Providing situational device settings for consumer electronics and discovering user-preferred device settings for consumer electronics

A system comprising:

• at least one processor;
• and
• a non-transitory processor-readable memory device storing instructions that when executed by the at least one processor causes the at least one processor to perform operations including:
• receiving device setting behavioral data collected from different consumer electronic (CE) devices, wherein the device setting behavioral data is indicative of different user-initiated adjustments to one or more device settings of the different CE devices, and the device setting behavioral data is further indicative of different user contexts in which the different user-initiated adjustments occurred;
• generating one or more machine learning models based on training data that includes a portion of the device setting behavioral data;
• predicting, via the one or more machine learning models, a device setting suitable for a CE device based on a current user context, wherein the current user context is similar to one of the different user contexts;
• and
• providing a recommendation comprising the predicted device setting to the CE device.

A non-transitory processor-readable medium that includes a program that when executed by a processor performs a method comprising:

• receiving device setting behavioral data collected from different consumer electronic (CE) devices, wherein the device setting behavioral data is indicative of different user-initiated adjustments to one or more device settings of the different devices, and the device setting behavioral data is further indicative of different user contexts in which the different user-initiated adjustments occurred;
• generating one or more machine learning models based on training data that includes a portion of the device setting behavioral data;
• predicting, via the one or more machine learning models, a device setting suitable for a CE device based on a current user context, wherein the current user context is similar to one of the different user contexts;
• and
• providing a recommendation comprising the predicted device setting to the CE device.

A method comprising:

• receiving device setting behavioral data collected from different consumer electronic (CE) devices, wherein the device setting behavioral data is indicative of different user-initiated adjustments to one or more device settings of the different CE devices, and the device setting behavioral data is further indicative of one or more device properties of the different CE devices and different user contexts in which the different user-initiated adjustments occurred;
• generating one or more machine learning models based on a portion of the device setting behavioral data;
• clustering, via the one or more machine learning models, at least one user associated with the different user-initiated adjustments into at least one user group;
• and
• for each user group, determining one or more user-preferred device settings that the user group prefers most, wherein, based on a current user context, the one or more user-preferred device settings are provided to a CE device as one or more new device settings available for user selection, and the current user context is similar to one of the different user contexts.