Qualcomm incorporated (20240103119). PERSONAL DEVICE SENSING BASED ON MULTIPATH MEASUREMENTS simplified abstract

PERSONAL DEVICE SENSING BASED ON MULTIPATH MEASUREMENTS

Organization Name

qualcomm incorporated

Inventor(s)

Jamie Menjay Lin of San Diego CA (US)

Tong Tang of Escondido CA (US)

PERSONAL DEVICE SENSING BASED ON MULTIPATH MEASUREMENTS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240103119 titled 'PERSONAL DEVICE SENSING BASED ON MULTIPATH MEASUREMENTS

Simplified Explanation

The present disclosure provides techniques for training and using machine learning models to predict locations of stationary and non-stationary objects in a spatial environment.

• Measuring a plurality of signals within a spatial environment using a device.
• Extracting timing information from the measured signals.
• Predicting locations of stationary reflection points and non-stationary reflection points in the spatial environment based on a machine learning model, the measured signals, and the extracted timing information.
• Taking actions based on the predicted locations of reflection points.

Potential Applications

This technology could be applied in various fields such as:

• Autonomous vehicles for detecting and avoiding obstacles.
• Augmented reality for enhancing user experiences in real-world environments.
• Surveillance systems for tracking movement of objects in a given space.

Problems Solved

This technology helps in:

• Improving safety by predicting the locations of objects in the environment.
• Enhancing efficiency by automating the process of object detection.
• Providing real-time information about the spatial environment.

Benefits

The benefits of this technology include:

• Increased accuracy in predicting object locations.
• Faster response time to potential obstacles.
• Enhanced decision-making based on predictive analytics.

Potential Commercial Applications

With its predictive capabilities, this technology could be valuable in:

• Security systems for monitoring and tracking intruders.
• Industrial automation for optimizing processes and workflows.
• Retail analytics for understanding customer behavior in physical spaces.

Possible Prior Art

One possible prior art could be the use of machine learning models in object detection and tracking systems. However, the specific application of predicting locations of stationary and non-stationary objects in a spatial environment may be a novel aspect of this technology.

How does this technology handle dynamic environments where objects are constantly moving?

The technology uses machine learning models to predict the locations of non-stationary objects in real-time, but the specific mechanisms for adapting to dynamic environments are not detailed in the abstract.

What types of signals are measured within the spatial environment, and how are they utilized in predicting object locations?

While the abstract mentions measuring a plurality of signals, it does not specify the nature of these signals or how they contribute to the prediction process.

Original Abstract Submitted

certain aspects of the present disclosure provide techniques for training and using machine learning models to predict locations of stationary and non-stationary objects in a spatial environment. an example method generally includes measuring, by a device, a plurality of signals within a spatial environment. timing information is extracted from the measured plurality of signals. based on a machine learning model, the measured plurality of signals within the spatial environment, and the extracted timing information, locations of stationary reflection points and locations of non-stationary reflection points in the spatial environment are predicted. one or more actions are taken by the device based on predicting the locations of stationary reflection points and non-stationary reflection points in the spatial environment.