Intellectual Property for Business: Strategy and Analysis

A.Y. 2022/2023
6
Max ECTS
40
Overall hours
SSD
SECS-P/10
Language
English
Learning objectives
This module has been developed to connect cutting-edge techniques for data management and analysis in the field of management of intellectual property rights to teaching in the classroom. It aims to facilitate learning about key developments and applications in the intellectual property rights field. The module helps students to develop up-to-date empirical skills for the formulation of appropriate strategies in the area of intellectual property rights 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 module 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.
Expected learning outcomes
During the module all learning outcomes below will be introduced, developed and assessed.
Knowledge and Understanding
Having successfully completed this module you will be able to demonstrate knowledge and understanding of:
1. The key and advanced dimensions of data management for intellectual property rights - Retrieval, Cleaning, Analysis, Choice and Implementation;
2. Diagnostic, practical and creative skills to analyse and evaluate a range of business solutions in relation to intellectual property rights;
3. Organisations' ability to implement chosen strategies and identify the areas requiring change.
Subject Specific Intellectual (Cognitive) skills
Having successfully completed this module you will be able to:
4. 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;
5. Evaluate these solutions, analysing the impact of potential outcomes on the various stakeholder groups;
6. Analyse alternative strategies for intellectual property development in differing operating contexts;
7. Assess the importance of data quality and design of indicators to effective intellectual property management;
Transferable (Key general) skills
Having successfully completed this module you will be able to:
8. Obtain, analyse and apply information from a variety of sources in the public and private domains;
9. Master new hard skills in relation to specialised software for the retrieval and analysis of intellectual property data;
10. Work collectively as an effective and efficient group member, including, where appropriate, organising, guiding and motivating others;
11. Plan and enact the successful completion of a personal workload;
12. Communicate both orally and in written form, using and justifying arguments within reports, presentations and debates.
Single course

This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.

Course syllabus and organization

Single session

Responsible
Lesson period
First trimester
Course syllabus
1. IP Rights and Competitive Advantage
2. Technological Innovation & Tech Mining (TM)
- How tech mining works
3. Doing TM
- finding the right sources
- TM models
4. Patents, Trademarks, Copyrights
- Main characteristics
- Patent analysis
- Patent quality indicators
- Patent valuation
5. NFT (Non-Fungible Tokens)
6. Patents retrieval and analysis
- Google patents
- Patstat
Prerequisites for admission
There are no specific prerequisites for following this course.
Teaching methods
Teaching methods include theoretical lesson, hands-on experience with patents data bases, teamwork (for attending students), and seminars.
Attending lessons is warmly suggested.
Teaching Resources
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
Assessment methods and Criteria
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.
The evaluation of the project will consider both the completeness and quality of the result presented and the quality of the presentation.

Evaluation methods are the same both for attending and non-attending students. In this latter case, students must contact the professor in due time to be assigned a topic for the project and are allowed to work alone (not in a team).
SECS-P/10 - ORGANIZATION AND HUMAN RESOURCE MANAGEMENT - University credits: 6
Lessons: 40 hours
Professors: Ripamonti Laura Anna, Staccioli Jacopo