Surveillance Record Search with Dynamic LLM Query Translation
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Solution Overview
Problem
Existing surveillance systems require manual, time-consuming searches through vast amounts of surveillance records due to the lack of efficient query methods, especially with dynamic and evolving searchable parameters.
Innovation Solution
A system utilizing a Large Language Model (LLM) translates natural language queries into structured data source queries, allowing for on-the-fly updates without reprogramming, to efficiently search surveillance repositories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual searching methods are used to query surveillance records, then operators can access surveillance data, but the search process becomes extremely time-consuming and labor-intensive when dealing with vast amounts of records
Solution Approach 1:
The patent introduces an intermediary system comprising a language model and query generation module that acts as a mediator between the operator's natural language input and the surveillance record database. This intermediary automatically translates human language queries into structured database queries, eliminating the need for operators to manually search through vast amounts of records while maintaining accurate and efficient data retrieval.
2Adaptability or versatility
If traditional deterministic algorithmic conversion or trained AI models are used to translate queries, then search functionality is provided, but the system becomes rigid and requires complex reprogramming or AI re-training when new searchable parameters are added
Solution Approach 1:
The patent implements a dynamic system where the language model and query generation module can adapt to new surveillance record parameters and data structures in real-time without requiring complex reprogramming or AI re-training. The system dynamically adjusts its query translation capabilities based on the current database schema and searchable parameters, allowing operators to query new types of surveillance data immediately when they are added to the system.
3Reliability
If comprehensive surveillance records are retained for extended periods to ensure safety and compliance, then more surveillance data is available for searching, but the volume of records to be manually searched increases dramatically
Solution Approach 1:
The patent replaces the mechanical manual searching process with an automated intelligent system that uses natural language processing and query generation. Instead of operators manually reviewing surveillance records one by one, the system automatically translates natural language queries into database queries that efficiently retrieve relevant records, maintaining high reliability for safety and compliance while dramatically increasing search throughput even with large volumes of retained surveillance data.
Data Source
AI summary
A system for causing a searching of a data source of surveillance records; it has a processor; and memory comprising program code that, when executed by the processor, cause the processor to obtain an input query, the input query comprising natural language; provide the input query to a large language model (LLM), for translating the natural language into a structured data source query based on searchable categories associated with the surveillance records stored in the data source; obtain, from the LLM, a structured data source query based on the input query; transmit the structured data source query to the data source to perform a search of the data source in accordance with the structured data source query to identify at least one query result; and receive the at least one query results; a method of use thereof.