Adaptive sensory environment facilitating psychoactive therapies

Abstract

An augmented sensory environment (ASE) system includes a control system, a set of biosensors, and a virtual / augmented-reality (XR) human interface system. The ASE system, when operatively deployed, is interfaced with a subject via the biosensors and the XR human interface system. The ASE system may present an XR-based program to the subject that provides a virtual environment and sensory stimulation to the subject to modulate an emotional state of the subject. The system may monitor a plurality of physiologic measurements of the subject via the set of biosensors, process the physiologic measurements to infer an emotional state of the subject, apply predefined criteria to the inferred emotional state of the subject to determine if the subject is in a target emotional state, and adjust the XR-based program to further modulate the emotional state of the subject towards the target emotional state.

Description

[0001] ADAPTIVE SENSORY ENVIRONMENT

[0002] FACILITATING PSYCHOACTIVE THERAPIES

[0003] Priority Application

[0004] This Application claims the benefit of U.S. Provisional Application Serial No. 63 / 509,197 filed June 20, 2023, the disclosure of which, including the Appendix, is incorporated by reference herein.

[0005] Technical Field

[0006] The present subject matter relates generally to systems and algorithms for employing virtual- or augmented-reality environments as diagnostic or therapeutic instruments, which may be used in conjunction with psychoactive-pharmacologic therapies.

[0007] Background

[0008] Drug-assisted therapies involving the controlled use of psychoactive substances have gained significant attention in recent years as potential breakthrough treatments for a variety of mental health conditions. Substances such as ketamine, MDMA, psilocybin, ibogaine, ayahuasca, DMT, LSD, and others have shown promising results in clinical research settings. However, it is crucial to approach these therapies with caution, ensuring careful patient preparation, monitoring, and addressing the challenges associated with their use.

[0009] Ketamine, initially used as an anesthetic, has shown promising results in treating treatment-resistant depression (TRD) and acute suicidal ideation. Its rapid onset of action and potential to enhance neuroplasticity make it a compelling candidate for addressing severe depression. However, its dissociative effects and potential for abuse necessitate careful patient selection and controlled administration.

[0010] 3, 4-Methylenedi oxymethamphetamine (MDMA), commonly known as ecstasy, has demonstrated potential as an adjunct to psychotherapy for post-traumatic stress disorder (PTSD). By promoting empathy, trust, and emotional openness, MDMA-assisted therapy can help individuals process traumatic memories. Nonetheless, concerns regarding neurotoxicity and long-term safety need to be addressed. Found in "magic mushrooms," psilocybin has shown promise in treating depression, anxiety, and addiction. It acts on serotonin receptors in the brain, leading to altered states of consciousness and facilitating profound introspection. Well-designed studies with psilocybin have reported significant therapeutic benefits, but potential risks, such as triggering psychotic episodes, must be carefully managed.

[0011] Derived from the iboga plant, ibogaine has gained attention for its potential to treat substance use disorders, particularly opioid addiction. It has been observed to interrupt withdrawal symptoms and reduce cravings. However, its cardiovascular risks and potential for hallucinations necessitate careful monitoring and medical supervision during its administration.

[0012] Ayahuasca, a brew containing DMT (N,N-Dimethyltryptamine), has been used traditionally by indigenous communities for healing and spiritual purposes. Research indicates its potential in treating depression, addiction, and trauma. However, the complexity of the brew, potential adverse effects, and the need for an experienced facilitator present significant challenges.

[0013] Lysergic acid diethylamide (LSD) is a potent hallucinogen that has shown potential in alleviating anxiety in patients with life-threatening illnesses. Its ability to induce mystical experiences and enhance emotional insight can aid in psychotherapy. Yet, concerns about the potential for psychotic episodes and its long-term effects have limited its therapeutic application.

[0014] While these substances hold therapeutic potential, they also carry risks of potential adverse reactions, exacerbation of pre-existing conditions, and the potential for abuse. Practical and effective solutions are needed to facilitate careful selection of patients, and preparation, monitoring, and management of the patients before, during, and after treatment. The successful implementation of drug-assisted therapies relies on thorough patient preparation and meticulous monitoring throughout the treatment process. Rigorous screening is crucial to identify suitable candidates and exclude individuals with contraindications or increased vulnerability to adverse effects. Creating a supportive and safe environment is paramount to ensure positive treatment outcomes. The therapy sessions should take place in controlled settings with trained professionals to provide psychological support and guide the experience. The integration phase, following the psychedelic experience, is essential for patients to make sense of their insights and experiences. Professional support and appropriate aftercare plans are necessary to maximize the therapeutic potential and prevent potential harm.

