Patent Publication Number: US-2021169389-A1

Title: Mood tracking and delivery of a therapeutic based on Emotional or Mental State of a User

Description:
TECHNICAL FIELD 
     This invention relates generally to the field of mood tracking and delivery of therapeutics). More specifically, the invention discloses a new and useful method for delivering a digital therapeutic through an eco-system of digital content as well as a dosage-dependent drug therapeutics based on a users&#39; emotional and mental state. 
     BACKGROUND 
     In the past few decades, the availability and use of electronic computing devices, such as desktop computers, laptop computers, handheld computer systems, tablet computer systems, and cellular phones have grown tremendously, which provide users with a variety of new and interactive applications, business utilities, communication abilities, and entertainment possibilities. 
     One such communication ability is electronic messaging, such as text-based, user-to-user messages. Electronic messaging has grown to include a number of different forms, including, but not limited to, short message service (SMS), multimedia messaging service (MMS), electronic mail (e-mail), social media posts and direct messages, and enterprise software messages. Electronic messaging has proliferated to such a degree that it has become the primary mode of communication for many people. 
     While electronic messaging can be a particularly efficient mode of communication for a variety of reasons—instant delivery, limitless distance connectivity, recorded history of the communication—electronic messaging does not benefit from the advantages of in-person communication and telecommunication. For example, when communicating via telecommunication, a person can adjust, alter, or augment the content of their message to an intended recipient through tone, volume, intonation, and cadence. When communicating in-person, or face-to-face, a person can further enhance or enrich their spoken words with eye contact and shift of focus, facial expressions, hand gestures, body language, and the like. In electronic messaging, users lack these critically important signals, clues, and cues, making it difficult for people to convey the subtler aspects of communication and deeper intent. As a result, issues of meaning, substance, and sentiment are often lost or confused in electronic messages, which can, and very often does, result in harmful or damaging misunderstandings. Miscommunications can be particularly damaging in interpersonal and business relationships. 
     Another unintended effect of our overreliance on electronic communication is the impairment of emotional and mental health. In a recent article published in the American Journal of Psychiatry, Dr. Jerald Block wrote “technology addiction is now so common that it merits inclusion in the Diagnostic and Statistical Manual of Mental Disorders, the profession&#39;s primary resource to categorize and diagnose mental illnesses.” He went on to further state that the disorder leads to anger and depression when the tech isn&#39;t available, as well as lying, social isolation and fatigue. Our devices and experiences from said devices (receiving likes, comments and shares on social media) are in essence a drug dealer and drugs, respectively: Having the capability of doling out the same kind of dopamine hit as a tiny bump of cocaine. In effect, creating the typical addiction/dependency vicious cycle and all of the attendant consequences. 
     According to psychotherapist, Nancy Colier, author of “The Power of Life”, “We are spending far too much of our time doing things that don&#39;t really matter to us . . . [and become] disconnected from what really matters, from what makes us feel nourished and grounded as human beings.” Based on her findings, the average person checks their smartphones 150 times per day, or every six minutes. Furthermore, the average young adult sends on average 110 texts per day and 46% of respondents checked that their devices are something that they couldn&#39;t live without. 
     With this kind of digital ubiquity, it is becoming readily apparent that any solution to the problem involving curtailing or augmenting user behavior is not a realistic approach. Current approaches espoused by experts involve any one of, or combination of, the following: Downloading an app (Moment, Alter, etc.) that locks or limits phone usage upon reaching a pre-specified limit; disabling notifications from your phone settings; keeping the blue-hued light of your smartphone away from your place of rest; and even buying and carrying around a dummy phone. 
     There is a void for a solution that takes into account ubiquitous usage and provides delivery of pro-mental and emotional health content—personalized to the user, much like the way therapeutics have become narrowly tailored—to counter all of the digital-mediated ill effects plaguing our society. These effects will only logarithmically grow as we transition into the IoT era—where we will be exposed to thousands of internet-enabled objects (each capable of delivering contextualized analytics and provisioning) as part of our day-to-day living. Finally, there is a void in the market for delivering hyper-personalized digital-based therapeutics based on a higher-resolution assessment of an EMS (a more dynamic EMS or dEMS). 
     Further yet, drugs have been applied to the skin to treat superficial disorders, for the transdermal administration of therapeutics to manage systemic ailments and as cosmetics, dating back to the oldest existing medical records of man. For instance, the use of salves, ointments, potions and even patches, consisting of plant, animal or mineral extracts, was already popular in ancient Egypt and in Babylonian medicine (around 3000 BC). However, the routine use of transdermal delivery systems only became a common practice in the latter third of the 20th century when delivery technology was developed to enable precise and reproducible administration through the skin for systemic effects. More importantly, there is also a vacuum in the market that detect the release of a mood-governing chemicals and delivers a dosage-dependent therapeutic to regulate the emotional or mental state of the user. 
     A dynamic assessment based on a multi-correlate coordinate system (mood map) that allows users to plot as a single point along at least two correlates of behavior—resulting in a push of hyper-personalized digital content as well as a dosage-dependent therapeutic via an adhesive detector patch with therapeutic value to reinforce or counter the user-mapped dynamic assessment. 
     SUMMARY 
     Disclosed is a method and system for imposing a dynamic sentiment vector to an electronic message. In one embodiment of the invention, the method comprises: receiving a text input comprising message content from an electronic computing device associated with a user; parsing the message content comprised in the text input for emotionally-charged language; assigning a sentiment value, based on the emotionally-charged language, from a dynamic sentiment value spectrum to the text input; and, based on the sentiment value, imposing a sentiment vector, corresponding to the assigned sentiment value, to the text input, the imposed sentiment vector rendering a sensory effect on the message content designed to convey a corresponding sentiment. 
     In another embodiment of the invention, the method comprises: receiving a text input comprising message content from an electronic computing device associated with a user; converting the message content comprised in the text input received from the electronic computing device into converted text in a standardized lexicon; parsing the converted text for emotionally-charged language; generating a sentiment value for the text input from a dynamic sentiment value spectrum by referencing the emotionally-charged language with a dynamic library of emotionally-charged language; and, based on the sentiment value, imposing a sentiment vector to the text input, the imposed sentiment vector rendering a sensory effect on the message content designed to convey a corresponding sentiment. 
     For example, in one application of the invention, a user can write and submit a text message on the user&#39;s cellular phone for delivery to the user&#39;s best friend. After receiving the text message, the invention can analyze the message content of the text message and determine, based on the verbiage, syntax, and punctuation within the message content, that the user is attempting to convey excitement through the text message. The invention can then apply a visual filter of red exclamation points or other illustrative, performative, or kinetic attributes to the text message, indicating the excitement of the user, before the text message is delivered to the user&#39;s best friend. 
     In another example of one application of the invention, a user can write and submit a direct message through a social media application (e.g., Instagram, Facebook, SnapChat) on the user&#39;s mobile phone for delivery to a second user. After receiving the direct message, the invention can use a camera built into the user&#39;s mobile phone to capture an image of the user&#39;s face and analyze aspects of the user&#39;s face (e.g., curvature of the lips, motion of the eyes, etc.) to determine the user&#39;s mood or expression. Based on the user&#39;s mood or expression, the invention can then apply a vibration pattern to the direct message before the direct message is delivered to the second user. 
     In another object of the invention, sentiment and cues of the users emotional or mental state is not gleamed by referencing a parsed user input against a dynamic library of emotionally-charged language to generate a sentiment value and vector for overlaying the said input. Rather, the emotional and mental state (EMS) of the user is chosen by the user or determined by the system based on user engagement with the interface or content. Once the EMS of the user is defined, carefully curated and efficacious content is delivered to the user to combat the defined EMS. 
     In one aspect, a method is provided for delivering a digital therapeutic, specific to a user-chosen emotional or mental state (EMS), the method comprising the steps of: recognizing at least one EMS selected by the user from a plurality of EMS, the selected EMS indicating at least one of a feeling, sensation, type of discomfort, mood, mental state, emotional condition, or physical status of the user. Once the EMS is defined, the method then calls for pushing a primary-level message personalized to the user based on at least one stored message coupled to the selected EMS. Finally, pushing at least a secondary-level message personalized to the user based on a threshold-grade match of the user response to the pushed primary-level message with at least one stored response coupled to a stored primary-level message, whereby the user and stored response is a measure of at least one of a reaction, compliance, engagement, or interactivity with the pushed and, or stored primary-level message. The primary and secondary-level messages may contain at least one of a text, image, sound, video, art asset, suggested action or recommended behavior. The efficaciousness or therapeutic value of the primary or secondary messages are validated by at least one—and typically two—independent sources of clinical research or peer-reviewed science, as verified by a credentialed EMS expert. 
     In another aspect, once the EMS is defined, the method may call for pushing at least a single-level message. The at least single message may contain at least one of a text, image, sound, video, art asset, suggested action or recommended behavior. Again, the efficaciousness or therapeutic value of the primary or secondary messages are validated by at least one—and typically two—independent sources of clinical research or peer-reviewed science, as verified by a credentialed EMS expert. 
     In yet another aspect, a system is described and claimed for delivering the digital content of validated therapeutic efficacy. The system may comprise an EMS store; at least a primary message prescriber; a processor coupled to a memory element with instructions, the processor when executing said memory-stored instructions, configure the system to cause: at least one EMS from a plurality of EMS in the EMS store to be selected by the user, said selected EMS indicating at least one of a feeling, sensation, type of discomfort, mood, mental state, emotional condition, or physical status of the user; and the at least primary message prescriber pushing a primary-level message personalized to the user based on at least one stored message coupled to the selected EMS. 
     In yet other aspects, at least a secondary message prescriber is included, wherein the at least secondary message prescriber pushes at least a secondary-level message personalized to the user based on a threshold-grade match of the user response to the pushed primary-level message with at least one stored response coupled to a stored primary-level message, whereby the user and stored response is a measure of at least one of a reaction, compliance, engagement, or interactivity with the pushed and, or stored primary-level message. 
     In both aspects (primary or at least secondary message prescribers), the messages or content may contain at least one of a text, image, sound, video, art asset, suggested action or recommended behavior. Much like in the method aspects, the therapeutic value of the messages or content are validated by at least one—and typically two—independent sources of clinical research or peer reviewed published science and selected by a credentialed EMS expert. 
     In one other aspect, a system and method for generating a more dynamic assessment allowing for more hyper-personalized digital content delivery is provided. Users may plot as a single point on a multi-correlate coordinate system (mood map), wherein each axis represents a unique correlate of behavior and where a single point is a representation of a user-mapped assessment along at least two correlates of behavior, such as active/inactive and positive/negative. Other reinforcing or countering correlates may be provided. Moreover, the mood map may be three-dimensional to include a third correlate of behavior. Finally, the mood map and plotted assessment along the at least two correlates of behavior may be system enabled, as opposed to user-plotted. The system may capture emotional metrics from at least one of a facial image capture, heart/respiration rate, skin conductance, sensor gathered, digital footprint crawled, response to cognitive/physical tasks, engagement to pushed content, etc. 
     Whether the sentiment or cues are generated by the system or defined by the user, content is being overlaid or delivered to enhance intonation, heighten digital communication, obviate ambiguity, boost mood, support self-esteem, inspire wellness, and aid in the longitudinal and non-interventional care for people in distress or need—leveraging a familiar and known modality (digital devices). According to the claimed invention, a whole ecosystem of receiving and delivering modalities are provided for a host of digital therapeutics. The digital therapeutic offerings—with the aid of Artificial Intelligence (AI), machine learning, and, or predictive EMS assessment tools—may deliver increasingly personalized solutions uniquely tailored to aid each subscriber. Such non-interventional, anonymous, and device-centric solutions are far more appropriate to combat the rising ill-effects of device dependency—rather than pharmaceutical dosing, in-patient treatment, and altering device behavior. 
     Further yet in another preferred embodiment of the invention, a method for mood tracking and delivery of a digital therapeutic, comprising the steps of: detecting a release of a mood-governing chemical in a user&#39;s body via an adhesive detector patch (ADP); assessing and tracking of the mood-governing chemical to determine an emotional or mental state (EMS) of a user; and delivering at least a primary-level message (digital therapeutic) personalized to the user based on at least one of a stored message coupled to the emotional and mental state (EMS). In yet another embodiment of the invention, the mood-governing chemical is at least one of a neurotransmitter, neuromodulator or a hormone. Further yet in an embodiment of the invention, the neurotransmitter is at least any one of acetylcholine, dopamine, serotonin, epinephrine, norepinephrine, glutamate, oxytocin, or gamma-aminobutyric (GABA). Preferably, in an embodiment of the invention, the neuromodulator is at least one of opioid peptides, enkephalins, endorphins or dynorphins. In another embodiment of the invention, wherein the hormone is at least one of growth hormone (GH), thyroid-stimulating hormone (TSH), adrenocorticotropic hormone (ACTH), follicle-stimulating hormone (FSH), luteinizing hormone (LH), beta endorphin, and prolactin. 
