Artificial intelligence-driven adaptive learning and assessment in virtual reality environments

Abstract

Provided are platforms, methods, and systems to customize virtual or augmented reality (collectively, XR) content for an individual or a group of individuals, such as to treat a user having a mental or neurodevelopmental disorder (e.g., autism), train or educate an individual (e.g., student) or a group of individuals, or otherwise attend to a special need of an individual or a group of individuals. A graphical user interface may be used to edit the XR content.

Description

ARTIFICIAL INTELLIGENCE-DRIVEN ADAPTIVE LEARNING AND ASSESSMENT IN VIRTUAL REALITY ENVIRONMENTSCROSS-REFERENCE

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 709,932, filed 10 / 21 / 2024, which application is incorporated herein by reference.BACKGROUND

[0002] Virtual or augmented reality is the coordinated experience of a user in or with an environment manufactured by, and housed in, computer processors and memory, where the environment is coordinated with the user’s motion and use of the senses such as sight, sound, and touch. Augmented reality systems may be used to augment, emphasize, or otherwise modify the perception both of real -world objects and completely virtual objects. Virtual or augmented reality systems may be used to create experiences so that a user may encounter, learn, or train while being exposed to little or no physical risk. For example, virtual or augmented reality systems may be used for neurodevelopmental therapies for disorders such as autism or education.

[0003] Autism, also known as autism spectrum disorder (ASD), refers to a group of complex [DG1] heterogeneous neurodevelopmental conditions characterized by variable degrees of impairment in social and communication skills, as well as restricted and repetitive patterns of behavior and interests. Autism affects over 2% of the US population, and is becoming more prevalent in the population every year. While the impact of autism is lifelong and includes reduced social engagement and quality of life, autism therapy can lead to positive lifelong impacts. Although there is no known cure, today, autism therapy ranges across a myriad of specialties including neurology, psychiatry, physical therapy, occupational therapy, behavioral therapy, and speech language pathology. Such therapeutic methods can help autistic individuals gain social and communication skills.SUMMARY

[0004] In many virtual or augmented reality (collectively referred to as XR) systems, users often find themselves limited to a standard set of XR environments. This limitation is especially significant for individuals utilizing XR for neurodevelopmental, behavioral, and psychiatric therapies or as learning aids. The one-size-fits-all approach neglects the unique traits, challenges, and learning profiles of each user. For instance, some users may prefer minimal auditory input to reduce sensory overload, while others might find visual schedules or cues more effective for navigating tasks. Providing options to customize the complexity of environments — such as adjusting lighting, colors, and movement — can help create a more comfortable and focusedexperience. Diverse educational levels, language proficiencies, and cultural backgrounds among users are often overlooked, making it difficult to create universally effective XR experiences. Therefore, there is a clear need for more flexible and personalized XR environments that address not only sensory and interaction preferences but also educational and cultural diversity to enhance accessibility and effectiveness for all users. With Al, learning plans can be personalized using data, such as patient age (chronological and cognitive), diagnosis, language level, co-morbid conditions, learner questionnaire results, coach goals, and current learner goals. The Al system can then suggest appropriate lessons, generate real-time lesson plans, and adjust lesson complexity according to learner progress. Further, Al-powered characters enhance natural interaction within the VR environment by adapting their conversations to the learner's needs, being paused, or redirected by the coach, and participating in group-based lessons. Al also permits the creation of diverse 3D content and animations, allowing for a variety of immersive XR environments.

[0005] On the analytical side, machine learning can process various data points, such as gaze patterns, gesture usage, physiological responses, and language interaction metrics, to generate valuable insights. It can assist in diagnosing and assessing conditions by identifying subcategories based on parameters like biometric data such as eye-tracking, voice, speech, pupillary responses, blink patterns and heart rate data. In addition, Al can automatically summarize session notes and minutes, detect significant deviations in learner behavior that might indicate issues, and verify if specific criteria or requirements, such as insurance renewals, have been met. As a result, Al can bring a new level of customization to XR content, whether used for the treatment of neurodevelopmental disorders such as autism, for education, or to accommodate the special needs or preferences of individuals or groups of users.

[0006] Unlike traditional systems and methods, the systems and methods provided herein may comprise Al-driven speech generation, allowing Al-controlled characters to interact with learners through conversational exchanges. These characters may adapt their dialogue based on the unique needs of individual learners, enhancing the level of engagement within the XR environment. Al-based speech generation is one of several components the system may use to personalize the XR experience, contributing to a more dynamic and immersive environment. Al may also facilitate the creation of more adaptable and inclusive XR environments that align with the diverse learning preferences and needs of various users.

[0007] The present disclosure may employ methods for creating, editing, and / or using a virtual world that provides visual, auditory, and / or haptic stimulation and experiences. In some instances, the methods may include creating, editing, and / or using a template XR experience or alibrary of templates. In some instances, the methods may include profiling one or more users and using the results of such profiling to create, edit, and / or use the virtual world. In some instances, the methods may include monitoring the user’s interaction with the virtual and real world, measuring the user’s progress toward one or more therapeutic or educational goals, and editing the virtual world based on such monitored results to improve the efficiency or accuracy of the XR experience. The present disclosure may employ platforms having graphical user interfaces (GUIs) for inputting user instructions for the creation, editing, and / or use of virtual world content. In some instances, the GUIs may output an intermediate and / or final result of the user instructions to aid in the creation, editing, and / or use of the virtual world content.

[0008] The virtual or augmented reality system of the present disclosure can comprise a display screen for visual engagement, headphones or speakers for auditory stimulation, and controllers for physical input as well as haptic feedback. The method may further comprise the user interacting with the virtual world using eye movement, pupillary responses, head movement, and / or one or more controllers attached to a body and / or limb of the user. One or more sensors integrated or external to the virtual or augmented reality system may detect the user’s interactions with the virtual world. In some instances, monitoring may be in real-time.

[0009] The platforms, methods, and systems of the present disclosure may be used for treating or supplementing the treatment of a user for a mental or neurodevelopmental disorder, such as autism, using a virtual or augmented reality system. The platforms, methods, and systems of the present disclosure may be used for educating or training, or supplementing the education or training, of a user, such as a student, using a virtual or augmented reality system.

[0010] In some embodiments, the system comprises at least one AI-Based Learning Customization Module. In some cases, the AI-Based Learning Customization Module comprises at least one User Profile Customization Component. In some instances, the User Profile Customization Component comprises information such as patient age, diagnosis, verbal / non-verbal level, co-morbid conditions, learner questionnaire results, coach goals, and learner emotions. In some cases, the AI-Based Learning Customization Module comprises at least one Real-time Lesson Plan Generator. In some instances, the at least one Real-time Lesson Plan Generator comprises Al algorithms. For example, at least one AI-Based Learning Customization Module may be configured to generate personalized learning plans based on real-time user data. In further examples, the generation of personalized learning plans is configured to enhance autism therapy.

[0011] The method may further comprise the user interacting with a therapist, parent, or peer to progress toward the one or more therapeutic or educational goals. For example, the user may bepaired with the therapist, parent, teacher, peer, or a plurality of therapists, parents, teachers, or peers, or any combination of the above. In some instances, a paired individual or entity may be capable of influencing the user’s experience in the virtual world, such as by creating, modifying, and / or removing the visual, auditory, and / or haptic stimulations provided to the user in the virtual world.

[0012] In some instances, the virtual or augmented reality experiences and therapies can be tailored to the needs of individual users either manually by a human expert (e.g., therapist, educators, parents etc.) or using computer algorithms or artificial intelligence. For example, the tailoring can be performed based at least on prior conditions and / or other assessments of the user and / or based on data collected throughout the user’s and / or others’ use of the system.

[0013] The platforms, methods, and systems of the present disclosure may be used by domain experts (e.g., teachers, educators, therapists, parents, etc.) to create new supervised interactive XR content. The interactive XR content may be published on the platform to be available for a target audience. For example, the target audience may be a specified user, group of users, groups of users, or the public. The creators and / or publishers of the interactive XR may interface with the platform with minimal technical understanding or expertise in the authoring, playback, or publication infrastructure.

[0014] The platforms, methods, and systems of the present disclosure may be used to review the XR content created from a clinical efficacy perspective, safety perspective, publication rights perspective, or other perspectives before permitting distribution of the content to a wider user base.

[0015] The platforms, methods, and systems of the present disclosure may provide for the authoring, editing, collaborative contribution, review, and / or troubleshooting (e.g., debugging) of the supervised interactive XR content

[0016] The platforms, methods, and systems of the present disclosure allow for a separation of XR content definitions that permits publication of new content without the need for conventional updates through the underlying operating system or distributor (e g., app store).

[0017] The present disclosure provides platforms, methods, and systems which may utilize an AI-Based Learning Customization Module. In some embodiments, the system comprises at least one AI-Based Learning Customization Module. In some cases, the AI-Based Learning Customization Module comprises at least one Real-Time Lesson Plan Generation Component. In some instances, the Real-Time Lesson Plan Generation Component comprises Al algorithms tailored to individual learner profiles. In some cases, the AI-Based Learning Customization Module comprises at least one Dynamic Content Update Component. For example, the ALBased Learning Customization Module may be configured to generate personalized lesson plans based on real-time user data. In further examples, the generation of personalized lesson plans is configured to provide a continuously updated personalized learning plan.

[0018] The present disclosure provides platforms, methods, and systems which may utilize an Al-Based Learning Progression Module. In some embodiments, the system comprises at least one AI-Based Learning Progression Module. In some cases, the AI-Based Learning Progression Module comprises at least one Dynamic Lesson Selector. In some instances, the Dynamic Lesson Selector comprises Al algorithms tailored to individual learner performance. In some cases, the AI-Based Learning Progression Module comprises at least one Real-time Progress Analysis Component. For example, the AI-Based Learning Progression Module may be configured to control progression of lessons based on user performance. In further examples, the progression of lessons is configured to create a personalized and dynamic learning plan.

[0019] The platforms, methods, and systems of the present disclosure may be used to publish XR content authored or modified for individual users in a clinical context, akin to the prescription of individualized regimens. Therapeutic or training regimens may be monitored and adjusted based on the progress of users by manual intervention of domain experts and / or by algorithms. For example, one or more processors may be programmed to execute one or more algorithms to adjust the XR content (e.g., a parameter, a duration, etc.) or prescription thereof with respect to the monitored user. Such monitoring and adjustment may be made without technical expertise in the platform or infrastructure.

[0020] In an aspect provided herein are systems for providing personalized learning support to users with neurodevelopmental disorders through a wearable device that provides a virtual reality environment and an Al module configured to receive user-specific data and generate a personalized lesson plan, where the lesson plan comprises virtual reality lessons displayed on the wearable device. In some embodiments, the neurodevelopmental disorders comprise one or more of Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), Specific Learning Disorders, Intellectual Disability (ID), Communication Disorders, Motor Disorders, Global Developmental Delay, and Other Specified or Unspecified Neurodevelopmental Disorders. In some embodiments, the neurodevelopmental disorders comprise at least one neurodevelopmental disorder. In some embodiments, the Al module is further configured to accumulate feedback on user learning experience and outcomes and utilize the feedback to refine and update the personalized lesson plan. In some embodiments, the system comprises a biometric feedback module configured to gather biometric data from the user. In some embodiments, the biometric data comprises one or more of eye gaze, voice tone, heart rate,pupillary response, voice analysis, facial expressions, hand movements, gestures, and sentiment analysis. In some embodiments, the biometric feedback module is configured to deduce the user’s engagement level and emotional state based at least in part on the biometric data. In some embodiments, the Al module is further configured to receive and interpret biometric data from the biometric feedback module. In some embodiments, the Al module is further configured to utilize the biometric data to refine and update the personalized lesson plan. In some embodiments, the Al module comprises a learning language model that functions as a recommendation engine to guide the user through the personalized lesson plan. In some embodiments, the user data used by the Al module for generating the personalized lesson plan comprises one or more of the following: the user's age, diagnosis, level of verbal or non-verbal communication, any co-morbid conditions, responses to a user questionnaire, goals set by a coach, and the current emotional state of the user. In some embodiments, the Al module uses lookalike data or comparison based on previous user experiences for refining and updating the personalized lesson plan. In some embodiments, the Al module utilizes a rating system to evaluate or score one or more aspects of the user's experiences or performance within the virtual reality environment. In some embodiments, the Al module uses the evaluations or scores from the rating system to dynamically adapt the virtual reality lessons in real-time, adjusting lesson tasks, stimuli, challenges, or strategies based on the user's performance and responses. In some embodiments, the continuous refinement and updating of the personalized lesson plan are implemented to enhance the user's skill acquisition, specifically catering to the needs of individuals with neurodevelopmental disorders. In some embodiments, the rating system integrates with a recommendation engine within the Al module, optimizing the selection and sequencing of virtual reality lessons for the user based on their previous performance and progression In some embodiments, the insights garnered from identified patterns, trends, or effective strategies as determined by the rating system are made accessible to a coach or instructor, thus permitting them to provide more effective support for the user's learning process within the virtual reality environment. In some embodiments, the rating system utilizes machine learning algorithms to accurately discern patterns and trends from the user's performance data, facilitating predictive modeling of the user's future progress and may comprise challenges. In some embodiments, the Al module is capable of tracking the progression of lessons in terms of complexity to adjust the lesson plan in real-time. In some embodiments, the biometric feedback module is capable of evaluating how effectively a lesson was delivered, based on learner or instructor feedback and the gathered biometric data. In some embodiments, the Al module isfurther configured to generate real-time updates of the lesson plans based on user data, feedback, and biometric data.

[0021] In another aspect, provided herein are systems for creating an interactive virtual environment for individuals with neurodevelopmental disorders, comprising a wearable device configured to provide a virtual reality environment, an Al module configured to receive userspecific data and generate a plurality of user-specific parameters, and an animation module configured to receive the user-specific parameters and generate personalized animated characters for the virtual reality environment based at least in part on the user-specific parameters. In some embodiments, the wearable device comprises a microphone or similar audio capture device to collect the user's audio data during a virtual reality session. In some embodiments, the user's audio data collected by the wearable device is sent to an API for processing, wherein the API is configured to transform the audio data into a textual format. In some embodiments, the processed audio data from the API is returned to the Al module. In some embodiments, a conversation module interfaces with the Al module, wherein the conversation module provides real-time response generation based at least in part on the user's audio data, gestural data, head tracking information, and eye tracking data to enrich the contextual accuracy of the responses. In some embodiments, the conversation module provides real-time response generation based at least in part on the user-specific parameters. In some embodiments, the conversation module utilizes Al to generate human-like speech for the personalized animated characters in the virtual reality environment. In some embodiments, the conversation module is configured to facilitate unscripted conversations. In some embodiments, the conversation module is configured to simulate a plurality of conversational scenarios. In some embodiments, the conversational scenarios may comprise one or more of job interview preparation, social interaction at a park, a visit to a grocery store, a classroom scenario, a sports game, a family gathering, a doctor's appointment, navigating a busy street, or an emergency situation like a fire alarm. In some embodiments, the conversation module is configured to monitor one or more neurodevelopmental skills of the user. In some embodiments, the neurodevelopmental skills of the user may comprise one or more of understanding and using language for social purposes, recognizing, and expressing emotions, initiating, and maintaining conversations, understanding social cues, adjusting speech style or content according to the social context, and developing empathy. In some embodiments, an intervention module comprises one or more components configured to direct or pause the conversation module. In some embodiments, the intervention module is controllable by a coach In some embodiments, the coach comprises one or more of a therapist, parent, teacher, caregiver, medical professional, or any other individual involved in theuser's therapy or care. In some embodiments, the intervention module is configured to permit the coach to choose conversation topics tailored to the user. In some embodiments, the conversation module further comprises a group learning module configured to facilitate interaction between the user and one or more additional users. In some embodiments, the group learning module is configured to identify and flag disruptive participants in a group. In some embodiments, the group learning support module is further configured to carry out actions such as removing, warning, correcting disruptive users during group lessons, and / or blocking user communications if necessary. In some embodiments, the system comprises a voice modulation component, wherein the voice modulation is configured to modulate the voice of the animated character, the user, the coach, and / or additional users. In some embodiments, the voice modulation comprises modulation of one or more of pitch, tone, speed, accent, language, emotional context, and verbal cues. In some embodiments, the Al module is further configured to provide real-time updates and modifications to the lesson plan based at least in part on feedback and / or performance metrics of the user.

[0022] In yet another aspect, provided herein are systems for virtualizing an environment to treat a neurodevelopmental disorder, comprising a wearable device configured to provide a virtual reality environment, an Al module configured to receive user-specific data and generate a plurality of user-specific parameters, and an animation module configured to receive the userspecific parameters and either generate virtual reality environments or modify virtual reality environments based on the user-specific parameters. In some embodiments, the user-specific parameters comprise one or more real-life scenarios. In some embodiments, the real-life scenario comprises one or more active shooter drills or independent living situations. In some embodiments, the user-specific parameters comprise real-world video footage. In some embodiments, the real-world video footage is captured in real-time or near real-time. In some embodiments, the system converts the real-world video footage into semi and / or fully animated virtual reality environments. In some embodiments, the semi and / or fully animated virtual reality environments comprise a hybrid virtual reality environment, wherein the hybrid environment comprises one or more real-world elements. In some embodiments, the real-world elements comprise one or more of buildings, nature landscapes, furniture, vehicles, familiar figures or objects, or any other elements present in the user's real-life environment. In some embodiments, a scanner is configured to scan one or more images. In some embodiments, the user-specific parameters comprise one or more scanned images. In some embodiments, the scanned images comprise one or more entities from the user's life. In some embodiments, the entity comprises one or more persons. In some embodiments, the generation module is configured to generate oneor more animated characters resembling the persons. In some embodiments, the user-specific parameters comprise one or more characteristics of the persons. In some embodiments, the modification module is configured to modify the animated character based on the characteristics. In some embodiments, the characteristics comprise one or more of physical appearance, voice properties, mannerisms, habitual behaviors, or any other identifiable traits associated with the persons. In some embodiments, the modification is of the animated character’s physical features, voice features, and personality controls. In some embodiments, a system derives valuable data through analytics and machine learning to support individuals with neurodevelopmental disorders, comprising a wearable device configured to provide a virtual reality environment and an Al module configured to process user interaction data within the virtual reality environment and generate automated analytics, wherein the analytics comprise an assessment of one or more conditions associated with the neurodevelopmental disorder. In some embodiments, the neurodevelopmental disorders comprise Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), Specific Learning Disorders, Intellectual Disability (ID), Communication Disorders, Pervasive Developmental Disorder, Global Developmental Delay, and Other Specified or Unspecified Neurodevelopmental Disorders. In some embodiments, the neurodevelopmental disorders comprise one or more of difficulties in social interaction, nonverbal communication, using language for social purposes, repetitive patterns of behavior or interests, hyperactivity, impulsivity, and impairments in personal, academic, or occupational functioning. In some embodiments, the analytics assessment indicates that the user has improved in one or more neurodevelopmental conditions. In some embodiments, the analytics assessment indicates that the user has declined in one or more neurodevelopmental conditions. In some embodiments, the analytics identify causative factors of the decline and assign probabilities to those factors In some embodiments, the analytics assess user-specific behavior in the virtual reality environment, including emotional state, task completion status, and need for prompting. In some embodiments, the system comprises a voice recorder module configured to receive and / or transcribe the user’s audio data. In some embodiments, the automated analytics are generated at least in part based on the user’s audio data. In some embodiments, the automated analytics are generated using machine learning algorithms. In some embodiments, a modification module is configured to modify the virtual reality environment based at least in part on the automated analytics.