[0015] Traditionally, skilled and experienced therapists or practitioners have performed the screening, patient preparation, and patient management tasks. This basic approach has some notable shortcomings. For one, the job of the skilled practitioner can be challenging and time-intensive. As a practical matter, this limits the scalability of such therapies as there are a limited number of qualified practitioners. In addition, the practitioner’s time required for each patient is substantial, meaning that the cost of therapy is commensurately high. The challenge and intensity of the tasks performed by the practitioners, including assessing patient receptiveness and responsiveness to administered psychoactive therapies through conventional observation, coupled with the large variability among patients, results in largely subjective assessments, which can be expected to vary considerably from one practitioner to another, and even among a common practitioner as that practitioner naturally experiences variability in their energy level, mood, etc.

[0016] Furthermore, patients may have different emotional experiences in connection with psychoactive therapies in different settings. Clinical settings differ considerably from one another, and even more so from each patient’s familiar surroundings such as their homes.

[0017] Summary of the Disclosure

[0018] In some aspects, the techniques described herein relate to an adaptive sensory environment (ASE) system, including: a control system; a set of biosensors operatively coupled to the control system; and a virtual / augmented-reality (XR) human interface system operatively coupled to the control system; wherein the ASE system, when operatively deployed, is interfaced with a subject via the biosensors and the XR human interface system, and the control system is operative to cause the ASE system to: present an XR-based program to the subject via the XR human interface system, the XR-based program providing a virtual environment and sensory stimulation to the subject to modulate an emotional state of the subject; monitor a plurality of physiologic measurements of the subject via the set of biosensors; process the physiologic measurements to infer an emotional state of the subject; apply predefined criteria to the inferred emotional state of the subject to determine if the subject is in a target emotional state; and in response to a determination that the subject is not in the target emotional state, adjust the XR-based program to further modulate the emotional state of the subject towards the target emotional state.

[0019] In some aspects, the techniques described herein relate to a ASE system, wherein the control system is further operative to cause the ASE system to recommend medicinal dosing of the subject based on the inferred emotional state of the subject corresponding to the target emotional state.

[0020] In some aspects, the techniques described herein relate to a ASE system, wherein adjustment of the XR-based program to further modulate the emotional state of the subject by the control system includes presenting an interactive experience via the XR human interface system to the subject that reduce stress of the subject.

[0021] In some aspects, the techniques described herein relate to a ASE system, wherein adjustment of the XR-based program to further modulate the emotional state of the subject by the control system includes presenting an interactive experience via the XR human interface system to the subject that increase sensory stimulation of the subject to expose a defined vulnerability of the subject.

[0022] In some aspects, the techniques described herein relate to a ASE system, wherein the control system is further operative to initiate machine-learning based assessment of the emotional state of the subject based on prior-gathered data of the subject.

[0023] In some aspects, the techniques described herein relate to a ASE system, further including a user interface device operatively coupled with the control system, and operative to receive input from a clinician, the input from the clinician including observation of the subject during execution of the XR-based program, and wherein the control system is further operative to incorporate the observation of the subject in processing of the physiologic measurements to infer the emotional state of the subject.

[0024] In some aspects, the techniques described herein relate to a ASE system, wherein the control system is further operative to process the physiologic measurements to infer an emotional state of the subject based on a patient-specific machine-learning -based model.

[0025] In some aspects, the techniques described herein relate to a ASE system, wherein the control system is further operative to generate an assessment of whether the subject qualifies for psychoactive therapy according to a plurality of predefined factors indicative of human psychological traits.

[0026] In some aspects, the techniques described herein relate to a ASE system, wherein the control system is further operative to track gathered data of emotional state of the subject over a course of XR-based sessions, and to determine assessments of emotional state of the subject in later ones of the sessions based on baseline assessments of the subject from earlier ones of the sessions.