     In yet another aspect of the invention, a method for mood tracking and delivery of a digital therapeutic comprises the steps of: detecting the release of a mood-governing chemical in a user&#39;s body via an adhesive detector patch (ADP); assessing and tracking of the mood-governing chemical to determine an emotional or mental state (EMS) of a user; and delivering at least a primary level dosage-dependent therapeutic via the ADP personalized to the user based on at least one of a stored message coupled to the emotional and mental state (EMS). 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a graphical representation of one embodiment of the electronic messaging system. 
         FIG. 2  is a graphical representation of one embodiment of the electronic messaging system. 
         FIGS. 3A and 3B  are graphical representations of one embodiment of the electronic messaging system. 
         FIGS. 4A, 4B, 4C and 4D  are graphical representations of one embodiment of the electronic messaging system. 
         FIGS. 5A, 5B and 5C  are graphical representations of one embodiment of the electronic messaging method. 
         FIG. 6  is a graphical representation of one embodiment of the electronic messaging method. 
         FIGS. 7A and 7B  are graphical representations of one embodiment of the electronic messaging system. 
         FIGS. 8A, 8B, 8C, and 8D  is a graphical representation of one embodiment of the electronic messaging system. are flow diagrams of one embodiment of the electronic messaging system. 
         FIG. 9  illustrates a network diagram in accordance with an aspect of the invention. 
         FIG. 10  illustrates a block diagram depicting the digital therapeutic system in accordance with an aspect of the invention. 
         FIG. 11  illustrates a block diagram depicting the digital therapeutic system in accordance with an aspect of the invention. 
         FIG. 12  illustrates a flow diagram depicting the digital therapeutic method in accordance with an aspect of the invention. 
         FIG. 13  illustrates a representative screen shot depicting an exemplary user interface in accordance with an aspect of the invention. 
         FIG. 14  illustrates a representative screen shot depicting an exemplary user interface in accordance with an aspect of the invention. 
         FIG. 15  illustrates a representative screen shot depicting an exemplary user interface in accordance with an aspect of the invention. 
         FIG. 16  illustrates a representative screen shot depicting an exemplary user interface in accordance with an aspect of the invention. 
         FIG. 17  illustrates a block diagram representing a system including the mood mapper module in relation to the EMS store and prescribers in accordance with an aspect of the invention. 
         FIG. 18  illustrates a graphical representation of the mood map including the at least two correlates of behavior underlying the user-plotted assessment of behavior in accordance with an aspect of the invention. 
         FIG. 19A ,  FIG. 19B ,  FIG. 19C , and  FIG. 19D  are exemplary screen shots of the mood map interface in accordance with an aspect of the invention. 
         FIG. 20A  and  FIG. 20B  are exemplary screen shots of the hyper-personalized digital therapeutic pushed to the user-plotted dynamic EMS. 
         FIG. 21  illustrates a method flow chart for generating the hyper-personalized digital therapeutic pushed to the user-plotted dynamic EMS. 
         FIG. 22  illustrates a method flow chart for mood tracking and delivery of a digital therapeutic. 
         FIG. 23  illustrates a method flow chart for mood tracking and delivery of a dosage-dependent therapeutic. 
         FIG. 24  illustrates a method flow for mood tracking and delivery of a targeted advertisement based on tracked psycho-emotional effect on an individualized health profile. 
         FIGS. 25A and 25B  is an exemplary representation of the adhesive detection patch (ADP). 
         FIGS. 26A and 26B  is an exemplary representation of a perspective view the adhesive detection patch (ADP) with microneedles. 
         FIGS. 27A and 27B  are an exemplary representation of the adhesive detection patch (ADP) showing the microneedle array connected to the central processor unit. 
     
    
    
     DETAILED DESCRIPTION OF DRAWINGS 
     Numerous embodiments of the invention will now be described in detail with reference to the accompanying figures. The following description of the embodiments of the invention is not intended to limit the invention to these embodiments but rather to enable a person skilled in the art to make and use this invention. Variations, configurations, implementations, and applications described herein are optional and not exclusive to the variations, configurations, implementations, and applications they describe. The invention described herein can include any and all permutations of these variations, configurations, implementations, and applications. 
     In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. It will be apparent, however, to one skilled in the art that the invention can be practiced without these specific details. 
     Reference in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others. Similarly, various requirements are described which may be requirements for some embodiments but no other embodiments. 
       FIG. 1  depicts a schematic of a system  100  for imposing a dynamic sentiment vector to an electronic message. In one embodiment, a system  100  can include: a sentiment vector generator  110 , a processor  120 , and an electronic computing device  140  associated with a particular user  130 . The sentiment vector generator  110 , the processor  120 , and the electronic computing device  140  are communicatively coupled via a communication network. The network may be any class of wired or wireless network including any software, hardware, or computer applications that can provide a medium to exchange signals or data. The network may be a local, regional, or global communication network. 
     The electronic computing device  140  may be any electronic device capable of sending, receiving, and processing information. Examples of the computing device include, but are not limited to, a smartphone, a mobile device/phone, a Personal Digital Assistant (PDA), a computer, a workstation, a notebook, a mainframe computer, a laptop, a tablet, a smart watch, an internet appliance and any equivalent device capable of processing, sending and receiving data. The electronic computing device  140  can include any number of sensors or components configured to intake or gather data from a user of the electronic computing device  140  including, but not limited to, a camera, a heart rate monitor, a temperature sensor, an accelerometer, a microphone, and a gyroscope. The electronic computing device  140  can also include an input device (e.g., a touchscreen or a keyboard) through which a user may input text and commands. 
     As further described below, the sentiment vector generator  110  is configured to receive an electronic message  160  (e.g., a text input) from the particular user  130  associated with the electronic computing device  140  and run a program  116  executed by the processor  120  to analyze contents of the electronic message, determine a tone or a sentiment that the particular user  130  is expressing through the electronic message  160 , and apply a sentiment vector to the electronic message  160 , the sentiment vector designed to convey the tone or sentiment determined by the sentiment vector generator  110 . The electronic message  160  can be in the form of a SMS message, a text message, an e-mail, a social media post, an enterprise-level workflow automation tool message, or any other form of electronic, text-based communication. The electronic message  160  may also be a transcription of a voice message generated by the particular user  130 . For example, in one embodiment, from a messaging application installed on the electronic computing device  140 , the user  130  may select to input a voice (i.e., audio) message through a microphone coupled to the electronic computing device  140  or initiate a voice message through a lift-to-talk feature (e.g., the user lifts a mobile phone to the user&#39;s ear and the messaging application automatically begins recording a voice message). In this example, the system  100  can generate a transcription of the voice message or receive a transcription of the voice message from the messaging application. After receiving or generating the transcription (i.e., an electronic message), the sentiment vector generator  110  can then analyze the message content within the electronic message, determine the mood or sentiment of the message content, and apply a corresponding sentiment vector to the electronic message, as further described below. 
     In one embodiment, the system  100  may receive an electronic message  160  in the form of an electroencephalograph (EEG) output. For example, in this embodiment, a user can generate a message using an electronic device communicatively coupled to the user and capable of performing an electroencephalograph to measure and record the electrochemical activity in the user&#39;s brain. In this example, the system  100  can transcribe the EEG output into an electronic message  160  or receive a transcription of the EEG output from the electronic device communicatively coupled to the user. After receiving or generating the electronic message  160  from the EEG, the sentiment vector generator  110  can then analyze the message content within the electronic message  160 , determine the mood or sentiment of the message content, and apply a corresponding sentiment vector to the electronic message. In one example of this embodiment, a user is connected to an augmented reality (AR) or virtual reality (VR) headset capable of performing an EEG or an equivalent brain mapping technique. The user can generate a message simply by thinking of what the user is feeling or would like to say. The headset can monitor and record these thoughts and feelings using the EEG and transcribe the thoughts and feelings into an electronic message or send the EEG output signals directly to the system  100 . The system  100  can then analyze the message content included within the electronic message  160 , determine the mood or sentiment of the message content, and apply a corresponding sentiment vector to the electronic message  160 , creating a vectorized message. The system  100  can then send the vectorized message to the user&#39;s intended recipient (e.g., a recipient that the user thought of). 
     In one embodiment, the particular user  130  may submit an electronic message  160  through a mobile application (e.g., a native or destination app, or a mobile web application) installed on the particular user&#39;s mobile phone or accessed through a web browser installed on the user&#39;s phone. In one example of this embodiment, the user accesses the mobile application, submits the electronic message  160  in the form of a text input. The sentiment vector generator 110  can then analyze the message content included within the electronic message  160 , determine the mood or sentiment of the message content, and apply a corresponding sentiment vector to the electronic message  160 , creating a vectorized message. In this example, the user can then send the vectorized message to the user&#39;s intended recipient(s)  131  (e.g., by copying and pasting the vectorized message into a separate messaging application or selecting to export the vectorized message to a separate application, as further described below). In one variation of this embodiment, the user may send the vectorized message to the intended recipient  131  directly through the mobile application. In one embodiment, the user may submit an electronic message  160 , or a component of an electronic message (e.g., a single word or phrase within the message content of an electronic message) using a touch input gesture. In one example of this embodiment, the user may submit the electronic message  160  through an electronic computing device by swiping a finger on a touch screen coupled to the electronic computing device  140  in a U-shaped gesture on the electronic message. 
     In another embodiment, the user may input an electronic message  160  into an entry field of a third-party application such as an email client (e.g., Gmail, Yahoo Mail) or a social media application (e.g., Facebook, Twitter, Instagram). For example, the user may input a message into the body of an email, or into a status update on Facebook. In this embodiment, the system  100  can detect the input of the electronic message  160  into the third-party application and upload the electronic message  160  to the sentiment vector generator  110 . The sentiment vector generator  110  can then analyze the message content contained within the electronic message  160 , determine the mood or sentiment of the message content, and apply a corresponding sentiment vector to the electronic message  160 , creating a vectorized message. The sentiment vector  110  can then replace the electronic message  160  within the third-party application with the vectorized message. Alternatively, the user may select to replace the electronic message  160  with the vectorized message (e.g., by copying and pasting the vectorized message into the entry field). 
       FIG. 2  depicts a [schematic] of the sentiment vector generator  110 . In one embodiment, the sentiment vector generator  110  includes a parsing module  112 , a dynamic sentiment value spectrum  114 , a program  116 , and a library of sentiment vectors. In this embodiment, after receiving an electronic message  160 , the sentiment vector generator  110  can activate the program  116  executed by a processor  120  to analyze message content contained within the electronic message  160  using the parsing module  112 , the sentiment value spectrum  114 , and the library of sentiment vectors, which are discussed in further detail below. Part or all of the sentiment vector generator  110  may be housed within the electronic computing device 140 . Likewise, part of all of the sentiment vector generator  110  may be housed within a cloud computing network. 
       FIG. 3  depicts a schematic of the parsing module  112 . The parsing module  112  is configured to parse message content contained within an electronic message  160  received by the sentiment vector generator  110  for emotionally-charged language and determine a sentiment value for the electronic message  160  from the dynamic sentiment value spectrum  114 . In one embodiment, the parsing module  112  can include one or both of a heuristic layer  112   a  and a semantic layer  112   b . The heuristic layer  112   a  is configured to recognize, within the message content contained within the electronic message  160 , shorthand script, symbols, and emotional icons (emoticons). For example, the message “r u okay? :(” contains the shorthand character “r” to represent the word “are,” the shorthand character “u” to represent the word “you,” and the emoticon “:(,” representing an unhappy face, each of which the heuristic layer  112   a  is configured to recognize. The heuristic layer  112   a  can be further configured to translate recognized shorthand script, symbols, and emoticons into a standardized lexicon. For example, referring back to the previous example, the heuristic layer can translate “u” into “you,” “r” into “are,” and “:(” into “[sad].” The heuristic layer  112   a  can thus translate the entire message from “r u okay? :(” to “are you okay? [sad]” in order to compare the sentiments expressed within different messages in a more objective manner and determine the nature of the emotionally-charged language contained within the message of content of the electronic message  160 . 
     The semantic layer  112   b  is configured to recognize, within the message content contained within the electronic message  160 , natural language syntax. For example, in the message “is it ok if we text on WhatsApp?” the construction of the phrases “is it ok” and “WhatsApp?” reflect natural language syntax that can express particular sentiments. “is it ok[?]” can express tentativeness in addition to the objective question that the phrase asks. For reference, inverting and contracting the first two words to create the phrase “it&#39;s okay[?]” results in a phrase that can express more confidence. Likewise, the space inserted between “WhatsApp” and “?” can have the effect of “softening” the question mark in comparison to “WhatsApp?” The semantic layer  112   b  is configured to recognize the use of natural language syntax such as “is it ok” and “WhatsApp?” and can be further configured to translate the recognized natural language syntax into a standardized lexicon. The standardized lexicon can be a standard set of words and terms (e.g., an Oxford dictionary) that the parsing module  112  is able to parse for emotionally-charged language. In one embodiment, the standardized lexicon is a standard set of words and terms with predefined attributes. For example, again referring to the previous example, the semantic layer  112   b  can translate the entire message from “is it ok if we text on WhatsApp?” to “can[soft] we text on WhatsApp?[soft]” in order to compare the sentiments expressed within different messages in a more objective manner and determine the nature of the emotionally-charged language contained within the message of content of the electronic message  160 . 
