PRESAGE

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$448,286.00
Award Year:
2009
Program:
SBIR
Phase:
Phase II
Contract:
N00014-09-C-0474
Award Id:
87635
Agency Tracking Number:
N081-084-0664
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
4515 Seton Center Parkway, Suite 320, Austin, TX, 78759
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
158034665
Principal Investigator:
Matthew McClain
Principal Investigator
(512) 342-0010
mmcclain@21technologies.com
Business Contact:
Irene Williams
Principal Investigator
(512) 342-0010
SBIR_ADMIN@21technologies.com
Research Institute:
n/a
Abstract
PRESAGE is a robust computational tool that employs artificial neural network machine learning technologies to automate the analysis of extremely large sets of unstructured information. The PRESAGE solution takes a broad view of unstructured information analysis, learning correlations between on-the-ground threat risk and trends in sentiment, themes, and other textual features extracted from large amounts of open source information. PRESAGE uses this learned knowledge to detect emerging threats from changing sentiment extracted from newly acquired information. This provides the situational analysis that enables troops to take a proactive stance and conduct operations when, where, and how they will be most effective. In the PRESAGE Phase I effort, 21CT demonstrated the effectiveness of a neural network based approach to threat risk estimation. The results are significant for the future development of PRESAGE technology. We have proven the concept that recurrent neural architectures can be used to estimate information analogous to threat levels from sentiment and theme features extracted from unstructured data. We believe this technology can also be used to estimate threat risk if applied to wide-area OSINT sources. We have identified critical research questions that need to be addressed by future work for successful application of PRESAGE to operational scenarios.

* information listed above is at the time of submission.

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