20240075895.CRASH DETECTION ON MOBILE DEVICE simplified abstract (apple inc.)

CRASH DETECTION ON MOBILE DEVICE

Organization Name

apple inc.

Inventor(s)

Vinay R. Majjigi of Mountain View CA (US)

Sriram Venkateswaran of Sunnyvale CA (US)

Aniket Aranake of San Jose CA (US)

Tejal Bhamre of Mountain View CA (US)

Alexandru Popovici of Santa Clara CA (US)

Parisa Dehleh Hossein Zadeh of San Jose CA (US)

Yann Jerome Julien Renard of San Carlos CA (US)

Yi Wen Liao of San Jose CA (US)

Stephen P. Jackson of San Francisco CA (US)

Rebecca L. Clarkson of San Francisco CA (US)

Henry Choi of Cupertino CA (US)

Paul D. Bryan of San Jose CA (US)

Mrinal Agarwal of San Jose CA (US)

Ethan Goolish of Mountain View CA (US)

Richard G. Liu of Sherman Oaks CA (US)

Omar Aziz of Santa Clara CA (US)

Alvaro J. Melendez Hasbun of San Francisco CA (US)

David Ojeda Avellaneda of San Francisco CA (US)

Sunny Kai Pang Chow of San Jose CA (US)

Pedro O. Varangot of San Francisco CA (US)

Tianye Sun of Sunnyvale CA (US)

Karthik Jayaraman Raghuram of Foster City CA (US)

Hung A. Pham of Oakland CA (US)

CRASH DETECTION ON MOBILE DEVICE - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240075895 titled 'CRASH DETECTION ON MOBILE DEVICE

Simplified Explanation

The patent application describes a method for crash detection on mobile devices using sensor data and machine learning models.

• Detect crash event on a mobile device
• Extract multimodal features from sensor data
• Compute crash decisions using machine learning models
• Determine severe vehicle crash based on decisions and severity model

Potential Applications

This technology could be applied in:

• Automotive safety systems
• Emergency response systems
• Wearable technology for personal safety

Problems Solved

This technology addresses:

• Timely detection of vehicle crashes
• Enhanced emergency response coordination
• Improved safety features on mobile devices

Benefits

The benefits of this technology include:

• Increased safety for users of mobile devices
• Faster response times in emergency situations
• Enhanced data collection for crash analysis

Potential Commercial Applications

This technology could be utilized in:

• Automotive industry for integrated safety systems
• Insurance companies for claims processing
• Mobile device manufacturers for enhanced features

Possible Prior Art

One possible prior art could be traditional crash detection systems that rely on single sensor data rather than multimodal features and machine learning models.

How does this technology impact user privacy?

The article does not address the potential privacy concerns related to collecting and analyzing sensor data from mobile devices for crash detection purposes.

What are the limitations of this technology in different environmental conditions?

The article does not discuss how this technology performs in various environmental conditions such as extreme weather or poor visibility.

Original Abstract Submitted

embodiments are disclosed for crash detection on one or more mobile devices (e.g., smartwatch and/or smartphone). in some embodiments, a method comprises: detecting, with at least one processor, a crash event on a crash device; extracting, with the at least one processor, multimodal features from sensor data generated by multiple sensing modalities of the crash device; computing, with the at least one processor, a plurality of crash decisions based on a plurality of machine learning models applied to the multimodal features; and determining, with the at least one processor, that a severe vehicle crash has occurred involving the crash device based on the plurality of crash decisions and a severity model.