     In one embodiment, the parsing module  112  can include a library of emotionally-charged language  112   c . In this embodiment, after parsing the message content contained within the electronic message  160 , the parsing module  112  can cross-reference the words and terms contained with the message content to the library of emotionally-charged language  112   c . The words and terms contained within the library of emotionally-charged language  112   c  may be tagged with attributes according to the sentiments they most commonly express. For example, the library of emotionally-charged language  112   c  may include the terms “disastrous,” “splendid,” “terrible,” and “awesome.” Within the library of emotionally-charged language  112   c , “disastrous” may be tagged with the attribute [bad] or [negative]; “splendid” may be tagged with the attribute [good] or [positive]. In one embodiment, the terms contained within the library of emotionally-charged language  112   c  may additionally or alternatively be tagged with a numeric value. For example, “disastrous” may be tagged with the attributes [negative; 7], and “terrible” may be tagged with the attributes [negative; 5], indicating that while “disastrous” and “terrible” may express similar “negative” sentiments, “disastrous” is more negative than “terrible.” In one embodiment, the parsing module  112  (or, alternatively, any component of the system  100 ) can dynamically add or remove words or terms to and from the library of emotionally-charged language  112   c . The parsing module  112  may use any technique to tag or evaluate the sentiments of emotionally-charged language. 
     In one embodiment, the library of emotionally-charged language  112   c  is specific to the particular user  130 . In this embodiment, each particular user  130  of the system  100  access a unique library of emotionally-charged language  112   c  associated only with that particular user. In one variation of this embodiment, the particular user  130  may manually add or remove words and terms to and from the library of emotionally-charged language  112   c . In one embodiment of the system  100 , the system  100  can be accessed by multiple users. In one variation of this embodiment, the library of emotionally-charged language  112   c  employed by the parsing module  112  is the same for each user. 
     In one embodiment of the system  100 , the parsing module additionally includes a neural network  150  and a library of inputs  151 . In this embodiment, after parsing the message content of an electronic message  160  received by the sentiment vector generator  11 , the parsing module  112  can store the electronic message  160  in the library of inputs  151 , along with the emotionally-charged language found within the message content and any accompanying attributes, creating a database of messages and their accompanying emotionally-charged language. In this embodiment, the neural network  150  can employ machine learning techniques to analyze this database for patterns and trends in order to dynamically improve the performance of the sentiment vector generator  110 . For example, the neural network  150  may determine through the application of an algorithm that the particular user  130  uses the term “disastrous” ten times more often than the particular user  130  uses the term “terrible.” Thus, even though “disastrous” may be a more negative term than “terrible” for the average user or person, the neural network can determine that, for the particular user  130 , “disastrous” generally carries less emotional weight than “terrible.” In this example, the neural network  150  can then update the parsing module  112  and the library of emotionally-charged language accordingly. For example, in the example in which the terms “disastrous” and “terrible” begin as tagged within the library of emotionally-charged language  112   c  as [negative; 7] and [negative; 5], respectively, the neural network  150  can update the attributes to read [negative; 5] and [negative 7], respectively. In one embodiment, the parsing module  112  can store electronic messages into the library of inputs  151  along with their standardized lexicon conversions. 
       FIG. 4  depicts graphical representations of the parsing of electronic messages by the parsing module  112 .  FIG. 4A  depicts the parsing of three separate electronic messages  160 , “it definitely has given me more time and flexibility and channels creativity differently”  160   a , “is it ok if we text on WhatsApp?”  160   b , and “Oh u live in Williamsburg”  160   c  for emotionally-charged language by the parsing module  112 . In, this example, in the message content of  160   a , the parsing module  112  determines three emotionally-charged words and terms: “definitely has,” “and,” and “differently;” in the message content of  160   b : “ok,” “we,” and “WhatsApp?” and in the message content of  160   c : “u” and “Williamsburg.” In one embodiment, as discussed above, after parsing the message content, the parsing module  112  can determine attributes for the emotionally-charged language found in the message content, as depicted by S 123  in  FIG. 4B . In the example depicted in  FIG. 4B , the parsing module  112  tags “definitely has” with [positive, active], “and” with [neutral], and “differently” with [negative]. In one embodiment, as discussed above, the parsing module  112  includes a semantic layer  112   b  configured to recognize, within the message content contained within the electronic message  160 , natural language syntax, as depicted by S 122  in  FIG. 4B . In the example depicted in  FIG. 4B , the semantic layer  112   b  recognizes the space between “WhatsApp” and “?” in “is it ok if we text on WhatsApp?” as an instance of natural language syntax. In one embodiment, as discussed above, the parsing module  112  includes a heuristic layer  112   a  configured to recognize, within the message content contained within the electronic message  160 , shorthand script, symbols, and emoticons, as depicted by S 124  in  FIG. 4B . In the example depicted in  FIG. 4B , the heuristic layer  112   a  recognizes “u” as a shorthand term for “you.” 
     In one embodiment, as discussed above, after parsing the message content contained within the electronic message  160 , the parsing module  112  can cross-reference the words and terms contained with the message content to a library of emotionally-charged language  112   c , as depicted in  FIG. 4C . In the example depicted in  FIG. 4C , the parsing module  112  cross-references electronic message  160   a  with the library of emotionally-charged language  112   c  and determines that “differently,” “more,” “flexibility,” and “differently” are emotionally-charged words or terms. In one embodiment, as discussed above, before parsing the message content of an electronic message  160 , the parsing module  112  can convert the message content into a standardized lexicon, as depicted in  FIG. 4D . In the example depicted in  FIG. 4D , the parsing module  112  converts “is it ok if we text on WhatsApp?” into the converted text, “is it okay if we text on WhatsApp?” in step S 126  before parsing the converted text for emotionally-charged language in step S 128 . 
       FIGS. 5A, 5B, and 5C  depict a graphical representation of a dynamic sentiment value spectrum  114 . In one embodiment, after parsing message content of an electronic message  160  for emotionally-charged language, the sentiment vector generator  110  can generate a sentiment value from a dynamic sentiment value spectrum  114  for the electronic message  160 . In one variation of this embodiment, the dynamic sentiment value spectrum  114  can be represented as a coordinate system, as depicted in  FIG. 5A . In the example depicted in  FIG. 5A , the dynamic sentiment value spectrum  114  is a Cartesian coordinate system consisting of two axes: a horizontal axis  115   a  ranging from positive to negative (henceforth, the positivity axis) and a vertical axis  115   b  ranging from passive to active (henceforth, the activity axis). In this example, the dynamic sentiment value spectrum  114  consists of a multitude of different sentiments, each occupying a different position on the coordinate system. For example, the sentiments “Happy,” “Astonished,” and “Inquisitive” ( 114   a - 114   c , respectively) all occupy the second quadrant of the coordinate system, defined by a positive position on the positivity scale and an active position on the activity scale (i.e., each of these sentiments are determined by the sentiment vector generator  110  to be positive and active sentiments). In this example, the sentiment vector generator considers Inquisitive  114   c  to be a more active but less positive sentiment than Astonished  114   b  and Astonished to be a less positive and less active sentiment than Happy  114   a . Also, in this example, the sentiments “Shocked,” “Sad,” and “Mad” ( 114   d - 114   f , respectively) all occupy the first quadrant of the coordinate system, defined by a negative position on the positivity scale and an active position on the activity scale (i.e., each of these sentiments are determined by the sentiment vector generator to be active and negative sentiments). However, the dynamic sentiment value spectrum  114  need not be a coordinate system. Rather, the dynamic sentiment value spectrum  114  may take on any appropriate form (e.g., a list, a linear scale, etc.). Additionally, the sentiment value spectrum does not need to be dynamic. 
     In one embodiment, as discussed above, after parsing message content contained within an electronic message  160  for emotionally-charged language, the parsing module  112  can assign attributes to the emotionally-charged language found in the message content of the electronic message  160 . In one embodiment, the sentiment vector generator  110  can analyze the emotionally-language and their accompanying attributes to generate a sentiment value from the dynamic sentiment value spectrum  114 , as depicted in  FIG. 5B . For example, in the example depicted in  FIG. 5B , the parsing module  112  can assign each emotionally-charged term found in the message content of an electronic message with respective coordinate values on the positivity and activity axes of the Cartesian coordinate dynamic sentiment value spectrum discussed in the example above. In this example, the sentiment vector generator  110  can take the coordinate position of each emotionally-charged term, calculate an average position of the emotionally-charged terms, and plot the average positon on the dynamic sentiment value spectrum  114  depicted in  FIG. 5A . Then, in this example, the sentiment vector generator  110  can generate a sentiment value for the electronic message by determining the sentiment value on the dynamic sentiment value spectrum  114  closest to the average position of the emotionally-charged terms. 
     In one embodiment, the sentiment vector generator  110  can generate a sentiment value for an electronic message  160  by determining which of the emotionally-charged terms found in the message content of the electronic message carries the most emotional weight. For example, in one embodiment, the parsing module  112  can parse the message content of an electronic message  160  for emotionally-charged language and assign each emotionally-charged term with a positivity scale value, an activity scale value, and an emotional weight value. In this embodiment, the sentiment vector generator  110  can then determine a sentiment value for the electronic message by determining which of the emotionally-charged terms has the highest emotional weight value, and then determining the sentiment value on the dynamic sentiment value spectrum  114  closest to the position of emotionally-charged term with the highest emotional weight value. 
     In one embodiment, the library of emotionally-charged language  112   c  associates each emotionally-charged term contained within the library with a sentiment value from the dynamic sentiment value spectrum  114 . For example, the library of emotionally-charged language  112   c  may associate the words “gleeful,” “splendid,” and “terrific” with a “happy” sentiment value. In this example, if the message content of an electronic message  160  includes any of the terms “gleeful,” “splendid,” or “terrific,” the sentiment vector generator  110  can generate a “happy” sentiment value for the electronic message  160 . However, the sentiment vector generator can generate a sentiment value for an electronic message  160  using any other methodology. 
     In one embodiment, the particular user  130  may select a sentiment value from the dynamic sentiment value spectrum for an electronic message  160 . In one variation of this embodiment, after the parsing module  112  parses the message content of an electronic message  160  submitted by the particular user  130 , the sentiment vector generator  110  can generate multiple sentiment values for the electronic message  160  and present the multiple sentiment values for the electronic message  160  to the particular user  130  for selection. For example, after receiving electronic message  160   a  (depicted in  FIG. 4A ), the sentiment vector generator  110  may generate an “excited” sentiment value and a “melancholy” sentiment value for electronic message  160   a . In this example, the particular user  130  may be given the choice to pick between the “excited” sentiment value and the “melancholy” sentiment value, in order to further ensure that the proper (i.e., intended) sentiment will be expressed. 
     In one embodiment, as discussed above, the system  100  includes a neural network  150  and a library of inputs  151  communicatively coupled to the sentiment vector generator  110 . In one variation of this embodiment, after generating a sentiment value for an electronic message  160 , the sentiment vector generator  110  store the electronic message  160  and its accompanying sentiment value in the library of inputs  151  creating a database of messages and their accompanying sentiment values. In this embodiment, the neural network  150  can employ machine learning techniques to analyze this database for patterns and trends in order to dynamically improve the performance of the sentiment vector generator  110 . In one variation of this embodiment, the neural network  150  can dynamically edit or rearrange the dynamic sentiment value spectrum  114 . In the rearranged version, the sentiment values have adjusted and coalesced into more discrete sections ( 115   c - 115   e ). This may reflect that a particular user  130  associated with the rearranged sentiment value spectrum  117  generates messages most of their messages with a similar tone, making the difference between similar sentiments subtler than that of the average person. 
     In one embodiment, the sentiment vector generator  110  can generate a sentiment value for an electronic message  160  at least in part by utilizing information about a particular user  130 . For example, in one embodiment, the system  100  can generate sender context associated with a particular user  130 . The sender context can include, but is not limited to: social media data associated with the particular user, data obtained from IoT (internet of things) devices associated with the particular user, data obtained from wearable devices associated with the particular user, genetic profile data associated with the particular user, and stress data of the particular user. In one variation of this embodiment, the system  100  can leverage sensors and inputs coupled to an electronic computing device  140  associated with the particular user  130  to generate sender context associated with the particular user  130 , as depicted by step S 160  in  FIG. 6 . For example, in the example depicted in  FIG. 6 , the system  100  can leverage a camera built into a mobile phone associated with the particular user  130  to capture images of the face of the particular user. In this example, the system  100  can then analyze the images of the face of the user (e.g., the eye motion or lip curvature of the user) and determine the mood of the user at the time that the electronic message  160  is generated. The sentiment vector generator  110  can then generate a sentiment value using the determined mood of the user. In one variation of this embodiment, the system  100  can leverage sensors coupled to wearable devices associated with a particular user, such as a smart watch, intelligent contact lenses, or cochlear implants. For example, the system  100  can leverage a microphone built into a cochlear implant to capture the heartrate of a user at the time that the user is generating an electronic message  160 . Using the captured heartrate, the sentiment vector generator  110  can then determine a stress level of the user at the time that the user generated the electronic message  160  and generate a sentiment value using the determined stress level of the user. Sender context can additionally or alternatively include: facial expression, motion or gesture, respiration rate, heart rate, and cortisol level. 
