System, methods, and apparatuses for managing data for artificial intelligence software and mobile applications in digital health therapeutics

Abstract

Disclosed herein are systems and methods of a digital therapy service to generate therapy regimen addressing a health condition, which may require the customer to perform various tasks and instruct devices to capture data related to the customer's therapy, including body metric measurements and information related to the number and quality of interactions between the user and aspects of the digital therapy service, sometimes referred to as “user-generated” inputs. The digital therapy service may calculate various metrics, such as scores and milestone determinations, to measure the customer's progress. The scores can be determined using dynamically generated and updated scoring models. Artificial intelligence chatbots may be used to deliver and capture information to and from customers during interactive sessions. Each customer may have a unique chatbot queue that contains the various chatbots that will be used to deliver particular aspects of the customer's therapy.

What is claimed: 
     
         1 . A computer-implemented method for generating and monitoring a therapy regimen, the method comprising:
 generating, by machine learning software within a computer, a therapy regimen associated with a condition of a user, wherein the therapy regimen comprises one or more machine-readable computer files containing machine-executed instructions for a mobile application and indicates a set of one or more pre-stored parameters associated with the condition;   receiving, by the computer, a plurality of inputs associated with the therapy regimen of the user from one or more devices via a network, wherein an input contains a value of a parameter;   comparing, by the computer, the value of the parameter against a corresponding pre-stored parameter stored in a first database;   determining, by the machine learning software within the computer, a regimen status for the therapy regimen of the user based at least in part on interactions between the user and aspects of the mobile application and the value of the parameter with respect to the corresponding pre-stored parameter, wherein the computer receives indicators of the interactions and tracks the frequency and/or amount of interactions between the user and the mobile application; and   transmitting, by the computer, to a user device associated with the user a milestone achievement interface that is based on the regimen status, wherein the milestone achievement interface is configured to be displayed on a graphical user interface at the user device.   
     
     
         2 . The method of  claim 1 , wherein receiving the plurality of inputs further comprises storing, by the computer, each respective input into a user record of a second database. 
     
     
         3 . The method of  claim 2 , wherein at least one of the inputs is a user-generated input received from the user device, and wherein the method further comprises calculating, by the computer, a score indicating a likelihood of success associated with the therapy regimen based upon a frequency of receiving each user-generated input. 
     
     
         4 . The method of  claim 3 , further comprising updating, by the computer, the user record of the second database to include the score indicating the likelihood of success. 
     
     
         5 . The method of  claim 4 , wherein the computer updates the score indicating the likelihood of success at a predetermined interval. 
     
     
         6 . The method of  claim 2 , wherein at least one of the inputs is received from a device selected from the group comprising: a coach device, a third-party server, and an artificial intelligence server. 
     
     
         7 . The method of  claim 2 , further comprising:
 receiving, by the computer, via the network one or more coach inputs from a coach device; and   updating, by the computer, the user record of the second database to at least one coaching input received from the coach device.   
     
     
         8 . The method of  claim 7 , further comprising:
 generating, by the computer, a coaching interface configured to display the one or more coaching inputs received from the coach device; and   transmitting, by the computer, the coaching interface to the user device.   
     
     
         9 . The method of  claim 7 , further comprising transmitting, by the computer, to the user device at least a portion of the machine-readable computer files of the therapy regimen to be executed by the mobile application of the user device. 
     
     
         10 . The method of  claim 9 , further comprising:
 updating, by the computer, the therapy regimen associated with the condition of the user based upon the regimen status for the therapy regimen; and   updating, by the computer, the therapy regimen and parameters in the user record of the second database.   
     
     
         11 . The method of  claim 1 , wherein at least one input includes a body metric measurement received from a device configured to generate the body metric measurement. 
     
     
         12 . An apparatus for generating and monitoring a therapy regimen, the apparatus comprising:
 a processor configured to:
 generate, by machine learning software within the processor, a therapy regimen associated with a condition of a user, wherein the therapy regimen comprises one or more machine-readable computer files containing machine-executed instructions for a mobile application and indicates a set of one or more pre-stored parameters associated with the condition; 
 receive a plurality of inputs associated with the therapy regimen of the user from one or more devices via a network, wherein each input contains a value of a parameter; 
 compare the value of the parameter against a corresponding pre-stored parameter stored in a first database; 
 determine, by the machine learning software within the processor, a regimen status for the therapy regimen of the user based at least in part on interactions between the user and aspects of the mobile application and the value of the parameter with respect to the corresponding pre-stored parameter, wherein the computer receives indicators of the interactions and tracks the frequency and/or amount of interactions between the user and the mobile application; and 
 transmit, to a user device associated with the user, a milestone achievement interface that is based on the regimen status, wherein the milestone achievement interface is configured to be displayed on a graphical user interface at the user device. 
   