[0023] In yet another aspect, provided herein are systems configured for neurodevelopmental diagnosis and monitoring, comprising a wearable device that provides a virtual reality environment and a diagnostic and monitoring module that uses Al to receive and interpret user-specific data to determine the presence or severity of one or more neurodevelopmental conditions. In some embodiments, the user-specific data comprises interaction data, performance metrics, and biometric data obtained during the user's interaction with the virtual reality environment. In some embodiments, the user-specific data comprises phenotypic data, which includes genetic data and biometric data. In some embodiments, the diagnostic assessment module uses phenotypic data to identify subcategory phenotypes. In some embodiments, the subcategories comprise Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), Specific Learning Disorders, Intellectual Disability (ID), Communication Disorders, Motor Disorders, and Global Developmental Delay. In some embodiments, the system includes biometric components configured to collect biometric data, including user eye movements, voice, speech, facial expressions, and pupillary response. In some embodiments, the biometric data comprises heart rate and positional data. In some embodiments, an assessment module compares current and previous determinations of neurodevelopmental conditions. In some embodiments, the assessment module generates or modifies personalized lessons based on this comparison. In some embodiments, the assessment module adjusts the lessons to enhance skill acquisition based on the user’s progression. In some embodiments, the system includes an anomaly detection module interfacing with the Al module to detect anomalies in the user's progress. In some embodiments, the user-specific parameters may include the user's interests, physical appearance, preferred genre, significant people, role models, favorite activities, cultural background, language proficiency, cognitive abilities, sensory preferences, emotional state, and any other personal attributes that may influence the user's engagement and learning outcomes in the virtual reality environment. In some embodiments, the user-specific parameters may be virtualized within the virtual reality environment based on the user’s request or when the Al module assesses that incorporating these parameters is beneficial for the user’s treatment or learning process. In some embodiments, the Al module assesses and limits lessons based on parameters such as age, diagnosis, verbal or non-verbal level, co-morbid conditions, questionnaire responses, and user goals. In some embodiments, the Al uses a Lookalike Model (LLM) as a recommendation engine for predicting lesson progression and updating lesson plans in real-time based on performance, feedback, and biometric data. In some embodiments, the virtual reality environment includes Al-powered characters that interact with the user in a less scripted manner, providing a conversational partner with the ability for the coach to intervene, as necessary. In some embodiments, the Al-powered characters are used in group lessons, with Al flagging disruptive participants and accommodating all learners. In some embodiments, the Al generates 3D content or animations for the virtual reality environment, transforming real-worldvideo into fully animated VR environments or modifying the physical and voice features of characters. In some embodiments, the Al utilizes analytics and machine learning to create valuable data for diagnosis or assessment, identifying subcategory phenotypes based on user data, including tracking eye movement, voice, speech, facial expressions, pupillary response, heart rate data, gesture recognition, and other behavioral and physiological data during interaction with the virtual reality environment. In some embodiments, the Al is used for summarizing notes, spotting anomalies, and determining whether users have met predetermined goals for healthcare or insurance decisions.

[0024] Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in this art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments, and its several details are capable of modifications in various obvious respects, all without departing from the disclosure.Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.INCORPORATION BY REFERENCE

[0025] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and / or take precedence over any such contradictory material.BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The novel features of the disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings (also “Figure” and “FIG.” herein), of which:

[0027] FIG. 1 shows a schematic illustration of a virtual or augmented reality system, in accordance with some embodiments

[0028] FIG.2 shows an illustration of a mobile companion application display, in accordance with some embodiments.

[0029] FIG.3 shows a logical model of a learning module, in accordance with someembodiments.

[0030] FIG. 4 shows a logical model of a learning card, in accordance with some embodiments.

[0031] FIG. 5 illustrates a process flow for editing XR content of the present disclosure, in accordance with some embodiments.

[0032] FIG. 6A illustrates a user interface platform for editing XR content, in accordance with some embodiments.

[0033] Fig. 6B illustrates a user interface platform for editing XR content, in accordance with some embodiments.

[0034] FIG. 7A shows illustrations of progress reports generated from a user’s response to an augmented reality or virtual reality experience, in accordance with some embodiments.

[0035] FIG. 7B shows illustrations of progress reports generated from a user’s response to an augmented reality or virtual reality experience, in accordance with some embodiments /

[0036] FIG. 8 shows a computer control system that is programmed or otherwise configured to implement methods provided herein, in accordance with some embodiments.DETAILED DESCRIPTION

[0037] The present disclosure provides systems, methods, and platforms for delivering personalized and adaptive XR experiences using artificial intelligence (Al). These systems may incorporate real-time data such as user age, diagnosis, language level, learning goals, and physiological signals to dynamically generate customized learning plans and therapeutic content. Through utilizing Al-driven features, immersive environments tailored to each user’s needs may be created. In addition, the system may support collaborative content creation by educators, therapists, or caregivers. Innovations disclosed herein enhance the accessibility, flexibility, and efficacy of XR-based interventions.

[0038] In some embodiments, an artificial intelligence module may receive user-specific data (e.g., diagnosis, emotional state, cognitive abilities) and generate a set of personalized parameters. These parameters are then used by an animation module may generate virtual reality environments that are customized to the user's needs based on the user-specific parameters and / or modify existing virtual reality environments to reflect changes or adaptations required by the user's progression or current state.

[0039] The virtualized environment may simulate real-life scenarios (such as social interactions, job interviews, or educational settings) or fully imaginative spaces configured for therapeutic or learning purposes, permitting users to engage in controlled, personalized experiences aimed at treating or managing neurodevelopmental disorders.

[0040] The term "animation module," as used herein, generally refers to a system or software component that generates or manipulates animated characters and objects within a virtual reality(VR) or augmented reality (AR) environment. This module is configured to receive a plurality of user-specific parameters, such as the user’s age, cognitive abilities, emotional state, or specific learning goals, and use these parameters to create personalized animated characters that are tailored to the user's unique needs. In some embodiments, the animation module generates these personalized animated characters based at least in part on the received user-specific parameters, ensuring that the characters' appearance, behavior, and interactions are adapted to the user's therapeutic, educational, or developmental objectives. These animated characters may serve as virtual guides, companions, or interaction partners, facilitating personalized learning, therapy, or engagement within the virtual environment. The characters can also dynamically adjust based on user feedback and progress, providing a customized experience that evolves in response to the user's ongoing development.

[0041] The platforms, methods, and systems of the present disclosure may be used to test play or experiment with new content or modifications to existing content for research or development purposes.

[0042] In some instances, different virtual or augmented reality experience may be organized in a library and be available for prescription or recommendation by the human expert or computer algorithms (e.g., artificial agent) to caregivers, parents, or the users themselves.

[0043] In some embodiments, the system comprises at least one AI-Based Learning Evaluation Module. In some cases, the AI-Based Learning Evaluation Module comprises at least one Real-Time Feedback Component. In some instances, the Real-Time Feedback Component comprises Al algorithms for real-time analysis of user or instructor feedback. In some cases, the AI-Based Learning Evaluation Module comprises at least one Lesson Optimization Component. For example, the AI-Based Learning Evaluation Module may be configured to evaluate how well the lesson went based on user or instructor feedback. In further examples, the evaluation of how well the lesson went is configured to create a tailored and optimized lesson plan.

[0044] In further examples, this advanced anomaly detection feature contributes to the overall effectiveness of the system, providing a responsive and adaptive learning environment that can cater to the individual needs and learning pace of users with neurodevelopmental disorders.

[0045] In an aspect, provided is a platform for editing virtual or augmented reality content, comprising: (a) a user interface comprising an editable network of nodes and connectors representing a sequence of logical flow paths in a virtual or augmented reality experience, wherein a node is associated with a state of an asset in the virtual or augmented reality experience, and a connector between two nodes defines a chronological sequence between two states of the respective two nodes in the virtual or augmented reality experience; and (b) aninteractive control module configured to define a first node, wherein the interactive control module is presented upon selection of the first node from the editable network of nodes in the user interface.

[0046] In some embodiments, the state of the asset in the virtual or augmented reality experience is configured to provide visual, auditory, or haptic stimulation to a user of the virtual or augmented reality experience.

[0047] In some embodiments, the asset comprises a plurality of states and a plurality of transitions associated between different states of the plurality of states, wherein the state of the asset represents an instance of the asset in the virtual or augmented reality experience.

[0048] In some embodiments, the asset is defined by one or more data files selected from the group consisting of a model file, an audio file, a video file, an image file, an animation controller file, a mesh file, an object template file, and an animation file.

[0049] In some embodiments, the sequence of logical flow paths in the virtual or augmented reality experience is configured to direct a user of the virtual or augmented reality experience to achieve one or more therapeutic or educational goals.

[0050] In some embodiments, the interactive control module is configured to define a first state of a first asset associated with the first node. In some embodiments, the interactive control module is configured to replace the first state of the first asset associated with the first node with a second state of the first asset. In some embodiments, the interactive control module is configured to delete the first state of the first asset associated with the first node.

[0051] In some embodiments, the first node is a conditional node connected to a second node, wherein the second node is connected to a plurality of different conditional nodes including the first node, wherein the plurality of different conditional nodes are each associated with a condition.

[0052] In another aspect, provided is a method for editing virtual or augmented reality content, comprising: (a) providing a user interface comprising an editable network of nodes and connectors representing a sequence of logical flow paths in a virtual or augmented reality experience, wherein a node is associated with a state of an asset in the virtual or augmented reality experience, and a connector between two nodes defines a chronological sequence between two states of the respective two nodes in the virtual or augmented reality experience; and (b) editing the editable network of nodes and connectors by adding a node to the editable network, removing a node form the editable network, modifying a connectivity between a plurality of nodes in the editable network, changing a definition of a node in the editable network of nodes, or a combination of the above.

[0053] In some embodiments, the method further comprises publishing the editable network of nodes or one or more nodes of the editable network to a library accessible by a plurality of different users.

[0054] In some embodiments, the method further comprises further editing the editable network of nodes published in the library to generate an edited network of nodes.

[0055] In some embodiments, the method further comprises publishing and distributing the edited network of nodes.

[0056] In some embodiments, the method further comprises prescribing or assigning the virtual or augmented reality experience to a target user, target group of users, or target groups of users.

[0057] In some embodiments, the state of the asset in the virtual or augmented reality experience is configured to provide visual, auditory, or haptic stimulation to a user of the virtual or augmented reality experience.

[0058] In some embodiments, the sequence of logical flow paths in the virtual or augmented reality experience is configured to direct a user of the virtual or augmented reality experience to achieve one or more therapeutic or educational goals.

[0059] In some embodiments, the asset comprises a plurality of states and a plurality of transitions associated between different states of the plurality of states, wherein the state of the asset represents an instance of the asset in the virtual or augmented reality experience.

[0060] In some embodiments, the asset is defined by one or more data files selected from the group consisting of a model file, an audio file, a video file, an image file, an animation controller file, a mesh file, an object template file, and an animation file.

[0061] In some embodiments, the editing in (b) comprises replacing a first state of a first asset associated with the node with a second state of the first asset.

[0062] In some embodiments, the editing in (b) comprises deleting a first state of a first asset associated with the node.

[0063] In one aspect, platforms, systems, and methods are provided for offering a user an XR experience. This may involve configuring, creating, modifying, and / or selecting the appropriate virtual or augmented world for the user. Such activities can be performed by assessing and / or profiling the user and pairing them with specific therapeutic or education goals. The XR experience provided to the user is associated with one or more of these goals and configured to help the user achieve or progress towards them. In some cases, each user may be uniquely profiled prior to, during, or subsequent to the user’s XR experience. In some cases, each user may be categorized into a type of pre-existing user profile, such as from a library of user profiles. In some cases, a XR experience may be created for each user or each type of user. In somecases, a XR experience template may be created for a type of user profile. For example, a XR experience template may be selected from a pre-existing library of XR experience templates. The library of templates may be updated with new templates or templates modified or otherwise refined based on new users and / or uses of the existing templates.

[0064] In some instances, the XR experiences can be configured, created, modified, and / or selected manually by a human domain expert (e.g., therapist, educators, teachers, parents, etc.) and / or using computer algorithms or artificial intelligence. For example, the configuring, creating, modifying, and / or selecting can be performed based at least in part on prior conditions and / or other assessments of the user and / or based on data collected throughout the user’s and / or others’ use of the system. The human expert or computer algorithms may recommend or prescribe different virtual or augmented reality experiences by selecting from the library of XR experience templates, creating a new XR experience, and / or modifying an existing XR experience template for the user. The prescription or recommendation of a new or existing XR experience may be provided to caregivers of the users, parents of the users, the users themselves, or other individuals or entities (e g., schools, hospitals, etc.) affiliated with the users.

[0065] In the virtual or augmented world, the user can be presented with visual, auditory, and / or haptic stimulation. The user’s interactions with the virtual, augmented, and / or real world can be monitored, and the user’s progress towards one or more goals can be measured. For example, the user’s interactions, reactions, and / or responses to the simulations presented in the virtual world can be quantified based at least in part on sensory data measured for the user, such as a reaction time, gaze accuracy, gaze stability, response volume, pupillary response, blink rate, voice or facial emotion recognition, heart rate and / or other forms or units of outputs by the user. In some cases, the user’s progress toward one or more goals can be quantified based at least in part on such quantifications. In some cases, the user’s progress toward one or more goals can also be measured based on qualitative observations made by another user monitoring the user’s interactions, reactions, and responses in the virtual world, such as a therapist, educator, direct support professional, or parent. For example, the user’s progress toward one or more goals can be measured based on subjective and / or objective feedback from the monitoring user. In some instances, the user’s progress toward one or more goals can be measured by comparing the user’s performance in an evaluation, whether in or outside of the XR experience, prior to and subsequent to the user’s XR experience. In some cases, the platforms, systems, and methods may further include editing or otherwise modifying a XR content to improve the potency, efficiency, or accuracy of the XR experience, such as based on the user’s current or prior performance with the XR content.

[0066] The present disclosure may employ platforms having graphical user interfaces (GUIs) for inputting user instructions for the configuring, creating, modifying, selecting, and / or use of virtual or augmented reality content. In some instances, the GUIs may output an intermediate and / or final result of the user instructions to aid in the creation, editing, and / or use of the virtual or augmented world content. In some instances, one or more parameters and / or stimulations in a virtual world can be provided or adjusted in real-time, such as by another user (e g., therapist, educator, operator, supervisor) monitoring the user in the virtual or augmented world, for example, based on the user’s interactions, reactions, or responses (or lack thereof) to a previous stimulation.

[0067] The virtual or augmented reality system may be a virtual reality system in which the user is presented with content in an environment that may be separate from the surroundings of the user. Alternatively, the virtual or augmented reality system may be an augmented reality system in which the user is presented with content that may be overlaid or at least partially integrated with the environment of the user.

[0068] Such system can comprise a display for presenting the user with content. Such display can be provided to the user through a user device (e g., mobile device, head mounted display, computer, tablet, laptop, etc.), for instance. The user device may or may not be portable. Visual stimulation may be provided by presenting one or more images or video on a display screen. The system can further comprise one or more headphones, earphones, or speakers for providing auditory stimulation by presenting the user with audio. The one or more headphones, earphones, or speakers may be synchronized with images or video provided by the display screen to the user. In some instances, the user may access the virtual or augmented reality system with the use of a supplemental headgear (e.g., Google® Cardboard Compatible headsets, Meta® Quest, Pico®, Apple® Vision Pro, and HTC® Vive). The system can further comprise one or more controllers for presenting the user with haptic stimulation. The controllers can be configured to, for example, vibrate. The controllers can comprise an actuator. The controllers may be attached (or otherwise coupled) to one or more body parts (e g., limbs) of the user. The display can receive the one or more images or video, the headphone, earphone, or speaker can receive the audio, and the controllers can receive the haptic output to present to the user through a computer control system.

[0069] The user may interact with the virtual or augmented world using eye movement, head movement, or one or more controllers attached to the body and / or the limb of the user. To track the user’s movement, the system may employ one or more sensors, including one or more cameras. A camera may be a charge coupled device (CCD) camera. The camera may record astill image or video. The image or video may be a two-dimensional image or video or a three-dimensional image or video. The system may employ other optical sensors, auditory sensors (e.g., microphones), touchpads, touchscreens, motion sensors, heat sensors, inertial sensors, touch sensors, or other sensors. The one or more sensors may be capable of measuring sensory data indicative of an output by a user (e.g., eye movement, head movement, facial expressions, speech, etc.) or lack thereof. The user may be tracked or monitored in real-time using the sensors. The monitoring may or may not be remote. The monitoring may be over one or more networks.

[0070] The method may further comprise the user interacting with one or more other individuals to progress toward the one or more goals. For example, the user may be paired with an operator, therapist, educator, parent, peer, or a plurality of operators, therapists, educators, parents, or peers, or any combination of the above. In some instances, a paired individual or entity may be capable of influencing the user’s experience in the virtual or augmented world, such as by creating, modifying, and / or removing the visual, auditory, and / or haptic stimulations provided to the user in the virtual or augmented world. In some instances, the user may interact with a paired individual in the virtual or augmented world (e.g., as avatars). In some instances, the paired individual may monitor the user without influencing or otherwise interacting with the user in the virtual or augmented world. The paired individual can be located remotely from the user. The paired individual may or may not be the individual responsible for configuring, creating, modifying, and / or selecting the XR experience for the user.

[0071] Beneficially, the platforms, methods, and systems described herein may flexibly customize XR content for an individual or a group of individuals, such as to treat a user having a mental or neurodevelopmental disorder (e.g., autism), train or educate an individual (e.g., student) or a group of individuals, or otherwise attend to a special need of an individual or a group of individuals. Further, the platforms, methods, and systems described herein may customize XR content for a specific goal, such as a therapeutic goal (e.g., learn or train a certain social sill, etc ) and / or an educational goal (e g , leam the alphabet, improve mathematical skill, etc.).

