Risk-based Customer Profiler and Work Prioritisation

Location: Phillip, ACT

Duration: 5 months

Proposed start date: ASAP

Keywords: Large Datasets, Emerging technologies, Machine Learning, Research methodology, Multi-disciplinary, Data Science

Please note: Due to funding requirements, students must have Australian Citizenship or Permanent Residency to apply. Any applicants not meeting this requirement will be ineligible for this project.

Project Background

IP Australia is the Federal Government agency responsible for the administration of Intellectual Property (IP) rights and legislation relating to patents, trademarks, designs and plant breeder’s rights within Australia.

IP Australia is pursuing opportunities presented by new technologies such as data analytics and cognitive computing to transform the administration of IP rights. IP Australia is actively developing smart systems that are capable of learning, solving problems, making decisions and improving the way we do business. These systems leverage emerging technologies such as machine learning (ML) and natural language processing (NLP) to support IP Australia’s staff in the examination and processing of IP rights.

A key area of interest is the application of data analytics and data science techniques, using both structured and unstructured data, to assess the risk and complexity associated with the examination of IP rights, in particular trade mark applications.

IP Australia is seeking a PhD candidate with experience in analysing both structured and unstructured data using data science techniques to identify insights associated with the complexity of a patent application. These insights include the identification of factors or features which indicate whether an application is at risk of receiving adverse findings during the examination process. Building on these insights, the successful candidate will be required to develop a model which will be used to predict the risk rating of future applications based on identified factors or features. The model will be used to support business outcomes, such as streamlined processing of trade mark applications.

Research to be Conducted

Key objectives to be achieved:

  1. Identify and extract relevant trade mark data from multiple sources;
  2. Conduct data analysis of trade mark applications and processes;
  3. Determine risk factors relevant to trade mark application assessment (including examination, pre-examination and post acceptance processes);
  4. Research and identify algorithms and approaches applicable to the project;
  5. Develop machine learning models to support trade mark application assessment, based on attributes contributing to the risk factors and risk ratings.

Skills Required

We are looking for a PhD student with the following skills:

  • Able to work in a multi-disciplinary team
  • Ability to determine a suitable research approach/methodology for a unique problem;
  • Ability to analyse, transform, process and manipulate large datasets, utilising emerging technologies (such as spark or other machine learning algorithms); and
  • Ability to contribute to the development of machine learning models

Expected Outcomes

  1. Delivery of a report detailing the:
  • Research methodology for the project;
  • Trade mark data sources utilised and techniques used to extract relevant data;
  • Data analysis conducted and insights derived;
  • Risk factors relevant to trade mark application assessment; and
  • ML and NLP algorithms and approaches applicable to the project.
  1. Delivery of machine learning models capable of being run standalone as well as being integrated into existing applications/frameworks.

Additional Details

The intern will receive $3,000 per month of the internship, usually in the form of stipend payments.

It is expected that the intern will primarily undertake this research project during regular business hours, spending at least 80% of their time on-site with the industry partner.  The intern will be expected to maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.

The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.

To participate in the APR.Intern program, all applicants must satisfy the following criteria:

  • Be a PhD student currently enrolled at an Australian university
  • PhD candidature must be confirmed
  • Applicants must have the written approval of their Principal Supervisor to undertake the internship. This approval must be submitted at the time of application.
  • Internships are also subject to any requirements stipulated by the student’s and the academic mentor’s university
Applications Close

26 September 2018


INT – 0455


03 8344 1785