     In another variation of the previous embodiment, the sentiment vector generator  110  can generate a sentiment value for an electronic message  160  at least in part by utilizing information about an intended recipient of the electronic message  160 . In this embodiment, after receiving an electronic message  160 , the system  100  can determine an intended recipient  131  of the electronic message  160 . The system  100  can then generate recipient context associated with the intended recipient  131 . The recipient context can include but is not limited to: social media data associated with the intended recipient, data obtained from IoT (internet of things, e.g., a smart home assistant such the Amazon Echo) devices associated with the intended recipient, data obtained from wearable devices associated with the intended recipient, genetic profile data associated with the intended recipient, and stress data associated with the intended recipient. For example, in one embodiment, the system  100  can leverage sensors built into an electronic device  141  associated with the intended recipient to determine a mood of the intended recipient  131  at the time that the electronic message  160  is generated. The sentiment vector generator  110  can then generate a sentiment value for the electronic message  160  based at least in part on the determined mood of the intended recipient  131 . 
     After generating a sentiment value for an electronic message  160 , the sentiment vector generator  110  can then select a sentiment vector from a library of sentiment vectors  118 , the selected sentiment vector designed to convey a sentiment corresponding to the generated sentiment value, and impose the selected sentiment vector to the electronic message  160 , as depicted in  FIG. 7 . The library of sentiment vectors  118  can include but is not limited to: a color change of a component of the message content, a change in the text font of a component of the message content, an audio effect, a haptic effect, and a graphical addition to the message content. For example, in one embodiment, after generating a “mad” sentiment value, the sentiment vector generator  110  may change the background of the electronic message  160 , as depicted by step S 141   a  in  FIG. 7A , such as changing the background of the electronic message  160  to red to reflect the mad sentiment. Or, for example, in one variation of this embodiment, the sentiment vector generator  110  may opt to highlight only key words or terms in red, or change the fonts of key words or terms to red. The sentiment vector generator  110  can impose any sort of color change to the electronic message  160  in order to convey a corresponding sentiment. 
     In one embodiment, for example, after generating an “inquisitive” sentiment value for an electronic message  160 , the sentiment vector generator  110  may impose a graphic onto the electronic message  160 , as depicted by step  141   b  in  FIG. 7A , such as adding question mark graphics to the background of the electronic message  160 . In one variation of this example, the sentiment vector generator  110  can add one question mark to the end of the message content of the electronic message  160  in a font size that is larger than the font size of the rest of the message content. In another variation of this example, the sentiment vector generator  110  may impose a .gif file to the background of electronic message  160 , in which one question mark grows and shrinks in periodic intervals. The sentiment vector generator  110  can impose any sort of static or dynamic graphic to the electronic message  160  in order to convey a corresponding sentiment. 
     In one embodiment, for another example, after generating a “judgmental” sentiment value for an electronic message  160 , the sentiment vector generator  110  can edit the font of a key word in the message content, as depicted by step S 141   c  in  FIG. 7A , such as italicizing one of the words contained in the message content. Such font effects can include, but are not limited to, italicizing the font, changing the size of the font, bolding, underlining, and changing the spacing between characters, words, and lines. The sentiment vector generator  110  can impose any sort of font change to the electronic message  160  in order to convey a corresponding sentiment. 
     In one embodiment, the sentiment vector generator  110  can impose an animated character or personality to the electronic message  160 , or transpose the electronic message  160  into a graphic of an animated character or personality. For example, in one variation of this embodiment, the library of sentiment vectors  118  may include a series of the same animated character (take, for example, an animated llama or chicken) performing various actions associated with various corresponding sentiments. For example, the library of sentiment vectors  118  may include a static or dynamic graphic of an animated chicken stomping with red eyes (expressing anger), another graphic of the animated chicken laying in a hammock and basking in the sun (expressing contentedness), and another graphic of the animated chicken blowing a kiss (expressing affection). In this example, after generating an “anger” sentiment value for an electronic message  160 , the sentiment vector generator  110  can transpose the electronic message into the graphic of the animated chicken stomping and saying the message content of the electronic message  160 . 
     In one embodiment, the sentiment vector generator  110  can impose a haptic effect onto an electronic message  160 . For example, after generating an “anger” sentiment value for an electronic message  160 , the sentiment vector generator  110  can impose a vibration or vibration pattern onto the electronic message  160 , as depicted by step S 141   d  in  FIG. 7B , such as three short vibrations. In another example, after generating a “contented” sentiment value for an electronic message  160 , the sentiment vector generator  110  can impose one long and muted vibration to the electronic message  160 . The sentiment vector generator  110  can impose any form of vibration or vibration pattern to an electronic message in order to convey a corresponding sentiment. 
     In one embodiment, the sentiment vector generator  110  can impose an audio effect onto an electronic message  160 . For example, after generating an “unhappy” sentiment value for an electronic message  160 , the sentiment vector generator  110  can impose an audio accompaniment onto the electronic message  160 , as depicted by step S 142  in  FIG. 7B , such as protracted “nooo.” In another example, the sentiment vector generator  110  can impose a voice accompaniment dictating the message content of the electronic message  160  and stressing key words contained within the message content. The voice accompaniment may stress key words contained within the message content in any number of ways including, but not limited to: increasing or decreasing in volume, changing the intonation of the voice, changing the speed of the voice, or changing the cadence of the voice accompaniment. In one embodiment, the voice accompaniment vector may be a recorded and processed version of the particular user&#39;s voice. In one embodiment, the voice accompaniment vector may be the voice of another individual, such as a celebrity, or a combination of the particular user&#39;s voice and the voice of another individual. 
     In one embodiment, after generating a sentiment value for an electronic message  160 , the sentiment vector generator  110  can impose a vector onto the electronic message  160  that adjusts the position of the words contained with the message content of the electronic message, as depicted by step S 141   e  in  FIG. 7B . In one variation of this embodiment, the adjustment of the words contained within the message content is static, such that the words occupy new positions in a static image. In one variation of this embodiment, the adjustment of the words contained within the message content is dynamic, such that the words contained within the message content move within the resulting vectorized message. 
     In one embodiment, a user may submit sentiment vectors to the sentiment vector generator  110 . For example, in one embodiment, a user may submit a picture or graphic design to impose onto the background of an electronic message and select a sentiment value for the picture or graphic design to be associated with. In this example, after generating a sentiment value for an electronic message  160  corresponding to the sentiment value that the user has selected to associate with the picture or graphic design, the sentiment vector generator  110  can impose the picture or graphic design to the background of the electronic message  160  to convey the corresponding sentiment. In another example, in one variation of this embodiment, a user can select a sentiment vector previously included in the library of sentiment vectors  118  and previously associated with a sentiment value and disassociate the sentiment vector from the associated sentiment value, or re-associate the sentiment vector with a different sentiment value. In yet another example, in one variation of this embodiment, a user can select one or more elements from existing sentiment vectors contained within the library of sentiment vectors  118  and combine them to create a new sentiment vector. In this example, the user can also choose a sentiment value to associate with the new sentiment vector. In another example, in one variation of this embodiment, a user can select a sentiment vector by scrolling through a list of sentiment vectors (e.g., a list including options to adjust text weight, height, font, color, highlight, or content animation) using a flicking gesture, within a mobile application, on a touch screen coupled to an electronic computing device. 
     The sentiment vector generator can include or generate, but is not limited to, sentiment vectors using any combination of the elements of the sentiment vectors described herein. Additionally, environmental conditions and factors for example, but not limited to, wind, heat, humidity, cold may also play a role in generating the sentiment vector. 
     In one embodiment of the system  100 , a user can submit an electronic message 160  to the sentiment vector generator  110  through a mobile application (e.g., a native application), as discussed above. In one variation of this embodiment, the mobile application can store vectorized messages generated by the sentiment vector generator and allow the user to search through the vectorized messages. In this embodiment, the user can search through the vectorized messages using different filters or queries including, but not limited to: mood, color, content, and sentiment. For example, in one embodiment, the user can enter a sentiment as “anger” as a search query, and a graphical user interface of the mobile application can display a list of all of the vectorized messages that the user has created through the sentiment vector generator  110  with a sentiment value corresponding to an “anger” sentiment. In one embodiment, the sentiment vector generator  110  can impose a hyperlink onto an electronic message  160 .  FIGS. 8A, 8B, 8C, and 8D  are flow diagrams of one embodiment of the electronic messaging system. 
     In an embodiment of the invention, the sentiment vector generator  110  can impose a hyperlink onto an electronic message  160 . An imperative function of the sentiment vector is GEEQ (genetics, emotion and electroencephalography) and its capacity to integrate messages and messaging with movement and thought as well as the ability to pair information with form and performative elements. In a nutshell, our technology will introduce, integrate, account for, and actively utilize GEEQ (Genetics, Emotion, and Electroencephalography). GEEQ, by its very desgn, integrates and intermingles the beliefs and postulates of Darwin, Mendel, Mendelssohn, Morgan, and Martha Graham. 
       FIG. 9  illustrates a network diagram of the digital therapeutic system in accordance with an aspect of the invention. As shown, at least one processor  204  is connected to the Internet (network)  206  via either a wireless (e.g. WiFi link) or wired link to an Internet connected router, usually via firewall. The network  206  may be any class of wired or wireless network including any software, hardware, or computer applications that can provide a medium to exchange signals or data. The network  206  may be a local, regional, or global communication network. Various servers  204 , such as a remote VCS Internet server, and associated database memory can connect with the at least a user device ( 1  . . . n). Additionally, various user devices (e.g. Smartphones, tablet computers, laptop computers, desktop computers and the like) can also connect to both the processor-controlled IoT hubs, sensors disposed on the device configured for data gathering, and/or the remote VCS Internet server  204 . 
     As will be discussed, often a plurality of different user devices may be used, but for simplicity this plurality of devices will often be spoken of in the singular form. This use of the singular form is not intended to be limiting, and in general the claims and invention should be understood as operating with a plurality of devices. Although for simplicity, often mobile client computerized devices such as Internet connected versions of the popular Android, iOS, or Windows smartphones and tablets will be used as specific examples of devices, these specific examples are not intended to be limiting. The electronic computing device may include any number of sensors or components configured to intake or gather data from a user of the electronic computing device including, but not limited to, a camera, a heart rate monitor, a temperature sensor, an accelerometer, a microphone, and a gyroscope. The electronic computing device can also include an input device (e.g., a touchscreen or a keyboard) through which a user may input text and commands. 
     While not shown, note that server, Internet connected storage device and database memory may all be located in the cloud. This is intended to both designate and remind the reader that the server, Internet connected storage device and database memory are in fact operating according to scalable Internet cloud-based methods that in turn operate according to automated service provisioning and automated virtual machine migration methods. As previously discussed, examples of such scalable methods include, but are not limited to, Amazon EC2, Microsoft Windows Azure platform, and the Google App Engine. Thus, for example, server and Internet connected storage device will often be implemented as automatically provisioned virtual machines under a cloud service system that can create a greater or lesser number of copies of server and Internet connected video storage device and associated database memory according to the underlying demands on the system at any given time. 
     Preferred embodiments may include the addition of a remote server  204  or cloud server to further provide for back-end functionality and support. Any one of the storage or processing may be done on-board the device or be situated adjacent or remotely from the system and connected to each system via a communication network  206 . In one embodiment, the server  204  may be used to support user behavior profiling; user history function; predictive learning/analytics; alert function; network sharing function; digital footprint tracking, etc. The remote server  204  may be further configured to authenticate the user and retrieve data of the user, device, and, or network and applies the data against a library of messages, content, validated user information, etc. 
     Now in reference to  FIGS. 10 and 11 .  FIGS. 10 and 11  both illustrate an exemplary embodiment of the digital therapeutic delivery system.  FIGS. 10 and 11  illustrate an exemplary processing unit with at least a one prescriber  305 ,  307  configured for displaying interactively therapeutic content from an EMS store  303 ,  403  based on a user-specific EMS. As shown, the system may comprise an EMS store  303 ,  403 ; at least a primary message prescriber  305 ; a processor coupled to a memory element with instructions, the processor when executing said memory-stored instructions, configure the system to cause: at least one EMS from a plurality of EMS in the EMS store  303 ,  403  to be selected by the user. 
