Systems and methods for treating memory disorders

Abstract

Disclosed herein are systems and methods for treating memory disorders, and more particularly, systems and methods relating to customized visual representations for treating memory-related disorders and diseases. The disclosed systems and methods can predict a patient's clinical status based on platform user behavior, e.g., patient behavior. This Abstract is intended as a screening tool for research in the particular technical field, and is not intended to limit the invention. [Selected Figure] Figure 3A

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to U.S. Provisional Patent Application No. 62 / 711,306, filed July 27, 2018, which is incorporated herein in its entirety. It is contemplated that the referenced applications may be applicable to the concepts and embodiments disclosed herein, even though such concepts and embodiments may be disclosed in the referenced applications having different limitations and configurations and described using different examples and terminology.

[0002] The present disclosure relates generally to systems and methods for treating memory disorders, and more particularly to customized visual representations for treating memory-related disorders and diseases. [Background technology]

[0003] Individuals with memory impairment and / or dementia, such as those with Alzheimer's disease (AD) and related dementias (ADRD), may be prone to stress, frustration, agitation, and frequent anger and withdrawal when they experience failure in tasks and activities of daily living (ADLs), especially those that they were previously able to undertake and complete without difficulty. For those with dementia, the inability to perform ADLs increases the risk of institutionalization and loss of independent living. For caregivers, this increases the levels of stress, depression, and physical strain.

[0004] The range of therapies available today to help individuals with AD / ADRD perform activities of daily living is very limited. Currently available pharmaceutical treatments provide only modest improvements in cognition and are associated with significant side effects. Furthermore, many current strategies for treating memory disorders rely heavily on strengthening dysfunctional areas of the memory system. However, these strategies to reinforce dysfunctional areas of the memory system can only work with respect to areas that progressively worsen and may only be effective for a relatively short time course of progressive disease. In the United States, AD / ADRD affects as many as 5 million people and nearly 40% of the population aged 85 years or older. By 2050, it is projected that approximately 13.2 million older Americans will have AD / ADRD.

[0005] Thus, there remains a need for new systems and methods for treating memory disorders and memory-related diseases, such as AD / ADRD. This and other needs are met by various aspects of the present disclosure. Summary of the Invention

[0006] This Summary is provided to introduce a selection of concepts in a simplified form that are described in more detail below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

[0007] Both the above summary and the following detailed description provide examples and are explanatory only. Thus, the above summary and the following detailed description should not be considered limiting. Furthermore, features or variations may be provided in addition to those described herein. For example, embodiments may be directed to combinations and subcombinations of various features described in the detailed description.

[0008] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present disclosure. The drawings contain various trademark and copyright notices owned by the applicant. In addition, the drawings may contain other marks owned by third parties and are used for illustrative purposes only. All rights to the various trademarks and copyrights shown herein, except as belonging to their respective owners, belong to and are the property of the applicant. The applicant retains and reserves all rights in the trademarks and copyrights contained herein and permits reproduction of the materials only in connection with reproduction of the granted patent and not for other purposes.

[0009] Additionally, the figures may include text or captions that may describe particular embodiments of the present disclosure, which text is included for illustrative, non-limiting, and explanatory purposes of the particular embodiments detailed in the present disclosure. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 illustrates a block diagram of an operating environment according to an exemplary embodiment of the present disclosure. [Diagram 2] 1 illustrates a template of a visual representation provided by a platform according to an exemplary embodiment of the present disclosure. [Figure 3A] 1 illustrates a customized visual representation provided by a platform according to an exemplary embodiment of the present disclosure. [Figure 3B] 1 illustrates a customized visual representation provided by a platform according to an exemplary embodiment of the present disclosure. [Figure 4] 1 illustrates a flowchart of a method for generating a customized visual representation of a platform according to an exemplary embodiment of the present disclosure. [Diagram 5] 1 shows a flowchart of a method for treating memory impairment using a customized visual representation of a platform according to an exemplary embodiment of the present disclosure. [Figure 6]1 illustrates an example of how the platform may track patient user activity according to an exemplary embodiment of the present disclosure. [Figure 7A] 1 illustrates a method for creating a user profile based on user behavior according to an exemplary embodiment of the present disclosure. [Figure 7B] 1 illustrates a method for creating a user profile based on user behavior according to an exemplary embodiment of the present disclosure. [Figure 7C] 1 illustrates a method for creating a user profile based on user behavior according to an exemplary embodiment of the present disclosure. [Figure 8] 1 illustrates a user's platform user behavior based on which a user profile may be created according to an exemplary embodiment of the present disclosure. [Figure 9] 9 illustrates a platform page 900 showing screenshots of user interface screens corresponding to attribute elements from a visual representation related to showering, according to an exemplary embodiment of the present disclosure. [Figure 10] 1 illustrates a neural network model for predicting and identifying future clinical outcomes according to an exemplary embodiment of the present disclosure. [Figure 11A] 1 illustrates a dashboard for displaying various visual representations of a platform, customized visual representations, user data, and / or analytical data, according to an exemplary embodiment of the present disclosure. [Figure 11B] 1 illustrates a dashboard for displaying various visual representations of a platform, customized visual representations, user data, and / or analytical data, according to an exemplary embodiment of the present disclosure. [Figure 11C] 1 illustrates a dashboard for displaying various visual representations of a platform, customized visual representations, user data, and / or analytical data, according to an exemplary embodiment of the present disclosure. [Figure 12] 1 shows the brain structure of a normal brain and an Alzheimer's brain. [Figure 13]1 illustrates a block diagram of a computing device consistent with an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] As a preliminary matter, those skilled in the relevant art will readily appreciate that the present disclosure has broad utility and applications. As will be understood, any embodiment may incorporate only one or more of the above-disclosed aspects of the present disclosure, and may further incorporate only one or more of the above-disclosed features. Furthermore, any embodiment discussed and identified as "preferred" is considered to be some of the best modes contemplated for carrying out the embodiments of the present disclosure. Other embodiments may also be considered for additional illustrative purposes in providing a complete and enabling disclosure. As will be understood, any embodiment may incorporate only one or more of the above-disclosed aspects of the display, and may further incorporate only one or more of the above-disclosed features. Furthermore, many embodiments, such as adaptations, variations, modifications, and equivalent arrangements, are implicitly disclosed by the embodiments described herein and fall within the scope of the present disclosure.

[0012] Thus, while the embodiments have been described in detail herein with reference to one or more embodiments, it should be understood that this disclosure is illustrative and exemplary of the present disclosure, and is made only for the purpose of providing a complete and enabling disclosure. The detailed disclosure herein of one or more embodiments is not intended, and should not be construed, as limiting the scope of patent protection conferred in any claim of an issued patent, which scope is defined by the claims and their equivalents. The scope of patent protection is not intended to be defined by reading into any claim any limitations found herein that are not expressly set forth in the claim itself.

[0013] Thus, for example, any sequence(s) and / or chronological order of steps of various processes or methods described herein are illustrative and not limiting. Thus, although various process or method steps may be shown and described in a sequence or chronological order, it should be understood that any such process or method steps are not limited to being performed in any particular sequence or order unless otherwise indicated. In fact, steps in such processes or methods can generally be performed in a variety of different sequences and orders while still falling within the scope of the present invention. Thus, it is intended that the scope of patent protection be defined by the issued claim(s) rather than by the description set forth herein.

[0014] In addition, it is important to note that each term used herein refers to what a person of ordinary skill in the art would understand that term to have based on the contextual use of such term in this specification. To the extent that the meaning of a term used herein, as understood by a person of ordinary skill in the art based on the contextual use of such term, differs in any way from any particular dictionary definition of that term, it is intended that the meaning of the term as understood by a person of ordinary skill in the art shall control.

[0015] With respect to the applicability of 35 U.S.C. ...

[0016] Additionally, it is important to note that, as used herein, "a" and "an" each generally mean "at least one," but do not exclude plurals unless contextual usage dictates otherwise. When used herein to add to a list of items, "or" means "at least one of the items," but does not exclude multiple items of the list. Finally, when used herein to add to a list of items, "and" means "all of the items of the list."

[0017] Ranges can be expressed herein as from "about" one particular value and / or to "about" another particular value. When such a range is expressed, another embodiment includes from one particular value and / or to the other particular value. Similarly, when values ​​are expressed as approximations, by using the antecedent "about," it will be understood that the particular value forms another embodiment. It will be further understood that each endpoint of a range is significant in relation to the other endpoint, and independently of the other endpoint. It is also understood that there are a number of values ​​disclosed herein, and that each value is also disclosed herein as "about" that particular value in addition to the value itself. For example, if the value "10" is disclosed, then "about 10" is also disclosed. It is also understood that each unit between two particular units is also disclosed. For example, if 10 and 15 are disclosed, then 11, 12, 13, and 14 are also disclosed.

[0018] Reference in this specification and in the eventual claims to parts by weight of a particular element or component refers to the weight relationship between that element or component and any other element or component in the article for which the parts by weight are expressed. Thus, in a compound containing 2 parts by weight of component X and 5 parts by weight of component Y, X and Y are present in a weight ratio of 2:5, and are present in such ratio whether or not the compound includes additional components.

[0019] As used herein, the term "optionally" or "optionally" means that the subsequently described event or circumstance may or may not occur, and that the description includes cases where the event or circumstance occurs and cases where it does not occur.

[0020] As used herein, the term "subject" may be a vertebrate, such as a mammal, fish, bird, reptile, or amphibian. Thus, the subject of the methods disclosed herein may be a human, a non-human primate, a horse, a pig, a rabbit, a dog, a sheep, a goat, a cow, a cat, a guinea pig, or a rodent. The term does not imply a particular age or sex. Thus, it is intended to encompass adult and newborn subjects, whether male or female, as well as fetuses. In one embodiment, the subject is a mammal. A patient refers to a subject suffering from a disease or disorder. The term "patient" includes human and veterinary subjects. In some embodiments of the disclosed methods, the subject has been diagnosed as needing treatment for one or more disorders prior to the presenting step. In some embodiments of the disclosed methods, the subject has been diagnosed as needing treatment for one or more age-related disorders or diseases prior to the presenting step. In some embodiments of the disclosed methods, the subject has been diagnosed with a chronic lung disease prior to the presenting step. In some embodiments of the disclosed methods, the subject has been diagnosed with a chronic lung disease prior to the presenting step.

[0021] As used herein, the term "treatment" or "treating" refers to the medical management of a patient with the intent to cure, ameliorate, stabilize, or prevent a disease, condition, or disorder. This term includes active treatment, i.e., treatment specifically aimed at ameliorating a disease, condition, or disorder, and also includes causal treatment, i.e., treatment aimed at eliminating the cause of the associated disease, condition, or disorder. In addition, this term includes symptomatic treatment, i.e., treatment designed to relieve symptoms rather than cure the disease, condition, or disorder; preventive treatment, i.e., treatment aimed at minimizing or partially or completely inhibiting the onset of the associated disease, condition, or disorder; and supportive treatment, i.e., treatment used to complement another specific therapy aimed at ameliorating the associated disease, condition, or disorder. In various embodiments, the term encompasses any treatment of a subject, including a mammal (e.g., a human), including (i) preventing the disease from occurring in a subject who may be predisposed to the disease but has not yet been diagnosed as having it, (ii) inhibiting the disease, i.e., arresting its onset, or (iii) relieving the disease, i.e., causing regression of the disease. In one embodiment, the subject is a mammal, such as a primate, and in a further embodiment, the subject is a human. The term "subject" also includes domestic animals (e.g., cats, dogs, etc.), livestock animals (e.g., cows, horses, pigs, sheep, goats, etc.), and laboratory animals (e.g., mice, rabbits, rats, guinea pigs, fruit flies, etc.).

[0022] As used herein, the term "prevent" or "preventing" refers to preventing, avoiding, forestalling, preventing, stopping, or impeding something from happening, especially by prior action.When reduce, inhibit, or prevent are used herein, it should be understood that the use of the other two words is also expressly disclosed unless otherwise indicated.For example, preventing memory-related disease or disorder means reducing, delaying, or reversing the incidence of memory-related or related disease or disorder.

[0023] As used herein, the term "diagnosed" means that a person has been physically examined by a person skilled in the art, e.g., a physician, and has been found to have a condition that can be diagnosed or treated by the compounds, compositions, or methods disclosed herein. In some embodiments of the disclosed methods, the subject has been diagnosed as needing treatment for a memory-related disorder or disease prior to the presenting step. As used herein, phrases such as "identified as needing treatment for a disorder" refer to the selection of a subject based on the need for treatment for a disorder. In one embodiment, it is contemplated that the identification can be performed by a person different from the person who performs the diagnosis. It is also contemplated that in further embodiments, the presentation can be performed by a person who subsequently performs the presentation.

[0024] As used herein, the term "providing" refers to any method of administering or dispensing a therapeutic agent to a subject.

[0025] As used herein, the terms "effective amount" and "effective amount" refer to an amount sufficient to achieve a desired result or affect an undesired condition. For example, a "therapeutically effective amount" refers to an amount sufficient to achieve a desired therapeutic result or affect an undesired condition, but generally insufficient to cause adverse side effects. The specific therapeutically effective level for any particular patient will depend on a variety of factors, including the disorder being treated and the severity of the disorder, the patient's age, weight, general health, sex and diet, administration time, administration frequency, and duration of treatment. In further various embodiments, the therapeutic agent can be administered in a "prophylactically effective amount", i.e., an amount effective for the prevention of a disease or condition.

[0026] As used herein, the term "memory-related disorder" or "memory-related disease" refers to a disorder or disease in which memory impairment is a major risk factor. In some embodiments, memory-related disease or disorder can be based on the type of disease, including but not limited to degenerative diseases, including neuronal degenerative diseases (Alzheimer's disease, Parkinson's disease, vascular dementia, mixed dementia, frontotemporal dementia, Huntington's disease, chronic traumatic encephalopathy (CTE)); mental health disorders, including schizophrenia, mood disorders, including depression and anxiety; and functional decline disorders or conditions, including age-related decline, or other so-called "benign age-related changes" that do not have a specific neurodegenerative or biological basis, such as memory decline, cognitive decline. In other embodiments, memory-related disease or disorder can also be classified based on the type of brain cell or region involved, such as the hippocampus of the brain in Alzheimer's disease. In yet other embodiments, memory-related disease or disorder can also be classified based on the type of brain cell or region spared from memory-related disease or disorder, such as the neostriatum of the brain in Alzheimer's disease.

[0027] As used herein, the term "page" or "screen" refers to a user screen or page within the platform that is displayed to a user for viewing and / or interacting with the platform. For example, an "attribute element screen" or "attribute element page" refers to a platform page or user screen that is shown to a user, such as a patient, and that corresponds to an attribute element in a visual representation that includes content related to the attribute element.

[0028] The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. Although many embodiments of the present disclosure may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements shown in the drawings, and the methods described herein may be modified by substituting, reordering, or adding steps to the disclosed methods. Therefore, the following detailed description does not limit the disclosure. Instead, the appropriate scope of the disclosure is defined by the appended claims. The present disclosure includes headings. It should be understood that these headings are used as a reference and should not be interpreted as limiting the subject matter disclosed under them.

[0029] The present disclosure provides various embodiments, features, and aspects that solve problems presented by the current state of the art, either individually or in various combinations, as described herein.

[0030] A. Platform Overview Consistent with various embodiments and aspects of the present disclosure, provided herein are devices, systems, methods, and / or techniques (hereinafter referred to as "platforms") for treating memory disorders using customized visual representations of a theme, such as events or activities. A platform consistent with embodiments of the present disclosure can be used by a clinician or medical institution to relatively accurately determine statistics regarding patients and groups of such patients using the platform. Such statistics can be used by the platform to predict, for example, but not limited to, the clinical status of a patient or group of patients. The present disclosure, in one aspect, recognizes and addresses the problem of presenting large amounts of information to an individual with memory disorders during a visual-based therapeutic technique. This Summary is provided to introduce a selection of concepts in a simplified form that are described in more detail below. This Summary is not intended to identify key features or essential features of the claimed subject matter. Nor is this Summary intended to be used to limit the scope of the claimed subject matter.

[0031] In various embodiments, the platform can be used by individuals (e.g., users, patients, etc.) with dementia or other forms of memory impairment to overcome individual challenges of daily living activities. In further embodiments, the platform can generate personalized visual representations using visual representation templates that visually lay out the daily living activities that these individuals with dementia or other forms of memory impairment will encounter. By repeatedly using the visual representations of the platform, individuals with memory impairment can eventually acquire habit routines through repeated consultation of the visual representations and maps of the present invention. In still further embodiments, the platform can maintain or strengthen the habit regions of the patient's brain (e.g., striatum and basal ganglia) by acquiring and remembering daily habits, even in certain patients with Alzheimer's disease.

