostna. V okviru multikonference smo letos združili dvanajst vsebinsko raznolikih srečanj, ki odražajo širino in globino informacijskih ved: od umetne inteligence v zdravstvu, demografskih in družinskih analiz, digitalne preobrazbe zdravstvene nege ter digitalne vključenosti v informacijski družbi, do raziskav na področju kognitivne znanosti, zdrave dolgoživosti ter vzgoje in izobraževanja v informacijski družbi. Pridružujejo se konference o legendah računalništva in informatike, prenosu tehnologij, mitih in resnicah o varovanju okolja, odkrivanju znanja in podatkovnih skladiščih ter seveda Slovenska konferenca o umetni inteligenci. Poleg referatov bodo okrogle mize in delavnice omogočile poglobljeno izmenjavo mnenj, ki bo pomembno prispevala k oblikovanju prihodnje informacijske družbe. »Legende računalništva in informatike« predstavljajo domači »Hall of Fame« za izjemne posameznike s tega področja. Še naprej bomo spodbujali raziskovanje in razvoj, odličnost in sodelovanje; razširjeni referati bodo objavljeni v reviji Informatica, s podporo dolgoletne tradicije in v sodelovanju z akademskimi institucijami ter strokovnimi združenji, kot so ACM Slovenija, SLAIS, Slovensko društvo Informatika in Inženirska akademija Slovenije. Vsako leto izberemo najbolj izstopajoče dosežke. Letos je nagrado Michie-Turing za izjemen življenjski prispevek k razvoju in promociji informacijske družbe prejel Niko Schlamberger, priznanje za raziskovalni dosežek leta pa Tome Eftimov. »Informacijsko limono« za najmanj primerno informacijsko tematiko je prejela odsotnost obveznega pouka računalništva v osnovnih šolah. »Informacijsko jagodo« za najboljši sistem ali storitev v letih 2024/2025 pa so prejeli Marko Robnik Šikonja, Domen Vreš in Simon Krek s skupino za slovenski veliki jezikovni model GAMS. Iskrene čestitke vsem nagrajencem! Naša vizija ostaja jasna: prepoznati, izkoristiti in oblikovati priložnosti, ki jih prinaša digitalna preobrazba, ter ustvariti informacijsko družbo, ki koristi vsem njenim članom. Vsem sodelujočim se zahvaljujemo za njihov prispevek — veseli nas, da bomo skupaj oblikovali prihodnje dosežke, ki jih bo soustvarjala ta konferenca. Mojca Ciglarič, predsednica programskega odbora Matjaž Gams, predsednik organizacijskega odbora i FOREWORD TO THE MULTICONFERENCE INFORMATION SOCIETY 2025 The 28th International Multiconference on the Information Society takes place at a time of remarkable growth in artificial intelligence, its applications, and its impact on humanity. Each year we enter a new era in which generative AI and other innovative approaches shape the path toward superintelligence and singularity — phenomena that will shape the future of human civilization. The conference is both a traditional scientific forum and an academically open incubator for new, bold ideas and perspectives. In addition to artificial intelligence, this year’s conference addresses other pressing issues of our time: environmental preservation, demographic challenges, healthcare, and the transformation of social structures. The rapid development of AI offers potential solutions to many of today’s challenges and highlights the importance of collaboration among researchers, experts, and policymakers in designing sustainable strategies. We are acutely aware that we live in an era of profound change, where innovative approaches and deep knowledge are essential to creating an information society that is safe, inclusive, and sustainable. This year’s multiconference brings together twelve thematically diverse meetings reflecting the breadth and depth of the information sciences: from artificial intelligence in healthcare, demographic and family studies, and the digital transformation of nursing and digital inclusion, to research in cognitive science, healthy longevity, and education in the information society. Additional conferences include Legends of Computing and Informatics, Technology Transfer, Myths and Truths of Environmental Protection, Knowledge Discovery and Data Warehouses, and, of course, the Slovenian Conference on Artificial Intelligence. Alongside scientific papers, round tables and workshops will provide opportunities for in-depth exchanges of views, making an important contribution to shaping the future information society. Legends of Computing and Informatics serves as a national »Hall of Fame« honoring outstanding individuals in the field. We will continue to promote research and development, excellence, and collaboration. Extended papers will be published in the journal Informatica, supported by a long-standing tradition and in cooperation with academic institutions and professional associations such as ACM Slovenia, SLAIS, the Slovenian Society Informatika, and the Slovenian Academy of Engineering. Each year we recognize the most distinguished achievements. In 2025, the Michie-Turing Award for lifetime contribution to the development and promotion of the information society was awarded to Niko Schlamberger, while the Award for Research Achievement of the Year went to Tome Eftimov. The »Information Lemon« for the least appropriate information-related topic was awarded to the absence of compulsory computer science education in primary schools. The »Information Strawberry« for the best system or service in 2024/2025 was awarded to Marko Robnik Šikonja, Domen Vreš and Simon Krek together with their team, for developing the Slovenian large language model GAMS. We extend our warmest congratulations to all awardees. Our vision remains clear: to identify, seize, and shape the opportunities offered by digital transformation, and to create an information society that benefits all its members. We sincerely thank all participants for their contributions and look forward to jointly shaping the future achievements that this conference will help bring about. Mojca Ciglarič, Chair of the Program Committee Matjaž Gams, Chair of the Organizing Committee ii KONFERENČNI ODBORI CONFERENCE COMMITTEES International Programme Committee Organizing Committee Vladimir Bajic, South Africa Heiner Benking, Germany Se Woo Cheon, South Korea Howie Firth, UK Olga Fomichova, Russia Vladimir Fomichov, Russia Vesna Hljuz Dobric, Croatia Alfred Inselberg, Israel Jay Liebowitz, USA Huan Liu, Singapore Henz Martin, Germany Marcin Paprzycki, USA Claude Sammut, Australia Jiri Wiedermann, Czech Republic Xindong Wu, USA Yiming Ye, USA Ning Zhong, USA Wray Buntine, Australia Bezalel Gavish, USA Gal A. Kaminka, Israel Mike Bain, Australia Michela Milano, Italy Derong Liu, Chicago, USA Toby Walsh, Australia Sergio Campos-Cordobes, Spain Shabnam Farahmand, Finland Sergio Crovella, Italy Matjaž Gams, chair Mitja Luštrek Lana Zemljak Vesna Koricki Mitja Lasič Blaž Mahnič Programme Committee Mojca Ciglarič, chair Bojan Orel Franc Solina Viljan Mahnič Cene Bavec Tomaž Kalin Jozsef Györkös Tadej Bajd Jaroslav Berce Mojca Bernik Marko Bohanec Ivan Bratko Andrej Brodnik Dušan Caf Saša Divjak Tomaž Erjavec Bogdan Filipič Andrej Gams Matjaž Gams Mitja Luštrek Marko Grobelnik Nikola Guid Marjan Heričko Borka Jerman Blažič Džonova Gorazd Kandus Urban Kordeš Marjan Krisper Andrej Kuščer Jadran Lenarčič Borut Likar Janez Malačič Olga Markič Dunja Mladenič Franc Novak Vladislav Rajkovič Grega Repovš Ivan Rozman Niko Schlamberger Gašper Slapničar Stanko Strmčnik Jurij Šilc Jurij Tasič Denis Trček Andrej Ule Boštjan Vilfan Baldomir Zajc Blaž Zupan Boris Žemva Leon Žlajpah Niko Zimic Rok Piltaver Toma Strle Tine Kolenik Franci Pivec Uroš Rajkovič Borut Batagelj Tomaž Ogrin Aleš Ude Bojan Blažica Matjaž Kljun Robert Blatnik Erik Dovgan Špela Stres Anton Gradišek iii iv KAZALO / TABLE OF CONTENTS 18. Mednarodna konferenca o prenosu tehnologij / 18th International Technology Transfer Conference ................................................................................................................................................................... 1 PREDGOVOR / FOREWORD ............................................................................................................................... 3 PROGRAMSKI ODBORI / PROGRAMME COMMITTEES ............................................................................... 5 Innovations in patent valuation: testing Smart5 on Slovenian spin-out and start-up companies / Hafner Ana...... 7 Academic entrepreneurs in Slovenia: Entrepreneurial Competences, Intellectual Property, and Academic Culture / Hafner Ana, Kolar Janez, Lamut Urša, Dobravc Škof Karin ........................................................................ 11 University - Business Cooperation in Slovakia / Pastor Rudolf ........................................................................... 15 Developing University-Industry Cooperation through Liaison Offices in Organized Industrial Zones: The KTÜ TTC Example / Değermenci Beril, İskender Balaban Dilek, Aykut Yalçın, Ayvaz Emrah, Kalyoncu Sedanur, Yildiz İslam, Sönmez Kerim, Yilmaz Eren, Gültekin Güler Tuğba, Sağlam Gözde, Değirmenci Samet Can, Ünver Müslüm Serhat, Aydin Aleyna, Sabir Hülya, Koç Ayhan ................................................. 19 Knowledge Sharing, Protection of Trade Secrets, and Sensitive Practices in the Circular Economy / Lužar Magda ............................................................................................................................................................... 23 Self-evaluation of Research Organizations in the Field of Knowledge Transfer / Lutman Tomaž, Vindišar Jure .......................................................................................................................................................................... 27 Strengthening Knowledge and Technology Transfer Ecosystems through Transnational Collaboration: The Case of the STEIDA Project / Sabir Hülya, Kalyoncu Sedanur, Sağlam Gözde, Gültekin Güler Tuğba, Koç Ayhan, Yildiz İslam, Ünver Müslüm Serhat, Yilmaz Eren, Değermenci Beril, İskender Balaban Dilek, Ayvaz Emrah, Değirmenci Samet Can, Sönmez Kerim, Aykut Yalçın, Aydin Aleyna, Tancheva Mariana, Paunov Dimitar, Pastor Rudolf, Noskovic Jaroslav, Florjancic Urska, Blatnik Robert, Leban Marijan, Kireta Sanja, Lale Orsat, Perez Berta, Atienza Vicente, Hehn Leonie .................................................................................. 31 Evaluating Skill Development and Collaboration Outcomes in the INDUSAC Project / Kunej Špela, Odić Duško, Mrgole Urška, Trobec Marjeta ............................................................................................................ 35 Digital Persona Generation: Historical Figure Emulation in Learning / Kaliappan Velu .................................... 39 Trends in Brain-Computer Interface Technologies: Patent Analysis / Aničić Čandrlić Rahela, Jagodič Gregor 43 Indeks avtorjev / Author index ................................................................................................................... 47 v 6 Innovations in Patent Valuation: Testing SMART5 on Slovenian Spin-out and Start-up Companies Ana Hafner† Centre for Technology Transfer and Intellectual Property Rudolfovo – Science and Technology Centre Novo mesto Faculty of Information Studies Novo mesto [email protected] Abstract This study presents a brief review of patent valuation techniques, followed by a specific case study of SMART5, an online patent evaluation service. SMART5 was tested on U.S. and European patents of eight successful Slovenian firms that had previously been start-up companies, four of which originated as spin-outs from universities or public research institutes. The results show that SMART5 is a reliable and attractive tool; however, some improvements are needed, particularly regarding the transparency of its scoring methodology and the evaluation of patents belonging to the same family, where the system yields conflicting results for identical inventions. Keywords IP valuation, patent valuation, SMART5, quantitative valuation, spin-out/start-up enterprises 1 Introduction The valuation of patents has become an increasingly important field of research and practice, as intellectual property (IP) is now widely recognized as a critical driver of innovation, competitiveness, and economic growth. Start-up companies and university spin-outs in particular rely heavily on patents not only to protect their technological advancements but also to attract investment, secure partnerships, and strengthen their market position. Yet, despite the growing strategic significance of patents, their valuation remains a complex task. Universities or other public research institutions’ (PROs) spinout companies are typically formed to commercialize intellectual property (IP) generated within these academic institutions. Here patents often play a central role in this process. The creation and success of these spin-outs are closely linked to how universities and PROs manage and protect their IP, especially through formal mechanisms like patents and trademarks. However, research indicates that while formal IP protection (such as patents) is commonly used, it can sometimes negatively impact the competitiveness of spin-outs [9]. In contrast, informal protection strategies, like maintaining trade secrets, may be more beneficial for competitiveness in certain contexts [9]. This paper contributes to the ongoing debate on patent valuation methods by presenting an empirical test of SMART5 applied to the patents of eight Slovenian start-ups, four of which are university or PRO spin-outs. By combining a short review of existing valuation techniques with a critical assessment of SMART5’s performance, the study highlights both the potential and the limitations of automated patent evaluation tools. In doing so, it provides insights into how such tools could be further improved to better serve the needs of innovative enterprises, IP practitioners and technology transfer staff. 2 Patent valuation techniques Patent valuation techniques can broadly be grouped into qualitative and quantitative approaches. Qualitative methods have interpretative and subjective nature and they attempt to determine patent value by understanding the processes and the behavioural patterns [3]. They often include expert judgment [6]. Quantitative methods, on the other hand, attempt to measure patent value using economic frameworks. These include costbased approaches, which estimate the resources required to develop and protect the invention; market-based approaches, which rely on comparable patent transactions or licensing deals; and income-based approaches, which calculate expected future cash flows derived from exploiting the patent [1, 12, 13]. A classification of methods for patent valuation as analysed by Munari and Oriani [7] is presented on Figure 1. Figure 1: A classification of methods for patent valuation, source [7] †Corresponding author Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia © 2025 Copyright held by the owner/author(s). 7 Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia A. Hafner We will focus only on quantitative non-monetary methods, i.e., patent indicators. Typical indicators are legal status, international and technological scope, number of forward citations and the existence of opposition and litigation [7]. Such valuation has many advantages: the method is fast, objective and inexpensive and can be fully automated once the valuation system is set up [7]. International scope (size of patent family) and forward citations (citations received from patents applied later) are probably the most frequent measures for assessing patent value. For example, patent valuation using forward citations has been increasingly used by practitioners when a patent’s value has not been otherwise established [10]. Og et al. [8] divide patent value indicators into ex-ante indicators (family size, backward citations, backward references to non-patent literature, number of claims, and number of inventors) and ex-post indicators (forward citations). Such indicators can be further integrated into indexes, composite measures that can combine multiple indicators into a single value. Grimaldi and Cricelli [4] in their paper “Indexes of Patent Value: A Systematic Literature Review and Classification” identified even 37 different indexes. Despite the variety of available methods, no single approach offers a universally accepted or comprehensive solution. Traditional valuation models often face limitations when applied to early-stage companies. Valuing patents in start-ups presents unique challenges due to limited financial history, uncertain market prospects, and evolving technologies. As a result, traditional valuation models often require adaptation or supplementation [2]. In response to various challenges connected to patent valuation – along with a lengthy and complex assessment – SMART5 was developed under the auspices of the Korea Invention Promotion Association (KIPA) and offers an online platform for patent evaluation where each patent can be evaluated in some seconds. The system claims to “objectively evaluate the superiority of a patent in different countries” by leveraging patent specifications, bibliographic information, and administrative data [14]. By transforming bibliometric indicators into comparative scores, SMART5 aims to provide accessible and standardized insights into patent quality across jurisdictions. While such automated evaluation systems hold considerable promise for reducing information asymmetries and enhancing decision-making, they must be rigorously tested for reliability, transparency, and contextual relevance. This is particularly important for research-based spin-outs and start-ups, which often operate with limited resources and for whom misleading or inconsistent patent assessments may have serious strategic consequences. 3 About SMART5 To illustrate how patent evaluation systems can support innovation and technology transfer, this chapter introduces SMART5 with a brief overview of its purpose, methodology and application. SMART5 [14, 15] is an acronym for System to Measure, Analyse and Rate patent Technology. It is an online patent evaluation service in which the “superiority of a patent in different countries is objectively evaluated using patent information extracted from patent specification, bibliographic information, and administrative information”, as claimed by owner and developer KIPA. It is not very clear, at least from the documents translated in English, what does the number “5” mean in the name. The “5” may refer to a software version (like the 5th release) or maybe to the five (broad) evaluation dimensions that the system applies when assessing patents. The system is designed to objectively evaluate a patent across different countries using a combination of patent specifications, bibliographic information, application information, examination information, information about litigations, licences, changes of ownership, citations and patent family information. SMART5 emphasizes a data-driven scoring system grounded in patent documentation rather than subjective expert opinion alone. It supports evaluation for patents registered in China, Europe (European patent), Japan, Korea, and the United States. Figure 2: Patent evaluation model, source [15] SMART5 was first launched already in 2010 and it has processed approximately 1.7 million evaluations (up to 2023) and has emerged as a leading patent evaluation system in South Korea, taking the forefront in popularizing patent assessment [16]. However, SMART5 is not known in Slovenia and it was first presented on the IP Valuation Workshop in Ljubljana in June 2025 [11] and it has received a lot of interest from technology transfer professionals. KIPA kindly provided us with possibility to test 10 patents free of charge. 