WO2022174342A1 - Système et procédé pour générer une recommandation pour améliorer la santé mentale d'un utilisateur - Google Patents

Système et procédé pour générer une recommandation pour améliorer la santé mentale d'un utilisateur Download PDF

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Publication number
WO2022174342A1
WO2022174342A1 PCT/CA2022/050226 CA2022050226W WO2022174342A1 WO 2022174342 A1 WO2022174342 A1 WO 2022174342A1 CA 2022050226 W CA2022050226 W CA 2022050226W WO 2022174342 A1 WO2022174342 A1 WO 2022174342A1
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WO
WIPO (PCT)
Prior art keywords
user
application
accordance
data
gui
Prior art date
Application number
PCT/CA2022/050226
Other languages
English (en)
Inventor
Nikolay VASSEV
Jack BUNCE
Nicholas Martin
Simon ABOU-ANTON
Danielle WISE
Rick Barnett
Original Assignee
Mindleap Health Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Mindleap Health Inc. filed Critical Mindleap Health Inc.
Publication of WO2022174342A1 publication Critical patent/WO2022174342A1/fr

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
    • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/20—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring

Definitions

  • Telemedicine and telehealth involves the distribution of health-related services and information via electronic information and telecommunications technology.
  • Mental health professionals are known to hold videoconferences with their patients.
  • videoconferences still rely on the patient’s memory to recall the patient’s history of moods, emotions, habits and activities.
  • Video on Demand is a media distribution system that allows user to access selectable videos at any time using a television or personal computer without needing to use a traditional video playback device.
  • on-demand media content has not yet been put to good use in the realm of mental health services.
  • An object of the invention is to address the above shortcomings.
  • aspects disclosed herein may provide simplified and modernized mental health services through systems and software applications. For example, there may offered a first of its kind telemedicine, on demand mental health support through audio, and video programs combined with advanced mental health analytics.
  • the disclosure describes a computer-implemented method of generating a recommendation for improving mental health of a user, the method comprising: receiving user data indicative of a plurality of events and a plurality of time-stamps associated with the plurality of events, the plurality of events being indicative of at least one moods of the user, emotional states of the user, habits of the user, or activities of the user; determining a time-based trend associated with the plurality of events by using the user data; in response to the trend, determining a suggested activity to improve the mental health of the user; and transmitting alert data indicative of the suggested activity to a user device associated with the user to alert the user.
  • the disclosure describes a non-transitory computer-readable medium having stored thereon machine interpretable instructions which, when executed by a processor, cause the processor to perform the computer-implemented method.
  • FIG. 1 is a schematic architecture of an application for improving mental health of users (e.g. referred to as “mindleap” in some cases), in accordance with an embodiment
  • FIG. 2 is a schematic diagram of the infrastructure, showing a user interacting via HTTPS, in accordance with an embodiment
  • FIG. 3 is a first view 300 of a graphical user interface (GUI) of the application and/or system, in accordance with an embodiment
  • FIG. 4 is a second view of the GUI of the application and/or system configured to allow a user to enter their negative habits, in accordance with an embodiment
  • FIG. 5 is a third view of the GUI of the application and/or system configured to allow a user to enter their positive habits, in accordance with an embodiment
  • FIG. 6 is a fourth view of the GUI of the application and/or system showing a reminder screen, in accordance with an embodiment
  • FIG. 7 is a fifth view of the GUI of the application and/or system showing a login screen, in accordance with an embodiment
  • FIG. 12 is a ninth view of the GUI of the application and/or system showing a mood selection screen, in accordance with an embodiment
  • FIG. 13 is a tenth view of the GUI of the application and/or system showing a current habit editing screen, in accordance with an embodiment
  • FIG. 14 is an 11th view of the GUI of the application and/or system showing a habit adding screen, in accordance with an embodiment
  • FIG. 15 is a 12th view of the GUI of the application and/or system showing a habit adding screen with a negative habit selected for input, in accordance with an embodiment
  • FIG. 16 is a 13th view of the GUI of the application and/or system showing a habit adding screen with a negative habit being edited for input, in accordance with an embodiment
  • FIG. 17 is a 14th view of the GUI of the application and/or system showing a habit adding screen with a positive habit being edited for input, in accordance with an embodiment
  • FIG. 18 is a 15th view of the GUI of the application and/or system showing an emotion selection screen, in accordance with an embodiment
  • FIG. 19 is a 16th view of the GUI of the application and/or system showing an emotion adding screen, in accordance with an embodiment
  • FIG. 20 is a 17th view of the GUI of the application and/or system showing an emotion adding screen with a positive emotion being selected and entered, in accordance with an embodiment
  • FIG. 21 is an 18th view of the GUI of the application and/or system showing an emotion adding screen with another positive emotion being selected and entered, in accordance with an embodiment
  • FIG. 22 is an 19th view of the GUI of the application and/or system showing a goal selection screen, in accordance with an embodiment
  • FIG. 23 is an 20th view of the GUI of the application and/or system showing a journey tab screen, in accordance with an embodiment
  • FIG. 26 is a 23rd view of the GUI of the application and/or system showing a menu allowing sharing of current habits, moods, and goals of a user with a specialist (e.g. during a video call therewith), in accordance with an embodiment;
  • FIG. 29 is a 26th view of the GUI of the application and/or system showing a journey tab with scheduled sessions and tracked moods, habits, and goals, in accordance with an embodiment
  • FIG. 31 is a 28th view of the GUI of the application and/or system showing an emotion tracking screen obtained from the plus tab or button, in accordance with an embodiment
  • FIG. 32 is a 29th view of the GUI of the application and/or system showing a habit tracking screen obtained from the plus tab or button, in accordance with an embodiment
  • FIG. 33 is a 30th view of the GUI of the application and/or system showing a screen listing specialists, in accordance with an embodiment;
  • the list of all specialists may be generated once a user clicks on the Specialist tab on the bottom bar.
  • FIG. 35 is a 32nd view of the GUI of the application and/or system showing a search screen for specialists after a name has been entered into a search bar, in accordance with an embodiment
  • FIG. 36 is a 33rd view of the GUI of the application and/or system showing a fdtering tool for fdtering specialists based on one or more criteria, in accordance with an embodiment
  • FIG. 37 is a 34th view of the GUI of the application and/or system showing a fdtering tool for fdtering specialists based on one or more criteria, with a price range selected, in accordance with an embodiment
  • FIG. 38 is a 35th view of the GUI of the application and/or system showing a fdtering tool for fdtering specialists based on one or more criteria, including the ability to fdter specialists based on the type of session offered (introduction or full sessions, as shown), in accordance with an embodiment;
  • FIG. 39 is a 36th view of the GUI of the application and/or system showing a filtering tool for filtering specialists based on one or more criteria, including based on the specialization of the specialist, in accordance with an embodiment;
  • FIG. 40 is a 38th view of the GUI of the application and/or system showing a tool for saving favourite specialists, in accordance with an embodiment
  • FIG. 41 is a 39th view of the GUI of the application and/or system showing a profile of a specialist shown after selection of a particular specialists, in accordance with an embodiment
  • FIG. 43 is a 41st view of the GUI of the application and/or system showing a selected specialist’s presentation page, in accordance with an embodiment
  • the 40th view 4200 may be overlayed on the 41st view 4300.