[0027] In some aspects, the techniques described herein relate to a method for performing a psychological assessment of a subject using an adaptive sensory environment (ASE) system, the method including: interfacing the ASE system with a human subject via a set of biosensors and a virtual / augmented-reality (XR) human interface system; presenting an XR-based program to the subject via the XR human interface system, the XR-based program providing a virtual environment and sensory stimulation to the subject to modulate an emotional state of the subject; autonomously monitoring a plurality of physiologic measurements of the subject via the set of biosensors; processing the physiologic measurements to infer an emotional state of the subject; applying predefined criteria to the inferred emotional state of the subject to determine if the subject is in a target emotional state; in response to a determination that the subject is not in the target emotional state, adjusting the XR-based program to further modulate the emotional state of the subject towards the target emotional state; and generating an assessment of the subject based on a response of the subject to the adjusting of the XR-based program.

[0028] In some aspects, the techniques described herein relate to a method, further including: executing, by the control system, a set of operational phases, including: screening to monitor biometric indicia of the subject to ascertain qualification of the subject to psychoactive medicinal therapy; in response to a result of the screening, preparation of the subject for dosing of the medicinal therapy based on XR-based training of the subject via the XR-based program of the ASE system; following the preparation of the subject, predosing assessment and modulation of an emotional state of the subject via the ASE system; and following the pre-dosing assessment and modulation, providing ASE-system-based acute dosing support of the subject, including determining if the emotional state of the subject during administration of the medicinal therapy, and modulating the emotional state of the subject via the XR-based program to facilitate a therapeutic effect of the medicinal therapy.

[0029] In some aspects, the techniques described herein relate to at least one non-transitory machine-readable storage medium including instructions that, when executed by an adaptive sensory environment (ASE) system that is interfaced with a subject via a set of biosensors and a virtual / augmented-reality (XR) human interface system, cause the ASE system to: present an XR-based program to the subject via the XR human interface system, the XR-based program providing a virtual environment and sensory stimulation to the subject to modulate an emotional state of the subject; monitor a plurality of physiologic measurements of the subject via the set of biosensors; process the physiologic measurements to infer an emotional state of the subject; apply predefined criteria to the inferred emotional state of the subject to determine if the subject is in a target emotional state; and in response to a determination that the subject is not in the target emotional state, adjust the XR-based program to further modulate the emotional state of the subject towards the target emotional state.

[0030] In some aspects, the techniques described herein relate to an at least one machine- readable storage medium, wherein the instructions further cause the ASE system to recommend medicinal dosing of the subject based on the inferred emotional state of the subject corresponding to the target emotional state.

[0031] In some aspects, the techniques described herein relate to an at least one machine- readable storage medium, wherein the instructions cause the ASE system to adjust adjustment the XR-based program to further modulate the emotional state of the subject by presenting an interactive experience via the XR human interface system to the subject that reduce stress of the subject.

[0032] In some aspects, the techniques described herein relate to an at least one machine- readable storage medium, wherein the instructions cause the ASE system to adjust of the XR-based program to further modulate the emotional state of the subject by causing the ASE system to present an interactive experience via the XR human interface system to the subject that increase sensory stimulation of the subject to expose a defined vulnerability of the subject.

[0033] In some aspects, the techniques described herein relate to an at least one machine- readable storage medium, wherein the instructions cause the ASE system to initiate machine-learning based assessment of the emotional state of the subject based on prior- gathered data of the subject.

[0034] In some aspects, the techniques described herein relate to an at least one machine- readable storage medium, wherein the ASE system further includes a user interface device operatively coupled with the control system, wherein the instructions cause the ASE system to receive input from a clinician, the input from the clinician including observation of the subject during execution of the XR-based program, and wherein the control system is further operative to incorporate the observation of the subject in processing of the physiologic measurements to infer the emotional state of the subject.

[0035] In some aspects, the techniques described herein relate to an at least one machine- readable storage medium, wherein the instructions cause the ASE system to process the physiologic measurements to infer an emotional state of the subject based on a patientspecific machine-learning-based model.

[0036] In some aspects, the techniques described herein relate to an at least one machine- readable storage medium, wherein the instructions cause the ASE system to generate an assessment of whether the subject qualifies for psychoactive therapy according to a plurality of predefined factors indicative of human psychological traits.

