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A Generative AI-Based Technical Data Extraction Tool for IoT Application Systems.

Dezheng Kong¹, Nobuo Funabiki¹, Htoo Htoo Sandi Kyaw¹

¹Department of Information and Communication Systems, Okayama University, Okayama 700-8530, Japan.

Sensors (Basel, Switzerland)

|February 27, 2026

Summary

This summary is machine-generated.

A new generative AI tool extracts technical data from IoT device documents, improving AI setup assistance reliability. This enhances support for new devices by integrating technical specifications into Retrieval-Augmented Generation (RAG).

Keywords:

AI data sheet internet of things retrieval-augmented generation schema-based extraction technical information vector database

Related Experiment Videos

Area of Science:

Computer Science
Artificial Intelligence
Internet of Things

Background:

Internet of Things (IoT) systems are crucial for smart applications but challenging for non-experts to configure.
Current AI setup tools struggle with new or unseen devices, leading to configuration errors.
Retrieval-Augmented Generation (RAG) can improve AI reliability by incorporating technical document data.

Purpose of the Study:

To develop a generative AI tool for extracting technical data from IoT device datasheets.
To enhance AI-based setup assistance for IoT devices, especially novel ones.
To improve the reliability and accuracy of IoT configuration tasks.

Main Methods:

Proposed a generative AI tool using schema-based extraction for PDF/HTML datasheets.
Implemented a local vector database for semantic similarity retrieval and RAG.
Evaluated the tool on sensor and device datasheets, comparing extracted data against ground truth.
Assessed end-to-end configuration question-answering (QA) reliability against a commercial baseline (ChatPDF).

Main Results:

The tool reliably extracts key IoT device specifications.
Significantly improved end-to-end configuration QA reliability compared to ChatPDF.
Achieved higher Recall (0.926 vs. 0.636) and Accuracy (0.807 vs. 0.595) across 960 QA pairs.

Conclusions:

The generative AI technical data extraction tool effectively supplements AI knowledge for IoT configurations.
The proposed method ensures consistent support for previously unseen IoT devices.
This approach enhances the practical usability of AI in complex IoT system setups.