  • FIG. 45 is a 43rd view of the GUI of the application and/or system showing a medical assessment screen where booking a session has been refused, in accordance with an embodiment
  • FIG. 47 is a 45th view of the GUI of the application and/or system showing first payment options including bundles, a final price, and an option to proceed, in accordance with an embodiment
  • FIG. 48 is a 46th view of the GUI of the application and/or system showing second payment options including available credit cards, in accordance with an embodiment
  • FIG. 49 is a 47th view of the GUI of the application and/or system showing a final payment screen, in accordance with an embodiment
  • FIG. 52 is a 50th view of the GUI of the application and/or system showing a daily reminder tab under the profile tab, in accordance with an embodiment
  • FIG. 53 is a 51st view of the GUI of the application and/or system showing a daily reminder tab allow a choice of a daily reminder time, in accordance with an embodiment
  • FIG. 54 is a 52nd view of the GUI of the application and/or system showing a main screen for a specialist allowing login and/or sign-up, in accordance with an embodiment
  • FIG. 56 is a 54th view of the GUI of the application and/or system showing a main settings section for a user and a qualifications section thereof, in accordance with an embodiment
  • FIG. 57 is a 55th view of the GUI of the application and/or system showing a main settings section for a user and documents uploaded to a qualifications section thereof, in accordance with an embodiment
  • FIG. 60 is a 58th view of the GUI of the application and/or system showing a main settings section for a user and a calendar section thereof showing settings for normal daily hours, in accordance with an embodiment
  • FIG. 61 is a 59th view of the GUI of the application and/or system showing a main settings section for a user and a calendar section thereof, in accordance with an embodiment
  • FIG. 62 is a 60th view of the GUI of the application and/or system showing a main settings section for a user and a calendar section thereof showing settings for working hours, in accordance with an embodiment
  • FIG. 63 is a 61st view of the GUI of the application and/or system showing a main settings section for a user and a calendar section thereof showing settings for normal daily hours after publishing of a specialist’s profde, in accordance with an embodiment
  • FIG. 66 is a 64th view of the GUI of the application and/or system showing editing of a session and an ability to add a note to a session in a specialist’s dashboard, in accordance with an embodiment
  • FIG. 67 is a 65th view of the GUI of the application and/or system showing a specialist’s dashboard and a notification indicating a client has joined a session, in accordance with an embodiment
  • FIG. 69 is a 67th view of the GUI of the application and/or system showing a specialist’s screen prior to joining a session with a client and a series of notes taken by the specialists about the client in a notes utility provided by the system/application, in accordance with an embodiment
  • FIG. 70 is a 68th view of the GUI of the application and/or system showing a screen during a session of a specialist with a client, in accordance with an embodiment
  • FIG. 71 is a 69th view of the GUI of the application and/or system showing a screen during a session of a specialist with a client and a series of notes (“My notes”) taken by the specialist and which may be associated with the client, in accordance with an embodiment;
  • FIG. 72 is a 70th view of the GUI of the application and/or system showing a screen during the session with no notes yet taken, in accordance with an embodiment
  • FIG. 73 is a 71st view of the GUI of the application and/or system showing a screen during the session and the client conducting sharing with the specialist, in accordance with an embodiment
  • FIG. 74 is a 72nd view of the GUI of the application and/or system showing a screen during the session and representation of data and/or data analytics (analysis) of the client displayed simultaneously with the audio/video call session (overlay ed on the screen or position adjacent to each other), in accordance with an embodiment
  • FIG. 76 is a 74th view of the GUI of the application and/or system showing an administrators panel managing a plurality of administrators, in accordance with an embodiment
  • FIG. 77 is a 75th view of the GUI of the application and/or system showing an administrators panel and creation of a new administrator profile, in accordance with an embodiment
  • FIG. 80 is a 78th view of the GUI of the application and/or system showing appointments associated with various specialists as viewed by an administrator, in accordance with an embodiment
  • FIG. 81 is a 79th view of the GUI of the application and/or system showing a discount section and a plurality of created discounts as viewed by an administrator, in accordance with an embodiment
  • FIG. 82 is an 80th view of the GUI of the application and/or system showing a screen for editing a (or adding a new) discount by an administrator, in accordance with an embodiment
  • FIG. 83 is an 81st view of the GUI of the application and/or system showing a screen listing reports as view by an administrator, in accordance with an embodiment
  • FIG. 87 is a schematic of a system, in accordance with an embodiment
  • FIG. 89 is a chart of a sensor-feature-behaviour hierarchy, in accordance with an embodiment
  • FIG. 90 is a plot of PHQ-9 score vs. entropy, in accordance with an embodiment
  • FIG. 91 is a plot of PHQ-9 score vs. total distance, in accordance with an embodiment
  • FIG. 92 is a plot of PHQ-9 score vs. normalized entropy, in accordance with an embodiment
  • FIG. 93 is a plot of PHQ-9 score vs. circadian movement, in accordance with an embodiment
  • FIG. 94 is a plot of PHQ-9 score vs. location variance, in accordance with an embodiment
  • FIG. 96 is a plot of PHQ-9 score vs. home stay, in accordance with an embodiment
  • FIG. 98 is a plot of PHQ-9 score vs. transition time, in accordance with an embodiment
  • FIG. 99 is a plot of PHQ-9 score vs. usage frequency, in accordance with an embodiment
  • FIG. 100 is an 84th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 101 is an 85th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 103 is an 87th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 104 is an 88th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 105 is an 89th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 106 is a 90th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 107 is a 91st view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 108 is a 92nd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 109 is a 93rd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 110 is a 94th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. Ill is a 95th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 112 is a 96th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 113 is a 97th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 114 is a 98th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 115 is a 99th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 116 is a 100th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 117 is a 101st view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 118 is a 102nd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 119 is a 103rd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 120 is a 104th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 121 is a 105th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 122 is a 106th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 123 is a 107th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 124 is a 108th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 125 is a 109th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 126 is a 110th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 127 is a 111st view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 128 is a 112nd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 129 is a 113rd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 130 is a 114th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 131 is a 115th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 132 is a 116th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 133 is a 117th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 134 is a 118th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 135 is a 119th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 136 is a 120th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 137 is a 121st view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 138 is a 122nd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 139 is a 123rd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 140 is a 124th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 141 is a 125th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 142 is a 126th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 143 is a 127th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 144 is a 128th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 145 is a 129th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 146 is a 130th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 147 is a 131st view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 148 is a 132nd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 149 is a 133rd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 150 is a 134th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 151 is a 135th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 152 is a 136th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 153 is a 137th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 154 is a 138th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 155 is a 139th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 156 is a 140th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 157 is a 141st view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 158 is a 142nd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 159 is a 143rd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 160 is a 145th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 161 is a 145th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 162 is a 146th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 163 is a 147th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 164 is a 148th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 165 is a 149th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 166 is a 150th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 167 is a 151st view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 168 is a 152nd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 169 is a 153rd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 170 is a 154th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 171 is a 155th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 172 is a 156th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 173 is a 157th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 174 is a 158th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 175 is a 159th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 176 is a 160th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 177 is a 161st view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 178 is a 162nd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 179 is a 163rd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 180 is a 164th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 181 is a 165th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 182 is a 166th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 183 is a 167th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 184 is a 168th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 185 is a 169th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 186 is a 170th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 187 is a 171st view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 188 is a 172nd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 189 is a 173rd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 190 is a 174th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 191 is a 175th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 192 is a 176th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 193 is a 177th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 194 is a 178th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 195 is a 179th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 196 is a 180th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 197 is a 181st view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 198 is a 182nd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 199 is a 183rd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 200 is a 184th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 201 is a 185th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 202 is a 186th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 203 is a 187th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 204 is a 188th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 205 is a 189th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 206 is a 190th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 207 is a 191st view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 208 is a 192nd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 209 is a 193rd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 210 is a 194th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 211 is a 195th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 212 is a 196th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 213 is a 197th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 214 is a 198th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 215 is a 199th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 216 is a 200th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 217 is a 201st view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 218 is a 202nd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 219 is a 203rd view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 220 is a 204th view of the GUI of the application and/or system, in accordance with an embodiment
  • FIG. 221 illustrates a block diagram of a computing device, in accordance with an embodiment of the present application.
  • FIG. 222 is a flow chart of a computer-implemented method of generating a recommendation for improving mental health of a user, the in accordance with an embodiment.
  • the following disclosure relates to methods, systems, and devices for generating recommendations for improving mental health of patients.
  • a system and/or software application may connect patients to specialists who can help them work through their most pressing problems and improve their wellbeing via private and confidential video calls.
  • the system and/or software application also leverages the latest technology tools to provide people advanced analytics surrounding their mental health and wellbeing combining over 21 different data sources.
  • the system and/or software application may be able to analyze and standardize this information to come up with a wellbeing score. This score can then be used by individuals for self-reflection, mental health specialists for personalized support, HR (Human Resources) departments for a view into wellbeing of their workforce and clinics as a way to prove treatment efficacy.
  • the application may include on demand digital programs, offering audio and video support that can help people improve their mental health and wellbeing.
  • the system may include visual dashboards for external parties like HR departments and clinics and also has intuitive community features that allow specialists to build stronger relationships with their clients.
  • the system/application in some embodiments may be operable to model the relationships between how users interact with their phone, user behaviors and the status of their mental health to make accurate, real-time predictions and alerts to contribute to improved wellbeing.
  • the application may allow anyone to mental healthcare providers by leveraging technology, i.e. mental health care on an individual’s own terms.
  • Today, customers may want increasing control of their healthcare. Freedom to access care virtually from the comfort and privacy of their homes may be desired.
  • Virtual mental healthcare is a new concept that is increasingly in-demand.
  • the system/application may provide a win-win situation for both mental health professionals and application users.
  • a user may be able to track their mood, track their goals, track their negative habits, purchase mental health treatments, find mental health specialists, purchase virtual consultations, schedule virtual consultations to improve well-being.
  • features may include: HIPAA compliant, GDPR compliant, virtual appointments are fully supported through secure HD video conferencing, international payment solution supporting currencies and all payment types, easy to use web portals for helping specialists build their profiles, multiple scheduling options - schedule consultations easily, the infrastructure is extremely stable, robust, have long term support and have a proven track record in high volume transactional ecosystems the platform in some embodiments meets scalability needs as the cliental base grows worldwide in some cases, an attractive user interface and extremely user friendly.
  • a computer-implemented method of generating a recommendation for improving the mental health of a user involves: (a) receiving by a computer processor into a computer memory user data that includes at least one of user-mood data, user-emotional-state data, user-habit data, and user-activity data, the user data further including time-stamp data; (b) determining by the processor a time-based trend associated with the user data; (c) in response to the trend, determining by the processor a suggested activity; and (d) transmitting by the processor the suggested activity to a user device associated with the user.
  • Step (c) may involve determining the suggested activity when the trend is a negative trend indicating a deterioration of the mental health of the user.
  • Step (c) may involve determining the suggested activity associated with a counteraction of the negative trend.
  • Step (c) may involve determining the suggested activity to be participation in an online educational program.
  • the method may further involve streaming by the processor to the user device the online educational program in response to receiving by the processor an on-demand request.
  • the method may further involve: (e) determining by the processor an alarm condition in response to the user data.
  • the method may further involve: (f) transmitting by the processor to a specialist device the alarm condition.
  • the method may further involve: (g) determining by the processor a mental-health score in response to the user data.
  • Step (g) may involve determining by the processor an alarm condition in response to the mental-health score.
  • Step (g) may involve determining an average mental-health score associated with a plurality of the users.
  • Step (b) may involve determining a plurality of the trends in response to a plurality of subsets of the user data, respectively, each of the subsets being associated with a different one of a plurality of the users, and determining an average trend in response to the plurality of trends.
