Using context based machine learning for generation of customized driving outputs

A method comprising:

• at a computing platform comprising at least one processor, a communication interface, and memory:
• training a machine learning model using historical data, wherein training the machine learning model comprises:
• classifying the historical data as corresponding to one or more of:
• a trip context, a device interaction context, a physical condition context, or a personality context corresponding to a driver or a passenger, and
• training a model corresponding to one or more of the trip context, the device interaction context, the physical condition context, or the personality context, using the corresponding classified historical data;
• monitoring a data source system to collect new data, wherein the new data includes information from at least a first computing device corresponding to a first driver and a second computing device corresponding to one of:
• a second driver or a passenger of the first driver or the second driver;
• generating, by inputting the new data into the machine learning model, a customized driving output for the first driver, wherein generating of the customized driving output for the first driver is based at least in part on the new data corresponding to the second driver, wherein:
• the first driver and the second driver are within a predetermined distance of each other during a driving trip, and
• generating the customized driving output for the first driver is based on an identified personality score for the second driver;
• and
• sending, to the first computing device, the customized driving output for the first driver and one or more commands directing the first computing device to display the customized driving output for the first driver, wherein sending the one or more commands directing the first computing device to display the customized driving output for the first driver causes the first computing device to display the customized driving output for the first driver.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:

• train a machine learning model using historical data, wherein training the machine learning model comprises:
• classifying the historical data as corresponding to one or more of:
• a trip context, a device interaction context, a physical condition context, or a personality context, and
• training a model corresponding to one or more of the trip context, the device interaction context, the physical condition context, or the personality context, using the corresponding classified historical data;
• monitor a data source system to collect new data, wherein the new data includes information from at least a first computing device corresponding to a first driver and a second computing device corresponding to one of:
• a second driver or a passenger of the first driver or the second driver;
• generate, by inputting the new data into the machine learning model, a customized driving output for the first driver, wherein generating of the customized driving output for the first driver is based at least in part on the new data corresponding to the second driver, wherein:
• inputting the new data into the machine learning model comprises:
• classifying the new data for analysis by one or more of the trip context model, the device interaction context model, the physical condition context model, or the personality context model, and
• analyzing the classified new data using the corresponding model, wherein one or more of the trip context model, the device interaction context model, the physical condition context model, or the personality context model are uniquely weighted within the machine learning model to generate the customized driving output;
• and
• send, to the first computing device, the customized driving output for the first driver and one or more commands directing the first computing device to display the customized driving output for the first driver, wherein sending the one or more commands directing the first computing device to display the customized driving output for the first driver causes the first computing device to display the customized driving output for the first driver.