AI Search and Rescue Platform with LLM Decision Support
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Solution Overview
Problem
Emergency and disaster response operations in remote and underserved areas face challenges due to limited resources and infrastructure, including unreliable communication and unstructured critical knowledge that is often held by local residents.
Innovation Solution
A specialized AI-based technology platform that utilizes a large language model (LLM) to enhance decision support for emergency and disaster response efforts, integrating data from unmanned aerial systems (UAS) and augmented reality (AR) devices, and providing flexible and collaborative decision support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a specialized AI-based platform with LLM is deployed to enhance decision support, then the quality and effectiveness of decision-making is improved, but the device complexity and resource requirements increase
Solution Approach 1:
The AI platform is divided into distinct functional modules including data ingestion components, processing engines, and output delivery systems. Each module handles specific tasks independently, allowing the complex system to be managed through modular components that can be developed, deployed, and maintained separately while working together to provide comprehensive decision support.
Solution Approach 2:
The patent introduces intermediary components such as standardized APIs, data transformation layers, and integration interfaces that mediate between various data sources, processing engines, and output devices. These intermediaries simplify connections between system components and enable flexible configuration without increasing overall system complexity.
2Loss of information
If real-time processing of structured and unstructured data from multiple sources is performed, then the completeness of information is improved, but the processing time and computational resources increase
Solution Approach 1:
The system implements periodic processing cycles where data is collected, processed, and delivered in structured intervals rather than attempting continuous real-time processing of all data streams. This approach maintains information completeness by systematically processing all data sources while managing computational load through rhythmic operation patterns.
Solution Approach 2:
The patent applies partial processing strategies where not all data is processed to the same depth simultaneously. Critical data streams receive immediate full processing, while less time-sensitive data undergoes batch processing or lighter analysis, ensuring important information is delivered promptly without being bottlenecked by comprehensive processing of all data sources.
3Adaptability or versatility
If the platform integrates multiple data feeds including UAS data, then the versatility of decision support is improved, but the system complexity and integration difficulty increase
Solution Approach 1:
The platform is designed with universal data interfaces and standardized protocols that enable multiple data feeds including UAS data to be integrated through common entry points. The system architecture supports multiple functions and data types through unified processing mechanisms, allowing versatile decision support without proportionally increasing integration complexity for each additional data source.
4Loss of information
If AI responses are generated to augment mission support information, then the value of information is improved, but the computational load and energy consumption increase
Solution Approach 1:
The system generates AI responses selectively rather than continuously, applying computational resources to augment information only when adding value is determined to be beneficial. This partial action approach ensures high information value is delivered while avoiding unnecessary computational energy expenditure on redundant or low-impact processing.
Data Source
AI summary
A search and rescue (SAR) application platform configured to provide SAR support services. A process includes: receiving, at a mission support system, mission data from structured and unstructured data feeds including data from an unmanned aerial system (UAS); outputting mission support information to a set of output nodes having disparate user interfaces (UIs), wherein at least one of the output nodes includes an augmented reality (AR) equipped device; and receiving, at a large language model (LLM), queries from the mission support system, and generating AI responses that augment the mission support information, wherein the LLM is trained with a model training system using structured and unstructured data sources involving SAR content.