4 Method During August 2025 we tested SMART5 on ten patents from eight different patent families from eight Slovenian start-up or universities or other PROs spin-out companies. Start-up enterprises were selected from enterprises which received support of Slovenian Enterprise Fund – Tender P2 – which is a grant intended to co-finance the setting up of innovative enterprises [17]. PROs spin-outs were selected from the online news and university websites such as University of Ljubljana presentation of their spin outs [18]. The basic criteria for the selected patents were, that they are not “too young and too old”. With not “too young” we mean patents 8 Innovations in patent valuation: testing SMART5 on Slovenian spin-out and start-up companies Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia which are more than five years old, and they are applied and already granted outside Slovenia. With not “too old” we mean that patens are still maintained. Two patent families from our spin-outs were tested with different patents within the same family – one applied in Europe and the other in the U.S. Since a “patent family” presents the same patent, applied in different jurisdictions, we were interested, will SMART5 recognize the same patents and evaluate them equally. SMART5 evaluates patents based on three core aspects: IP Rights valuation – legal strength and enforceability of the patent; Technology valuation – the technical sophistication and innovativeness of the invention; and Usability valuation – the potential for practical application and commercialization of the patent [15]. These grades are in the end summarized in one overall grade of a patent which can receive a hierarchical grade from AAA (the highest grade) to C (the lowest grade) as presented on the Figure 3. Figure 3: SMART5 evaluation grades, source [15] 5 Results We can confirm that SMART5 offers several clear strengths that make it an attractive tool for patent evaluation. The results are presented in a clear and user-friendly way, with well-structured grades and visualizations that allow even non-specialists to quickly grasp the relative strength of a patent. The interface and design are intuitive, supporting ease of use even for small companies which may lack in-house IP expertise. Our results are listed in the Table 1. It is also important to note that the patents were filed in the past, while the company size reflects the present data. Table 1: Results of testing SMART5 Patent no. Priority date Company Grade Company size US2016194054 (A1) 2013 Start-up A micro US2020395832 (A1) 2017 Spin-out CCC medium US2016134220 (A1) 2013 Start-up AA small US2017100755 (A1) 2015 Spin-out A micro US2021068752 (A1) 2019 Start-up B medium EP2868662 (A1) 2013 Spin-out A medium EP3197906 (A1) 2014 Spin-out BBB micro EP3001203 (A1) 2014 Start-up B micro EP3153246 (A1) 2015 Spin-out AA micro EP3711147 (A1) 2017 Spin-out BB medium Seven (five) patents received a grade of A, AA, BB, or BBB which is according to SMART5 above-average to strong quality. Only one patent received score CCC, standing out as significantly weaker. It should be noted that this patent belongs to an enterprise that already holds around 20 different patent families, and it may have been a coincidence that our sample included one of the weakest patents from its portfolio. By contrast, a micro start-up with only a single patent family received an A grade for its U.S. patent. It can therefore be argued that the overall quality of a company’s patent portfolio cannot be inferred from a single evaluation result, especially when the portfolio is large and heterogeneous. Larger enterprises may hold a mix of both strong and weak patents, depending on the stage of development, research focus, and patenting strategy. Conversely, for very small firms or start-ups, even one patent can represent the core of their business model, and thus its evaluation result is highly consequential. This highlights the importance of interpreting SMART5 results not only at the level of individual patents but also within the broader context of portfolio structure and company strategy. Most importantly, the scores generally align with expectations based on the technological and legal aspects of these specific patents, which indicates that the tool captures meaningful aspects of patent quality. These features demonstrate that SMART5 has considerable value as a first-level screening and benchmarking instrument, capable of guiding companies and IP professionals toward more informed decision-making. However, from the Table 1 we cannot see evidence of influence of quality of patent to the present size of enterprise. Micro firms have patents graded from AA (very strong) to B (solid) what shows that even the smallest firms can secure relatively strong patents. At the same time medium sized firms showed mixed performance, ranging from CCC (weak) to A (strong). Of course, this might be a consequence of a small sample size. Among the tested cases, two patents (marked with blue and green colour) belong to the same patent family (parallel filings of the same invention in different jurisdictions). Ideally, such patents should receive identical grades, because their technical content, inventive step, and core claims are essentially the same. Minor differences may occur due to jurisdiction-specific citation practices, examiner reports, or legal events, but these should not result in substantial differences in the overall valuation. 9 Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia A. Hafner 6 Discussion and Conclusion Some previous studies already tested SMART5. For example, study of Lee, Jeong, and Kong [5] examines determinants of successful technology commercialization. It focuses on 883 U.S. patents issued in 2020 that originated from government-funded R&D in South Korea. The key aim of the study was to understand which patent-related characteristics contribute to revenue generation through licensing and transfers. Among the variables analysed, the study highlights SMART5 scores as a central qualitative indicator. In statistical tests (linear and logistic regression), the SMART5 grade emerged as the only variable with a statistically significant positive impact on technology commercialization outcomes. Of course, our sample was very small, therefore we found no evidence of correlation with commercialisation outcomes (translated into our study we may have predicted that start-ups which had more valuable patents in the past are now larger firms with more employees and more revenue). Because of small sample size, we cannot conclude anything except that SMART5 is an attractive tool for patent evaluation. For technology transfer professionals, the system may provide structured, evidencebased evaluations that support better patent portfolio management, stronger industry confidence, and higher commercialization potential. However, SMART5 has two disturbing issues. First, if a user of the system wants to find more information about how scores and how the final score are calculated, what specific indicators are taken and how are they weighted, there are not many available information – at least not in English. So, while SMART5 provides clear numerical indicators of patent quality and market relevance, the underlying algorithms and weightings used in the evaluation remain largely undisclosed. This lack of transparency can limit user trust and also raises questions about the value of SMART5 for technology transfer offices: if it is better at assessing the impact of a patent that is already well established and widely cited than at evaluating an initial patent application, then it is more useful as a tool for monitoring and demonstrating the value of mature patents rather than for making early-stage decisions about which patent applications to prioritize. However, it might also be reasonable that KIPA is keeping the methodology as a business secret. Second, in the SMART5 evaluation, we observed inconsistent grading of patent family members. It seems from our two cases that European patents automatically get a higher grade than those from the U.S. This inconsistency can be interpreted in two ways: 1) Algorithmic sensitivity to jurisdictional data: SMART5 may weigh bibliographic and administrative data differently across jurisdictions (e.g., USPTO vs. EPO citation patterns, costs of patent or procedural timelines). This could lead to artificially divergent results for otherwise equivalent inventions. 2) Lack of family-level normalization: SMART5 appears to evaluate each filing in isolation, without consolidating information across the patent family. In practice, this risks misrepresenting the value of an invention, since a strong patent family can be unfairly weakened by one low score, or vice versa. Despite these limitations, the application of SMART5 to Slovenian start-ups/spin-outs demonstrated its usefulness as a benchmarking tool. It allows companies to compare their patent portfolios against competitors across jurisdictions, identify relative strengths, and detect areas where additional IP strategy development might be necessary. With targeted improvements, SMART5 could evolve into a more robust and widely adopted instrument for patent evaluation in both academic and privatesector innovation ecosystems. With inclusion of artificial intelligence, occupations such as expert for patent valuation, may become obsolete. We believe that this can happen in the next three years. Acknowledgments Author acknowledges that this research was supported by the project KTO3 funded under the Call for Proposals to support the activities of Knowledge Transfer Offices (JR KTO). References [1] Martin A Bader and Frauke Rüether. Still A Long Way To Value-Based Patent Valuation. [2] Aysun Beyazkilic Koc and Nihan Yildirim. 2023. A multi-criteria decision framework for IP valuation method selection: “Valuation case” matters. World Patent Information 73, (June 2023), 102176. https://doi.org/10.1016/j.wpi.2023.102176 [3] Nil Girgin Kalıp, Yaman Ömer Erzurumlu, and Nur Asena Gün. 2022. Qualitative and quantitative patent valuation methods: A systematic literature review. World Patent Information 69, (June 2022), 102111. https://doi.org/10.1016/j.wpi.2022.102111 [4] Michele Grimaldi, Livio Cricelli, Martina Di Giovanni, and Francesco Rogo. 2015. The patent portfolio value analysis: A new framework to leverage patent information for strategic technology planning. Technological Forecasting and Social Change 94, (May 2015), 286–302. https://doi.org/10.1016/j.techfore.2014.10.013 [5] Jaeheon Lee, Myoungsun Jeong, and Heejung Kong. 2024. Qualitative factors of patents affecting technology commercialization in 2020: An analysis of U.S. registered patents. Journal of Intellectual Property 19, 3 (September 2024), 111–129. https://doi.org/10.34122/JIP.2024.19.3.111 [6] Pineda Martinez and Katherine Michelle. 2025. Strategic patent portfolio management : an expert-based framework for IP value assessment. (2025). Retrieved August 23, 2025 from https://lutpub.lut.fi/handle/10024/169544 [7] Federico Munari and Raffaele Oriani. 2011. The Economic Valuation of Patents: Methods and Applications. Edward Elgar Publishing. [8] Joo Young Og, Krzysztof Pawelec, Byung-Keun Kim, Rafal Paprocki, and EuiSeob Jeong. 2020. Measuring Patent Value Indicators with Patent Renewal Information. Journal of Open Innovation: Technology, Market, and Complexity 6, 1 (March 2020), 16. https://doi.org/10.3390/joitmc6010016 [9] Aurora A. C. Teixeira and Cátia Ferreira. 2019. Intellectual property rights and the competitiveness of academic spin-offs. Journal of Innovation & Knowledge 4, 3 (July 2019), 154–161. https://doi.org/10.1016/j.jik.2018.12.002 [10] Dan Werner and Huy Dang. 2021. Patent Valuation Using Citations: A Review and Sensitivity Analysis. 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Retrieved August 24, 2025 from https://www.uni-lj.si/raziskovanje/inovacije-in-prenosznanja/odcepljena-podjetja 10 Academic Entrepreneurs in Slovenia: Entrepreneurial Competences, Intellectual Property, and Academic Culture Ana Hafner† Centre for Technology Transfer and Intellectual Property Rudolfovo - Science and Technology Centre Novo mesto and Faculty of Information Studies [email protected] Janez Kolar Centre for Technology Transfer and Intellectual Property Rudolfovo - Science and Technology Centre Novo mesto [email protected] Urša Lamut Centre for Technology Transfer and Intellectual Property Rudolfovo - Science and Technology Centre Novo mesto [email protected] Karin Dobravc Škof Centre for Technology Transfer and Intellectual Property Rudolfovo - Science and Technology Centre Novo mesto [email protected] Abstract Academic entrepreneurship is a key channel for linking research and economic development, yet little is known about its dynamics in smaller national innovation systems. Based on indepth interviews with the founders of three internationally successful Slovenian spin-out enterprises, we examine how do Slovenian academic entrepreneurs in the natural and technical sciences acquire entrepreneurial competences, use intellectual property, and perceive the entrepreneurial culture in their academic environment. Although their academic backgrounds are rooted in highly technical fields, our findings reveal that these founders have engaged extensively in entrepreneurial learning, management and intellectual property, acquiring knowledge and skills far beyond their original scientific expertise. They emphasise the importance of patents and intellectual property rights knowledge, however, intellectual property is not viewed merely as a legal safeguard but as a strategic resource for signalling credibility and positioning firms at different growth stages. At the same time, they express a critical perspective on the prevailing entrepreneurial culture within Slovenian academia, which they perceive as underdeveloped, contrasting it with more supportive environments abroad. Keywords Academic entrepreneurs, spin-outs, academic entrepreneurship, intellectual property, patents, entrepreneurial competences 1 Introduction Academic entrepreneurship is often defined as the direct involvement of academicians in valorising research results in the market, often through the creation of new firms or academic spinoffs [14]. This process has gained increasing attention in recent decades, but in smaller countries such as Slovenia, where the institutional setting is less developed and the number of cases is limited, the dynamics of academic entrepreneurship are not yet well understood. Our research question is: how do Slovenian academic entrepreneurs in the natural and technical sciences acquire entrepreneurial competences, use intellectual property, and perceive the entrepreneurial culture in their academic environment? Previous studies on academic entrepreneurship have shown that institutional culture, resource availability, and the presence of role models play important roles in shaping academic entrepreneurship, but their influence can vary by context. For example, in Brazil, institutional initiatives had limited direct impact on academic entrepreneurship, suggesting some level of ineffectiveness in initiatives aiming at promoting academic entrepreneurship in Brazilian universities [6] while study in China highlights that supportive university environment and favourable government policies significantly enhance entrepreneurial intentions [1]. Academic entrepreneurs more likely engage in commercial activities such as founding or advising companies compared to their non-entrepreneurial peers. Ding and Choi [4] showed that founding activity occurred earlier during a scientist's career than advising and that factors such as gender, research productivity, social networks and employer characteristics also play important roles. Academic entrepreneurs often develop a dual identity, balancing their roles as scientists and entrepreneurs [3] and the interaction between scientific and entrepreneurial identities can strengthen the intention to engage in entrepreneurship: academic entrepreneurs who are also “hybrid scientists” can positively promote the development of the firms’ knowledge breadth, and the “hybrid entrepreneurs” deepen the knowledge depth of academic start-ups. Academic entrepreneurs with prior business ownership experience had broader social networks and were more effective in developing network ties while less experienced entrepreneurs likely encounter structural holes between their scientific research networks and industry networks [11]. International mobility experiences further differentiate academic entrepreneurs, as returnees with international exposure are more than 50% more likely to become academic entrepreneurs than those who have not worked abroad [12]. Our study draws on in-depth interviews with the founders of three internationally successful Slovenian spin-out enterprises to examine how academic entrepreneurs from the natural and technical sciences acquire and integrate entrepreneurial competencies into their professional trajectories. We explore the ways in which they engage with entrepreneurial learning, management practices, and intellectual property rights, as well as their critical views on the prevailing entrepreneurial culture within Slovenian academia. By analysing these cases, we aim to contribute to four strands of literature: (1) the study of academic †Corresponding author Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia © 2025 Copyright held by the owner/author(s). 11 Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia A. Hafner et al. entrepreneurship in smaller, less-researched national systems; (2) the understanding of how technical and scientific expertise is complemented by non-technical competencies in successful spin-out ventures; and (3) the examination of the role of intellectual property for spin-out (start-up) enterprises. 2 Methodology This study employs a qualitative research design. Given the complexity of individual decision-making processes, an in-depth interview approach is well-suited to uncover nuanced perspectives that may not be evident in quantitative studies. This approach enables us to delve into the participants’ experiences, motivations, and perceptions regarding entrepreneurship, intellectual property and innovation management. The study focuses on three top Slovenian spin-outs which successfully sell their products internationally, and they are also well known to the wider Slovenian public. Companies had to meet the following criteria: - their founders were employed at universities/ PROs before they founded a start-up, - they sell scientifically based product or services, i.e., they are deep tech companies, - they are international companies: their market is extended outside Slovenia, - they are older than five years. Additional characteristics of companies are presented in Table 1. Table 1: Properties of spin-out companies R1 R2 R3 Year of foundation 2006 2017 2005 Number of patent families 20 0 (owners of a patent are founders/ institute) 5 Number of trademarks 3 1 20 Number of employees 100 - 200 1 - 10 200 - 300 Company’s size Medium sized company Micro company Large company Educational background of founder Physics, electrotechnics Physics, chemistry Physics Patents were obtained from Espacenet database, while trademarks from TM View database. Interviews were held either face-to-face or online in June and July 2025, lasting between 60 and 90 minutes. All interviews were audio-recorded and subsequently transcribed verbatim for analysis. We applied thematic analysis, which involved the following steps: transcription, independent coding by two researchers to ensure data validity, identification of recurring themes, and interpretation of findings. 