  • Step (a) may involve receiving a user- selected reminder time.
  • Step (a) may involve receiving the user-habit data such that the user data includes at least one of a negative habit and a positive habit, and may involve receiving the user- emotional-state data such that the user data includes at least one of a negative emotion and a positive emotion.
  • Step (a) may involve receiving the user data such that the user data includes user-selected goal data.
  • Step (a) may involve receiving the user data such that the user data further includes user-device data received by the processor from a wearable device associated with the user.
  • Step (a) may involve receiving the user data such that the user data further includes additional data associated with the user, the additional data including at least one of weight data, biometric data, nutrition data, supplementation data, sleep data, community- connection data, meditation data, social-media consumption data, entertainment consumption data, stress-predictive data, eye-tracking data, EEG (Electroenchephalograhpy) data, ECG (electrocardiogram) data, genetic data, and blood-test data.
  • Step (a) may involve receiving the stress-predictive data such that the stress-predictive data includes user-smartphone interaction data.
  • the method may further involve determining by the processor a mental-health score in response to a combination of the user-mood data, the user-emotional-state data, the user-habit data, the user-activity data, the sleep data, the social-media consumption data, the entertainment consumption data, the community-connection data, the stress-predictive data, and the meditation data.
  • the method may further involve: (h) scheduling by the processor a videoconference between the user and a user-selected specialist.
  • the method may further involve communicating by the processor user-selected portions of the user data to a specialist device associated with the user-selected specialist.
  • FIG. 1 is a schematic architecture 100 of an application for improving mental health of users (e.g. referred to as “mindleap” in some cases), in accordance with an embodiment.
  • the application may employ a client-server architecture.
  • native Android mobile applications, iOS mobile applications and/or web applications may be the clients, and the backend may provide the application(s) with data via a REST API (Representational-State-Transfer Application-Program-Interface).
  • REST API Real-State-Transfer Application-Program-Interface
  • the web application for specialists and the admin panel may be frontend applications written in Angular and operable to connect to the REST API provided by the backend.
  • communication between apps and the backend may be secured with the latest or up-to-date version of HTTPS/TLS (Hypertext Transfer Protocol Secure/Transport Layer Security) protocol.
  • HTTPS/TLS Hypertext Transfer Protocol Secure/Transport Layer Security
  • the data in some embodiments are stored in a PostgreSQL database.
  • the system in some embodiments is deployed on the AmazonTM cloud infrastructure and in some embodiments utilizes AWS (AmazonTM Web Services) dedicated services as is possible (ex. a managed PostgreSQL database service).
  • AWS AmazonTM Web Services
  • cloud services may be services such as AWS and/or Microsoft AzureTM.
  • a backend/ Application Programming Interface may include one or more of the following components (or be associated with related features):
  • a web admin panel and specialist web application may be associated with one or more of the following features:
  • an iOS mobile app may be associated with one or more of the following features:
  • external integration may include integration with one or more of the following features, components and/or services:
  • preventing unauthorized product access may be achieved by outsourced processing, physical and environmental security, authentication, authorization, and/or API access as described below.
  • the application and/or system
  • the application may host its Service with AWS or another outsourced cloud infrastructure provider.
  • the application/system may maintain contractual relationships BAA (Business Associate Agreement) with vendors to provide the Service in accordance with the Data Processing Agreement.
  • BAA Business Associate Agreement
  • the application/system may rely on contractual agreements, privacy policies, and vendor compliance programs to protect data processed or stored by these vendors.
  • the application/system may host product infrastructure with multi-tenant, outsourced infrastructure providers.
  • Physical and environmental security controls may be audited for HIPAA/GDPR/PIPEDA (Health Insurance Portability and Accountability Act/General Data Protection Regulation/Personal Information Protection and Electronic Documents Act) compliance, among other certifications.
  • HIPAA/GDPR/PIPEDA Health Insurance Portability and Accountability Act/General Data Protection Regulation/Personal Information Protection and Electronic Documents Act
  • the application/system may implement a uniform password policy for its customer products. Users using a mobile application may be required to authenticate to save their information. On the other hand, specialists who interact with the products via the Specialist web interface may be required to authenticate before accessing non-public customer data.
  • customer data may be stored in multi-tenant storage systems accessible to customers via only application user interfaces and application programming interfaces. Customers may not be allowed direct access to the underlying application infrastructure.
  • the authorization model in each of the associated products may be operable to ensure that only the appropriately assigned individuals access relevant features, views, and customization options.
  • Authorization to data sets may be performed through validating the user’s permissions against the attributes associated with each data set.
  • public product APIs may be accessed using an API key or through Oauth authorization.
  • preventing unauthorized product use may be achieved by access controls, intrusion detection and prevention, static code analysis, and/or penetration testing.
  • the application/system may implement access controls and detection capabilities for the internal networks that support its products.
  • network access control mechanisms may (in some embodiments) prevent network traffic using unauthorized protocols from reaching the product infrastructure.
  • the technical measures implemented may differ between infrastructure providers and may include Virtual Private Cloud (VPC) implementations, security group assignment, and/or traditional firewall rules.
  • VPC Virtual Private Cloud
  • the application/system may implement a Web Application Firewall (WAF) solution to protect hosted customer websites and other internet- accessible applications.
  • WAF Web Application Firewall
  • the WAF may be operable to identify and prevent attacks against publicly available network services.
  • source code repositories may be performed, and checking for coding best practices and identifiable software flaws may be carried out.
  • the application/system may include employing industry recognized penetration testing service providers to run four annual penetration tests.
  • the penetration tests may facilitate identifying and resolving foreseeable attack vectors and potential abuse scenarios.
  • limitations of privilege & authorization requirements may be provided via product access.
  • a subset of the associated employees may have access to the products and to customer data via controlled interfaces.
  • the intent of providing access to a subset of employees may be to provide effective customer support, to troubleshoot potential problems, to detect and respond to security incidents and implement data security.
  • Access may be enabled through “just in time” requests for access; all such requests are logged.
  • Employees may be granted access by role, and reviews of high-risk privilege grants are initiated daily. Employee roles may be reviewed at least once every six months.
  • transmission control may be achieved in-transit and at-rest.
  • the application/system may utilize HTTPS encryption (also referred to as SSL (Secure Sockets Layer) or TLS) available on every one of its login interfaces and for free on every customer site hosted on the associated products.
  • HTTPS implementation may use algorithms and certificates.
  • at-rest the application/system may store user passwords following policies for security.
  • the application/system may be implemented technologies to ensure that stored data is encrypted at rest.
  • input control may be achieved detection, and response and tracking.
  • the application/system may include (in some embodiments) infrastructure operable to log extensive information about the system behavior, traffic received, system authentication, and other application requests.
  • Internal systems aggregated log data and alert appropriate employees of malicious, unintended, or anomalous activities.
  • Various personnel, including security, operations, and support personnel, may be responsive to known incidents.
  • the application/system may maintain (in some embodiments) a record of known security incidents that includes description, dates and times of relevant activities, and incident disposition.
  • suspected and confirmed security incidents may be investigated by security, operations, or support personnel; and appropriate resolution steps may be identified and documented.
  • the system/application in some embodiments may take appropriate steps to minimize product and Customer damage or unauthorized disclosure. Notification to Customer in some embodiments is in accordance with the terms of the DPA or Agreement.
  • availability control may be achieved by ensuring infrastructure availability, fault tolerance, and online replicas and backups.
  • AWS may use (or be asked to use) commercially reasonable efforts to make the Included Services each available for each AWS region with a Monthly Uptime Percentage of at least 99.99%, in each case during any monthly billing cycle.
  • the application/system may use Multi-Zone and Multi-Region features to minimize any downtime.
  • backup and replication features may be operable to ensure redundancy and fail-over protections during a significant processing failure.
  • customer data may be backed up to multiple durable data stores and replicated across multiple availability zones.
  • production databases are operable to replicate data between no less than 1 primary and 1 secondary database. All databases may be backed up and maintained.
  • associated products may be, in some embodiments, operable to ensure redundancy and seamless failover.
  • the server instances that support the products are also architected for advantageously preventing single points of failure. This assists the inventive operations in maintaining and updating the product applications and backend while limiting downtime.
  • FIG. 2 is a schematic diagram 200 of the infrastructure, showing a user 210 interacting via HTTPS, in accordance with an embodiment.
  • the infrastructure may include one or more Elastic Containers (EC) and/or Elastic Container Services (ECS).
  • EC Elastic Containers
  • ECS Elastic Container Services
  • a memory circuit in accordance with an embodiment may contain blocks of code comprising computer program instructions for directing a processor of the invention to perform inventive operations.
  • the application/system may be configured to act as a “buddy” and/or a personal assistant and/or therapist.
  • FIG. 3 is a first view 300 of a graphical user interface (GUI) of the application and/or system, in accordance with an embodiment.
  • GUI graphical user interface
  • the GUI may be implemented using a computing device configured to be operable via input and output interfaces.
  • the GUI may provide access to such input and output interfaces.
  • the computing device may be a smartphone or a tablet PC.
  • FIG. 4 is a second view 400 of the GUI of the application and/or system configured to allow a user to enter their negative habits, in accordance with an embodiment.
  • FIG. 5 is a third view 500 of the GUI of the application and/or system configured to allow a user to enter their positive habits, in accordance with an embodiment.
  • the application/system may be a user’s day to day buddy to track their wellbeing and may thereby start by helping the user setup their habits and their goals.
  • habit may come in two flavors or classes: positive and negative.
