DON26BZ05-DV081 TITLE: DIRECT TO PHASE II: Intelligent Data Analysis of Post-Mission Reporting Artifacts & Data

OUSW (R&E) CRITICAL TECHNOLOGY AREA(S): Applied Artificial Intelligence (AAI)

COMPONENT TECHNOLOGY PRIORITY AREA(S): Trusted AI and Autonomy

PROJECTED CMMC LEVEL REQUIREMENT: Level 2 (Self)

The technology within this topic is restricted under the International Traffic in Arms Regulation (ITAR), 22 CFR Parts 120-130, which controls the export and import of defense-related material and services, including export of sensitive technical data, or the Export Administration Regulation (EAR), 15 CFR Parts 730-774, which controls dual use items. Offerors must disclose any proposed use of foreign nationals (FNs), their country(ies) of origin, the type of visa or work permit possessed, and the statement of work (SOW) tasks intended for accomplishment by the FN(s) in accordance with the Announcement. Offerors are advised foreign nationals proposed to perform on this topic may be restricted due to the technical data under US Export Control Laws.

OBJECTIVE: Establish a framework using advanced, automated analytics, allowing various users to parse and query different data types from large datasets. The framework will use innovative and automated methods for comprehensive and efficient data analysis to support data-driven decisions based on post-mission reporting databases.

DESCRIPTION: The U.S. Navy collects vast amounts of high-quality data for training and operational use. While the quantity and quality of this data are crucial for advanced automation and smart systems, effectively using it presents a significant challenge. The Navy requires a solution that enables personnel who are not data scientists to easily connect, analyze, and visualize data from various sources. The goal is to empower them to make data-driven decisions that improve operational and training efficiency.

The increasing volume and complexity of data within the Fleet demand an advanced analysis solution that surpasses traditional methods. This topic seeks a platform that allows personnel to "conversationally" access and utilize extensive data resources, thereby enhancing the Fleet's analytical capabilities. The ideal solution will be a user-friendly platform for creating and sharing interactive reports and dashboards, fostering a culture of data-driven collaboration and decision-making.

Key Solution Requirements:

The proposed solution must be robust, scalable, and capable of efficiently handling large, heterogeneous datasets. It should include the following features:

• Connect to a wide range of data sources, including spreadsheets, SQL databases, cloud services, and on-premises resources. It must also transform raw data by cleaning and preparing it for analysis.

• Offer an intuitive interface that allows naval personnel of varying expertise to interact with the tools and interpret results in real-time or near real-time. Users should be able to create relationships between data tables, define measures, and build calculations.

• Enable users to ask questions and propose analyses in plain language and receive relevant visualizations as answers. This conversational approach will bridge the gap between data experts and non-specialists.

• Provide comprehensive data visualization functions, allowing users to explore and present insights through interactive charts, graphs, tables, and other visual representations like maps and figures.

• Integrate automated data preprocessing and cleansing techniques to ensure data quality and consistency.

• The accuracy of the outputs is paramount. The solution must provide transparent, explainable methods so personnel can understand and trust the rationale behind data-driven recommendations. Users must also have access to manual methods to verify the results.

• The solution must adhere to the Department of War’s (DoW) ethical principles (Responsible, Equitable, Traceable, Reliable, Governable), current policies on automated technology, and all Risk Management Framework (RMF) guidance.

Work produced in Phase II may become classified. Note: The prospective contractor(s) must be U.S. owned and operated with no foreign influence as defined by 32 U.S.C. § 2004.20 et seq., National Industrial Security Program Executive Agent and Operating Manual, unless acceptable mitigating procedures can and have been implemented and approved by the Defense Counterintelligence and Security Agency (DCSA) formerly Defense Security Service (DSS). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances. This will allow contractor personnel to perform on advanced phases of this project as set forth by DCSA and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material during the advanced phases of this contract IAW the National Industrial Security Program Operating Manual (NISPOM), which can be found at Title 32, Part 2004.20 of the Code of Federal Regulations.

PHASE I: For a Direct to Phase II topic, the Government expects that the small business will have accomplished the following in a Phase I-type effort and developed a concept for a workable prototype or design to address, at a minimum, the basic requirements of the stated objective above. The following actions would be required to satisfy the requirements of Phase I:

• Previous work designing and developing advanced analytic techniques, methods, and models with technical approaches relevant to this topic.

• Evidence that a previous capability is feasible within a relevant domain or using publicly available training data, with clear parallels to the needs of DoW post-mission analysis use cases.

• The ability to ingest various data types, prepare the data, and output initial visualizations. This includes identifying advanced and automated processing solutions for text, audio, and graphics.

• A clear description of existing prototypes, matured capabilities, or modules to be leveraged, developed, and extended under the Phase II effort.