[0072] The XR systems described herein may further provide a low cost, accessible therapeutic solution for users. Alternatively or in addition, the XR systems described herein may provide educational and / or entertainment value to users. User may or may not have a mental or developmental disorder.

[0073] For example, the platforms, methods, and systems of the present disclosure may be used for treating or supplementing the treatment of a user for a mental, behavioral, orneurodevelopmental disorder, such as autism, attention deficit hyperactivity disorder (ADHD), and social anxiety disorder, using a virtual or augmented reality system. In another example, the platforms, methods, and systems of the present disclosure may be used for educating or training, or supplementing the education or training, of a user, such as a student, using a virtual or augmented reality system.

[0074] Users who can benefit from these methods can be categorized into distinct profiles based on their unique traits and needs. One profile includes individuals with cognitive or perceptual conditions who benefit from gradual and structured exposure to various real-world scenarios. These conditions may encompass various anxiety disorders and phobias (e.g., generalized anxiety disorder, panic disorder, social anxiety, separation anxiety, selective mutism), body perception disorders (e g., anorexia nervosa), and neurological conditions that can cause sensory overload (e.g., ADHD). Another profile consists of individuals whose conditions impede the acquisition of essential skills, such as social skills, through typical developmental processes or recovery from trauma or illness. This group includes individuals with autism, social (pragmatic) communication disorder, intellectual and developmental disabilities, as well as those requiring speech-language therapy, occupational therapy, behavioral therapy, or physical therapy.Additionally, there is a diverse group of individuals who can benefit from novel intervention methods permitted by virtual reality (VR) or augmented reality (AR). This includes individuals undergoing cognitive behavioral therapy or interpersonal psychotherapy to enhance interpersonal functioning, such as self-compassion or self-esteem, and members of the general population seeking to practice social skills or routines in a controlled environment. By recognizing these distinct user profiles, XR systems can be tailored to address the specific needs and preferences of each group, enhancing the effectiveness of therapeutic and educational interventions.

[0075] Users can also be categorized into other types of groups, or distinct types of user profiles, based on other factors, such as gender (e.g., male, female, unidentified, etc.), age (e.g., 10, <10, >20, etc.), educational level (e.g., elementary school level, 3rdgrade, 4thgrade, middle school level, high school diploma, bachelors and above, doctorate level, etc ), type of disorder, prior exposure to XR therapies (e.g., yes / no, less than 1 year experience, more than 3 years’ experience, geographic region, etc.), and other factors. Beneficially, users in the different groups can be provided different types of XR experiences either selected from a pre-existing library of templates, or created and / or modified based on group characteristics and / or user characteristics.

[0076] Furthermore, the XR system may facilitate data collection during or subsequent to treatment. For example, a user’s progress toward one or more goals may be measured with significantly higher accuracy and precision than traditional methods of therapy or education. TheXR system may be capable of collecting data that was previously unavailable for measurements via traditional methods. For example, the XR system may comprise one or more integrated or external sensors that are capable of measuring sensory data indicative of an output by a user (e.g., eye movement, head movement, facial expressions, speech, etc.). The one or more sensors may be capable of measuring sensory data indicative of a user’s progress toward one or more goals, such as a reaction time, gaze stability, gaze accuracy, response volume, pupillary response, blink rate, heart rate, emotional response, voice analysis, the presence or lack of a movement, the presence or lack of speaking, or other outputs by the user. A user’s progress toward achieving a goal (e.g., increasing ability to concentrate) may be quantified more accurately and precisely than traditional methods (e.g., that rely on manual observations of a user or of a video recording of a user). For example, the XR system may track a user’s gaze with one or more integrated sensors that are much more accurate than manual observations that predict a direction of a gaze.Additionally, the integrated sensors in the XR system allow the measuring of a user’s interactions, reactions, and responses to a situation without interrupting, or being exposed to, the user who is wholly immersed in a XR environment — for example, in contrast, traditionally, the user may become aware of the artificiality of a therapy or education session, such as via presence of another human or a camera, and change his or her behavior based on such awareness, resulting in biased or otherwise inaccurate results.

[0077] The systems and methods may allow a therapist or educator to prescribe or otherwise assign one or more XR sessions to achieve one or more goals to a user. The user may perform the prescribed sessions without direct supervision from the prescriber. In some instances, the sessions are capable of being reviewed at a later time and place. Alternatively or in addition, the sessions are capable of being monitored directly or indirectly (e g., remotely) in real time. The therapy sessions can be monitored by any other individual, such as a parent or guardian in real time or at a later time and place. As used herein, real time can include a response time of less than 1 second, tenths of a second, hundredths of a second, or a millisecond. Real time can include a process or operation (e g., monitoring of an action by a user) occurring simultaneously or substantially simultaneously with another processor or operation (e.g., performing the action by the user).

[0078] In some cases, data collected (or recorded) for one or more users may be aggregated to build behavior models for one or more conditions (e.g., mental, or developmental disorders). Such behavior models can be leveraged as diagnostic tools for users to be evaluated through the XR system. The behavior models can be used to profile a user and / or assign one or more goals (e.g., therapeutic, educational) to the user. For example, a plurality of behavior models fordifferent mental or developmental disorders can be stored in a library of behavior models, such as in one or more databases. A behavior model for a first type of developmental disorder can comprise data exhibited by one or more users known to suffer from the first type of developmental disorder when placed in a first XR environment. When a user to be diagnosed is placed in the same first XR environment, or an environment similar to the first XR environment, the data collected on the user may be compared to the behavior model for the first type of developmental disorder to determine whether the user has the first type of developmental disorder or not, or a degree to which the user suffers from the first type of developmental disorder. In some instances, the data collected for the user to be diagnosed may be compared to a plurality of behavior models to determine which one or more conditions the user may suffer from (and to what degree). By way of example, the higher the % similarity between the collected data for the user and the data stored for the behavior model, the more likely it is (and with higher degree) that the user suffers from the condition of the behavior model.

[0079] In some instances, where users are students and the goals relate to educational goals, XR content can be keyed to traditional educational methods. As an example, the XR content may adopt a spaced learning method where the learning content is repeated three times, with two short breaks in between the repetition during which students are directed to perform other activities (e.g., physical activity). To parallel the spaced learning method, the XR content may direct a user through five environments, the first including a learning content, the second directing the student to perform an unrelated activity (e.g., a puzzle game), the third repeating the first, the fourth directing the student to perform another unrelated activity (e.g., a physical activity such as jumping), and the fifth again repeating the first. In another example, the XR content may adopt a flipped classroom learning method, and the XR content may direct a user to teach an avatar to perform an activity in which the user is supposed to be learning themselves.

[0080] In instances where users have neurodevelopmental disorders and therapeutic goals, XR content can incorporate evidence based strategies from traditional therapy methods. For example, naturalistic behavioral intervention techniques, such as Natural Environment Teaching (NET), can be adapted for XR therapy. NET typically occurs in the user's natural environment and focuses on teaching skills within meaningful contexts. XR systems can simulate these environments, allowing users to select virtual settings (e g., a zoo or a grocery store) and permitting clinicians to apply evidence based strategies like modeling, prompting, and reinforcement. Clinicians may observe the user's interests as they explore the virtual environment, provide prompts to encourage interaction, provide verbal scripts to model interactions, and reinforce positive actions based on the user’s behavior. Additionally, thesystem can create opportunities for communication or play if the user remains inactive by introducing engaging scenarios or interactive challenges. For instance, if a user silently stares at one animal, an avatar might interact with another animal to encourage the user to shift attention or initiate a game to engage them. Once the user begins interacting, the system continues using modeling, prompting, and reinforcement strategies. Furthermore, the system can offer varying levels of prompts to assist the user in completing specific tasks and reward successful task completion with pleasing stimuli, such as images, audio, video, or compliments from avatars or a clinician. These adaptations support the delivery of targeted, goal-oriented therapies, including the development of social skills, communication skills, and coping skills.

[0081] In doing so, the system may provide prompts of varying levels of intrusiveness throughout the process. For example, the prompts may range from least intrusive to most intrusive as follows: subtle prompts by the software (e.g., a clickable item that glows or is slightly translucent, or a virtual hand suggesting where to move certain blocks); verbal cues from a teacher or therapist (e.g., phrases such as “I see something interesting” or “That animal looks hungry”); direct verbal instruction (e.g., “Put the can in your cart”); modeling (e.g., a therapist providing a script to use in conversation, or an avatar in the virtual environment, demonstrating a desired action so that the user can imitate); and physical prompts (e.g., directing the user by using hand-over-hand support through a desired action, or moving the user’s head in an intended direction). By integrating these strategies, the system can create and provide XR environments with varying levels of difficulty, in which the user always succeeds in a given task, with or without supportive prompts. These adaptations support the delivery of targeted, goal-oriented therapies, including the development of social skills and sensory regulation.

[0082] In another example, XR content can be configured to build social connections between the user and others, including peers undergoing treatment, by developing essential skills such as joint attention (e.g., following a shared focus), social reciprocity (e.g., taking turns), and pragmatic intentions (e.g., problem-solving or communicating acceptance). For instance, therapy modules may help users recognize and interpret facial expressions or understand others' perspectives. Additionally, the system can teach functional skills through immersive storytelling, creating practice environments for routine tasks like crossing the street, navigating TSA security lines, using public transportation, practicing safe behaviors on car rides or around water, interacting with police officers, and going to the grocery store. Personal safety skills, such as setting and respecting boundaries or safely navigating online interactions with new and familiar people, are also integrated. Moreover, XR systems enhance social skills by providing opportunities to invite others to join activities, use imagination to play, and engage inconversations. Beneficially, such experiences provide an effective and entertaining (‘fun’) solution to teach users how to navigate everyday interactions by placing them in a controlled virtual or augmented reality environment. This approach shields users from may comprise real-world harms and allows clinicians to guide them effectively at a flexible and repeatable pace.

[0083] In another example, the system may provide sensory-based therapies tailored for users with neurodevelopmental disorders, such as autism. These therapies utilize XR environments to create specific calming or stimulating experiences, helping users manage and respond to challenging situations. For instance, a calming module might allow a user to play a simple and delightful musical instrument using gaze direction, promoting relaxation and focus.Additionally, the system can incorporate guided meditation sessions, permitting users to practice mindfulness techniques within a controlled virtual setting. XR also offers experiences that allow learners to practice coping strategies in real-life environments, such as navigating a car or using public transportation. Transportation can be particularly challenging for individuals with severe behaviors, limiting their opportunities to participate in activities outside the home, such as social gatherings, obtaining food, or attending medical and therapy appointments. Access to transportation is a critical social determinant of health, and barriers in this area can lead to a range of negative health and quality-of-life outcomes for people with autism. By providing a safe and controlled environment, XR permits users to build and refine these essential skills, thereby reducing real-world barriers and enhancing their ability to engage in everyday activities confidently.

[0084] The XR content may include a network or graph of interactions that are triggered either by one or more actions of the user or by a supervisor or other third party that is monitoring the user. In some instances, an interaction, reaction, or response may be triggered by a peer (e.g., another user user). Every discrete action from the user can trigger a combination of animation, audio, and / or visual instructions to the supervisor. A supervisor, who may or may not be a therapist or educator, can directly monitor and supervise the user receiving the XR therapy through the use of a companion application. Such monitoring may occur remotely, wherein the user and the supervisor are physically and geographically at a significant distance from each other (e g., different buildings, different countries, etc.). Alternatively or in addition, such monitoring may occur as in-room monitoring. Additionally, the user may also have the option to self-administer the XR therapy without direct supervision, utilizing the companion application to guide and track their progress independently.

[0085] The platforms, systems, and methods may allow a user to edit the XR content, such as by modifying the network or graph of interactions. The platforms, systems, and methods may allow a user to edit the content of a companion application that is paired and used with the XR content.

[0086] The platforms, systems, and methods presented in this patent may allow a user, or a supervisor with suitable permissions, to edit the XR content. This may include modifying the network or graph of interactions within the XR environment or even adjusting the content of the paired companion application used alongside the XR content. The ability to tailor the XR content and the companion application may allow for greater flexibility in customizing the therapeutic or learning experience to suit the user's needs.

[0087] The system can comprise at least one, at least two, or at least three devices: one device capable of providing a virtual reality or augmented reality environment (e.g., smartphone, dedicated headset device like an Google® Cardboard Compatible headsets, Meta® Quest, Pico®, Apple® Vision Pro, and HTC® Vive headset) to the user, a second device (or same device as the first device), capable of providing a companion application to the supervisor, and a third device (or same device as the first and / or second device), capable of providing a user interface for editing XR content. The at least three devices can be communicatively coupled together, such as via tangible connection cable or wireless or Bluetooth pairing. Through the companion application, a supervisor may access, and, if needed, intervene in, the user’s virtual or augmented reality experience. As an alternative, the system can comprise a single integrated device.

[0088] FIG. 1 shows a schematic illustration of a virtual or augmented reality system, in accordance with some embodiments. In some embodiments, the system may comprise components for synchronizing and mirroring VR content between a primary VR headset, a user device, and supplemental headsets, permitting real-time interaction and content sharing between users.

[0089] In some embodiments, the system may synchronize and mirror virtual reality (VR) content between a primary VR headset (102), a user device (104), and supplemental headsets (115, 120).

[0090] The primary VR headset (102) is wirelessly paired (110) with the user device (104), which could be a tablet (105) or similar device. The system facilitates mirroring (125) of the VR content from the primary headset (102) to the user device (104), permitting real-time content sharing. A trigger action component (130) is also shown, representing the ability of the user device (104) to initiate certain actions within the VR environment, such as pausing or interacting with the content. A central server (106) manages the data flow between devices, ensuring synchronized interaction across the system. An external development environment (108) isincluded, represented by the Unity symbol, suggesting compatibility with or development within that platform. Supplemental VR headsets (115, 120) are shown at the bottom, indicating that additional devices such as Google Cardboard, Meta Quest, and Pico can be integrated into the system to allow multiple users to experience the same VR content. The system may also comprise additional sensors and input mechanisms, such as motion controllers or hand-tracking devices, to enhance user interaction within the VR environment. The system may include various wireless communication protocols, such as Bluetooth or Wi-Fi, to facilitate pairing between the primary headset (102) and the user device (104). It may allow for cloud-based data storage or processing via the central server (106) to optimize performance across connected devices. The user device (104) may be a smartphone, tablet, or desktop computer capable of receiving mirrored content from the VR headset (102). The supplemental headsets (115, 120) may include other VR headsets, smartphones using VR viewers, or any compatible device capable of displaying VR content. The system may support multiplayer or collaborative VR experiences, allowing multiple users to interact within the same virtual space via different headsets.Additionally, the system may be integrated with other development platforms besides Unity (108), allowing flexibility in content creation and customization.

[0091] FIG.2 shows an illustration of a mobile companion application display, in accordance with some embodiments. In some embodiments, the mobile companion application may comprise a dashboard that tracks user performance metrics, such as task completion, session time, and success rates, providing feedback for both the user and the supervisor in a VR or AR experience. The virtual reality (VR) environment may comprise an interactive user interface configured to initiate or interact with VR content. For example, FIG. 2 may comprise a virtual scene with graphical elements such as trees, a cauldron, and a steam effect. The scene may also include multiple labels (225), which may represent selectable or interactive objects within the VR environment. Additionally, the interface may feature a prominent blue banner (220) with the instruction, "PRESS THE GREEN START BUTTON TO BEGIN," indicating the initial action required by the user to engage with the system. A green start button (210) may be visible in the lower-right comer, inviting the user to start interacting with the VR experience. Furthermore, the interface may contain text labeled "MagicMix" and "Magic Mix: Who's Next?" (205), which suggests the name of the module or activity that the user will engage with The operational mode of the system may be indicated by a label, such as "VR Space Mode," located in a sidebar, providing further context for the system's current state. These elements shown in FIG.2 are examples of how the interface may be structured, and the specific visual and interactive components may vary depending on the particular VR experience or application being used.

[0092] In some embodiments, the system may display a mobile companion application to a supervisor overseeing a user. The application may include lesson information, comprising the lesson title, a short description, and the language level. A start button may be provided, allowing the supervisor to initiate the lesson. The version number may inform the supervisor of the current software version the device is running. An information banner may convey necessary information, such as instructions ("Press start to begin"), notifications ("It's Ayanna's turn next"), scripts for the supervisor to say to the user ("Nice job naming your ingredient! "), or captions spoken by characters. Field of view markers may distinguish the boundary between what the user is viewing and the extended view of the clinician, and may delineate boundaries in various directions, including left, right, diagonal, up, or down. Once the lesson begins, an END button may be available to immediately terminate the lesson if needed. The system may also include a VR sleep mode, allowing the clinician to disable the iPhone screen by turning it black.Additionally, control buttons may be provided to allow the clinician to manage the experience by progressing the lesson forward, repeating character scripts or actions, and collecting data.

[0093] In some embodiments, the system may comprise a virtual reality (VR) environment with an interactive user interface configured for initiating or interacting with VR content. The central area of the interface may feature a virtual scene with graphical elements like trees, a cauldron, and a steam effect. Multiple labels may represent selectable or interactive objects within the VR environment. A prominent banner may display instructions for the user, such as "PRESS THE GREEN START BUTTON TO BEGIN," indicating the user action required to engage with the system. A start button may be visible to initiate interaction with the VR experience. Additionally, the interface may contain text referencing the module or activity the user is engaging with, such as "MagicMix" or "Magic Mix: Who's Next?" The VR system mode may be displayed in a sidebar, indicating the operational mode of the system.

[0094] In one embodiment, more devices may be paired, as needed, for increased monitoring. For example, a second companion application that provides a camera view can be used as an in-room monitor. A supervisor may view the user in a XR session through a small video window on the companion application interface. For example, a user device of a user may be paired with at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more devices of supervisors, each having their own device. Alternatively or in addition, a user device of a user may be paired with at most about 50, 45, 40, 35, 30, 25, 20, 15, 10, 9, 8, 7, 6, 5, 4, 3, or 2 devices of supervisors. In some cases, a supervisor may be paired with a plurality of users. For example, a user device of a supervisor may be paired with at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more devices of users, each having their own device. Alternatively or inaddition, a user device of a supervisor may be paired with at most about 50, 45, 40, 35, 30, 25, 20, 15, 10, 9, 8, 7, 6, 5, 4, 3, or 2 devices of users. In some cases, a virtual reality experience may allow a plurality of users (e.g., peer users) to interact with each other in the virtual reality experience, such as for a group therapy session.