[0032] In a further aspect, the positive results of using a platform that allows memory disorder patients to continuously access the information they need can include, but are not limited to, reduced stress, confusion, frustration, and anger, because they do not have to constantly worry that they may have forgotten something.In addition, patients may become less dependent on caregivers and more independent, despite their illness.At the same time, the stress and burden experienced by caregivers can be greatly reduced, because patients may not constantly ask the same questions.Overall, the relationship and communication between patients and caregivers can become more meaningful and productive.

[0033] In an exemplary aspect, the present disclosure provides a method for preparing or customizing a visual representation of a theme, the method including: providing a parent visual element displaying information related to a selected or specified theme, the parent visual element being linked or associated with at least one attribute visual element; providing at least one attribute visual element based on at least the selected theme, the at least one attribute visual element being configured to be linked with the parent visual element in a linking pattern or arrangement; modifying the at least one attribute visual element to convey information related to at least one of the parent visual element and the theme, and displaying visual indicators configured to determine a relationship between the parent visual element and the at least one attribute visual element, the at least one attribute visual element and the theme, and at least one of the at least one attribute visual element and another attribute visual element; generating a visual representation including the parent visual element linked with the modified at least one attribute visual element in a linking pattern or arrangement based on at least a predetermined visual representation template; and displaying the visual representation, the visual representation being configured for interaction with a subject or user.

[0034] In another exemplary aspect, the present disclosure also provides a system for preparing a visual representation of a theme, the system comprising: at least one memory having computer-accessible instructions; and at least one processor operatively coupled to the at least one memory and configured by at least a portion of the computer-accessible instructions to: provide a parent visual element displaying information related to a selected or specified theme, the parent visual element being linked or associated with at least one attribute element; provide at least one attribute element based on at least the selected theme, the at least one attribute element configured to be linked with the parent visual element in a linking pattern or arrangement; modify the at least one attribute element to convey information related to at least one of the parent visual element, the theme, and display content configured to determine a relationship between the parent visual element and the at least one attribute element, the at least one attribute element and the theme, and the at least one attribute element and at least one of another attribute element; generate a visual representation including the parent visual element linked with the at least one attribute element modified in a linking pattern or arrangement based on at least a predetermined visual representation template; and display the visual representation, the visual representation configured for interaction with a subject or user.

[0035] In another exemplary embodiment, the present disclosure also provides a computer-readable non-transitory storage medium that includes a set of instructions that executes the disclosed method.In yet another exemplary embodiment, the present disclosure also provides a device and a system that are configured to execute one or more steps of the disclosed method.In yet another exemplary embodiment, the present disclosure also provides a method of treating memory-related disorders using the disclosed system and visual representation template, comprising presenting a customized visual representation to a subject, the visual representation being configured for interaction with the subject.

[0036] In another exemplary embodiment, the present disclosure also provides a method and system for dynamically adjusting or modifying a platform, such as a visual representation, based on user characteristics and behavior. In yet another exemplary embodiment, the present disclosure also provides a method and system for relatively accurately determining statistics about users and groups of such users who interact with the platform. Such statistics can be used by the platform, for example, but not limited to, to predict the clinical status of the user and develop models of trajectories through the disease. In some embodiments, the user behavior data can indicate the clinical status of the user. In other embodiments, data associated with user behavior can be used to help guide clinicians' treatment choices and allow families time to prepare for upcoming changes.

[0037] Thus, in yet another exemplary aspect, the present disclosure also provides a method and system for predicting a patient's clinical status based on the patient's platform user behavior. The embodiments of the present disclosure may operate in a number of different environments. For example, in a first aspect, the platform may receive a notification that the patient has visited a visual representation. The platform may then scan the page to collect raw data from the page. For example, the platform may search the visual representation for keywords or phrases using various algorithms, including, but not limited to, machine learning techniques, natural language processing, and digital signal processing (audio / image / video data). Still consistent with the embodiments of the present disclosure, the platform may receive raw data, for example, as it tracks and / or monitors the patient-user through navigation and interaction with the visual representation. Tracking may include, for example, but not limited to, scanning each user interface page visited or pages in the visual representation to create a profile for the visual representation. As described in more detail below, the profile may be generated, for example, by the aforementioned algorithms used to collect the raw data of the visual representation.

[0038] Thus, in some embodiments, a user's interaction with the platform and multiple platform servers, e.g., content servers, visual representation servers, etc., may be monitored. For example, when a user views a visual representation, a tracker or monitor may be initiated to store information related to the user and / or the user's interaction with the visual representation. For example, the tracker may be initiated on the server side and may include information such as a timestamp corresponding to the user's viewing of the visual representation and one or more identifiers associated with the user. The one or more identifiers may be, for example, a network identifier such as a platform protocol (IP) number and / or MAC number, a device identifier such as an IMEI number, a software environment identifier such as an OS name, a browser name, a user identifier such as an email address, a first name, a last name, a middle name, a zip code, etc., as well as values ​​of context variables such as a GPS location of the device used to access the visual representation, sensor readings of the device or a wearable device while accessing the visual representation.

[0039] In some embodiments, the one or more identifiers may uniquely identify the user while preserving the user's anonymity. In other embodiments, the one or more identifiers may be encrypted to render the one or more identifiers unreadable to other users while maintaining the ability of the one or more identifiers to uniquely identify the user. For example, in some cases, the tracker may be instantiated on the client side, where the tracker may reside on a user device, such as a tablet or laptop computer. Thus, any information collected by the tracker may remain accessible in a human-readable form only within the user device. However, prior to transmitting the tracker to the server side, the collected information may be hashed. Thus, in some embodiments, information related to the user in a human-readable form may not be available on the server side. In this manner, the user may be assured of privacy protection.

[0040] In further aspects, embodiments of the platform may be used by a clinician or caregiver to determine the clinical status and / or identify patient-users of the platform who may be "in need" of increased monitoring or clinical intervention, e.g., using the calculated confidence scores. Thus, targeted information, e.g., patient-user behavior and / or biometric data, may be presented to such clinician or caregiver to assist them in making informed patient care decisions, while also increasing the likelihood of effective use and compliance of the patient-user with the platform. Although certain embodiments of the present disclosure may be disclosed with reference to a "clinician" or "caregiver" as a platform user, any individual or entity may be a platform user.

[0041] In a further aspect, the present disclosure provides a method and system for predicting a patient-user who needs a clinical intervention based on collecting and scoring (either statistically or using machine learning) data extracted from user behavior data by the patient-user's interaction with the platform. One possible user of the platform can be, for example, a clinician. The clinician can join or access a network with multiple patients (e.g., a patient network) to collectively determine the clinical status of the patient-user. The network can be associated with multiple trackable patient-users. Thus, the present disclosure allows the patient-user to determine whether or not he / she is "in need" of clinical services provided by the clinician. This is advantageous because if the patient is "in need," the clinician can reassess the patient, perform appropriate interventions, and / or adjust the personalized patient platform instance to increase the likelihood that the patient will remain compliant and not experience a worsening clinical status.

[0042] In some embodiments, a platform user may be a caregiver or family member, rather than a clinician. To this end, a platform user, such as a family member or caregiver, may specify certain criteria for determining a particular patient user's clinical status or state of need, without having to join a network. As an example, a platform user may specify that they intend to evaluate a particular patient on the platform, regardless of whether the platform user is a caregiver or family member. The platform may then begin analyzing aggregated platform behavioral data from only the particular patient's use of the platform.

[0043] Upon analysis, the platform may determine that the patient visited a particular ADL visual representation on multiple occasions. Thus, by tracking the patient's platform activity, the methods and systems disclosed herein may determine that the patient viewed the visual representation of the "medication ADL" five times and focused on the attribute element page corresponding to "pills." As described in detail herein, such information may be extracted by the platform from multiple data sources using multiple techniques, such as, but not limited to, using a tracker or monitor that runs on the patient device when the user visits them. Furthermore, as described in detail below, key elements extracted from the accessed pages may be analyzed to determine whether the patient is in need of one or more clinical interventions. In some embodiments, the disclosed methods and systems may also calculate a confidence score associated with the person's need status with respect to clinical intervention. For example, the methods and systems disclosed herein may determine that 73% of the patients require increased monitoring, 59% require adjustments to the complexity or content of the patient's visual representation, and 32% require a neurobehavioral evaluation.

[0044] Embodiments of the platform may further be used to enable platform users (e.g., clinicians, caregivers, or patient care facilities) to better monitor their patients. Thus, data obtained, aggregated, and processed by the platform may be provided to platform users. For example, an application program interface (API) may provide statistics regarding single patients (e.g., the likelihood that a patient's clinical condition is deteriorating), or groups of patients (e.g., patients residing in nursing homes and / or patients sharing a common diagnosis). Such statistics may be provided, for example, in lists, charts, and graphs. Additionally, searchable and filterable raw data may be provided. In some embodiments, the data may be provided to licensed users, such as authorized clinicians within a medical institution. For example, for a patient with identified data, such as a diagnosis of progressive Alzheimer's disease with a list of known patients, the clinician may use the data, for example, to further monitor those list of known patients or to predict changes in clinical status.

[0045] Both the above summary and the following detailed description provide examples and are explanatory only. Thus, the above summary and the following detailed description should not be considered limiting. Furthermore, features or variations may be provided in addition to those described herein. For example, embodiments may be directed to combinations and subcombinations of various features described in the detailed description.

[0046] B. Platform Configuration In one aspect, FIG. 1 illustrates one possible operating environment in which a platform according to an exemplary embodiment of the present disclosure can be provided. In a further aspect, and as a non-limiting example, the platform 100 can be hosted on a centralized server 110, such as, for example, a cloud computing service. The centralized server can communicate with other network entities, such as, for example, multiple host servers and user devices (e.g., laptop computers, smartphones, tablet computers, desktop computers, wearable devices, smart speakers, etc.). In addition, in some embodiments, the centralized server can also communicate with other entities, such as databases, wearable devices, smart speakers, etc. In general, the centralized server can be configured to communicate with any entity that can provide user behavioral data or clinical data representative of a patient's clinical status. A user 105, who may be a patient ("patient user") or a clinician ("clinician user"), can access the platform 100 through a software application. The software application can be embodied, for example, but not limited to, as a website, a web application, a desktop application, and a mobile application compatible with the computing device 1600. One possible embodiment of the software application may be provided by the MapHabit System™ (MHS) suite of products and services offered by MapHabit™ Inc.

[0047] In various aspects, the customized visual representation of the platform may be based on visual representation templates and / or historical information indicative of one or more user (e.g., patient) behavioral responses or user behavior data during interactions with the platform, such as, for example, visual-based interactions of attribute elements within visual representations associated with a theme, such as steps in an activity or event details, etc. Such interactions may relate to, for example, confirming steps to take medication, preparing a recipe, or remembering details of a planned movie outing. In further aspects, a parent visual element may display content, such as visual cues or information indicative of a theme, and an attribute visual element may display content, such as visual cues or information, e.g., textual data, indicative of an attribute of the theme. In still further aspects, the historical information may include information indicative of a group of attribute visual elements that correspond to a theme. In still further aspects, the visual elements may be associated with content of a theme or an attribute of the theme, such that the historical information may include information indicative of user behavior associated with content related to the theme or attribute. To this end, the historical information may enable the generation of user interaction and / or viewing statistics, such as, for example, frequency of presentation of a visual element or content, interval of display of a visual element or content, etc., as described herein. In further aspects, at least a portion of the content associated with the visual elements may be generated by the platform based on visual representation templates, selection criteria, and / or in response to user-based interactions or behavioral evaluations of different users. In still further aspects, each of the attribute visual elements may include content including multiple media assets or visual landmarks associated with a particular attribute, and each such media asset or visual landmark may correspond to a particular aspect or characteristic of the attribute visual element.

[0048] In further embodiments, the platform can determine one or more groups of salient attribute elements for a theme. Each group of salient attribute elements can have a salience level. The salience level can be associated with or correspond to a user condition, such as a level of memory impairment. The salient attribute elements can be visual elements that are collectively preferred or otherwise perceived by users utilizing a visual representation of a given theme. One or more groups of salient attribute elements can be used to create a visual representation template for a given theme, and in one embodiment, build a customized visual representation for a user. Thus, some visual representation templates can result in multiple customized visual representations. For a particular group of visual representation templates, various selection criteria can be utilized to identify a visual representation template that is appropriate for a user characteristic or theme.

[0049] In further aspects, various themes generally associated with an activity, event, or thing represented by a parent visual element, and multiple attribute elements associated with thematic attributes, may be available for user-based interaction or browsing activity (e.g., persisted in a network repository or in its memory) as further described herein. A visual element may embody, include, display, or otherwise present visual cues and / or content such as media assets. In some aspects, visual cues may include at least one of alphanumeric characters, text, objects, shapes, pictures, symbols, icons, colors, etc., or combinations thereof. In other aspects, media assets may include digital content (digital image(s) and / or video, digital audio, combinations thereof, etc.). In further aspects, content may include content associated with one or more sensory modalities, including (but not limited to) visual, auditory, and tactile. In still further aspects, sensory modalities may include sensory aspects of the content or types and locations of sensory receptors activated by the content. In still further aspects, content may include content associated with multiple sensory modalities. In some aspects, content related to multiple sensory modalities may be presented simultaneously. Presenting content associated with a parent element or attribute element to a user may include a simultaneous modality presentation (CMP) in which one or more visual cues and one or more media assets, or multiple media assets, are presented to a user simultaneously. For example, a simultaneous modality presentation (CMP) may be when a static or moving photograph (i.e., visual modality) is displayed to a user while simultaneously playing a voice-over audio (i.e., auditory modality) describing the static or moving photograph to the user. Thus, a user sees a static or moving photograph while simultaneously listening to a voice-over audio describing the photograph.The CMP employed through the platform of the present invention can provide numerous benefits, including, but not limited to, aiding in habit memory formation, enhancing safety, enhancing the individualization of visual representations, and the reinforcing effect of reinforcement. CMPs can aid in habit memory formation by providing opportunities for information redundancy and enhancing the development of habit memories. For example, a visual representation of a drug may include at least two CMPs (i.e., auditory and auditory) for every attribute element or step, and for some attribute elements or steps, may have three CMPs (i.e., visual, auditory, and tactile). In some embodiments, the more modalities that can capture the user's attention or be made available for interaction, the more effective information is understood, recognized, and retained by the user. To this end, it should be appreciated that redundancy is a well-established strategy for successful learning and retention. Additionally, CMPs can enhance safety by providing built-in checks for accuracy, reducing the likelihood of error. For example, a visual template of a drug may show a picture of a large, round pill, and a simultaneous voice describes the same characteristics of that pill, but the pill the user obtains from the pill bottle is square. A mismatch can communicate to the user that the wrong pill has been selected and that the selection should be corrected. Additionally, the CMP can enhance the individualization of the map. For example, the visual representation can include personalized content or information from family members in multiple modalities, including visual (i.e., photos, videos, text notes) and auditory (i.e., audio recordings). Using a CMP with a visual representation for family member individualization can enhance engagement by the memory-impaired user, and content such as family members' messages can serve to encourage the user to engage and complete the activity. Additionally, the CMP can enhance the effect of reinforcement. For individuals with memory impairments, music in the form of a favorite melody, song, or performer, and / or photos of a scene or person can often serve as reinforcing stimuli to maintain a habit. The effect of reinforcement can be further enhanced by presenting strong reinforcing stimuli simultaneously in multiple modality representations.For example, an ADL visual representation for taking a shower may be incorporated into a sequence of attribute elements such as a favorite melody by a favorite singer (e.g., Frank Sinatra) or a recording (e.g., Strangers in the Night) where the picture is linked or associated with the music, both stimuli being linked to the specific step of taking a shower. As a further example, when the shower step is completed, an "Activity Completed" button may be displayed on the user interface or device screen. The user sees the button, touches the button, hears a ding sound, followed by a short introductory piece of music. To this end, the convergence of multiple modalities can serve as a strong reinforcement for completing a sequence of steps, helping to ensure compliance in subsequent interactions with the platform.

[0050] In various embodiments, the platform provides methods and systems for relatively accurately determining statistics regarding users interacting with the platform. In further embodiments, each of a plurality of attribute visual elements provided in a visual representation may be used for the generation of various user statistics. For example, a customized visual representation may include a plurality of attribute visual elements indexed or mapped in a linked pattern, and the viewing statistics and / or interaction statistics associated with such visual elements may include a histogram of interactions and / or viewing instances of the visual elements. In further embodiments, user behavior, such as interactions and / or viewing instances of a visual representation or visual element, may be represented by the display of user interactions and / or visual representations and / or visual elements within a range of predetermined interaction or viewing conditions. For example, conveying that certain user interactions and / or viewing instances associated with a visual representation may be more frequent than others, and that certain content associated with a visual representation may be less recognizable or memorable than others. In further examples, conveying that certain user interactions and / or viewing instances associated with a visual representation may be of longer duration than others, and that certain content associated with a particular attribute element may be less recognizable or memorable than others. Further examples convey that certain user interactions and / or viewing instances of content associated with attribute elements may be more frequent than others, and that certain content associated with certain attribute elements may be less recognizable or memorable than others. Further examples convey that certain user interactions and / or viewing instances of content associated with attribute elements may be of longer duration than others, and that certain content associated with certain attribute elements may be less recognizable or memorable than others.

