IoT Data Inconsistency Prediction Patent Application

ChangeBridge: Patent Apps - Networking (H04L)

Detected March 27th, 2026

Summary

The USPTO has published a patent application (US20260089068A1) from TELEFONAKTIEBOLAGET LM ERICSSON (PUBL) for a method to predict Internet of Things (IoT) data inconsistency. The application details a machine learning model trained to identify and flag inconsistent IoT data, along with rules for handling such data.

View original document View source feed page

What changed

This document is a publication of a patent application filed by TELEFONAKTIEBOLAGET LM ERICSSON (PUBL) for a method and apparatus to predict inconsistency in Internet of Things (IoT) data. The proposed method involves training a machine learning model using labeled IoT data to identify features of data inconsistency and then generating rules to apply to live IoT data predicted as inconsistent.

As this is a patent application, it does not impose direct regulatory obligations or compliance deadlines on entities. However, it signals potential future technological developments and intellectual property in the area of IoT data integrity and AI-driven data analysis. Companies operating in the IoT space, particularly those developing or utilizing IoT monitoring systems and AI for data validation, may wish to review this application for competitive intelligence and to understand emerging patent landscapes.

Source document (simplified)

← USPTO Patent Applications

METHOD FOR PREDICTING INTERNET OF THINGS (IOT) DATA INCONSISTENCY

Application US20260089068A1 Kind: A1 Mar 26, 2026

Assignee

TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)

Inventors

Carla MOURADIAN, Mbarka SOUALHIA

Abstract

The disclosure relates to a method and apparatus for predicting Internet of Things (IoT) data inconsistency. The method comprises obtaining labelled IoT data, the IoT data being collected from a plurality of IoT devices by a monitoring system. The method comprises analyzing characteristics of the labelled IoT data and identifying features of IoT data inconsistency. The method comprises training, using the labelled IoT devices data and the features of IoT data inconsistency, a ML model to predict the IoT data inconsistency. The method comprises generating, using the labelled IoT data and the features of IoT data inconsistency, a set of inconsistency rules to be applied to live IoT data predicted as inconsistent by the ML model.