[0095] In one embodiment, the pairing of these devices (e.g., devices for supervisors, devices for users, etc.) may be remote which allows for teletherapy or tele-education sessions. The devices can be paired over a network, such as over the internet, intranet, and / or extranet, so that the companion application may operate remotely from the user.

[0096] In one embodiment, the system can use a gaze tracking algorithm to determine the object of focus for the user and trigger actions and prompts based on that gaze. The system may use such tracking algorithm to collect metrics and provide an analytics dashboard comprising data such as reaction time to a task, whether the user is taking fixation brakes, gaze accuracy, and how steady their gaze is on an object versus a neurotypical user. Alternatively, the system can use other algorithms and / or sensors to monitor a different type of user response. Alternatively, the system may compare the user with other users receiving the same or similar XR content. These analytics may provide not only the metrics tracked during the user’s session but also a longitudinal view of the user’s performance progress.

[0097] FIG. 7A - FIG. 7B show illustrations of progress reports generated from a user’s response to an augmented reality or virtual reality experience, in accordance with some embodiments.

[0098] FIG. 7A illustrates a graphical dashboard that tracks user progress over time, displaying metrics such as session duration, task completion, and success rates, allowing trainers or users to monitor trends and performance.

[0099] In some embodiments, the dashboard may provide a comprehensive user progress report, offering a detailed breakdown of user activity and insights into areas of improvement, success patterns, and progress across various learning modules or sessions. The dashboard may include metrics such as task categories, task names, start dates, total number of sessions, total time spent, total trials completed, successes, and success rates 701. Additionally, it may display information like the user's best performance periods, profile data (e.g., age, gender, and other demographic information), and other relevant contextual details 702. A graphical representation of user progress over time may be shown, with metrics such as reaction time, gaze accuracy, gaze stability, and fixation breaks 703, providing a comprehensive view of performance across different activities.

[0100] The dashboard may track progress across several key training categories, such as Social Interactions, Emotional Regulation, Focusing, Safety, Descriptive Language, School Social Skills, Impulse Control, and Imitation 704. Each category may be represented by distinct colored lines on the graph, illustrating cumulative monthly scores and allowing trainers or users to visualize trends and improvements over a defined period (e.g., from January to October) 705. In this example, the total cumulative points earned over the past 12 months may amount to 405 points, reflecting the user’s overall progress across all categories 706.

[0101] In some embodiments, the dashboard may also present additional analytics for each progress category, aligned with the objectives of the training program. For example, it may show feedback such as “Exceeds 10 minutes per session effective session duration” for total time spent, “Meets minimum effective repetitions per session” for total trials, “Exceeds expected 75% success rate” for success rates, or “18 sessions remaining out of 150 recommended total” for session completion. Such feedback permits trainers or users to monitor the effectiveness of the training sessions and gauge the user’s progression toward their goals.

[0102] In addition to task performance metrics, the dashboard may incorporate sensory data captured by one or more sensors. This sensory data may include information such as reaction time, gaze stability, gaze accuracy, response volume, and the presence or absence of physical movements or speech. These metrics provide deeper insights into the user’s progress toward specific training goals that may not be readily observable through traditional assessments.

[0103] Moreover, the dashboard may feature results from quizzes, tests, or physical activities, such as scores from a mathematical quiz or a physical task like completing a set of exercises. This functionality permits comprehensive tracking of both cognitive and physical progress within a single, integrated system. By combining task-specific data, sensory feedback, and test results, the dashboard offers a holistic view of the user’s overall development, permitting more personalized and precise analysis of their progress over time.

[0104] FIG. 7B illustrates a illustrates a graphical dashboard 707 that provides a detailed breakdown of performance on specific tasks within a lesson, highlighting key performance indicators such as lesson success, pass rates, and individual task completion statuses. In some embodiments, the graphical dashboard 707 may track key performance indicators for a specific lesson, such as "Magic Mix: Who's Next?" 709, providing feedback on a user's performance in individual lessons. The dashboard may focus on metrics such as lesson duration, lesson success, and lesson pass rate 708. Key performance metrics may be displayed at the top of the graphical dashboard 707 For example, the lesson duration may represent the time spent by the user on a specific lesson, such as 2.61 minutes in this case 715. The lesson success status may indicatewhether the user successfully completed the lesson, with a status such as "Passed" 716. The lesson pass rate may show the number of successful completions out of a total number of attempts, such as 11 out of 43,717, providing insights into the user's consistency in completing the lesson.

[0105] The graphical dashboard 707 may include a section that provides a detailed breakdown of specific tasks associated with the lesson, offering a comprehensive checklist that reflects the user’s performance on each task 718. This section may be located directly beneath the summary of key performance indicators, giving users or trainers a clear and accessible view of taskspecific data for deeper analysis. The tasks may outline various activities configured to assess the user’s ability to engage in interactive and social scenarios within the lesson. For example, tasks may include Identifying when it was their own turn 710, where the system tracks whether the user can correctly determine their turn in a group setting, and Identifying when it was a peer's turn 711, which may evaluate the user’s ability to recognize when it is appropriate for others to participate. Additional tasks may involve Sharing what their ingredient was with peers 712, configured to assess the user’s ability to communicate specific information in a collaborative environment, and Introducing themselves to peers 713, which may focus on the user’s ability to initiate social interactions. Each task may be marked as completed (e.g., "Yes") or incomplete, offering a clear indication of whether the user successfully accomplished the task. This checklist format may provide valuable insights into individual task performance, helping trainers or users identify areas of strength and areas that require further development.

[0106] This graphical dashboard 707 may be configured for trainers, users, or administrators to monitor overall progress and performance across individual lessons, providing insights into both specific task completions and broader lesson outcomes 714. This information may support the evaluation of user development and inform necessary adjustments to learning strategies or goals 714, allowing the learning experience to be customized to better meet the individual needs of the user.

[0107] In some embodiments, a lesson duration may comprise between about 0 minutes and about 60 minutes. In some cases, the method may comprise a lesson duration of at most 60 minutes, at most 55 minutes, at most 50 minutes, and continue incrementally down to at most 5 minutes, at most 4 minutes, at most 3 minutes, at most 2 minutes, and at most 1 minute. In some instances, shorter lesson durations of at most 5 minutes may be particularly effective for providing quick, focused interactions that allow users to practice specific social skills without being overwhelming For example, lessons within this range may comprise concentrated exercises that offer multiple brief interactions, permitting users to progressively refine theirabilities. In further examples, the method may comprise tracking lesson success and pass rates, which may range from about 0% to about 100%. As an example, pass rates may range from at most 100%, at most 90%, at most 80%, and continue incrementally down to at most 30%, at most 20%, and at most 10%, highlighting areas where the user may need additional practice. In some embodiments, task completion rates may also comprise ranges, such as between about 0% and about 100%. In some cases, task completion rates may range from at most 100%, at most 90%, at most 80%, and continue down to at most 10%. In some instances, users with higher completion rates may show progress in recognizing conversational cues and participating in social interactions. For example, task completions like identifying turns in conversation or sharing information with peers may indicate mastery of specific social skills. In further examples, the method may comprise using performance metrics, such as task completions and pass rates, to tailor lesson plans and provide personalized feedback. As an example, the system may help trainers adjust sessions to match the user’s needs, optimizing the learning experience by focusing on areas where improvement is required.

[0108] In one embodiment, the system integrates motion controllers into therapy modules to accurately track the user’s movements within the virtual or augmented reality environment. For example, in a joint attention module configured to assess the user's ability to respond to pointing gestures, the user can use a motion controller to physically point toward objects in their real space.

[0109] The system maps these gestures to specific targets in the virtual environment, triggering appropriate responses and prompts based on the user’s actions. Similarly, in a module focused on training safe interactions with law enforcement, motion tracking can monitor the user’s hand positions and body movements to identify behaviors that may be perceived as threatening or nonthreatening. The system can then provide feedback or simulate different interaction scenarios to help the user practice maintaining a calm and respectful demeanor. Additionally, motion controllers can be utilized in modules that teach social gestures, such as waving goodbye, shaking hands, or using expressive body language. By tracking the precision and naturalness of these movements, the system offers real-time feedback and guidance to enhance the user’s neurodevelopmental skills. These advanced motion tracking capabilities permit dynamic and responsive therapy sessions, allowing for personalized skill development and improved outcomes for users.

[0110] In one embodiment, the system can integrate voice recognition technology to permit therapeutic conversation training for the user in the XR space. For instance, a user may be given simple scenarios where the user answers the door and finds themselves having a back and forthconversation with a virtual character that leverages voice recognition mixed with override input from the supervisor to create a coherent training environment.[OHl] In some embodiments, XR content may be segmented into distinct units, such as “lessons” or “lesson cards.” Each lesson targets at least one therapeutic or educational skill. The system includes a collection of lessons from which a clinician can sequence together to provide more opportunities for practice, increase difficulty, or offer the chance to learn and practice multiple related skills with the user. For instance, a clinician might sequence lessons to first build foundational neurodevelopmental skills before advancing to more complex interactions, ensuring a gradual and supportive learning progression. This modular approach allows for personalized therapy sessions tailored to the unique needs of each individual, enhancing the effectiveness of interventions.

[0112] In some embodiments, each learning card may comprise one or more models and one or more sequences. Sequences, which can be triggered by user events or application prompts, may comprise one or more events as well as one or more prompts. Each of the events and the prompts may, in turn, comprise one or more sequences. For example, a sequence may trigger an event, and the event may also trigger a sequence. Additionally, each event and prompt may comprise one or more audio / visual updates (A / V updates). Each A / V update may include a banner update in the supervisor’s application as well as an animation and audio element in the game scene. One model (e.g., a humanoid character) may relate to one or more game scenes (e.g., a cafe or park) and may comprise one or more animation elements and one or more audio elements. An audio element may also comprise one or more models.

[0113] A learning card may be a distinct playable unit with a set of learning objectives. A learning card may represent a single exercise that helps move the user toward a specific skill or developmental milestone. A learning card may define which of a game scene’s models are present and may include a collection of sequences. These sequences may interact both with a supervisor’s companion application, such as through banner updates, and with the game scene and models, such as through animation and audio elements. Each learning card may be defined by parameters such as title, thumbnail image, a collection of sequences, and a description, which is a long-form content that describes the skills addressed and the objective of the particular learning card.

[0114] FIG.3 shows a process flow and logical model of a learning module, in accordance with some embodiments. In some embodiments, the learning module may comprise a set of learning cards, sequences, events, prompts, and A / V updates, each contributing to guiding the user through developmental, educational, or training content in a structured manner. In some cases,the process may begin at Start (300), where the system may initialize the lesson. In some instances, the process may then proceed with animations for walking non-player characters, shown as NPC_1 (301) and NPC_2 (302). Next, the process may comprise a microphone check step (303). In some instances, the system may prompt the supervisor to determine whether the user is interacting with the lesson for the first time. If this is the user’s first time, the system may remove the VR headset from the player to request microphone permissions (304). In some instances, the microphone check can be skipped (305) if the supervisor chooses not to perform it. In some instances, the system may then verify whether the microphone access has been granted (306). In some instances, if microphone access is confirmed, the flow continues to the next step. In some instances, if not, the user or supervisor may be guided to recheck the microphone permissions (307). In some instances, once microphone access is confirmed, the system may proceed to initialize the in-world components (308), setting up elements within the lesson environment. In some instances, the user may then be prompted to select a character to interact with, such as Marcie (309) or Jordan (310), depending on the lesson scenario. In some instances, following the character selection, the system may start a timer (311) that tracks user interactions within the lesson. In some instances, as time progresses, a message prompt (312) may appear, reminding the user to press the continue button (313) in order to maintain their engagement with the lesson. In some instances, the message may reappear every 15 minutes, ensuring ongoing interaction within the lesson. In some instances, finally, if the microphone access is disabled or the user struggles with the interaction, the lesson may provide the option for the supervisor to permit microphone access (314) and help guide the user through the lesson.

[0115] In some embodiments, a logical model for a learning module may comprise each line ending with a circle may denote a "one to many" relationship, in which a line connector may represent "one" and a circle connector may represent "many." In some cases, a line ending with circles on both ends may denote a "one to many" relationship in both directions. In some instances, each triangle may denote an "is a" relationship. For example, the logical model may illustrate a number of editable components, including learning modules, learning cards, sequences, events, prompts, audio / visual updates, game scenes, models, animation, audio, and banner updates. In further examples, the XR content may be edited at any level of component via a graphical user interface. As an example, the editable components may be edited with respect to a timeline or other time dimension. For example, the XR content may include one-dimensional (1-D), two-dimensional (2-D), three-dimensional (3-D), and / or four-dimensional (4-D) content. The XR content may define a 360° experience, wherein the user may view the content in 360°. The XR content may also define more limited experiences, such as a 180° experience.

[0116] In some cases, in this logical model, each game scene may comprise one or more learning modules, one or more models (e.g., avatars, 2-D or 3-D animated characters), and one or more audio elements. In some instances, each learning module may then comprise one or more learning cards. In some instances, each learning card may comprise one or more models and one or more sequences. For example, sequences may be triggered by user events or application prompts and may comprise one or more events as well as one or more prompts. In some instances, each of the events and the prompts may, in turn, comprise one or more sequences. For example, a sequence may trigger an event, and the event may also trigger a sequence.Additionally, in some instances, each of the events and the prompts may comprise one or more audio / visual updates. For example, each audio / visual update may comprise a banner update in the supervisor's application as well as an animation and audio element in the game scene. In some instances, one model (e.g., an animal character) may relate to one or more game scenes (e.g., a zoo or train station) and may comprise one or more animation elements and one or more audio elements. For example, an audio element may also comprise one or more models.

[0117] In some cases, a learning module may represent a developmental milestone in the form of a distinct set of therapeutic, educational, and / or training content that attempts to improve one or more target skills in the user. In some instances, a supervisor may opt into a learning module to "play" with a user. For example, each learning module may be defined by parameters such as title (e.g., "Safari"), educational focus (e.g., joint attention), unity scene (e.g., "Safari"), thumbnail image, and a collection of learning cards, including an Intro Learning Card. In further examples, a unity scene, or the game scene, may comprise the game board, including the scene background and audio elements independent of the models (e.g., introductory music, generic background sounds), and a collection of two or three dimensional models, which may include the supervisor's avatar.

[0118] A learning card may be a distinct playable unit within a learning module that has a set of learning objectives. A learning card may represent a single exercise that helps move the user toward the milestone encompassed by the learning module. A learning card may define which of a game scene's models are present and may include a collection of sequences. These sequences may interact both with a supervisor's companion application, such as through banner updates, and with the game scene and models, such as through the animation and audio elements Each learning card may be defined by parameters such as title, thumbnail image, a collection of sequences, and an educational description, which may be long-form content that describes the purpose of the particular learning card. The logical flow of a learning card may be discussed in further detail in relation to other figures. A sequence may be a collection of prompts and eventsthat together progress the user through some or all of the objectives of the learning card. A sequence may be triggered either by an event (e.g., the user fixes their gaze on an animal) or by an action prompt on the learning card, which is initiated by a supervisor (e.g., the supervisor presses a button to "Point" at a Giraffe model). A sequence may be defined by parameters such as the event or prompt that triggers the sequence and the collection of events and prompts within the sequence.

[0119] An event may be a user action that may be explicitly captured by the XR system for the purposes of triggering audio / visual updates and / or updating active sequences that contain the event. An event may be defined by parameters such as the user's triggering action (e.g., maintaining gaze on a model for a specified duration), and a defined set of audio / visual updates that may execute as a post-action to the triggering action (e.g., a banner status update in the supervisor's companion application or the model initiating an audio element such as a sound in the game scene).

[0120] A prompt may be a supervisor-initiated component of a sequence. It may be defined by parameters such as a named action, which may appear as a button for the learning card (e.g., a "Point" button), clear logic for a user to complete the prompt, and a collection of audio / visual updates in case the user fails or succeeds in following the given prompt. In the event of failure, the prompt may trigger updates such as a banner status update to the supervisor (e.g., "Tell the user to look at the Pig if they are not picking up the visual pointing cue") or animation or audio updates in the game scene. In the event of success, the prompt may trigger updates such as a banner status update (e.g., "Congratulate user for looking at the Pig Model") or animation or audio updates in the game scene (e.g., the Pig model being animated or playing a celebratory sound).

[0121] An audio / visual update may be defined by parameters such as the type of update (e.g., banner, animation, or audio) and the specific content of the update (e.g., in the case of a banner update, the specific text displayed).

[0122] A sequence may be a collection of prompts and events that together progresses the user through some or all of the objectives of the learning card. A sequence may be triggered either by an event (e.g., the user orients their gaze toward an avatar) or by an action prompt on the learning card which is initiated by a supervisor (e g., the supervisor presses a button to cue the avatar to ask a clarifying question). A sequence may be defined by the following parameters: the event or prompt that triggers the sequence and the collection of events and prompts within the sequence.

[0123] An event may be a user action that may be explicitly captured by the XR system for the purposes of triggering A / V updates and / or updating active sequences that contain the event. Anevent may be defined by the following parameters: the user’s triggering action (e.g., maintaining gaze on the avatar 10 seconds), and a defined set of A / V updates that execute as a post-action to the triggering action (e.g., a banner status update in the supervisor’s companion application reading, “User has gazed at Marcie,” or the avatar model initiating an audio element such as “Hey, what’s up?” in the game scene).

[0124] A prompt may be a supervisor-initiated component of a sequence. It may be defined by the following parameters: a named action which appears as a button for the learning card (e.g., a “Can you say that again?”” button), clear logic for a user to complete the prompt, a collection of A / V updates should the user fail to follow the given prompt which may include a banner status update to the supervisor (e.g., “Explain to the user why Marcie is confused.”” displayed to the supervisor in a companion application) or an animation or audio update in the game scene (e g., Avatar is animated and / or makes a sound), a collection of A / V updates should the user succeed in following the given prompt which may include a banner status update to the supervisor (e.g., “Praise user for clarifying their comment.” displayed to the supervisor in a companion application) or an animation or audio update in the game scene (e.g., avatar is animated or an audio is initiated for celebratory music).

[0125] An A / V update may be in the form of updates to the supervisor through a banner update, the triggering of animation or audio of the models in the game scene, or the triggering of audio in the game scene level independent of the models. For example, an A / V update may be defined by the following parameter: which of banner, animation, or audio is to be updated (e.g., in the case of a banner update, the text should be specified).

