nep-ipr New Economics Papers
on Intellectual Property Rights
Issue of 2024‒05‒13
two papers chosen by
Giovanni Battista Ramello, Università di Turino

  1. Strategic evaluation of the technology transfer and IPR protection systems of Bulgaria, Croatia and Romania and recommendations for their enhancement By BOLE Domen; GALABOVA Lidia; HALEY Christopher; KOKOROTSIKOS Paris; MATANOVAC-VUČKOVIĆ Romana; RIZZUTO Carlo; TAYLOR Stephen; VLADUT Gabriel; ZAMBELLI Mauro
  2. Machine learning-based similarity measure to forecast M&A from patent data By Giambattista Albora; Matteo Straccamore; Andrea Zaccaria

    Abstract: With a view to maximise the impact of EU Cohesion Policy and RRP investments in the field of Research and Innovation (R&I) in Bulgaria, Croatia and Romania, this report provides a strategic evaluation of the three countries’ Technology Transfer systems with concrete recommendations for enhancing academia-industry collaboration and research commercialisation. The report aims to help Bulgaria, Croatia, and Romania to build up effective Research and Innovation (R&I) systems, in particular through the strengthening of Technology Transfer (TT) and Intellectual Property Rights (IPR). The report is the result of extensive research, primarily interviews and consultations with approximately 100 stakeholders, the majority of which from academia (Technology Transfer Offices (TTOs) at Universities and Research Institutes) and government authorities (ministries; agencies at national and, where relevant, regional level), as well as some industry representatives, cluster associations and other innovation-focused stakeholders (science and technology parks, incubators, accelerators) from the three countries at focus. The interviews were conducted in the second half of 2022 and first half of 2023. This study presents a country-by-country analysis of the technology transfer landscape; regulatory frameworks, skills-capacities-capabilities; academia-industry collaboration dynamics; financing instruments and mechanisms; patenting of academic inventions, among others. The targeted findings and recommendations contained in the three country-focused chapters are followed by two horizontally-applicable chapters on Financing mechanism and capacity building activities; and TT Tools and IP Guidelines, which have a relevance to and could benefit all three countries. The work was commissioned by DG REGIO to the Joint Research Centre, which coordinated and directed the group of independent experts that authored the present report.
    Date: 2024–01
  2. By: Giambattista Albora; Matteo Straccamore; Andrea Zaccaria
    Abstract: Defining and finalizing Mergers and Acquisitions (M&A) requires complex human skills, which makes it very hard to automatically find the best partner or predict which firms will make a deal. In this work, we propose the MASS algorithm, a specifically designed measure of similarity between companies and we apply it to patenting activity data to forecast M&A deals. MASS is based on an extreme simplification of tree-based machine learning algorithms and naturally incorporates intuitive criteria for deals; as such, it is fully interpretable and explainable. By applying MASS to the Zephyr and Crunchbase datasets, we show that it outperforms LightGCN, a "black box" graph convolutional network algorithm. When similar companies have disjoint patenting activities, on the contrary, LightGCN turns out to be the most effective algorithm. This study provides a simple and powerful tool to model and predict M&A deals, offering valuable insights to managers and practitioners for informed decision-making.
    Date: 2024–04

This nep-ipr issue is ©2024 by Giovanni Battista Ramello. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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