[0037] In some aspects, the techniques described herein relate to an at least one machine- readable storage medium, wherein the instructions cause the ASE system to track gathered data of emotional state of the subject over a course of XR-based sessions, and to determine assessments of emotional state of the subject in later ones of the sessions based on baseline assessments of the subject from earlier ones of the sessions.

[0038] In some aspects, the techniques described herein relate to an at least one machine- readable storage medium, wherein the instructions cause the ASE system to execute, by the control system, a set of operational phases, including: screening to monitor biometric indicia of the subject to ascertain qualification of the subject to psychoactive medicinal therapy; in response to a result of the screening, preparation of the subject for dosing of the medicinal therapy based on XR-based training of the subject via the XR-based program of the ASE system; following the preparation of the subject, pre-dosing assessment and modulation of an emotional state of the subject via the ASE system; and following the predosing assessment and modulation, providing ASE-system-based acute dosing support of the subject, including determining if the emotional state of the subject during administration of the medicinal therapy, and modulating the emotional state of the subject via the XR- based program to facilitate a therapeutic effect of the medicinal therapy.

[0039] Brief Description of the Drawings

[0040] The invention may be more completely understood in consideration of the following detailed description of various embodiments of the invention in connection with the accompanying drawings, in which: FIG. l is a diagram illustrating an exemplary hardware and software architecture of a computer system on which aspects of the embodiments described above may be implemented, and in which various interfaces between hardware components and software components are shown according to an example;

[0041] FIG. 2 is a high-level diagram of ASE system for facilitating an adaptive sensory environment for a subject according to various aspects;

[0042] FIG. 3 is a simplified example illustrating virtual environment which may be implemented by an ASE control system as one of a plurality of virtual environments designed to provide a pleasant or soothing experience for subjects;

[0043] FIG. 4 is a diagram illustrating an extra-pharmacological (EP) model of drug action;

[0044] FIG. 5 is a diagram illustrating phases of psychedelic therapy that are facilitated by an ASE system according to various embodiments;

[0045] FIG. 6 is a flow diagram illustrating a process executable by an ASE system to implement a screening phase to screen for potential treatment deterrents according to an example embodiment according to an example;

[0046] FIG. 7 is a flow diagram illustrating a process executable by an ASE system to implement a preparation phase for initial XR-based assessment of preparedness according to an example;

[0047] FIG. 8 is a flow diagram illustrating a process executable by an ASE system to implement a pre-dosing phase to briefly assess subject preparedness just prior to administration of dosing according to an example;

[0048] FIG. 9 is a flow diagram illustrating a process executable by an ASE system to implement acute a dosing phase to establish and maintain the subject in a desired state(s) while the dosing is administered according to an example;

[0049] FIG. 10 is a decision tree diagram illustrating an example set of assessed subject states and recommended responses according to an example implementation;

[0050] FIG. 11 is a flow diagram illustrating a process executable by an ASE system to implement a post-dosing phase to assess the subject's state immediately following administration of dosing according to an example; and

[0051] FIG. 12 is a flow diagram illustrating a process executable by an ASE system to implement an integration phase according to an example. Description

[0052] Aspects of this disclosure are directed to an adaptive sensory environment (ASE) that is realized using a combination of human-interface technologies that measure the state of the user, patient, or subject (these terms are used interchangeably), and modulate the subject’s senses to achieve a target effect on the person’s psychological or emotional state. In some aspects, the sensing and modulation are automated according to an algorithm which is externally configurable or internally self-adjusting to assist the patient and caregiver in bringing the patient to a desired condition.

[0053] Sensing may be achieved using a plurality of biosensors including, without limitation, sensors suitable for measuring heart rate, inter-beat interval (IBI), heart rate variability (HRV), respiration (RSP, audio), blood oxygenation, blood pressure, eye movement (saccades, dilation), perspiration (EDA), temperature (SKT), facial muscle tension, electrocardiography, (ECG), electroencephalography (EEG), motor signaling, movement, digestive activity, voice tone, text semantics, and the like.

[0054] In the present context, sensory modulation or stimulation (which are used interchangeably herein) include sensory excitation or sensory deprivation. Sensory modulation may be achieved using visual and auditory stimulation, such as with a subjectwearable virtual-reality (XR), augmented-reality (AR), or variable / combined system (XR) (these terms are used interchangeably herein). In addition, aromatherapy, thermal, touch, or electrostimulation may be employed in combination with XR systems to further enhance the desired effect on the subject.