3 Results From Table 1 we can see that two companies are now 20 years old while one is more than 10 years younger. The two older spinouts have significantly more registered industrial property as well as they have more employees. It is very interesting that all our respondents have bachelor’s degree from physics, however, two of them later specialized in another fields. For example, R1 graduated in astrophysics, but, as he explained, he had always wanted to do something more practical. This is why he decided to pursue a master’s degree in electrical engineering. One of the reasons of spin-out’s success was founders' willingness to receive additional education in entrepreneurship and intellectual property, before and/or after founding the company. After finishing his PhD from physics, R3 also completed MBA study with the highest grades. R2 describes: “When I started, I didn’t have any knowledge about how to be a manager. Then I began educating myself a little, and I basically internalized that the most important thing is the team – how you hold the team together, what you have to do? /…/ How do you make sure that everyone is motivated, that you figure out what each person is best at, and place them exactly there so that they feel the most comfortable? These are the kinds of skills that are very important, especially in small companies.” R1 explains: “Here, I would like to praise our supportive environment. The technology park organized a bunch of workshops, as did the business incubator, so I was somehow “infected” with these /entrepreneurial/ things, but I was also proactive. This also applies to my master’s studies. I arranged my electrical engineering program in such a way that I also took the Innovation Management course at the Faculty of Economics /…/ So I switched from physics to electrical engineering, but I set it up in a rather interdisciplinary way.” R2 also attended several management and entrepreneurial lectures at the university incubator. R1 also took World Intellectual Property Organization’s Distance Learning courses from intellectual property (IP): “…so I would also advise all researchers interested in technology to simply be proactive and to also take advantage of online courses and various workshops and participate in them. It’s not about how much they will gain from the content itself, but primarily it will stimulate their thinking and make it easier for them to understand something on their own later.” IP is very important for our respondents, but not only in the “classical” sense, such as the legal protection of patents or trademarks as a means of achieving market exclusivity. Rather it is understood in a broader, more strategic sense. Respondents emphasized its role in shaping competitive advantage, facilitating collaboration with external partners, and signalling credibility to investors and stakeholders. In this perspective, IP functions less as an offensive mechanism and more as an active resource within innovation processes and organizational strategy. R1 claimed: “We know for sure that some larger companies potentially infringe our patents. But that doesn’t necessarily mean that we will react to it. This is because a patent has a specific purpose, and in the industry it has a certain value that you add. And it’s not necessarily about wanting to block others who are doing something similar. So yes, a patent does not necessarily have an offensive role.” R3 explained: “I would 12 Academic Entrepreneurs in Slovenia: Entrepreneurial Competences, Intellectual Property, and Academic Culture Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia say that patent is one of the defensive mechanisms. Just like a trademark is a defensive mechanism against competition. We want to compete, we want to beat the competition. How will we beat them? With quality? Certainly. With price? No. /…/ We achieve that through other elements: quality, brand, reputation as such. If patents or regulatory protections are part of that brand, then yes. If we are producing medical products, they must be certified. That is quite demanding. But once we have certification, we know the competition will need two years, not only to copy us but also to go through that same process. In short, that’s it. One of these defensive mechanisms.” R2 believes that patents may enhance the company’s reputation and gain the trust of customers: “Patents are very important because people ask you whether you have patented something. And if you say yes, then that’s it. They feel safe, because someone has reviewed it and confirmed that it is truly an invention.” R1 emphasized that patents are very important for attracting investors: “Without this, it is completely impossible to gain either a customer or an investor, because, of course, the customer needs to be protected in order to use your technology. The same goes for the investor.” How you use IP, also depends on the size of a company. R1 explained: “For example, if you are a small company, you have a patent so that you can show others that you have it and that this makes you worth something. It also protects your customers, since someone bigger cannot just use protected technology. If you are a medium-sized company, say with a few hundred or thousand employees, then you have patents in order to defend yourself against the large players. When you step on their toes and capture a share of the market, they start putting pressure on you. And if you are a very large company, then you have patents to secure and prolong your monopolistic position for some time. So, it depends on the role you are in. But as a small company, of course, it is very useful to have patents. At the same time, I am aware that patents come with costs, which means you cannot have an unlimited number of them. Writing patents is also not easy. Obtaining them involves lengthy procedures, including abroad. You need a patent attorney to handle communication with patent examiners, and these are things that cost money.” R3 concluded: “I think we should patent more. We are still learning, and I am also to blame, so in a way I am criticizing myself. But I think that our culture is too scientific. We tend to believe that if we have done something, it is nothing special. I should have patented every little thing. When I look at American companies, they patent every little stupid thing.” How supportive are Slovenian academic institutions toward entrepreneurship? R2 explained: “Though I had very good experiences at my institute, and I think others do as well, there are also objective obstacles. For example, there is this new law – or rather, it’s already a few years old. It does state that public institutions can be co-owners of spin-outs, but in reality this doesn’t happen because there are no implementing regulations. /…/ For example, when I go abroad, I know some companies that are spin-outs (where public institution is a co-owner). Portugal is very good with these small companies and startups, and I asked them do they have to pay a rent when they had a sit at their institute, they were surprised: ‘How do you pay rent, what kind of rent?’” R1 believes that “in USA there is now significantly more (spinout) tradition. This is something that is taken for granted. Does it give professors the highest rating? That means students go to the professor who is the most successful – not academically, but entrepreneurially. Because this way they ensure, let’s say, good conditions for life and for interesting research. We don’t have yet these traditions and experiences in Europe. But it’s not forbidden. I believe the legislation is not particularly unfriendly to this. It’s just that people need to start doing it. The more good stories are there, the more it will happen naturally on its own.” R2 agrees: “The US has significantly more spin-outs than Europe. Europe is very diverse. In some countries, there is enough support and a lot of startups. In others, there is less – for example, in Croatia, there are already more startups than in Slovenia. We are very poor here.” Our respondents were very critical to basic academic requirements, such as excessive publishing and metrics tracked by academic institutions. R2 said: “What really annoys me are these articles with a huge number of authors, and in most cases, people are just listed there. I am not listed on any such article, because I don’t want to be listed if I didn’t read it and participate in it. No, this is pointless to me, because if someone tells me they have 50 articles per a year – I say, just don’t try to fool me!” To encourage more spin-outs, R3 believes, the achievements should be equivalently rewarded, both academic and entrepreneurial: “The reward and reputation should be equivalent, or you do pure basic science and publish articles, or you start a company. /…/ I think I have enough knowledge, experience, and everything to be a professor and teach. However, the system does not allow me to be a university professor, because I do not meet the requirements for habilitation.” R3 concluded: “Here in science, for example, where I am, the professors have a secure academic job and on top of that they also have some extra private business and have a good time. And this is fine. With that money they can buy a Mercedes, a yacht and a weekend house. And that's it. But wouldn't it be better if this professor would use this knowledge and created a company where he could employ 100 people, and they all would earn so much that each of them could buy a Mercedes, a yacht and a weekend house?” 4 Discussion and Conclusion Our study highlights the critical role of spin-out founders’ proactive learning and entrepreneurial education in the success of spin-out companies. Our respondents consistently emphasized that formal and informal education in entrepreneurship, management, innovation, and IP provided them with essential skills to build and lead effective teams. For instance, R2 described the importance of understanding team dynamics, highlighting the significance of human capital management in small companies, while R1 and R3 actively sought interdisciplinary knowledge through structured courses, workshops, and online programs, demonstrating that continuous learning fosters both confidence and competence in entrepreneurial endeavours. These findings align with prior research emphasizing the value of absorptive capacity in entrepreneurial education [9] and continuous learning and skill development in technology-based entrepreneurship [2, 10]. Academic founders must acquire competencies in entrepreneurship, management, and IP that go far beyond their original disciplinary expertise. The ability to navigate these 13 Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia A. Hafner et al. domains is particularly important in environments where the entrepreneurial culture within academia is underdeveloped or where institutional support for commercialization is limited. Intellectual property emerged as a multifaceted strategic resource rather than merely a legal instrument for market exclusivity. Respondents consistently described patents and trademarks as defensive mechanisms, tools for signalling credibility to investors and safeguards for customer trust. Our respondents recognized that IP strategy is context-dependent, varying with company size and market position. Small firms use patents to establish legitimacy and protect customers, medium-sized firms use them defensively against larger competitors, and large firms leverage them to maintain monopolistic advantages. Some of these insights are supported by previous research: small firms often use patents to attract customers and venture capital, so patenting has an important role to play even in firms where the protective function of patents is secondary [7]. Huges and Mina [8] also showed that an increasingly important factor for high tech small firms is the role of patents in obtaining financial backing by venture capitalists. However, respondents also highlighted cultural and structural challenges in patenting and entrepreneurship. R3 noted that European research culture tends to undervalue the commercialization of inventions, and that many innovations remain unpatented due to overly academic mindsets or other obstacles. R2 and R1 pointed to the uneven institutional support across European countries, contrasting the US and Portugal with Slovenia, where regulatory ambiguity hinder spin-out formation. These findings suggest that supportive policy frameworks and a culture that values entrepreneurship are critical for translating scientific research into commercial ventures. The study also revealed tension between academic values and entrepreneurial incentives. Respondents criticized the overemphasis on publication metrics and authorship inflation, arguing that current academic reward systems insufficiently recognize entrepreneurial achievements. R3 emphasized the need for equivalently rewarding scientific and entrepreneurial paths. This echoes broader debates in higher education policy regarding the balance between research excellence and technology transfer [5]. Additionally, here we can add critics of higher educational system who claim that universities are too bureaucratic, they educate mainly 'job seekers', they suffer from decreasing number of science and technology students and are weak at technology transfer [13]. In conclusion, academic entrepreneurs who succeeded in Slovenia, demonstrate a greater willingness to step outside the boundaries of traditional research roles (shaped by discoveries and publications), proactively acquire managerial and legal knowledge, and engage with external stakeholders such as incubators, investors, patent attorneys and customers. They perceive intellectual property not only as a legal safeguard but as a strategic asset that enhances credibility, facilitates partnerships, and supports long-term competitiveness. Acknowledgments Hafner and Lamut gratefully acknowledge CRP - Podpora IL project (https://www.rudolfovo.eu/en/nacionalni-projekti/crpil%3A-) co-funded by Ministry for Higher Education, Science and Innovation (MVZI) and Slovenian Research and Innovation Agency (ARIS). Dobravc Škof and Kolar gratefully acknowledge the research was co-funded by the Slovenian Research and Innovation Agency (ARIS) through the annual work program of Rudolfovo. References [1] Muhammad Sibt e Ali and Furrukh Bashir. 2024. The Role of Individual Resource Capital, University Support Environment and Government Policy in Academic Entrepreneurship: Evidence from China. Pakistan Journal of Humanities and Social Sciences 12, 1 (March 2024), 645–664. https://doi.org/10.52131/pjhss.2024.v12i1.2111 [2] Margherita Bacigalupo. 2021. Entrepreneurship as a competence. In World Encyclopedia of Entrepreneurship. Edward Elgar Publishing, 186–189. Retrieved August 16, 2025 from https://www.elgaronline.com/edcollchap/edcoll/9781839104138/97 81839104138.00030.xml [3] Yuanyuan Chen, Wei Liu, Stavros Sindakis, and Sakshi Aggarwal. 2024. Transferring Scientific Knowledge to Academic Startups: the Moderating Effect of the Dual Identity of Academic Entrepreneurs on Forming Knowledge Depth and Knowledge Breadth. J Knowl Econ 15, 1 (March 2024), 1823–1844. https://doi.org/10.1007/s13132-023-01110-5 [4] Waverly Ding and Emily Choi. 2011. Divergent paths to commercial science: A comparison of scientists’ founding and advising activities. Research Policy 40, 1 (February 2011), 69–80. https://doi.org/10.1016/j.respol.2010.09.011 [5] Joanne Duberley, Laurie Cohen, and Elspeth Leeson. 2007. Entrepreneurial Academics: Developing Scientific Careers in Changing University Settings. Higher Education Quarterly 61, 4 (2007), 479–497. https://doi.org/10.1111/j.14682273.2007.00368.x [6] Bruno Brandão Fischer, Gustavo Hermínio Salati Marcondes de Moraes, and Paola Rücker Schaeffer. 2019. Universities’ institutional settings and academic entrepreneurship: Notes from a developing country. Technological Forecasting and Social Change 147, (October 2019), 243–252. https://doi.org/10.1016/j.techfore.2019.07.009 [7] Marcus Holgersson. 2013. Patent management in entrepreneurial SMEs: a literature review and an empirical study of innovation appropriation, patent propensity, and motives. R&D Management 43, 1 (2013), 21–36. https://doi.org/10.1111/j.14679310.2012.00700.x [8] Alan Hughes and Andrea Mina. THE IMPACT OF THE PATENT SYSTEM ON SMEs. [9] Minjung Kim and Min Jae Park. 2023. Absorptive capacity in entrepreneurial education: Rethinking the Kolb’s experiential learning theory. The International Journal of Management Education 21, 3 (November 2023), 100873. https://doi.org/10.1016/j.ijme.2023.100873 [10] Lahcene Makhloufi, Abderrazak Ahmed Laghouag, Alhussain Ali Sahli, and Fateh Belaid. 2021. Impact of Entrepreneurial Orientation on Innovation Capability: The Mediating Role of Absorptive Capability and Organizational Learning Capabilities. Sustainability 13, 10 (January 2021), 5399. https://doi.org/10.3390/su13105399 [11] Simon Mosey and Mike Wright. 2007. From Human Capital to Social Capital: A Longitudinal Study of Technology–Based Academic Entrepreneurs. Entrepreneurship Theory and Practice 31, 6 (November 2007), 909–935. https://doi.org/10.1111/j.15406520.2007.00203.x [12] Wolf-Hendrik Uhlbach, Valentina Tartari, and Hans Christian Kongsted. 2022. Beyond scientific excellence: International mobility and the entrepreneurial activities of academic scientists. Research Policy 51, 1 (January 2022), 104401. https://doi.org/10.1016/j.respol.2021.104401 [13] Tsvi G. Vinig. 2007. Scientists are Entrepreneurs so Why Universities are Not Entrepreneurial? Retrieved August 16, 2025 from https://papers.ssrn.com/abstract=1020580 [14] Alessandra Micozzi. 2020. Academic Entrepreneurship. In The Entrepreneurial Dynamics in Italy: A Focus on Academic SpinOffs, Alessandra Micozzi (ed.). Springer International Publishing, Cham, 43–112. https://doi.org/10.1007/978-3-030-55183-4_2 14 University - Business Cooperation in Slovakia Rudolf Pástor† Department of International Cooperation, Slovak Centre of Scientific and Technical Information, Bratislava/Slovakia [email protected] Abstract This paper contributes to our understanding of the university-business cooperation in Slovakia. We have assessed university-business cooperation activities on the example of the case studies of 2 Slovak universities – Pavol Jozef Šafárik University in Košice and Slovak University of Technology in Bratislava. The paper is based on the qualitative research provided in frame of the STEIDA project under the Erasmus+ program, with the aim to strengthen technology transfer ecosystem through an innovative and holistic approach. The output of this research was included as the “Best Practices in Technology Transfer Ecosystem in Slovakia.” Keywords Technology transfer, university, business, cooperation, innovation ecosystem 1 Introduction University-Business Cooperation (UBC) is a relationship in flux, reflecting issues of transition from an industrial to a knowledge society. UBC links are no longer confined to a relatively small academic sector, leaving most of the academy untouched, but have expanded from engineering and medicine to the social sciences and the arts [1]. The aim of this paper is to get a more profound, comprehensive and up to date understanding of the state of UBC in Slovakia: what is the state of play of a wide range of UBC activities, what are the main drivers and barriers for the different stakeholders and at what levels; what is the regulatory framework and socio-economic conditions and what kind of measures/initiatives exist on a national level to support the development of UBC. This paper compiles the best practices identified by Slovak Centre of Scientific and Technical Information after conducting desk and field research, carried out within the scope of STEIDA (Strengthening Technology Transfer Ecosystem through Innovative and Digital Approaches) project, ref. № 2023-1-TR01-KA220-HED-000157242. The project, which is funded under the “ERASMUS + programme”, aims to strengthen the technology transfer ecosystem through an innovative and holistic approach which will foster national and international collaboration among HEIs, academics, businesses, students, entrepreneurs and other stakeholders by developing/using digital platforms and networks. This objective will be achieved through the implementation of the following main activities: - Conducting a comprehensive study on the technology transfer ecosystem; - Compilation of a report on best practices in technology transfer; - Development of a curriculum for higher education institutions and students; - Development of training modules for technology transfer professionals and newcomers; - Conducting pilot training for building the capacity of technology transfer professionals/newcomers/students; - Development of digital platform for cooperation between actors in the technology transfer ecosystem. The present paper of 2 best practices from Slovakia has been developed in parallel with the Research on Best Practices in Technology Transfer, carried out within the scope of Activity 5, with both outputs to be used as a basis in the implementation of the other foreseen project activities. This paper is structured within five main sections. Section 1 sets out the introduction. Section 2 discusses methodology of the research. Section 3 provides an overview of the 2 best practices within Pavol Jozef Šafárik University in Košice and , Slovak University of Technology in Bratislava. Section 4 provides conclusions, stemming from the analysis of the case studies. 