     
     
         13 . The apparatus of  claim 12 , wherein the processor is further configured to store each respective input into a user record of a second database. 
     
     
         14 . The apparatus of  claim 13 , wherein at least one of the inputs is a user-generated input received from the user device, and wherein the processor is further configured to calculate a score indicating a likelihood of success associated with the therapy regimen based upon a frequency of receiving each user-generated input. 
     
     
         15 . The apparatus of  claim 14 , wherein the processor is further configured to update the user record of the second database to include the score indicating the likelihood of success. 
     
     
         16 . The apparatus of  claim 15 , wherein the computer updates the score indicating the likelihood of success at a predetermined interval. 
     
     
         17 . The apparatus of  claim 13 , wherein at least one of the inputs is received from a device selected from the group comprising: a coach device, a third-party server, and an artificial intelligence server. 
     
     
         18 . The apparatus of  claim 13 , wherein the processor is further configured to:
 receive via the network one or more coaching inputs from a coach device; and   update the user record of the second database to at least one coaching input received from the coach device.   
     
     
         19 . The apparatus of  claim 18 , wherein the processor is further configured to:
 generate a coaching interface configured to display the one or more coaching inputs received from the coach device; and   transmit the coaching interface to the user device.   
     
     
         20 . The apparatus of  claim 18 , the processor is further configured to transmit to the user device at least a portion of the machine-readable computer files of the therapy regimen to be executed by the mobile application of the user device. 
     
     
         21 . The apparatus of  claim 20 , wherein the processor is further configured to:
 update the therapy regimen associated with the condition of the user based upon the regimen status for the therapy regimen; and   update the therapy regimen and parameters in the user record of the second database.   
     
     
         22 . The apparatus of  claim 12 , wherein at least one input includes a body metric measurement received from a device configured to generate the body metric measurement. 
     
     
         23 . A computer-implemented method for generating and monitoring a therapy regimen, the method comprising:
 receiving, by machine learning software within a computer, a therapy regimen associated with a condition of a user, wherein the therapy regimen comprises one or more machine-readable computer files containing machine-executed instructions for a mobile application and indicates a set of one or more parameters associated with the condition;   receiving, by the computer, via a graphical user interface one or more inputs associated with the therapy regimen of the user, wherein an input contains a value of a parameter;   comparing, by the computer, the value of the parameter against a corresponding pre-stored parameter stored in a first database;   determining, by the machine learning software within the computer, a regimen status based at least in part on interactions between the user and aspects of the mobile application and the value of the parameter with respect to the corresponding pre-stored parameter, wherein the computer receives indicators of the interactions and tracks the frequency and/or amount of interactions between the user and the mobile application; and   generating, by the computer, a milestone interface configured to display, via the graphical user interface, a milestone achievement based upon the regimen status.   
     
     
         24 . The method of  claim 23 , wherein receiving the one or more inputs further comprises transmitting, by the computer, each respective input to a second database configured to store data associated with the user in a database record for the user. 
     
     
         25 . The method of  claim 24 , further comprising determining, by the computer, a score indicating a likelihood of success associated with the therapy regimen based upon a frequency of receiving each respective input via the graphical user interface. 
     
     
         26 . The method of  claim 25 , further comprising transmitting, by the computer, the score indicating the likelihood of success to the second database to update the database record for the user. 
     
     
         27 . The method of  claim 26 , wherein the computer updates the score indicating the likelihood of success at a predetermined interval. 
     
     
         28 . The method of  claim 23 , further comprising receiving, by the computer, one or more body metric inputs containing one or more values, each representing a body metric measurement. 
     
     
         29 . The method of  claim 28 , wherein at least one body metric measurement input is received from a device configured to generate the body metric measurement. 
     
     
         30 . The method of  claim 29 , wherein the device configured to generate the body metric measurement is selected from the group comprising: a smart home device, a wearable device, and a fitness tracker. 
     
     
         31 . The method of  claim 23 , further comprising:
 receiving, by the computer, via the network one or more coaching inputs from a coach device; and   generating, by the computer, a coaching interface configured to display the one or more coaching inputs received from the coach device.   
     
     
         32 . The method of  claim 23 , further comprising generating, by the computer, a prompt interface at a given time according to the instructions of the therapy regimen, the prompt interface configured to display a field that receives the input from the user via the graphical user interface of the computer. 
     
