Research Assistant for an Artificial Intelligence Adoption in Trade Mark Registration Systems Project

Internal Applicants Only

Deadline: 30 March 2023

Professor Dev Gangjee is looking for a research assistant to help with a research project on Artificial Intelligence Adoption in Trade Mark Registration Systems.

The project is being conducted in collaboration with Dr Anke Moerland at the University of Maastricht. While there has been considerable interest in the implications of artificial intelligence technology for patent or copyright law, there has been far less in relation to trade marks. Yet machine learning technology has already had a considerable influence on the everyday practice of registering marks.

Machine learning and rules-based systems are increasingly adopted in relation to assisting applicants seeking to register marks and when checking for conflicts with prior rights. Here, image recognition, semantic search based on natural language processing, as well as goods and services classification tools are prominent case studies, along with machine learning tools which analyse past decisions of IP registrars to predict or suggest whether a mark is registrable. The project seeks to map these developments at WIPO, the USPTO, IP Australia, the UK IPO and other leading trade mark registries. We seek to conduct a comparative study which looks at (i) mapping these new technologies; (ii) looking at the degree to which a select group of registries have adopted these technologies; and (iii) the limits of these technologies (when is human judgment or legal knowledge required when interpreting results).

The tasks for the Research Assistant will be:

• Doing background research on relevant machine learning technologies, such as machine vision/image recognition that are being developed or already in use by registries. The successful candidate will have a strong background in computer science, with the technical skills necessary to assist legal researchers in understanding the functional parameters of the technology.
• In consultation with Dr Moerland and Professor Gangjee, preparing briefing reports on the relevant technology.
• Attending online interviews with registry officials; especially those involved in designing and implementing the technology.

Duration and hours

Working hours: a total of up to 30 hours across the duration of the project, to be agreed with the Supervisor.

Start date: 5 April 2023

Date by which the work must be completed: 31 July 2023, or 12 weeks after start date.

Reporting

The Research Assistant will report to Professor Dev Gangjee.

Requirements

• A knowledge of machine learning technology is essential.
• A prior degree in computer science is desirable.
• Some familiarity with law and intellectual property law in particular is desirable.

The work can be done in any place in the UK where you have access to a good library and internet. Meetings with the PI/team members will be held online.

Eligibility

This opportunity is open to current graduate students in the Faculty of Law and Faculty of Computer Science, and the hours are in line with the restrictions on working hours for students within the Faculties.

It is expected that the work will be undertaken in the UK. 

Rate of Pay

The work will be paid at £16.49 per hour (including the exceptional non-consolidated uplift for 2022-23), which equates to University Grade 6, point 1, on the basis of completed and approved timesheets, which must be submitted to payroll@law.ox.ac.uk by the last Friday of each calendar month for payment on the last working day of the following month.

In addition, annual leave will be assumed to be taken in the month in which it was accrued.

Funding

This opportunity is funded by a Law Faculty Officer allowance.

How to Apply

A short CV and cover letter (including the name of your supervisor) should be sent by email to dev.ganjee@law.ox.ac.uk by Thursday 30 March 2023. Please explain how you meet the requirements for the role, and give details of your availability. Supervisors may be asked for a reference. 

Enquiries about the project are welcome, and may be addressed to dev.gangjee@law.ox.ac.uk. General queries, e.g. about the appointment process, or eligibility, may be addressed to research@law.ox.ac.uk.