[0051] The salient attribute elements (or associated content) can be selected according to one or more selection criteria. In one embodiment, the selection criteria can be "highest viewed interval". Thus, the attribute elements or content associated with the attribute elements with the highest viewed interval can be selected as the salient attribute or image from among a group of attribute elements or content. Selection of salient attribute elements, such as evocative images of the respective visual representations or templates, can enable or facilitate dynamic adjustment and effectiveness of the visual representation and user understanding of the theme. In some embodiments, rather than providing a large amount of information indicating a large number of aspects or attribute elements representing a theme, including less salient or less effective attribute elements of the theme that are unlikely to be understood by the user or patient, collective user preferences for suggestive or memorable visual elements or views, as suggested or evidenced by various measures of user behavior, allow for the provision of a limited amount of information indicating such salient elements.

[0052] In further embodiments, the platform may be used by individuals or clinicians to relatively accurately determine statistics about users and groups of such users who interact with the platform. Such statistics may be used by the platform, for example, but not limited to, to predict the clinical status of the user and develop models of trajectories through the disease. In some embodiments, user behavior data may indicate the clinical status of the user. In other embodiments, data associated with user behavior may be used to help guide clinicians' treatment choices and allow families time to prepare for upcoming changes. Primary outcome measures in users may include fluctuations in neurobehavioral status as indicated by functional measures and neuropsychiatric instruments, as well as transfers from independent living to nursing homes. In some embodiments, primary outcome measures may be selected because they are generalizable and likely to significantly impact the lives of individuals with AD / ADRD and their caregivers.

[0053] Thus, in further embodiments, the platform may be configured to monitor user behavior and create a user profile indicative of the user's clinical status. In still further embodiments, user behavior may include, for example, platform activities performed by the user such as interacting with visual representations, biometric data generated when interacting with the platform, navigating user interface screens of desktop applications and / or mobile applications of the platform, etc. User behavior may include, for example, data associated with current user interactions and / or past user interactions with the platform.

[0054] In further embodiments, the platform may receive user behavior variables and thresholds associated with the patient list. The patient list may include a plurality of users associated with user behavior data corresponding to the primary filtering criteria. The patient list may include user behavior variables and / or user identifiers of users who may have exhibited a particular user behavior in the past or are currently exhibiting such user behavior. For example, the clinician may specify the primary filtering criteria to be the duration of the user viewing instance and a corresponding threshold (e.g., "time to display attribute visual element";>2 minutes) to identify the patient list. In addition, the clinician may reduce the size of the patient list by applying secondary filtering criteria such as further specifying a theme (e.g., medication ADL) or a particular attribute element (e.g., medication ADL step-"take pill" or "swallow"). Thus, patient subpopulations of users may be created based on an analysis of the user behavior data of users present in the patient list. In the present example, the user must view at least one ADL step or user interface screen in the medication ADL visual representation for more than 2 minutes without proceeding to the next step or user interface screen to be included in the patient subpopulation list. The patient subpopulation list may represent a set of users that may be more relevant to a clinician, and thus the clinician may specify an increased amount of oversight when administering a drug to users of the patient subpopulation.

[0055] In further aspects, the platform may collect biometric data from a wearable device connected to the platform that is worn by the user while interacting with the platform. The biometric data may include data associated with one or more of high-density accelerometry, actigraphy, quantitative and qualitative measures of sleep, average heart rate, and instantaneous heart rate variability. Sleep disorders and changes in sleep architecture in older adults have been reported to be associated with higher incidence of dementia over time. Furthermore, instantaneous heart rate variability has been shown to independently reflect autonomic dysfunction seen in Alzheimer's and Parkinson's diseases. In various embodiments, and as described in detail herein, the data may not be limited to platform-generated data. For example, external clinical data, diagnostic data, medical history data, and many other data sources may be used.

[0056] In some embodiments, data derived from how a user interacts with the platform may be separated into two or more categories, such as time series data and success rate. User behavior data variables measuring time series data may look at factors such as the duration of an instance view, the duration a user needs to click and / or navigate a user interface screen, e.g., "time to view a slide or text prompt before making a selection." In further embodiments, the platform may capture data related to physical measurements of a user's interaction with a device within the platform, such as, but not limited to, the strength of screen pressure, finger dwell time on the screen, navigation strategies, typos, etc. In other embodiments, user data variables measuring success rate may include the percentage of time a user is assigned a task and successfully completes it. As a non-limiting example, in a first embodiment, the platform may notify or alert a user to take a medication via a user interface or application interface. The platform may then record and collect data from the user interface, such as, for example, whether the user was able to navigate the user interface and mark the medication as taken. Changes in comprehension and executive function are expected to be manifested by how quickly the user can navigate the visual representation, with trends in change correlating with clinical changes in the user's ability to function independently. Additionally, biometric data from the device can also provide multiple additional indicators of the user's functional ability and / or clinical status.

[0057] The platform can search the user interface for key elements using various algorithms, including, for example, but not limited to, machine learning techniques and digital signal processing (audio / image / video data). Still consistent with embodiments of the present disclosure, the platform can receive raw data, for example, as it tracks the user across user interface screens and within customized visual representations. Tracking can include, for example, but not limited to, monitoring each navigated screen or view to create a profile of the screen. As described in more detail below, the profile may be generated, for example, by the aforementioned algorithms used to collect raw data of the user interface and visual representation screens.

[0058] For example, in some embodiments where a user may access the same and / or different visual representations through multiple user devices, correlation of information collected by the multiple trackers may be performed to track the user. In some cases, each of the multiple trackers may not include all of the one or more identifiers. For example, a user may access a first visual representation associated with a first ADL using a smartphone, while the user may access a second visual representation associated with a second ADL using a tablet computer. In other aspects, a user may access a first visual representation associated with a first ADL using a smartphone, while in certain situations, the user may only access the first visual representation using a smart speaker, such as when taking a shower.

[0059] When scanning, the platform may perform, for example, natural language processing (NLP) to further process the context of words and phrases in the text. In addition, the platform may utilize image recognition, voice recognition, and / or video recognition to collect data regarding the patient's platform usage. FIG. 9 shows a platform page 900 showing a screenshot of a user interface screen corresponding to attribute elements from a visual representation related to taking a shower. In various embodiments, FIG. 9 shows how and where text, image, video, and audio information may be obtained to provide platform usage information for a particular patient. For example, the text 902 may be scanned with optical character recognition (OCR). The OCR scan may generate words or phrases to characterize the visual representation. The text 902 from the visual representation may also be extracted and analyzed using NLP. Additionally, image recognition software may be used to characterize the visual representation. For example, artificial intelligence (AI) software may be used to determine whether the image 904 shows, for example, a soap. The image 904 may also be analyzed to characterize image properties such as flux, color, saturation, etc. Audio content 906 from the visual representation or related sources can also be scanned, for example using voice recognition software, to provide further information to further characterize the visual representation. In a further embodiment, video content 908 can be converted into a series of images from regular patient screens and scanned in the same manner as the images. In yet a further embodiment, audio associated with the video content can be scanned to provide data regarding the visual representation. Similarly, a combination of text, image, audio and video recognition can provide a humanistic view of what the visual representation provides. The humanistic view can enable the platform to optimize the characterization of the visual representation.

[0060] In various further aspects, the platform may receive general data, which may include, for example, data from a particular visual representation (e.g., text, image, audio, and video data associated with the visual representation) as well as data from individual users (e.g., visual representations visited, information from a user's medical records, etc.).

[0061] In further aspects, the platform can then analyze the collected information associated with the patient's use of the platform. In some embodiments, the platform may perform natural language processing (NLP), as well as image, audio and video recognition to analyze the information. For example, the platform may use certain keywords and phrases found in the visual representation, as well as keywords associated with images, videos and audio files, to attach "saliency" to attribute elements displayed on a page or screen. For example, for a visual representation related to taking medication, the platform may return hundreds of "keywords," including "pills" with high salience, "glass of water" with medium salience, and "cabinet" with low salience. The platform may then interpret the information based on the patient's use of the platform to create a profile associated with the salience of each attribute element in the visual representation.

[0062] For example, a patient may continue or repeatedly access some attribute elements within a visual representation that have high salience visual content and / or keywords associated with "pills." Such patients are statistically more likely to not be able to remember what their medication looks like. Such statistical predictions may be associated with a confidence level. Additionally, statistical predictions may be made for numerous other characteristics, such as, for example, but not limited to, age, clinical status, and disease status.

[0063] FIG. 6 illustrates an example 600 of how the platform may track an individual's platform usage. The platform may track a patient-user 602 who may access multiple visual representations, such as visual representation A and visual representation B, at different time instants, as shown as steps 604 and 606, respectively. Thus, the platform may scan each of the visual representations visited by the patient-user 602, as shown as steps 608 and 610. Thus, based on an analysis, such as NLP, as shown as step 612, key elements, such as content, keywords, and corresponding salience, may be extracted. For example, content, such as textual content and image content, may be extracted from a platform page, e.g., a user interface screen. Furthermore, non-textual content, such as images, audio, and video, may be converted to textual content using one or more processes, such as optical character recognition, voice recognition, image processing, and the like. The textual content may then be analyzed to identify keywords and associated salience, which may be stored in a user profile and associated with the patient-user in step 614. After the platform analyzes the information about each patient, the method may proceed to a stage where the platform may group users based on certain characteristics. For example, patients who are more likely to have certain characteristics, such as gender, age, clinical condition, and location (e.g., at home vs. nursing home), may be grouped. Additionally, patients may be grouped based on selected preferences, such as, for example, attending physician or location of patient care. Further, in some embodiments, the platform may be configured to predict a first portion of a first profile corresponding to a first user based on a first portion of a second profile corresponding to a second user. Additionally, the prediction may be based on a result of a comparison between the second portion of the first profile and the second portion of the second profile.

[0064] Thus, the user's profile may further include one or more characteristics of the user, such as, for example, clinical characteristics, such as the level of memory impairment. Thus, in some embodiments, the first portion may include one or more characteristics of the first user. Additionally, the second portion may include at least one keyword, and one or more corresponding salience values. Thus, based on the matching of the keywords and salience values ​​of the first user and the second user, one or more characteristics, such as demographic characteristics, of the second user may be predicted based on one or more characteristics of the first second user. In other words, based on the matching of the keywords and / or salience values ​​between the two users, one or more characteristics of one user may be associated with the other user. Additionally, in some embodiments, the first portion may include one or more keywords, and one or more corresponding salience values. Additionally, the second portion may include one or more characteristics of the first user. Thus, based on the matching of the one or more characteristics of the first user and the second user, one or more keywords and / or corresponding salience values ​​of the second user may be predicted based on one or more keywords and / or corresponding salience values ​​of the first second user. In other words, the keywords and / or corresponding salience values ​​of one user may be associated with another user based on, for example, a match of demographic characteristics between the two users. Furthermore, in some embodiments, the result of the comparison of the keywords and corresponding salience values ​​corresponding to each of the first user and the second user may be associated with a confidence value. In addition, a prediction may be associated with an aggregated confidence value calculated based on the aggregated confidence values ​​corresponding to each keyword and corresponding salience value of the multiple keywords and corresponding salience values. For example, each of user A and user B may be associated with a clinical characteristic, such as a disease diagnosis. In some cases, each of user A and user B may have a corresponding disease. Thus, user A may be identified as having Alzheimer's disease (AD) and user B may be identified as having Parkinson's disease (PD).Further, each of user A and user B is associated with a user profile including user behavior data and / or biometric data. User A may be associated with user behavior data affected by Alzheimer's disease, and user B may be associated with user behavior data affected by Parkinson's disease. Thus, each of user A and user B may be an instance of a first user. Furthermore, a first portion of the profile associated with user A and user B may include a level of cognitive impairment. Furthermore, user C may be an instance of a second user who may not have been initially identified by a disease diagnosis. However, user behavior data, biometric data, and / or salience values ​​corresponding to user C may be available. Thus, in an example, user behavior data associated with user C may be compared with user behavior data associated with each of user A and user B. The results of the comparison may indicate a higher degree of agreement between user C and user A. In other words, the user behavior of user C may be more similar to the behavior of user A than the behavior of user B. Thus, it is more likely that user C may be associated with similar characteristics. As a result, user C may be determined to have a diagnosis of Alzheimer's disease.

[0065] Similarly, in some cases, a user's characteristics, such as a disease diagnosis, may be predicted based on a match between the user's user behavior data and aggregated user behavior data of a group of other users with known information. For example, by aggregating user behavior data values ​​of declared user characteristics, such as diagnosis or location, a first set of keywords indicative of neurobehavioral variables corresponding to a clinical condition primarily influenced by the user characteristics may be identified. Thus, when a user with an unknown disease condition encounters the platform, the keywords associated with the user may be compared to each of the first set of keywords and the second set of keywords. Furthermore, based on some matches, the stage of the user's disease progression may be predicted. For example, if five of the most important keywords associated with the user match the first set of keywords associated with late stage, while only two of the most important keywords match the second set of keywords associated with early stage, the platform may predict that the user is in late stage. In further embodiments, the key elements, keywords, and user behavior data in the user profile may be associated with a confidence value in relation to one or more user characteristics and clinical conditions to be predicted. For example, each keyword in the user's profile may be associated with a confidence value in the form of a pair of numbers. For example, the keyword "pills" may be associated with a confidence value of 50 / 50, indicating that the confidence that "pills" is indicative of an AD diagnosis is the same as the confidence that "pills" is indicative of a PD diagnosis. Similarly, instantaneous heart rate variability may be shown to be associated with a confidence value of 52 / 48, indicating that the confidence indicative of AD is not substantially greater than the confidence indicative of PD. Conversely, sleep disorders may be shown to be associated with a confidence value of 82 / 18, indicating that the confidence indicative of dementia is substantially greater than the confidence indicative of the absence of dementia.

[0066] In some embodiments, the confidence value may be derived based on a statistical analysis of the user behavior data variables and key elements, such as keywords and / or corresponding salience values, of a large group of patients, including both AD and PD patients. Furthermore, in some embodiments, by aggregating the user behavior data variables, key elements, keywords, and / or salience values ​​across a large group of users, the accuracy of predicting the clinical status and / or unknown characteristics of the patient may be improved. Furthermore, a suitable mathematical function may be applied to the group to balance and reduce skew with the keywords per person. A prediction of the clinical status of the patient may then be determined with a higher confidence value based on a comparison of the patient's keywords and / or salience with the group's keywords and / or salience. Logical functions (e.g., AND, OR, and NOT) may be used to group and filter out certain types of patients. Furthermore, in some embodiments, the patients may be aggregated using statistical functions, such as, for example, weighted averages. Thus, a group profile corresponding to multiple users may be created. Furthermore, the group profile may include multiple keywords and corresponding multiple group salience values. The group salience value of a keyword may be based on an aggregation of the salience values ​​of the keywords associated with multiple users. For example, as shown, multiple users may serve a group of patients in a care facility, and so on.

[0067] Further, each user may be associated with a user profile that includes keywords, corresponding salience values, and one or more other features, such as demographic characteristics. In addition, in some embodiments, one or more of the keywords, corresponding salience values, and demographic characteristics may be determined based on an analysis of visual representations accessed and created by the user. A group salience value may be obtained for each theme by aggregating the salience values ​​of each theme across all users of the group. As a result, user groups may be more accurately modeled for reporting purposes.

[0068] In further embodiments, the platform allows for adjusting platform characteristics, such as visual representations and content, based on user characteristics and behavior. In still further embodiments, the methods and systems allow for generating adaptive visual representations that can be dynamically adjusted based on user characteristics and behavior, such as the degree of cognitive and / or memory impairment. In still further embodiments, the cognitive impairment can range from mild to severe, and the degree of impairment can affect the user's ability to understand and effectively use the platform. As further described herein, the degree of impairment may be initially determined independently of the platform. For example, the initial degree of impairment may be assessed by administration of standard techniques and analysis of a battery of neuropsychological assessment tools. In various embodiments, the degree of impairment may be used by the platform to generate visual representations that may be individualized. For example, the platform may use the degree of impairment to determine at least one of the following attributes incorporated into the visual representation: the number of visual elements or steps, increasing the size of the text, reducing the content or image, the complexity of the pictures, and adding additional CMPs.