     
         33 . An apparatus for generating and monitoring a therapy regimen, the apparatus comprising:
 a processor configured to:
 receive, by machine learning software within the processor, a therapy regimen associated with a condition of a user, wherein the therapy regimen comprises one or more machine-readable computer files containing machine-executed instructions for a mobile application and indicates a set of one or more parameters associated with the condition; 
 receive, via a graphical user interface, one or more inputs associated with the therapy regimen of the user, wherein an input contains a value of a parameter; 
 compare the value of the parameter against a corresponding pre-stored parameter stored in a first database; 
 determine, by the machine learning software within the processor, a regimen status based at least in part on interactions between the user and aspects of the mobile application and the value of the parameter with respect to the corresponding pre-stored parameter, wherein the computer receives indicators of the interactions and tracks the frequency and/or amount of interactions between the user and the mobile application; and 
 generate a milestone interface configured to display via the graphical user interface a milestone achievement based upon the regimen status. 
   
     
     
         34 . The apparatus of  claim 33 , wherein the processor is further configured to transmit each respective input to a second database configured to store data associated with the user in database record for the user. 
     
     
         35 . The apparatus of  claim 34 , the processor is further configured to determine a score indicating a likelihood of success associated with the therapy regimen based upon a frequency of receiving each respective input via the graphical user interface. 
     
     
         36 . The apparatus of  claim 35 , wherein the processor is further configured to transmit the score indicating the likelihood of success to the second database to update the database record for the user. 
     
     
         37 . The apparatus of  claim 36 , wherein the processor is configured to update the score indicating the likelihood of success at a predetermined interval. 
     
     
         38 . The apparatus of  claim 33 , the processor is further configured to receive one or more body metric measurement inputs containing one or more values, each representing a body metric measurement. 
     
     
         39 . The apparatus of  claim 38 , wherein the computer receives at least one body metric input from a device configured to generate the body metric measurement. 
     
     
         40 . The apparatus of  claim 39 , wherein the device configured to generate the body metric measurement is selected from the group comprising: a smart home device, a wearable device, and a fitness tracker. 
     
     
         41 . The apparatus of  claim 33 , wherein the processor is further configured to:
 receive via the network one or more coaching inputs from a coach device; and   generate a coaching interface configured to display the one or more coaching inputs received from the coach device.   
     
     
         42 . The apparatus of  claim 33 , wherein the processor is further configured to generate a prompt interface at a given time according to the instructions of the therapy regimen, the prompt interface configured to display a field that receives the input from the user via the graphical user interface of the computer. 
     
     
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         86 . The method of  claim 1 , wherein the condition of the user is a diabetes health condition. 
     
     
         87 . The method of  claim 1 , wherein a health score model is trained with and uses data fields relevant to monitoring diabetes. 
     
     
         88 . The method of  claim 87 , wherein the data fields are selected from the group consisting of blood glucose, cholesterol, blood pressure, weight, and number of interactions with one or more of the one or more devices and a coach. 
     
     
         89 . The method of  claim 1 , wherein the condition of the user is type II diabetes. 
     
     
         90 . The method of  claim 1 , wherein the machine learning software executes machine learning algorithms and processes selected from the group consisting of generalized linear models, random forests, support vector machines, unsupervised and/or supervised clustering, and deep learning, wherein deep learning includes neural networks. 
     
     
         91 . The method of  claim 1 , further comprising comparing, by the computer, one or more data fields relevant to the user's condition against pre-stored milestone parameters or data values at predetermined milestone intervals, wherein the prestored milestone values may operate as threshold values. 
     
     
         92 . The method of  claim 91 , wherein there are multiple pre-stored milestone values for multiple data fields, to compare multiple different customer values of different fields against multiple different pre-stored values. 
     
     
         93 . The apparatus of  claim 12 , wherein the condition of the user is a diabetes health condition. 
     
     
         94 . The apparatus of  claim 12 , wherein a health score model is trained with and uses data fields relevant to monitoring diabetes. 
     
     
         95 . The apparatus of  claim 94 , wherein the data fields are selected from the group consisting of blood glucose level, cholesterol, blood pressure, weight, and number of interactions with one or more of the one or more devices and a coach. 
     
     
         96 . The apparatus of  claim 12 , wherein the condition of the user is type II diabetes. 
     
     
         97 . The apparatus of  claim 12 , wherein the machine learning software executes machine learning algorithms and processes selected from the group consisting of generalized linear models, random forests, support vector machines, unsupervised and/or supervised clustering, and deep learning, wherein deep learning includes neural networks. 
     
     
         98 . The apparatus of  claim 12 , wherein the processor is further configured to compare one or more data fields relevant to the user's condition against pre-stored milestone parameters or data values at predetermined milestone intervals, wherein the prestored milestone values may operate as threshold values. 
     
     
         99 . The apparatus of  claim 98 , wherein there are multiple pre-stored milestone values for multiple data fields, to compare multiple different customer values of different fields against multiple different pre-stored values. 
     
     
         100 . The method of  claim 23 , wherein the condition of the user is a diabetes health condition. 
     
