Intellectual Property for Business: Strategy and Analysis

A.Y. 2020/2021
Overall hours
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.
Course syllabus and organization

Single session

Lesson period
Second trimester
Depending on the evolving sanitary emergency, lessons could be moved online, on Teams. Lectures videos will be also downloadable from Ariel.
Course syllabus
Please find below the schedule for this course module. Please note that there are sometimes unforeseen circumstances, such as staff illness and deeper coverage of some topics, that may necessitate some changes to this schedule (e.g. order and coverage of topics). The lecturer will make every effort to communicate any changes to students in good time.

1. Introduction
- Introduction to the course
- IP and strategy

2. Foundations I
- Intellectual capitalism and the appropriation of innovation
- IPRS and the need for data
- Which data from a patent document?
- Main data sources for patent
- Individual presentations
- Hand-on activities

3-4. Foundations II
- Patent indicators
- Hand-on tutorials on data retrieval, patent databases and software for analysis

5-7. Capturing value: profiting from innovation and the market for ideas
- Mentoring session
- Managing IPRs
- Implications of IPRs in a global environment
- Complementary assets
- The influence of standards
- Organising to capture value
- Individual presentations

7-9. Analysis of competition and cooperation
- Mentoring sessions
- Patent intermediaries
- Patent transactions
- Citations, Co-inventions and Co-applications
- Patenting strategic groups
- Technological evolutions
- Individual presentations

10. Wrap-up
- Final workshop
Prerequisites for admission
Prerequisites refer to core topics in the following areas: Industrial Organisation, Economics and Management of Innovation and Quantitative Methods. Good knowledge of English is required.
Teaching methods
A number of teaching methods will be used in lectures in order to cater to a variety of learning styles and address the learning needs of the diverse student population. Please find a non-exhaustive list of the teaching/learning methods that might be employed during classes:

- Face-to-face lectures
- Guest speaker's talks (in class or in distance)
- Exercises (exercises, database, software etc.)
- Case studies
- Individual assignments
- Group assignments
- Interactive class activities (role playing, business game, simulation, online forum, instant polls)

Class sessions will be interactive. A number of the classes will be devoted to case discussions where the task of analysing the issues in the case will be undertaken by the class as a whole or in small groups. In other classes, students are expected to contribute their ideas on the issues being discussed and examples drawn from their personal experiences and from reading the business/academic press. These classes will involve a discussion of academic articles or other in-class activities.
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. Most of them are available in the library as a printed or an electronic copy or available on Ariel or external repositories.
Assessment methods and Criteria
Assessment methods - attending students
Note that this course does not have a final exam for attending students and the evaluation will be based on a combination of class participation (20%), Individual paper presentation and write-ups (40%) and final group presentation (40%).

Class participation - 20%
Class participation reflects the assessment of students' contribution to class learning:
- Does the student interact with fellow students and the professor regularly?
- Are his/her comments and questions well informed, reflecting careful analysis?
- Do student's comments and questions add to the understanding of the situation and show creativity?
- Does the student ask questions to other students? Do his/her comments link previous contributions and follow the overall direction of the discussion?

Individual paper presentation and write-ups - 40%
Students will be asked to prepare and deliver at least two presentations and hand in two brief memos related to two academic/professional papers (the exact number will depend on the number of attending students). During the first week, the students will be randomly assigned to the papers. The presentations and write-ups are individually prepared.

Final group presentation - 40%
Students are expected to deliver and hand in a presentation aimed at analysing an important strategic issue at a firm using the concepts and frameworks from class.

Assessment methods - non-attending students
Non-attending students will be evaluated through a nal exam that covers all the material discussed in class (e.g., cases, readings, relevant theoretical framework, exercises, hands-on tutorials, etc.). All the material (including slides for each class) can be found on Ariel. The nal exam will consist of a combination of multiple-choice and short questions and will be up to 2 hours long.

Assessment Details [DSE STUDENTS ONLY]
Students from the MSc programme in Data Science and Economics are expected to earn 6 ECTS credits instead of 9. Information about the assessment for attending and non-attending students is provided below:

Assessment methods - DSE attending students
DSE attending students should attend classes for a total of 40 contact hours. They are free to pick the hours they prefer but their attendance will be checked through appropriate means. Provisions for attendance are similar to the ones for non-DSE attending students. Assessment will be similar to the one for non-DSE students (please refer to section above) as per the following:

- 20% - Their mark on class participation will be referred to the reduced number of total contact hours (40 contact hours).
- 40% - They are expected to prepare and deliver fewer paper presentations (the exact number will depend on overall number of attending students)
- 40% - They will conduct the group work together with the students from the assigned group and participate to mentoring sessions

Assessment methods - DSE non-attending students
DSE non-attending students will be evaluated through a nal exam that covers all the material discussed in class (e.g., cases, readings, relevant theoretical framework, exercises, hand-on tutorials, etc.) expect for the material for weeks 7, 8 and 9 (see the section with the schedule). All the material (including slides for each class) can be found on Ariel. The nal exam will consist of a combination of multiple choice and short questions and will be up to 2 hours long.
Lessons: 40 hours