[0069] In further embodiments, at least a portion of the visual elements may be personalized or modified by the user to display or present selected content. In some embodiments, the user modifying the content may be the patient. In other embodiments, the user modifying the content may not be the patient, such as a caregiver, family member, or clinician, or a combination thereof. In other embodiments, modifications associated with the methods and systems of the present invention may be based on user conditions, such as the severity or level of impairment of the user using the visual representation, which in some embodiments may be derived through use of the platform. Importantly, changes to the visual representation may be modified as the user's level of impairment progresses, which may serve to enhance the ability of individuals with more severe impairments to continue to effectively engage with the visual representation. Users with low levels or mild impairments may require visual representations with only a few steps to successfully complete an ADL, while individuals with more severe impairments may require the ADL to be presented and illustrated in many separate, detailed steps to successfully complete. As a non-limiting example, in the early stages of Alzheimer's disease, when an individual is still functioning well, visual representations with relatively complex features, such as those with upper limits of attributes or branches, smaller text, complex images, etc., may be effectively used.

[0070] Conventional visual methods and systems use numerous features to allow individuals to do very complex things in creating visual maps. These include, for example, making available numerous types of fonts, graphics, links to multiple websites, and the choice of the number and complexity of branches that can be generated within a single map. While the complexity of features may be desirable in certain applications and uses, it is considered counterproductive for the purposes of the present invention. In some embodiments, for individuals with memory impairments and progressive neurodegenerative diseases such as Alzheimer's disease, an abundance of options and information may contribute to their current state of stress and confusion. Instead, having visual representations and maps with fewer options and less information to process may benefit them. Furthermore, the visual representations of the platform of the present invention, which are easy to create and have relatively simple steps to follow or information to impart, are necessary conditions for them to be effective in helping users develop habit routines such as those described herein.

[0071] Again, as the disease progresses, the user may be less likely to effectively utilize the baseline or low impairment level visual representation. Instead, as part of the personalization process, one or more visual representation features need to be modified, adjusted, or personalized for the user, such as reducing the number of attributes or branches, reducing the number of words in the text, reducing the complexity of the image, increasing the font size, and / or larger images. In further embodiments, additional means of modifying or personalizing the visual representation may include incorporating personal information. For example, the visual representation may be personalized by incorporating user-specific or personal information, such as content associated with the memory-impaired user's family (music, sounds, stories), and content such as photos of the environment in which the user resides. Incorporating user-specific or personally relevant content or components into the visual representation not only enhances memory, but also aids in compliance: because the use of these components may increase engagement with the visual representation and ensure that the habit is continually developed.

[0072] In further embodiments, the visual representations may be tailored for different levels of technological competence. For example, the visual representations may be individualized in terms of levels of technological expertise, ranging from the full use of smart devices, wearables, and voice-activated assistants (e.g., Amazon's Alexa) to low-tech kits that utilize paper printouts of the visual representations and magnetic boards that can show the steps of a particular visual representation. In still further embodiments, the printouts and magnetic boards can be attached to specific locations in the user's residence (e.g., bathroom, bedroom, kitchen) adjacent to where the user performs a particular ADL. The close contingency between the behavior to be performed and the stimuli showing the habit (whether a smart device, a paper map, or a magnetic board) enhances the individual's ability to engage with the behavioral habit and the habit regions of the brain. In still further embodiments, the visual representations may be tailored for different living environments. For example, the visual representations may be individualized with respect to the user's living environment, which may vary from a private home to a residential facility. In some embodiments, the ADL visual representations may utilize content in the form of real-time photos of the actual places (e.g., bathroom shower) and things (e.g., medicine bottles) in which the user currently resides. As a user's circumstances change or the user moves to a different residence or environment, the visual representation can be easily adjusted to reflect new details or attributes associated with the new environment while the ADL theme of the visual representation remains the same throughout. Furthermore, the ability to maintain consistency of general attribute elements of ADL habit behaviors (e.g., the shower is still in the bathroom) can result in better engagement with small changes related to the specific content associated with the attribute elements (e.g., the medicine bottle is now on the shelf and not in the medicine cabinet) and reduce confusion due to change. Furthermore, maintaining routine behaviors can be a key component in successful habit building and memory enhancement.

[0073] In various embodiments, the platform may work in conjunction with a virtual assistant, such as Amazon's Alexa, and / or a voice-enabled "smart speaker," such as Amazon Echo. The platform may work in conjunction with Alexa as a "skill" or add-on feature. To this end, and by way of non-limiting example, a user may press an activation button and / or say the activation phrase "Alexa, open MapHabit" to activate and otherwise access the add-on feature of the platform. Because the platform may use private data and / or sensitive information, the user may be prompted to perform validation. In this case, the user may provide a password or other form of authentication. In further embodiments, the platform may work in conjunction with additional voice-enabled products, such as SIRI, Google Assistant, etc. The user may then be prompted to make a request and may respond with a command or say "settings" to edit settings for the platform or the language used by the platform. In some embodiments, some or all of the content may be presented to the user via a smart speaker. In other embodiments, some of the content may be presented to the user via a smart speaker, and some of the content may be presented to the user using another smart device, such as a mobile device, or using a paper-based medium, such as a printout. For example, in simultaneous modality presentation (CMP), static or moving photographs (i.e., visual modality) can be displayed to a user using a mobile device or printout, and simultaneously, a voice-over audio describing the static or moving photograph can be played using a smart speaker.

[0074] In further embodiments, the platform may be accessed via various other internet-connected devices. To this end, the platform may include features that allow access to highly personalized visual representations via various other internet-connected devices and enable interoperability between the user's mobile device and other technology platforms, including internet-connected devices or smart devices, to bring healthcare to the home. For example, the platform may be activated by voice or by a wearable that is individually worn on the body, thereby minimizing cognitive and physical barriers to using the platform. In further embodiments, the platform may include multiple repositories or databases. In still further embodiments, the database may include an image repository, or a visual representation template repository, or a customized visual representation repository for the user, or a combination thereof. In still further embodiments, the platform may include at least one data received from a database or repository. In still further embodiments, the data may include multiple images or templates. In other embodiments, the templates may include a theme related to a category, topic, type, or a combination thereof. In still further embodiments, the platform may analyze landmarks on the attribute visual elements to identify one or more keywords. In still further embodiments, the keywords may be used in selecting images or media assets in preparation of the customized visual representation.

[0075] In other embodiments, non-patient clinician users, such as medical personnel or clinical managers of a medical institution, may also access the platform 100 through a software application. The user may provide input parameters to the platform. The input parameters may be a specific device ID, a patient residing in a specific nursing home location with care, a display of a list of visual representations, a display of one or more attribute elements contained within a visual representation from a list of visual representations, a display of a list of recorded events indicative of loss of independence, and a display of one or more interventions targeted to patients identified as meeting a predetermined clinical level (which may be referred to herein as patients in need). In response, the platform may identify patients in need based on actions on the platform and allow the clinician user to view the patients in need along with their associated trust values.

[0076] To determine a patient's clinical status, the platform may be configured to track the patient's platform activity using the platform. For example, in some embodiments, the platform may be configured to use a client-side or server-side user activity monitor or tracker to track a patient user across multiple attribute element pages from various visual representations. Thus, a tracker stored on the patient device may be used to monitor and / or log each attribute element page of the multiple attribute element pages from the visual representation accessed by the patient device. The platform may extract user behavior data while creating a log of the patient device represented by a unique ID, such as, for example, a network address, an IMEI number, a combination of software, hardware, and demographic information associated with the person operating the patient device.

[0077] As a result of the tracking, for each unique ID representing a patient, a list of customized visual representations may be identified for that patient. The platform may then access and scan attribute element pages from within the list of visual representations to obtain key elements present in the content of each visual representation. For example, the platform may scan, scrape, OCR, etc. to analyze the content of each page. Additionally, the key elements may be aggregated and analysis may be performed on the aggregated key elements to determine clinical status and / or identify patient need status for potential clinical intervention. The key elements may be analyzed based on a set of criteria to determine the corresponding patient need status. For example, the platform may be configured to evaluate various factors related to the key elements, including, but not limited to, keywords, keyword density (e.g., frequency of occurrence in the visual representation), themes related to the page and / or visual representation, user behavior data such as time spent on the page and / or visual representation, amount of pages visited with the associated key element, and parameters related thereto. The analysis may be embodied, at least in part, using various machine learning methods and techniques. The analysis may then identify a confidence value associated with the need for a clinical intervention that corresponds to the clinical condition and / or the condition of the patient in need.

[0078] In some embodiments, the platform behavioral data may be combined with data obtained from an EMR database, electronic medical record (EMR) data, biometric data obtained from a wearable, etc. The combination may then be used to identify patterns of user activity indicative of clinical conditions, such as levels of memory impairment. Such patterns may be identified by performing machine learning over past user behavioral data. Machine learning may then be used to identify patient conditions of need based on currently received user behavioral data.

[0079] In other embodiments, the platform can build predictive models that can determine a patient's clinical status and / or identify future clinical outcomes. In a further aspect, the predictive model can include a feed-forward artificial neural network with back-propagation as shown in FIG. 10. The nodes can be arranged as an input layer 1002, a hidden layer 1004, and an output layer 1006. The input layer can include user behavioral data and / or biometric data and / or user characteristic information. The output layer can include various neurobehavioral measurements and various losses of independent functional outcomes. In a further aspect, the variables in the input layer and the output layer can be separated by a fixed time interval to ensure consistency. In yet a further aspect, users can be randomly divided into a training and cross-validation group (70%) and a validation group (30%). The k-fold cross-validation during training and the internal validation group can prevent overfitting of the data and help ensure internal validity. As described in more detail with reference to FIG. 13 below, computing devices that can access the platform can include, but are not limited to, for example, a desktop computer, a laptop, a tablet, or a mobile communication device.

[0080] C. Operation of the Platform According to various aspects of the disclosure, provided herein are devices, systems, methods, and techniques (collectively referred to as "platforms") for treating memory disorders. According to further aspects, the platform uses customized visual representations to treat memory disorders. In yet further aspects, the platform may be comprised of various disclosed methods and systems implemented by a computing device. As described in more detail below with reference to FIG. 13, a computing device (e.g., computing device 1600) may include various computing modules having software instructions for performing and operating at least a portion of the various disclosed methods and techniques. Furthermore, in some embodiments, different operations may be performed by different network elements in operative communication with computing device 1600. For example, a server may be used for the operation of some or all of the steps in the disclosed methods. Furthermore, the server may be configured substantially similarly to computing device 1600.

[0081] In one aspect, an initial user interface (UI) may be provided by the platform. The initial UI may be used as an interface for the platform to collect user profile data, such as user characteristics and demographics, themes selected for generating customized visual representations, and selection criteria. The UI may include various input fields and buttons to allow the user to provide such data and make selections to guide underlying calculations and / or generation of personalized visual representations. These data may also be used by the platform in generating and configuring customized visual representations, including visual element branching, as described herein, the results of which may be used to treat memory disorders, e.g., dementia. Still consistent with embodiments of the present disclosure, the platform may generate customized visual representations in the form of visually branched visual elements for a specified theme. FIG. 2, FIG. 3A-FIG. 3B show various exemplary visual elements of thematic visual representation customization in accordance with one or more aspects of the present disclosure.

[0082] Consistent with embodiments of the present disclosure, the platform may include a visual representation generation module or component. The visual representation generation module 1622 (FIG. 13) may enable a customized visual representation to be generated. As shown in FIG. 13, the visual representation generation module 1622 may be a module included within the platform 1620. In other embodiments, the visual representation generation module 1622 may be independent of the platform 1620. In various aspects, the visual representation according to the present invention may include an individualized or customized visual map including a layout and style used to compose content such as visual landmarks and images that may enhance processing of information and memory function in memory-impaired individuals, including those with dementia. In further aspects, the theme of the customized visual representation may focus on a single component of an individual's Activities of Daily Living (ADLs) and / or Instrumental Activities of Daily Living (IADLs). In still further aspects, the customized visual representation may be based on user characteristics including at least one of cognitive level, degree of cognitive impairment, level of memory impairment, age, gender, nationality, spoken language, disease progression, and the like. To this end, individuals recently diagnosed with AD / ADRD (early stages) will benefit from assistive technology, unlike individuals who have progressed to severe impairments (late stages) who require assistance with basic operations. Thus, visual representations can be tailored by the platform to suit the needs of users with a wide range of cognitive impairments, including, but not limited to, adjusting the number of attributes or branches associated with a single visual representation, adjusting the font size used, adjusting the amount of text on each attribute or branch, adjusting color and contrast, adjusting the complexity of symbols, adjusting image visibility (e.g., in the range of visual angles from about 15 to about 65 degrees, with exemplary values ​​of 20, 25, 30, 35, 40, 45, 50, 55, and 60), adjusting the focus or salience of attributes within a visual representation or theme, and modulating the complexity of the visual representation.In further embodiments, the visual representation may include a baseline or template representation having a set of pre-defined levels of severity of the disorder and / or stage of the condition that the individual is experiencing. For example, the visual representation may include three levels of complexity that can be toggled to suit an individual patient's level of mild, moderate, or severe memory impairment.

[0083] As described herein, the individualized or customized visual representations may be based on a representation template and / or user characteristics. In some embodiments, a representation template may have about 2 to about 10 attribute branches or steps, such as, for example, about 5 to 7 or 7 to 9 branches or steps. In still further embodiments, a template of the present invention may have 1 to 10 levels corresponding to a user characteristic, such as a level of disability. As a non-limiting example, a representation template may be targeted to a particular activity type, such as a patient appointment. In further embodiments, the representation template for that particular activity may have several attribute elements or branches that provide information related to the theme or activity. Display templates may be retrieved from a display template repository or database and presented to a user according to one or more selection criteria provided by the user. For example, the selection criteria may be "events" for the theme and "movie night" as the event type, and thus, one or more display templates matching the selection criteria may be provided to the user.

[0084] In still further embodiments, the attribute element may include one or more of each of the six "W's": "what", "who", "why", "when", "where", and "other". In a particular example, the attribute element may be branches including: branch 1: ("what") - appointment; branch 2: ("who") - doctor's name, assistant or receptionist's name, who will be with me; branch 3: ("why") - purpose of the visit; initial consultation? screening? drug evaluation? will there be lab tests performed?; branch 4: ("when") - appointment date and time, alarm notifications; branch 5: ("where") - location of clinic, driving or public transportation instructions, parking information including fees, instructions for using transportation, and branch 6: ("other") - should I bring medication? list of questions since last visit, discussing side effects or unusual symptoms, other supporting materials for the visit. In some embodiments, non-patient platform users, such as caregivers, can modify or add content to the display template for and / or in addition to the patient. In other embodiments, non-patient users may modify or add content together with the patient. In further embodiments, the display templates can facilitate medical visits and facilitate medical personnel's interaction with the patient, since many questions or concerns are already located on the customized visual representation generated from the template, and the patient can recall them by simply accessing the customized visual representation. Although the customized visual representation may be initially generated or accessed via a mobile device or computer, a printed version of the customized visual representation can be created for the patient to take to the appointment or otherwise available for review and memory training.

[0085] In an exemplary embodiment, FIG. 2 illustrates a visual representation template 200 and components in the form of attribute elements 202 for generating a customized visual representation according to a platform. As shown, the attribute elements 202 have an arrangement within the display template with three levels corresponding to three levels of impairment: low, medium, and high. Each of the template levels includes two, five, or eight attribute elements that are presented as steps in a sequence within the template. During the creation or setup phase, all of the multiple attribute elements 202 may be present at once in an arrangement for the user to modify each attribute element to reflect personalized information and details. Alternatively, each attribute visual element may be presented individually to the user to modify or provide associated content or details, and a personalized visual representation is generated or constructed upon providing all the information required for each attribute visual element. In some embodiments, the platform may prompt the user to provide a complexity level that corresponds to a user characteristic, such as a level of memory impairment. In some embodiments, the platform may provide the user with several attribute elements or steps that correspond to the complexity level. In other embodiments, the platform may prompt the user to provide several attribute elements or steps. In further embodiments, each of the attribute elements 202 may be assigned a default salience or relevance level. In the present example, an attribute element designated as L or mild impairment may be assigned a salience or relevance level of 3, corresponding to an attribute element having a higher salience or relevance to the theme of the visual representation, an attribute element designated as M or moderate impairment may be assigned a salience or relevance level of 2, corresponding to an attribute element having a medium salience or relevance to the theme of the visual representation, and an attribute element designated as H or high impairment may be assigned a salience or relevance level of 1, corresponding to an attribute element having a lower salience or relevance to the theme of the visual representation.To this end, a patient with mild memory impairment may only require two L attribute elements with highly salient and / or relevant content to complete the visual representation and corresponding activity or task. A patient with moderate memory impairment may require two L attribute elements and three M attribute elements with highly and moderately salient and / or relevant content, respectively, to complete the visual representation and corresponding activity or task. A patient with severe memory impairment may require two L attribute elements, three M attribute elements, and three H attribute elements with highly, moderately, and more mildly salient and / or relevant content, respectively, to complete the visual representation and corresponding activity or task. In some embodiments, the platform may assign adjusted or aggregated salience and / or relevance values ​​to each attribute element based at least on user behavioral data and / or biometric data from the patient's interaction with the platform and visual representation. In other embodiments, the platform and / or platform user may adjust or modify the customized visual representation between levels L (2 steps), M (5 steps), and H (8 steps) based at least on user behavioral data and / or biometric data from the patient's interaction with the platform and visual representation.