     
         101 . The method of  claim 23 , wherein a health score model is trained with and uses data fields relevant to monitoring diabetes. 
     
     
         102 . The method of  claim 101 , wherein the data fields are selected from the group consisting of blood glucose, cholesterol, blood pressure, weight, and number of interactions with one or more of the one or more devices and a coach. 
     
     
         103 . The method of  claim 23 , wherein the condition of the user is type II diabetes. 
     
     
         104 . The method of  claim 23 , wherein the machine learning software executes machine learning algorithms and processes selected from the group consisting of generalized linear models, random forests, support vector machines, unsupervised and/or supervised clustering, and deep learning, wherein deep learning includes neural networks. 
     
     
         105 . The method of  claim 23 , further comprising comparing, by the computer, one or more data fields relevant to the user's condition against pre-stored milestone parameters or data values at predetermined milestone intervals, wherein the prestored milestone values may operate as threshold values. 
     
     
         106 . The method of  claim 105 , wherein there are multiple pre-stored milestone values for multiple data fields, to compare multiple different customer values of different fields against multiple different pre-stored values. 
     
     
         107 . The apparatus of  claim 33 , wherein the condition of the user is a diabetes health condition. 
     
     
         108 . The apparatus of  claim 33 , wherein a health score model is trained with and uses data fields relevant to monitoring diabetes. 
     
     
         109 . The apparatus of  claim 108 , wherein the data fields are selected from the group consisting of blood glucose level, cholesterol, blood pressure, weight, and number of interactions with one or more of the one or more devices and a coach. 
     
     
         110 . The apparatus of  claim 33 , wherein the condition of the user is type II diabetes. 
     
     
         111 . The apparatus of  claim 33 , wherein the machine learning software executes machine learning algorithms and processes selected from the group consisting of generalized linear models, random forests, support vector machines, unsupervised and/or supervised clustering, and deep learning, wherein deep learning includes neural networks. 
     
     
         112 . The apparatus of  claim 33 , wherein the processor is further configured to compare one or more data fields relevant to the user's condition against pre-stored milestone parameters or data values at predetermined milestone intervals, wherein the prestored milestone values may operate as threshold values. 
     
     
         113 . The apparatus of  claim 112 , wherein there are multiple pre-stored milestone values for multiple data fields, to compare multiple different customer values of different fields against multiple different pre-stored values. 
     
     
         114 . The method of  claim 1 , wherein indicators of interactions between the user and certain aspects of the mobile device are selected from the group consisting of whether a meal plan was followed; whether a therapy regimen task was completed; whether a coaching call was completed; whether one or more, or all, ingredients on a shopping list were procured; whether the subject consumed a specified amount of water or whether the subject consumed water; a number of meal plan meals consumed, a number of tasks completed, a total number of calories burned in one or more activities, a number of coaching calls completed, length of one or more coaching calls, a number or fraction of ingredients procured from a shopping list, a number of hydration events, or a total volume of hydration. 
     
     
         115 . The apparatus of  claim 12 , wherein indicators of interactions between the user and certain aspects of the mobile device are selected from the group consisting of whether a meal plan was followed; whether a therapy regimen task was completed; whether a coaching call was completed; whether one or more, or all, ingredients on a shopping list were procured; whether the subject consumed a specified amount of water or whether the subject consumed water; a number of meal plan meals consumed, a number of tasks completed, a total number of calories burned in one or more activities, a number of coaching calls completed, length of one or more coaching calls, a number or fraction of ingredients procured from a shopping list, a number of hydration events, or a total volume of hydration. 
     
     
         116 . The method of  claim 23 , wherein indicators of interactions between the user and certain aspects of the mobile device are selected from the group consisting of whether a meal plan was followed; whether a therapy regimen task was completed; whether a coaching call was completed; whether one or more, or all, ingredients on a shopping list were procured; whether the subject consumed a specified amount of water or whether the subject consumed water; a number of meal plan meals consumed, a number of tasks completed, a total number of calories burned in one or more activities, a number of coaching calls completed, length of one or more coaching calls, a number or fraction of ingredients procured from a shopping list, a number of hydration events, or a total volume of hydration. 
     
     
         117 . The apparatus of  claim 33 , wherein indicators of interactions between the user and certain aspects of the mobile device are selected from the group consisting of whether a meal plan was followed; whether a therapy regimen task was completed; whether a coaching call was completed; whether one or more, or all, ingredients on a shopping list were procured; whether the subject consumed a specified amount of water or whether the subject consumed water; a number of meal plan meals consumed, a number of tasks completed, a total number of calories burned in one or more activities, a number of coaching calls completed, length of one or more coaching calls, a number or fraction of ingredients procured from a shopping list, a number of hydration events, or a total volume of hydration.