A method for analyzing non-intrusive human activity in a connected environment

By simulating an autonomous agent's activity in a digital twin of the environment, the method addresses the inefficiencies of existing AI training methods, enabling rapid and accurate human activity analysis in connected environments using non-intrusive sensors.

FR3169001A1Pending Publication Date: 2026-05-29ORANGE SA
View PDF 0 Cites 0 Cited by

Patent Information

Authority / Receiving Office
FR · FR
Patent Type
Applications
Current Assignee / Owner
ORANGE SA
Filing Date
2024-11-25
Publication Date
2026-05-29

AI Technical Summary

Technical Problem

Existing methods for training artificial intelligence algorithms to analyze human activity in connected environments are inefficient due to their dependence on specific sensor arrangements and environments, requiring time-consuming and expensive data collection, and often necessitate human intervention.

Method used

A method utilizing a digital twin of the environment to simulate an autonomous agent's activity, generating training data autonomously and accurately, allowing for fully automated AI training without human intervention.

Benefits of technology

This approach enables rapid, automated, and accurate training of AI to infer human activity using non-intrusive sensors, improving the efficiency and accuracy of user monitoring and security systems.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure 00000000_0000_ABST
    Figure 00000000_0000_ABST
  • Figure 00000000_0001_ABST
    Figure 00000000_0001_ABST
Patent Text Reader

Abstract

A method is proposed for determining human activity in an environment comprising at least one sensor for monitoring the use of at least one piece of environmental equipment. The method includes artificial intelligence (AI) training and AI inference to determine human activity in said environment. It is characterized in that the method comprises, for the AI ​​training: - the generation of AI training data from a simulation of the activity of at least one autonomous agent evolving in an environmental simulation generated from a digital twin of the real environment and using at least one digital replica of said equipment. Abstract: Figure 3
Need to check novelty before this filing date? Find Prior Art