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Grant Award View - GA4121-V1
Identifying technological trajectories using machine learning algorithms
GA ID:
GA4121-V1
Agency:
Australian Research Council
Approval Date:
26-Feb-2018
Variation Publish Date:
14-Apr-2020
Variation Date:
14-Apr-2020
Category:
Humanities, Arts and Social Sciences (HASS) Research
Grant Term:
1-Jul-2018 to 31-Dec-2020
Value (AUD):
$256,551.00
(GST inclusive where applicable)
Varies:
GA4121
- Identifying technological trajectories using machine learning algorithms
One-off/Ad hoc:
No
Aggregate Grant Award:
No
PBS Program Name:
ARC 17/18 Linkage
Grant Program:
Linkage Projects
Grant Activity:
Identifying technological trajectories using machine learning algorithms
Purpose:
This project aims to improve our understanding of why scientific knowledge progresses in certain directions and what causes it to grow faster or slower across fields. The project will create new neural-network machine-learning algorithms to scan patent and scientific article texts (specifications and claims) for natural language concepts. The results will potentially be used by patent offices to improve their own database search, by business analytics companies to reveal new technologies and potential collaborators, and by academic economists to understand how knowledge travels and accumulates.
GO ID:
GO Title:
Linkage Projects commencing in 2017
Internal Reference ID:
LP17 Round 1
Selection Process:
Targeted or Restricted Competitive
Confidentiality - Contract:
No
Confidentiality - Outputs:
No
Grant Recipient Details
Recipient Name:
Swinburne University of Technology
Recipient ABN:
13 628 586 699
Grant Recipient Location
Suburb:
HAWTHORN
Town/City:
HAWTHORN
Postcode:
3122
State/Territory:
VIC
Country:
AUSTRALIA
Grant Delivery Location
State/Territory:
VIC
Postcode:
3122
Country:
AUSTRALIA