  • goals of a user may be those aspects that a user would like to improve in their behavior.
  • a user may be able to enter their mood, emotions (negative or positive emotions), what improvement in their habits they may have achieved or wish to achieve.
  • a user may have the ability to change their goals.
  • the lower bar of the GUI of the software application may comprise 4 parts: Home, Journey, Specialists, Profde. Additional a “+” may be provided, as will be explained in detail below.
  • the application/system may be configured for gamification to facilitate improvement in mental health.
  • users may find their scores starting from 0 Points before they sign up and it may be configured to increase with every activity. Users may be able to collect points, this activity having been created to motivate users to improve their wellbeing.
  • the application/system may be configured to rewards users based on their achievements with some tangible rewards such as discounts on sessions booking, swag or others.
  • TABLE 1 explains the different level that a user may achieve, in some embodiments.
  • TABLE 2 shows the activities and the relevant points collected for the corresponding activities, in some embodiments.
  • Event Type one Type of badge Number off/repeatable if applicable of points Finish Self Assessment One time 30 Buy first session One time 30
  • FIG. 6 is a fourth view 600 of the GUI of the application and/or system showing a reminder screen, in accordance with an embodiment.
  • the user may be able to enter a reminder to update the tracking.
  • FIG. 7 is a fifth view 700 of the GUI of the application and/or system showing a login screen, in accordance with an embodiment.
  • to save the data entered users must sign up to the software application.
  • FIG. 8 is a fifth view 800 of the GUI of the application and/or system showing a first login/sign- up screen, in accordance with an embodiment.
  • FIG. 9 is a sixth view 900 of the GUI of the application and/or system showing a second login/sign-up screen, in accordance with an embodiment.
  • sign-up and login may be provided.
  • the application/system may make it easy for users to start exploring the application without signing up right away .
  • This feature in some embodiments may be operable to give users the chance to learn about the software application without having to share their information .
  • users Once users decide to keep moving with the application they have to sign up and the invention would provide users with multiple options to sign up to the application such as FacebookTM, GoogleTM and AppleTM.
  • users may have the option to sign up using their email address by sharing some basic information such as first name, last name, email address and password.
  • FIG. 10 is a seventh view 1000 of the GUI of the application and/or system showing a home screen, in accordance with an embodiment.
  • FIG. 11 is an eighth view 1100 of the GUI of the application and/or system showing a mood tracking (or historical view of mood) screen, in accordance with an embodiment.
  • FIG. 12 is a ninth view 1200 of the GUI of the application and/or system showing a mood selection screen, in accordance with an embodiment.
  • FIG. 13 is a tenth view 1300 of the GUI of the application and/or system showing a current habit editing screen, in accordance with an embodiment.
  • FIG. 14 is an 11th view 1400 of the GUI of the application and/or system showing a habit adding screen, in accordance with an embodiment.
  • FIG. 15 is a 12th view 1500 of the GUI of the application and/or system showing a habit adding screen with a negative habit selected for input, in accordance with an embodiment.
  • FIG. 16 is a 13th view 1600 of the GUI of the application and/or system showing a habit adding screen with a negative habit being edited for input, in accordance with an embodiment.
  • FIG. 17 is a 14th view 1700 of the GUI of the application and/or system showing a habit adding screen with a positive habit being edited for input, in accordance with an embodiment.
  • FIG. 18 is a 15th view 1800 of the GUI of the application and/or system showing an emotion selection screen, in accordance with an embodiment.
  • FIG. 19 is a 16th view 1900 of the GUI of the application and/or system showing an emotion adding screen, in accordance with an embodiment.
  • FIG. 20 is a 17th view 2000 of the GUI of the application and/or system showing an emotion adding screen with a positive emotion being selected and entered, in accordance with an embodiment.
  • FIG. 21 is an 18th view 2100 of the GUI of the application and/or system showing an emotion adding screen with another positive emotion being selected and entered, in accordance with an embodiment.
  • FIG. 22 is an 19th view 2200 of the GUI of the application and/or system showing a goal selection screen, in accordance with an embodiment.
  • FIG. 23 is an 20th view 2300 of the GUI of the application and/or system showing a journey tab screen, in accordance with an embodiment.
  • journey under Journey (or the Journey tab), users may be able to review daily the information that they have entered already. In addition, any session with specialists may be reflected therein. In short, journey may be the diary of the user in various embodiments.
  • a user may be able to start their session with the specialist they booked with.
  • FIG. 24 is a 21st view 2400 of the GUI of the application and/or system showing interaction of a user with a specialist (care professional or healthcare professional) via text, e.g. prior to launching a session, in accordance with an embodiment.
  • a specialist care professional or healthcare professional
  • FIG. 25 is a 22nd view 2500 of the GUI of the application and/or system showing interaction of a user with a specialist (care professional or healthcare professional) via video call with a button for facilitating sharing of the user’s current habits, in accordance with an embodiment.
  • FIG. 26 is a 23rd view 2600 of the GUI of the application and/or system showing a menu allowing sharing of current habits, moods, and goals of a user with a specialist (e.g. during a video call therewith), in accordance with an embodiment.
  • FIG. 27 is a 24th view 2700 of the GUI of the application and/or system showing interaction of a user with a specialist (care professional or healthcare professional) via video call without a button for facilitating sharing of the user’s current habits, in accordance with an embodiment.
  • a specialist care professional or healthcare professional
  • FIG. 28 is a 25th view 2800 of the GUI of the application and/or system showing a post interaction screen of a user showing several options provided to the user, in accordance with an embodiment.
  • a user may typically launch the session on their end using a user device.
  • the specialist may join in after that.
  • users may share the analytics from their journey and that include their Habits, Mood tracking and Goals. This information may help the specialist learn more about their patients what would give them an edge to provide service. Patients may be in control of their data and they may stop sharing at any time.
  • Specialists and Patients may be able to do text chatting during their video session to exchange some information.
  • FIG. 29 is a 26th view 2900 of the GUI of the application and/or system showing a journey tab with scheduled sessions and tracked moods, habits, and goals, in accordance with an embodiment.
  • FIG. 30 is a 27th view 3000 of the GUI of the application and/or system showing a mood tracking screen obtained from the plus tab or button, in accordance with an embodiment.
  • FIG. 31 is a 28th view 3100 of the GUI of the application and/or system showing an emotion tracking screen obtained from the plus tab or button, in accordance with an embodiment.
  • FIG. 32 is a 29th view 3200 of the GUI of the application and/or system showing a habit tracking screen obtained from the plus tab or button, in accordance with an embodiment.
  • users may be able to choose from a wide range of highly qualified specialists to connect with and work with them on improving their wellbeing. Once the client finds the right specialist, they may book a session with them and connect via a HIPAA compliant video conferencing session. In addition to the video chatting, patients may be able to do text chatting and to share confidentially their data with their specialist. The data shared may be the collection of inputs entered by users during their own health tracking using the app.
  • This feature of sharing the data may help make the specialists more informed about the history of the patients what would help them devise the appropriate curing techniques. It is important to stress that sharing the data may only take place if a consent has been awarded by the patient.
  • the system/application may offer different tools to narrow down the list.
  • FIG. 33 is a 30th view 3300 of the GUI of the application and/or system showing a screen listing specialists, in accordance with an embodiment.
  • the list of all specialists may be generated once a user clicks on the Specialist tab on the bottom bar.
  • FIG. 34 is a 31st view 3400 of the GUI of the application and/or system showing a search screen for specialists, in accordance with an embodiment.
  • FIG. 35 is a 32nd view 3500 of the GUI of the application and/or system showing a search screen for specialists after a name has been entered into a search bar, in accordance with an embodiment.
  • users may be able to search for a specialist by their name or a specific word that could part of their profile such as experience or even education.
  • FIG. 36 is a 33rd view 3600 of the GUI of the application and/or system showing a filtering tool for filtering specialists based on one or more criteria, in accordance with an embodiment.
  • FIG. 37 is a 34th view 3700 of the GUI of the application and/or system showing a filtering tool for filtering specialists based on one or more criteria, with a price range selected, in accordance with an embodiment.
  • FIG. 38 is a 35th view 3800 of the GUI of the application and/or system showing a filtering tool for filtering specialists based on one or more criteria, including the ability to filter specialists based on the type of session offered (introduction or full sessions, as shown), in accordance with an embodiment.
  • FIG. 39 is a 36th view 3900 of the GUI of the application and/or system showing a filtering tool for filtering specialists based on one or more criteria, including based on the specialization of the specialist, in accordance with an embodiment.
  • specialization may refer to a medical specialization or a category of a specialist (social worker, psychiatrist, and so on).
  • users may be able to filter specialists based on the price range, type of sessions, and specialization.
  • FIG. 40 is a 38th view 4000 of the GUI of the application and/or system showing a tool for saving favourite specialists, in accordance with an embodiment.
  • users could save a specialist to their favorite by clicking the heart on their profile. With this feature, users could come back to their favorite list and book a session.
  • booking a session may be facilitates via the application/system.
  • FIG. 41 is a 39th view 4100 of the GUI of the application and/or system showing a profile of a specialist shown after selection of a particular specialists, in accordance with an embodiment.
  • FIG. 42 is a 40th view 4200 of the GUI of the application and/or system showing available specialists, in accordance with an embodiment.
  • FIG. 43 is a 41st view 4300 of the GUI of the application and/or system showing a selected specialist’s presentation page, in accordance with an embodiment.
  • the 40th view 4200 may be overlay ed on the 41st view 4300.
