Lyten, Inc.

Kyle Matthys

Bruce Lanning

Daniel Cook

Carlos Montalvo

George Clayton Gibbs

John Chmiola

Michael Wayne Stowell, Jr

Karel Vanheusden

Sung Hyun Lim

Jacques F Nicole

Methods and system to learn precise sensing fingerprints based on machine learning integration are disclosed herein. In use, the system receives at least one first parameter associated with at least one sensor and associates the first parameter with a pre-identified first digital signature in a signature database. A machine learning system is trained based on the first parameter and the pre-identified digital signature. The system then receives at least one second parameter from the at least one sensor and determines that the second parameter is independent of a digital signature in the signature database. Using the machine learning system, a second digital signature for the second parameter is identified and saved in the signature database.

Reconfiguring a second type of sensor based on sensing data of a first type of sensor

Disclosed herein is a sensors-as-a-service ecosystem. In use, the system includes functions for receiving first sensor data at a sensors as a service platform, where the first sensor data corresponds to a first level of capabilities for a first sensor. The system also receives a selection of a sensor upgrade for the first sensor and provisions enhanced sensor capabilities for the sensor upgrade based on the selection. Furthermore, the system sends a sensor update with the enhanced sensor capabilities from the sensors as a service platform to the first sensor. Finally, the system receives second sensor data from the first sensor at the sensors as a service platform, where the second sensor data corresponds to a second level of capabilities for the first sensor.

Configuration of wearable sensors based on a sensors-as-a-service platform

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Los Altos Hills, CA, United States of America

Kyle Matthys