2 Methodology The process of collection of best practices included the following 4 stages: Initial Desk Research, Internal Review and Selection of preliminary identified best practices, Field research, Involvement of Stakeholders. Data was gathered through two-step qualitative research with the use of †Corresponding author Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia © 2025 Copyright held by the owner/author(s). 15 B. Değermenci et al. Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia 3.1. Contribution to Literature This case contributes to the literature by operationalizing a placebased interface model for technology transfer an underresearched area in TTO studies. Most existing models situate TTOs within universities or technoparks; however, this study shows that embedding liaison offices directly within industrial clusters yields faster, more targeted, and trust-enhanced interactions. These findings are consistent with evolving perspectives in innovation policy that emphasize spatial proximity, context-sensitivity, and relational trust [7]. The model appears transferable to other regions with similar structural gaps between academia and industry, provided that administrative support, qualified human resources, and sector-sensitive approaches are in place. 4. Conclusion and Policy Implications This study examined the impact of establishing Technology Transfer Office (TTO) liaison offices within Organized Industrial Zones (OIZs) as a strategy to strengthen university industry collaboration and stimulate regional innovation. Drawing on the case of Karadeniz Technical University Technology Transfer Application and Research Center (KTU TTC), the findings clearly demonstrate that embedding TTO services within industrial zones significantly improves access, communication, and trust between academic institutions and industrial firms. Over a six-year period, the liaison offices contributed to measurable outcomes, including a substantial increase in firm engagement, academic industry partnerships, publicly funded R&D projects, and institutional advancements such as the establishment of R&D Centers. Furthermore, the model facilitated a behavioral transformation to direct collaboration signifying a positive cultural shift in how firms approach innovation and research partnerships. By physically positioning TTO representatives in proximity to industrial actors, this place-based model overcame spatial and perceptual barriers that often limit effective technology transfer. In doing so, it addressed a key gap in the literature, which has largely overlooked the potential of interface structures located outside of universities or technoparks. This research offers empirical evidence that such structures can catalyze not only transactional outcomes (e.g., projects, incentives, certifications) but also relational outcomes (e.g., trust, communication habits, innovation culture). 4.1 Policy Implications The results of this study offer several important implications for policymakers, regional planners, and university administrators: Support for Field-Based TTO Models: National and regional innovation policies should support the expansion of liaison office models in industrial zones, particularly in regions with weak university industry ties or limited technopark access. Institutional Incentives and Funding: Dedicated funding mechanisms can be developed to support the operational costs of liaison offices, including staffing, mobility, and digital tools for academic industry matchmaking. Integration into Smart Specialization Strategies: Regional development agencies should integrate liaison-based TTO models into smart specialization and cluster development strategies to ensure that knowledge flows and innovation incentives reach local firms effectively. In conclusion, the liaison office model implemented by KTU TTC represents a replicable and scalable good practice for strengthening innovation ecosystems in industrial regions. It not only increases the operational reach of TTOs but also contributes to a more balanced, inclusive, and responsive innovation infrastructure at the regional level. 4.2. Transferability and Future Research The OIZ-based TTO liaison office model presented in this study is not limited to the specific context of the Black Sea region but can also be applied to comparable environments with similar structural conditions. In particular, regions where university– industry interaction is weak, access to technoparks is limited, or firms have low awareness of R&D incentives present high potential for transferability. However, the successful implementation of the model requires the establishment of supportive administrative mechanisms, the provision of qualified human resources, and the development of strategies sensitive to regional sectoral dynamics. For future research, comparative case studies across different universities and industrial zones would provide opportunities to test the scalability of the model. Moreover, surveys and in-depth interviews with firm representatives could offer richer insights into the effects of the model from the industry perspective. Multicase analyses and international comparisons would further validate the applicability of this approach in diverse contexts, thereby contributing more comprehensively to the literature on university–industry collaboration. REFERENCES [1] Perkmann, M., Tartari, V., McKelvey, M., Autio, E., Broström, A., D’este, P., ... & Sobrero, M. (2013). Academic engagement and commercialisation: A review of the literature on university–industry relations. Research policy, 42(2), 423-442. [2] Bozeman, B., Rimes, H., & Youtie, J. (2015). The evolving state-ofthe-art in technology transfer research: Revisiting the contingent effectiveness model. Research Policy, 44(1), 34-49. [3] Yin, R. K. (2018). Case study research and applications (Vol. 6). Thousand Oaks, CA: Sage. [4] Laursen, K., & Salter, A. (2004). Searching high and low: what types of firms use universities as a source of innovation?. Research policy, 33(8), 1201-1215. [5] Siegel, D. S., Waldman, D., & Link, A. (2003). Assessing the impact of organizational practices on the relative productivity of university technology transfer offices: an exploratory study. Research policy, 32(1), 27-48. [6] Bruneel, J., d’Este, P., & Salter, A. (2010). Investigating the factors that diminish the barriers to university–industry collaboration. Research policy, 39(7), 858-868. [7] Fitjar, R. D., & Rodríguez-Pose, A. (2011). Innovating in the periphery: Firms, values and innovation in Southwest Norway. European Planning Studies, 19(4), 555-574. 22 Knowledge Sharing, Protection of Trade Secrets, and Sensitive Practices in the Circular Economy Magda Lužar† Faculty of Information Studies in Novo mesto, Slovenia [email protected] Abstract The paper addresses the research problem of how companies in the circular economy reconcile open knowledge sharing with the protection of trade secrets and other sensitive practices. A secondary analysis was conducted on the basis of relevant articles from the WoS and Scopus databases, which were published internationally in the last decade. Knowledge sharing and protection approaches that appear in the business environment at the content, organizational, technological, cultural and strategic levels are identified. The findings show that companies use combinations of selective, phased, organizational and digitally enabled knowledge sharing. The mentioned sharing is often coordinated with protection approaches: through restricted access, trust and technological security measures. Added value leads to an understanding of how to establish a balance between collaboration and protection of knowledge in the transition to a circular economy, which is often overlooked in companies, primarily due to market existence and achieving of competitive advantage. Keywords Sharing knowledge, trade secrets, sensitive practices, knowledge protection, circular economy 1 Introduction Companies today operate in a competitive global environment, facing ever-increasing customer demands and at the same time pursuing rapidly evolving technological progress. To overcome these challenges, innovative and strategic actions need to be oriented towards sustainability. Knowledge sharing refers to the intentional exchange of information, experiences, ideas and skills among stakeholders, which contributes to knowledge application, innovation and optimization of the organization [22]. Companies are increasingly dependent on knowledge sharing in all aspects of their business if they want to operate in a circular, innovative and sustainable manner. The growing need for collaboration accompanies them and presents them with the challenge of how to share knowledge without jeopardizing their own trade secrets or other sensitive practices that are a competitive advantage for their business. Trade secrets are information with economic value that is not publicly available and is subject to protective measures as defined by the EU Directive [7]. Often in practice, companies consider as trade secrets even their own knowledge that is not formally protected by legal means. Nevertheless, knowledge is strategically important and therefore they do not want to disclose it to external partners. In this paper, we therefore provide a perspective on the sharing and protection of knowledge and the inclusion of formally protected information and informal forms of protecting sensitive content that companies (do not) want to disclose to partners. In this paper, we follow the research question of how companies in the circular economy reconcile open exchange and sharing of knowledge with the protection of trade secrets and other sensitive practices. 2 Theoretical Background An economic system that aims to eliminate waste and continuously use resources through reuse, recovery and recycling can be understood as a circular economy [11]. There are numerous definitions of the circular economy in literature. The transition of companies to a circular economy creates a need for collaboration within and outside the industry. The challenge for organizations is to overcome organizational, technological, financial and regulatory barriers [12]. Companies are under pressure to maintain a competitive advantage, and this poses the challenge of how to share knowledge without compromising sensitive information and trade secrets. Interorganizational relationships and exploiting different aspects of collaboration in line with company goals and with partners and geographical proximity are crucial for maintaining competitive advantage [6, 15]. A successful transition to a circular economy requires an understanding of knowledge, which is a key source of competitiveness. It can be documented or possessed by individuals in experience and in competence [16]. Its sharing involves a process for innovation and learning [22]. Often, the knowledge to be shared is limited and protected. A trade secret is information that (1) is secret in the sense that it is not generally known or readily accessible, (2) has commercial value because it is secret, and (3) has been subject to reasonable steps to keep it confidential [7]. In addition to legally protected secrets, organizations manage sensitive practices. Their disclosure could threaten competitiveness and therefore they use formal or informal protection approaches [6, 21]. Knowledge sharing is crucial for successful collaboration in circular models. Selective sharing [14, 21], modular sharing [3] and phased sharing [10, 13] are practices that allow companies †Corresponding author. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia © 2025 Copyright held by the owner/author(s). 23 Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia M. Lužar. to decide what knowledge to share and when. Often, knowledge is shared in an abstract or generalized form [21] to avoid revealing technical details. Organizational filtering or sharing is implemented within the company through organizational roles depending on the context of collaboration [13]. Contemporary digital platforms and technologies (open data channels, digital twins, IoT, AR glasses and other smart devices) [10, 12] enable structured, personalized and controlled knowledge sharing. Platforms (e.g. Circular Living Lab) are a space for knowledge and information sharing between companies, consultants and research institutions [2, 19]. The collaborative system is strengthened through the aforementioned platforms, which enable two-way learning and structured and transparent communication between stakeholders [2, 20]. Due to the sharing of knowledge, information, content, sensitive practices and trade secrets, companies develop protective approaches or strategies. Selective disclosure, where only part of the knowledge is shared, is based on mapping and classifying competencies, sensitive information and roles in the collaboration [2, 3, 10]. Informal protective practices are used, when stakeholders share information only with a sufficiently high level of trust and predictable behavior. In the form of informal conversations or psychological contracts and relational trust, they regulate information sharing if stable partnership relationships are present [6, 21]. Organizations reduce risk through controlled partner selection, often using intermediaries who act as filters that determine what knowledge enters the network and what remains protected. [19]. Access to knowledge is regulated by technological solutions for filtering and control through roles and competencies. [12] Coordinated strategies have been reported when companies combine sharing and protection, through digital sharing controls, [3, 4] through gradual knowledge disclosure [13, 20] and when sharing is tailored to the user according to their competency profile [10]. Most research addresses knowledge sharing and knowledge protection separately. In a circular economy, collaboration and competitiveness are intertwined and comprehensive approaches that include both aspects need to be explored. This paper then addresses this gap by identifying approaches used to implement a circular economy. 3 Methods To prepare the paper, a systematic literature review was conducted in June and July 2025, in accordance with the PRISMA guidelines [17]. The aim was to identify knowledge sharing and exchanges and the protection of sensitive content and trade secrets within the circular economy. The analysis included scientific articles, regardless of the research methodology used (e.g. qualitative, quantitative, mixed), as the inclusion criterion focused on the relevance of the findings to the research question. The literature was selected from the extensive WoS and Scopus databases, and selective filtering was used on keywords. A single search string was used for the WoS and Scopus databases: “exchange of knowledge”, “knowledge sharing”, “trade secrets”, “confidential information”, “circular economy”, “circular business models”. The circular economy is present in all aspects of business, so we did not exclude any area. We limited ourselves to papers that were published internationally in the last ten years. 99 records were identified in Scopus and 59 records in WoS. After removing 46 duplicates, 112 documents remained, which were reviewed based on title and abstract. After the first reading, 95 were eliminated as irrelevant documents. 17 articles were submitted for assessment of relevance and included in the final analysis. (Figure 1). Figure 1: PRISMA 2020 flow diagram of the study selection process 4 Results and Discussion The review of the contributions allowed for the definition and interpretation of perspectives that enable companies in the circular economy to share knowledge in a coordinated manner while protecting sensitive practices, information and trade secrets. Approaches occur at the content, organizational, technological, cultural and strategic levels. Opportunities were identified that reflect the coordinated use of approaches. At the content level, companies implement selective and modular knowledge sharing. They selectively filter knowledge content within “safe” topics and share only non-sensitive information. Sensitive information, such as detailed processes, is protected [2, 14, 21]. Organizations implement knowledge and competence mapping to coordinate collaborations [3]. If goals are aligned, companies share information about production processes between partners, but under conditions of relational trust [6]. Phased knowledge selection means more protection in development, more openness in implementation, sharing is in steps [10, 13, 20]. Modular content distribution allows controlled access only to individual content sets. Knowledge is preclassified and only what is safe or necessary for the process is shared. Partial internalization is present, when appropriate external knowledge is accepted from a security perspective and internal resources are protected. External competencies are converted into potential internal resources [3]. At the organizational level, knowledge is shared through organizational roles (project manager, manager, consulting organizations), which filter the content and level of shared 24 Knowledge Sharing, Protection of Trade Secrets, and Sensitive Practices in the Circular Economy Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia knowledge and prevent uncontrolled disclosure of knowledge by experts. They determine which knowledge enters the network and which remains protected [2, 13, 19]. In addition, partners are carefully selected based on the level of trust and previous experience, which enables effective collaborative relationships [6, 14]. At the same time, research and design teams are established to promote knowledge sharing [8]. At the technological level, digital technologies provide central support, which are the central tools for secure, personalized and continuous knowledge exchange [3, 4, 10, 18]. They use technological tools (open data channels, visualization support with smart devices, decision-support technologies) that enable the transfer of complex knowledge through graphic or data displays. Technological solutions enable traceability and access restrictions [1, 12]. Communication is adapted to the user profile through digital interfaces. [10] Researchers, consultants and companies develop and test solutions and collaborate in innovation laboratories (Circular Living Lab) [19]. Organizations collaborate in circular ecosystems with industry, researchers and governments to find solutions and share knowledge [5, 11, 20]. At the cultural level, the aspect of trust proves to be an essential component of successful knowledge