Systems and methods for empathy-based machine learning

A method for controlling generation of one or more computer-generated insights using empathy-based machine learning features, the method comprising:

• maintaining, in a first set of machine learning models each tracking an empathy-based aspect of a user, a trained empathy based representation of the user, each of the machine learning models of the first set of machine learning models tracking a different empathy-based aspect of the user;
• maintaining, in a second machine learning model, a trained circumstance based representation of the user;
• receiving one or more candidate insight data objects representing potential computer-based interactive notifications;
• generating a tuning matrix from the second machine learning model to be applied as biasing weights to the trained empathy based representation of the user;
• processing each of the one or more candidate insight data objects using at least the biasing weights applied to the trained empathy based representation of the user to generate a real-time prediction score for each of the one or more candidate insight data objects;
• and
• transmitting the candidate insight data object having a highest score to a user interface associated with the user.

A non-transitory computer readable medium storing machine interpretable instruction sets, which when executed by a processor, cause the processor to perform a method for controlling generation of one or more computer-generated insights using empathy-based machine learning features, the method comprising:

• maintaining, in a first set of machine learning models each tracking an empathy-based aspect of a user, a trained empathy based representation of the user, each of the machine learning models of the first set of machine learning models tracking a different empathy-based aspect of the user;
• maintaining, in a second machine learning model, a trained circumstance based representation of the user;
• receiving one or more candidate insight data objects representing potential computer-based interactive notifications;
• generating a tuning matrix from the second machine learning model to be applied as biasing weights to the trained empathy based representation of the user;
• and
• processing each of the one or more candidate insight data objects using at least the biasing weights applied to the trained empathy based representation of the user to generate a real-time prediction score for each of the one or more candidate insight data objects;
• and transmitting the candidate insight data object having a highest score to a user interface associated with the user.