  • FIG. 44 is a 42nd view 4400 of the GUI of the application and/or system showing a calendar of available and not available appointments (distinguished by shadowing on buttons representing sessions) of a selected specialist, in accordance with an embodiment.
  • the patient may transmit a request to see a counsellor as soon as he or she is available.
  • the system /application may receive the request and then carry out processor actions such that it then shows (via the GUI) the time slots available by the doctor to show up for the user to allow easy scheduling.
  • the system offers open time slots the medical professional has made available to see patients.
  • FIG. 45 is a 43rd view 4500 of the GUI of the application and/or system showing a medical assessment screen where booking a session has been refused, in accordance with an embodiment.
  • FIG. 46 is a 44th view 4600 of the GUI of the application and/or system showing a medical assessment screen to be fdled in, in accordance with an embodiment.
  • two critical conditions may be required to be met in order to be able to complete the session registration. Firstly, patients may have to be 13 years old and older, otherwise they may not be allowed to proceed and should have to clearly acknowledge their age status. Secondly, if patients are in a Life Threatening Situation they may not be allowed to proceed with the booking. They may be directed to contact an emergency life line for immediate help. The system/application and associated specialists may take no responsibility for users booking a session in such situations.
  • FIG. 47 is a 45th view 4700 of the GUI of the application and/or system showing first payment options including bundles, a final price, and an option to proceed, in accordance with an embodiment.
  • FIG. 48 is a 46th view 4800 of the GUI of the application and/or system showing second payment options including available credit cards, in accordance with an embodiment.
  • FIG. 49 is a 47th view 4900 of the GUI of the application and/or system showing a final payment screen, in accordance with an embodiment.
  • FIG. 50 is a 48th view 5000 of the GUI of the application and/or system showing a profile tab, in accordance with an embodiment.
  • FIG. 51 is a 49th view 5100 of the GUI of the application and/or system showing a notification tab under the profile tab, in accordance with an embodiment.
  • FIG. 52 is a 50th view 5200 of the GUI of the application and/or system showing a daily reminder tab under the profile tab, in accordance with an embodiment.
  • FIG. 53 is a 51st view 5300 of the GUI of the application and/or system showing a daily reminder tab allow a choice of a daily reminder time, in accordance with an embodiment.
  • users may be able to check multiple features such as Notifications, Habits, Emotions, Goals, Level, Health questions, and others.
  • the current tabs available may be as follows:
  • FIGS. 54-75 shows various views of specialist panels of the GUI.
  • FIG. 54 is a 52nd view 5400 of the GUI of the application and/or system showing a main screen for a specialist allowing login and/or sign-up, in accordance with an embodiment.
  • FIG. 55 is a 53rd view 5500 of the GUI of the application and/or system showing a main sehings section for a user and an account sehings section thereof, in accordance with an embodiment.
  • FIG. 56 is a 54th view 5600 of the GUI of the application and/or system showing a main sehings section for a user and a qualifications section thereof, in accordance with an embodiment.
  • FIG. 57 is a 55th view 5700 of the GUI of the application and/or system showing a main sehings section for a user and documents uploaded to a qualifications section thereof, in accordance with an embodiment.
  • FIG. 58 is a 56th view 5800 of the GUI of the application and/or system showing a main sehings section for a user and a services section thereof, in accordance with an embodiment.
  • FIG. 59 is a 57th view 5900 of the GUI of the application and/or system showing a main settings section for a user and a finances section thereof, in accordance with an embodiment.
  • FIG. 60 is a 58th view 6000 of the GUI of the application and/or system showing a main settings section for a user and a calendar section thereof showing settings for normal daily hours, in accordance with an embodiment.
  • FIG. 61 is a 59th view 6100 of the GUI of the application and/or system showing a main settings section for a user and a calendar section thereof, in accordance with an embodiment.
  • FIG. 62 is a 60th view 6200 of the GUI of the application and/or system showing a main settings section for a user and a calendar section thereof showing settings for working hours, in accordance with an embodiment.
  • FIG. 63 is a 61st view 6300 of the GUI of the application and/or system showing a main settings section for a user and a calendar section thereof showing settings for normal daily hours after publishing of a specialist’s profile, in accordance with an embodiment.
  • FIG. 64 is a 62nd view 6400 of the GUI of the application and/or system showing a specialist’s dashboard, in accordance with an embodiment.
  • FIG. 65 is a 63rd view 6500 of the GUI of the application and/or system showing a specialist’s dashboard and a timeline of events, in accordance with an embodiment.
  • FIG. 66 is a 64th view 6600 of the GUI of the application and/or system showing editing of a session and an ability to add a note to a session in a specialist’s dashboard, in accordance with an embodiment.
  • FIG. 67 is a 65th view 6700 of the GUI of the application and/or system showing a specialist’s dashboard and a notification indicating a client has joined a session, in accordance with an embodiment.
  • FIG. 68 is a 66th view 6800 of the GUI of the application and/or system showing a specialist’s screen prior to joining a session with a client, in accordance with an embodiment.
  • FIG. 69 is a 67th view 6900 of the GUI of the application and/or system showing a specialist’s screen prior to joining a session with a client and a series of notes taken by the specialists about the client in a notes utility provided by the system/application, in accordance with an embodiment.
  • FIG. 70 is a 68th view 7000 of the GUI of the application and/or system showing a screen during a session of a specialist with a client, in accordance with an embodiment.
  • FIG. 71 is a 69th view 7100 of the GUI of the application and/or system showing a screen during a session of a specialist with a client and a series of notes (“My notes”) taken by the specialist and which may be associated with the client, in accordance with an embodiment.
  • FIG. 72 is a 70th view 7200 of the GUI of the application and/or system showing a screen during the session with no notes yet taken, in accordance with an embodiment.
  • FIG. 73 is a 71st view 7300 of the GUI of the application and/or system showing a screen during the session and the client conducting sharing with the specialist, in accordance with an embodiment.
  • FIG. 74 is a 72nd view 7400 of the GUI of the application and/or system showing a screen during the session and representation of data and/or data analytics (analysis) of the client displayed simultaneously with the audio/video call session (overlayed on the screen or position adjacent to each other), in accordance with an embodiment.
  • FIG. 75 is a 73rd view 7500 of the GUI of the application and/or system showing a financials screen for a specialist showing daily appointments with patients and amounts paid from the patient(s), in accordance with an embodiment.
  • administrators may be able to create a new specialist under this tab and provide the credentials by email.
  • FIG. 76 is a 74th view 7600 of the GUI of the application and/or system showing an administrators panel managing a plurality of administrators, in accordance with an embodiment.
  • FIG. 77 is a 75th view 7700 of the GUI of the application and/or system showing an administrators panel and creation of a new administrator profile, in accordance with an embodiment.
  • FIG. 78 is a 76th view 7800 of the GUI of the application and/or system showing an administrators panel showing an account profile of an administrator, in accordance with an embodiment.
  • FIG. 79 is a 77th view 7900 of the GUI of the application and/or system showing an administrators panel showing editing of a administrator’s account profde, in accordance with an embodiment.
  • an appointments tab which may provide an effective tool for administrators to check the status of appointments and may facilitate exporting the list of appointment into an excel spreadsheet, including with additional details.
  • FIG. 80 is a 78th view 8000 of the GUI of the application and/or system showing appointments associated with various specialists as viewed by an administrator, in accordance with an embodiment.
  • FIG. 81 is a 79th view 8100 of the GUI of the application and/or system showing a discount section and a plurality of created discounts as viewed by an administrator, in accordance with an embodiment.
  • FIG. 82 is an 80th view 8200 of the GUI of the application and/or system showing a screen for editing a (or adding a new) discount by an administrator, in accordance with an embodiment.
  • dictionary sections may be provided for each of emotions, goals, and habits, respectively.
  • Each dictionary may include a plurality of associated words, e.g. an Emotion dictionary may include “Calm”, “Content”, “Glad”, “Enthusiastic”, and so on.
  • Each word in a dictionary may be edited or deleted, and new words may be added to a dictionary.
  • FIG. 83 is an 81st view 8300 of the GUI of the application and/or system showing a screen listing reports as view by an administrator, in accordance with an embodiment.
  • FIG. 84 is an 82nd view 8400 of the GUI of the application and/or system showing a screen of reports associated with a specialist as view by an administrator, in accordance with an embodiment.
  • FIG. 85 is an 83rd view 8500 of the GUI of the application and/or system showing a screen where the status of a specialist may be changed and/or notes added on the specialist by an administrator, in accordance with an embodiment.
  • FIG. 86 is a schematic of a system 8600 showing interfacing between data and devices, in accordance with an embodiment.
  • a web/mobile application (or app) and backend may communicate with health applications and devices, send/receive medical record, and send/receive lab results. Sending and receiving via the API may allow standardization of data and/or data types.
  • products may be updated to meet consumers requests.
  • a product such as an application and/or system that provides patients and specialists with advanced capabilities that can improve treatment outcomes may be provided.
  • the tangible benefits of mental health coaching and therapy may be desired.
  • services may be sold at a price that is too high for some people, which is undesirable.
  • the application/system may make world-class mental health technologies and services available to many users, a series of free and paid digital products may be launched.
  • these products may be available at a significantly cheaper price point than coaching while still creating similarly lasting and impactful results for users.
  • Each of these programs may feature daily audio and/or video sessions created by experts (e.g. world-class, leadership) in each of the respective topics.
  • these on-demand offerings may be offered alongside the live telemedicine sessions to provide more value to customers.
