Risk-based Administration and Processing of Intellectual Property Applications
Location: Phillips, ACT
Duration: 5 months
Proposed start date: ASAP
Keywords: Python, Statistical Modelling, Datasets, Stakeholder Communication, Machine Learning Models, Multidisciplinary, Team-focused
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.
IP Australia is the Federal Government agency responsible for the administration of Intellectual Property (IP) rights and legislation relating to patents, trade marks, 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 land 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 patent 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 drive cognitive automation of business processes where appropriate, targeted inflight quality review and streamlined work allocation.
Research to be Conducted
Key objectives to be achieved:
- Undertake data analysis of patent applications and relevant business processes;
- Identification of factors or features associated with patent applications which can be used to indicate whether a patent application is at risk of an adverse finding;
- Development of a model based on identified factors or features, as above, to enable the risk and complexity of future patent applications to be predicted.
We are looking for a PhD student with the following skills:
- Able to work in a multi-disciplinary team
- Experience/Knowledge in the application of mathematical or statistical modelling to draw insights from large and complex datasets (both structured and unstructured);
- Experience in the manipulation of large datasets using python-based tools;
- Ability to communication results and findings to stakeholders; and
- Prior experience in the development of machine learning models is highly desirable, not essential.
1. Delivery of a report outlining the process for the:
a. Identification of factors or features of a patent application which can be used to indicate whether a patent application is at risk of an adverse finding; and
b. Development of a risk prediction model to be used against future patent applications based on risk factors or features as outlined above.
2. Delivery of the model (as above) enabling the prediction of the risk or complexity associated with a future patent applications based on identified factors or features.
The model to be delivered by the successful candidate will be a stand-alone product and will not be expected to be integrated into existing systems upon the completion of this project.
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
26 September 2018
INT – 0456
FOR ANY ENQUIRIES ABOUT THIS INTERNSHIP03 8344 1785