FEASIBILITY DOCUMENTATION: Offerors interested in participating in Direct to Phase II must include in their response to this topic Phase I feasibility documentation that substantiates the scientific and technical merit and Phase I feasibility described in Phase I above has been met (i.e., the small business must have performed Phase I-type research and development related to the topic NOT solely based on work performed under prior or ongoing federally funded SBIR/STTR work) and describe the potential commercialization applications. The documentation provided must validate that the proposer has completed development of technology as stated in Phase I above.

PHASE II: Develop and prototype the proposed solution, demonstrating its ability to integrate with sample Navy post-mission air data files or similar data types. The prototype must be capable of the following:

• Ingest and prepare a variety of synthetic or real-world data types.

• Allow users to conduct analysis through a natural and user-friendly interface that supports human-in-the-loop and human-on-the-loop interaction.

• Present relevant data visualizations based on the analysis.

• Ensure that all outputs are transparent and include a method for verifying their accuracy.

Given the rapidly evolving nature of this domain, the development strategy should focus on:

• Leveraging available tools and infrastructures to maximize the potential for transition to government users.

• Minimizing lifecycle sustainment costs and accelerating the maturation of the solution.

The project must adhere to all appropriate DoW policies and address cybersecurity requirements to support a future Authority to Operate (ATO).

Work in Phase II may become classified. Please see note in the Description section.

PHASE III DUAL USE APPLICATIONS: Mature the prototype developed in Phase II and transition it into a fully operational capability for deployment across relevant Navy platforms and acquisition programs. This includes:

• Completing final integration and conducting operational testing and evaluation with designated partners.

• Ensuring the technology is robust, scalable, and ready for sustained use within the Naval Aviation training and/or operational environment.

The intelligent data analysis framework developed under this topic has significant commercial potential across numerous private sectors. Any industry that generates large volumes of complex data, such as finance, healthcare, logistics, and manufacturing, could benefit from a platform that allows non-technical users to perform advanced analytics through natural language queries, with verified results. Commercial applications include business intelligence, market trend analysis, supply chain optimization, and predictive maintenance. The technology's ability to democratize data analysis makes it a highly valuable tool for any organization seeking to foster a data-driven culture and gain a competitive advantage.

REFERENCES:

1. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson Education Limited. https://www.worldcat.org/title/1124776132
2. Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P. F., Leike, J., & Lowe, R. (2022). Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35, 27730-27744. https://proceedings.neurips.cc/paper_files/paper/2022/hash/b1efde53be364a73914f58805a001731-Abstract-Conference.html
3. Wei, J., Tay, Y., Bommasani, R., Raffel, C., Zoph, B., Borgeaud, S., Yogatama, D., Bosma, M., Zhou, D., Metzler, D., Chi, E. H., Hashimoto, T., Vinyals, O., Liang, P., Dean, J., & Fedus, W. (2022). Emergent abilities of large language models. https://arxiv.org/abs/2206.07682
4. Goodfellow, I., Bengio, Y. & Courville, A. (2016). Deep learning. MIT Press. https://www.deeplearningbook.org/
5. National Science & Technology Council, Artificial Intelligence Research & Development Interagency Working Group, Subcommittee on Networking & Information Technology Research & Development, Subcommittee on Machine Learning & Artificial Intelligence, and the Select Committee on Artificial Intelligence of the National Science & Technology Council. (2019). 2016–2019 Progress report: advancing artificial intelligence R&D. Executive Office of the President of the United States. https://www.nitrd.gov/pubs/AI-Research-and-Development-Progress-Report-2016-2019.pdf
6. Select Committee on Artificial Intelligence of the National Science & Technology Council. (2019). The national artificial intelligence research and development strategic plan: 2019 update. Executive Office of the President of the United States. https://www.nitrd.gov/pubs/National-AI-RD-Strategy-2019.pdf
7. Defense Innovation Board. (2019). AI principles: recommendations on the ethical use of artificial intelligence by the Department of Defense. Department of Defense. https://media.defense.gov/2019/Oct/31/2002204458/-1/-1/0/DIB_AI_PRINCIPLES_PRIMARY_DOCUMENT.PDF
8. National Industrial Security Program Executive Agent and Operating Manual (NISP), 32 U.S.C. § 2004.20 et seq. 1993". https://www.ecfr.gov/current/title-32/subtitle-B/chapter-XX/part-2004

KEYWORDS: Artificial Intelligence; Machine Learning; Data Analytics; Natural Language Processing; Data Visualization; Big Data

TPOC 1 : Beth Atkinson
(407) 380-4773 Y
beth.f.atkinson.civ@us.navy.mil

TPOC 2 : John Hodak
(407) 380-4737
john.c.hodak.civ@us.navy.mil

TPOC3 : John Killilea
(407) 380-4670
john.p.killilea.civ@us.navy.mil