  • a major part of a software application may be the combination of various data sources into the platform in order to provide the ability to have intelligent systems provide automated actions to users based on certain behaviors.
  • a platform may combine certain medical records and various wearable devices (including devices from FitbitTM, GarminTM, AppleTM Watch, HuaweiTM, SamsungTM, LGTM, SonyTM, GoogleTM, MicrosoftTM, MisfitTM ) with software and services to give:
  • the system and/or software application may then control how the data is displayed, analyzed, and shared.
  • the system and/or application may pull data from hundreds of clinical and consumer health devices as well as provide a manual import of CSV fries.
  • the software application may automatically standardize the data into the system to allow ( inter alia) manual and automated analysis.
  • the system/application may integrate data from BluetoothTM Smart devices, Apple’sTM Health Kit, and GoogleTM Health kit into the iOSTM and AndroidTM mobile applications.
  • data may then be made available to clients via the mobile apps and specialists, researchers, & clinics through the web portal.
  • fitness data may provide insight into user’s activities that are undertaken with the purpose of exercising. These activities may have a defined duration, time, distance, calories, elevation, etc. Activities include running, biking, walking, swimming, elliptical, rowing, skating, and so on.
  • routine data may provide insight into user’s activities that occur regularly throughout the day, without the specific goal of exercising. These activities are able to provide a regular and daily aggregate of calories burned, distance traveled, water consumed, steps taken, stairs climbed, and so on.
  • Weight data may provide insight into a user’s weight, height, and body mass measurements. Detailed values may include fat percentage, BMI, free mass, and so on.
  • biometric data may provide insight into a user’s vitals data taken routinely or situationally. These measurements may provide insights into a person’s blood pressure, cholesterol, heart rate, blood and hormone levels, and so on.
  • nutrition data may provide insight into a user’s activities related to calorie intake and consumption, as well as ability to meet nutritional needs with diet. Details may include the amount of carbohydrates, fat, protein, sodium, water, and fiber consumed by a person, as well as the meal name.
  • supplementation data may involve the micronutrients users get from various foods, supplements, and nutraceuticals they are taking. Research has demonstrated that nutrient intake (i.e. Vitamins) can have significant effects on improving mental health.
  • sleep data may provide insight into measurements related to the length of time spent in various sleep cycles, as well as the number of times a person wakes during the night.
  • Total sleep, REM sleep, deep sleep and other values may be included in the data delivered to provide sleep insights.
  • analysis of community-related activities and events may be conducted. Analyzing calendar data for post work activities, looking at post work phone calls and text messages may be conducted. In some cases, such actions may be conducted together with collection and/or reception of self-reported data.
  • analysis of meditation may be carried out. Looking at the amount of time a person spends meditating, looking at program usage associated with embodiments of the application/system, self-reported data, and connectivity to the (e.g. top) meditation software applications may be conducted.
  • social media consumption may be considered. Looking at the amount of time each person spends on FBTM, instagramTM, TwitterTM, ParlerTM, PintrestTM, SnapchatTM, Tik TokTM may be conducted.
  • entertainment consumption may be considered. Looking at the amount of time each person spends on YoutubeTM, NetflixTM, AmazonTM Prime, HBOTM Max, DisneyTM Plus and all other streaming services including sports applications may be carried out.
  • stress may be estimated, measured, and/or inferred.
  • the software application may be operable to automatically collect and analyze calendar, notifications, usage of phone for productivity and blood pressure (hypertension) among other factors to predict stress.
  • habit data that is self-reported within the software application may be considered.
  • emotion data that is self-reported within the software application may be considered.
  • mood data that is self-reported within the software application may be considered.
  • meditation Data - time spent meditating may be considered.
  • the system/application may used for collection of scientific data for research.
  • the system/application may also incorporate additional data sources in each patient profile to help support clinical trials and research studies performed by researchers.
  • This data collection may be aimed at evaluating if a medical, or behavioral interventions has certain affect mental health.
  • This data may be used by researchers so they can learn if a new treatment is more effective and/or has less harmful side effects than other treatments. For example, eye tracking data, EEG data, ECG data, genetic data, and/or blood testing data may be collected.
  • Eye Tracking Data - Eye tracking is the process of measuring either the point of gaze or the motion of an eye relative to the head.
  • EEG Data - Electroencephalography is an electrophysiological monitoring method to record electrical activity of the brain.
  • ECG Data - An electrocardiogram (ECG) is a simple test that can be used to check the heart's rhythm and electrical activity. Sensors from wearables are used to detect the electrical signals produced by the heart.
  • Genetic Data - Studying the genes in a patient's cells may help researchers select the best treatment for patients that have certain genetic changes from new treatments.
  • Blood Testing Data Integrating data from the most common blood tests into each patient profile.
  • one or more of the blood tests below are available: Complete Blood Count, Prothrombin Time, Basic Metabolic Panel, Comprehensive Metabolic Panel, Lipid Panel, Liver Panel, Thyroid Stimulating Hormone, Hemoglobin AIC.
  • the system/application may include aspects related to data processing, standardization, visualization.
  • the system/application may be operable to establish a standardized data model to be used to process information in a unified way.
  • the system may be operable to utilize a technology that allows the creation of production level business intelligence dashboards.
  • this service may use “Super-fast, Parallel, In-memory Calculation Engine” which may be known as SPICETM to perform the computations and create graphs.
  • FIG. 87 is a schematic of a system 8700, in accordance with an embodiment.
  • data sources may include S3, Redshift, and/or Direct Upload.
  • the system/application may create visualizations for its end users within the Mobile application and utilize visualizations out of the analytics tool for Specialist, Corporate and Clinic clients.
  • the system/application may generate an inventive well-being (or wellbeing) score.
  • the system/application may be operable to combine the most important data points that affect each person’s wellbeing and run it through an inventive algorithm to determine a standardized wellbeing score. Individual users may use this score for personal reflection/growth and also choose to share that data with their practitioner in order to help improve treatment outcomes. In various embodiments, this data may also be provided to researchers, HR departments upon user consent.
  • a mood chart may be provided at the GUI. In some embodiments, the mood chart may provide a scoring of 75 for a very good mood, for example.
  • wellbeing visualizations may be provided for or in the form of one or more of the following: Mood Chart , % Positive Emotions, % Positive Habits, Time spent on Social Media, Time spent on Entertainment, Time spent Exercising, Time Spent Sleeping, Time Spent Meditating, Movement and Steps, Most Common Habits, Most Common Emotions, Stress Level.
  • the Wellbeing Score in some embodiments combines data from: Moods, Emotions, Habits, Sleep, Movement, Exercise, Social Media Consumption, Entertainment Consumption, Community, Stress, meditation
  • the inventive Wellbeing score may allow users, specialists, researchers, corporate clients to: Measure progress, Gauge the effectiveness of treatments, Sustain motivation, Form healthy habits, Understand how lifestyle choices are affecting wellbeing, Make informed decisions about what to do to improve
  • Wellbeing snapshot This capability allows users to toggle Across weekly, monthly, yearly stats. The monthly score is only available after 1 week is completed, the yearly after 3 months of usage.
  • Inventive Score Descriptors may be as follows: 100-90 - Amazing!; 89-75 - Very Good; 74-65 - Good; 64 and less Needs Improvement.
  • the application/system may allows for individual defined goals. For example, the recommended sleep time to achieve 100% of points is 7 hours but it can be adjusted based on each individual person’s goals.
  • automated alerts may be provided.
  • reminders and alerts in the form of push notifications, email and SMS (Short Message Service) may be used to reach patients and provide personalized wellbeing recommendations. These alerts also may be sent to mental health specialists and other parties to provide proactive interventions based on certain factors such as user behavior, data from the data sources, wellbeing score, self- assessment data and artificial intelligence algorithm determination.
  • the system/application may support patient self-management and shared accountability for outcomes by improving end-user adherence to treatments.
  • the inventive Alerts and reminders provide timely and relevant information which has the potential to improve health outcomes. Alerts and reminders are delivered in several ways that reflect the urgency of the information based on how they are delivered. Alerts can be scheduled, or real time and the system has conditions for triggering the alert based on result or result field counts. When a set of results meets the trigger conditions, the alert can trigger one time or once for each of the results.
  • alerts that the system can make:
  • Behavior recommendations based on change in mental health data sources Behavior recommendations based on Wellbeing score drop Medication adherence alerts
  • Threshold Alerts based on data sources Alerts based on time periods
  • Standard Deviation based alerts where a low standard deviation indicates that most of the values in the data set are close to the average, a high standard deviation indicates that they are distributed over a wide range of values.
  • Each one of the data points can provide a maximum of 10 points based on the user behavior and the inventive algorithm.
  • system/ application may provide digital phenotyping.
  • digital phenotyping capabilities may mean that the intelligent system can sense and mine mental health states, support smart decisions, maximize the treatment outcomes and facilitate prevention and surveillance based on the ubiquitous ‘digital footprints’ from multiple data sources.
  • the platform may be operable to monitor how a user interacts with their smartphone in combination with other data about them such as user emotion, mood, habit inputs, data from wearables and is operable to serve as a sufficient proxy for making ongoing, accurate predictions about mental health states.
  • passive data tracking may make digital phenotyping possible.
  • the application/system may begin continuously tracking data on a number of “sensors”, such as: keyboard interaction dynamics, social phone utilization metrics (calls, texts, social media post language), application usage, steps (pedometer), application events (i.e. open, close, tapping), and/or any of the 21 data sources.
