This course has been developed to connect cutting-edge techniques for data management, analysis and protection in the field of management of innovation and organizational transformation. It aims to facilitate learning about key developments and applications in the management of innovation field. The course helps students to develop up-to-date empirical skills for the formulation of appropriate strategies in the area of innovation at the business level. It examines available data sources of intellectual property rights and equips students with up-to-date knowledge and thinking from experts in this area. This course aims to provide a dynamic platform for learning, which exploits the latest research and practice from experts (academics and practitioners) exploiting a hand-on approach.
Risultati apprendimento attesi
Having successfully completed this module, students will be able to demonstrate knowledge and understanding of: the key and advanced dimensions of data management for intellectual property rights - Retrieval, Cleaning, Analysis, Choice and Implementation; diagnostic, practical and creative skills to analyse and evaluate a range of business solutions in relation to intellectual property rights; organisations' ability to implement chosen strategies and identify the areas requiring change. Students will develop skills in generating alternative solutions to complex issues surrounding intellectual property rights management at the business level, underpinning each with a supportive and well researched rationale in order to achieve critical success; evaluate these solutions, analysing the impact of potential outcomes on the various stakeholder groups; analyse alternative strategies for intellectual property development in differing operating contexts; assess the importance of data quality and design of indicators to effective intellectual property management. In order to acquire these skills, students will learn how to obtain, analyse and apply information from a variety of sources in the public and private domains; master new skills in relation to the retrieval and analysis of intellectual property data; work collectively as an effective and efficient group member, including, where appropriate, organising, guiding and motivating others; plan and enact the successful completion of a personal workload; communicate both orally and in written form, using and justifying arguments within reports, presentations and debates.
1. IP Rights and Competitive Advantage - Patents, Trademarks, Copyrights and other solutions for IP protection - Patent analysis - Patent quality indicators - Patent valuation
2. Technological Innovation & Tech Mining (TM) - How tech mining works - finding the right sources - TM models
6. Data retrieval, analysis and protection - Introduction to databases, data models, and database querying - Patents databases (e.g., Google patents, Patstat) - Basics of sentiment analysis - Main issues about IP and data protection
5. Introduction to blockchains and their applications - Blockchain characteristics and cryptocurrencies - NFTs (Non-Fungible Tokens)
There are no specific prerequisites for following this course.
Teaching methods include theoretical lesson, hands-on experience with patents data bases, teamwork (for attending students), and seminars. Attending lessons is warmly suggested.
Materiale di riferimento
There is no core textbook that students should read in preparation for this course. As this is an advanced course, the core of the preparation comes from a variety of material (slides, cases, academic and professional readings, exercises, hands-on tutorials etc.). Details on all the material (including slides for each class) can be found on Ariel. Some sections (specified during lessons) of the following textbooks will be used: M. Cantamessa, F. Montagna (2016) Management of Innovation and Product Development: Integrating Business and Technological Perspectives. Springer-Verlag London Alan L. Porter & Scott W. Cunningham (2005) TECH MINING: exploiting new technologies for competitive advantage. John Wiley & Sons, Inc. M. Fortnow & Q. Terry (2022) The NFT handbook: how to create, sell and buy non-fungible tokens. John Wiley & Sons
Modalità di verifica dell’apprendimento e criteri di valutazione
Students will be evaluated based on a (team) project, that will be presented on the day of the exam, followed by an oral exam, consisting in a discussion about the project and its related theoretical assumptions. The topic of the project will be assigned to each team/student at least 1 month before the exam, and will focus on assessing some aspects related to a specific technological field. To develop the project, students are expected to exploit tools and techniques learned during the lectures. At the end of the course, attending students are expected to present publicly their projects, according to the timing and modalities defined during the first lessons. The evaluation of the project will consider both the completeness and quality of the results presented and the overall quality of the presentation. PLEASE NOTE: evaluation methods are the same both for attending and non-attending students. In this latter case, students must contact the professors in due time to be assigned a topic for the project and are allowed to work alone (not in a team).