Abstract

This document describes techniques that enable a mobile computing device (e.g., a smartphone, smart watch, wearable device, augmented reality glasses, vehicle infotainment system, etc.) to generate context data (e.g., location data, movement patterns, audio data, biometrics, traffic hazards, recent crimes, etc.) for use by a proactive personal safety system. The proactive personal safety system may include an intelligent software agent (e.g., artificial intelligence (AI), machine learning model, etc.) hosted locally on the mobile computing device, on a companion device (e.g., tethered smartphone), on a cloud server, etc. The mobile computing device may continuously monitor diverse environmental factors and user behavior patterns to generate the context data. The proactive personal safety system may use the context data to assess the user’s safety status, identify potential risks, and recognize deviations from typical user behavior. If the proactive personal safety system detects an unsafe situation, the mobile computing device may prompt the user to take immediate safety measures. For instance, if the proactive personal safety system determines that the user’s current location is an unsafe area (e.g., by analyzing crime rates, recent hazards, ambient light, etc.) and the user is walking, the mobile computing device may prompt the user to set up safety checks with a trusted contact, set up safety beacons, take an alternate route, call a taxi, etc. In this way, the techniques of this document may provide an enhanced personal safety experience, offering context-aware guidance and risk mitigation suggestions in real-time to help reduce the potential for unsafe situations.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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