METHODS AND SYSTEMS FOR EXPLOITING VALUE IN CERTAIN DOMAINS

1 . A method of exploiting value within a certain domain, the method comprising:

receiving, by a computing device:

scheduling data;

at least a domain, wherein a quantity of domains in the at least a domain is between one and a predetermined maximum number of domains selected by a user; and

domain-specific data, wherein the domain-specific data is a function of the at least a domain;

generating, using a target-setting machine learning model that has been trained on target-setting training data comprising an exemplary plurality of domain-specific data correlated to an exemplary domain target, at least a domain target for the at least a domain as a function of the domain-specific data;

generating, using a scheduling machine learning model that has been trained on scheduling training data comprising exemplary domain targets with exemplary user schedules, the at least a user schedule, wherein generating the at least a user schedule comprises:

receiving a status of at least a domain;

assigning one or more state variables to the at least a domain, wherein the one or more state variables represent the status of the at least a domain; and

generating the at least a user schedule as a function of the one or more state variables; and

displaying, by the computing device, the at least a user schedule and the at least a domain target to the user.

2 . The method of claim 1 , further comprising identifying an effective motivation path for the user using a machine learning model that has been trained on training data comprising previous outputs correlated with subsequent updates for users generally, wherein the machine learning model is further updated as a function of user-specific data to refine the effective motivation path for the user.

3 . The method of claim 1 , further comprising identifying an effective motivation path for the user, wherein identifying an effective motivation path for the user comprises:

classifying the user into a cohort of similar users;

selecting a machine learning model that has been trained on training data comprising previous outputs correlated to updates for the cohort of similar users; and

identifying the effective motivation path as a function of the selected machine learning model.

4 . The method of claim 1 , further comprising determining an inferred courage type for the user using a courage typing layer, wherein the courage typing layer is configured to identify a form of motivational courage from a set of predefined courage types arranged in a progressive order.

5 . The method of claim 4 , wherein determining the inferred courage type comprises:

receiving, by the computing device, user input comprising at least textual input;

processing the user input using a natural language processing engine to extract at least a semantic indicator of motivational state; and

classifying the user, using the courage typing layer and as a function of the at least a semantic indicator of motivational state, into at least one of the set of predefined courage types.

6 . The method of claim 4 , further comprising modifying, by the computing device, one or more of the at least a user schedule and a motivational feedback message as a function of the inferred courage type.

7 . The method of claim 4 , further comprising surfacing, through a graphical user interface, one or more of courage-aligned coaching prompts, media content, and milestone suggestions selected from a content delivery engine as a function of the inferred courage type.

8 . The method of claim 4 , further comprising:

receiving, by the computing device, user data comprising at least textual input and schedule adherence data;

identifying, using a natural language processing engine and the courage typing layer comprising a classifier trained to detect transitional motivational states; and

triggering an eliminator intervention as a function of the transitional motivational state.

9 . The method of claim 4 , further comprising generating at least an adaptive check-in point as a function of a system time constraint and the inferred courage type.

10 . The method of claim 4 , further comprising:

monitoring, using the computing device, a sequence of inferred courage types over time for the user;

detecting, using the computing device, at least one courage-type transitions within the sequence; and

triggering, as a function of detecting the at least one courage-type transitions, a retraining of the courage typing layer using historical transition data of the user as training data.

11 . A system for exploiting value within a certain domain, the system comprising a computing device configured to:

receive at the computing device:

scheduling data;

at least a domain, wherein a quantity of domains in the at least a domain is between one and a predetermined maximum number of domains selected by a user; and

domain-specific data, wherein the domain-specific data is a function of the at least a domain;

generate, using a target-setting machine learning model that has been trained on target-setting training data comprising an exemplary plurality of domain-specific data correlated to an exemplary domain target, at least a domain target for the at least a domain as a function of the domain-specific data;

generate, using a scheduling machine learning model that has been trained on scheduling training data comprising exemplary domain targets with exemplary user schedules, the at least a user schedule, wherein generating the at least a user schedule comprises:

receiving a status of at least a domain;

assigning one or more state variables to the at least a domain, wherein the one or more state variables represent the status of the at least a domain; and

generating the at least a user schedule as a function of the one or more state variables; and

display, at the computing device, the at least a user schedule and the at least a domain target to the user.

12 . The system of claim 11 , wherein the computing device is further configured to identify an effective motivation path for the user using a machine learning model that has been trained on training data comprising previous outputs correlated with subsequent updates for users generally, wherein the machine learning model is further updated as a function of user-specific data to refine the effective motivation path for the user.

13 . The system of claim 11 , wherein the computing device is further configured to identify an effective motivation path for the user, wherein identifying an effective motivation path for the user comprises:

classifying the user into a cohort of similar users;

selecting a machine learning model that has been trained on training data comprising previous outputs correlated to updates for the cohort of similar users; and

identifying the effective motivation path as a function of the selected machine learning model.

14 . The system of claim 11 , wherein the computing device is further configured to determine an inferred courage type for the user using a courage typing layer, wherein the courage typing layer is configured to identify a form of motivational courage from a set of predefined courage types arranged in a progressive order.

15 . The system of claim 14 , wherein determining the inferred courage type comprises:

receiving, by the computing device, user input comprising at least textual input;

processing the user input using a natural language processing engine to extract at least a semantic indicator of motivational state; and

classifying the user, using the courage typing layer and as a function of the at least a semantic indicator of motivational state, into at least one of the set of predefined courage types.

16 . The system of claim 14 , wherein the computing device is further configured to modify one or more of the at least a user schedule and a motivational feedback message as a function of the inferred courage type.

17 . The system of claim 14 , wherein the computing device is further configured to surface, to a graphical user interface, one or more of courage-aligned coaching prompts, media content, and milestone suggestions selected from a content delivery engine as a function of the inferred courage type.

18 . The system of claim 14 , wherein the computing device is further configured to:

receive user data comprising at least textual input and schedule adherence data;

identify, using a natural language processing engine and the courage typing layer comprising a classifier trained to detect transitional motivational states; and

trigger an eliminator intervention as a function of the transitional motivational state.

19 . The system of claim 14 , wherein the computing device is further configured to generate at least an adaptive check-in point as a function of a system time constraint and the inferred courage type.

20 . The system of claim 14 , wherein the computing device is further configured to:

monitor a sequence of inferred courage types over time for the user;

detect at least one courage-type transitions within the sequence; and

trigger, as a function of detecting the at least one courage-type transitions, a retraining of the courage typing layer using historical transition data of the user as training data.