| CPC G06F 3/011 (2013.01) [A61B 5/165 (2013.01); A61B 5/7264 (2013.01); G06F 18/22 (2023.01); G06F 21/31 (2013.01); G06N 20/00 (2019.01); G16H 20/00 (2018.01); G16H 20/70 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 80/00 (2018.01); A61M 2021/0044 (2013.01)] | 18 Claims |
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1. A system for emotional pattern matching, the system comprising a computing device, wherein the computing device is configured to:
receive current user emotional activity data from a user and an authorized contact from an authorization list utilizing a support response module operating on the computing device, wherein the current user emotional activity data comprises a location and direction of travel of the user;
train a user state classifier as a function of state training data and a machine learning algorithm, wherein the state training data comprises a plurality of elements of user emotional activity data correlated with elements of past user emotional state data;
identify a current user emotional state as a function of the trained user state classifier and the current user emotional activity data, wherein the current user emotional state is a function of an emotional wellbeing of the user, wherein the location and the direction of travel of the user are provided to the trained user state classifier as an input to output the current user emotional state;
match the current user emotional state to an emotional therapy by:
receiving emotional therapy training data, wherein the emotional therapy training data includes a plurality of combinations of user emotional state data and correlated emotional therapies;
generating, as a function of a machine-learning process, an emotional wellbeing model as a function of the emotional therapy training data;
adding user rejection of the emotional therapy to the emotional therapy training data; and
retraining the emotional wellbeing model as a function of the emotional therapy training data; and
generating the emotional therapy as a function of the current user emotional state and the emotional wellbeing model, wherein generating the emotional therapy comprises:
selecting one authorized contact from the authorization list of contacts as a function of the location of the user, such that the one authorized contact is closest to the location of the user from the authorization list of contacts, wherein the authorization list comprises family members for the user; and
initiating a call to the authorized contact to provide assistance to the user;
transmit the emotional therapy to the user;
receive a user response to the emotional therapy;
update the state training data as a function of the user response; and
retrain the user state classifier using the updated state training data to generate an updated user state classifier.
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