STOTTLER HENKE ASSOCIATES, INC — Department of Defense SBIR Phase I: AF222-0013
STOTTLER HENKE ASSOCIATES, INC — SBIR Phase I award from Department of Defense.
Phase I SBIR feasibility signal
- Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from Department of Defense in a technical approach.
- Watch for Phase II follow-ons from the same firm/topic family — that conversion path is where budgets and transition pressure rise.
- Obligated amount $149,981. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code AF222-0013 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $149,981
- Agency
- Department of Defense · Air Force
- Program / Phase
- SBIR · Phase I
- Topic
- AF222-0013
- Solicitation
- 22.2
- NAICS
- —
- Place of performance
- CA
- Period
- 2022-12-15 → 2023-09-15
Description
Predictive analytics tools, such as Insight, rely on example behavior models to feed their internal forecasting and inferencing components. These behavior models represent potential courses of action (COAs) for targets of interest. Currently, they are produced from scratch by human experts—a costly and inefficient use of specialized human resources that bottlenecks the analytic process. To address this inefficiency, Stottler Henke proposes to develop the SCAFFOLD system to automatically construct the core “who/what/when/where” of these behavior models from reports of real events. SCAFFOLD will advance the state of the art by addressing three shortcomings of automated event extraction. First, the SCAFFOLD system will build COAs from co-referring events and entities in different sources—even when they are represented differently among those sources. Second, SCAFFOLD will correlate multi-source observations to “fill in the gaps” between geospatially referenced observations. Finally, SCAFFOLD will infer implicit relationships to identify which other events may influence an extracted COA. This effort leverages our past work and experience in the areas of event and entity extraction, natural language processing, and predictive modeling. Phase I development of a limited prototype will provide a solid foundation for the complete implementation of SCAFFOLD in Phase II, and its eventual commercialization.