     As shown in  FIG. 11 , any number of EMS or EMS types may be included in the EMS store  303 ,  403 . Each EMS may indicate at least one of a feeling, sensation, type of discomfort, mood, mental state, emotional condition, physical status of the user, and, or a behavioral intervention or training regimen.  FIG. 11  also illustrates the fact that any number of messages or interactively therapeutic content may be associated with each EMS type. Each message; or interactively therapeutic content; or pushed therapeutic may contain at least one of a text, image, sound, video, art asset, suggested action or recommended behavior. The matching of message; interactively therapeutic content; or pushed therapeutic with EMS type may be pre-defined by at least one of an accredited expert or source; probabilistic; or deep learned. In a preferred embodiment, an accredited expert or source will require at least two independent sources of peer-reviewed scholarship or data in order to validate the match. 
     The at least primary message prescriber  305  may push a message or interactively therapeutic content personalized to the user based on at least one stored message matched to the selected EMS. For example, within the EMS store  403 , if EMS 1 (lethargic) is selected as defined by the user or the system, any one of message 1, 2 . . . n may be selected by the prescriber  305 . The pre-defined messages validated by the accredited expert may all be messages with documented utility in elevating mood and energy (rubric). The mood and energy documented for each message may be on a scale. For instance, EMS 1 message 1 may be low-moderate; EMS 1/message 2 may be moderate; and EMS 1/message n may be high-severe, etc. Any variant of the scale may be featured without departing from the scope of the invention. In other embodiments, the messages, while falling under the same rubric and un-scaled, can vary along design cues. For instance, the prescriber  305  may choose EMS 1/message 2, over other available messages, due to the fact that the message is comprised of traditionally feminine cues (pink-colored bauhaus typeface) for a female user. Other user profile or demographic information may further inform the prescribers  305  choice of message type, such as age, education level, voting preference, etc. User profile or demographic information may be user inputted or digitally crawled. 
     Still in reference to  FIG. 11 , the prescriber&#39;s  305  choice of message type is not specific to a user, user profile, or crawled user data. In a certain embodiment, the prescriber  305  may have to choose between any one of the message types (message 1, message 2 . . . message n) from the selected EMS type. This type of message assignment may be completely arbitrary. In other embodiments, the message assignment may be not specific to a user-generated or crawled profile but may be based on user history. In other words, a user&#39;s tracked level of engagement with a previous message or message from a previous session may inform message assignment by the prescriber  305 . Tracking engagement of a user with a pushed or prescribed therapeutic message may be by camera-captured eye gazing, touch-screen interaction, time span between pushed therapeutic and user follow-up action, choice of follow-up action, etc. 
     In some embodiments, the full list of message types is not grouped by EMS type or along any design categories, but rather simply listed arbitrarily and mapped or matched to an appropriate EMS type. In this arbitrarily listed manner, the prescriber  305  may match to more than one EMS type. Likewise, a user may be defined by more than one EMS type and be prescribed the same message type. 
       FIG. 12  illustrates a flow diagram depicting the method of delivering a digital therapeutic in accordance with an aspect of the invention. In a preferred embodiment, the method may comprise the steps of: (1) recognizing at least one EMS selected by the user from a plurality of EMS, the selected EMS indicating at least one of a feeling, sensation, type of discomfort, mood, mental state, emotional condition, or physical status of the user  508 . Once the EMS is defined, the method then calls for (2) pushing at least a primary-level message personalized to the user based on at least one stored message coupled to the selected EMS  509 . 
     In some embodiments, the system or method may call for pushing at least a secondary-level message personalized to the user based on a threshold-grade match of the user response to the pushed primary-level message with at least one stored response coupled to a stored primary-level message, whereby the user and stored response is a measure of at least one of a reaction, compliance, engagement, or interactivity with the pushed and, or stored primary-level message. Much like the primary message or primary-level message, the secondary-level messages may also contain at least one of a text, image, sound, video, art asset, suggested action or recommended behavior. Again, the efficaciousness or therapeutic value of the primary or secondary messages are validated by at least one—and typically two—independent sources of clinical research or peer-reviewed science, as verified by a credentialed EMS expert. 
     In order to facilitate the at least secondary message or secondary-level message, the primary prescriber  305  may be used: Assigning a second message to the same user in the same session for the first defined EMS type. As is with the the assignment of the first message, the assignment of the second may arbitrarily choose among EMS-grouped messages or from the full arbitrary list of messages in the EMS store. Moreover, the primary prescriber  305  may perform the secondary assignment in a logic-defined manner, wherein gathered, contextualized, or profiled data informs the assignment. In yet other aspects, second-level assignment may be performed by at least a secondary message prescriber  307 , wherein the at least secondary message prescriber  307  pushes at least a secondary-level message personalized to the user based on a threshold-grade match of the user response to the pushed primary-level message with at least one stored response coupled to a stored primary-level message, whereby the user and stored response is a measure of at least one of a reaction, compliance, engagement, or interactivity with the pushed and, or stored primary-level message. 
     For instance, when a user-generated or system-generated EMS is defined as ‘unfulfilled’ for user A, a primary prescriber  305  assigns message 2 (uplifting; inspiring message) from EMS 1 (unfulfilled). In one embodiment, a secondary prescriber  307  prescribes a pro-social behavior, such as a local community service, immediately upon a touch interaction with the first inspiring message pushed. In other embodiments, a level of engagement, interaction or compliance may be tracked by the system to infer severity of the EMS. For instance, if user A does not comply with the touch-interaction requests from the first inspiring message or pro-social behavior recommendation of the second message, then the secondary prescriber  307  may push a less physically strenuous pro-social recommendation, such as suggesting to call an in-network licensed expert or simply make a cash donation to a charitable organization of the users choosing via a linked micro-payment method. For the purposes of inferring severity of EMS, any number of diagnostics that leverage any one of the on-device tools may be used, such as gyroscopic sensors or cameras. Secondary assignment may also be based on learned history, such as a past positive reaction (compliance) to a receiving a message from a loved one that a donation was made in user A&#39;s name to a charitable organization. Based on such history, a secondary prescriber  307  may assign a primary or secondary message recommending to make a donation in the name of a loved one during an ‘unfulfilled’ EMS experienced by user A. 
     The processing unit may further be communicatively coupled to at least one of an interface module, display module, input module, logic module, a context module, timeline module, tracking module, notification module, and a payment/gifting module. In accordance with one aspect, the notification module may be configured to generate reports at regular intervals (such as daily at 12:00 PM, weekly and monthly), on-demand (when the user requests for a report corresponding to the user), when triggered by an event, or upon a detected severe EMS. In an embodiment of the present invention, the notification module may also be configured to send a notification to the user or to a chosen loved one of the user. The notification may be a message, a phone call or any other communication means. 
     In an embodiment of the present invention, a timeline module may push already pushed messages in at least one of a static, dynamic, and, or scheduled fashion based on at least one of the user&#39;s scheduler criteria. The line of static, dynamic, and, or scheduled messages may be curated by the user, pre-set, or dynamically pushed based on any one of a user parameter. In some embodiments, the timeline module enables the displayed line of static, dynamic, and, or scheduled messages to be further replicated on at least one of a social media timelines or stories. In other words, the timeline module enables the displayed messages to be further shared with social media outlets. 
     In an embodiment of the present invention, a payment or gifting module may enable purchasing and gifting donations, physical objects, or digital assets. The gifting module may further be coupled to a distributive digital ledger, wherein each transaction among any user is represented as a unique node in the digital ledger. Each node tagged with meta data facilitating at least one of a transaction, validation and, or registration for each transaction. 
       FIG. 13  is a representative screen shot depicting an exemplary user interface in accordance with an aspect of the invention. As shown, the top layer  602  depicts a spotlighted EMS and the bottom layer is a scroll menu of EMS. In this case, the concept of EMS, as earlier defined, also includes behavioral interventions or training regimens, in addition to an emotional and mental state. In some embodiments, an exemplary user experience may have both top layer  602  and bottom layer  604  within the same screen, wherein the top layer  602  is a spotlighted rendering of the focused EMS from the EMS menu depicted in the bottom layer  604 . In other embodiments, the window may only feature the scrolling EMS menu as depicted in the bottom layer  604 , wherein the focused EMS from the plurality of EMS may pop-out, or be emphasized anyhow. In yet other embodiments, the window may only feature the one EMS at a time, allowing for the user to go through the entire menu, one window (EMS) at a time. In yet other embodiments, the menu may be featured in a thumbnail format, allowing the user to choose at least one EMS from a thumbnail menu, sized to fit in a single window, or alternatively, configured for scrolling. 
       FIG. 14  is a representative screen shot depicting an exemplary user interface in accordance with an aspect of the invention. Once the EMS (behavioral intervention or training regimen) is defined, users can read more about the intervention or training regimen they&#39;re going to start and self-administer (have pushed to their device) from a top portion of the card (window)  702 . On the same card (window), the bottom portion may highlight proven benefits, and then provide directions for use, mixing real guidance with elements of humor  704 . The medical-inspired alliteration and iconography are intended to invoke a sense of prescriptive health care or wellness. 
       FIGS. 15 and 16  are a representative screen shot depicting an exemplary user interface in accordance with an aspect of the invention. As shown on  FIG. 15 , once the EMS (regimen) is defined and a particular course of treatment (message) is started, on the top-right portion of the next card explicitly identifies the specific drug benefit  802 . While not shown, by tapping the drug abbreviation, users can see the source of supporting scientific research  802 . By tapping the hamburger icon, users can choose to save the individual card, or share the card and its contents with friends across social media. It is to be understood by a person of ordinary skill in the art that these icons, or any icons, on this card (window), or any card (window), may be positioned elsewhere (or anywhere), without departing from the inventive scope. 
     As shown on  FIGS. 15 and 16 , the focal point of the card (window) is the actual EMS-defined message (treatment), and in the case of this window, is a suggested action—jump for 5 seconds.  FIG. 15  represents an exemplary card formatted for a mobile phone, while  FIG. 16  represents an exemplary card formatted for a smart watch. Jumping for 5 seconds is a suggested action to restore the oxytocin neurotransmitter, which is documented for building happiness and confidence—the initially chosen EMS or behavioral intervention by the user ( FIG. 13 ). The veracity of the message or suggested action is supported by the referenced peer-reviewed research and co-signed credentialed expert  802 . As a person, skilled in the art will recognize from the previous detailed description and from the figures and claims, modifications and changes can be made to the embodiments of the cards, windows, icons, design elements, EMS types, behavioral intervention types, message types, without departing from the scope of this invention as defined in the following claims. 
     While not shown in  FIGS. 15 and 16 , the messages (cards/windows) may comprise a single or battery of physical and, or cognitive tasks and based on responses, further indicate a more nuanced EMS for a more tailored initial or subsequent message. Responses may include a level of compliance, engagement, interaction, choices, etc. Furthermore, for deeper and more nuanced EMS definition, assigning an indication score or color-coded range to further convey EMS severity may be achievable. As a result, matching of message type to scored or color-coded EMS may produce a more refined match for pushing of even more personalized digital content or therapeutics. 
       FIG. 17  illustrates a block diagram representing a system including the mood mapper module  308  in relation to the EMS store and prescribers in accordance with an aspect of the invention. In combination, the system enables a specific sub-routine that ultimately provisions a dynamic assessment of a user&#39;s EMS (dEMS) based on a multi-correlate coordinate system (mood map).  FIG. 18  illustrates a graphical representation of the mood map including the at least two correlates of behavior (active/passive  902 ; positive/negative 904) underlying the user-plotted assessment of behavior in accordance with an aspect of the invention. The mood map allows users to plot as a single point along at least two correlates of behavior—resulting in a push of hyper-personalized digital content with therapeutic value to reinforce or counter the user-mapped dynamic assessment. 
     In a preferred embodiment, as demonstrated in  FIG. 17 / 18 , the system featuring a user-plotted mood map for deriving a dEMS for a hyper-personalized digital therapeutic comprises a message prescriber  305 ,  307 ; an EMS store  303 ; a processor coupled to a memory element stored with instructions, said processor when executing said memory-stored instructions, configure a mood mapping module (mood mapper)  308  to cause display of a coordinate-based sentiment value spectrum (mood map) comprising one positive to negative-scaled axis  904  and one perpendicular active to passive scaled axis  902  forming a two-dimensional plot of a sentiment value along a positive to negative line (positivity correlate) and an active to passive line (activity correlate); at least one user-plotted point on the displayed mood map to reflect a two-dimensional EMS (dynamic EMS) along the two correlates of positivity and activity, said dynamic EMS indicating a granular assessment of at least one of a feeling, sensation, mood, mental state, emotional condition, or physical status of the user; and the message prescriber  305 ,  307  delivering at least a primary-level message (digital therapeutic) personalized to the user based on at least one of a stored message coupled to the dynamic EMS (hyper-personalized digital therapeutic). 
     In alternative embodiments, the mood map may comprise at least three axis in a three-dimensional representation, wherein one axis represents the positivity correlate; the second axis the activity correlate; and the third axis a time or duration correlate. In yet other embodiments, any type of correlates of behavior along any number of axis may be represented to capture a dEMS based on a user-plot on the mood map. 
