Language-Vision Building Security for Precise Anomaly Queries
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Solution Overview
Problem
Existing building security systems struggle to accurately generate relevant and precise data for specific security threats due to limitations in predictive AI models, such as large language models (LLMs), which often produce incorrect, imprecise, or irrelevant outputs, and lack transparency and agility in responding to novel queries.
Innovation Solution
Implementing a system that utilizes generative AI models, like LLMs and multi-modal models, to process unstructured data from various sources, including video and audio, and integrate expert feedback for real-time anomaly detection and response, enabling automated and conversational interfaces for security personnel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generative AI models like LLMs are used to process security data, then the system can handle unstructured data and provide conversational interfaces, but the models produce incorrect, imprecise, or irrelevant outputs
Solution Approach 1:
The patent introduces an intermediary layer between the LLM and the security data processing. This layer includes prompt engineering mechanisms and contextual framing that mediate the interaction between the generative model and the security domain, improving output precision while maintaining conversational capabilities
Solution Approach 2:
The system implements feedback loops where the outputs of the LLM are evaluated against ground truth security data and expert annotations. This feedback is used to refine prompts and adjust the model's responses, progressively improving precision while maintaining ease of operation
2Adaptability or versatility
If existing predictive AI models are used, then the system can process security data, but the models lack transparency and agility in responding to novel queries
Solution Approach 1:
The patent segments the AI system into multiple components: the LLM for handling novel queries, expert feedback mechanisms for validation, and rule-based systems for transparency. This segmentation allows each component to fulfill its strength while mitigating weaknesses
Solution Approach 2:
Expert feedback acts as an intermediary that bridges the gap between the flexible LLM and the need for transparent, reliable decision-making. The expert layer provides interpretability and validates the adaptability of the model responses
3Measurement precision
If traditional security systems are used, then the system structure is simple, but the systems struggle to accurately generate relevant and precise data for specific security threats
Solution Approach 1:
The patent merges traditional security system structures with generative AI capabilities. The LLM is integrated into the existing security workflow, combining the simplicity and reliability of traditional systems with the advanced pattern recognition and natural language processing of generative models
Solution Approach 2:
The LLM component serves multiple functions: processing unstructured security data, generating relevant threat information, providing conversational interfaces, and adapting to novel query types. This multi-functionality achieves high detection precision without proportionally increasing system complexity
Data Source
AI summary
A building security system including computer-readable storage media having instructions stored thereon that, when executed by processors, cause the processors to: provide one or more machine learning models, at least one of the one or more machine learning models trained to identify contextual information within video data, the at least one machine learning model trained using at least one of video data or image data and annotations to the at least one of the video data or the image data, and provide a chatbot configured to: receive and process one or more input videos using the at least one machine learning model to identify the contextual information from the one or more input videos, receive a query from a user relating to the one or more input videos, and generate, by the one or more machine learning models, a response to the query using the contextual information.