[0126] FIG. 4 shows a logical model of a learning card, in accordance with some embodiments. In some embodiments, the learning card may comprise individual tasks or exercises with defined objectives, including sequences triggered by user events or prompts, helping the user progress toward specific skills or milestones within the lesson. As an example, FIG. 4 shows a logical model for an illustrative conversation-style lesson with an Al avatar, titled "Get Social." This model represents a single configuration among many possible, highlighting the logical components and methods discussed in this document. The "Get Social" experience takes place in one of two different locations, depending on the avatar selected. In this particular experience, each avatar is trained to discuss specific topics Marcie offers guidance and support around employment, including job interview practice and workplace readiness, while Jordan provides advice on friendships and making plans. The user profile for the lesson includes individuals who may benefit from structured social practice, with the behavioral goal focused on engaging with and responding to social cues presented by the avatar. The experience may include the followingparameters: Unity Scene (Cafe or Park); Avatars (Marcie, Jordan); Thumbnail Image (figure in conversation posture); and Sequence ([Avatar] [discusses chosen topic] with personalized guidance).

[0127] In an example, the "Get Social" lesson may provide the following notes and instructions to the supervisor in the companion application: The lesson may help the user practice conversation skills relevant to social situations. It may not be intended as a ‘scripted response’ exercise, allowing the Al avatar to guide the conversation, offering a natural and engaging interaction for the user. For Marcie, the supervisor may encourage the user to engage in discussions about job interviews and employment skills, waiting for the user to respond to Marcie’s questions or prompts and offering guidance only if needed. For Jordan, the supervisor may listen as the player interacts with Jordan around making friends and planning activities, supporting the user by responding naturally to their dialogue and providing clues or encouragement as necessary. The supervisor may provide specific verbal praise or verbal prompts after the user responds to the avatar’s prompts. If the user struggles to engage with the topics, the supervisor may consider starting with a simpler lesson or practicing related social cues.

[0128] The Card’s 402 first event 406 occurs when the user enters the XR scene. Upon the occurrence of this event 406, the supervisor receives a banner update 408 on the companion application reading, "Press ‘Begin’ when you are ready to start." The supervisor initiates a prompt 410 when the supervisor presses the “Begin” button on their companion application. Upon initiation of the prompt 410, there is an A / V update 412 in the XR scene. Simultaneously, the A / V update 412 starts to collect metrics on the user. Once the microphone access is confirmed, an icon representing a microphone appears above the avatar's head, indicating a listening state. As the user starts speaking, their words appear as text in the XR scene. Following the user's speech, the selected avatar responds, with their captions appearing under the user's text.

[0129] After confirming microphone access, the user may proceed with one of two events: the first event 416, in which the user interacts with one Al avatar, and the second event 424, in which the user interacts with another avatar. During this interaction, every five minutes, the supervisor receives a banner update asking them to select 'Continue.' This ensures ongoing monitoring during the lesson. If 'Continue' is not selected, the avatar's microphone turns off, suspending the avatar's listening and response capabilities until 'Continue' is selected.

[0130] Such data may be presented to a supervisor as a progress report in the dashboard format illustrated in FIG. 7A - FIG. 7B or, alternatively, as any other format or report. Alternatively, the user may self-administer the lesson without the need for direct supervision, using thecompanion application to guide their own experience, and progress metrics would be logged for later review by a supervisor if necessary.

[0131] In some aspects, the system may provide teaching modules that teach functional skills using stories. These teaching modules can include placing a user in a practice environment for both routine and non-routine tasks. Beneficially, such stories provide an effective and entertaining solution to teach users how to navigate everyday interactions by placing the users in a controlled virtual or augmented reality environment, thereby shielding the users from potential actual harms and retaining control to effectively guide the users at a flexible pace.

[0132] FIG. 5 illustrates a process flow for editing XR content of the present disclosure, in accordance with some embodiments. In some embodiments, the process flow may comprise steps for modifying virtual or augmented reality content, such as editing audio / visual elements, sequences, or prompts through an interactive user interface. In some cases, a user interface platform 506 may comprise a set of tools and technologies to create, update, modify, remove, and / or select a XR experience. The user interface platform 506 may comprise a graphical user interface comprising one or more user interactive objects (e.g., buttons, sliders, etc ). For example, the user interface platform 506 may be a web-based interface. Alternatively, the user interface platform 506 may be a non-web based interface. The user interface platform 506 may allow a user to manipulate one or more editable components, such as the learning card or learning module described elsewhere herein. For example, the user interface platform 506 may be used to create, modify, or otherwise edit a network or graph of interactions between the editable components. The editable components may be edited with respect to a timeline or other time dimension.

[0133] An editable component may include any component of the XR content, such as learning cards, sequences, events, prompts, audio / visual updates, game scenes, models (e.g., avatars), animation, audio (e.g., dialogue files, sound effect files, etc.), and banner updates. An editable component may be edited by editing an asset and / or a state of the component. An asset can be any media or data that can be used in the XR content. An asset may be internally created within the user interface platform 506. Alternatively an asset may be sourced from an external source 502. For example, an asset may include, be associated with, and / or be defined by a model file (e g., defining meshes, bones, animations, textures, etc ), an audio file, a video file, an image file, animation controller files, mesh files, animation files, html (e.g., for documents and guides, etc.) prefabricated (e.g., object template files), and other data. In some instances, a combination of assets may be bundled as an asset package. The asset package may be compressed and stored as single files. A state may describe a particular instance of an asset, such as an animationcharacter. An asset may have a plurality of states with different transitions (or a set of transitions) associated between the different plurality of states. For example, a state type can include an animation, audio, banner, conditional, delay, dialogue, fixation check, inverse kinematics, monitor user interface update, randomization, reward bell generator, shuffler, teleport, variable counter, variable flipper, variable setter, vehicle, and other elements. An editable component, asset, and / or state may have a plurality of versions.

[0134] Using the user interface platform 506, a user may create, edit, or remove any editable component. For example, a user may create a new learning card. A user may modify the collection and / or order of learning cards in a learning module. A user may edit or copy (e.g., duplicate) an existing learning card from a library 504. A user may localize an existing learning card, such as to swap out different assets and / or states (e.g., dialogue or audio files, banner text, environment, animation meshes). A user may add, modify, or remove one or more assets in the learning card. A user may change or upgrade the version of one or more assets, change or upgrade the version of one or more states, and / or change or upgrade one or more learning cards. A user may pair the platform 506 with a XR experience application 508 to play, monitor, or supervise the XR content on the XR experience application 508. A user may publish XR content to the library 504. For example, the user may publish a XR experience template, publish individual editable components such as leaning cards, and / or publish individual assets (or asset packages) or individual states to the library 504. Beneficially, other users may access the library 504 to download the XR content to select or customize their own XR experiences. The XR content created or modified by the user may be published for further development and / or for production (e.g., by the XR experience application 508). In some instances, a user may also gather metrics for a set of states.

[0135] The library 504 may be accessed to download one or more components for editing and / or execution. For example, as described earlier, the user interface platform 506 may download one or more assets, states, or learning cards, or other editable components from the library 504, such as for use in creation, modification, and / or updates to a XR experience, or for pairing with and execution on the XR experience application 508. In another example, the XR experience application may directly access the library 504 to download one or more components, such as a learning card, to execute a XR experience for a XR user. In another example, a companion application 510 may access the library 504 to download one or more components, such as a learning card, to execute, supervise, and / or monitor a XR experience in the XR experience application 508 (e.g., after pairing with the XR experience application).

[0136] FIGs. 6A-6B illustrate a user interface platform for editing XR content, in accordance with some embodiments. FIG. 6A illustrates a user interface comprising a canvas for creating or editing a state machine, such as a finite state machine. The state machine may define a set of states and transitions that partially or wholly govern the flow of a user experience within a learning card. In some embodiments, the user interface platform may include a sequence of state nodes and connectors, representing the logical progression between various states.

[0137] The process may begin with state nodes for initiating actions, such as Animation: walkingNPCl: Path 601 and Animation: walkingNPC2: Path 602, which may trigger specific actions or animations that transition to subsequent states. For example, these animation states could correspond to NPCs moving through a virtual environment, signaling the beginning of user engagement with the system.

[0138] In some instances, after initiating animations, the process may progress to input prompt nodes like Coach Question: Microphone check 603, where the system may ask the user whether they have interacted with a system component, such as a microphone. Depending on the user’s response (e.g., "Yes" or "Skip"), the state machine can transition to the appropriate next step. For instance, if the user confirms microphone interaction, the system may proceed to configuration nodes such as take the headset off and permit microphone permissions 604, where the system may adjust settings related to microphone access, ensuring the user can participate in further interactions.

[0142] In further examples, after configuring user settings, the flow may lead to verification nodes like Custom: CheckMicrophoneAccess 605, where the system verifies the necessary permissions. If microphone access is granted, the process may proceed to a decision node such as Switch: hasMicrophoneAccess 606, which checks the condition of whether access has been successfully permitted. In some instances, if access is denied, the system may prompt instructional nodes like Please permit microphone access 606, providing the user with guidance on permitting the required permissions.

[0139] As an example, once microphone access is verified, the system may transition to selection nodes, such as Coach Question: Who would you like to talk to? 607, offering the user the option to interact with specific characters in the virtual experience. The system may store the user's selection using assignment nodes like SetVariable: character set to Marcie 608 or SetVariable: character set to Jordan 609. Based on the user’s choice, the system may update the character variables, and both of these transitions may lead to initialization nodes such as Custom:InitializelnWorldComponents 610, which may activate and prepare relevant components for the upcoming interaction. These initialization states ensure that the virtual environment is ready toreflect the user’s choice and continues the logical flow of the experience. In some embodiments, the system may include engagement check nodes like Custom: AreYouStillThere 611 to verify whether the user is actively engaged with the system. If the user remains engaged, the system may present continued interaction prompts, such as Coach Question: To keep interacting with Marcie 612, asking the user to confirm their desire to continue the interaction with the selected character. For example, if the user confirms continued interaction, the system may extend the engagement by initiating new tasks or interactions with the character.

[0140] In further examples, the system may also utilize notification nodes like Banner:Information: This message will appear again in 5 minutes 613, which may provide the user with periodic updates or reminders to re-engage with the system. These notifications ensure that the user remains aware of any necessary actions, even if they have temporarily disengaged from the current interaction. In some instances, a sequence of state nodes and connectors that may define the logical flow of a user experience in a learning card. The system may transition from state to state, processing user input, verifying conditions, configuring system components, and guiding the user through predefined steps within the virtual environment. This structure may allow the system to ensure proper interaction and progression through the learning card’s predefined flow, adapting dynamically based on user inputs and system conditions.

[0141] FIG. 6B illustrates an example of a user interface for creating and editing state machine logic within a virtual learning environment. The interface may include several state nodes that control animations, input prompts, and system verifications. For instance, the process may begin with an animation node, such as walkingNPCl: Path 614, which initiates an animated sequence where a non-player character (NPC) starts moving through the virtual environment. This may be followed by walkingNPC2: Path 615, continuing the movement of a second NPC, signaling progression in the user’s experience. In some cases, the system may prompt the user with an input node, such as Microphone check 616, asking if the user has interacted with the microphone before. If the user confirms, the flow may proceed to a configuration node, where the system instructs the user to remove the headset and permit microphone permissions 616, ensuring the proper setup for the next steps. The system may then verify if the necessary permissions are in place using CheckMicrophoneAccess 617. For example, if microphone access is granted, the process may flow to a decision node like hasMicrophoneAccess 618, where the system checks the access status. If access has not been permitted, the user may receive a prompt to follow instructions for permitting microphone access 619. Once permissions are confirmed, the system may move to interaction nodes, such as Who would you like to talk to? 620, allowing the user to select a character, either Marcie 621 or Jordan 622. The system records this choice and initializesthe corresponding in-world components using InitializelnWorldComponents 623, preparing the virtual environment for the interaction. In further examples, the system may check if the user remains engaged through AreYouStillThere 624, which verifies the user’s activity and prompts them to continue interacting. If the user opts to continue, the system may present a prompt, such as To keep interacting with Marcie, press L 625, encouraging further engagement. If no response is received, a notification like Banner: Information 626 may remind the user after a configured time, such as 5 minutes, to re-engage with the lesson.

[0142] The platform may facilitate collaborative authoring and editing of XR content. In some instances, a plurality of users may, together, create and / or edit XR content. For example, a first user may create at least a first portion of XR content and publish, save, and / or distribute the first portion of XR content on the platform (e g., in a library), and a second user may edit the first portion of XR content and / or add other portions of XR content to the existing XR content, and publish, save, and / or distribute such modifications on the platform to complete or modify an XR module (e.g., learning card, learning module, etc ). Any number of users may contribute to a single unit of XR content, such as about 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 100 or more users. In some instances, a creator or author of XR content may control the number of users or identify of users with permissions to modify the XR content. In some instances, a user may be granted permission, such as by the creator or by another user, to modify the XR content. In other instances, any user may have permission to modify the XR content. Such permissions may be associated with the XR content (e g., as metadata). Different users may have different permissions to execute different actions on particular XR content, such as reading, modifying, playback, testing, reviewing, or evaluating, troubleshooting (e.g., debugging), publishing, distributing, and the like.

[0143] In some instances, the platform may have one or more pre-publication or pre-distribution tests for the XR content before publication or distribution. For example, there may be a clinical efficacy test, a safety test, a publication rights test, a technical test, and the like. In such cases, a particular XR content may be published or distributed only upon passing the one or more tests. In some cases, a particular XR content may be published or distributed to a desired user base only upon passing the one or more tests. For example, if an XR content fails a first test, it may still be distributed to a targeted user or group of users, but not allowed to be distributed to the public. In some instances, a test may have a binary passing or failing evaluation. In some instances, a test may be scored, wherein there may or may not be a passing score. In such cases, the test score may be associated with the XR content after publication — the score may be viewable to one or more users or to the public, or alternatively, the score may be viewable uponrequest, or alternatively, the score may not be viewable. In some instances, a test may be automatically performed by one or more algorithms programmed to perform the tests. In some instances, a test may require submission of one or more evidence documents (e.g., a clinician’s approval, regulatory approval, peer review, etc.). In some instances, a test may require voluntary statements (e.g., that a user has publication rights and has no knowledge of infringement of others’ rights). In some instances, a test may be performed by other users of the platform, such as for beta testing to pass a technical test before publication to a wider user base.

[0144] In some aspects, provided are systems and methods for profiling a user. For example, the user may be diagnosed with a condition, such as a neurodevelopmental or learning disorder. In some cases, data collected (or recorded) for one or more users may be aggregated to build behavior models for one or more conditions (e.g., mental, or developmental disorders). Such behavior models can be leveraged as diagnostic tools for users to be evaluated through the XR system. For example, a plurality of behavior models for different mental or developmental disorders can be stored in a library of behavior models, such as in one or more databases. A plurality of behavior models for each XR environment, scene, or experience may be stored in a library of behavior models, such as in one or more databases. A behavior model for a first type of developmental disorder can comprise data exhibited by one or more users known to suffer from the first type of developmental disorder when placed in a first XR environment. When a user to be diagnosed is placed in the same first XR environment, or an environment similar to the first XR environment, the data collected on the user may be compared to the behavior model for the first type of developmental disorder to determine whether the user has the first type of developmental disorder or not, and / or determine a degree to which the user suffers from the first type of developmental disorder. In some instances, the data collected for the user to be diagnosed may be compared to a plurality of behavior models to determine which one or more conditions the user may suffer from (and to what degree). By way of example, the higher the % similarity between the collected data for the user and the data stored for the behavior model, the more likely it is (and with higher degree) that the user suffers from the condition of the behavior model. In some instances, a user being diagnosed may be placed in a plurality of different XR environments, scenes, and / or experiences (e.g., sensory calming modules, teaching modules, learning modules, etc.) and an overall performance by the user may be compared to the library of behavior models. Such comparisons may be made individually by XR environments (for each type of condition) and / or by condition (for each type of XR environments), and then aggregated (e.g., average, mean, median, other statistical computation or evaluation). Beneficially, the XR systems may accurately and precisely diagnose a user with a condition based on comparisons ofaccurately measured empirical data (for example, compared to general guidelines or hunches that a child is not attentive enough), and determine a degree of intensity or progression of a condition. This may be particularly beneficial for diagnosing and treating mental or developmental disorders where it is difficult to quantify symptoms. In some instances, the diagnosis of the XR systems may be implemented by one or more computer algorithms, such as machine learning algorithms, which are trained with increasing data input (e.g., more users, more therapy sessions using the XR systems, etc.). For example, the accuracy of the diagnosis may increase as the iterations increases.

[0145] Beneficially, the platforms, systems, and methods of the present disclosure may be used to flexibly customize a XR experience for a profiled or diagnosed user. For example, a particular XR experience may be prescribed or recommended to a user based on the user profile. In another example, an existing XR experience may be customized for the user based on the user profile. In another example, an entirely new XR experience may be created for the user based on the user profile. A XR experience may be published and shared in a commonly accessible library to prevent duplication of effort. In some instances, the prescription, recommendation, and / or customization can be performed by human experts. Alternatively, or in addition, they may be prescribed by the system via one or more computer algorithms (e.g., machine learning algorithms). In some cases, the diagnosis, prescription, or recommendation may be made without human input. The accuracy of the diagnosis, prescription, or recommendation may increase with increasing iterations. In some instances, the virtual or augmented reality experiences and therapies can be tailored to the needs of individual users either manually by the human expert (e.g., therapist) or using computer algorithms. For example, the tailoring can be performed based at least on prior conditions defined in the user profile and / or based on data collected throughout the user’s and others’ use of the XR system.

[0146] The platforms, methods, and systems of the present disclosure may provide systems for providing personalized learning support to users with one or more neurodevelopmental disorders. In some embodiments, the system comprises at least one wearable device configured to provide a user with a virtual reality environment. In some cases, the user has or is suspected of having one or more neurodevelopmental disorders. In some instances, the system may comprise an artificial intelligence module. In some cases, the artificial intelligence module is configured to receive user-specific data. For example, based on the user-specific data, the artificial intelligence module may generate a personalized lesson plan for the user. In further examples, the personalized lesson plan may comprise one or more virtual reality lessons that are configured to be displayed on the wearable device.

[0147] In some embodiments, the system comprises at least one artificial intelligence module. In some cases, the artificial intelligence module is configured to accumulate feedback on a user's learning experience and outcomes. In some instances, the system comprises a personalized lesson plan. For example, the artificial intelligence module may be configured to utilize the accumulated feedback for the continued refinement and updating of the personalized lesson plan. In further examples, the at least one artificial intelligence module may comprise one or more Machine Learning, Deep Learning, Reinforcement Learning, Natural Language Processing (NLP), Predictive Analytics, and Decision Tree Learning.

[0148] In some embodiments, the system is configured for users with at least one neurodevelopmental disorder. In some instances, the system is configured to cater to specific types of neurodevelopmental disorders. For example, these disorders may include one or more of Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), Specific Learning Disorders, Intellectual Disability (ID), Communication Disorders, Motor Disorders, Global Developmental Delay, and Other Specified and Unspecified Neurodevelopmental Disorders.

