SYSTEMS FOR ADAPTIVE HEALTHCARE SUPPORT, BEHAVIORAL INTERVENTION, AND ASSOCIATED METHODS

1 . A method for operating a health guidance system, the method comprising:

obtaining new data from one or more user devices, wherein the new data represents a biometric condition, a user input, a user motion, a user location, or a combination thereof for a user;

accessing one or more user history items associated with the user, the user history items defining at least one of a past user state, a past action presented to the user, and a past user behavior, wherein

the past user state represents a physiological or a health condition of the user occurring or processed at a past time,

the past action represents a previously identified action taken by the user, and

the past user behavior represents a repeated action occurring with a temporal pattern;

estimating a recent state of the user based on the new data and the one or more user history items, wherein the recent state represents a current or a most recent health condition of the user;

estimating a likely outcome based on the recent state, wherein the likely outcome represents a thresholding health condition of the user likely to occur at a future time;

identifying an action for the user based on the recent state of the user using an adaptive support machine-learning model, wherein

the action represents an action performed by the health guidance system to affect a targeted user action before the future time to prevent or adjust the likely outcome, and

identifying the action includes identifying a set of delivery details for adjusting a content and/or a delivery timing for the recommended action;

executing the identified action for the user according to the set of delivery details;

receiving an indication of a response of the user performed in response to the action, wherein the response corresponds to the past user behavior; and

updating the adaptive support machine-learning model based on the received indication of the response.

2 . The method of claim 1 , wherein the adaptive support machine-learning model is a deep neural network model.

3 . The method of claim 1 , wherein the identified action is identified from a group of actions using a determined likely compliance value for each action and a corresponding set of delivery details, the determined likely compliance value for each action being generated by the adaptive support machine-learning model configured to assist the user in changing a user behavior over time.

4 . The method of claim 1 , wherein the user recent state is further estimated using parameters associated with the user.

5 . The method of claim 4 , wherein the parameters associated with the user are estimated using a maximum likelihood estimation function.

6 . The method of claim 1 , wherein the action is at least one of a prompt for encouraging the user to perform the targeted user action, a warning regarding the likely outcome, and a reinforcement for the user for performing the targeted user action.

7 . The method of claim 1 , wherein the received indication represents at least one of a performance of the targeted user action, a partial performance of the targeted user action, and non-performance of the targeted user action.

8 . A computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform a process, the process comprising:

obtaining new data from one or more user devices, wherein the new data represents a biometric condition, a user input, a user motion, a user location, or a combination thereof for a user;

accessing one or more user history items associated with the user, the user history items defining at least one of a past user state, a past action presented to the user, and a past user behavior, wherein

the past user state represents a physiological or a health condition of the user occurring or processed at a past time,

the past action represents a previously identified action taken by the user, and

the past user behavior represents a repeated action occurring with a temporal pattern;

estimating a state of the user based on the new data and the one or more user history items, wherein the recent state represents a current or a most recent health condition of the user;

estimating a likely outcome based on the recent state, wherein the likely outcome represents a thresholding health condition of the user likely to occur at a future time;

identifying an action for the user based on the state of the user using an adaptive support model, wherein

the adaptive support model is a machine-learning model,

the action represents an action performed by the health guidance system to affect a targeted user action before the future time to prevent or adjust the likely outcome, and

identifying the action includes identifying a set of delivery details for adjusting a content and/or a delivery timing for the recommended action;

executing the identified action for the user according to the set of delivery details;

receiving an indication of a response of the user performed in response to the action, wherein the user action corresponds to the past user behavior; and

updating the adaptive support model based on the received indication of the response.

9 . The computer-readable medium of claim 8 , wherein the adaptive support model is a deep neural network model.

10 . The computer-readable medium of claim 8 , wherein the identified action is identified from a group of actions using a determined likely compliance value for each action and a corresponding set of delivery details, the determined likely compliance value for each action being generated by the adaptive support model configured to assist the user in changing a user behavior over time.

11 . The computer-readable medium of claim 8 , wherein the user state is further estimated using parameters associated with the user.

12 . The computer-readable medium of claim 11 , wherein the parameters associated with the user are estimated using a maximum likelihood estimation function.

13 . The computer-readable medium of claim 8 , wherein the action is at least one of a prompt for encouraging the user to perform the targeted user action, a warning regarding the likely outcome, and a reinforcement for the user for performing the targeted user action.

14 . The computer-readable medium of claim 8 , wherein the received indication represents at least one of a performance of the targeted user action, a partial performance of the targeted user action, and non-performance of the targeted user action.

15 . A computing system comprising:

one or more processors; and

memory having stored thereon instructions that, when executed by the one or more processors, cause the one or more processors to perform a process, the process comprising:

obtaining new data from one or more user devices, wherein the new data represents a biometric condition, a user input, a user motion, a user location, or a combination thereof for a user;

accessing one or more user history items associated with the user, the user history items defining at least one of a past user state, a past action presented to the user, and a past user behavior, wherein

the past user state represents a physiological or a health condition of the user occurring or processed at a past time,

the past action represents a previously identified action taken by the user, and

the past user behavior represents a repeated action occurring with a temporal pattern;

estimating a state of the user based on the new data and the one or more user history items, wherein the recent state represents a current or a most recent health condition of the user;

estimating a likely outcome based on the recent state, wherein the likely outcome represents a thresholding health condition of the user likely to occur at a future time;

identifying an action for the user based on the state of the user using an adaptive support model, wherein

the adaptive support model is a machine-learning model,

the action represents an action performed by the health guidance system to affect a targeted user action before the future time to prevent or adjust the likely outcome, and

identifying the action includes identifying a set of delivery details for adjusting a content and/or a delivery timing for the recommended action;

executing the identified action for the user according to the set of delivery details;

receiving an indication of a response of the user performed in response to the action, wherein the user action corresponds to the past user behavior; and

updating the adaptive support model based on the received indication of the response.

16 . The computing system of claim 15 , wherein the identified action is identified from a group of actions using a determined likely compliance value for each action and a corresponding set of delivery details, the determined likely compliance value for each action being generated by the adaptive support model configured to assist the user in changing a user behavior over time.

17 . The computing system of claim 15 , wherein the user state is further estimated using parameters associated with the user.

18 . The computing system of claim 17 , wherein the parameters associated with the user are estimated using a maximum likelihood estimation function.

19 . The computing system of claim 15 , wherein the action is at least one of a prompt for encouraging the user to perform the targeted user action, a warning regarding the likely outcome, and a reinforcement for the user for performing the targeted user action.

20 . The computing system of claim 15 , wherein the received indication represents at least one of a performance of the targeted user action, a partial performance of the targeted user action, and non-performance of the targeted user action.

21 - 40 . (canceled)