       FIG. 17  illustrates an exemplary processing unit with at least a one prescriber  305  configured for displaying interactively therapeutic content from an EMS store  303  based on a user-plotted dEMS (hyper-personalized digital therapeutics). As shown, the system may comprise an EMS store  303 ; at least a primary message prescriber  305 ; a processor coupled to a memory element with instructions, the processor when executing said memory-stored instructions, configure the system to cause: at least one EMS from a plurality of EMS in the EMS store  303  to be pushed based on the user-plotted point, and alternatively, the dynamic sentiment value represented by the user-plotted point based on the intersecting correlates of behavior. 
     While not shown in  FIG. 17 , any number of EMS or EMS types may be included in the EMS store  303 . Each EMS may indicate at least one of a feeling, sensation, type of discomfort, mood, mental state, emotional condition, physical status of the user, and, or a behavioral intervention or training regimen. Any number of messages or interactively therapeutic content may be associated with each EMS type. Each message; or interactively therapeutic content; or pushed therapeutic may contain at least one of a text, image, sound, video, art asset, suggested action or recommended behavior. The matching of message; interactively therapeutic content; or pushed therapeutic with EMS type may be pre-defined by at least one of an accredited expert or source; probabilistic; or deep learned. In a preferred embodiment, an accredited expert or source will require at least two independent sources of peer-reviewed scholarship or data in order to validate the match. 
     The at least primary message prescriber  305  may push a message or interactively therapeutic content hyper-personalized to the user based on at least one stored message matched to the selected EMS. For example, within the EMS store  303 , if EMS 1 (amused) is selected as plotted in the far bottom left corner ( FIG. 18, 115   c ) the by the user, any one of message 1, 2 . . . n may be selected by the prescriber  305 . The pre-defined messages validated by the accredited expert may all be messages with documented utility in elevating mood and energy (counter-effect) or playful, light-hearted (reinforcing). The effects documented for each message may be on a scale. For instance, EMS 1 message 1 may be low-moderate; EMS 1/message 2 may be moderate; and EMS 1/message n may be high-severe, etc. EMS types or message types may be color coded or scored to indicate severity. Any variant of the scale may be featured without departing from the scope of the invention. In other embodiments, the messages, while falling under the same rubric and un-scaled, can vary along design cues. For instance, the prescriber  305  may choose EMS 1/message 2, over other available messages, due to the fact that the message is comprised of traditionally feminine cues (pink-colored bauhaus typeface) for a female user. Other user profile, demographic information, or contextual information may further inform the prescribers  305  choice of message type, such as age, education level, voting preference, etc. User profile or demographic information may be user inputted, digitally crawled, sensed or captured. 
     Still in reference to  FIG. 17 , the prescriber&#39;s  305  choice of message type may not specific to a user, user profile, or crawled user data. In a certain embodiment, the prescriber  305  may have to choose between any one of the message types (message 1, message 2 . . . message n) from the selected EMS type. This type of message assignment may be completely arbitrary. In other embodiments, the message assignment may be not specific to a user-generated or crawled profile but may be based on user history. In other words, a user&#39;s tracked level of engagement with a previous message or message from a previous session may inform message assignment by the prescriber  305 . Tracking engagement of a user with a pushed or prescribed therapeutic message may be by camera-captured eye gazing, touch-screen interaction, time span between pushed therapeutic and user follow-up action, choice of follow-up action, etc. 
     In some embodiments, the full list of message types is not grouped by EMS type or along any design categories, but rather simply listed arbitrarily and mapped or matched to an appropriate EMS type. In this arbitrarily listed manner, the prescriber  305  may match to more than one EMS type. Likewise, a user may be defined by more than one EMS type and be prescribed the same message type. For instance, as illustrated in  FIG. 18 , a plot may indicate for an intermediary EMS between astonished and indifferent  115   d  or between sad and mad  115   e . In such an example, two EMS types may be diagnosed/assigned. Message types may be prescribed suited for the intermediary EMS diagnosis. For example, in the case of the intermediary astonished/indifferent, the plot point may indicate that the user is stronger leaning towards astonished than indifferent, and as a result, exclude certain message types associated with the traditional indifferent EMS type. In other embodiments, the mood mapper module  308  can take the coordinate position of each plot, calculate a precise position, and plot the positon on the dynamic sentiment value spectrum, wherein every coordinate position correlates with a precise EMS. Precise EMS types may be on a scale or depicted with certain leanings/dispositions toward a specific correlate or other EMS types. 
       FIGS. 19A-19D  are exemplary screen shots of the mood map interface in accordance with an aspect of the invention. In  FIG. 19A , the mood maps x-axis is an activity correlate represented as an ocean surface horizontally bisecting the display, whereby a wave action increases the further right or left from a center point and the wave action calms further left or right from the center point. The mood maps y-axis is a positivity correlate represented as a sky above the ocean surface and ocean depth below the ocean surface, whereby the sky light becomes brighter (indicating positivity) the further up from the center point and the ocean depth becomes dimmer (indicating negativity) the further down from the center point ( FIGS. 19B, 19C, 19D ). 
     While not illustrated, in some embodiments, the mood maps z-axis is a time or duration correlate, wherein the mood map shifts ninety degrees to reveal a side-sectional view of the ocean surface and shore, whereby the shore at either end of the display represents day zero and time or duration increases the further from the shore. Other correlates of behavior may be represented on any one of the two or three axis of the mood map, without departing from the scope of the invention. 
     The mood map/mapper may allow for a user to plot a single point on the x/y or x/y/z map, wherein each axis represents a unique and complementary behavioral attribute. In other embodiments, the user may plot multiple points on the x/y or x/y/z map to provide at least four behavioral attributes to inform a dEMS assessment by creating a coefficient, which may be eventually converted into at least one of a dEMS score, dEMS behavioral characteristic, d/EMS type, neurotransmitter implicated, treatment regimen, digital therapeutic type, etc. In other embodiments, at least one of a dEMS score, dEMS behavioral characteristic, d/EMS type, neurotransmitter implicated, treatment regimen, or digital therapeutic type may be derived without the need of a coefficient or algorithmically, statistically, probabilistically, or deep learned. 
     In yet other embodiments, the user may engage the mood map/mood mapper by finger-tip scrolling across the map and removing the finger to pinpoint the exact location of the circle/cursor point to define the at least two-axis correlates of behavior. In other embodiments, the user may finger-tip scroll across the map and double-tap to pinpoint the exact location of the circle/cursor point. 
     While not illustrated, in some embodiments, the mood map/mood mapper may be configured in alternate ways to interactively engage the user in defining the user&#39;s dEMS. For instance, in one embodiment, the map/mapper may may be an interface comprising a series of vertically oriented scales requiring the user to slide a bar up or down the scale in response to a question designed to infer wholly or partially a dEMS of the user. For instance, “what is the likelihood of the polar ice caps completely melting by 2050?” The user would be prompted to slide the bar in response to the question, wherein the furthest top of the scale represents an extremely high likelihood and the furthest bottom of the scale represents an extremely low likelihood. Questions may extend to questions of a more personal nature, such as, “do you believe you will lose your patience, exhibited by an outburst of some kind, during the course of the work day today?” The system may capture the responses to each of the questions and create a coefficient, which may be eventually converted into at least one of a dEMS score, dEMS behavioral characteristic, d/EMS type, neurotransmitter implicated, treatment regimen, digital therapeutic type, etc. In other embodiments, at least one of a dEMS score, dEMS behavioral characteristic, d/EMS type, neurotransmitter implicated, treatment regimen, or digital therapeutic type may be derived without the need of a coefficient or algorithmically, statistically, probabilistically, or deep learned. 
       FIG. 20A  and  FIG. 20B  are exemplary screen shots of the hyper-personalized digital therapeutic pushed to the user-plotted dEMS. Once the EMS (regimen) is defined and a particular course of treatment (message) is started, the first card (window) explicitly identifies the specific EMS and its associated neurotransmitter/effects. While not shown, by tapping the specific EMS and its associated neurotransmitter/effects, users can see the source of supporting scientific research. By tapping the hamburger icon, users can choose to save the individual card, or share the card and its contents with friends across social media. It is to be understood by a person of ordinary skill in the art that these icons, or any icons, on this card (window), or any card (window), may be positioned elsewhere (or anywhere), without departing from the inventive scope. 
     The focal point of the same or next card (window) in series is the actual EMS-defined message (treatment), and in the case of this window, a suggested action—to plan a day outdoors with a friend. This suggested action is to restore/maintain/improve on the dynamic EMS of happiness by expanding on the associated serotonin neurotransmitter, which is documented for building happiness and confidence. The veracity of the message or suggested action is supported by the referenced peer-reviewed research and co-signed credentialed expert. As a person skilled in the art will recognize that modifications and changes can be made to the embodiments of the cards, windows, icons, design elements, EMS types, behavioral intervention types, message types, without departing from the scope of this invention. 
     While not shown in  FIGS. 20A and 20B , the messages (cards/windows) may comprise a single or battery of physical and, or cognitive tasks and based on responses, further indicate a more nuanced EMS for a more tailored initial or subsequent (secondary) message. Responses may include a level of compliance, engagement, interaction, choices, etc. Secondary messages may be pushed from secondary prescribers or primary prescribers coupled to the EMS store. Furthermore, for deeper and more nuanced EMS definition, assigning an indication score or color-coded range to further convey EMS severity may be achievable. As a result, matching of message type to scored or color-coded EMS may produce a more refined match for pushing of even more personalized digital content or therapeutics to boost mood, alleviate anxiety, reduce stress, and improve psychological health or mental fitness by directing users to follow procedures proven to increase the production of beneficial molecules and neurotransmitters like Dopamine, Oxytocin, Acetylcholine, Serotonin, and GABA to deliver positive mood and mind-altering effects. 
       FIG. 21  illustrates a method flow chart for generating the hyper-personalized digital therapeutic pushed to the user-plotted dynamic EMS. The first step is: selecting at least one EMS for the user based on a user-plotted point on a displayed mood map to reflect at least a two-dimensional EMS along at least two correlates of behavior, said EMS indicating a granular assessment of at least one of a feeling, sensation, mood, mental state, emotional condition, or physical status of the user  1008 ; and step two entails delivering at least a primary-level message (digital therapeutic) personalized to the user based on at least one of a stored message coupled to the EMS  1009 . 
     Map engagement and hyper-personalized digital therapeutic content may be pushed to any number of user devices, such as a smart phone, smart watch, tablet, smart tv, or any device with a display feature. Dynamic EMS (dEMS) assessment via the mood map and content pushing may be rendered/delivered identically across the ecosystem of devices. In other embodiments, the rendering/delivery may be specifically formatted based on the device form factor/configurations. For instance, a smart watch format may alter the map presentation, plot mechanisms, and EMS store. The pushed content on a smaller form factor, such as a smart watch, may feature more text-based, static imagery, haptic effects, as opposed to long-form animated or video content. Another user device may feature the use of a home automation vocal-based hub, configured to recognize natural based language input and output. In such embodiments, dEMS may be rendered from vocal tone or responses to targeted question/s. In other embodiments, dEMS may be user-selected. The hyper-personalized digital therapeutic in response to the rendered or user-selected dEMS may be an audio output of a select EMS type from an audio-based EMS store. The audio output may comprise at least one of a narrative, sound, song, tune, voice message, etc. 
       FIG. 22  illustrates a method flow for real-time mood tracking and delivery of a digital therapeutic. In an embodiment of the invention, a method for mood tracking and delivery of a digital therapeutic comprising the steps of: detecting a release of a mood-governing chemical in a user&#39;s body via an adhesive detector patch (ADP)  2201 , assessing and tracking of the mood-governing chemical to determine an emotional or mental state (EMS) of a user  2202  and delivering at least a primary-level message digital therapeutic personalized to the user based on at least one of a stored message coupled to the emotional and mental state (EMS)  2203 . 
     Further yet, the mood-governing chemical is at least one of a neurotransmitter, neuromodulator or hormone wherein the neurotransmitter is at least one of, but not limited to acetylcholine, dopamine, serotonin, epinephrine, norepinephrine, glutamate, oxytocin, or gamma-aminobutyric (GAB A). Further yet, in an embodiment of the invention, the neuromodulator is at least one of opioid peptides, enkephalins, endorphins or dynorphins and wherein the hormone is at least one of growth hormone (GH), thyroid-stimulating hormone (TSH), adrenocorticotropic hormone (ACTH), follicle-stimulating hormone (FSH), luteinizing hormone (LH), beta endorphin, and prolactin. 
     In a preferred embodiment of the invention, the ADP is attached via at least one of subcutaneous, percutaneous, cutaneal and dermal on at least one part of the user&#39;s body. The detection of the release of the mood-governing chemical is performed via at least one of a sample of blood, sweat, heart rate, interstitial fluid using at least one of microneedle patch, Electrocardiography (ECG), photoplethysmography (PPG), Galvanic skin response (GSR), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), computed tomography (CT), Positron emission tomography (PET), electroencephalography (EEG), magnetoencephalography (MEG), near infrared spectroscopy (NIRS), surface-enhanced Raman scattering (SERS) and magnetic resonance spectroscopy (MRS). 