[0055] The sensory modulation according to aspects of this disclosure achieves an adaptive sensory environment in which certain imagery, sound, and other sensory stimulus is presented to the subject and dynamically varied as a function of time and as a function of the subject’s varying state as the subject experiences the effects of the sensory environment and, when administered, the pharmacological therapy. Based on real-time (or near-realtime, as may be used interchangeably) biometric sensing of the subject, the subject’s psychological or emotional state (or changes in that state) may be inferred according to defined assessment criteria. In response to the subject’s state, change of state, or stasis of state, the adaptive sensory environment is adjusted according to environment-variation criteria to change or maintain the subject’s state, as appropriate for the present goals to be achieved for the subject. In one example, the subject’s measured biomarkers and inferred state may indicate that the subject is experiencing increasing anxiety (e.g., increasing heart rate, facial tension, voice, perspiration, ventilation, etc.). In response to this assessment, the adaptive sensory environment may vary the imagery and other stimuli presented to the subject in a manner designed to soothe the subject. In another example, the level of excitation may be varied by the adaptive sensory environment in order to raise the arousal of the subject while monitoring the subject’s biomarkers, in order to assess the receptiveness of the subject to such stimulus. Different types of imagery may be presented as party of the variation of stimulation to determine what type is most effective to impact the subject’s responsiveness thereto. In yet another example, the adaptive sensory environment is modulated in a controlled manner to bring the subject to a target level of arousal, and maintain (or shape) that arousal level over time while the subject is administered a psychoactive drug. These are just a few examples illustrating ways in which the adaptive sensory environment may be used to steer the subject’s state, in a controlled manner.

[0056] In related embodiments, machine-learning (ML) algorithms are utilized to facilitate one or more operational aspects of the ASE system. In one type of implementation, a ML algorithm is utilized to facilitate fusion of multiple biosensors in order to infer the subj ect’ s psychological or emotional state. In another aspect, a ML system is utilized to dynamically determine the manner of variation of the stimulus presented to the subject based on the subject’s biomarker-measured responsiveness to prior types of stimulation presented via the adaptive sensory environment. Other uses of ML systems are contemplated as well. For instance, to accommodate the reality of different subjects having differences in pre-state factors (e.g., set, rapport, and intention - as discussed in greater detail herein) which affects their varying responses to psychoactive therapy, and likewise, different subjects having different sensitivities to varying dosages of the medications, subjects may be assessed according to multiple characteristics using a ML system to determine a suitable therapy protocol or treatment model.

[0057] Embodiments of the inventive subject matter may address a number of practical challenges. One such challenge relates to scalability and providing access to therapy for patients. Conventional therapy sessions are resource-intensive, requiring hours of human therapist time for patient preparation, dosing support, and integration sessions. There are insufficient therapist resources to meet expected patient demand. As another practical challenge, a substantial fraction of patients are non-respondent to psychoactive treatments. Therapist resources may be spent on failed treatments, further exhausting the limited resources which could otherwise be used to help respondent patients. Another challenge has to do with the fact that different patients respond differently to psychedelic medicines. This may be due to the patient’s prior psychedelic experience, demographics, comorbidities, and other factors. Further, a significant fraction of patients may experience relapse following initially-successful treatment, which would require another round of therapy. As explained below, aspects of the subject matter improve the patient preparation, dosing, integration, and maintenance phases of therapy.

[0058] The adaptive sensory environment may be implemented as part of a computer system. The computer system may be one physical machine, or may be distributed among multiple physical machines, such as by role or function, or by process thread in the case of a cloud computing distributed model. In various embodiments, aspects of the invention may be configured to run in virtual machines that in turn are executed on one or more physical machines. It will be understood by persons of skill in the art that features of the invention may be realized by a variety of different suitable machine implementations.

