T-Mobile USA, Inc. (20240347071). USING MACHINE LEARNING TO LOCATE MOBILE DEVICE simplified abstract
Contents
USING MACHINE LEARNING TO LOCATE MOBILE DEVICE
Organization Name
Inventor(s)
Yasmin Karimli of Kirkland WA (US)
Ryan Cyrus Khamneian of Kirkland WA (US)
Jie Hui of Mercer Island WA (US)
Antoine T. Tran of Bellevue WA (US)
USING MACHINE LEARNING TO LOCATE MOBILE DEVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240347071 titled 'USING MACHINE LEARNING TO LOCATE MOBILE DEVICE
The patent application describes techniques for training machine learning models to determine the location of a mobile device based on audio and contextual data associated with the device.
- Machine learning models are trained to analyze movement patterns in data to infer contextual information about users, such as identifying their home or office locations.
- The trained models can classify a mobile device's location as indoors or outdoors at a specific time, improving accuracy.
- The technology aims to enhance the accuracy of determining a mobile device's location, among other technical benefits.
Potential Applications: - Location-based services for mobile applications - Enhanced security features for mobile devices - Personalized user experiences based on location data
Problems Solved: - Improving accuracy in determining a mobile device's location - Enhancing contextual understanding of user behavior
Benefits: - Enhanced user experience with personalized services - Improved security measures for mobile devices - Efficient location tracking for various applications
Commercial Applications: Title: Enhanced Location-Based Services for Mobile Applications This technology can be utilized in various commercial applications such as targeted advertising, location-based notifications, and personalized recommendations for users.
Questions about the Technology: 1. How does this technology improve the accuracy of determining a mobile device's location? - The technology uses machine learning models to analyze audio and contextual data associated with the device to infer its location accurately. 2. What are the potential privacy concerns associated with using this technology? - Privacy concerns may arise due to the collection and analysis of user data for location tracking purposes. Implementing robust data protection measures is essential to address these concerns.
Original Abstract Submitted
described herein are techniques, devices, and systems for training a machine learning model(s) and/or artificial intelligence algorithm(s) to determine where a mobile device (and, hence, a user of the mobile device) is located based on audio data associated with the mobile device and/or contextual data associated with the mobile device. the machine learning techniques may be used to determine contextual information about users, such as determining that a particular location is likely to be a user's home, office, or the like, based on movement patterns exhibited in the data associated with a user's mobile device. once trained, the machine learning model(s) is usable to classify a mobile device as having been located at one of multiple candidate locations, such as indoors or outdoors, at a particular time. the described techniques can improve the accuracy of determining a mobile device's location, among other technical benefits.