     Further yet, in another preferred embodiment of the invention, the ADP further comprises at least one of a drug capsule, micro-needle sensor array, ADP central processor, analogue signal preprocessor, memory element and radio transceiver. Once the micro-needle sensor array sends a signal to the ADP central processor, the ADP central processor reads, filters, calculates, saturates, and assesses at least one of the pulse rate value, salts, sweat, electrical conductance, interstitial fluid (ISF) and a blood oxygen value to assess the EMS of the user. Further yet, in an embodiment of the invention, the ADP central processor aggregates and assesses at least one of, heart rate, pulse rate value, interstitial fluid (ISF) or blood oxygen value and transmits aggregated data via at least one of a mobile communication device, wearable device, tablet, personal digital assistant (PDA) and Internet of Things (IoT). 
     In an embodiment of the invention, serotonin, Oxytocin, Endorphins, and Testosterone can be measured in the blood using ADP with micro-needle sensor array. Concentrations of these neurotransmitters, neuromodulators and hormones can be detected through micro blood samples in real-time using the ADP. Minimally invasive ADP detects molecules in dermal interstitial fluid (ISF). The adhesive detect patch (ADP) may have microneedles that collect ISF for on-patch biomarker analysis by surface-enhanced Raman scattering (SERS). Surface-enhanced Raman spectroscopy or surface-enhanced Raman scattering (SERS) is a surface-sensitive technique that enhances Raman scattering by molecules adsorbed on rough metal surfaces or by nanostructures such as plasmonic-magnetic silica nanotubes. This technique allows for the detection of single molecules. Further yet, the micro-needles (MNs) create micropores in the skin surface, through which microliter quantities of ISF is collected and placed onto plasmonic paper on the back surface of the patch. The plasmonic paper is prepared by immobilizing poly(styrene sulfonate) (PSS) coated gold nanorods (AuNRs) using a thin strip of filter paper using plasmonic calligraphy. Negatively charged PSS are used to bind positively charged rhodamine 6G (R6G), which serve as a model compound and thereby localize R6G on the AuNR surface. R6G bound on the AuNR surface is further detected and quantified by acquiring SERS spectra from the ADP. This approach is used to measure pharmacokinetic profiles of R6G in ISF and serum. 
     Additionally, in a preferred embodiment, further comprising generating an individualized health profile of the user based on at least one of detected release of the mood-governing chemical, physiological state, or the EMS state of the user. Further yet, the physiological state is at least one of, heart rate, pulse rate, respiratory rate, rate, skin conductance, eye tracking, sweat, blood oxygen and body temperature. 
     In another preferred embodiment of the invention, the EMS indicates a granular assessment of at least one of a feeling, sensation, mood, mental state, emotional condition, or physical status of the user. Additionally, in an embodiment of the invention wherein at least primary-level messages contain at least one of a text, image, sound, video, art asset, suggested action, or recommended behavior. These primary-level message maybe a suggestion or recommendation for the user to perform a task or consume content with clinically proven benefits to improve at least one of a mood, anxiety, stress, psychological state, emotional state, or physical state by altering levels of at least one neurotransmitter of the user. Further yet, an embodiment further comprises a personalized secondary-level message delivered to the user based on the user response to the delivered primary-level message, whereby the user response is a measure of at least one of a reaction, compliance, engagement, or interactivity with the delivered primary-level message. Additionally, in an embodiment the user response to at least the primary-level message is tracked based on at least one of a compliance or performance to at least one of a cognitive or physical task request. In an embodiment of the invention, the ADP are disposable. 
       FIG. 23  illustrates a method flow for mood tracking and delivery of a dosage-dependent therapeutic. In an embodiment of the invention, a method for mood tracking and delivery of a dosage-dependent therapeutic comprising the steps of: detecting the release of a mood-governing chemical in a user&#39;s body via an adhesive detector patch (ADP)  2301 ; assessing and tracking of the mood-governing chemical to determine an emotional or mental state (EMS) of a user  2302 ; and delivering at least a primary level dosage-dependent therapeutic via the ADP personalized to the user based on at least one of a stored message coupled to the emotional and mental state (EMS)  2303 . 
     Further yet, the mood-governing chemical is at least one of a neurotransmitter, neuromodulator or hormone wherein the neurotransmitter is at least one of acetylcholine, dopamine, serotonin, epinephrine, norepinephrine, glutamate, oxytocin, or gamma-aminobutyric (GAB A). Further yet, in an embodiment of the invention, the neuromodulator is at least one of opioid peptides, enkephalins, endorphins or dynorphins and wherein the hormone is at least one of growth hormone (GH), thyroid-stimulating hormone (TSH), adrenocorticotropic hormone (ACTH), follicle-stimulating hormone (FSH), luteinizing hormone (LH), beta endorphin, and prolactin. 
     In a preferred embodiment of the invention, the ADP is attached via at least one of subcutaneous, percutaneous, cutaneal and dermal on at least one part of the user&#39;s body. The detection of the release of the mood-governing chemical is performed via at least one of a sample of blood, sweat, heart rate, interstitial fluid using at least one of microneedle patch, Electrocardiography (ECG), photoplethysmography (PPG), Galvanic skin response (GSR), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), computed tomography (CT), Positron emission tomography (PET), electroencephalography (EEG), magnetoencephalography (MEG), near infrared spectroscopy (NIRS), surface-enhanced Raman scattering (SERS) and magnetic resonance spectroscopy (MRS). 
     In another preferred embodiment of the invention, the ADP further comprises at least one of a drug capsule, exterior flexible adhesive, micro-needle sensor array and ADP central processor. Alternatively, the ADP may be further comprised of a permeation enhancer, preservative, drug enhancer and a matrix filler. 
     Additionally, in an embodiment the ADP central processor is at least one of analogue signal preprocessor, memory element, radio transceiver. Further yet, in another preferred embodiment of the invention, the ADP further comprises at least one of a drug capsule, micro-needle sensor array, ADP central processor, analogue signal preprocessor, memory element and radio transceiver. Once the micro-needle sensor array sends a signal/message, the ADP central processor reads, filters, calculates, saturates, and assesses at least one of the pulse rate value, salts, sweat, electrical conductance, interstitial fluid (ISF) and a blood oxygen value to assess the EMS of the user. Further yet, in an embodiment of the invention, the ADP central processor aggregates and assesses at least one of, heart rate, pulse rate value, interstitial fluid (ISF) or blood oxygen value and transmits aggregated data via at least one of a mobile communication device, wearable device, tablet, personal digital assistant (PDA) and Internet of Things (IoT). 
     Additionally, in a preferred embodiment, further comprising generating an individualized health profile of the user based on at least one of detected release of the mood-governing chemical, physiological state, or the EMS state of the user. Further yet, the physiological state is at least one of, heart rate, pulse rate, respiratory rate, rate, skin conductance, eye tracking, sweat, blood oxygen and body temperature. 
     In another preferred embodiment of the invention the dosage-dependent therapeutic is delivered via at least one of a transdermal patch, gel-based transdermal patch, nasal spray, oral inhaler, tablet, sub-lingual tablet, and lozenge. Further yet, the primary level therapeutic is at least one of, specific dosage, sustained-release dosage, immediate-release dosage or modified-release dosage. Additionally, the ADP is at least one of single layer drug-in-adhesive, multi-layer drug-in-adhesive, reservoir, matrix or vapor patch. 
     In yet another embodiment of the invention, ADP uses sweat to collect data to track mood in real-time and deliver a dosage-dependent therapeutic. The ADP are ultrathin disposable with a stretchable design which enables conformal contact with the skin for efficient sweat collection and high performance under physical deformation. These ADP may have micro-needles on a bottom surface to deliver an appropriate amount of dosage-dependent drug for example, metformin or chloropropamide is transdermally delivered via microneedles. Additionally, in an embodiment the micro-needle sensor array performs a dual function of detecting a mood-governing chemical and delivering the dosage-dependent drug from the drug capsule. Alternatively, the micro-needle sensor array may perform a single function of detecting the release of a mood-governing chemical or delivering the dosage-dependent drug from the drug capsule. Further yet, in an embodiment of the invention, the micro-needle sensor array is at least solid, hollow, coated or dissolvable type. The solid micro-needles are a two-part system, wherein the microneedle array is first applied to the skin to create microscopic wells just deep enough to penetrate the outermost layer of skin, and then the drug is applied via transdermal patch. 
     Hollow microneedles are like solid microneedles in material. They contain reservoirs that deliver the drug directly into the site. Coated microneedles are usually designed from polymers or metals. In this method the drug is applied directly to the microneedle array instead of being applied through other patches or applicators. Dissolvable microneedles encapsulate the drug in a nontoxic polymer which dissolves once inside the skin. The polymer would allow the drug to be delivered into the skin and could be broken down once inside the body. 
     In an embodiment, the ADP may be assembled on the multichannel thermal actuator that can be periodically replaced with new ones. Additionally, the ADP&#39;s may be fabricated on a handle substrate and then transfer-printed onto a thin silicone patch. The ADP may alternatively, be fabricated on a thin polyimide (PI) substrate. Further yet, a micro-needle sensor array may be connected to the ADP central processor in a patterned electrochemical functionalization to complete the fabrication. 
     In yet another embodiment of the invention, palmar sweat along with electrical measurements on the skin—detected and measured via the ADP along with a combination of ECGs, PPG or HRV may be used to detect emotional, physiological and mental changes in the user. For example, galvanic skin response (GSR) is a continuous measurement of electrical parameters of human skin. Emotional changes induce sweat reactions, which are mostly noticeable on the surface of the hands, fingers, and the soles of the feet. Sweat reaction causes a variation of the amount of salt in the human skin which leads to the change of electrical resistance of the skin. When sweat glands become more active, they secrete moisture towards the skin surface which causes changes in the balance of positive and negative ions and affects the electrical currents&#39; flow property on the skin. Skin conductance is mainly related with the level of arousal—if the arousal level is increased, the conductance of the skin also increases. GSR signal amplitude is associated with stress, excitement, engagement, frustration, and anger, and the obtained measurement results correlate with the self-reported evaluation of arousal. In the GSR method, the electrical conductance of the skin is measured using one or two ADP&#39;s which may consist of special electrodes containing Ag/AgCl (silver-chloride) contact points with the skin. There are a variety of possibilities for placing ADP&#39;s but are usually attached to the fingers, wrist, shoulder, or feet. 
     For example, consider a senior year college student Jen has her final exams and hence she is stressed/nervous. The ADP would detect a change in the palmar sweat along with an in increase heart rate via ECG, HRV or PPG—assessed and tracked EMS of Jen relates to high cortisol levels—delivery of a digital therapeutic—“text a message on Jen&#39;s phone to take 5 deep breaths”. Alternatively, the ADP would deliver a dosage-dependent therapeutic—“ketoconazole” via the microneedles from the drug capsule. 
     In yet another embodiment of the invention, the dosage-dependent therapeutic is delivered via at least one of a gel-based transdermal patch, gum, rapid-release gum, nasal spray, oral inhaler, tablet, sub-lingual tablet, and lozenge. Additionally, multiple forms of delivery of dosage-dependent therapeutics, for example, nasal spray along with a sustained-release patch may be used to track a users&#39; mood. For example, Jen uses a nasal spray of “ketoconazole” along with a dermal patch to deliver a strong dose of “ketoconazole”. Alternatively, dosage-dependent therapeutic along with a digital therapeutic may be used to track a users&#39; mood. 
     In yet another embodiment of the invention, a method for mood tracking and delivery of a therapeutic comprising the steps of: detecting a release of a mood-governing chemical in a user&#39;s body via an adhesive detector patch (ADP); assessing and tracking of the mood-governing chemical to determine an emotional or mental state (EMS) of a user; delivering at least a primary level dosage-dependent therapeutic via the ADP personalized to the user based on at least one of a stored message coupled to the emotional and mental state (EMS) and delivering at least a secondary-level message digital therapeutic personalized to the user based on at least one of a stored message coupled to the emotional and mental state (EMS). 
     For example, Peter an ICU nurse and a father of newborn triplets. With the current health crisis of Covid-19, Peter works 14 hour days—Stress level is high—ADP would detect the high cortisol levels—Oura ring may detect lack of sleep—EMS selected/assessed Cortisol levels high—the ADP would release “Ashwagandha, holy basil, ginseng or any adaptogen” from the drug capsule via the microneedles for calmness. Additionally, a visual of the triplets would appear on the phone to calm Peter thus, lowering the stress levels thus, lowering cortisol. 
     Alternatively, associated between selected/assessed EMS and the therapeutic release may be pre-defined based on peer-reviewed research/credentialed opinions. Further yet, in an embodiment of the invention, the selected/assessed EMS may be user-selected scrolled, inputted, graphed, or plotted) or system-assessed (from an on-board camera; an on-board/off-board sensor; or third-party. The user inputted data for assessing EMS is at least one of a textual input, scrolled selection, and/or plotted/mapped in at least one of a x/y graph and/or wheel. The wheel, graph, plot or map may comprise a three-dimensional coordinate, wherein each coordinate represents a different EMS attribute. For example, the three-dimensional wheel, graph, or map may comprise a z-axis that appears projecting into or out of the display, indicative of a time-line (duration of time the user has experienced the assessed x- and y-EMS attributes). 
