Inferring User Activities from Internet of Things (IoT) Connected Device Events Using Machine Learning Based Algorithms

Thu, 05 Dec 2024 00:00:00 +0000

Abstract

An Internet-of-Things (IoT) learning framework (IoT learning framework) may train AI models to infer user activities from IoT connected device events using machine learning based algorithms. According to such an example, processing circuitry obtains a training dataset indicating IoT device events and extracts representative user activity patterns from the sequences of IoT device events. In such an example, processing circuitry trains the AI model to learn an optimal subset of the sequences of IoT device events corresponding to a smallest quantity of the sequences of IoT device events to predict user activities with accuracy that satisfies a threshold and outputs the AI model. According to such an example, processing circuitry may obtain new data indicating new sequences of IoT device events and generates output indicating one or more user activities predicted by the AI model to have occurred based on the new sequences of IoT device events.

Guoliang Xue, Yinxin Wan, Xuanli Lin, Kuai Xu, and Feng Wang. “Inferring User Activities from Internet of Things (IoT) Connected Device Events Using Machine Learning Based Algorithms.” U.S. Patent Application Publication US20240403648A1, published December 5, 2024.

This record is a published patent application. Application 18/734,240 was filed June 5, 2024, with a provisional application 63/506,316 filed June 5, 2023. The assignee listed on the publication is the Arizona Board of Regents on Behalf of Arizona State University.

Patent | Xuanli Lin

Inferring User Activities from Internet of Things (IoT) Connected Device Events Using Machine Learning Based Algorithms

Abstract