[0086] In another exemplary embodiment, Figures 3A-3B show a customized visual representation 300 with attribute elements 302 in an arrangement with each attribute element modified to include content reflecting personalized information and details related to the theme, medication management, of this example. The table in Figure 3B provides additional details related to the content included within each attribute element of instructions for taking medication. In further embodiments, the platform may receive selection criteria that may include "instructions" or "activity" for the theme and "drugs" as the type, and visual representation templates matching the selection criteria may be provided to the user. The platform may then prompt the user to provide a low, medium, or high complexity level corresponding to the level of memory impairment. The platform may then provide two, six, or eight steps corresponding to the complexity level. In some embodiments, the platform may prompt the user to provide a drug name or drug number. In some embodiments, the platform may assign adjusted or aggregated salience and / or relevance values ​​to each attribute element based at least on user behavioral data and / or biometric data from the patient's interaction with the platform and the visual representation. In other embodiments, the platform and / or platform user may adjust or modify the customized visual representation between levels L (2 steps), M (6 steps), and H (8 steps) based on at least user behavioral and / or biometric data from the patient's interaction with the platform and the visual representation. For example, all three levels may be completed and / or utilized by the user to enable switching between levels based on the user's clinical condition and / or level of memory impairment.

[0087] As another non-limiting example, a customized visual representation related to instructions for preparing oatmeal may include a selection criterion of the theme "instructions" and "oatmeal" as an instruction type, such that visual representation templates matching the selection criteria may be provided to the user. The visual representation may include a parent element represented by a shape and the text "oatmeal" and multiple attribute visual elements linked to the parent element. The primary attribute elements may be linked to the parent element in a linked arrangement or layout. The attribute elements may be associated with steps of the instructions including "kitchen," "measure," "pour and stir," "microwave," and "enjoy!". In some embodiments, the attribute elements may include at least one of a primary attribute element, a secondary attribute element, a tertiary attribute element, etc., which may correspond to a level of complexity. The first attribute element may include "kitchen" as a primary attribute element, "pantry" as a secondary attribute element including an image of a pantry, and a tertiary attribute element including a "middle shelf" including an image of oatmeal and a "bottom shelf" including an image of a measuring cup. Similarly, all of the multiple attribute elements may exist at once in a linked arrangement for the user to modify the content within each attribute visual element to reflect personalized information and details and / or add additional secondary and tertiary attribute elements. Alternatively, each attribute element may be presented individually to the user to modify or provide related information or details and / or add secondary and tertiary attribute elements, and a personalized visual representation is generated or configured upon providing all the information required for each attribute visual element. In further embodiments, the customized visual representation may further include anchor points. The anchor points may include or be embodied in the form of attribute visual elements and are configured to create a sense of security, reduce stress, enhance mood, or create a sense of autonomy upon successfully completing a task independently. In further embodiments, the anchor points may also function as a reward to the user, for example, a reward for eating oatmeal.

[0088] In further aspects, the platform can analyze or otherwise process the content and information contained in the visual representation or template and, accordingly, determine a relevance weight for each attribute visual element associated with the theme or template. In one aspect, the relevance weight of an attribute element can be a numerical value representing the importance or importance level (e.g., impact on behavior, stress, etc.) associated with an attribute or characteristic specific to the theme or template. In another aspect, the relevance weight of an attribute element can be a visual representation (such as a thickness of connecting branches, a sequence, or a designated color) of the importance or importance level (e.g., impact on behavior, stress, etc.) associated with an attribute or characteristic specific to the theme or template. In yet another aspect, the relevance weight of an attribute element can be a numerical value representing the viewing time of the attribute visual element or element type. In some aspects, such a value can be greater than or equal to zero and less than or equal to one. In other aspects, to determine the relevance weight of an attribute element, the analysis component can assign a value (e.g., a real number) of a predetermined function of the importance level for the attribute element to be indicative of the theme or template associated with the attribute element or to be salient. In one embodiment, interactions or views of a given attribute element may be extended to have long viewing times, and therefore such views may be assigned a high relevance weight to represent a low level of user awareness or preference with the given attribute element. The analysis component may maintain information (e.g., data and / or metadata) indicative of one or more relevance weights respectively associated with one or more attribute elements of a template or theme. The relevance weights of various attribute elements may allow for adjusting the number of attribute elements to establish a preference weight of a template or theme. In a further aspect, the relevance or importance of an attribute (or branch or step) may be visually represented on the visual representation. For example, the thickness of a connecting branch may represent the relative importance of that attribute to a central theme. Additionally, designated colors and / or sequence orders may be utilized to provide information related to the theme, attribute, relevance and / or importance.In still further embodiments, one or more of the default attribute elements in the visual representation template may be removed or adjusted by the user or the platform. For example, the selection criteria may be "level of cognitive impairment," and the platform may remove or eliminate a given attribute element based on the cognitive relevance weights assigned to each attribute element, where the attribute element fails to meet a threshold relevance for the cognitive level. To this end, a user designated as having a high level of cognitive impairment may be tolerated with a limited number of attribute visual elements. Alternatively, the user may choose to remove or add attribute elements based on observed past behavior or other factors related to the end user's clinical condition.

[0089] FIG. 4 is a flow chart illustrating the general steps involved in a method 400 for generating a customized visual representation. The method 400 may be performed by the visual representation generation module 1622. The method 400 may also be implemented using a computing device 1600, as described in more detail below with reference to FIG. 13. Although the method 400 is described as being performed by the computing device 1600, it should be understood that in some embodiments, different operations may be performed by different network elements in operative communication with the computing device 1600. For example, the server 110 and / or the computing device 1600 may be used for the operation of some or all of the steps in the disclosed method. Furthermore, the server 110 may be configured substantially similarly to the computing device 1600, and in some cases, may be one in the same embodiment. The method 400 may begin at step 405, where data including themes and / or selection criteria may be received. The data may be received, for example, as a file accessible by the computing device 1600. As discussed above, the data may be received or accessed from a database or repository. The data may be provided via a user interface, such as the user interfaces described herein. Alternatively, the user interface may have a section where files can be uploaded. Once the computing device 1600 receives the data entered at stage 405, the method 400 may proceed to stage 410, where the computing device 1600 may generate a customized visual representation using the entered data. The customized visual representation may be based on keywords associated with various visual representation templates and thematic attributes. Once the computing device 1600 generates a customized visual representation at stage 410, the method 400 may proceed to stage 415, where the computing device 1600 may provide or display the customized visual representation.In some embodiments, dashboards such as those shown in FIGS. 11A-C may be provided to display various visual displays, customized visual representations, user data, and / or analytical data of the platform described herein. In further embodiments, a standard set of views and templates may be used to display customized visual representations and / or associated data. In still further embodiments, the dashboards may include multiple dashboard views for each user type (patient, family, caregiver, clinician, and / or facility) that allow platform users to set configuration properties to customize customized visual representations, views, graphs, and / or charts. In still further embodiments, these configurations may include, for example, attribute identifiers, feature identifiers, branch groups, single or multiple, aggregation functions, default and customizable labels. The dashboards may also include the ability to define baselines, which can be used to provide visual cues on the representations and provide alarms or notifications related to the patient or user's interaction with the customized visual representations. In further embodiments, the platform may provide customized visual representations to users, caregivers, or clinics. For example, the platform may facilitate delivery of a customized visual representation to a caregiver or clinic, e.g., the day before a scheduled visit.

[0090] In yet further embodiments, a number of standardized neuropsychological assessment tools can be used to measure the initial and subsequent clinical status of the user, for example, the severity of memory impairment can be determined by assessment (Wechsler Memory Scale-Revised [WMS-R], Warrington Recognition Memory Test [WRMT], and Doors and People Test [D&P]). The clinical status can be used to provide patients and non-patient platform users with the initial and subsequent burden levels they face with respect to the memory impairment challenge. Memory impairment can be associated with the development of negative emotional states that grow concurrently with the memory impairment. In particular, patients with Alzheimer's disease and other conditions that impair memory commonly report feeling frustrated, stressed, angry, anxious, and confused. In addition, they often have poor sleep quality, less physical activity, and are diagnosed with comorbid chronic depression. It is important to note that diagnoses of chronic emotional stress and emotional disorder conditions are also common in caregivers. Thus, the discussion of the burden of emotional dysfunction applies to both patients and caregivers. Moreover, as in the case of memory disorders, the burden of emotional disorder conditions can be easily and systematically assessed using standardized neuropsychological measurement tests. For example, additional standardized neuropsychological measurement tests that can be used in the platform include the Mind and Body Questionnaire (PHQ-9), which assesses a wide range of symptoms related to depression (9 questions: Do you have little interest in things? Do you feel like a failure? Do you feel tired or have no energy?); the Generalized Anxiety Disorder (GAD-7), which assesses general anxiety (7 questions: Do you feel tense, anxious or irritable? Do you tend to be irritable and angry?); and the Repeatable Battery for the Assessment of Neuropsychological Conditions (RBANS), a brief test to detect cognitive decline.

[0091] In yet further embodiments, the platform may utilize several objective and systematic tools to diagnose and evaluate patients, such as their memory impairment, and emotional dysfunction with memory impairment. Furthermore, emotional dysfunction also applies to caregivers. In this sense, by reducing the emotional burden on caregivers and patients, the methods and systems of the present invention can have a measurable and significant positive impact on quality of life measures for both patients and caregivers. A further problem and challenge is the medical costs of hospitals and clinics. Part of this cost comes from failure in both compliance with appointments and compliance with medications and instructions. In further embodiments, the use of customized visual representations and specialized templates of the platform of the present invention can reduce such costs, such as the cost of care and the cost of implementing personalized care for individual patients. In still further embodiments, the use of the disclosed customized visual representations of the platform of the present invention can enable a higher throughput of patient visits with better communication so that patients are more compliant.

[0092] As described herein, the inventive devices, systems, techniques and methods of the inventive platform can utilize customized visual representations and can be used to improve memory and / or improve and / or treat memory disorders, such as dementia and / or Alzheimer's disease. Figure 5 shows a flow chart illustrating the general steps involved in a method 500 for treating memory disorders consistent with embodiments of the present disclosure for providing the platform 100. Various steps of the method 500 can be implemented using a computing device 1600, as described in more detail below with reference to Figure 13.

[0093] Although the method 500 has been described as being performed by the platform 100, it should be understood that the computing device 1600 can be used to perform various stages of the method 500. Additionally, in some embodiments, different operations may be performed by a user or by different network elements in operative communication with the computing device 1600. For example, the server 110 may be used to perform some or all of the operations of the stages in the method 500. Additionally, the server 110 may be configured substantially similarly to the computing device 1600. Although the stages depicted by the flowcharts are disclosed in a particular order, it should be understood that the order is disclosed for illustrative purposes only. The stages can be combined, separated, reordered, and various intermediate stages can be present. Thus, it should be understood that the various stages depicted in the flowcharts may be performed in different arrangements than the stages depicted in various embodiments. Additionally, various stages can be added to or deleted from the flowcharts without altering or inhibiting the basic scope of the depicted methods and systems disclosed herein.

[0094] The method 500 can begin at start block 525 and proceed to stage 530, where the platform 100 can generate a customized visual representation based on input data, templates, and selection criteria associated with the platform 100. Input can include importing and mapping data by accessing various sources, e.g., user profiles, electronic medical records, templates, and / or image repositories or databases. Consistent with embodiments of the present disclosure, the platform can be configured to integrate data from multiple sources and different formats. The platform can be configured to communicate with various data sources, e.g., via application programming interface integration between the platform 100 and the data sources. In this manner, data integration can be at least partially computer-implemented. From stage 530, the platform can proceed to stage 540, where the customized visual representation is presented to a subject, such as a patient. Through the user interface of the platform, various views and / or formats can be provided, such as those shown in FIGS. 11A-11C. Method 520 can proceed to step 550, where the platform can enter a presentation or implementation mode, and the customized visual representation can continue to be provided or presented to the subject or patient until the treatment goal or outcome is achieved. For example, the platform can provide or display the customized visual representation to a user as part of a program, such as a memory training program. For example, the platform can facilitate presentation of the program or treatment plan, monitor usage, and track the progress of the user or subject.

[0095] In various further aspects, the present invention also provides a method for preventing memory impairment in a subject. To treat or inhibit a memory-related disease or disorder, the treatment is offered to a subject, e.g., a human, in need thereof. In further aspects, the subject has been diagnosed as needing memory impairment treatment prior to the offering step. In further aspects, the subject has been diagnosed as having a memory-related disease or disorder associated with age-related memory loss. Prior to offering the treatment, the subject may be diagnosed as needing treatment for a memory-related disorder or disease, such as dementia. In further aspects, the subject may be identified as needing treatment for memory impairment treatment, as described herein. The treatment may be offered to the subject according to any method, such as computer implemented. In determining the effective amount of the use of the present invention, the response to the preventive and / or therapeutic method of the present invention may also be measured by determining the behavioral and / or physiological effects of the therapeutic, such as, for example, a reduction or absence of disease symptoms following administration of the therapeutic. Other assays are known to those skilled in the art and may be used to measure the level of response. For example, the diagnostic method used to identify the likelihood that a subject has a memory-related disorder or disease may be used to identify the level of response to the preventive and / or therapeutic method of the present invention. The amount of therapeutic agent may be changed, for example, by increasing or decreasing the amount of therapeutic agent. The effective amount varies according to the specific condition being treated, the age and physical condition of the subject being treated, the severity of the condition, the duration of treatment, the nature of concurrent therapy (if any), and similar factors within the knowledge and expertise of the clinician or medical practitioner. For example, the effective amount may depend on the degree to which an individual has abnormal disorder level and / or activity. In some embodiments, the effective amount is a therapeutically effective amount. In other embodiments, the effective amount is a prophylactically effective amount. In further embodiments, the subject is a mammal, preferably a human.

[0096] In a further embodiment, the method further comprises identifying a subject in need of memory disorder treatment. In a further embodiment, the subject in need of memory disorder treatment comprises having at least one risk factor for developing a memory-related disease or disorder. In another embodiment, the diagnostic method comprises performing an experiment on the subject and / or identifying a level of a biological marker. In another embodiment, the diagnostic method comprises performing an assessment on the subject. In various embodiments, the platform method may comprise performing an assessment on a platform user and may comprise collecting biometric data from the patient. In yet a further embodiment, the assessment may comprise a multi-dimensional set of scales providing cognitive, affective, motor, and sensory assessments.

[0097] As described herein, the platform and method of the present invention can be effective in resulting in habit memory formation and supporting the habit area of ​​the brain. Everyday conscious memory, often referred to as declarative memory, is based on active learning and conscious memorization and relies on the brain area of ​​the temporal lobe, including the hippocampus. When the hippocampus and related brain structures are damaged or destroyed, as in the case of Alzheimer's disease, individuals lose the ability to learn new memories and consciously access recent memories (declarative memories). In contrast, habit learning occurs when information is unconsciously stored through repetition and trial-and-error learning. These memories are believed to be managed in a different area of ​​the brain called the neostriatum. In further embodiments, the habit area of ​​the brain can support the ability to unconsciously learn and retain new information while retaining habit memory, even in the absence of conscious or declarative learning, as in the case of memory loss from Alzheimer's disease.