     EMS assessment may also be based on contextual data. Contextual data may be captured from at least one of an on-board camera, off-board camera, sensor (accelerometer, etc.), and/or third-party Application Programming Interface (API). Furthermore, the contextual data may be at least one of a current date, time, weather, temperature, geo-location, tracked physical activity, captured facial image, and/or tracked digital activity (digital footprint) as it relates to the user. 
       FIG. 24  illustrates a method for mood tracking and delivery of a targeted advertisement based on tracked psycho-emotional effect on an individualized health profile comprising the steps of: detecting the release of a mood-governing chemical in a user&#39;s body via an adhesive detector patch (ADP)  2401 ; assessing and tracking of the mood-governing chemical to determine an emotional, physiological or mental state (EMS) of the user  2402 ; generating the individualized health profile of the user based on at least one of the detected release of the mood-governing chemical, physiological state, or the EMS state of the user  2403 ; assigning a digital nutrition (DN) label to the individualized health profile, wherein the label is an indication of the intended psycho-emotional effect on the individualized health profile  2404 ; and triggering delivery of a targeted advertisement from a store, wherein the targeted advertisement is labeled with a digital diet score range covering for a score corresponding to a last viewed labeled content prior to an advertisement delivery trigger point  2405 . 
     Further yet, in an embodiment of the invention, Further yet, the mood-governing chemical is at least one of a neurotransmitter, neuromodulator or hormone wherein the neurotransmitter is at least one of acetylcholine, dopamine, serotonin, epinephrine, norepinephrine, glutamate, oxytocin, or gamma-aminobutyric (GABA). Further yet, in an embodiment of the invention, the neuromodulator is at least one of opioid peptides, enkephalins, endorphins or dynorphins and wherein the hormone is at least one of growth hormone (GH), thyroid-stimulating hormone (TSH), adrenocorticotropic hormone (ACTH), follicle-stimulating hormone (FSH), luteinizing hormone (LH), beta endorphin, and prolactin. 
     Further yet, in another embodiment of the invention, the DN label is the intended psycho-emotional effect expressed in terms of at least one of an intended neurotransmitter (NT), neuromodulator or hormone triggered. More importantly, in an embodiment of the invention, the DN label further comprises a prompted informational for at least one of an individualized health profile, intended pyscho-emotional effect, intended neurotransmitter triggered, therapeutics, or behavioral recommendations. Additionally, the DN labeling parses the individualized health profile into frames of cropped defined structures; process said frames through at least one of a convolutional classifier network or convolutional semantic segmentation network; match at least one processed frame against a library of stored content indicating at least one intended psycho-emotional effect on the user; and assign the DN label to the individualized health profile matched to the stored and indicated content, wherein the DN label is the intended psycho-emotional effect of the individualized health profile on the user. 
     In another embodiment of the invention, the individualized health profile labeled is based in part by a user context fed from at least one of a geo-location, social media engagement, and sensor-tracked motion or activity. Further yet, the advertisement delivery trigger point is based on a counter threshold, wherein the counter threshold is based on a duration of labeled content played or the number of labeled content played. Additionally, in an embodiment of the invention the advertisement delivery trigger point causes a DN advertisement player to search an DN advertisement store for an advertising content labeled with a DN diet score range covering the users DN diet score for delivery to the user. 
     In yet another embodiment of the invention, a method for mood tracking and delivery of a therapeutic, comprising the steps of: detecting a release of a mood-governing chemical in a user&#39;s body via an adhesive detector patch (ADP); assessing and tracking of the mood-governing chemical to determine an emotional or mental state (EMS) of a user; delivering at least a primary level dosage-dependent therapeutic via the ADP personalized to the user based on at least one of a stored message coupled to the emotional and mental state (EMS); and delivering at least a secondary-level message digital therapeutic personalized to the user based on at least one of a stored message coupled to the emotional and mental state (EMS). 
     Further yet, in an embodiment of the invention, once the EMS is defined, the method then calls for pushing a primary-level message personalized to the user based on at least one stored message coupled to the selected EMS. The primary level message is a dosage-dependent therapeutic via the ADP. Finally, pushing at least a secondary-level message personalized to the user based on a threshold-grade match of the user response to the pushed primary-level message with at least one stored response coupled to a stored primary-level message, whereby the user and stored response is a measure of at least one of a reaction, compliance, engagement, or interactivity with the pushed and, or stored primary-level message. The secondary-level messages may contain at least one of a text, image, sound, video, art asset, suggested action or recommended behavior. The efficaciousness or therapeutic value of the primary or secondary messages are validated by at least one—and typically two—independent sources of clinical research or peer-reviewed science, as verified by a credentialed EMS expert. 
     In another aspect, once the EMS is defined, the method may call for pushing at least a single-level message. The at least single message may contain at least one of a text, image, sound, video, art asset, suggested action or recommended behavior. Again, the efficaciousness or therapeutic value of the primary or secondary messages are validated by at least one—and typically two—independent sources of clinical research or peer-reviewed science, as verified by a credentialed EMS expert. 
     Alternatively, in an embodiment of the invention, it is also worth nothing that the delivery of either the digital therapeutic or dosage-dependent therapeutic from the ADP may or may not be dependent on a sensed condition (selected/assessed EMS, patch-detected cortisol levels, micro-needle-captured interstitial fluids, subcutaneously embedded sensors, accelerometer-detected physical exertion, etc.). In certain embodiments, the fixed-interval dispensing of a single or a plurality of therapeutics may be dependent on a user&#39;s pre-defined drug schedule. 
       FIGS. 25A and 25B  illustrate a perspective view of the ADP.  FIG. 25A  shows a top down view of the ADP while,  FIG. 25B  shows the drug capsule  2501 , ADP central processor  2502  and the flexible exterior adhesive  2503 . The main components of the ADP&#39;s are at least one of liner, drug capsule  2501 , exterior flexible adhesive  2503 , ADP central processor  2502 , membrane, backing with microneedle, microneedle array, permeation enhancer, stabilizer, preservatives, and matrix filler. The liner protects the patch during storage and is removed prior to use. The drug solution is in contact with the release liner. The adhesive flexible exterior layer  2503  serves to adhere the various components of the ADP with each other as well as to the skin. The membrane layer (not shown) controls the release of the drug from the drug capsule  2501 . Additionally, not shown the backing protects the ADP from the outer environment. Further yet, the permeation enhancer (not shown) promotes permeation of the therapeutics from the drug capsule  2501  to increase therapeutic delivery. Further yet, in an embodiment of the invention, the matrix filler (not shown) provides bulk to the matrix. 
     Further yet in an embodiment of the invention, the ADP may be the ADP is at least one of single layer drug-in-adhesive, multi-layer drug-in-adhesive, reservoir, matrix or vapor patch. Additionally, the ADP releases a primary level dosage-dependent therapeutic via at least one of, specific dosage, sustained-release dosage, immediate-release dosage or modified-release dosage. Modified-release dosage is a mechanism that delivers a drug with a delay after its administration (delayed-release dosage) or for a prolonged period of time (extended-release [ER, XR, XL] dosage) or to a specific target in the body (targeted-release dosage). Alternatively, in an embodiment of the invention Sustained-release dosage forms are dosage forms designed to release a drug at a predetermined rate in order to maintain a constant drug concentration for a specific period of time with minimum side effects. This can be achieved through a variety of formulations, including liposomes and drug-polymer conjugates (an example—hydrogels). Sustained release&#39;s definition is more akin to a “controlled release” rather than “sustained”. Extended-release dosage consists of either sustained-release (SR) or controlled-release (CR) dosage. SR maintains drug release over a sustained period but not at a constant rate. CR maintains drug release over a sustained period at a nearly constant rate. 
       FIGS. 26A and 26B  illustrate a perspective view of the ADP.  FIG. 26A  shows a side angle perspective of the ADP while,  FIG. 26B  shows a front view with micro-needles at the bottom end of the ADP. The main components of the ADP&#39;s are at least one of liner, drug capsule  2601 , exterior flexible adhesive  2603 ,  2606 , ADP central processor  2602 , membrane (not shown), microneedle  2605 ,  2607 , microneedle array  2604 , permeation enhancer (not shown), stabilizer (not shown), preservatives (not shown), and matrix filler (not shown). The liner protects the patch during storage and is removed prior to use. 
     In an embodiment of the invention, the ADP central processor  2602  further comprises at least one of (not shown) analogue signal preprocessor, memory element, radio transceiver. Additionally, the ADP central processor  2602  reads, filters, calculates, saturates, and assesses at least one of the pulse rate value, salts, sweat, electrical conductance and a blood oxygen value, interstitial fluid (ISF) value to assess the EMS of a user. Further yet, in an embodiment of the invention, the ADP central processor  2602  aggregates and assesses at least one of the heart rate, pulse rate value, interstitial fluid (ISF) or blood oxygen value and transmits aggregated data via at least one of a mobile communication device, wearable device, tablet, personal digital assistant (PDA) and Internet of Things (IoT). 
     In yet another embodiment of the invention, the mood tracking method further comprises generating an individualized health profile of the user based on at least one of detected release of the mood-governing chemical, physiological state, or the EMS state of the user, wherein the physiological state is at least one of, heart rate, pulse rate, interstitial fluid (ISF), respiratory rate, skin conductance, eye tracking, sweat, blood oxygen and body temperature. 
       FIGS. 27A and 27B  illustrate a perspective view of the microneedle array  2704 ,  2707  along with the ADP central processor  2702 .  2705  along the back of the exterior flexible adhesive  2703 ,  2706 . Serotonin, Oxytocin, Endorphins, and Testosterone can be measured in the blood using ADP with microneedle technology. Concentrations of these neuromodulators and hormones can be detected through micro blood samples in real-time. Minimally invasive ADP detect molecules in dermal interstitial fluid (ISF). An ADP collects ISF for on-patch biomarker analysis by surface-enhanced Raman scattering (SERS). The microneedles (MNs) create micropores in the skin surface, through which microliter quantities of ISF is collected and placed onto plasmonic paper on the ADP backing. The plasmonic paper is prepared by immobilizing poly(styrene sulfonate) (PSS) coated gold nanorods (AuNRs) using a thin strip of filter paper using plasmonic calligraphy. Negatively charged PSS are used to bind positively charged rhodamine 6G (R6G), which serve as a model compound and thereby localize R6G on the AuNR surface. R6G bound on the AuNR surface is detected and quantified by acquiring SERS spectra from the plasmonic paper ADP. This approach is used to measure pharmacokinetic profiles of R6G in ISF and serum. 
     The ADP central processor  2702 ,  2705  then reads, filters, calculates, saturates, and assesses at least one of the pulse rate value, salts, sweat, electrical conductance and a blood oxygen value, interstitial fluid (ISF) value to assess the EMS of a user. Further yet, in an embodiment of the invention, the ADP central processor  2702 ,  2705  aggregates and assesses at least one of the heart rate, pulse rate value, interstitial fluid (ISF) or blood oxygen value and transmits aggregated data via at least one of a mobile communication device, wearable device, tablet, personal digital assistant (PDA) and Internet of Things (IoT). 
     In yet another embodiment of the invention, the mood tracking method further comprises generating an individualized health profile of the user based on at least one of detected release of the mood-governing chemical, physiological state, or the EMS state of the user, wherein the physiological state is at least one of, heart rate, pulse rate, interstitial fluid (ISF), respiratory rate, skin conductance, eye tracking, sweat, blood oxygen and body temperature. 
     The claimed invention leverages existing clinical research and proven science (already published in peer-reviewed journals) and repackages insights as content modules or behavioral interventions that are simpler, more seductive, and profoundly more fun than traditional analogue therapies or digital treatment regimen. Described more simply, the system and platform curates existing digital content, and creates entirely new content programs, informed by and centered around techniques proven to boost mood, alleviate anxiety, reduce stress, and improve psychological health or mental fitness by directing users to follow procedures proven to increase the production of beneficial molecules and neurotransmitters like Dopamine, Oxytocin, Acetylcholine, Serotonin, and GABA to deliver positive mood and mind-altering effects. 
     Embodiments are described at least in part herein with reference to flowchart illustrations and/or block diagrams of methods, systems, and computer program products and data structures according to embodiments of the disclosure. It will be understood that each block of the illustrations, and combinations of blocks, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the block or blocks. 
     These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function/act specified in the block or blocks. 
     The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus, to produce a computer implemented process such that, the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts specified in the block or blocks. 
     In general, the word “module” as used herein, refers to logic embodied in hardware or firmware, or to a collection of software instructions, written in a programming language, such as, Java, C, etc. One or more software instructions in the unit may be embedded in firmware. The modules described herein may be implemented as either software and/or hardware modules and may be stored in any type of non-transitory computer-readable medium or other non-transitory storage elements. 
     Some non-limiting examples of non-transitory computer-readable media include CDs, DVDs, BLU-RAY, flash memory, mobile device, remote device, and hard disk drives.