IoT Security Chatbot Query Translation for Faster Data Retrieval
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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 leveraging generative AI, specifically 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 navigate dashboard systems manually or directly interface with databases, then they can access IoT security information, but the operation becomes cumbersome and inefficient due to lack of familiarity with dashboard format or database query language
Solution Approach 1:
The patent introduces a natural language processing intermediary layer between the user and the database system. Users can query IoT security information using natural language instead of requiring knowledge of dashboard navigation or SQL query language. The system translates natural language queries into appropriate database queries, retrieves results, and presents them in an accessible format, thereby eliminating the need for users to learn complex interfaces while maintaining efficient information retrieval.
2Adaptability or versatility
If a chatbot system uses a pre-trained LLM adapted through prompt engineering, then it can generate database queries from natural language, but the system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the LLM on relevant IoT security domain data and performing prompt engineering adjustments before deployment. The model is pre-adapted to understand technical terminology, query structures, and database schemas specific to IoT security. This upfront preparation reduces the complexity of real-time processing during actual use, as the model already possesses domain-specific knowledge and requires only simple prompt adjustments rather than complex runtime modifications.
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.