Patent ID: 11928621
Assignee: ALLSTATE INSURANCE COMPANY
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 8:
9. A computing platform comprising:
at least one processor;
a communication interface communicatively coupled to the at least one processor; and
memory storing computer-readable instructions that, when executed by the at least one processor, cause the computing platform to:
receive sensor data collected during operation of a vehicle, wherein the sensor data is associated with a plurality of timestamps associated with recorded audio signals to detect a voice of at least one passenger of the vehicle;
assign, based on the timestamps, a first subset of the sensor data to one or more first periods during which a driver was transporting passengers associated with a shared mobility service, and a second subset of the sensor data to one or more second periods during which the driver was not transporting passengers associated with the shared mobility service;
train a first machine learning model to determine a first safety score indicating a performance of the driver during the one or more first periods based on the first subset of the sensor data, wherein the first machine learning model is trained by trip data that correlates to a passenger mobility status associated with carrying passengers;
train a second machine learning model to determine a second safety score indicating a performance of the driver during the one or more second periods based on the second subset of the sensor data, wherein the second machine learning model is trained by trip data that correlates to a no passenger mobility status associated with not carrying passengers;
determine a portion of a trip fare and driver-specific feedback to provide to the driver based on the first safety score and the second safety score, wherein the driver-specific feedback is associated with a condition of the one or more first periods or the one or more second periods;
provide real-time driver-specific feedback including the driver-specific feedback in a display of a mobile system associated with the driver in real-time upon a next detection of the condition;
retrain the first machine learning model and the second machine learning model based on additional real-time driving data collected after providing the driver-specific feedback; and
provide updated real-time driver-specific feedback based on updated safety scores calculated by the retrained first machine learning model and the retrained second machine learning model.