[0149] In some embodiments, the system comprises at least one biometric feedback module. In some cases, the biometric feedback module is configured to gather biometric data from a user. In some instances, the system may comprise specific types of biometric data. For example, these types of biometric data may include one or more of eye gaze, pupillary response, voice tone, heart rate, facial expressions, and sentiment analysis. In some instances, the system may comprise one or more biometric components configured to obtain biometric data. For example, the one or more biometric components may comprise an eye tracker for monitoring eye movement and gaze, a microphone for capturing voice tone and inflections, a heart rate monitor for tracking the user's heart rate, a facial recognition system for capturing and analyzing facial expressions, and / or a sentiment analysis algorithm for assessing the user's emotional state based on the collected biometric data. In further examples, the one or more biometric components may comprise a galvanic skin response sensor to measure changes in the user's emotional arousal, a motion sensor to track physical movements and gestures, and / or a brain-computer interface to record brainwave patterns for more detailed cognitive analysis.

[0150] In some cases, the biometric feedback module is configured to deduce the user’s engagement level and / or emotional state based on the biometric data. In some instances, the biometric feedback module may determine the user's engagement level by analyzing a combination of biometric data. For example, an increased heart rate, rapid eye movement, or specific patterns in voice tone may indicate high engagement. Conversely, lack ofmovement, sustained or fixed eye gaze, extended blinking times, or monotone voice may suggest low engagement or disinterest. In some instances, the biometric feedback module may determine the user's emotional state by employing sentiment analysis algorithms on voice data to identify changes in tone, pitch, or volume correlating with particular emotions. Additionally, voice patterns such as speech rate, pauses, or rhythm could be analyzed to further understand the user's emotional state. Additionally, it may utilize facial recognition software to analyze expressions and recognize emotions such as happiness, sadness, anger, or surprise. Eye gaze patterns can be monitored and analyzed to understand the user's focus and interest level. For instance, a user frequently shifting their gaze may indicate distraction or lack of engagement, whereas a steady gaze could suggest attention and focus. If the system is equipped with a braincomputer interface, it could interpret brainwave patterns to provide further insights into emotional states. For instance, specific patterns or frequencies of brainwaves could be associated with states of stress, relaxation, or focus.

[0151] In some instances, the artificial intelligence module is configured to receive and interpret the biometric data from the biometric feedback module. For example, the artificial intelligence module may utilize the biometric data for continued refinement and updating of a personalized lesson plan.

[0152] In some cases, the artificial intelligence module may comprise a learning language model. In some instances, the learning language model functions as a recommendation engine to guide a user through a personalized lesson plan. For example, the learning language model may comprise natural language processing (NLP) algorithms for understanding and generating human language, machine learning algorithms for predicting user needs and preferences, and sentiment analysis tools for interpreting the user's emotional state and adjusting the lesson content accordingly. In addition, it may include a sequence prediction model to suggest the next appropriate steps in the lesson plan based on user's past performance and an adaptive learning model that continuously refines its recommendations based on ongoing user feedback and performance data.

[0153] In some cases, the artificial intelligence module may utilize at least in part user data to generate a personalized lesson plan. In some instances, the artificial intelligence module may utilize specific types of user data. For example, the specific types of user data may be predefined and may include demographic information (e.g., age, gender, and educational background), performance data (e.g., past scores, completion times, and success rates), interaction data (e.g., mouse clicks, page views, time spent on tasks), and feedback data (e.g., user ratings, comments, suggestions). This user data can be collected from a range of sources (e.g., user profiles, learningmanagement systems, direct user interaction with the system), and the artificial intelligence module may use it to generate a personalized lesson plan tailored to the user's needs, learning style, and progression pace.

[0154] In some instances, the specific types of user data may comprise one or more of the user's interests (e.g., preferred subjects, hobbies, recreational activities), physical appearance (e.g., hair color, eye color, height, which can be relevant for creating personalized avatars in a virtual environment), favorite genre (e.g., fiction, non-fiction, fantasy, mystery, for personalizing content), significant people in their life (e.g., family members, friends, mentors, which can be useful in creating contextual learning scenarios), preferred role models or animated characters (e.g., superheroes, anime characters, which may be used to enhance user engagement and motivation), among other personal attributes and preferences.

[0155] In some cases, the artificial intelligence module may utilize lookalike data or comparison based on previous user experiences. In some instances, the artificial intelligence module may utilize this lookalike data for the continued refinement and updating of a personalized lesson plan. As an example, this approach may allow the system to learn from past experiences and continually enhance and personalize the learning support provided to the user. In some instances, the lookalike data may comprise attributes or behavioral patterns of successful learners who have similar demographic information or learning preferences to the current user. For example, the data may include a user learning pace, preferred learning modalities (e.g., visual, auditory, kinesthetic), time spent on tasks, interaction patterns with the system, feedback provided, as well as their progress and achievement data. By analyzing this lookalike data, the artificial intelligence module may identify strategies and approaches that were effective for similar users, and incorporate these insights into the refinement and updating of the current user's personalized lesson plan.

[0156] In some cases, the artificial intelligence module utilizes a rating system to evaluate or score one or more aspects of the user's experiences or performance within the virtual reality environment. In some instances, the system may adjust the lessons on a scale (e g., such as 1-5), based on the user's performance and responses. For example, the system may adjust the difficulty level of the lessons based on the user's performance rating. If the user consistently scores high on a (e.g., such as 1-5), the system may interpret this as a sign that the user is ready to progress to more challenging content. Conversely, if the user's scores are consistently low, the system may reduce the difficulty level or provide additional support and resources to help the user improve.

[0157] Alternatively, the system may adapt the pace of the lessons based on the user's response times and engagement levels. If the user completes lessons quickly and maintains a high level of engagement, the system may increase the pace to keep the user challenged and engaged. If the user takes longer to complete lessons or shows signs of disengagement, the system may slow down the pace to ensure the user has sufficient time to understand and absorb the content.

[0158] In some instances, the system comprises configured for dynamic adaptation of virtual reality lessons. In some cases, the artificial intelligence module uses the evaluations or scores from the rating system to dynamically adapt the virtual reality lessons in real-time. For example, the system may adjust lesson tasks, stimuli, challenges, or strategies based on the user's performance and responses. This dynamic adaptation may permit a personalized and responsive learning environment that can cater to the specific needs and learning pace of the user.

[0159] In some cases, the artificial intelligence module continuously refines and updates a personalized lesson plan to enhance a user's skill acquisition. In some instances, the artificial intelligence module specifically catering to the needs of individuals with neurodevel opmental disorders. For example, the artificial intelligence module may be configured to generate lessons that focus on key areas of neurodevelopmental such as recognizing and expressing emotions, understanding social cues, initiating, and maintaining conversations, and developing empathy. These lessons may employ a variety of strategies tailored to the user's unique needs, such as social stories, role-playing activities, and interactive games. Furthermore, the module can integrate real-time feedback and performance data to adapt these lessons to the user's progress and preferences. For instance, if the user struggles with recognizing facial expressions, the module could focus more on lessons that incorporate facial recognition exercises. If the user excels in empathy-related tasks, the module could introduce more advanced empathy-building activities.

[0160] By continuously refining and updating the personalized lesson plan, the artificial intelligence module may provide targeted, effective support to enhance skill acquisition for individuals with neurodevelopmental disorders, such as those on the autism spectrum or with attention-deficit / hyperactivity disorder (ADHD). In some cases, the rating system may integrate with a recommendation engine within the artificial intelligence module. In some instances, the recommendation engine may optimize the selection and sequencing of virtual reality lessons for the user based on their previous performance and progression. For example, if a user consistently scores high in lessons focusing on verbal communication but lower in non-verbal communication, the recommendation engine could suggest more lessons or activities that strengthen non-verbal communication skills. The engine could also sequence lessons in a waythat alternates between areas of strength and areas needing improvement, to maintain engagement and provide a balanced learning experience. Alternatively, if a user progresses rapidly through beginner-level lessons, the recommendation engine may propose moving to intermediate or advanced level lessons to maintain an appropriate level of challenge. The recommendation engine may also use the rating system to suggest revisiting lessons that received low scores, providing users with opportunities to improve their understanding and mastery of the material. Overall, by integrating with the rating system, the recommendation engine can deliver a highly personalized, dynamic, and effective virtual reality learning experience.

[0161] In some cases, the artificial intelligence module may be configured to derive insights from identified patterns, trends, or effective strategies, as determined by the rating system, which are made accessible to a coach or instructor. In some instances, the coach or instructor can provide more effective support for the user's learning process within the virtual reality environment based on these insights. For example, the coach or instructor could receive detailed reports on the user's performance, progress, and areas of difficulty. They may be able to see which types of lessons or activities have been most effective for the user, as well as the user's engagement levels and completion times. This valuable data can assist the coach or instructor in tailoring their instruction and support to the user's specific needs, making the learning experience more efficient and targeted.

[0162] Furthermore, the artificial intelligence module may provide predictive analytics to the coach or instructor, forecasting may comprise future challenges or areas of growth for the user based on current performance and progression trends. With these insights, the coach or instructor can proactively adjust their approach or provide additional resources to mitigate anticipated challenges or foster areas of growth.

[0163] In some cases, the rating system may utilize machine learning algorithms. In some instances, the machine learning algorithms may accurately discern patterns and trends from the user's performance data, facilitating predictive modeling of the user's future progress and may comprise neurodevelopmental challenges. For example, the functionality may permit the system to provide highly personalized and targeted learning support, continually adapting to the user's specific needs and progress.

[0164] In some cases, the artificial intelligence module is capable of tracking the progression of lessons, in terms of complexity, to adjust the lesson plan in real-time. In some instances, if the artificial intelligence module detects that the user is mastering the material quickly, it may escalate the complexity of the lessons, introducing more advanced concepts or more challenging tasks to maintain the user's engagement and facilitate their continued learning. In otherinstances, if the system observes that the user is struggling with the current complexity level, it may scale back the difficulty of the lessons, providing simpler tasks or revisiting earlier concepts. As an example, the artificial intelligence module may ensure that the user has a solid understanding of the foundational material before progressing to more complex lessons. In each instances, this adaptive approach may offer a personalized learning experience that adjusts to the user's needs and abilities in real-time.

[0165] In some instances, the system comprises a biometric feedback module. For example, the biometric feedback module may be capable of evaluating how effectively a lesson was delivered, based on learner or instructor feedback and the gathered biometric data.

[0166] In some instances, the artificial intelligence module is further configured to generate realtime updates of the lesson plans based on user data, feedback, and biometric data. For example, the artificial intelligence module may provide a dynamic and responsive learning environment that can continually adapt to the user's progress and specific needs, enhancing the effectiveness of the learning support provided by the system.

[0167] In some embodiments, the system comprises a wearable device that provides a user with a virtual reality environment. In some cases, the user has or is suspected of having a neurodevelopmental disorder. In some instances, the system comprises an artificial intelligence module. For example, the artificial intelligence module may be configured to receive userspecific data and generate a plurality of user-specific parameters. In some embodiments, the system comprises a wearable device that provides a user with a virtual reality environment. In some cases, the user has or is suspected of having a neurodevelopmental disorder. In some instances, the system comprises an artificial intelligence module. For example, the artificial intelligence module may be configured to receive user-specific data and generate a plurality of user-specific parameters. Additionally, the system may include Al-driven sentiment analysis of the learner's speech, Al-driven gestural and facial expressions of animated characters in response to the learner, and the capture and analysis of the learner's hand gestures, enhancing the personalized and adaptive nature of the virtual learning environment.

[0168] In some instances, the system may comprise an animation module. For example, the animation module may be configured to receive the user-specific parameters and generate personalized animated characters for the virtual reality environment. In further examples, the dynamic and personalized generation of animated characters may improve the user's engagement and learning outcomes in the virtual reality environment.

[0169] In some cases, the system may comprise a microphone or similar audio capture device that collects the user's audio data during a virtual reality session. In some instances, the systemmay comprise a heart rate monitor that captures the user's physiological responses during the virtual reality session. The system may also comprise eye-tracking technology that records the user's gaze and eye movements, providing insight into where the user is focusing their attention. Additionally, the system may comprise motion-sensing technology to track the user's physical movements and gestures within the virtual environment, which could help assess their level of engagement and the effectiveness of their motor skills. In some instances, the system may comprise a facial recognition system to analyze the user's facial expressions, which could provide valuable information about the user's emotional responses during the virtual reality session.

[0170] In some cases, the system comprises an Application Programming Interface (API). In some instances, the API may be configured to process the audio data from the wearable device, and transform it into a machine-readable format. For example, the processed audio data from the API may be returned to the artificial intelligence module. In further examples, the system may analyze and utilize the user's audio data to personalize the interactive virtual environment to achieve specific learning outcomes.

[0171] In some cases, the system may comprise a conversation module. In some instances, the conversation module may interface with the artificial intelligence module. In some instances, the conversation module may provide real-time response generation based at least in part on the user's audio data. In some instances, the conversation module may provide real-time response generation based at least in part on the user-specific parameters.

[0172] In some cases, the system may comprise a conversation module that is part of an interactive virtual reality environment for users with neurodevelopmental disorders. In some instances, the conversation module utilizes artificial intelligence to generate human-like speech for personalized animated characters in the virtual reality environment. For example, this functionality can create a more immersive and interactive virtual reality experience, and leading to improved learning outcomes for users.

[0173] In some instances, the conversation module may facilitate unscripted conversations. In some instances, the conversation module may simulate a variety of conversational scenarios. For example, the conversational module may simulate a job interview, a social interaction at a park, a visit to a grocery store, a classroom scenario, a sports game, or a family gathering. Additional scenarios may include a doctor's appointment, navigating a busy street, or an emergency situation like a fire alarm. These simulations can help users practice social skills, decision-making, and appropriate reactions in a safe and controlled environment. This may be particularly beneficial for users with neurodevelopmental disorders such as autism, helping them to improve their social and communication skills.

[0174] In some instances, the conversation module is configured to monitor one or more neurodevelopmental skills of the user. For example, one or more neurodevel opmental skills may include verbal and non-verbal communication, understanding of social cues, the ability to maintain a conversation, adjust speech style or content according to the social context, comprehend different points of view, and understand and use humor appropriately. In further examples, this functionality may permit the system to assess the user's progress and adapt the virtual reality environment and scenarios accordingly (e.g., providing more effective and personalized learning support).

[0175] In some cases, the system may comprise an intervention module. In some instances, the intervention module may comprise part of an interactive virtual reality environment for users with neurodevelopmental n disorders. In some instances, the intervention module may comprise one or more components configured to direct or pause the conversational module. For example, the intervention module may provide an additional layer of control and customization for the user's experience.

[0176] In some instances, a coach may control the intervention module. For example, the coach could be a therapist, parent, teacher, caregiver, medical professional, or any other individual involved in the user's therapy or care. In further examples, this function may permit professionals and caregivers to directly influence and shape the user's virtual reality experience (e.g., enhancing its relevance and effectiveness).

[0177] In some instances, the intervention module is configured to allow the coach to choose conversation topics that are tailored to the user. For example, the personalized selection of conversation topics can create a more focused and relevant conversational scenarios, promoting the user's engagement and learning progress in the virtual reality environment.

[0178] In some cases, the conversation module may comprise a group learning module. In some instances, the group learning module is configured to facilitate interaction between the user and one or more additional users. For example, group learning module may accommodate group learning scenarios and foster interactive learning experiences among multiple users.

[0179] In some instances, the group learning module includes a feature to identify and flag disruptive participants in a group. For example, the group learning module can carry out actions such as removing, warning, or correcting disruptive users during group lessons, and / or blocking user communications if necessary. In further examples, the group learning module may facilitate a conducive and respectful learning environment for all users participating in the group lesson.

[0180] In some cases, the system comprises a voice modulation component. In some instances, the voice modulation component may comprise part of an interactive virtual reality environmentfor users with neurodevel opmental disorders. In some instances, the voice modulation component is configured to modulate the voice of the animated character, the user, the coach, and / or additional users. For example, the voice modulation feature may contribute to the customization and personalization of the virtual reality environment.

[0181] In some instances, the voice modulation component may modulate one or more of pitch, tone, speed, accent, language, emotional context, and / or verbal cues. For example, the ability to modulate these aspects of voice can enhance the realism and engagement of the virtual reality environment, providing a more immersive and effective learning experience for users.

[0182] In some cases, the artificial intelligence module is configured to provide real-time updates and modifications to the lesson plan. In some instances, the updates and modifications may be based at least in part on feedback and / or performance metrics of the user. For example, this feature may permit the system to adapt and tailor the lesson plan dynamically in response to the user's progress and needs. In further examples, this feature may enhance the effectiveness and personalization of the learning support provided by the system.

[0183] In some embodiments, the system comprises a wearable device that provides a user with a virtual reality environment configured to treat neurodevelopmental disorders. In some cases, the user is or is suspected to be suffering from a neurodevelopmental disorder.

[0184] In some instances, the system comprises an artificial intelligence module. For example, the artificial intelligence module is configured to receive user-specific data and generate a plurality of user-specific parameters.

[0185] In some cases, the system comprises an animation module that receives the user-specific parameters. In some instances, the animation module may either generate a virtual reality environment or modify an existing one based on the user-specific parameters. For example, this functionality may permit a highly personalized and adaptive virtual reality environment In further examples, this feature may lead to more effective treatment outcomes for users with neurodevelopmental disorders.

[0186] In some cases, the system comprises a mechanism for generating a plurality of userspecific parameters within a virtual environment configured to treat neurodevelopmental disorders. In some instances, the plurality of user-specific parameters may comprise one or more real-life scenarios. For example, this feature may help provide users with a contextually relevant and engaging learning experience. In further examples, the real-life scenarios may include one or more situations like active shooter drills or independent living situations. As an example, by simulating these scenarios, the system may help users with neurodevelopmental disorders practice and enhance their skills in a safe and controlled environment. For example, after doing aleaming card on one specific topic, Al could generate similar content and the coach could play it with the learner to test the learner's understanding of that topic.

[0187] In some cases, the plurality of user-specific parameters comprises real-world video footage. In some instances, the real-world video footage may be captured in real-time or near real-time, providing an authentic and contextually relevant base for the virtual environment. In some instances, the system is configured to convert the real-world video footage into semi and / or fully animated virtual reality environments. For example, these environments may comprise a hybrid virtual reality environment that includes one or more real-world elements. In further examples, the real-world elements may include buildings, nature landscapes, furniture, vehicles, familiar figures or objects, or any other elements present in the user's real-life environment. In even further examples, the system may provide an immersive and familiar virtual reality experience for the user, may comprise enhancing their engagement and learning outcomes.