[0098] In various embodiments, the methods and techniques of the platform of the present invention can utilize an intact neostriatal system to bypass the impaired hippocampal declarative memory system. With repeated experience, memory-impaired individuals can learn to routinely utilize the visual representation of the platform, even though they may not have a conscious recollection (declarative memory) that they learned to use the visual representation. In further embodiments, the platform's focus on neostriatal and habit memory treatments has several advantages over typical approaches that seek to strengthen the routine memory (hippocampal) system instead for memory-impaired individuals. As shown in FIG. 12, the hippocampus shows evidence of pathology in the form of plaques and tangles in early diagnosis of AD, corresponding to the behavioral symptoms of memory impairment, as the most common early defining marker of Alzheimer's Disease (AD). In contrast, the neostriatum does not show these pathological markers for much longer times (often years) after diagnosis. The platform of the present invention is effective in utilizing spare areas of the brain to acquire habits related to various ADLs encompassed in the visual representation, thus reducing the impact of the damaged hippocampal system on memory. Moreover, the hippocampal system is an early and primary target of AD pathology, which gradually deteriorates as the disease progresses. Thus, the hippocampus becomes less and less useful as a target for behavioral intervention, while the neostriatum remains disease-free for a longer period of time and is a more suitable target for behavioral intervention. To this end, the neostriatum can convert a series of actions, as described in connection with the methods and systems disclosed herein, into an automatic routine, a habit. The process in the brain includes a three-step loop, which can be used with the platform to target these habit brain regions. In further embodiments, the disclosed method may first provide a cue, trigger, or stimulus that is configured to put the brain into automatic mode and use the habit. Next, the daily habit may be provided in a visual representation, etc., or may include physical, mental, or emotional activities. For example, the disclosed routine of the platform of the present invention may include the patient utilizing a visual representation or map.Finally, the platform can then provide the patient with rewards that can help the brain understand that a particular loop is worth remembering. In further embodiments, the cues, routines, and rewards of this loop method can become increasingly automated and habitual. In still further embodiments, the cues can include tones or flashing light stimuli, or vibrations from a wrist device or band. In still further embodiments, the routine can include the individual looking at a visual map and being guided by the information contained therein. In still further embodiments, the rewards can include stress reduction, a sense of autonomy, or clearer understanding.

[0099] According to various further aspects, the platform method may include a set-up or "on-boarding" phase in which targeted user behavioral data and themes for a patient may be determined. In some embodiments, determining the targeted user behavioral data and themes may be done automatically by scraping / parsing the patient's user activity history within the platform for key elements. In further aspects, a platform user may provide input indicating targeted user behavioral data, biometric data, and / or patient information.

[0100] In various embodiments, key elements associated with one or more attribute element pages and / or visual representations may be determined. These key elements may then serve as fiducial reference points in analyzing future interactions and / or other pages to track an individual patient's progress, develop a trajectory model through the disease, make predictions about clinical status, and / or determine the patient's state of need. In further embodiments, the platform may use the fiducial reference points to perform one or more steps in the disclosed method. In various embodiments, the platform's ability to track and transmit individual user actions to a server may be built directly into a software application installed on the user device. When a potential user in need of the platform (hereinafter referred to as a "patient in need") visits a page, computer code on the user device may perform one or more steps of the disclosed method, for example, the code may search for a monitor or tracker on the user device associated with the patient. If a unique identifier (ID) has not been previously assigned, the code may assign a unique ID to the patient. The code may then store a tracker on the patient device with the ID, among other information. On the other hand, if a tracker is found on the patient device, a unique ID stored on the device is obtained. In this case, this information may have been previously stored when the patient device accessed the page and / or any other visual representation on the device in the past. During the onboarding or set-up phase for the clinician's use of the platform, the platform user may provide at least one of the following: 1) a list of targeted input user data (which may include, but is not limited to, at least one non-personally definable data point about the patient), and 2) a targeted clinical interest (which may include, but is not limited to, for example, user behavior variables, neurobehavioral measures, and / or keywords related to the clinical interest). In other embodiments, the platform user may provide a desired threshold "confidence level" associated with the targeted clinical interest.To this end, the platform may be able to evaluate a population of unique IDs to determine which of those unique IDs may be associated with a threshold confidence level of a key element that corresponds to a targeted clinical interest.

[0101] Consistent with various embodiments, the method of the platform of the present invention may begin with a step that may include identifying and / or recording a list of pages previously visited by the patient represented by the unique ID of interest. Additionally, the platform may be able to maintain or access an up-to-date list of all page visits by the identified patient after a trigger event. The method may include automatically accessing each page from the list of visual representations and analyzing the content on each page for key elements. As used herein, key elements may include, but are not limited to, text, images, video, audio, and combinations thereof. In some embodiments, the pages may have been previously processed according to this step. Additionally, the method may include aggregating key elements from the list of pages. In some embodiments, the pages may have been previously processed according to this step. Additionally, the method may include analyzing the key elements. As an example of the analysis step, the platform may be configured to assign scores to the key elements to determine whether there are any key elements associated with a set of reference key elements (e.g., established during a setup or onboarding phase). Thus, scores may be assigned based on a comparison of the key elements obtained during the setup phase to the reference key elements. Additionally, the method may include identifying one or more patterns in the key elements. The one or more patterns may be identified based on raw data including the key elements, machine learning, AI processing of the key elements, etc. It should be understood that the method of "scoring" is but one of many possible techniques for conducting an analysis consistent with the present disclosure.

[0102] In addition, in some cases, the method may further include a step of weighting more recent key elements. In other words, the method may incorporate a time factor. Thus, for example, if two or more attribute element pages with recent key elements are identified, such key elements are identified as "younger key elements" and thus given a higher weight in determining whether the patient is in clinical need. It should be understood that the "weighting" method is only one of many possible techniques for performing an analysis consistent with the present disclosure. For example, if a patient visits two attribute element pages with content related to medications within a predetermined time period (which may be established during creation of a customized visual representation, such as during a setup or onboarding phase), they may be determined to be in need of clinical intervention related to the management of medications. Upon analysis, the method may further include a step for determining the patient's need status (e.g., trend). To this end, a corresponding data field associated with the unique ID may be set to "true", or "in need", or "evaluation required". Furthermore, according to various embodiments, the method may include cross-referencing the patient with an electronic medical record (EMR) database associated with the patient and / or a platform user, such as a medical institution. For example, data associated with a patient may be stored in a record associated with the patient in an EMR database. The data associated with a patient may include, but is not limited to, data related to the patient's medical history, clinical data, biometric data, past platform data, along with respective trust values, key elements, lists of visited visual representations, etc. The data may further include user behavioral data and neurobehavioral assessments for each patient cross-referenced in the EMR database. This may be done, for example, based on a common reference point. For example, a login provided by a patient may be associated with the patient's medical record number as stored in the EMR database. Other common reference points may be used, such as, but not limited to, addresses, phone numbers, and other non-PHI (protected health information) and / or PHI cross-reference elements.

[0103] In some embodiments, the method may include triggering a clinical evaluation based on the patient's condition of need. For example, the clinical evaluation may include that the clinical evaluation may be conducted over one or more channels, such as through the platform, SMS, messaging within the software application, phone call, video call, in-person evaluation, etc. In various embodiments, the method steps may be repeated for a dynamically updated list of pages determined to have been visited by the patient. In such updates, data fields and associated user data may be modified based on the new data.

[0104] According to further embodiments, the platform may utilize a computer-implemented method, such as method 700A, to create a profile of a user based on user behavior, which may be provided as shown in Figure 7A. User behavior may include platform activities performed by a user, such as interacting with visual representations, biometric data associated with the user, completing tasks, downloading content from the platform, uploading content to the platform, and interacting with desktop and / or mobile applications. By way of example, user behavioral data and biometric data from which a user profile may be created is shown in Figure 8.

[0105] Further, in some embodiments, the data representing the user behavior may be de-identified. In other words, the data representing the user behavior may not include identifiable information such as a name. Thus, the user's privacy may be protected. To create a user profile, the method may include step 710 of receiving a plurality of platform pages (e.g., user interface screens) corresponding to a plurality of attribute elements from one or more visual representations that the user has interacted with. Further, the method may include step 720 of acquiring user behavior data and content from each of the plurality of visual representations based on the platform pages. For example, the monitoring program may be executed on the processor to automatically acquire user behavior data and content from each of the plurality of visual representations by monitoring the plurality of platform pages.

[0106] After obtaining the content, the method may include analyzing 730 the user behavior data and content from each of the plurality of visual representations. In some embodiments, analyzing the content from the visual representations may include analyzing content corresponding to each content type present on the visual representations. For example, both textual content on the visual representations and non-textual content such as audio, images, video, and multimedia may be analyzed.

[0107] Further, in some embodiments, the analysis may include performing natural language processing (NLP) of the textual content in the visual representation. Additionally, in some embodiments, if the visual representation consists of non-textual content, a step of converting the non-textual content to textual content may be performed. NLP may then be performed on the converted content. As a non-limiting example, the content of the visual representation may be analyzed using NLP, for example, which may result in the identification of themes or categories of content, such as "drugs". Furthermore, NLP may also identify other features of the visual representation, such as, for example, "morning pills" that may provide greater contextual relevance and awareness to the user. Additionally, NLP may also include event detection, which involves the identification of specific time-sensitive triggers or alerts, such as, for example, the next "scheduled dose". Furthermore, NLP may also identify significant topics featured in the content of the visual representation and associate those topics as concept tags with the visual representation, such as, for example, ADLs related to "taking a shower". Furthermore, NLP may also include entity extraction, which involves identifying relevant proper nouns, such as people and / or places.

[0108] In addition, the method may include identifying 740 a number of key elements, such as, for example, keywords, that correspond to the visual representations based on the analysis. For example, a set of user-specific keywords may be identified for a user based on the user's interactions with various customized visual representations. For example, the keywords "pill" and "swallow" may be identified and associated with the user based on the user's viewing of visual representations related to medication management.

[0109] Furthermore, multiple keywords may be associated with multiple salience and / or relevance values. Multiple keywords and multiple salience and / or relevance values ​​may constitute a user's profile. For example, the salience and / or relevance value of a keyword in a visual representation may represent how strongly the content of the visual representation is related to the keyword. In other words, the salience and / or relevance value may represent the relative importance of the keyword in the content. Thus, in some cases, a keyword that appears in an important attribute element page of a visual representation may be associated with a relatively greater salience and / or relevance value compared to a keyword that appears in a less important attribute element page of the visual representation. Similarly, a keyword that appears frequently within the content of a visual representation may be associated with a relatively greater salience and / or relevance value compared to a keyword that appears only once or a few times. In addition, a keyword that may appear in different media types present in the visual representation, such as text, image, and audio / video, may be associated with an even higher salience and / or relevance value.

[0110] 7B, the method may further include determining 750 an aggregated salience and / or relevance value corresponding to the keyword based on a first salience and / or relevance value of the keyword corresponding to a first attribute element page of the visual representation and a second salience and / or relevance value of the keyword corresponding to a second attribute element page of the visual representation. In other words, the aggregated salience and / or relevance value may represent an overall salience and / or relevance of the keyword to a user based on the user's interactions with multiple attribute element pages in the visual representation that include the keyword.

[0111] Additionally, in some embodiments, the aggregated salience and / or relevance value may be further based on a time decay value associated with each of the first salience and / or relevance value and the second salience and / or relevance value. For example, each of the first salience and / or relevance value and the second salience and / or relevance value may be weighted based on the time decay value. Thus, the influence of the salience and / or relevance values ​​on the aggregated salience and / or relevance value may be controlled according to, for example, the freshness associated with the salience and / or relevance value. For example, the salience and / or relevance value of a keyword associated with a first visual representation accessed one day ago may be weighted more heavily than the salience and / or relevance value of a keyword associated with a second visual representation visited one month ago.

[0112] Thus, the time decay value may take into account the dynamic nature of user activity in some embodiments. In other words, keywords from a visual representation visited one day ago may represent a current need or a changing clinical condition of a user that may be stronger than a past interest represented by a keyword from a visual representation visited one month ago. Thus, the salience and / or relevance values ​​corresponding to these keywords may be weighted relatively higher compared to those of keywords from a visual representation visited one month ago. Furthermore, in some embodiments, as shown in FIG. 7C, the method 700C may further include a step 760 of predicting a first portion of a first profile corresponding to the first user based on at least the previous analysis and / or the first portion of a second profile corresponding to the second user. Additionally, the prediction may be based on a result of a comparison between the second portion of the first profile and the second portion of the second profile.

[0113] Thus, the user's profile may further include one or more characteristics of the user, such as age, disease, or gender. Thus, in some embodiments, the first portion may include one or more characteristics of the first user. Furthermore, the second portion may include at least one keyword and one or more corresponding salience and / or relevance values. Thus, based on the match of keywords and the salience and / or relevance values ​​of the first user and the second user, one or more characteristics of the second user, such as demographic characteristics, may be predicted based on one or more characteristics of the first second user. In other words, based on the match of keywords and / or salience and / or relevance values ​​between the two users, one or more characteristics of one user may be associated with another user.

[0114] In addition, in some embodiments, the first portion may include one or more keywords and one or more corresponding salience and / or relevance values. Furthermore, the second portion may include one or more characteristics of the first user. Thus, based on a match of one or more characteristics of the first user and the second user, one or more keywords and / or corresponding salience and / or relevance values ​​of the second user may be predicted based on one or more keywords and / or corresponding salience and / or relevance values ​​of the first second user. In other words, based on a match of, for example, demographic characteristics between the two users, the keywords and / or corresponding salience and / or relevance values ​​of one user may be associated with another user.

[0115] Further, in some embodiments, the result of the comparison of the keywords and the corresponding salience and / or relevance values ​​corresponding to each of the first user and the second user may be associated with a confidence value. In addition, the prediction may be associated with an aggregated confidence value calculated based on the aggregated confidence values ​​corresponding to each keyword and corresponding salience and / or relevance values ​​of the multiple keywords and corresponding salience and / or relevance values.

[0116] According to various embodiments, the advantages of the platform can provide many benefits to many different patients and applications. In further embodiments, the platform can address existing problems and challenges common to memory-impaired patients, such as medical appointments. In still further embodiments, the platform users and patients can have a better ability to have meaningful discussions with others, such as physicians and caregivers, because at least the patient's concerns are pre-evident on the personalized visual representation and important information related to the activity is easily accessible. Prior to the platform of the present invention, healthcare providers would have to rely on the patient's (or caregiver's / family's) verbal recollection of the experience. Thus, in some embodiments, the platform can facilitate dialogue with physicians and patients, providing clearer means and tools of communication than traditional patient-clinician dialogue.

[0117] Without wishing to be bound by any particular theory, there is evidence to suggest that the use of visual maps as provided through the platform of the present invention can make patients more compliant with both medical appointments and medication schedules, as well as other instructions from doctors.Furthermore, there is further evidence to suggest that patients may show and experience less stress, and family members may be more involved in the patient's treatment process, reducing the burden on primary caregivers.In further embodiments, the burden may include various types of challenges that must be managed by caregivers.In one embodiment, the first type of burden may be memory impairment of the individual they are caring for.For people with Alzheimer's disease, for example, simply remembering appointments, or taking medicine at a certain time, or remembering new faces, or following the flow of conversation, becomes increasingly difficult as the disease continues to progress.

[0118] D. Platform Architecture The platform 1620 and the visual representation generation module 1622 may be embodied, for example, but not limited to, as a website, a web application, a desktop application, and a mobile application compatible with a computing device. The computing device may include, but is not limited to, a desktop computer, a laptop, a tablet, or a mobile communication device. Additionally, the platform 1620 and the visual representation generation module 1622 may be hosted on a centralized server, such as, for example, a cloud computing service. FIG. 13 is a block diagram of a system including a computing device 1600. Consistent with embodiments of the present disclosure, the memory storage and processing units described above may be implemented in a computing device, such as the computing device 1600 of FIG. 13. Any suitable combination of hardware, software, or firmware may be used to implement the memory storage and processing units. For example, the memory storage and processing units may be implemented with the computing device 1600, or in combination with the computing device 1600, or with any of the other computing devices 1618. The systems, devices, and processors described above are examples, and other systems, devices, and processors may include the memory storage and processing units described above, consistent with embodiments of the present invention. Additionally, the computing device 1600 may include the operating environment for the above-described methods 400 and / or 500. The methods 400 and / or 500 may operate in other environments and are not limited to the computing device 1600.

[0119] With reference to FIG. 13, a system consistent with embodiments of the present disclosure may include a computing device, such as computing device 1600. In a basic configuration, computing device 1600 may include at least one processing unit 1602 and system memory 1604. Depending on the configuration and type of computing device, system memory 1604 may include, but is not limited to, volatile (e.g., random access memory (RAM)), non-volatile (e.g., read only memory (ROM)), flash memory, or any combination. System memory 1604 may include an operating system 1605, one or more programming modules 1606, and may also include program data 1607. Operating system 1605 may be suitable, for example, for controlling the operation of computing device 1600. In one embodiment, programming module 1606 may include platform 1620 and visual representation generation module 1622. Additionally, embodiments of the present disclosure may be implemented in conjunction with graphics libraries, other operating systems, or any other application programs, and are not limited to any particular application or system. This basic configuration is illustrated in FIG. 13 by those components within dashed line 1608.

[0120] Computing device 1600 may have additional features or functionality. For example, computing device 1600 may also include additional data storage devices (removable and / or non-removable), such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 13 by removable storage 1609 and non-removable storage 1610. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. System memory 1604, removable storage 1609, and non-removable storage 1610 are all examples of computer storage media (i.e., memory storage). Computer storage media may include, but is not limited to, RAM, ROM, Electrically Erasable Read Only Memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage, or any other medium that can be used to store information and that can be accessed by computing device 1600. Any such computer storage media may be part of device 1600. Computing device 1600 may also have input device(s) 1612, such as a keyboard, mouse, pen, audio input device, touch input device, etc. Output devices 1614, such as a display, speaker, printer, etc. may also be included. The aforementioned devices are examples and other devices may be used.

