Ubiety Technologies, Inc.

Joseph Loftus

Michael Cox

Keith Puckett

Augusto Savaris

Samuel Frakes

Victoria Nakagawa

Erik Hanson

Luis Castañeda Lopez

Cameron Miller

Machine learning-based methods are disclosed to identify the types of electronic devices present in an area using emitted passive electromagnetic signals (e.g., RF signals such as Bluetooth, WiFi, and/or cellular). The identification of the electronic devices improves private and public security in determining human presence and device presence. The disclosed methods use trained machine learning models that learn the relationship between the metadata present within the broadcast electromagnetic signals and the types of electronic devices present. The disclosed methods, apparatuses and systems can include use of several wireless data transfer protocols, such as Wi-Fi, Bluetooth and cellular.

Electronic device identification using emitted electromagnetic signals

Machine learning-based methods are disclosed to intelligently generate dynamic workflows, such as dynamic call lists and priority levels of alarm notifications. To generate a dynamic call list based on an alarm notification, the system may identify electronic devices present in an area using emitted passive electromagnetic signals (e.g., RF signals such as Bluetooth, WiFi, and/or cellular). The identification of the electronic devices may be associated with known and/or unknown individuals on the premises. The RF signal data may be processed using at least one ML model to determine the severity of the alarm. Based on the processed data, the system may assign a priority level to the alarm, where the priority level may range from level 0 (no action) to level 4 (dispatch law enforcement immediately). The disclosed methods use trained machine learning models to generate a dynamic call list and identify false alarms with increased accuracy.

Intelligent dynamic workflow generation

Methods are disclosed to identify and/or count a number of electronic devices present in an area using emitted passive Bluetooth Low Energy (BLE) signals. The identification and/or counting of Bluetooth-enabled devices improves private and public security in determining human presence. Bluetooth-enabled devices passively emit BLE signals for inter-device communication in the form of Bluetooth Advertising Packets. The packets are sent by BLE-enabled devices to search for other known or compatible BLE devices, and advertise information such as media access control (MAC) addresses, device manufacturers, connection capabilities, and manufacturer-specific data. By passively listening to and decoding the observed BLE signals, access to the packets and the metadata they contain is gained. The disclosed methods can include use of other wireless data transfer protocols, such as Bluetooth and Cellular.

Detection of electronic device presence using emitted bluetooth low energy signals

Machine learning-based methods are disclosed to identify and/or count a number of electronic devices present in an area using emitted passive Wi-Fi signals. The identification and/or counting of Wi-Fi-enabled devices improves private and public security in determining human presence. The disclosed methods use trained machine learning models that learns the relationship between real-time Wi-Fi probe request broadcast behavior distribution and the number of electronic devices present. The disclosed methods can include use of other wireless data transfer protocols, such as Bluetooth and Cellular.

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Seattle, WA, United States of America

Joseph Loftus