sharing and protection. Mentoring and informal conversations enable the exchange of employee experiences and knowledge transfer [4]. Informal conversations are based on mutual expectations and trust of the process stakeholders and predictable behavior [21]. Partners share information only when there is a sufficiently high level of trust, which indicates controlled disclosure of information [6]. In proactive collaboration, business ethics and transparency are of great importance in addition to trust [9]. At the strategic level, sharing and protection are aligned with the long-term goals of the company [4, 6, 20]. External stakeholders are involved gradually, depending on the nature and phase of the project. Depending on the level of involvement, the level of protection of disclosed information and sensitive practices changes [12, 19, 20]. Knowledge sharing serves as a mediator in the relationship between intermediaries and organizations. [23] The findings of the reviewed literature indicate that in the circular economy, companies use different approaches to balance open knowledge sharing and protection of sensitive practices and trade secrets. The identified approaches are: content selectivity, organizational mechanisms, digital technologies, culture of trust, strategic orientation, personalization and collaboration in networks. The listed approaches occur at the content, organizational, technological, cultural and strategic levels. Companies often combine knowledge sharing and protection approaches, rarely using only one strategy. This confirms that openness to sharing and protection are processes through which collaboration is possible without jeopardizing key resources for competitive business. Balance is achieved through targeted control and shared content. A selective and modular view of knowledge sharing is essential, where companies filter content according to its sensitivity (more or less important information) and the phase of the project. Only non-sensitive or secure content (non-confidential information) is shared, while measuring (mapping) competencies to align collaboration. In this approach, competitive advantages are not revealed during sharing [3, 6, 10, 14, 21]. Partners are provided with enough data to be able to collaborate effectively. The alignment between capabilities and needs is important, so companies create an overview of existing knowledge and skills and identify what, how much and how to include in the collaboration. At the organizational level, knowledge sharing is controlled by intermediaries who take on certain roles between experts and external partners. At the same time, companies carefully select partners based on trust and previous experience [6, 13, 19]. This approach reduces the risk of uncontrolled disclosure of key information with partners. Intermediaries act as a filter to control the dynamics and content of the exchange. Careful selection of partners further reduces the risk, but also narrows the network of potential collaborations and wider openness. Secure, structured and flexible sharing and exchange of knowledge is enabled by digital platforms and technologies. Blockchain, IoT, digital identity, AR technologies are advanced solutions that provide traceability and access control and increase the transparency of processes [3, 4, 10, 12]. The findings show that digital technologies are becoming an important element in addressing the challenges of sharing and protecting key content. Complex information and knowledge can be reliably and securely transferred through controlled mechanisms. Long-term successful cooperation is also based on softer, human factors. A culture of trust stands out, which strengthens work in the process with experience, mentoring and psychological contracts between stakeholders. It is precisely a high level of trust that enables the secure exchange of sensitive information and long-term cooperation, which are key to the functioning of the circular economy. Mutual trust creates the foundations on which a company can more easily build long-term practices. The approaches are aligned with the broader strategic goals of the companies, gradually involving stakeholders in the project phases. In doing so, they take into account the levels of information protection or sensitivity of the collaboration [4, 12, 19, 20]. Companies develop the ability to internalize external competencies and personalized knowledge transfer based on user competencies. Effective and targeted learning of employees and stakeholders in the process and support in the introduction of plate practices [3, 4, 8, 10]. Within the framework of the collaborative approach in networks and incubators, companies, researchers and other stakeholders jointly develop innovations and solutions. In the organizational and socio-technological framework for collaboration (Circular Living Labs), companies, consultants and users can come together to collaborate and develop solutions [5, 12, 19]. The combination of approaches allows organizations to simultaneously seize the opportunity of open innovation and reduce the risk of losing competitive advantage. This is important for governance in the circular economy and for cooperation between stakeholders in the process involved in sharing, using and protecting knowledge, sensitive practices and trade secrets. 5 Conclussion The paper identifies and formulates key approaches that companies in the circular economy use to balance open 25 Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia M. Lužar. knowledge sharing with the protection of sensitive practices and trade secrets. The findings show that selective, phased, organizationally and digitally supported combinations are used for knowledge sharing. These are often aligned with access restriction strategies, relational trust and technological safeguards. Coordination is considered in companies with regard to technological infrastructure, strategic goals, level of trust and context of collaboration. Creating a balance is a response to risks and at the same time part of the process in an open innovation environment. Sharing, simultaneous openness and protection are necessary and necessary for long-term competitive advantage in the circular economy. This research opens new research questions, namely, it should be examined which strategies or approaches are more effective depending on the industry or the size of the company. The nature of knowledge, sensitivity of information and the possibilities of its protection vary depending on the industry and the size of the company. Knowing these differences allows them to design appropriately tailored approaches in the process that encourage collaboration while maintaining the security of key content. It would also be necessary to analyze how the institutional environment influences the formation of a balance between openness and knowledge protection. An in-depth analysis of various combinations would provide a view of how different approaches to sharing and protecting knowledge change throughout the life cycle of project collaboration. The levels of sharing and protecting knowledge show that companies in the circular economy use comprehensive approaches where the elements are not exclusive, but rather complementary. The main challenge is to establish a balance between open access to knowledge and security and protection. Companies solve the challenges at the content (filtering), organizational (roles), technological (platforms), cultural (trust) and strategic (alignment with goals) levels. A coordinated balance between sharing and protecting knowledge is a dynamic process that must constantly adapt to market changes, technological changes and changes in relationships between partners in the circular economy. References [1] Ahmed, M. F., Mokhtar, M. B., Lim, C. K., Hooi, A. W. K., & Lee, K. E. (2021). Leadership roles for sustainable development: The case of a Malaysian green hotel. Sustainability, 13(18), 10260. DOI: https://doi.org/10.3390/su131810260 [2] Berndt, R., Cobârzan, D., & Eggeling, E. (2024). A Platform Architecture for Data-and AI-supported human-centred Zero Defect Manufacturing for Sustainable Production. IFAC-PapersOnLine, 58(19), 622-627. DOI: https://doi.org/10.1016/j.ifacol.2024.09.231 [3] Castillo-Ospina, D. A., Ormazabal, M., de Vasconcelos Gomes, L., & Ometto, A. R. (2025). 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DOI: https://doi-org.nukweb.nuk.unilj.si/10.1108/JEIM-03-2025-0188 26 Self-evaluation of Research Organizations in the Field of Knowledge Transfer Tomaž Lutman Technology Transfer Office Jožef Stefan Institute Ljubljana, Slovenia tomaz.lu[email protected] Jure Vindišar Technology Transfer Office National Institute of Biology Ljubljana, Slovenia jure.vin[email protected] Abstract Knowledge transfer plays a central role in transforming research outcomes into social and economic value, addressing both technological progress and societal challenges. The European Council Recommendation 2022/2415 calls for systematic approaches to knowledge transfer, supported by clear metrics, appropriate policies, and stakeholder collaboration. This work examines self-evaluation in research organisations, highlighting its potential to improve performance, align activities with longterm vision, and identify both strengths and gaps. It includes international comparison and prevalence of quantitative indicators as well as stresses the importance of combining them with qualitative indicators such as case studies, narratives, and relationship assessments. The SCOPE methodology is presented as a practical, values-based framework for responsible research evaluation. By integrating both quantitative and qualitative indicators, organisations can achieve fairer, more transparent, and more effective evaluations that foster societal impact and sustainable growth. The paper explores the existing approaches for self-evaluation in the field of knowledge transfer and gives recommendations for those being responsible for selfevaluations at research organisations as well for policy makers, setting the frame for performing evaluations. Keywords knowledge transfer, self-evaluation, indicators, SCOPE methodology 1 INTRODUCTION Knowledge transfer (KT) is key to transforming research and innovation into social and economic value, as it enables technological progress and addresses societal challenges. In its Recommendation 2022/2415, the EU Council stresses that knowledge transfer must be systematic and supported by appropriate measures. Its effective implementation requires good management of intellectual property, including protection and transfer into practice. The cooperation of various stakeholders, from researchers and entrepreneurs to decision-makers and civil society, is crucial. The Recommendation calls on Member States to provide financial and political support that will enable better integration of research with industry and society. Only in this way will knowledge transfer have the greatest possible impact on sustainable development and economic growth [1]. The Council of the EU encourages joint efforts to develop and adopt definitions, metrics and indicators covering the different channels of valorisation, with the aim of improving its performance in the EU. Monitoring and evaluation practices should be aligned with the broader framework for monitoring the European Research Area. [1]. Across the EU, systematic self-evaluation of knowledge transfer in research organizations remains uneven and often limited in scope. While many organizations collect quantitative indicators, fewer engage in structured, organisation-wide selfevaluation that also captures informal, collaborative, and societal dimensions of knowledge transfer. The European Commission has acknowledged these gaps and is developing common frameworks and self-assessment tools, but uptake is still at an early and variable stage [2]. 2 Methodology In the period between November 2024 and August 2025 we reviewed the relative literature. We were interested to know which are relevant guidelines for self-evaluation in the field of KT as well as which are most relevant quantitative indicators and how to balance them with the qualitative assessment. We are members of The Coalition for Advancing Research Assessment (COARA), European Association of Research Managers and Administrators (EARMA), Association of European Science and Technology Transfer Professionals (ASTP) and other relevant organizations, relevant for self-evaluation of KT and have thus insight in most relevant literature. Internal documents of relevant working groups not cited here as well as our experience as technology transfer officers have been used to elaborate our recommendations. 3 Guidelines for self-evaluation of knowledge transfer 3.1 Monitoring and evaluating research organizations is reasonable and necessary The reasons behind implementation of evaluating research organisations (RO) are not only accountability for public funding, improvement of research quality and international Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia © 2025 Copyright held by the owner/author(s). 27 Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia T. Lutman et al. competitiveness but they focus on broader impact reflecting the growing expectation that science should benefit society, not only the academic community. Thus, the evaluation of RO in the field of knowledge transfer has several positive effects. One of them is to shed light on those research organisations that have a high potential for economic impact. The Redstone Venture Capital Fund recently carried out a study of 457 research organisations from 34 European countries. The study showed differences in the effectiveness of research organisations in creating companies. Some organisations achieve the same economic impact with €2 million as others with €200 million. The study highlights the importance of (self)evaluation in the field of knowledge transfer [3]. 3.2 A research organization must establish a vision Vision is crucial to the long-term success of an organization, setting its direction, inspiring employees, and aligning decisions with its overarching goals. A good vision goes beyond ambitious goals—it must be meaningful, inspiring, and easily understood. When effectively crafted, it acts as a compass that guides the organization through change and challenges. In addition to providing internal direction, a good vision also communicates externally the organization’s values and commitment to a larger purpose, which can build trust with customers, partners, and communities. Without a clear vision, organizations can get lost in the operational details and lose a sense of long-term perspective. Evaluation, metrics, and indicators are tools for measuring progress toward a specific vision. If indicators are not aligned with the vision, the organization may start optimizing the wrong things. Therefore, it is important that research organizations establish their vision. Measurements and evaluations must be meaningfully aligned with the long-term vision [4]. 3.3 Potential negative impacts of evaluation Evaluation can lead to potential negative effects. An example of this is the increased administrative burden for all involved, highlighted by the EU Council Recommendation 2022/2415 [1]. The process can even lead to an obsession with the quantitative measurement of human performance. In his book The Tyranny of Metrics, Jerry Z. Muller warns of the dangers of over-reliance on quantitative indicators in assessing performance in education, health, science, public administration and other fields. The author criticizes the idea that everything that matters can be measured, as such an approach often leads to distortion of behavior, manipulation of data and neglect of qualitative aspects of work. Metrics used without context can undermine the real goals of organizations. Muller therefore calls for a thoughtful and critical use of measurement, where numbers do not replace judgment and expertise [5]. 3.4 Guidelines for the evaluation of scientific excellence There are an increasing number of global and national initiatives focused on driving Responsible Research Assessment (RRA). Research organizations are typically evaluated in three categories: (1) scientific excellence (research quality), (2) viability and (2) societal relevance (impact). Guidelines for evaluating scientific excellence can also be useful in the field of evaluating knowledge transfer. The San Francisco Declaration on Research Evaluation (DORA) and Strategy Evaluation Protocol (SEP, Netherland) call for the elimination of the use of impact factor as the main measure of research quality. Instead, it encourages evaluation based on the content of the work and recognizes the diversity of research results and contributions. SEP in particular sets the organization’s strategy as a key stone of evaluation. The Leiden Manifesto offers ten principles for the responsible use of metrics, emphasizing that quantitative metrics should complement, not replace, professional judgment. It advocates for transparent, inclusive, and tailored evaluation approaches that take into account context and differences between disciplines. The COARA agreement on research evaluation reform builds on similar foundations and brings together organizations committed to long-term changes in the evaluation of researchers, projects, and institutions. COARA set up WG Responsible Metrics and Indicators, to deliver critical evaluation of the indicators used for evaluation. All those initiatives and instruments warn of the dangers of over-reliance on simple metrics and advocate for a holistic, fair, and qualityoriented evaluation of research work in support of scientific excellence and societal relevance [6], [7], [8] 4 SCOPE Methodology The SCOPE framework, developed by the International Network of Research Management Societies (INORMS), offers a values-based methodology to improve the fairness, transparency, and effectiveness of research evaluation. Traditional approaches often rely on citations, journal impact factors, or funding levels. While useful, these indicators can introduce bias, encourage quantity over quality, and increase pressure on researchers [9]. SCOPE provides a structured five-step process that helps research organisations, funders, and managers design evaluations that promote fairness, inclusion, and responsible use of indicators: 1. Start with what you value – Evaluation should reflect the true priorities of the organisation, not external pressures. For example, if open science is a key value, indicators might include open access, data sharing, or research transparency. 2. Consider context – Evaluation must be adapted to its purpose (understanding, self-praise, control, comparison, rewarding) and the unit of assessment (individuals, groups, or institutions). Since practices differ across disciplines, one-size-fits-all approaches are unfair. 3. Options for evaluating – Both qualitative and quantitative methods should be combined. Citation counts or other metrics should never stand alone; broader contributions such as mentoring, ethics, or societal impact should also be considered. 4. Probe deeply – Anticipate unintended consequences, such as bias, gaming, or harmful behaviours like overpublishing. Addressing risks ensures evaluations remain fair and effective. 28 Self-evaluation of Research Organizations in the Field of Knowledge Transfer Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia 5. Evaluate your evaluation – Reflect on whether the process met its goals, improved research culture, and what can be enhanced for the future. SCOPE warns against unconscious bias and discriminatory effects in evaluation processes. It emphasizes that evaluations must be co-designed with the evaluated community, tested for discriminatory impact, and continuously checked for unintended consequences. By following SCOPE, organisations can foster a more balanced, responsible, and values-driven research evaluation culture [9]. 