[0121] The computing device 1600 may also include communications connections 1616 that may enable the device 1600 to communicate with other computing devices 1618, such as over a network in a distributed computing environment (e.g., an intranet or the Internet). The communications connections 1616 are an example of communication media. Communication media may typically be embodied by computer-readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term "modulated data signal" may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media, such as a wired network or direct-wired connection, and wireless media, such as acoustic, radio frequency (RF), infrared, and other wireless media. As used herein, the term computer-readable media may include both storage media and communication media.

[0122] As mentioned above, a number of program modules and data files may be stored in the system memory 1604, including the operating system 1605. While executing on the processing unit 1602, the programming modules 1606 (e.g., applications 1620, visual representation generation module 1622) may perform processes including, for example, one or more of the disclosed method steps described above. The foregoing processes are exemplary and the processing unit 1602 may perform other processes. Other programming modules that may be used in accordance with embodiments of the present disclosure may include email and contact applications, word processing applications, spreadsheet applications, database applications, slide presentation applications, drawing or computer-aided application programs, and the like.

[0123] Generally, consistent with embodiments of the present disclosure, program modules can include routines, programs, components, data structures, and other types of structures that can perform particular tasks or implement particular abstract data types. Additionally, embodiments of the present disclosure can be practiced with other computer system configurations, including handheld devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. Embodiments of the present disclosure can also be practiced in distributed computing environments where tasks are performed by remote processing devices linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices.

[0124] Furthermore, embodiments of the present disclosure may be practiced on electrical circuits containing discrete electronic elements, packaged or integrated electronic chips containing logic gates, circuits utilizing microprocessors, or on a single chip containing electronic elements or a microprocessor. Embodiments of the present disclosure may also perform logical operations such as AND, OR, and NOT, and may be implemented using other technologies, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the present disclosure may be practiced in a general-purpose computer or in any other circuit or system.

[0125] The embodiments of the present disclosure may be implemented, for example, as a computer process (method), a computing system, or as an article of manufacture such as a computer program product or computer readable medium. The computer program product may be a computer storage medium readable by a computer system and encoding a computer program of instructions for executing a computer process. The computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process. Thus, the present disclosure may be embodied in hardware and / or software (including firmware, resident software, microcode, etc.). In other words, the embodiments of the present disclosure may take the form of a computer program product on a computer usable or computer readable storage medium having computer usable or computer readable program code embodied in the medium for use by or in connection with an instruction execution system. The computer usable or computer readable medium may be any medium that can contain, store, communicate, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device.

[0126] The computer usable or computer readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. In more specific examples of computer readable media (non-exhaustive list), the computer readable medium may include: an electrical connection with one or more wires, a portable computer diskette, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, and a portable compact disk read-only memory (CD-ROM). It should be noted that the computer usable or computer readable medium may be a paper or another suitable medium on which the program is printed, since the program may be captured electronically, for example, via optical scanning of the paper or other medium, and then compiled, interpreted, or otherwise processed in an appropriate manner as required, and then stored in the computer memory.

[0127] The embodiments of the present disclosure are described above with reference to, for example, block diagrams and / or operational diagrams of methods, systems, and computer program products according to the embodiments of the present disclosure. The functions / acts described in the blocks may be performed out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially simultaneously or the blocks may be executed in the reverse order depending on the functionality / acts involved.

[0128] Although specific embodiments of the present disclosure have been described, other embodiments may exist. Additionally, although the embodiments of the present disclosure are described as being associated with data stored in memory and other storage media, the data may also be stored in or read from other types of computer-readable media, such as secondary storage devices, such as hard disks, floppy disks, or CD-ROMs, carrier waves from the Internet, or other forms of RAM or ROM. Additionally, the steps of the disclosed methods may be modified in any manner, including rearranging steps and / or inserting or deleting steps, without departing from the present invention.

[0129] All rights, including copyright, in the code contained herein belong to and are the property of the Applicant, who retains and reserves all rights in the code contained herein and authorizes reproduction of the material solely in connection with reproduction of the granted patent and not for any other purpose.

[0130] The invention and platform include at least the following aspects:Aspect 1: A method for generating a visual representation of a theme, the method including: providing, by a system having at least one processor, a parent visual element displaying information related to a selected [or specified] theme, the parent visual element configured to be associated with at least one attribute visual element; providing at least one attribute visual element based on at least the selected theme, the at least one attribute visual element configured to be linked with the parent visual element in a link pattern or arrangement; modifying the parent visual element and the at least one attribute visual element to convey information related to at least one of the parent visual element, the theme, and displaying visual indicators configured to determine a relationship between the parent visual element and the at least one attribute visual element, the at least one attribute visual element and the theme, and at least one of the at least one attribute visual element and another attribute visual element; generating, by the system, a visual representation including the parent visual element linked with the at least one modified attribute visual element in a link pattern based on at least a predetermined visual representation template; and displaying, by the system, the visual representation for interaction with a subject or user. Aspect 2: A system for generating a visual representation of a theme, the system comprising: at least one memory having computer-accessible instructions; and at least one processor operatively coupled to the at least one memory and configured by at least a portion of the computer-accessible instructions to: provide a parent visual element displaying information related to a selected or specified theme, the parent visual element being linked or associated with at least one attribute visual element, providing at least one attribute visual element based on at least the selected theme, the at least one attribute visual element being configured to be linked with the parent visual element in a linking pattern or arrangement; modifying the at least one attribute visual element to convey information related to at least one of the parent visual element, the theme, display visual landmarks configured to determine a relationship between the parent visual element and the at least one attribute visual element, the at least one attribute visual element and the theme, and at least one attribute visual element and another attribute visual element; generate a visual representation including the parent visual element linked with the modified at least one attribute visual element in a linking pattern or arrangement based on at least a predetermined visual representation template; and display the visual representation for interaction with a subject or user. Aspect 3: A method for treating a memory impairment in a subject, comprising: presenting an interactive visual representation to the subject, the visual representation including: a parent visual element displaying information related to a selected theme, the parent visual element being associated with at least one attribute visual element; and at least one attribute visual element related to at least the selected theme, the parent visual element being configured to be linked with the parent visual element in a linking pattern or arrangement, each attribute visual element displaying visual landmarks configured to convey information related to at least one of the parent visual element, the theme, and determine a relationship between the parent visual element and the at least one attribute visual element, the at least one attribute visual element and the theme, and at least one of the at least one attribute visual element and another attribute visual element, the parent visual element being linked with each of the attribute visual elements in a linking pattern or arrangement. Aspect 4: The method or system of any preceding aspect, further comprising: sorting at least one attribute visual element based on at least one characteristic associated with the at least one attribute visual element.Aspect 5: The method or system of any preceding aspect, wherein generating the visual representation is further based on a selection criterion. Aspect 6: The method or system of any preceding aspect, further comprising a plurality of attribute visual elements.Aspect 7: The method or system of any preceding aspect, wherein the at least one attribute visual element comprises a primary attribute visual element, a secondary attribute visual element, or a tertiary attribute visual element, or a combination thereof.Aspect 8: The method or system of any preceding aspect, wherein the link pattern or arrangement comprises a map, chart, diagram, layout, or the like. Aspect 9: The method or system of any preceding aspect, wherein the theme relates to an event, an activity, an instruction, or the like. Aspect 10: The method or system of any preceding aspect, further comprising modifying the at least one attribute visual element to display image assets or media assets that match landmarks in the at least one attribute visual element. Aspect 11: The method or system of any preceding aspect, wherein the interaction comprises viewing the visual representation. Aspect 12: The method or system of any preceding aspect, wherein the interaction comprises viewing the at least one attribute visual element according to a link pattern or arrangement. Aspect 13: The method or system of any preceding aspect, wherein each of the at least one attribute visual element has a placement position, and the interaction comprises viewing the at least one attribute visual element according to a placement position within the link pattern or arrangement. Aspect 14: The method or system of any preceding aspect, wherein the interaction comprises navigating the visual representation. Aspect 15: The method or system of any preceding aspect, wherein the interaction comprises memory training using the visual representation. Aspect 16: The method or system of any preceding aspect, wherein the memory training comprises a predetermined number of repeated interactions or use of the visual representation. Aspect 17: The method or system of any preceding aspect, wherein the interaction with the visual representation is effective to improve memory [or treat a memory disorder] of the subject or user. Aspect 18: The method or system of any preceding aspect, wherein the interaction with the visual representation is effective to improve memory [or treat a memory disorder] corresponding to a selected structure or region of the brain of the subject or user. Aspect 19: The method or system of any preceding aspect, wherein the effectiveness of improving the memory of the subject or user is determined or measured by at least one of behavioral assessment, imaging scans of brain structure or activity, and brain activity assessment.Aspect 20: The method or system of any preceding aspect, wherein the behavioral assessment comprises at least one standardized assessment tool (e.g., Stebbins, GT, Neuropsychological Testing in Textbook of Clinical Neurology, 2007; Johns, P, Mild Cognitive Impairment in Clinical Neuroscience, 2014; Butler, c, and Zeman, A, The Neurology of Consciousness, 2016, etc.). Aspect 21: The method or system of any preceding aspect, wherein the assessment scale comprises determining the effect or impact on at least one of behavior, memory, emotion, stress, frustration, anger, confusion, agitation, agitation, mood (e.g., depression, anxiety, etc.), withdrawal, compliance, family involvement, physical complaints, attitude, self-involvement, need for medication, hospitalization, hospitalization, visuospatial function, general cognitive function, spatial memory, executive function, independence, autonomy, speech fluency, apathy, and disinhibition. Aspect 22: The method or system of any preceding aspect, wherein the image scan comprises a structural or functional magnetic resonance imaging (MRI) scan.Aspect 23: The method or system of any preceding aspect, wherein the selected structure or region of the brain is at least one of a habit region, a neostriatum, a striatum, a pallidum, and a basal ganglia of the brain.Aspect 24: A method of creating a profile of a plurality of users based on user behavior, the method being a computer-implemented method, the method comprising: receiving at least one attribute element from a visual representation accessed by a user of the plurality of users; obtaining content from each of the at least one attribute element of the visual representation; analyzing the content from each of the at least one attribute element of the visual representation, where analyzing the content from the visual representation comprises analyzing content corresponding to at least one content type present on the visual representation; and identifying a plurality of keywords corresponding to the visual representation based on the analysis, where the plurality of keywords constitute a profile of the user.Aspect 25: The method of any preceding aspect, wherein the plurality of keywords are associated with a plurality of salience values, and the user's profile further comprises the plurality of salience values. Aspect 26: The method of any preceding aspect, wherein the analyzing comprises performing at least one machine learning (ML) technique on the content included in the visual representation. Aspect 27: The method of any preceding aspect, wherein the analyzing comprises performing natural language processing (NLP) of the content included in the visual representation. Aspect 28: The method of any preceding aspect, wherein the analyzing comprises performing natural language processing (NLP) of textual content included within the visual representation. Aspect 29: The method of any preceding aspect, wherein the visual representation comprises textual content and non-textual content. Aspect 30: The method of any preceding aspect, wherein the non-textual content comprises at least one of audio content, images, video content, and multimedia content. Aspect 31: The method of any preceding aspect, wherein the analyzing further comprises converting the non-textual content to textual content. Aspect 32: The method of any preceding aspect, further comprising determining an aggregated salience value corresponding to the keyword based on a first salience value of the keyword corresponding to the first visual representation and a second salience value of the keyword corresponding to the second visual representation. Aspect 33: The method of any preceding aspect, wherein the aggregated salience value is further based on a time decay value associated with each of the first salience value and the second salience value. Aspect 34: The method of any preceding aspect, further comprising predicting a first portion of a first profile corresponding to the first user based on a first portion of a second profile corresponding to the second user, wherein the predicting is based on a result of a comparison between the second portion of the first profile and the second portion of the second profile. Aspect 35: The method of any preceding aspect, wherein the profile of the user further comprises at least one characteristic of the user. Aspect 36: The method or system of any preceding aspect further comprising predicting a clinical state of a user or patient. Aspect 37: A computer-implemented method for predicting a clinical state of a patient, comprising: monitoring user behavior on a user device operated by a patient during interaction across a plurality of visual representations having a plurality of attribute elements, each visual representation having a plurality of user interface pages corresponding to a plurality of attribute elements for interacting with the visual representation; analyzing the user behavior; and determining a clinical state of the patient based on at least the analysis of the user behavior. Aspect 38: The method of any preceding aspect, wherein the clinical state corresponds to at least one clinical intervention. Aspect 39: The method of any preceding aspect, further comprising determining a confidence value associated with the clinical state, the confidence value representing a certainty of the patient's need for the at least one clinical intervention. Aspect 40: The method of any preceding aspect, wherein the clinical state corresponds to at least one of a neurobehavioral assessment, a cognitive state, an emotional state, a motor state, and a sensory state. Aspect 41: The method of any preceding aspect, wherein the monitoring comprises extracting user behavior data captured during user interaction with the plurality of visual representations. Aspect 42: The method of any preceding aspect, wherein the monitoring further includes extracting biometric data associated with the user, and wherein determining the patient's clinical status is based at least on the analysis of the user behavior data and the biometric data. Aspect 43: The method of any preceding aspect, wherein the monitoring includes extracting at least one key element from at least one user page corresponding to a visual representation accessed by the patient. Aspect 44: The method of any preceding aspect, wherein the monitoring includes extracting content from the plurality of visual representations, and wherein the analyzing includes performing machine learning techniques on the content. Aspect 45: The method of any preceding aspect, wherein the machine learning techniques include at least one of supervised machine learning and unsupervised machine learning. Aspect 46: The method of any preceding aspect, wherein the user behavior includes at least one of visiting the plurality of visual representations, interacting with the plurality of visual representations, and the like. Aspect 47: The method of any preceding aspect, wherein the user behavior data includes time series data and success rate data. Aspect 48: The method of any preceding aspect, wherein the time series data includes a duration required for the user to navigate all user interface pages in the visual representation, a time interval between navigating each user interface page in the visual representation, a time to view a prompt before making a selection during an interaction, a frequency of accessing user interface pages in the visual representation, a sequence of accessing user interface pages in the visual representation, a duration of off-topic viewing, and a frequency of off-topic viewing. Aspect 49: The method of any preceding aspect, wherein the success rate data includes a percentage of time that the user successfully completes a visual representation corresponding to a task. Aspect 50: The method of any preceding aspect, wherein the user behavior data includes data associated with the user's physical interaction with the user device selected from one or more of an intensity of screen pressure during the user interaction, a finger dwell time on the screen, a navigation strategy, typos, and accelerometer data. Aspect 51: The method of any preceding aspect, wherein the biometric data includes eye measurements such as pupil size and reflexes, eye movements, facial expressions, accelerometer data, actigraphy data, quantitative and qualitative measures of sleep, heart rate, instantaneous heart rate variability, sleep disorders, changes in sleep architecture, single-lead EKG data, pulse oximetry data, GPS location data, multi-lead EKG data, blood pressure data, capnography data (e.g., respiratory gases such as CO2), gait speed variability, cadence, left and right leg symmetry, step time, stance time, step length, swing time, stance time, step velocity, step length asymmetry, step time asymmetry, and the like.

