Methods and systems of predicting total loss events

A mobile device comprising:

• one or more sensors;
• one or more processors;
• and
• a non-transitory computer-readable medium storing instructions which, when executed by the one or more processors, cause the mobile device to:
• operate the one or more sensors while the mobile device is positioned within a vehicle during a drive to collect movement measurements indicative of movements of the vehicle;
• detect, using the movement measurements, and as the movement measurements are collected, a change in the movements of the vehicle that exceeds a predefined threshold indicative of an occurrence of a vehicle collision;
• identify, in response to detecting the change in the movements of the vehicle that exceeds the predefined threshold, a first set of data from the movement measurements, wherein the first set of data includes a portion of the movement measurements collected over a time interval that begins at a first time before the change in the movements and ends at a second time after the change in the movements;
• generate a first feature vector using the first set of data and available values for one or more additional data types of a plurality of additional data types;
• execute a first machine-learning model on the first feature vector to generate a first confidence of a total loss event, wherein the first machine-learning model is selected for execution from a plurality of machine-learning models based on the one or more additional data types for which there are available values;
• and
• present the first confidence of the total loss event to the user of the mobile device or a remote computing system.

A non-transitory computer-readable medium storing instructions which, when executed by one or more processors of a mobile device, cause the one or more processors to perform operations comprising:

• operating one or more sensors of the mobile device while the mobile device is positioned within a vehicle during a drive to collect movement measurements indicative of movements of the vehicle;
• detecting, using the movement measurements, and as the movement measurements are collected, a change in the movements of the vehicle that exceeds a predefined threshold indicative of an occurrence of a vehicle collision;
• identifying, in response to detecting the change in the movements of the vehicle that exceeds the predefined threshold, a first set of data from the movement measurements, wherein the first set of data includes a portion of the movement measurements collected over a time interval that begins at a first time before the change in the movements and ends at a second time after the change in the movements;
• generating a first feature vector using the first set of data and available values for one or more additional data types of a plurality of additional data types;
• executing a first machine-learning model on the first feature vector to generate a first confidence of a total loss event, wherein the first machine-learning model is selected for execution from a plurality of machine-learning models based on the one or more additional data types for which there are available values;
• and
• presenting the first confidence of the total loss event to the user of the mobile device or a remote computing system.