  • sensors such as: keyboard interaction dynamics, social phone utilization metrics (calls, texts, social media post language), application usage, steps (pedometer), application events (i.e. open, close, tapping), and/or any of the 21 data sources.
  • Tools of the academic community are generally based on the development of clinically-viable solutions and attempts to offer the highest utility to patients.
  • collecting a large, high-quality data set may be helpful to developing robust, accurate Machine Learning (ML) models that generalize well to new data scenarios (i.e. new users).
  • ML Machine Learning
  • the following programming libraries have been identified as adequate for building this functionality into a smartphone app: Passive Data Kit and Aware Framework - note some features unavailable for iOS.
  • heterogeneous data may be advantageous. It is found that combining multiple data sources (heterogeneous data) may tend to improve model prediction. Therefore, with respect to developing this functionality, the collects user data from as many relevant sources as possible.
  • FIG. 88 is a schematic 8800 delineating various types of data, their collection method, their implications on mental health (and/or well-being), and their role in the application/system, in accordance with an embodiment.
  • FIG. 88 shows various types of data that may be used for digital phenotyping and how they can be processed.
  • Data pre-processing means preparing/transforming all ingested data into a format that can be best interpreted by the machine learning model. In some embodiments this includes converting categorical data (ie. biological sex being male / female) into numbers (0 / 1), fdling empty data cells, regularization (shrinking coefficient estimates), and reducing noise (distortions) in data. Preprocessing is advantageous because it prepares the data for machine learning models to learn correctly from it, leading to accurate predictions.
  • a form of data preprocessing called feature engineering involves transforming combinations of and inferences from raw data into features, then combinations of features into symptoms).
  • Machine Learning Models Developing this capability includes training machine learning models to predict a user’s mental state and symptoms, which can then be used to support a number of healthcare tools.
  • FIG. 89 is a chart 89000 of a sensor-feature-behaviour hierarchy, in accordance with an embodiment.
  • the chart demonstrates an example of what is known as a “sensor-feature-behavior hierarchy” to represent the relationships the models in some embodiments can understand and is able to predict about the users. (Note clinical states are not connected, reinforcing a focus on behaviours, not diagnoses (reflecting previously stated research feature #1)).
  • Sensors may be feed into low-level features (i.e. machine learning features), which may feed into high-level behavioural markers, which may then be associated with a variety of clinical states.
  • low-level features i.e. machine learning features
  • high-level behavioural markers which may then be associated with a variety of clinical states.
  • Passive detection of detection monitoring enhancement changes in self- Passive detection of Using activity and location Sensor-derived perceived stress in order activity changes data to discreetly monitor signals (e.g. location to foster self-regulation using accelerometry, mood changes as part of information) used to and resilience or trigger GPS, phone combined parent-child self tailor therapy in order proactive help-seeking utilization data in monitoring intervention to maximize user before the onset of frank order to identify engagement and mental health symptoms individuals at risk for BPD relapse prediction treatment effect or depression or anxiety. Passive monitoring for identify when High-risk alcohol use depressive (using keyboard treatment is not detection Suicidality detection signals) and manic (using working.
  • Passive identification of Automatic natural voice signals signs indicative high-risk drinking language processing of relapse, enabling “early episodes using activity of social media posts warning sign” interventions and phone utilization to identify at-risk data in order to trigger individuals.
  • Opioid overdose detection prevention interventions Active abnormal respiratory pattern detection post opioid use using smart phone “sonar” (combining speaker and microphone.)
  • Schizophrenia relapse prediction Passive monitoring for eariy- warming signs using accelerometry and heart variability in order to detect relapse early and enable medical intervention.
  • Deep learning is a type of machine learning which has enabled a number of breakthroughs over the past decade. Deep learning involves using connected layers of calculations (processing nodes) to represent increasingly complicated patterns - in the case of the models, the simplest layers intake raw data points (i.e. finger gestures, location updates), and the final layers determine (for example) a scale-based value of how severe each of a user’s symptoms are at any given point in time.
  • recurrent neural networks Using a deep learning tool called recurrent neural networks (RNNs), data captured over a period of time can also be processed, which allows the models to learn to represent not just patterns based on snapshots of a user’s behavior (i.e. what they are currently doing), but what their behaviour has collectively looked like over the past hour, day, week, or month - this time-series data can be used to make predictions about their current state and future behaviours.
  • this can be used to create far a more accurate understanding of each user’s behaviour and mental states than can, for example, prior-art weekly or monthly meetings with a psychiatrist, in which bias, lack of context, or misinterpretation can easily be introduced between the patient and the psychiatrists’ communication.
  • the inventive tools in some embodiments surpass the judgement and prediction abilities of professionals in certain skills.
  • the correlation coefficient may be a value that represents how much one variable depends on another, and may be commonly known as an r-value. In various embodiments, the correlation coefficient may be useful and/or used during the process of feature engineering as described earlier.
  • FIGS. 90-99 show a variety of correlations determined from data represented as scatter plots, in accordance with an embodiment.
  • FIG. 90 is a plot 9000 of PHQ-9 score vs. entropy, in accordance with an embodiment.
  • the r-value may be 0.42 and the p-value may be 0.082.
  • FIG. 91 is a plot 9100 of PHQ-9 score vs. total distance, in accordance with an embodiment.
  • the r-value may be 0.08 and the p-value may be 0.767.
  • FIG. 92 is a plot 9200 of PHQ-9 score vs. normalized entropy, in accordance with an embodiment.
  • the r-value may be 0.58 and the p-value may be 0.012.
  • FIG. 93 is a plot 9300 of PHQ-9 score vs. circadian movement, in accordance with an embodiment.
  • the r-value may be 0.63 and the p-value may be 0.005.
  • FIG. 94 is a plot 9400 of PHQ-9 score vs. location variance, in accordance with an embodiment.
  • the r-value may be 0.58 and the p-value may be 0.012.
  • FIG. 95 is a plot 9500 of PHQ-9 score vs. number of clusters, in accordance with an embodiment.
  • the r-value may be 0.09 and the p-value may be 0.728.
  • FIG. 96 is a plot 9600 of PHQ-9 score vs. home stay, in accordance with an embodiment.
  • the r-value may be 0.49 and the p-value may be 0.037.
  • FIG. 97 is a plot 9700 of PHQ-9 score vs. usage duration, in accordance with an embodiment.
  • the r-value may be 0.54 and the p-value may be 0.011.
  • FIG. 98 is a plot 9800 of PHQ-9 score vs. transition time, in accordance with an embodiment.
  • the r-value may be 0.21 and the p-value may be 0.401.
  • FIG. 99 is a plot 9900 of PHQ-9 score vs. usage frequency, in accordance with an embodiment.
  • the r-value may be 0.52 and the p-value may be 0.015.
  • the application/system may comprise community features. Community features are depicted in FIGS. 167-174.
  • the community features may allow specialists to highlight their expertise to the users. Specialists can share written content, photos, videos, audio files and written articles. End users can like or comment on the posts and can also directly book sessions from the posts and access all of the posts of specialists within each specialist profile. Admins of the system can schedule content to be posted within the community.
  • the news feed is the primary system through which users are exposed to content posted on the platform. The system enables users to easily interact with these updates by liking or commenting.
  • HR Human Resources
  • Clinic and External Dashboards may be provided in the system/application.
  • the invention offers dashboards utilizing the data sources of the system to display important information to various outside users.
  • the inventive interactive dashboard is a data management tool that tracks, analyzes, monitors, and visually displays key mental health and well-being metrics while allowing users to interact with data, enabling them to make well-informed, data- driven decisions. For example, from a corporate perspective, the wellbeing data can be extremely valuable and having the wellbeing score can allow deployment of both the digital programs and one-on-one coaching into companies more effectively because the invention can provide clear ROI (Return On Investment) to HR.