[0188] In some cases, the system may comprise a scanning device. In some instances, the scanner may be utilized to simulate / animate at least part of an interactive virtual reality environment for users with neurodevel opmental disorders. In some instances, the scanner is configured to scan one or more images. For example, the scanner may bring real-world elements into the virtual reality environment.

[0189] In some instances, the plurality of parameters includes the scanned images. For example, the scanned images may comprise one or more entities from the user's life. In further examples, the one or more entities from the user's life may comprise one or more persons. In even further examples, the inclusion of familiar entities can enhance the user's engagement within the virtual reality environment.

[0190] In some cases, the system comprises a generation module. In some instances, the generation module may comprise part of an interactive virtual reality environment for users with neurodevelopmental disorders. In some instances, the generation module is configured to generate one or more animated characters that resemble persons. For example, the generation of animated characters that resemble real persons can create a more immersive and personalized virtual reality experience. In further examples, the familiar appearances of the characters may increase user engagement and contribute to improved learning outcomes.

[0191] In some cases, the plurality of parameters comprises one or more characteristics of one or more persons. In some instances, the system includes a modification module. For example, this module may be configured to modify the animated character based at least in part on the one or more characteristics. In further examples, the one or more characteristics may be of the one or more persons. In even further examples, the one or more characteristics may include physicalappearance, voice properties, mannerisms, habitual behaviors, or any other identifiable traits associated with the one or more persons. In some instances, the system can modify one or more of the animated character’s physical features, voice features, and personality controls based on these characteristics. For example, this functionality may allow the system to provide a personalized and engaging virtual reality experience for the user.

[0192] In some embodiments, the system comprises a wearable device that provides a user with a virtual reality environment configured to support individuals with neurodevel opmental disorders. In some cases, the user has or is suspected of having a neurodevelopmental disorder.

[0193] In some cases, the system comprises an artificial intelligence module. In some instances, the artificial intelligence module is configured to process user interaction data within the virtual reality environment and generate automated analytics.

[0194] In some instances, the automated analytics generated by the artificial intelligence module comprise at least in part an assessment of one or more neurodevelopmental disorder conditions of the user. For example, this functionality may allow the system to derive valuable data about the user's condition, permitting more effective and personalized support for individuals with neurodevelopmental disorders.

[0195] In some cases, the system may comprise a mechanism capable of assessing a range of different neurodevelopmental conditions in a virtual environment. In some instances, the conditions may include Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, Social (Pragmatic) Communication Disorder, Anxiety and Phobias associated with social and environmental factors and any other conditions characterized by difficulties in the social aspect of communication.

[0196] In some instances, the system can address various behavioral characteristics typically associated with neurodevelopmental disorders. For example, these characteristics may include difficulties in social interaction, difficulties with non-verbal communication, difficulties understanding and using language for social purposes, repetitive patterns of behavior or interests, hyperactivity, impulsivity, attention difficulties, etc. in further examples, this comprehensive approach may allow the system to cater to a wide range of user needs and conditions, enhancing its effectiveness and applicability.

[0197] In some embodiments, the system comprises a mechanism for assessing a user's progress in an interactive virtual reality environment. In some cases, the system is capable of assessing whether a user has improved or declined in one or more neurodevelopmental conditions.

[0198] In some instances, the system includes an assessment module. For example, the assessment module may be configured to identify one or more causative factors of a user'sdecline and assign a probability to the causative factor. In further examples, this functionality may provide insights into the factors that might be impacting a user's progress.

[0199] In some instances, the automated analytics may include an assessment of user-specific behavior in the virtual reality environment. For example, the user-specific behaviors may include emotional state, task completion status, and the need for prompting within the virtual reality environment. In further examples, the data further permits the system to monitor, analyze, and adapt to the user's progress and needs in real-time.

[0200] In some cases, the system comprises a voice recorder module as part of an interactive virtual reality environment for users with neurodevelopmental disorders. In some instances, the voice recorder module is configured to receive audio data from the user and record and / or transcribe this data.

[0201] In some instances, the system includes automated analytics that are generated at least in part on the audio data. For example, these analytics may be assessed at least in part using machine learning algorithms (e.g., further enhancing the accuracy and efficiency of the data analysis).

[0202] In some instances, the system may comprise a modification module. For example, this module may be configured to modify the virtual reality environment based at least in part on the automated analytics. In further examples, the ability to adapt the virtual reality environment based on user-specific audio data can provide a more responsive and personalized learning experience for the user (e.g., may comprise leading to improved outcomes in treating neurodevelopmental disorders).

[0203] In some embodiments, the system comprises a wearable device configured to provide a user with a virtual reality environment for diagnosis and assessment of anxiety disorders, panic disorders, phobia, and other social and situational behavioral, neurodevelopmental, and psychiatric conditions. In some cases, the user is or is suspected of having a neurodevelopmental disorder.

[0204] In some instances, the system comprises a diagnosis assessment module For example, this module utilizes artificial intelligence to receive and interpret user-specific data. In further examples, based on this data, the module can determine the presence and / or severity of one or more neurodevelopmental disorder conditions of the user. In even further examples, this functionality may allow the system to provide a comprehensive and personalized assessment of the user's condition. Such insights can guide the adaptation of the virtual reality environment and interventions, and enhancing the effectiveness of treatment and support for individuals with neurodevelopmental disorders.

[0205] In some embodiments, the system comprises a user interface. In some cases, the user interface presents an editable network of nodes and connectors, representing the sequence of logical flow paths in a virtual or augmented reality experience. Each node within this network is associated with a state of an asset that is presented to the user in the virtual or augmented reality environment.

[0206] In some instances, the system allows for the modification of this network. This includes adding, removing, or changing the nodes within the network. This functionality permits the system to adapt and tailor the virtual or augmented reality experience according to the specific needs and preferences of the user.

[0207] For example, the system may be programmed to provide different types of stimulation to the user. These can be based on the specific states of the asset within the virtual or augmented reality environment. The types of stimulation could include visual, auditory, or haptic stimuli, enhancing the immersive nature of the user's virtual or augmented reality experience.

[0208] In some embodiments, the system comprises a sequence of logical flow paths in the virtual or augmented reality experience. These paths are configured to guide the user towards the achievement of specific therapeutic or educational goals. Such goals are tailored to the user's requirements, thereby enhancing the relevance and effectiveness of the virtual or augmented reality experience.

[0209] In some cases, the system is specifically configured for individuals diagnosed with mental or neurodevelopmental disorders. This includes, but is not limited to, Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder. By catering to the specific needs of these users, the system offers a highly personalized and effective method for providing therapeutic or educational support.

[0210] In some embodiments, the system comprises at least one virtual or augmented reality environment. In some cases, the virtual or augmented reality environment comprises at least one biometric component. In some instances, the at least one biometric component comprises one or more of a gaze tracker, voice recognition module, speech recognition module, facial expression analyzer, and pupillary dilation monitor. These modules are configured to capture and analyze user-specific biometric data within the virtual or augmented reality environment.

[0211] In some cases, the at least one biometric component further comprises a heart rate monitor and positional data module. In some instances, the heart rate monitor and positional data module comprise wearable sensors. For example, these wearable sensors may be configured to monitor the user's heart rate and physical position during their interaction with the virtual or augmented reality environment. Additionally, the system may also incorporate eye trackingtechnology as part of its biometric components. This technology can monitor and analyze the user's eye movements and gaze direction while interacting with the XR environment. The collection and interpretation of eye gaze data can provide valuable insights into user attention, engagement, and response to different stimuli within the XR scene.

[0212] In further examples, the system leverages the collected biometric data to personalize and adapt the virtual or augmented reality experience. For instance, this could involve modifying the difficulty level or content based on the user's heart rate or tailoring the visual stimuli based on the user's gaze patterns. This provides a responsive and adaptive learning environment, enhancing the effectiveness and personalization of neurodevelopmental therapies and education delivered via the system.

[0213] In some embodiments, the system comprises an assessment module. This assessment module is in communication with a diagnosis assessment module. The assessment module's key function is to compare current and previous determinations of the presence and / or severity of one or more neurodevelopmental disorder conditions in a user within the context of a virtual reality environment.

[0214] In some instances, the system is configured such that the assessment module, based on the aforementioned comparison, has the capability to generate and / or modify personalized lessons for the user within the virtual reality environment. This provides a highly personalized and adaptive learning experience, tailored to the user's specific needs and progression.

[0215] In some embodiments, the system comprises an enhanced feature wherein the assessment module identifies progression in the user's neurodevelopmental skills. This is done based on the comparison of current and previous determinations. Based on the identified progression, the assessment module adjusts the personalized lessons within the virtual reality environment to build upon and further enhance the user's progression.

[0216] In further examples, the system is configured to provide a dynamic and responsive virtual reality environment that can cater to the specific needs and learning pace of users with neurodevelopmental disorders. The system's ability to generate, modify, and adjust personalized lessons based on user's progression enhances its effectiveness in providing therapeutic or educational support.

[0217] In some embodiments, the system comprises at least one virtual or augmented reality environment. This environment is configured to provide a tailored therapeutic or educational experience for individuals with neurodevelopmental disorders.

[0218] In some cases, the system further comprises an anomaly detection module. The anomaly detection module interfaces with an artificial intelligence module, forming an integrated and cohesive unit within the system.

[0219] In some instances, the anomaly detection module is specifically configured to spot anomalies in the user's progress or interaction with the system. This ability to detect anomalies allows the system to identify may comprise issues or challenges the user may be experiencing in real-time.

[0220] For example, the system may recognize an unexpected decline in the user's performance, unusual patterns in user interaction, or signs of user distress. Upon detection of such anomalies, the system can alert therapists or educators, modify the virtual reality environment, or adjust the therapeutic or educational program to better support the user.Control systems

[0221] The present disclosure provides computer control systems that are programmed to implement methods of the disclosure. In some embodiments, the computer control system may comprise hardware and software components that manage data flow, user interactions, and system updates within the virtual or augmented reality environment, ensuring smooth operation and tracking user performance. FIG. 8 shows a computer system 801 that is programmed or otherwise configured to operate and display a user interface to edit virtual reality or augmented reality content, execute a XR environment with or without the use of supplemental headsets or gear, execute algorithms within the virtual reality or augmented reality platform or a companion application to the virtual reality or augmented reality platform, track, save, and analyze a user’s behaviors and responses in a XR environment, and / or execute and pair a companion application that may allow a supervising user to monitor and control in real-time a user’s virtual reality or augmented reality experience on a second, third, or nthdisplay screen. The computer system 801 can be an electronic device of a user or a computer system that is remotely located with respect to the electronic device. The electronic device can be a mobile electronic device. The computer system 801 may be used to treat a neurodevelopmental disorder, such as, for example, autism. The computer system 801 may communicate with the control system 106 of FIG. 1. In some cases, the control system 106 may comprise the computer system 801.

[0222] The computer system 801 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 805, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The computer system 801 also includes memory or memory location 810 (e g., random-access memory, read-only memory, flash memory), electronic storage unit 815 (e.g., hard disk), communication interface 820 (e.g., network adapter)for communicating with one or more other systems, and peripheral devices 825, such as cache, other memory, data storage and / or electronic display adapters. The memory 810, storage unit 815, interface 820 and peripheral devices 825 are in communication with the CPU 805 through a communication bus (solid lines), such as a motherboard. The storage unit 815 can be a data storage unit (or data repository) for storing data. The computer system 801 can be operatively coupled to a computer network (“network”) 830 with the aid of the communication interface 820. The network 830 can be the Internet, an internet and / or extranet, or an intranet and / or extranet that is in communication with the Internet. The network 830 in some cases is a telecommunication and / or data network. The network 830 can include one or more computer servers, which can permit distributed computing, such as cloud computing. The network 830, in some cases with the aid of the computer system 801, can implement a peer-to-peer network, which may permit devices coupled to the computer system 801 to behave as a client or a server.

[0223] The CPU 805 can execute a sequence of machine-readable instructions, which can be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 810. The instructions can be directed to the CPU 805, which can subsequently program or otherwise configure the CPU 805 to implement methods of the present disclosure. Examples of operations performed by the CPU 805 can include fetch, decode, execute, and writeback.

[0224] The CPU 805 can be part of a circuit, such as an integrated circuit. One or more other components of the system 801 can be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

[0225] The storage unit 815 can store files, such as drivers, libraries, and saved programs. The storage unit 815 can store user data, e.g., user preferences and user programs. The computer system 801 in some cases can include one or more additional data storage units that are external to the computer system 801, such as located on a remote server that is in communication with the computer system 801 through an intranet or the Internet.

[0226] The computer system 801 can communicate with one or more remote computer systems through the network 830. For instance, the computer system 801 can communicate with a remote computer system of a user (e.g., XR user, supervisor, therapist). Examples of remote computer systems include personal computers (e g., portable PC), slate or tablet PC’s (e g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android-permitted device, Blackberry®), or personal digital assistants. The user can access the computer system 801 via the network 830.

[0227] Methods as described herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 801, such as, for example, on the memory 810 or electronic storage unit 815. The machine executable or machine readable code can be provided in the form of software. During use, the code can be executed by the processor 805. In some cases, the code can be retrieved from the storage unit 815 and stored on the memory 810 for ready access by the processor 805. In some situations, the electronic storage unit 815 can be precluded, and machine-executable instructions are stored on memory 810.

[0228] The code can be pre-compiled and configured for use with a machine having a processor adapted to execute the code, or can be compiled during runtime. The code can be supplied in a programming language that can be selected to permit the code to execute in a pre-compiled or as-compiled fashion.

[0229] Aspects of the systems and methods provided herein, such as the computer system 801, can be embodied in programming. Various aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of machine (or processor) executable code and / or associated data that is carried on or embodied in a type of machine readable medium. Machine-executable code can be stored on an electronic storage unit, such as memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk.“Storage” type media can include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, may permit loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server. Thus, another type of media that may bear the software elements includes optical, electrical, and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links or the like, also may be considered as media bearing the software. As used herein, unless restricted to non-transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.

[0230] Hence, a machine readable medium, such as computer-executable code, may take many forms, including but not limited to, a tangible storage medium, a carrier wave medium orphysical transmission medium. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as may be used to implement the databases, etc. shown in the drawings. Volatile storage media include dynamic memory, such as main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and / or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[0231] The computer system 801 can include or be in communication with an electronic display 835 that comprises a user interface (UI) 840 for providing, for example an editing platform for editing XR content, and / or displaying images or videos used to simulate an augmented or virtual reality experience to the user. Examples of UI’s include, without limitation, a graphical user interface (GUI) and web-based user interface.

[0232] Methods and systems of the present disclosure can be implemented by way of one or more algorithms. An algorithm can be implemented by way of software upon execution by the central processing unit 805.

[0233] While preferred embodiments of the present disclosure have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the disclosure be limited by the specific examples provided within the specification. While the disclosure has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments herein are not meant to be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the disclosure. Furthermore, it shall be understood that all aspects of the disclosure are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of thedisclosure described herein may be employed in practicing the disclosure. It is therefore contemplated that the disclosure shall also cover any such alternatives, modifications, variations, or equivalents. It is intended that the following claims define the scope of the disclosure and that methods and structures within the scope of these claims and their equivalents be covered thereby.

[0234] Whenever the term “at least,” “greater than,” or “greater than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “at least,” “greater than” or “greater than or equal to” applies to each of the numerical values in that series of numerical values. For example, greater than or equal to 1, 2, or 3 is equivalent to greater than or equal to 1, greater than or equal to 2, or greater than or equal to 3.

[0235] Whenever the term “no more than,” “less than,” or “less than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “no more than,” “less than,” or “less than or equal to” applies to each of the numerical values in that series of numerical values. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1.

[0236] The term "virtual reality environment, " as used herein, generally refers to a simulated digital space that allows users to interact with and experience computer-generated surroundings as though they were real. Virtual reality environments may also include augmented reality (AR) environments, where digital content is overlaid on the real world. These environments may utilize a variety of devices, such as, for example, VR headsets, AR glasses, hand-held controllers, motion trackers, gloves with haptic feedback, full-body suits, spatial audio systems, holographic displays, and brain-computer interfaces (BCIs). In some cases, virtual and augmented reality technologies may be integrated into everyday devices, such as smartphones, cars, and televisions, allowing users to experience immersive environments through heads-up displays in vehicles, AR features in mobile devices, or holographic interfaces on smart TVs A given virtual reality or augmented reality environment may use one or a combination of these devices to create an immersive and interactive experience for the user.

[0237] The term "neurodevelopmental disorders," as used herein, generally refers to a group of conditions that affect the development and functioning of the brain and nervous system, leading to impairments in areas such as cognition, behavior, communication, and motor skills.Neurodevelopmental disorders may include, for example, Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), Specific Learning Disorders, Intellectual Disability (ID), Communication Disorders, Motor Disorders, Global Developmental Delay, and Other Specified and Unspecified Neurodevelopmental Disorders These conditions may manifest during early developmental stages and can impact a person’s ability to function in varioussettings, including home, school, and work environments. Treatment and intervention strategies may vary, including behavioral therapy, educational support, and, in some cases, medical treatment.

[0238] The term “personalized lesson,” as used herein, generally refers to an instructional module or unit of content that is customized to meet the specific needs, abilities, preferences, and learning objectives of an individual user. A personalized lesson may be tailored based on userspecific data, such as the user’s age, diagnosis, cognitive abilities, communication level (verbal or non-verbal), co-morbid conditions, emotional state, and real-time performance feedback. In some embodiments, the personalized lesson may be delivered in a virtual reality or augmented reality environment and may include customized activities, tasks, prompts, or simulations configured to address particular skills, behaviors, or therapeutic goals of the user. The content, difficulty, and pacing of the lesson may be dynamically adjusted by an artificial intelligence module based on the user’s interactions, biometric data, or other feedback to optimize learning outcomes.

[0239] The term “user data,” as used herein, generally refers to any information or dataset specific to an individual user that can be used to customize or tailor the system's functionality or response to meet that user’s unique needs. User data may include demographic information such as age and gender, diagnosis, cognitive abilities, level of verbal or non-verbal communication, co-morbid conditions, emotional state, biometric feedback such as heart rate, eye gaze, facial expressions, or gestures, responses to questionnaires, performance metrics, and learning goals set by a coach or instructor. In some embodiments, user data may be continuously collected and processed in real time to guide the adaptation and refinement of personalized lesson plans or virtual reality experiences based on the user’s interactions and progress within the system.

