Telematics Data Retrieval Through LLM-Generated Selective Queries
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Solution Overview
Problem
Managing and retrieving telematics data from a fleet of vehicles is challenging due to the vast amount of data, time-consuming report reviews, and difficulty in identifying specific outliers, trends, and patterns, especially when dealing with comprehensive reports.
Innovation Solution
A system utilizing a large language model (LLM) processes natural language requests to generate executable queries for retrieving specific telematics data without direct access to the telematics database, leveraging a context database for contextual information to respond to user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If comprehensive reports of telematics data are retrieved to gain insights about vehicle fleet, then complete information is obtained, but the reports are time-consuming to review and require a lot of data to acquire and store
Solution Approach 1:
The system extracts only the specific telematics data points that are relevant to the user's natural language question, rather than retrieving entire comprehensive reports. The query generation module parses the natural language input and constructs SQL queries that selectively retrieve only the necessary data fields and rows, eliminating the need to review extensive unrelated information.
Solution Approach 2:
The system segments the large telematics database into manageable queryable units by using structured queries that target specific tables, columns, and time periods. This allows the system to retrieve only the relevant subset of data needed to answer the specific question, rather than processing the entire database.
2Loss of information
If comprehensive reports of telematics data are retrieved to gain insights about vehicle fleet, then complete information is obtained, but a lot of data storage space is required
Solution Approach 1:
The system extracts only the specific telematics data points that are relevant to the user's natural language question, rather than retrieving entire comprehensive reports. This selective extraction significantly reduces the amount of data that needs to be stored and processed while maintaining the completeness of relevant information.
3Ease of operation
If natural language processing is implemented to allow users to query telematics data without coding knowledge, then ease of operation is improved, but system complexity increases
Solution Approach 1:
The system introduces a natural language processing intermediary layer that translates user-friendly natural language queries into SQL database queries. This intermediary module acts as a mediator between the user and the complex database system, allowing users to interact with the database using simple language while the system handles the complexity of query construction and execution.
4Loss of time
If specific portions of telematics data are retrieved based on natural language requests, then data retrieval time is reduced, but the system requires sophisticated query generation capabilities
Solution Approach 1:
The system employs self-service mechanisms where the natural language processing module automatically parses user input, identifies relevant data requirements, and generates appropriate SQL queries without human intervention. The system serves itself by autonomously translating natural language into executable database queries, reducing retrieval time while managing complexity through automation.
Data Source
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AI summary
The present disclosure relates to systems and methods for retrieving telematics data. A method for retrieving telematics data may comprise operating at least one processor to: provide at least one telematics database, the at least one telematics database storing telematics data originating from a plurality telematics devices installed in a plurality of vehicles, and at least one context database, the at least one context database storing contextual information relating to the at least one telematics database; receive a natural language request from a user; generate, using a large language model (LLM) that does not have access to the at least one telematics database, an executable query for retrieving a portion of the telematics data that is responsive to the natural language request from the at least one telematics database; execute the executable query for retrieving the portion of the telematics data from the at least one telematics database; and return at least the portion of the telematics data to the user, whereby the natural language request is responded to without providing the LLM with access to the telematics data stored on the at least one telematics database and the at least one context database.