5 Indicators Indicators are tools used to measure and track progress, performance, and impact in knowledge transfer and related activities. They provide valuable insights for policymakers, research organisations, and industry by capturing both inputs and outputs. A balanced approach combining quantitative metrics with qualitative evidence, such as case studies, is essential to reflect the complexity and societal value of KT processes. Quantitative indicators may also be useful to underpin the case studies as qualitative indicator. The choice of indicators depends on the exact argument for which they should provide evidence [10]. Evaluated RO shall explain the choice of the indicators as well as their link to RO's aims and strategy. 5.1 Quantitative indicators Joint Research Center of European Commission (JRC) has defined four groups of quantitative indicators: (1) KT Internal Context Indicators and (2) KT Environment Indicators represent inputs, while (3) KT Activity Indicators and (4) KT Impact Indicators represent outputs [2]. JRC also listed concrete indicators which we present here. We have reviewed three other sources and compared indicators that they use [11], [12], [13] and [14]. Indicators are presented in Table 1, Table 2, Table 3 and Table 4. JRC excluded most of intellectual property indicators from their metrics. Although they are right that the number of patents are less relevant than license agreements and are according to our information even sometimes recognized by researchers as the end of their knowledge transfer journey, we believe they are nevertheless important intermediate indicators. Most common quantitative indicators are research expenditure in RO, licences & assignments — number and gross revenue to RO and spin–offs — number (identified by 4 sources) as well as age of KTO, number of FTE in KTO, invention disclosures — number, spin–offs — gross revenue to RO from equity sale, research collaboration agreements with non– academic third parties — number and gross revenue to RO, research contracts with non–academic third parties — number and gross revenue to RO, and consultancy agreements with non– academic third parties— number and gross revenue to RO (3 sources). The prevalence of these indicators should be a sign for RO when establishing a quantitative system for self-evaluation. Table 1: KT Internal Context Indicators. JRC [2]; ASTP [11]; UK [12], [13]; US [14]. Indicator JRC ASTP UK US Existence of RO KT & IP Policies X RO KT Strategy X Direct funding via the RO for KT e.g. to KTO X Indirect funding via the RO for KT e.g. proof of concept X Existence of KTO X Age of KTO X X X Number of FTE in KTO X X X Research expenditure in RO X X X X Number of researchers X X Table 2: KT Environment Indicators Indicator JRC ASTP UK US National R&D spend as % GDP X National Higher Education Expenditure on R&D (HERD) X National Business Expenditure on R&D (BERD) X Availability of public funding programmes to support KT/Industry engagement X Availability of investment capital X Table 3: KT Activity Indicators Indicator JRC ASTP UK US Invention disclosures — number X X X Priority patent applications X X First patents granted X X Active patent families X X % of Licensed or optioned active patent families X X Licences & assignments — number X X X X Licences & assignments — gross revenue to RO X X X X Option agreement X X Spin–offs — number X X X X Spin–offs — gross revenue to RO from equity sale X X X Research collaboration agreements with non– academic third parties — number X X X 29 Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia T. Lutman et al. Research collaboration agreements with non– academic third parties — gross revenue to RO X X X Research collaboration agreements with non– academic third parties — length of relationship X Research contracts with non– academic third parties — number X X X Research contracts with non– academic third parties — gross revenue to RO X X X Research contracts with non– academic third parties — length of relationship X Consultancy agreements with non–academic third parties — number X X X Consultancy agreements with non–academic third parties — gross revenue to RO X X X Consultancy agreements with non–academic third parties — length of relationship X Income from courses X Number of courses held X Number of participants that attend courses X Table 4: KT Impact Indicators Indicator JRC ASTP UK US Jobs created in spin–offs X X Aggregate investment in spin–offs X X Products on market X X Culture change in RO X Societal benefits X Economic Benefits X 5.2 Qualitative indicators In line with many strategic directions, quantitative indicators need to be combined with qualitative ones. Examples include: Case studies of successful collaborations (e.g. AUTM’s Better World project, CURIE’s “20 years KT success stories”, or Knowledge Transfer Ireland’s impact cases) Narrative assessments of how KT contributed to outcomes like health, civil society engagement, or policy change. Relationship quality between universities and industry, highlighted as a key driver of success beyond measurable outputs Societal benefits such as improved well-being, new policies, or civil engagement [2]. 6 Conclusions Effective self-evaluation in knowledge transfer should rely on established global and national initiatives focused on driving Responsible Research Assessment (RRA) in combination with guidelines of respected organizations like JRC, ASTP, KT UK and AUTM. If not already in place, research organizations’ managers should use most common quantitative indicators like research expenditure, licences, spin–offs and others in their selfevaluation system. While quantitative indicators remain useful, they provide only a partial view of performance. Research organizations’ managers should integrate qualitative indicators - such as case studies, narrative assessments, and relationship quality - which offers a fuller understanding of KT outcomes and their societal relevance. Furthermore, they should follow the existing frameworks, which provide valuable guidance by emphasising values, context, and reflection in evaluation design. Ultimately, self-evaluation must align with the long-term vision of research organisations while supporting broader European goals of innovation, sustainability, and societal benefit. By embedding responsible and inclusive self-evaluation practices, KT can more effectively bridge research, industry, and society, strengthening its role as a driver of economic and social progress. Acknowledgments This project has received funding from the Slovenian Research and Innovation Agency and Ministry of Higher Education, Science and Innovation under grant agreement No. V5-24055, project ISERO. References [1] COUNCIL RECOMMENDATION (EU) 2022/2415 of 2 December 2022 on the guiding principles for knowledge valorisation. Accessed on 27 August 2025. https://eur-lex.europa.eu/eli/reco/2022/2415/oj/eng [2] Campbell, A., Cavalade, C., Haunold, C., Karanikic, P., Piccaluga, A., Knowledge Transfer Metrics. Towards a European-wide set of harmonised indicators, Karlsson Dinnetz, M. (Ed.), EUR 30218 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-18885-8, doi: 10.2760/907762 (online), JRC120716 . [3] Redstone, European Venture Capital. Redstone University Startup Index - Europe's Trillion Euro Opportunity. Accessed on 27 August 2025. https://www.redstone.vc/research/redstone-university-startup-index [4] Qualee Technology. Organizational Vision. Accessed on 27 August 2025. https://www.qualee.com/hr-glossary/organizational-vision [5] The Tyranny of Metrics. NED-New edition. Princeton University Press, 2018. https://doi.org/10.2307/j.ctvc7743t. [6] The Declaration on Research Assessment (DORA). Accessed on 27 August 2025. https://sfdora.org/ [7] The Coalition for Advancing Research Assessment (CoARA). The Agreement on Reforming Research Assessment. Accessed on 27 August 2025. https://coara.eu/agreement/the-agreement-full-text/ [8] Hicks, D., Wouters, P., Waltman, L. et al. Bibliometrics: The Leiden Manifesto for research metrics. Nature 520, 429–431 (2015). https://doi.org/10.1038/520429a [9] International Network of Research Management Societies - Research Evaluation Group (2023). The SCOPE Framework. The University of Melbourne. Report. https://doi.org/10.26188/21919527.v1 [10] The Dutch Research Council. The Strategy Evaluation Protocol 20212027. Accessed on 27 August 2025. https://www.nwo.nl/en/evaluationsnwo-institutes [11] Association of Knowledge Transfer Professionals (ASTP). ASTP 2022 Annual Survey. On the European Knowledge Transfer Landscape Financial year 2020. Accessed on 27 August 2025. https://www.astp4kt.eu/resources/impact/executive-data-report-2022fy2020.html [12] Holi, M.T. (2008). Metrics for the Evaluation of Knowledge Transfer Activities at Universities. https://api.semanticscholar.org/CorpusID:13959826 [13] Higher Education Statistics Agency. Higher Education Provider Data: Business and Community Interaction. Accessed on 27 August 2025. https://www.hesa.ac.uk/data-and-analysis/business-community [14] AUTM 2022 Licensing Activity Survey. A Survey of Technology Licensing Related Activity for US Academic and Nonprofit Research Institutions. Accessed on 27 August 2025. https://autm.net/AUTM/media/SurveyReportsPDF/2022-US-AUTMLicensing-Survey.pdf 30 Strengthening Knowledge and Technology Transfer Ecosystems through Transnational Collaboration: The Case of the STEIDA Project Hülya SABIR* Technology Transfer ARC Karadeniz Technical University Trabzon / TÜRKİYE [email protected] Sedanur KALYONCU Technology Transfer ARC Karadeniz Technical University Trabzon / TÜRKİYE [email protected] Gözde SAĞLAM Technology Transfer ARC Karadeniz Technical University Trabzon / TÜRKİYE [email protected] Güler Tuğba GÜLTEKİN Technology Transfer ARC Karadeniz Technical University Trabzon / TÜRKİYE [email protected] Ayhan KOÇ Technology Transfer ARC Karadeniz Technical University Trabzon / TÜRKİYE [email protected] İslam YILDIZ Technology Transfer ARC Karadeniz Technical University Trabzon / TÜRKİYE [email protected] Müslüm Serhat ÜNVER Technology Transfer ARC Karadeniz Technical University Trabzon / TÜRKİYE [email protected] Eren YILMAZ Technology Transfer ARC Karadeniz Technical University Trabzon / TÜRKİYE [email protected] Beril DEĞERMENCİ Technology Transfer ARC Karadeniz Technical University Trabzon / TÜRKİYE [email protected] Dilek İSKENDER BALABAN Technology Transfer ARC Karadeniz Technical University Trabzon / TÜRKİYE [email protected] Emrah AYVAZ Technology Transfer ARC Karadeniz Technical University Trabzon / TÜRKİYE [email protected] Samet Can DEĞİRMENCİ Technology Transfer ARC Karadeniz Technical University Trabzon / TÜRKİYE [email protected] Kerim SÖNMEZ Technology Transfer ARC Karadeniz Technical University Trabzon / TÜRKİYE [email protected] Yalçın AYKUT Technology Transfer ARC Karadeniz Technical University Trabzon / TÜRKİYE [email protected] Aleyna AYDIN Technology Transfer ARC Karadeniz Technical University Trabzon / TÜRKİYE [email protected] Mariana TANCHEVA Bulgarian Chamber of Commerce&Industry Sofia / BULGARIA [email protected] Dimitar PAUNOV Bulgarian Chamber of Commerce&Industry Sofia / BULGARIA [email protected] Rudolf PASTOR The Slovak Centre of Scientific and Technical Information Bratislava / SLOVAKIA [email protected] Jaroslav NOSKOVIC The Slovak Centre of Scientific and Technical Information Bratislava / SLOVAKIA [email protected] Urska FLORJANCIC Jožef Stefan Institute Ljubljana / SLOVENIA [email protected] Robert BLATNIK Jožef Stefan Institute Ljubljana / SLOVENIA [email protected] Marijan LEBAN Jožef Stefan Institute Ljubljana / SLOVENIA [email protected] Sanja KIRETA University of Zagreb Zagreb / CROATIA [email protected] Orsat LALE University of Zagreb Zagreb / CROATIA [email protected] Berta PEREZ Barcelona Chamber of Commerce, Industry, Services and Navigation Barcelona / SPAIN [email protected] Vicente ATIENZA Barcelona Chamber of Commerce, Industry, Services and Navigation Barcelona / SPAIN [email protected] Leonie HEHN Barcelona Chamber of Commerce, Industry, Services and Navigation Barcelona / SPAIN [email protected] 31 Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia S. Kunej et al. Overall interpretation. The main insights demonstrate that the INDUSAC project has been successful in achieving its primary objectives. Despite high baseline self-assessments, students and researchers improved key transversal skills. Both students and companies expressed increasing satisfaction with the process and platform, reflecting the value of continuous improvement. Companies recognised the innovativeness of student contributions, though the call for stronger market viability suggests a need to integrate more structured support for business analysis in future rounds. Taken together, these findings validate INDUSAC as a replicable and scalable co-creation model. Beyond skills and satisfaction, the project also strengthens knowledge transfer by linking collaborative problem-solving with structures for followup and application, ensuring that innovations generated within short-term projects can be sustained and further developed. Figure 5: Companies’ evaluation of solutions and the work of the co-creation teams. Of the categories surveyed, Innovativeness, Creativity, Improvement over existing solutions, Market Potential, and Relevance refer to the solution delivered by the co-creation team, whereas Soundness, Quality of Work, and Satisfaction refer to the work done by the co-creation team. The x-axis presents company evaluations from the first and second calls, while the y-axis indicates the percentage of company representatives who expressed satisfaction. The two lines represent average satisfaction scores for first and second calls, capturing industry perceptions of solution quality and teamwork. 4 Conclusions The INDUSAC project presents a strong case for integrating students into co-creative processes that bridge academia and industry. Although students and researchers initially rated their skills highly, post-project evaluations revealed clear improvements in several competencies. Negotiation skills increased by 7%, conflict management by 6%, and international teamwork by up to 15%. These gains confirm the value of experiential, collaborative learning and show that short, focused project cycles can enhance professional readiness in areas often difficult to develop in traditional academic settings. Student reflections indicate that even confident participants recognised growth, underscoring INDUSAC’s value as a developmental tool regardless of starting level. Companies likewise reported rising satisfaction with both the process and the solutions, appreciating creativity and innovation while calling for greater market orientation. This balance of skill enhancement and practical benefits illustrates how such collaborations provide value for all stakeholders. In addition to skills development and satisfaction, the project plays a significant role in knowledge transfer. By facilitating direct collaboration between students and companies on real-world challenges, INDUSAC promotes the exchange of knowledge, methodologies, and potential solutions that can be further refined and integrated into industrial practice. Direct student–company collaboration creates conditions for sustainable knowledge transfer, where ideas are co-created, critically tested, and adapted into practical industrial contexts. Overall, INDUSAC demonstrates effectiveness as a replicable model at the intersection of higher education, industry, and innovation. Key lessons include the importance of sustained engagement, feedback-driven refinement, and inclusive participation. Future work should strengthen market-oriented analysis and assess long-term impacts on student careers and company innovation. Acknowledgments Work described in this manuscript has received funding from the European Union’s Horizon Europe Programme under grant agreement No 101070297. References [1] Kempf, C., Albers, A., Hellwig, I., Bastian, A., & Ritzer, K. (2023). Success factors and barriers in industry–academia collaborations – a descriptive model. American Society of Mechanical Engineers (ASME), International Mechanical Engineering Congress and Exposition, V002T02A005. [2] Bastian, A., Kempf, C., Rudolph, P., & Albers, A. (2024). Breaking cultural barriers: an integrated methodology for challenge-driven cocreation projects. Proceedings of the Design Society, 4, 885–894. [3] Ejubovic, A., Avsec, S., Flogie, A., & Dolenc, K. (2017). State of University–Business Cooperation in Slovenia. University–Business Cooperation in Europe Project Report. [4] Kempf, C., Albers, A., & Bastian, A. (2024). Evaluation practices in short-term industry–academia projects: Response rates and engagement. Design Society Special Issue. [5] Morrison, A., & Pattinson, P. (2020). University–Industry collaboration in practice: Patterns and pathways. Journal of Technology Transfer, 45(5), 1234–1248. [6] Ratten, V. (2016). Knowledge transfer, entrepreneurship, and international collaboration: A framework for competitiveness. International Journal of Technology Management, 72(4), 217–229. [7] Cunningham, J.A., & Link, A. (2014). Fostering university–industry R&D collaboration in European contexts. Research Policy, 43(6), 1239– 1249. [8] INDUSAC (2023). Available at: https://indusac.eu/indusac/ [9] Odić, D., Mrgole, U., & Trobec, M. (2023). New initiatives for knowledge transfer between industry and academia: the INDUSAC Project. Proceedings of the 16th International Technology Transfer Conference, Information Society – IS 2023, vol. E: 58–61. [10] Odić, D., Mrgole, U., Trobec, M. (2023) New forms of upskilling in international cooperation between students and industry. Proceedings of the Education in Information Society conference, Information Society - IS 2023, vol. G: 119-123. [11] Odić, D., Mrgole, U., & Trobec, M. (2024). Feasibility analysis for the new mechanism of knowledge transfer within the INDUSAC project. Proceedings of the 17th International Technology Transfer Conference, Information Society – IS 2024, vol. E: 39-42. 38 Digital Persona Generation: Historical Figure Emulation in Learning Velu Kaliappan velum[email protected] Abstract This Machine Learning paper presents an innovative pipeline for generating interactive digital personas of historical figures, aiming to enhance educational engagement. Our system leverages large language models (LLMs) and RetrievalAugmented Generation (RAG) to ensure factual accuracy and employs sophisticated voice synthesis for authentic conversational experiences. A core aspect of our approach involves adaptive prompt engineering, which serves as a crucial feedback mechanism to continuously refine the historical figure’s knowledge base and conversational tone, ensuring high fidelity to the original persona. This iterative adaptation enables personalized learning interactions, allowing users to deeply engage with historical context through emulated figures like Pliny the Elder. Index Terms—Conversational AI, Historical Simulation, Retrieval-Augmented Generation (RAG), Educational Technology, Digital Humanities, Language Models, Virtual Persona, Interactive Learning, AI in Education, Prompt Engineering, Textto-Speech Synthesis, Knowledge Grounding, NLP for History, Whisper ASR, ElevenLabs TTS Introduction Traditional history education often relies on passive methods such as textbooks, lectures, and rote memorization, which can hinder engagement and knowledge retention, particularly in ancient history due to cultural and linguistic gaps. Students frequently struggle to connect meaningfully with historical content, reducing motivation and learning efficacy. Advances in artificial intelligence (AI) offer new opportunities to transform history education. Conversational agents powered by large language models (LLMs) can bring historical figures ”to life” as interactive, personality-driven digital personas. These reconstructions simulate speech, behavior, and ideologies based on historical texts, enabling dynamic, dialogue-based learning experiences. This paper presents a framework for reconstructing the persona of Pliny the Elder, a Roman author and naturalist, using a hybrid approach of Retrieval-Augmented Generation (RAG), prompt engineering, and voice synthesis. The system grounds responses in verified historical content and emulates Roman-era rhetorical style to provide an immersive, voice-interactive educational environment. We investigate three core hypotheses: 1) H1: Historical Accuracy – Responses are factually accurate and temporally consistent with Pliny’s writings. 