[0240] The term “wearable device,” as used herein, generally refers to any electronic or digital device that can be worn on the body and is configured to interact with or monitor the user in real time. A wearable device may include, for example, virtual reality (VR) or augmented reality (AR) headsets, smart glasses, wristbands, gloves, or full-body suits. These devices may be equipped with sensors, displays, cameras, microphones, or biometric monitors that collect data such as movement, eye tracking, heart rate, or other physiological signals. In some embodiments, a wearable device may be configured to provide immersive experiences, such as displaying virtual or augmented environments, and may be used to track the user’s interactions within these environments to support personalized learning or therapeutic goals.

[0241] The term "diagnostic and monitoring module," as used herein, generally refers to a system or component configured to assess, analyze, and track user-specific data to diagnose andmonitor the presence and / or progression of one or more conditions related to neurodevel opmental disorders. This module may utilize artificial intelligence, machine learning algorithms, or other computational techniques to interpret a wide range of user-specific data, including biometric data (such as heart rate, eye movements, voice tone, and facial expressions), performance metrics, and behavioral data collected during interactions within a virtual or augmented reality environment. The diagnostic and monitoring module may determine the existence of a neurodevelopmental disorder, such as Autism Spectrum Disorder (ASD) or Attention Deficit Hyperactivity Disorder (ADHD), and assess the severity or progression of these conditions over time. In some embodiments, the module may provide real-time assessments of a user's cognitive, emotional, or behavioral state and generate reports or feedback for clinicians, caregivers, or coaches to support individualized therapeutic interventions.

[0242] The term "virtualizing an environment," as used herein, generally refers to the process of creating or simulating a digital representation of a real or imagined environment within a virtual reality (VR) or augmented reality (AR) system. This process involves generating an interactive, immersive digital space that replicates or enhances real -world settings, objects, and experiences, which may be tailored to the specific needs of a user, such as an individual with a neurodevelopmental disorder. Virtualizing an environment may involve the use of a wearable device, such as a VR headset or AR glasses, which allows the user to enter and interact with the virtual environment.

Claims

CLAIMSWHAT IS CLAIMED IS:

1. A system for providing personalized learning support to users with one or more neurodevelopmental disorders, comprising:(a) a wearable device configured to provide a user with a virtual reality environment, wherein said user has or is suspected of having a neurodevelopmental disorder;(b) an artificial intelligence module configured to receive user data, wherein said user data is user-specific, and generate a personalized lesson plan for said user; wherein said personalized lesson plan comprises one or more virtual reality lessons configured to be displayed on said wearable device.

2. The system of claim 1, wherein said one or more neurodevelopmental disorders comprises one or more of Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), Specific Learning Disorders, Intellectual Disability (ID), Communication Disorders, Motor Disorders,, Global Developmental Delay, and Other Specified and / or Unspecified Neurodevelopmental Disorders.

3. The system of claim 1, wherein said one or more neurodevelopmental disorders comprises at least one neurodevelopmental disorder4. The system of any one of the preceding claims, wherein said artificial intelligence module is further configured to accumulate feedback on user learning experience and outcomes, and utilize said feedback for continued refinement and updating of said personalized lesson plan.

5. The system of any one of the preceding claims, further comprising a biometric feedback module configured to gather biometric data from said user.

6. The system of claim 5, wherein said biometric data comprises one or more of eye gaze, voice tone, heart rate, pupillary response, voice analysis, facial expressions, hand movements, gestures, and sentiment analysis.

7. The system of claim 5 or 6, wherein said biometric feedback module is configured to deduce said user’s engagement level and emotional state based at least in part on said biometric data.

8. The system of claim 7, wherein said artificial intelligence module is further configured to receive and interpret said biometric data from said biometric feedback module.

9. The system of claim 8, wherein said artificial intelligence module is further configured to utilize said biometric data for continued refinement and updating of said personalized lesson plan.

10. The system of any one of the preceding claims, wherein said artificial intelligence module comprises a learning language model, wherein said learning language model functions as a recommendation engine to guide the user through said personalized lesson plan.

11. The system of claim 1, wherein said user data utilized by the artificial intelligence module for generating the personalized lesson plan comprises one or more of the following: user's age, diagnosis, level of verbal or non-verbal communication, any co-morbid conditions, responses to a user questionnaire, goals set by a coach, and the current emotional state of the user.

12. The system of any one of the preceding claims, wherein said artificial intelligence module uses lookalike data or comparison based on previous user experiences for continued refinement and updating of said personalized lesson plan.

13. The system of claim 12, wherein said artificial intelligence module utilizes a rating system, wherein said rating system evaluates or scores one or more aspects of the user's experiences or performance within the virtual reality environment.

14. The system of claim 13, wherein said artificial intelligence module uses the evaluations or scores from said rating system to dynamically adapt the virtual reality lessons in real-time, adjusting lesson tasks, stimuli, challenges, or strategies based on the user's performance and responses.

15. The system of claim 14, wherein the continuous refinement and updating of the personalized lesson plan are implemented to enhance the user's skill acquisition neurodevelopmental, specifically catering to the needs of individuals with neurodevelopmental disorders.

16. The system of claim 14, wherein said rating system integrates with a recommendation engine within said artificial intelligence module, optimizing the selection and sequencing of virtual reality lessons for the user based on their previous performance and progression.

17. The system of claim 14, wherein the insights garnered from identified patterns, trends, or effective strategies as determined by said rating system are made accessible to a coach or instructor, thus permitting them to provide more effective support for the user's learning process within the virtual reality environment.

18. The system of claim 14, wherein said rating system utilizes machine learning algorithms to accurately discern patterns and trends from the user's performance data, facilitating predictive modeling of the user's future progress and may comprise neurodevelopmental challenges.

19. The system of any one of the preceding claims, wherein said artificial intelligence module is capable of tracking the progression of lessons, in terms of complexity, to adjust the lesson plan in real-time.

20. The system of claim 5 or 6, wherein said biometric feedback module is capable of evaluating how effectively a lesson was delivered, based on learner or instructor feedback and the gathered biometric data.

21. The system of claim 1, wherein said artificial intelligence module is further configured to generate real-time updates of the lesson plans based on user data, feedback, and biometric data.

22. A system for creating an interactive virtual environment for individuals with neurodevelopmental disorders, comprising:(a) a wearable device configured to provide a user with a virtual reality environment, wherein said user has or is suspected of having a neurodevelopmental disorder;(b) an artificial intelligence module configured to receive user data, wherein said user data is user-specific, and generate a plurality of user-specific parameters, (c) an animation module configured to receive said plurality of user-specific parameters and generate personalized animated characters for said virtual reality environment based at least in part on said plurality of user-specific parameters.

23. The system of claim 22, wherein said wearable device comprises a microphone or similar audio capture device to collect said user's audio data during a user virtual reality session.

24. The system of claim 23, wherein said user's audio data collected by said wearable device is sent to an Application Programming Interface (API) for processing, wherein said API is configured to transform said audio data into textual format.

25. The system of claim 24, wherein said processed audio data from said API is returned to said artificial intelligence module.

26. The system of claim 24, further comprising a conversation module interfacing with said artificial intelligence module, wherein said conversation module provides real-time response generation based at least in part on said user's audio data, gestural data, head tracking information, and eye tracking data to enrich the contextual accuracy of the responses.

27. The system of claim 26, wherein said conversation module provides real-time response generation based at least in part on said plurality of user-specific parameters.

28. The system of claim 26 or 27, wherein said conversation module utilizes artificial intelligence to generate human-like speech for said personalized animated characters in said virtual reality environment.

29. The system of any one of claims 26 to 28, wherein said conversation module is configured to facilitate unscripted conversations.

30. The system of any one of claims 26 to 29, wherein said conversation module is configured to simulate a plurality of conversational scenarios.

31. The system of claim 30, wherein said plurality of conversational scenarios may comprise one or more of a job interview preparation, a social interaction at a park, a visit to a grocery store, a classroom scenario, a sports game, a family gathering, a doctor's appointment, navigating a busy street, or an emergency situation like a fire alarm.

32. The system of claim 30, wherein said conversation module is configured to monitor one or more neurodevelopmental skills of said user.

33. The system of claim 30, wherein said one or more neurodevelopmental skills of said user may comprise one or more of understanding and using language for social purposes, recognizing, and expressing emotions, initiating, and maintaining conversations, understanding social cues, adjusting speech style or content according to the social context, and developing empathy.

34. The system of any one of claims 22-33, further comprising an intervention module, wherein said intervention module comprises one or more components configured to direct or pause the conversational module.

35. The system of claims 34, wherein said intervention module is controllable by a coach.

36. The system of claim 35, wherein said coach comprises one or more of a therapist, parent, teacher, caregiver, medical professional, or any other individual involved in the user's therapy or care.

37. The system of claim 36, wherein said intervention module is configured to permit said coach to choose conversation topics that are tailored to said user.

38. The system of any one of claims 24-37, wherein said conversational module further comprises a group learning module configured to facilitate interaction between said user and one or more additional users.

39. The system of claim 38, wherein said group learning module is configured to identify and flag disruptive participants in a group.

40. The system of claim 39, wherein said group learning support module is further configured to carry out actions such as removing, warning, correcting disruptive users during group lessons, and / or blocking user communications if necessary.

41. The system of any one of claims 22-40, further comprises a voice modulation component, wherein said voice modulation is configured to modulate a voice of said animated character, said user, said coach, and / or said additional users.

42. The system of claim 41, wherein said voice modulation comprises a modulation of one or more of pitch, tone, speed, accent, language, emotional context, and verbal cues.

43. The system of any one of claims 22-42, wherein said artificial intelligence module is further configured to provide real-time updates and modifications to said lesson plan based at least in part on feedback and / or performance metrics of said user.

44. A system for virtualizing an environment configured to treat a neurodevelopmental disorder, comprising:(a) a wearable device configured to provide a user with a virtual reality environment, wherein said user has or is suspected of having a neurodevelopmental disorder;(b) an artificial intelligence module configured to receive user data, wherein said user data is user-specific, and generate a plurality of user-specific parameters; and (c) an animation module configured to receive said plurality of user-specific parameters and(i) utilizing a generation module, generate a virtual reality environments based at least in part on said plurality of user-specific parameters, and / or(ii) utilizing a modification module, modify a virtual reality environments based at least in part on said plurality of user-specific parameters.

45. The system of claim 44, wherein said plurality of user-specific parameters comprises one or more real-life scenarios.

46. The system of claim 45, wherein said real-life scenario comprises one or more active shooter drills or independent living situations.

47. The system of any one of claims 44 to 46, wherein said plurality of user-specific parameters comprises real-world video footage.

48. The system of claim 47, wherein said real -world video footage is captured in real-time or near real-time.

49. The system of any one of claims 47 to 48, wherein said generation comprises converting said real-world video footage into semi and / or fully animated virtual reality environments.

50. The system of claim 49, wherein said semi and / or fully animated virtual reality environments comprises a hybrid virtual reality environment, wherein said hybrid virtual reality environment comprises one or more real-world elements.

51. The system of claim 50, wherein said one or more real-world elements comprises one or more of buildings, nature landscapes, furniture, vehicles, familiar figures or objects, or any other elements present in the user's real-life environment.

52. The system of any one of claims 44-51, further comprising a scanner configured to scan one or more images.

53. The system of claim 52, wherein said plurality of parameters comprises said one or more scanned images.

54. The system of claim 53, wherein said one or more scanned images comprises one or more entities from said user's life.

55. The system of claim 54, wherein said entity comprises one or more persons.

56. The system of claim 55, wherein said generation module is configured to generate one or more animated characters resembling said one or more persons.

57. The system of claim 55, wherein said plurality of parameters comprises one or more characteristics of said one or more persons.

58. The system of claim 56, wherein said modification module is configured to modify said animated character based at least in part on said one or more characteristics.

59. The system of claim 58, wherein said one or more characteristics comprises one or more of physical appearance, voice properties, mannerisms, habitual behaviors, or any other identifiable traits associated with said one or more persons.

60. The system of claim 52, wherein said modification is of one or more of said animated character’s physical features, voice features, and personality controls.

61. A system for deriving valuable data through analytics and machine learning to support individuals with neurodevelopmental disorders, comprising:(a) a wearable device configured to provide a user with a virtual reality environment, wherein said user has or is suspected of having a neurodevelopmental disorder; and (b) an artificial intelligence module configured to process user interaction data within said virtual reality environment and generate automated analytics, wherein said automated analytics comprise at least in part an assessment of one or more conditions associated with said neurodevelopmental disorder of said user.

62. The system of claim 61, wherein said one or more neurodevelopmental disorders neurodevelopmental of said user comprises one or more of Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), Specific Learning Disorders, Intellectual Disability (ID), Communication Disorders, Pervasive Developmental Disorder, Global Developmental Delay, and Other Specified and Unspecified Neurodevelopmental Disorders.

63. The system of claim 61, wherein said one or more neurodevelopmental disorders of said user comprises one or more of difficulties in social interaction, difficulties with non-verbalcommuni cation, difficulties understanding and using language for social purposes, repetitive patterns of behavior or interests, hyperactivity, impulsivity, attention difficulties, impairments in personal, academic, or occupational functioning, and any other behavioral characteristics typically associated with neurodevelopmental disorders.

64. The system of any one of claims 61 to 63, wherein said assessment is indicative that user has improved in on one or more of said neurodevelopmental conditions.

65. The system of any one of claims 61 to 64, wherein said assessment is indicative that user has declined in on one or more of said neurodevelopmental conditions.

66. The system of claim 65, wherein said assessment module is configured to identify one or more causative factors of said decline and assign a probability to said causative factor.

67. The system of any one of claims 61 to 66, wherein said automated analytics comprises at least in part an assessment of user-specific behavior in said virtual reality environment.

68. The system of claim 67, wherein said user's behavior comprises one or more of an emotional state, task completion status, and need for prompting within said virtual reality environment.

69. The system of any one of claims 61 to 68, further comprising a voice recorder module, wherein said voice recorder module is configured to receive audio data from said user and record and / or transcribe said audio data.

70. The system of claim 69, wherein said automated analytics are generated at least in part on said audio data.

71. The system of claim 70, wherein said automated analytics are assessed at least in part utilizing machine learning algorithms.

72. The system of any one of claims 61 to 71, further comprising a modification module configured to modify said virtual reality environment based at least in part on said automated analytics.

73. A system configured for neurodevelopmental diagnosis and monitoring, comprising:(a) a wearable device configured to provide a user with a virtual reality environment, wherein said user has or is suspected of having a neurodevelopmental disorder;(b) a diagnostic and monitoring module configured to utilize at least in part artificial intelligence to receive and interpret user-specific data, and determine a presence and / or severity of one or more conditions associated with said neurodevelopmental disorder of said user.

74. The system of claim 73, wherein said user-specific data comprises at least in part user interaction data, and / or performance metrics and / or biometric data obtained from the user during the interaction with the virtual reality environment.

75. The system of any one of claims 73-74, wherein said user-specific data comprises phenotypic data, wherein said phenotypic data comprises one or more of genetic data related to the user's observable traits and biometric data obtained from the user during the interaction with the virtual reality environment.

76. The system of claim 75, wherein said diagnosis assessment module utilizes said phenotypic data to identify phenotypes of sub categories.

77. The system of claim 76, wherein said subcategories comprise Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), Specific Learning Disorders, Intellectual Disability (ID), Communication Disorders, Motor Disorders, and Global Developmental Delay, and Other Specified and Unspecified Neurodevelopmental Disorders.

78. The system of claim 73 to 77, further comprising one or more biometric components configured to collect biometric data, wherein said biometric data comprises user-specific data.

79. The system of claim 78, wherein said biometric data comprises one or more of user eye movements, voice, speech, facial expressions, and pupillary reach on / dilati on.

80. The system of claim 78 or 79, wherein said biometric data comprises one or more of user heart rate and positional data module.

81. The system of any one of claims 73-80, further comprising an assessment module in communication with said diagnosis assessment module, configured to compare current and previous determinations of the presence and / or severity of said one or more neurodevelopmental conditions.

82. The system of claim 81, wherein said assessment module, based on said comparison, is further configured to generate and / or modify one or more personalized lessons for said user within the virtual reality environment.

83. The system of claim 82, wherein said assessment module identifies progression in said user's skill acquisition based on the comparison of current and previous determinations, and adjusts said one or more personalized lessons to build upon and further enhance said progression.

84. The system of any one of claims 73-83, further comprising an anomaly detection module interfacing with said artificial intelligence module, said anomaly detection module configured to spot anomalies in the user's progress or interaction with the system.

85. The system of any one of claims 73-84, wherein said user-specific parameters may comprise one or more of the user's interests, physical appearance, preferred genre, significant people in their life, preferred role models or animated characters, favorite activities, cultural background, language proficiency, educational level, cognitive abilities, sensory preferences,emotional state, and any other personal attributes or preferences that may influence the user's engagement and learning outcomes in the virtual reality environment.

86. The system of claim 85, wherein said user-specific parameters may be virtualized within the virtual reality environment based on a user's request or when the system's artificial intelligence module assesses that incorporating these user-specific parameters into the virtual reality environment is beneficial for the user's treatment, therapy, or learning process.

87. The system of any one of claims 73-86, wherein the artificial intelligence (Al) assesses and limits lessons of interest based on user-specific parameters such as patient age, diagnosis, verbal or non-verbal level, co-morbid conditions, a learner questionnaire, and the goals of the user.

88. The system of claim 87, wherein the Al uses a Lookalike Model (LLM) as a recommendation engine for predicting the progression of lessons and generating lesson plans that can be updated in real-time based on the user's performance, feedback, and biometric data such as eye gaze, voice, heart rate, expression, and sentiment analysis.

89. The system of any one of claims 73-88, wherein the virtual reality environment includes AI- powered characters that interact with the user in a less scripted manner and provide a conversational partner for the user, with the ability for the coach to stop or redirect the Al, as necessary.

90. The system of claim 89, wherein the Al-powered characters are used in group lessons, with the Al capable of flagging "bad actors" in the group, and accommodating all learners in a room.

91. The system of any one of claims 73-90, wherein the Al generates 3D content or animations for the virtual reality environment, transforming real-world video into fully animated VR environments or modifying the physical and voice features of characters within the environment.

92. The system of claim 91, wherein the Al utilizes analytics and machine learning to create valuable data for diagnosis or assessment, identifying phenotypes of subcategories based on one or more of user data, tracking eye movement, voice, speech, facial expressions, pupillary response, heart rate data, positional data, gesture recognition, blink patterns, gait analysis, user interactions within the VR environment, haptic responses, physiological responses, emotional responses inferred from biometric data, response times, completion rates of tasks within the VR environment, head movement patterns, and other behavioral and physiological data as gathered during the user's interaction with the virtual reality environment.

93. The system of claim 92, wherein the Al is used for summarizing notes or session minutes, spotting anomalies in the data, and determining if the user has met predetermined goals or renewals, informing healthcare or insurance decisions.

Images