[0131] Aspect 52: The method of any preceding aspect, further comprising employing a tracker on a user device operated by the patient, wherein the monitoring is performed by the tracker. Aspect 53: The method of any preceding aspect, wherein each of the plurality of visual representations comprises a tracker configured to perform the monitoring. Aspect 54: The method of any preceding aspect, further comprising analyzing content of at least one visual representation of the plurality of visual representations and identifying at least one theme based on the analysis. Aspect 55: The method of any preceding aspect, further comprising extracting a reference set of key elements associated with the at least one theme based on the analysis. Aspect 56: The method of any preceding aspect, further comprising identifying a patient based on at least one unique identifier associated with the request, presenting a visual representation of the plurality of visual representations to the patient on a user device operated by the patient, and extracting a set of key elements from the visual representation, wherein the analyzing includes analyzing the set of key elements, and wherein determining a clinical status of the patient is based on analyzing the set of key elements. Aspect 57: The method of any preceding aspect, wherein analyzing the set of key elements includes comparing the set of key elements to a reference set of key elements associated with the visual representation, the reference set of key elements representing at least one provided by the visual representation.Aspect 58: The method of any preceding aspect, further including receiving a request to access the visual representation by a user device operated by the patient, identifying the patient based on at least one unique identifier associated with the request, obtaining a clinical state associated with the patient, and sending at least one query to the user device based on the clinical state.Aspect 59: The method of any preceding aspect, wherein the query includes neurobehavioral assessments related to cognition, emotion, movement, and sensation, and cognitive tests related to drawing tasks, memory tasks, executive function tasks, and the like, and analyzing includes analyzing the neurobehavioral assessment data and cognitive test data from the user query, and determining the patient's clinical state is based at least on analyzing the neurobehavioral assessment data and cognitive test data from the user query. Aspect 60: The method of any preceding aspect, further comprising updating a patient database comprising a clinical status of the patient.Aspect 61: The method or system of any preceding aspect, wherein the memory comprises at least one of spatial memory, temporal memory, spatiotemporal memory, working memory and short-term memory.Aspect 62: The method or system of any preceding aspect, wherein improving memory or treating the memory impairment comprises preventing a memory-related disease or disorder.Aspect 63: The method or system of any preceding aspect, wherein the memory impairment comprises a memory-related disorder or disease. Aspect 64: The method or system of any of the preceding aspects, wherein the memory-related disease or disorder is associated with age-related memory impairment or brain function loss.Aspect 65: The method or system of any of the preceding aspects, wherein the memory-related disease or disorder comprises a degenerative disease or functional decline disorder.Aspect 66: The method or system of any of the preceding aspects, wherein the memory-related disease or disorder is selected from Alzheimer's disease, Parkinson's disease, vascular dementia, mixed dementia, frontotemporal dementia, Huntington's disease, chronic traumatic encephalopathy (CTE), mental health disorders including schizophrenia, mood disorders including depression and anxiety; and functional decline disorders or conditions including age-related decline, or other so-called "benign age-related changes" that do not have a specific neurodegenerative or biological basis, e.g., memory decline, cognitive decline. Aspect 67: The method of treatment of any of the preceding aspects, further comprising the step of identifying a subject in need of treatment for memory impairment. Aspect 68: The method or system of any of the preceding aspects, wherein the subject has been diagnosed as in need of memory impairment treatment prior to the presenting step. Aspect 69: The method or system of any of the preceding aspects, wherein the subject has been diagnosed as having a memory-related disease or disorder associated with age-related memory loss or brain function loss. Aspect 70: The method or system of any of the preceding aspects, wherein the subject is a mammal. Aspect 71: The method or system of any of the preceding aspects, wherein the mammal is a human. Aspect 72: The method or system of any of the preceding aspects, wherein the effective amount or effective is a therapeutically effective amount. Aspect 73: The method or system of any of the preceding aspects, wherein the effective amount or effective is a prophylactically effective amount. Aspect 74: The method or system of any of the preceding aspects, further comprising the step of identifying a subject in need of memory training or memory impairment treatment. Aspect 75: The method or system of any of the preceding aspects, wherein the subject in need of anti-aging treatment comprises having at least one risk factor for developing a memory-related disease or disorder. Embodiment 76: The method or system of any preceding embodiment, further comprising diagnosing the subject as having a memory-related disorder or disease. Aspect 77: The method or system of any preceding embodiment, wherein the diagnosing comprises performing a physical examination on the subject to state a finding. Aspect 78: The method or system of any preceding embodiment, wherein the diagnosing comprises performing an evaluation on the subject to state a finding. Aspect 79: The method or system of any preceding embodiment, wherein the finding comprises identifying at least one risk factor for developing a memory related disease or disorder. Aspect 80: The method or system of any preceding embodiment, wherein the diagnosing comprises performing an experiment on the subject to identify a level of a biological marker. Aspect 81: The method or system of any preceding embodiment, wherein the biological marker is a marker for a memory related disease or disorder. Aspect 82: The method or system of any preceding embodiment, wherein the biological marker is determined using a biological sample from the subject. Aspect 83: The method or system of any preceding embodiment, wherein the biological sample is selected from cells, blood, saliva, urine, tissue, or sputum. Aspect 84: A system comprising a memory storage and a processing unit coupled to the memory storage, wherein the processing unit is operative to perform the method of any preceding embodiment. Aspect 85: A computer-readable medium comprising a set of instructions that, when executed, performs the method of any preceding aspect.

[0132] Although aspects of the invention may be described and claimed in a particular statutory class, such as a system statutory class, this is for convenience only, and one of ordinary skill in the art will understand that each aspect of the invention may be raised and claimed in any statutory class. Unless otherwise expressly stated, it is never intended that any method or aspect described herein be construed as requiring that its steps be performed in a particular order. Thus, unless a method claim is specifically stated in the claim or specification to be limited to a particular order, no order is ever intended to be inferred in any respect. This applies to any possible non-express basis for interpretation, including logical matters regarding the arrangement of steps or operational flow, the obvious meaning derived from grammatical construction or punctuation, or the number or type of aspects described in the specification.

[0133] Throughout this application, various publications may be referenced. The disclosures of these publications are incorporated by reference into this application in order to more fully describe the state of the art to which they pertain. The references disclosed are also individually and specifically incorporated by reference herein for the material contained in them that is discussed in the sentence in which the reference is relied upon. Nothing herein should be construed as an admission that the present invention is not entitled to antedate such publications by virtue of prior invention. Further, the publication dates provided herein may be different from the actual publication dates, which may require independent confirmation. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements that have insubstantial differences from the literal language of the claims. To the extent the above description and accompanying drawings disclose any additional subject matter not falling within the scope of the following claims, the disclosure is not dedicated to the public and the right to file one or more applications to claim such additional disclosure is reserved.

Claims

1. 1. A method for presenting an interactive visual representation to a user for treating a memory-related disease or disorder, comprising: a) providing, by a system having at least one processor, a parent visual element that displays information related to a selected theme, the parent visual element configured to be associated with at least one attribute visual element; b) providing, by the system, at least one attribute visual element based at least on the selected theme, the at least one attribute visual element being configured to be linked with the parent visual element in a linking pattern; and c) displaying content configured to modify the parent visual element and at least one attribute visual element by the system to convey information related to at least one of the parent visual element, the theme, and to determine a relationship between the parent visual element and at least one attribute visual element, the at least one attribute visual element and the theme, and at least one of the at least one attribute visual element and another attribute visual element; d) generating, by said system, a visual representation including said parent visual element linked with said at least one modified attribute visual element in a linking pattern based on at least a predefined visual representation template; presenting, by the system, the visual representation to a user for interaction; said interaction with said visual representation is effective in treating a memory-related disease or disorder of said user; and creating a user profile for the user based on the user behavior, the creating the user profile comprising: receiving at least one attribute element from a visual representation accessed by the user; obtaining content from each of the at least one attribute visual element of the visual representation; Analyzing content from each of the at least one attribute visual element of the visual representation, where analyzing content includes analyzing content corresponding to at least one content type present in each of the at least one attribute visual element of the visual representation. and identifying a plurality of keywords corresponding to the visual representation based on the analysis, the plurality of keywords constituting a profile of the user, the plurality of keywords being associated with a plurality of salience values, the profile of the user further including the plurality of salience values; The method, wherein efficacy of treatment is determined based on improvement in at least one of rating scales, imaging scans of brain structure or activity, and brain activity assessments, wherein the rating scales include at least one measure of behavior, cognition, memory, emotion, stress, frustration, anger, confusion, agitation, agitation, mood, social withdrawal, compliance, family involvement, somatic complaints, attitude, self-involvement, need for medication, medical attendance, hospitalization, visuospatial function, general cognitive function, spatial memory, executive function, independence, autonomy, verbal fluency, apathy, and disinhibition.

2. The method of claim 1 , wherein the assessment includes at least one standardized assessment tool.

3. The method of claim 1 , wherein analyzing comprises performing at least one machine learning (ML) technique on content included in the visual representation.

4. and predicting a clinical state of the user, wherein predicting the clinical state of the user comprises: monitoring user behavior on a user device operated by the user during an interaction across a plurality of visual representations; Analyzing said user behavior; and and determining a clinical status of the user based on at least an analysis of the user behavior.

5. The method of claim 4 , wherein the clinical condition corresponds to at least one clinical intervention.

6. The method of claim 4 , further comprising determining a confidence value associated with the clinical condition, the confidence value representing a certainty of the user's need for at least one clinical intervention.

7. The method of claim 6 , wherein the clinical condition corresponds to at least one of neurobehavioral assessment data, a cognitive condition, an emotional condition, a motor condition, and a sensory condition.

8. 7. The method of claim 6, wherein monitoring comprises extracting user behavior data captured during user interaction with the plurality of visual representations and extracting biometric data associated with the user, and determining a clinical status of the user based at least on analysis of the user behavior data and the biometric data.

9. The method of claim 8 , wherein the machine learning technique includes at least one of supervised machine learning and unsupervised machine learning.

10. The method of claim 9 , wherein the user behavior data includes time series data and success rate data.

11. 11. The method of claim 10, wherein the time series data includes the duration required for the user to navigate through all user interface pages in the visual representation, the time interval between navigating through each user interface page in the visual representation, the time to view a prompt before making a selection during an interaction, the frequency with which user interface pages in the visual representation are accessed, the sequence in which user interface pages in the visual representation are accessed, the duration of off-topic viewing, and the frequency of off-topic viewing.

12. The method of claim 11 , wherein the success rate data comprises a percentage of time that the user successfully completes a visual representation corresponding to a task.

13. The user behavior data includes the strength of the screen pressure during user interaction, the time the finger is resting on the screen, 10. The method of claim 9, further comprising data associated with the user's physical interactions with the user device selected from one or more of: navigation strategies, typos, and accelerometer data.

14. 14. The method of claim 13, wherein the biometric data includes at least one of eye measurements, pupil size and reflexes, eye movements, facial expressions, accelerometer data, actigraphy data, quantitative and qualitative measures of sleep, heart rate, instantaneous heart rate variability, sleep disorders, changes in sleep architecture, single-lead EKG data, pulse oximetry data, GPS location data, multi-lead EKG data, blood pressure data, capnography data, gait speed variability, cadence, left and right leg symmetry, step time, stance time, step length, swing time, stance time, step velocity, step length asymmetry, and step time asymmetry.

15. a) receiving a request to access a visual representation by a user device operated by the user; b) identifying the user based on at least one unique identifier associated with the request; and c) obtaining the clinical condition associated with the user; and 20. The method of claim 14, further comprising: d) sending at least one query to the user device based on at least the clinical condition and user behavior data.

16. 16. The method of claim 15, wherein at least one of the queries includes neurobehavioral assessment data related to cognition, emotion, movement, and sensation, and cognitive testing data related to drawing tasks, memory tasks, and executive function tasks, and analyzing includes analyzing the neurobehavioral assessment data and the cognitive testing data from at least one of the queries, and determining the clinical condition of the user is based at least on analyzing the neurobehavioral assessment data and the cognitive testing data from at least one of the queries.

17. 17. The method of claim 16, further comprising identifying the user as in need of memory impairment treatment.

18. 20. The method of claim 17, further comprising diagnosing the user as having a memory-related disorder or disease, wherein diagnosing comprises performing an experiment on the user and determining a level of a biological marker.

19. 19. The method of claim 18, wherein the memory-related disease or disorder is selected from Alzheimer's disease, Parkinson's disease, vascular dementia, mixed dementia, frontotemporal dementia, Huntington's disease, chronic traumatic encephalopathy (CTE), mental health disorders, schizophrenia, mood disorders, depression and anxiety, functional decline disorders, and cognitive decline.

20. 1. A system for treating a user having a memory-related disease or disorder, comprising: at least one memory having computer accessible instructions; operatively coupled to the at least one memory; providing a parent visual element that displays information related to a specified theme, said parent visual element being associated with at least one attribute element; i. providing a plurality of attribute elements based on at least the specified theme, the plurality of attribute elements being configured to be linked with the parent visual element in a linking pattern; ii. displaying content configured to modify the plurality of attribute elements to convey information related to at least one of the parent visual element, the theme, and to determine a relationship between at least one of the parent visual element and at least one attribute element, the at least one attribute element and the theme, and the at least one attribute element and another attribute element; iii. generating a visual representation comprising the parent visual element linked with the modified attribute elements in a linking pattern based on at least a predefined visual representation template and at least one neurobehavioral assessment data of the user; presenting the visual representation for interaction by the user having a memory-related disease or disorder; at least one processor configured by at least a portion of the computer accessible instructions to create a user profile for the user based on user interactive behavior; said interaction with said visual representation is effective to treat a memory impairment corresponding to a selected structure or region of said user's brain selected from at least one of a habitus region of the brain, a neostriatum, a striatum, a globus pallidus, and a basal ganglia; Creating a user profile is receiving the plurality of attribute elements from a visual representation interacting with the user; obtaining content from each of the plurality of attribute elements of the visual representation; analyzing content from each of the attribute elements of the visual representation using at least one machine learning (ML) technique, the content corresponding to at least one content type present in each of the attribute elements of the visual representation; and identifying a plurality of keywords corresponding to the visual representation based on the analysis, the plurality of keywords constituting a profile of the user, the plurality of keywords being associated with a plurality of relevance values, the profile of the user further including the plurality of relevance values.

21. The system is further configured to predict a clinical state of the user, where predicting the clinical state of the user comprises: a) monitoring user behavior on a user device operated by the user during interaction across a plurality of visual representations having a plurality of attribute elements, each visual representation having a user interface page corresponding to each of the plurality of attribute elements for interacting with the visual representation; b) analyzing said user behavior; and c) determining a clinical status of the user based at least on the analysis of the user behavior; and d) determining a confidence value associated with said clinical condition; 21. The system of claim 20, wherein the clinical condition corresponds to at least one clinical intervention.

22. 22. The system of claim 21, wherein the clinical intervention includes presenting at least one query to the user device, the at least one query including the neurobehavioral assessment data related to at least one of cognition, emotion, movement, and sensation, and cognitive testing data related to at least one of a drawing task, a memory task, and an executive function task, and analyzing includes analyzing the neurobehavioral assessment data and the cognitive testing data from the at least one query, and determining the clinical status of the user is based at least on analyzing the neurobehavioral assessment data and the cognitive testing data from the at least one query.

23. 23. The system of claim 22, wherein the clinical condition corresponds to at least one clinical intervention.

24. 23. The system of claim 22, wherein the clinical condition corresponds to at least one of the neurobehavioral assessment data, a cognitive condition, an emotional condition, a motor condition, and a sensory condition.

25. 23. The system of claim 22, wherein monitoring includes extracting user behavior data captured during user interaction with the plurality of visual representations and extracting biometric data associated with the user, and determining a clinical status of the user based at least on analysis of the user behavior data and the biometric data.

26. 26. The system of claim 25, wherein the machine learning technique includes at least one of supervised machine learning and unsupervised machine learning.

27. 27. The system of claim 26, wherein the user behavior data includes time series data and success rate data.

28. 28. The system of claim 27, wherein the time series data includes the duration required for the user to navigate through all user interface pages within the visual representation, the time interval between navigating through each user interface page within the visual representation, the time to view a prompt before making a selection during an interaction, the frequency with which user interface pages within the visual representation are accessed, the sequence in which user interface pages within the visual representation are accessed, the duration of off-topic viewing, and the frequency of off-topic viewing.

29. 30. The system of claim 28, wherein the success rate data comprises a percentage of time that the user successfully completes a visual representation corresponding to a task.

30. 28. The system of claim 27, wherein the user behavior data includes data associated with the user's physical interactions with the user device selected from one or more of: intensity of screen pressure during user interaction, finger dwell time on the screen, navigation strategy, typos, and accelerometer data.

31. 28. The system of claim 27, wherein the biometric data includes at least one of eye measurements, pupil size and reflexes, eye movements, facial expressions, accelerometer data, actigraphy data, quantitative and qualitative measures of sleep, heart rate, instantaneous heart rate variability, sleep disorders, changes in sleep architecture, single-lead EKG data, pulse oximetry data, GPS location data, multi-lead EKG data, blood pressure data, capnography data, gait speed variability, cadence, left and right leg symmetry, step time, stance time, step length, swing time, stance time, step velocity, step length asymmetry, and step time asymmetry.

32. 32. The system of claim 31, wherein the system is further configured to identify the user as in need of memory impairment treatment.

33. The system is further configured to diagnose the user as having a memory-related disorder or disease prior to presentation of the visual representation, where diagnosing includes performing an experiment on the user. and determining a level of the biological marker.

34. 34. The system of claim 33, wherein the memory-related disease or disorder is selected from Alzheimer's disease, Parkinson's disease, vascular dementia, mixed dementia, frontotemporal dementia, Huntington's disease, chronic traumatic encephalopathy (CTE), mental health disorders, schizophrenia, mood disorders, depression and anxiety, functional decline disorders, and cognitive decline.

35. 35. The system of claim 34, wherein the biological sample is selected from cells, blood, saliva, urine, tissue, or sputum.

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