2) H2: Engagement and Learning Efficacy – Dialogue-based learning enhances engagement and knowledge retention compared to traditional methods. 3) H3: Stylistic Authenticity – The system emulates Pliny’s tone, language, and rhetorical style convincingly. Initial evaluations indicate strong alignment with all hypotheses, including contextually grounded answers and engaging voice-based interaction. This approach transforms passive content consumption into interactive, personalized historical learning. A. Relevance to Technology Transfer and Intellectual Property The modular framework—comprising speech recognition, retrieval, prompting, and synthesis—is transferable to domains beyond history, such as healthcare training, cultural preservation, and corporate knowledge management. Additionally, creating historically grounded digital personas raises intellectual property considerations related to synthesized voices, digital likenesses, and curated corpora, connecting educational innovation with technology transfer and IP governance. Related Work AI-driven educational systems, including virtual tutors and conversational agents, enable dynamic, personalized learning and immediate feedback [1]–[4]. A. Conversational Agents and Digital Personas Projects like SimSensei and New Dimensions in Testimony use AI avatars to simulate emotionally aware or historical interactions [2], [5], though often relying on firsthand recordings. Text-based approaches, such as Living Memories, generate digital representations from archival content [6]–[9]. B. Virtual Heritage and History Education AI supports digital humanities by preserving historical narratives and enabling interactive learning [10]–[12]. Projects like REACH, Europeana, and Time Machine provide interactive timelines and story-driven modules [13]–[15]. LLMbased tutoring shows potential for higher-order reasoning and curriculum-specific contextualization [?], [16]–[20]. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia © 2025 Copyright held by the owner/author(s). 39 Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia V. Kaliappan C. Retrieval-Augmented Generation and Knowledge Grounding RAG frameworks embed queries and source texts in a shared vector space to ground LLM responses in factual knowledge, reducing hallucination and maintaining style [21]–[26]. D. Gap in the Literature Few studies integrate RAG, persona simulation, and interactive dialogue for ancient history. Most digital heritage work focuses on static visualization or archives [27]. Our approach uniquely combines semantically indexed historical texts, realtime voice synthesis, and prompt engineering to create an interactive, educational experience with a Roman scholar. Methodology A. Overview The system enables immersive, historically grounded conversations with Pliny the Elder using four modules: Automatic Speech Recognition (ASR), Retrieval-Augmented Generation (RAG), Prompt-Conditioned Language Model, and Text-to-Speech (TTS) synthesis (Figure 1). B. Data Collection and Preprocessing Naturalis Historia [28] served as the primary knowledge source. Chapters were treated as atomic documents, cleaned, segmented, embedded with Sentence-BERT [29], and indexed in a FAISS vector database [30] for retrieval. C. System Architecture Figure 1. System architecture for Pliny the Elder simulation. D. Persona Simulation Approaches (1) Fine-Tuning: Supervised training on Q-A pairs from Naturalis Historia using cross-entropy loss, achieving tonal alignment but limited factual generalization. (2) RAG: Integrates document retrieval into generation, grounding responses in top-k passages to reduce hallucination: Context = TopK (sim(Embed(q),Embed(di))) (3) Prompt Engineering: Few-shot examples and stylistic meta-instructions capture Pliny’s tone but may struggle with complex queries. E. Hybrid Strategy Combining RAG + Prompting ensures factual accuracy via retrieval and stylistic authenticity via prompt templates [25]. Table I compares methods. Table 1: Persona Simulation Methods Comparison Method Factual Accuracy Stylistic Match Flexibility Fine-tuning Medium High Low Prompting Low High High RAG High Medium High RAG + Prompting High High High F. Illustrative Dialogue Table 2: Sample Interaction with AI Pliny User What are the main uses of sand in construction? Pliny (AI) Sand mixed with lime strengthens mortar; river and sea sand require one-third lime, fossil sand onefourth. Ground pottery fragments further reinforce structures, as observed in Rome. Evaluation A. Automated Evaluation Using 200 in-domain and 100 out-of-scope questions [31], results were: • In-domain accuracy: 194/200 (97%) • Out-of-domain: 100% correctly declined Table 3: Error Types in In-Domain Responses Error Type Count Incorrectly Declared Unavailable 4 Misinformation from Same Chapter 2 40 Digital Persona Generation: Historical Figure Emulation in Learning Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia B. Human Evaluation (Proposed) Learning Efficacy: Preand post-tests comparing control (text) vs. test (AI dialogue) groups. Engagement: Post-session Likert survey (1–5) on engagement, comprehension, and usability. Stylistic Fidelity: Participants compare AI-generated vs. original excerpts on tone, language, and historical alignment. C. Ethical Considerations All synthetic content is clearly marked as artificial. The model supplements, but does not replace, scholarly sources. Results and Discussion A. In-Scope Knowledge Accuracy The system correctly answered 194 of 200 in-domain questions from Naturalis Historia (97% accuracy), with errors limited to missed context (4) or minor factual inaccuracies (2) (Table IV). All 100 out-of-scope questions were correctly declined, demonstrating effective knowledge confinement. Table 4: In-Scope Accuracy Evaluation Result Type Count Percentage Correct Responses 194 97% Missed Context 4 2% Misinformation 2 1% B. Tone and Style Generated responses maintained factual fidelity but used simplified modern English rather than Pliny’s complex Roman sentence structures. This trade-off improved readability and engagement, although it slightly reduced stylistic authenticity. C. System Responsiveness Average response latency across 50 interactions was under 2.1 seconds, confirming near real-time performance suitable for interactive educational settings. D. Insights and Implications Accuracy vs. Style: High factual precision was balanced with simplified language for user comprehension. Informal testing suggested this trade-off enhanced engagement. RAG Effectiveness: Retrieval-augmented generation reliably grounded responses in source texts and prevented hallucinations, preserving temporal and historical integrity. Educational Potential: AI-driven historical personas can transform learning from passive text consumption to active, dialogic exploration, offering immersive experiences with figures like Pliny. Limitations: Fine-grained control over rhetorical style, humor, and philosophical nuance remains limited. Formal human evaluations and broader demographic testing are planned for future work. Conclusion This work presents a framework for AI-powered historical personas using RAG, prompt engineering, and speech synthesis. The system successfully reconstructs Pliny the Elder’s knowledge and voice, achieving 97% accuracy for in-domain queries and perfect rejection of out-of-scope questions, with sub2.1s response latency. By combining semantically indexed texts, context-aware language modeling, and voice synthesis, the approach ensures factual, temporally consistent, and stylistically guided interactions. While modernized syntax slightly reduces historical authenticity, it enhances readability and learner engagement. Overall, the framework demonstrates the feasibility of immersive, interactive historical learning, opening avenues for AI-driven education in digital humanities, museums, and cultural heritage platforms. Future work will refine stylistic fidelity, extend to other historical figures, and incorporate comprehensive human-subject evaluations. Future Work While the current implementation successfully demonstrates the feasibility of recreating historically grounded conversational agents, there are several promising directions for future research and system development. A. Expansion to Multiple Historical Figures One of the most immediate opportunities lies in extending the system to support multiple historical personas. Expanding beyond Pliny the Elder to include figures such as Socrates, Cleopatra, Leonardo da Vinci, or Confucius would allow users to explore diverse viewpoints across different eras and civilizations. This would require the creation of distinct RAG pipelines, vector databases, and persona-specific prompt templates for each character. A central challenge in this expansion would be ensuring that each virtual figure maintains not only factual accuracy but also individual linguistic style, philosophical perspective, and cultural context. B. Cross-Cultural and Multilingual Support Another avenue of future work involves supporting interactions in multiple languages. For example, recreating conversations with Pliny in Latin, alongside translations in English, could enhance authenticity and facilitate language learning. This would necessitate incorporating multilingual language models and translating source corpora while preserving semantic integrity. Moreover, cultural nuance in translation and tone must be carefully handled to maintain the integrity of the historical persona. C. Fine-Grained Stylistic Modeling While current prompt engineering techniques allow for general stylistic tuning, more sophisticated methods could be developed to capture specific rhetorical patterns, humor, dialect, and tone unique to each persona. This could include training specialized adapters or using reinforcement learning from human feedback (RLHF) to better align generated outputs with ancient writing 41 Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia V. Kaliappan styles. Additionally, integrating large-scale corpora of classical literature could further improve stylistic realism. D. Human Evaluation at Scale A critical limitation of the present work is the lack of largescale user studies. Future work will involve conducting controlled experiments with learners across different educational levels, ranging from middle school to university. These studies will assess learning outcomes, engagement metrics, usability, and overall educational impact. In particular, comparisons between AI-based interaction and traditional textbook-based learning will provide quantitative insight into the system’s pedagogical effectiveness. E. Ethical Safeguards and Historical Fidelity As the system is extended to simulate more figures, ensuring historical fidelity and ethical integrity becomes paramount. Mechanisms must be implemented to detect and prevent anachronistic statements, hallucinations, or culturally insensitive outputs. Additionally, the interface should clearly indicate that the persona is AI-generated and that any information provided should be cross-verified with scholarly resources when used for academic purposes. F. Educational Integration and Deployment Future work also includes integrating this system into learning management platforms, museum kiosks, and virtual reality environments. Instructors could use the AI personas to create curriculum-aligned conversations or simulate debates between historical figures. Moreover, real-time analytics could be used to monitor learner progress, adapt responses based on user history, and personalize educational journeys. G. Model Optimization and Scalability To support broader adoption, performance optimization will be necessary. Reducing latency while preserving response quality is crucial for scalability, especially in bandwidthconstrained environments. Research into model distillation, edge deployment of smaller LLMs, or serverless architectures could help bring such conversational agents to under-resourced regions and classrooms globally. H. Simulation of Historical Ecosystems In the long term, the framework could evolve into simulating entire historical ecosystems rather than individual personas. This would allow users to engage in conversations with multiple figures across time, participate in historical reenactments, or explore socio-political dynamics through AI-driven discourse. Such simulations could bring unparalleled depth to history education by enabling experiential, narrative-based exploration of the past. References [1] R. Winkler and M. S¨ollner, “Chatbots in education: A systematic literature review,” in Academy of Management Proceedings, 2020. [2] D. DeVault et al., “Simsensei kiosk: a virtual human interviewer for healthcare decision support,” in AAMAS, 2014, pp. 1061–1068. [3] C. Kulkarni et al., “Peerstudio: Rapid peer feedback emphasizes revision and improves performance,” in Learning@ Scale, 2015. [4] X. 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Kilpatrick, “Gemini 2.0: Flash, flash-lite and pro,” https://developers.googleblog.com/en/gemini-2-family-expands/, 2025, google Developer Blog 42 Trends in Brain-Computer Interface Technologies: Patent Analysis Rahela Anič ič Č andrlič PhD Čandidate at DOBA Business Sčhool DOBA Business Sčhool Maribor, Sloven ia rahela.aničič-čandrlič@net.doba.si PhD. Gregor Jagodič Dean of DOBA Business Sčhool DOBA Business Sčhool Maribor, Sloven ia gregor.jagodič@doba.si Abstract Brain-Čomputer Interfače (BČI) tečhnologies are expanding rapidly ačross medičal and čommerčial domains, yet little is known about their global innovation dynamičs. This paper addresses this gap through a patent analysis of BČI-related filings in the World Intellečtual Property Organisation (WIPO) P atentsčope database from 1993 to mid-2025. The study examines filing and publičation trends, geographičal distribution, appličant types, a nd International Patent Člassifičation (IPČ) čategories, supported by regression modelling. Results show 270 ide ntified patents, with the United States and Čhina leading (36% of filings). Nearly half were filed by individual inventors, reflečting vigorous entrepre neurial ačtivity. The dominant tečhnologičal areas are A61B (diagnostič and therapeutič in struments) and G06F (čomputing), čonfirming the čonvergenče of bio medičal and čomputational innovation. P roječtions suggest steady but modest growth through 2040. The paper presents one of the first longitudinal a nalyses of BČI pa tents, providing insights into tečhnologičal progress, key ačtors, and appličation areas relevant to researčhers, poličymakers, and industry. Keywords Brain-Čomputer Interfače (BČI), Neurotečhnology, Patent analysis, WIPO, Innovation trends 1 Introduction BČI tečhnology establishes a direčt čommuničation pathway between neural ačtivity and external devičes, enabling the čontrol of both hardware and software through brain signals. Initially čončeived for medičal purposes, sučh as supporting patients with paralysis or sensory impairments, BČIs are now rapidly expanding into domains inčluding gaming, autonomous driving, mobile tečhnology, and wellness [2, 5, 6]. This shift reflečts the broade r transformation of neurotečhnology, whičh has moved from narrowly defined čliničal appličations to a multidisčiplinary field with profound sočial, edučational, and čommerčial impličations [8, 15]. The innovative čharačter of BČI lies in bridging neurosčienče, engineering, and artifičial intelligenče (AI) while redefining human–mačhine interačtion. BČIs enable the translation of neural ačtivity into čommands without physičal movement, thereby čreating opportunities for čommuničation, rehabilitation, and human a ugmentation [1, 11]. Advančes in neuroadaptive systems extend this potential by allowing tečhnology to dynamičally adapt to the user’s mental state [3]. Beyond pračtičal appličations, čreative experiments sučh as multi-brain činema performančes [18] highlight the čultural and artistič signifičanče of BČIs. Patent ačtivity both ref lečts and ačče lerates these transformations. Authors [10] stress the importanče of reliable signal p ročessing, while others [14] underline the translation of neurotečhnologies into everyday use. At the same time, ethičal and regulatory čhallenges [5] emphasise the need for balančed progress. This researčh was undertaken to address the lačk of systematič evidenče on how global BČI innova tion is evolving, whičh ačtors are leading the development, and whičh tečhnologičal areas are čurrently dominating. Patent analysis provides a suitable method, as patents are not only legal instruments but also early indičators of tečhnologičal trends and čommerčialisation potential [9]. The added value of this artičle lies in offering one of the first čomprehensive global overviews of BČI-related patents, mapping innovation pathways, and identifying key ačtors and appličations. In doing so, it čontributes to a deeper understanding of how BČIs are reshaping human–mačhine integration and informs the responsible development of these tečhnologies. 2 BCI: Innovations Shaping the New Reality With growing čonvergenče between ne urosčienče, AI, and information tečhnologies, neurotečhnology has emerged as a čentral field of interdisčiplinary researčh. Neurotečhnology enčompasses medičine, engineering, psyčhology, and čomputer sčienče, enabling deeper čonnečtions between human čognition and tečhnology [14]. Historičally, neurotečhnology was p rimarily assočiated with medičine. Today, however, it extend s far beyond healthčare to inčlude appličations in edučation, workplače †Corresponding author rahela.aničič-ča ndrlič@net.doba.si, gregor.jagodič@doba.si Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee, provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honoured. For all other uses, contact the owner/author(s). Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia © 2025 Copyright held by the owner/author(s). 43 Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia R. Aničić Čandrlić et al. produčtivity, gaming, sports, and everyday life [8, 15]. At the čore of these developments a re BČIs, whičh translate brain ačtivity into čommands without physičal movement [1, 11]. BČIs are člassified into two broad ča tegories: noninvasive (wearable) and invasive (implanted). Non-invasive BČIs typičally utilise EEG to monitor čortičal ačtivity, enabling rea l-time feedbačk and čontrol of external devičes [8]. Invasive BČIs, on the other hand, require surgičal implantation to restore sensory or motor funčtions, partičularly in patients with paralysis or amputations [4]. The čivilian and military appličations of BČI are wide-ranging, from čommuničation and neurorehabilitation to drone čontrol and stress monitoring [6]. The market outlook is promising. The global neurotečhnology market is proječted to reačh $52.86 billion by 2034, with the BČI segment expečted to grow to $6.2 billion by 2030 [7]. This growth highlights the importanče of monitoring patent ačtivity to identify the most signifičant areas of innovation. 