IoT Security Chatbot for Natural Language Database Queries
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Solution Overview
Problem
Users often struggle to efficiently retrieve IoT security information due to the complexity of navigating dashboard systems and database query languages, lacking familiarity with these interfaces.
Innovation Solution
A stateful chatbot system utilizing a generative AI model, such as a pre-trained transformer-based LLM, converts natural language queries into database queries and generates summaries, simplifying the retrieval of IoT security information through intuitive interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually navigate dashboard systems or directly interface with databases to retrieve IoT security information, then they can access the required data, but the process becomes cumbersome and inefficient due to lack of familiarity with dashboard formats or database query language
Solution Approach 1:
The patent introduces a chatbot system as an intermediary between users and the database. The chatbot receives natural language queries from users, translates them into database queries, executes the queries, and returns results. This mediator eliminates the need for users to directly interact with complex dashboard interfaces or database query languages, thereby improving ease of operation and reducing time loss.
Solution Approach 2:
The patent replaces the mechanical interaction mechanism (manual navigation through dashboards and direct database interfacing) with an automated AI-based mechanism. The chatbot system uses natural language processing and AI models to automatically translate user queries into database queries and retrieve information, substituting the manual mechanical process with an intelligent automated system.
2Ease of operation
If a chatbot system translates natural language queries into database queries using generative AI, then user interaction becomes intuitive and efficient, but the system complexity increases due to the need for query translation and processing mechanisms
Solution Approach 1:
The patent employs generative AI models that have been trained to copy and translate patterns from natural language queries into corresponding database queries. The model learns from examples and replicates the transformation pattern, allowing it to accurately convert diverse natural language queries into appropriate database query formats without requiring complex manual translation rules.
Solution Approach 2:
The system dynamically adjusts its query translation approach based on the complexity and type of the input query. The AI model adapts its parameters and translation strategy according to the specific query requirements, database schema, and contextual information, enabling flexible and accurate query generation across different scenarios without fixed rigid rules.
Data Source
AI summary
A stateful chatbot system leverages generative AI to provide an interface by which users can retrieve information from backend IoT databases of a security provider via natural language queries. Upon receiving a natural language query that corresponds to a request for information from the database, the chatbot generates a corresponding database query having a format compatible with the database. The chatbot comprises a generative model adapted to generate database queries based on natural language queries via prompt engineering using natural language and database query pairs. The chatbot queries the database with the generated database query, retrieves results comprising data/metadata that satisfy the query, and generates a summary of the results, both of which it presents as a response to the user's query. The chatbot also has access to a vulnerability database from which it can obtain information about known vulnerabilities documented therein to respond to user queries.