  • ROI Return On Investment
  • FIG. 100 is an 84th view 10000 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 101 is an 85th view 10100 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 102 is an 86th view 10200 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 103 is an 87th view 10300 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 104 is an 88th view 10400 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 105 is an 89th view 10500 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 106 is a 90th view 10600 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 107 is a 91st view 10700 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 108 is a 92nd view 10800 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 109 is a 93rd view 10900 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 110 is a 94th view 11000 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. Ill is a 95th view 11100 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 112 is a 96th view 11200 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 113 is a 97th view 11300 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 114 is a 98th view 11400 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 115 is a 99th view 11500 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 116 is a 100th view 11600 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 117 is a 101st view 11700 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 118 is a 102nd view 11800 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 119 is a 103rd view 11900 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 120 is a 104th view 12000 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 121 is a 105th view 12100 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 122 is a 106th view 12200 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 123 is a 107th view 12300 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 124 is a 108th view 12400 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 125 is a 109th view 12500 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 126 is a 110th view 12600 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 127 is a 111st view 12700 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 128 is a 112nd view 12800 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 129 is a 113rd view 12900 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 130 is a 114th view 13000 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 131 is a 115th view 13100 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 132 is a 116th view 13200 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 133 is a 117th view 13300 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 134 is a 118th view 13400 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 135 is a 119th view 13500 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 136 is a 120th view 13600 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 137 is a 121st view 13700 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 138 is a 122nd view 13800 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 139 is a 123rd view 13900 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 140 is a 124th view 14000 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 141 is a 125th view 14100 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 142 is a 126th view 14200 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 143 is a 127th view 14300 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 144 is a 128th view 14400 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 145 is a 129th view 14500 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 146 is a 130th view 14600 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 147 is a 131st view 14700 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 148 is a 132nd view 14800 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 149 is a 133rd view 14900 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 150 is a 134th view 15000 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 151 is a 135th view 15100 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 152 is a 136th view 15200 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 153 is a 137th view 15300 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 154 is a 138th view 15400 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 155 is a 139th view 15500 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 156 is a 140th view 15600 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 157 is a 141st view 15700 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 158 is a 142nd view 15800 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 159 is a 143rd view 15900 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 160 is a 145th view 16000 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 161 is a 145th view 15100 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 162 is a 146th view 15200 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 163 is a 147th view 15300 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 164 is a 148th view 15400 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 165 is a 149th view 15500 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 166 is a 150th view 15600 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 167 is a 151st view 15700 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 168 is a 152nd view 15800 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 169 is a 153rd view 15900 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 170 is a 154th view 17000 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 171 is a 155th view 17100 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 172 is a 156th view 17200 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 173 is a 157th view 17300 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 174 is a 158th view 17400 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 175 is a 159th view 17500 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 176 is a 160th view 17600 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 177 is a 161st view 17700 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 178 is a 162nd view 17800 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 179 is a 163rd view 17900 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 180 is a 164th view 18000 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 181 is a 165th view 18100 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 182 is a 166th view 18200 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 183 is a 167th view 18300 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 184 is a 168th view 18400 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 185 is a 169th view 18500 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 186 is a 170th view 18600 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 187 is a 171st view 18700 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 188 is a 172nd view 18800 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 189 is a 173rd view 18900 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 190 is a 174th view 19000 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 191 is a 175th view 19100 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 192 is a 176th view 19200 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 193 is a 177th view 19300 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 194 is a 178th view 19400 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 195 is a 179th view 19500 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 196 is a 180th view 19600 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 197 is a 181st view 19700 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 198 is a 182nd view 19800 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 199 is a 183rd view 19900 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 200 is a 184th view 20000 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 201 is a 185th view 20100 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 202 is a 186th view 20200 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 203 is a 187th view 20300 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 204 is a 188th view 20400 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 205 is a 189th view 20500 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 206 is a 190th view 20600 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 207 is a 191st view 20700 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 208 is a 192nd view 20800 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 209 is a 193rd view 20900 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 211 is a 195th view 21100 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 212 is a 196th view 21200 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 213 is a 197th view 21300 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 214 is a 198th view 21400 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 215 is a 199th view 21500 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 216 is a 200th view 21600 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 217 is a 201st view 21700 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 218 is a 202nd view 21800 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 219 is a 203rd view 21900 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 220 is a 204th view 22000 of the GUI of the application and/or system, in accordance with an embodiment.
  • FIG. 221 illustrates a block diagram of a computing device 22100, in accordance with an embodiment of the present application.
  • the schematic architecture 100 may be implemented using the example computing device 22100 of FIG. 221.
  • the computing device 22100 includes at least one processor 22102, memory 1004, at least one I/O interface 22106, and at least one network communication interface 1008.
  • the processor 22102 may be a microprocessor or microcontroller, a digital signal processing (DSP) processor, an integrated circuit, a field programmable gate array (FPGA), a reconfigurable processor, a programmable read-only memory (PROM), or combinations thereof.
  • DSP digital signal processing
  • FPGA field programmable gate array
  • PROM programmable read-only memory
  • the memory 22104 may include a computer memory that is located either internally or externally such as, for example, random-access memory (RAM), read-only memory (ROM), compact disc read-only memory (CDROM), electro-optical memory, magneto-optical memory, erasable programmable read-only memory (EPROM), and electrically-erasable programmable read-only memory (EEPROM), Ferroelectric RAM (FRAM).
  • RAM random-access memory
  • ROM read-only memory
  • CDROM compact disc read-only memory
  • electro-optical memory magneto-optical memory
  • EPROM erasable programmable read-only memory
  • EEPROM electrically-erasable programmable read-only memory
  • FRAM Ferroelectric RAM
  • the I/O interface 22106 may enable the computing device 22100 to interconnect with one or more input devices, such as a keyboard, mouse, camera, touch screen and a microphone, or with one or more output devices such as a display screen and a speaker.
  • the networking interface 22108 may be configured to receive and transmit data sets representative of the machine learning models, for example, to a target data storage or data structures.
  • the target data storage or data structure may, in some embodiments, reside on a computing device or system such as a mobile device.
  • FIG. 222 is a flow chart of a method 22200, which may be computer-implemented, of generating a recommendation for improving mental health of a user, the in accordance with an embodiment.
  • Step 22202 of the method may include receiving user data indicative of a plurality of events and a plurality of time-stamps associated with the plurality of events.
  • the plurality of events may be indicative of at least one moods of the user, emotional states of the user, habits of the user, or activities of the user;
  • Step 22204 of the method may include determining a time-based trend associated with the plurality of events by using the user data
  • Step 22208 of the method may include transmitting alert data indicative of the suggested activity to a user device associated with the user to alert the user.
  • step 22206 may include determining the suggested activity when the trend is a negative trend indicating a deterioration of the mental health of the user.
  • step 22206 may include determining the suggested activity associated with a counteraction of the negative trend.
  • the step 22206 may include determining the suggested activity to be participation in an online educational program.
  • the method 22200 may further include transmitting the online educational program to a user device in response to receiving an on-demand request.
  • the method 22200 may further include determining an alarm condition in response to the user data.
  • the method 22200 may further include transmitting the alarm condition to a specialist device. In some embodiments, the method 22200 may further include determining a mental-health score in response to the user data.
  • the step 22204 may include determining a plurality of trends in response to a plurality of subsets of user data, respectively, each of the subsets being associated with a different user of the plurality of users, and determining an average trend in response to the plurality of trends.
  • the step 22202 mayeinclude receiving a user-selected reminder time.
  • the habits of the user may include at least one of a negative habit and a positive habit.
  • the emotional states of the user may include at least one of a negative emotion and a positive emotion.
  • step 22202 may include receiving the user data such that the user data is indicative of selected goals of the user.
  • the user data further may include device data of the user received from a wearable device associated with the user.
  • step 22202 may include receiving the user data such that the user data further includes additional data associated with the user, the additional data including at least one of weight data, biometric data, nutrition data, supplementation data, sleep data, community-connection data, meditation data, social-media consumption data, entertainment consumption data, stress-predictive data, eye-tracking data, electroenchephalograhpy data, electrocardiogram data, genetic data, or blood-test data.
  • additional data including at least one of weight data, biometric data, nutrition data, supplementation data, sleep data, community-connection data, meditation data, social-media consumption data, entertainment consumption data, stress-predictive data, eye-tracking data, electroenchephalograhpy data, electrocardiogram data, genetic data, or blood-test data.
  • step 22202 may include receiving the stress- predictive data such that the stress-predictive data includes data indicative of a user’s interaction with a smartphone.
  • the method 22200 may include determining a mental-health score by combining data indicative of the mood of the user, the emotional state of the user, the habits of the user, the activities of the user, sleep of the user, social-media usage of the user, entertainment consumption of the user, social interactions of the user, predictions of stress of the user, and meditation of the user.
  • the method 22200 may include scheduling a videoconference between the user and a specialist selected by the user.
  • the method 22200 may include transmitting portions of the user data selected by the user to a specialist device associated with the specialist.
  • a non-transitory computer-readable medium having stored thereon machine interpretable instructions which, when executed by a processor, cause the processor to perform the computer-implemented method described above.
  • a system may be equipped with the medium (e.g. memory).
  • a computer-implemented method of generating a recommendation for improving the mental health of a user comprising: (a) receiving by a computer processor into a computer memory user data comprising at least one of user-mood data, user-emotional-state data, user-habit data, and user-activity data, said user data further comprising time-stamp data; (b) determining by the processor a time-based trend associated with said user data; (c) in response to said trend, determining by the processor a suggested activity; and (d) transmitting by the processor said suggested activity to a user device associated with the user.
  • connection may include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements).

Abstract

L'invention concerne un procédé mis en œuvre par ordinateur pour générer une recommandation en vue d'améliorer la santé mentale d'un utilisateur, comprenant la détermination d'une tendance dans le temps associée à une pluralité d'événements en utilisant des données d'utilisateur qui sont reçues et qui indiquent la pluralité d'événements et une pluralité associée d'estampilles temporelles, la détermination d'une activité suggérée en réponse à la tendance, et la transmission de données d'alerte indicatives de l'activité suggérée à un dispositif d'utilisateur. La pluralité d'événements indiquant au moins un humeurs, des états émotionnels, des habitudes ou des activités de l'utilisateur. Un système comprenant un processeur, une mémoire lisible par ordinateur connectée au processeur et stockant des instructions exécutables par processeur qui, lorsqu'elles sont exécutées, configurent le processeur pour provoquer l'exécution du procédé mis en œuvre par ordinateur. Un support lisible par ordinateur non transitoire sur lequel sont stockées des instructions interprétables par machine qui, lorsqu'elles sont exécutées par un processeur, amènent le processeur à exécuter le procédé mis en œuvre par ordinateur.
PCT/CA2022/050226 2021-02-16 2022-02-16 Système et procédé pour générer une recommandation pour améliorer la santé mentale d'un utilisateur WO2022174342A1 (fr)

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CA3053245A1 (fr) * 2017-02-09 2018-08-16 Cognoa, Inc. Plate-forme et systeme de medecine personnalisee numerique
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KR101534328B1 (ko) * 2012-06-22 2015-07-07 주식회사 케이티 정신건강 관리 시스템을 위한 정신건강 측정 장치 및 정신건강 관리 장치
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