3 Methodology This researčh applies a patent analysis approačh to examine innovation trends in BČI tečhnologies. Patent data was retrieved from the World Intellečtual Property Orga nisation (WIPO) Patentsčope database, using advančed searčh filters, whičh čovers filings ačross 193 member states, inčluding international appličations submitted under the Patent Čooperation Treaty (PČT) [16]. The WIPO database was selečted bečause of its čomprehensive and standardised global čoverage, whičh enables čomparative analysis ačross čountries, institutions, and tečhnologičal fields. The dataset was čollečted on July 5, 2025, čovering published patents filed between 1993 and mid-2025. Due to the čonfidentiality regulations stipulated by WIPO, patent appličations remain undisčlosed for 18 months after the filing date [9]. For this reason, appličations filed in 2024 and 2025 are inčomplete and were exčluded from the analysis, as reliable čončlusions about their čontent and trends čannot yet be drawn. The searčh strategy was based on a čombination of keywords, inčluding BČI and “Brain-Mačhine Interfače (BMI).” Although the keyword “BMI” was initially čonsidered, it was exčluded due to the high inčidenče of irrelevant matčhes, inčluding terms sučh as Body Mass Index, Bio-renewable Čarbon Index, and unrelated protein or networking čončepts. Authors [12] highlight that sučh inčonsistenčies are typičal when applying keyword-based searčhes ačross interdisčiplinary fields, partičularly when terminology overlaps ačross domains. To mitigate this, searčh results were ča refully reviewed, though it is ačknowledged that some relevant patents may have been inadvertently exčluded or unrelated ones inčluded. The retrieved data were analysed by examining patent offičes to map the geographičal distribution of innovation, publičation, and filing dates, tračking temporal trends. This analysis also inčluded app ličants, sučh as čompanies, universities, researčh institutes, individuals, and foundations, as well as tečhnologičal člassifičations based on the IPČ and ČPČ systems. Patent člassifičations were partičularly relevant for this study, as they indičate the tečhnologičal areas in whičh innovation oččurs. Sinče BČI researčh spans multiple sčientifič disčiplines, inčluding engineering, čomputer sčienče, biology, and medičine, patents were often assigned to various IPČ čategories. That reflečts the interdisčiplinary čharačter of BČI and its potential appličations ačross healthčare, rehabilitation, human–čomputer interačtion, a nd neuroadaptive tečhnologies. To identify long-term innovation patterns, both desčriptive and inferential analyses were applied. Temporal patterns were examined using linear regression, with regression models estimating expečted patent ačtivity up to 2040. For instanče, based on the regression equation (y = 0.8203x – 1640.1), p roječtions suggest 25 BČI-related patents by 2030, 29 by 2035, and 33 by 2040. While these proječtions are indičative rather than definitive, they provide insight into the traječtory of tečhnologičal growth. The methodology builds on the understanding that p atent data is no t only a legal instrument for intellečtual property protečtion but also a strategič resourče for innovation analysis [9]. By systematičally analysing pa tents, it is possible to trače the čommerčialisation potential of BČIs, monitor tečhnologičal čompetition, and identify emerging areas of appličation. This approačh čomplements existing literature on neurotečhnology, whičh emphasises the translation of laboratory researčh into market-ready appličations [14]. Several methodologičal limitations must be ačknowledged. First, keyword-based searčhes čannot fully resolve issues of terminology, translation, or inčomplete metadata [12]. Sečond, the exčlusion of rečent patents due to čonfidentiality rules čreates a temporary data gap, whičh is unavoidable but limits čončlusions about the very latest developments. Finally, patent ačtivity d oes not nečessarily equate to suččessful čommerčialisation, as many filings never result in market-ready produčts. Nonetheless, patents remain one of the most reliable indičators of early-stage innovation and čompetitive tečhnologičal development. 4 Patent Analysis 4.1 Patents by PCT Offices The searčh resulted in 270 BČI-related p atent appličations filed between 1993 and July 2025. These were examined along four main dimensions: PČT jurisdičtions, publičation and filing dates, appličants, and IPČ člassifičations. Together, these čategories provide a čomprehensive pičture of the state of innovation in BČI tečhnologies and the trends shaping their evolution. Patent Čooperation Treaty (PČT) filings reflečt the global spread of innovation. The analysis indičates that the United States leads with 59 patents (22%), followed by Čhina with 37 patents (14%). The European Patent Offiče, the Republič of Korea, and Čanada eačh aččount for 7%, while India and Australia hold more minor but signifičant shares. Čollečtively, the United States and Čhina aččount for 36% of global BČI 44 Trends in Brain-Computer Interface Technologies: Patent Analysis Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia patents, undersčoring their dominant positions in neurotečhnology innovation. Figure 1: Patents by PCT offices (1993–2025) These results align with broader innovation trends, where North Američa and East Asia remain čentral hubs of high-tečhnology researčh. Notably, the diversity of čontributing čountries—inčluding Brazil, Japan, Mexičo, and the Russian Federation—demonstrates that BČI is not geographičally čonfined but inčreasingly globalised [17]. 4.2 Patents by Publication and Filing Dates Figures 2 and 3 illustrate the temporal distribution of patents. The earliest patents appeared in the mid-1990s, but growth remained limited until 2008, when filings rose signifičantly, čoinčiding with broader advančes in neurosčienče and čomputing. Additional peaks oččurred in 2019, 2021, and 2023, reflečting periods of intensified investment and tečhnologičal breakthroughs. Figure 2: Number of patents by publication date (1993– 2025) Figure 3: Number of patents by filing date (1993–2025) Between 1993 and 2023, the annual growth rate of published patents averaged 12%, with an overall mean of 9.2 patents per year. Regression analysis (R² = 0.4286) reveals a moderate čorrelation between time and patent volume, indičating that external fačtors (inčluding ečonomič čonditions, regulatory frameworks, and researčh funding) play a signifičant role in shaping innovation a čtivity. This interpretation is čonsistent with literature on neurotečhnology, whičh emphasises the impačt of črossdisčiplinary developments and funding dynamičs [11, 13]. Based on the regression model (y = 0.8203x – 1640.1), patent ačtivity is proječted to čontinue rising modestly, with 25 patents expečted by 2030, 29 by 2035, and 33 by 2040. These estimates highlight steady growth but also indičate that disruptive innovation čyčles, rather than linear trends, may drive the field forward. 4.3 Patents by Applicants Analysis of appličants shows that individual inventors play a disproportionately large role, representing 47% of all appličants and 61% of total patents (Table 1). That demonstrates the entrepreneurial and expe rimental čharačter of BČI researčh, where small-sčale innovators čomplement the efforts of čompanies and universities. Čompanies aččount for 10 appličants with 61 p atents, while universities čontributed 39 pa tents from 11 appličants. Researčh institutes and foundations play minor roles but provide essentia l čontributions to knowledge develop ment. The distributed nature of patent ačtivity suggests that a f ew large čorporations do not monopolise BČI innovation but are instead čharačterised by pluralism and diversity of approačhes. Table 1: Patent applicants and number of patents (1993–2025) Appličants Total No. of Patents Čompany 10 61 Foundation 1 3 Researčh institutes 4 17 Individuals 23 186 University 11 39 4.4 Patents by IPC Classification The International P atent Člassifičation (IPČ) system reveals whičh tečhnologičal areas dominate BČI innovation. The most prevalent čategory is A61B (Diagnostič and Therapeutič Instruments), čomprising 123 patents, and inčludes biomedičal devičes for brain signal pročessing, neurofeedbačk, rehabilitation, and the neural čontrol of prosthetičs. Figure 4: Number of published patents by IPC classification (1993–2025) G06F (Čomputing/Čalčulation) aččounts for 112 patents, reflečting the importanče of software, signal pročessing, and human–čomputer interačtion. Additional relevant čategories inčlude A61N (Elečtrotherapy, 29 patents), A61F (Medičal United States of America; 22% China; 14% Canada; 7% European Patent Office; 7% Republic of Korea; 7% India; 4% Australia; 4% Brazil; 3% Japan; 3% Mexico; 3% Russian Federation; 2% 1 1 1 31112 30 656124613 371420 10 30 15 2937 12 y = 0.0052x R² = 0.5021 y = 0.8203x - 1640.1 R² = 0.4286 -20 0 20 40 60 1990 1995 2000 2005 2010 2015 2020 2025 2030 No. Of Patents Year 1 1 3123 27 6578 8 61095 141719171923 35 24 y = 0.0056x R² = 0.6032 y = 0.8464x - 1691.6 R² = 0.5566 -20 0 20 40 60 1990 1995 2000 2005 2010 2015 2020 2025 2030 No. Of Patents Year 89 74 17 6 6 6 6 0 50 100 A61B G06F A61N A61F G06N H04L H04W No. Of Patents IPC classification 45 Information Society 2025, 6–10 October 2025, Ljubljana, Slovenia R. Aničić Čandrlić et al. devičes, 29 pa tents), and G06N (Biologičal čomputing systems, 24 patents). Together, these highlight the interdisčiplinary čharačter of BČI tečhnologies, whičh integrate hardware, software, and medičal sčienče [10]. Prominent čompanies inčlude Neuralink, NeuroSky Inč., Neurable Inč., MindMaze SA, Prečision Neurosčienče, and MindPortal Inč. Their fočus areas range f rom wearable EEGbased BČIs and ne uroadaptive tečhnologies to invasive implants and ne uroprosthetičs. Among individual innovators, Vijay VAD, Čhristoph Gugger, Gu nter Edlinger, Jose L. Čontreras-Vidal, and Rodolfo Llina s have made notable čontributions. Figure 5: Word cloud based on BCI patent titles (1993– 2025) 5 Conclusion This study addresses a gap in the literature by examining BČI patents to map global innovation dynamičs, an aspečt that has been less explored čompared to čliniča l or tečhničal studies. The analysis shows rapid a nd diverse p rogress, led by the United States and Čhina, with notable čontributions from Europe, Korea, Čanada, a nd India. Nearly half of all appličants are individual inventors, highlighting a broad, entrepreneurial ečosystem rather than dominanče by large čorporations. Patent člassifičations čonfirm the expansion of BČIs beyond medičine into edučation, entertainment, neurorehabilitation, and workplače p rodučtivity [3, 14], underlining their role both as a ssistive tečhnologies and as tools for human augmentation. Patent ačtivity reflečts funding čyčles, regulatory čhanges, and disruptive advančes, while the Geneva Ačademy [5 ] stresses that ethičal and human rights čonsiderations must guide development. Although patent data do not fully čapture čommerčialisation, they remain a valuable tool for tračing innovation, antičipating appličations, and assessing leadership. A logičal next step is to čomplement patent analysis with evidenče from čliničal trials, investment flows, and governanče studies, to tračk how AI-neurosčienče čonvergenče aččelerates BČI integration into everyday life while ensuring responsible innovation. 6 Acknowledgments The authors are deeply grateful to the PhD. Ana Hafner for her inva luable guidanče, enčouragement, and support. References [1] Bogue, R. (2010). Brain-computer interfaces: Control by thought. Industrial Robot, 37(2), 126–132. DOI:https://doi.org/10.1108/01439911011018894 [2] Bularka, S., & Gontean, A. (2016). Brain-computer interface review. 2016 12th IEEE International Symposium on Electronics and Telecommunications (ISETC), 219–222. DOI:https://doi.org/10.1109/ISETC.2016.7781096 [3] Fairclough, S. (2023). Neuroadaptive technology and the self: A postphenomenological perspective. Philosophy and Technology, 36(2). DOI:https://doi.org/10.1007/S13347-023-00636-5 [4] FDA. (2021, May 20). 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DOI:https://doi.org/10.1080/14626268.2016.1260593 46 Indeks avtorjev / Author index Aničić Čandrlić Rahela ................................................................................................................................................................ 43 Atienza Vicente ............................................................................................................................................................................ 31 Aydin Aleyna ......................................................................................................................................................................... 19, 31 Aykut Yalçın .......................................................................................................................................................................... 19, 31 Ayvaz Emrah .......................................................................................................................................................................... 19, 31 Blatnik Robert .............................................................................................................................................................................. 31 Değermenci Beril ................................................................................................................................................................... 19, 31 Değirmenci Samet Can ........................................................................................................................................................... 19, 31 Dobravc Škof Karin ..................................................................................................................................................................... 11 Florjancic Urska ........................................................................................................................................................................... 31 Gültekin Güler Tuğba ............................................................................................................................................................. 19, 31 Hafner Ana ............................................................................................................................................................................... 7, 11 Hehn Leonie ................................................................................................................................................................................. 31 İskender Balaban Dilek .......................................................................................................................................................... 19, 31 Jagodič Gregor ............................................................................................................................................................................. 43 Kaliappan Velu ............................................................................................................................................................................. 39 Kalyoncu Sedanur .................................................................................................................................................................. 19, 31 Kireta Sanja .................................................................................................................................................................................. 31 Koç Ayhan ............................................................................................................................................................................. 19, 31 Kolar Janez ................................................................................................................................................................................... 11 Kunej Špela .................................................................................................................................................................................. 35 Lale Orsat ..................................................................................................................................................................................... 31 Lamut Urša ................................................................................................................................................................................... 11 Leban Marijan .............................................................................................................................................................................. 31 Lutman Tomaž ............................................................................................................................................................................. 27 Lužar Magda ................................................................................................................................................................................ 23 Mrgole Urška ............................................................................................................................................................................... 35 Noskovic Jaroslav ........................................................................................................................................................................ 31 Odić Duško ................................................................................................................................................................................... 35 Pastor Rudolf .......................................................................................................................................................................... 15, 31 Paunov Dimitar ............................................................................................................................................................................ 31 Perez Berta ................................................................................................................................................................................... 31 Sabir Hülya ............................................................................................................................................................................. 19, 31 Sağlam Gözde ........................................................................................................................................................................ 19, 31 Sönmez Kerim ........................................................................................................................................................................ 19, 31 Tancheva Mariana ........................................................................................................................................................................ 31 Trobec Marjeta ............................................................................................................................................................................. 35 Ünver Müslüm Serhat ............................................................................................................................................................ 19, 31 Vindišar Jure ................................................................................................................................................................................ 27 Yildiz İslam ............................................................................................................................................................................ 19, 31 Yilmaz Eren ........................................................................................................................................................................... 19, 31 47