| CPC B60W 40/08 (2013.01) [A44C 9/0053 (2013.01); B60W 50/0097 (2013.01); B60W 50/14 (2013.01); G01S 19/42 (2013.01); G06N 5/04 (2013.01); G06N 20/00 (2019.01); H02J 7/00032 (2020.01); H02J 7/0042 (2013.01); H02J 7/0047 (2013.01); H02J 7/02 (2013.01); B60W 2040/0836 (2013.01); B60W 2040/0845 (2013.01); B60W 2040/0872 (2013.01); B60W 2540/221 (2020.02); B60W 2540/24 (2013.01); B60W 2540/26 (2013.01)] | 20 Claims |

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1. A method for predicting risk exposure, the method comprising:
receiving a particular set of data acquired via a sensor;
analyzing, via a machine learning (ML) model, the particular set of data, wherein the analyzing comprises:
determining that the particular set of data represents a particular light exposure pattern corresponding to a light exposure pattern correlated with a risk pattern, wherein:
the ML model is trained with a first set of data and a second set of data to identify a correlation between the light exposure pattern and the risk pattern;
the first set of data is indicative of the light exposure pattern acquired via a light exposure monitoring device; and
the second set of data is indicative of the risk pattern acquired via a monitoring device;
predicting a risk exposure for a user based on the analyzing the particular set of data; and
providing a notice indicating the risk exposure.
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