[0059] Generally, the ASE system includes various engines, each of which is constructed, programmed, configured, or otherwise adapted, to carry out a function or set of functions, as detailed below. The term engine as used herein means a tangible device, component, or arrangement of components implemented using hardware, such as by an application specific integrated circuit (ASIC) or field-programmable gate array (FPGA), for example, or as a combination of hardware and software, such as by a processor-based computing platform and a set of program instructions that transform the computing platform into a special-purpose device to implement the particular functionality. An engine may also be implemented as a combination of the two, with certain functions facilitated by hardware alone, and other functions facilitated by a combination of hardware and software.

[0060] In an example, the software may reside in executable or non-executable form on a non-transitory tangible machine-readable storage medium. Software residing in nonexecutable form may be compiled, translated, or otherwise converted to an executable form prior to, or during, runtime. In an example, the software, when executed by the underlying hardware of the engine, causes the hardware to perform the specified operations. Accordingly, an engine is physically constructed, or specifically configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a specified manner or to perform part or all of any operations described herein in connection with that engine. In examples in which engines are temporarily configured, each of the engines may be instantiated at different moments in time. For example, where the engines comprise a general-purpose hardware processor core configured using software; the general-purpose hardware processor core may be configured as respective different engines at different times. Software may accordingly configure a hardware processor core, for example, to constitute a particular engine at one instance of time and to constitute a different engine at a different instance of time.

[0061] In certain implementations, at least a portion, and in some cases, all, of an engine may be executed on the processor(s) of one or more computers that execute an operating system, system programs, and application programs, while also implementing the engine using multitasking, multithreading, distributed (e.g., cluster, peer-peer, cloud, etc.) processing where appropriate, or other such techniques. Accordingly, each engine may be realized in a variety of suitable configurations, and should generally not be limited to any particular implementation exemplified herein, unless such limitations are expressly called out.

[0062] In addition, an engine may itself be composed of more than one sub-engines, each of which may be regarded as an engine in its own right. Moreover, in the embodiments described herein, each of the various engines corresponds to a defined functionality; however, it should be understood that in other contemplated embodiments, each functionality may be distributed to more than one engine. Likewise, in other contemplated embodiments, multiple defined functionalities may be implemented by a single engine that performs those multiple functions, possibly alongside other functions, or distributed differently among a set of engines than specifically illustrated in the examples herein.

[0063] Underlying Computing Architecture

[0064] FIG. l is a diagram illustrating an exemplary hardware and software architecture of a computer system on which aspects of the embodiments described above may be implemented, and in which various interfaces between hardware components and software components are shown according to an example. As indicated by HW, hardware components are represented below the divider line, whereas software components denoted by SW reside above the divider line. On the hardware side, processing devices 102 (which may include one or more microprocessors, digital signal processors, etc., each having one or more processor cores, are interfaced with memory management device 101 and system interconnect 106. Memory management device 101 provides mappings between virtual memory used by processes being executed, and the physical memory. Memory management device 101 may be an integral part of a central processing unit which also includes the processing devices 102.

[0065] Interconnect 106 includes a backplane such as memory, data, and control lines, as well as the interface with input / output devices, e.g., PCI, USB, etc. Memory 108 (e.g., dynamic random access memory - DRAM) and non-volatile memory 109 such as flash memory (e.g., electrically-erasable read-only memory - EEPROM, NAND Flash, NOR Flash, etc.) are interfaced with memory management device 101 and interconnect 106 via memory controller 110. This architecture may support direct memory access (DMA) by peripherals in some embodiments. VO devices, including video and audio adapters, nonvolatile storage, external peripheral links such as USB, Bluetooth, etc., as well as network interface devices such as those communicating via Wi-Fi or LTE-family interfaces, are collectively represented as I / O devices and networking 112, which interface with interconnect 106 via corresponding I / O controllers 111.

[0066] On the software side, a pre-operating system (pre-OS) environment 116, which is executed at initial system start-up and is responsible for initiating the boot-up of the operating system. One traditional example of pre-OS environment 116 is a system basic input / output system (BIOS). In present-day systems, a unified extensible firmware interface (UEFI) is implemented. Pre-OS environment 116 is responsible for initiating the launching of the operating system, but also provides an execution environment for embedded applications according to certain aspects of the invention. Operating system 118 provides a kernel that controls the hardware devices, manages memory access for programs in memory, coordinates tasks and facilitates multi-tasking, organizes data to be stored, assigns memory space and other resources, loads program binary code into memory, initiates execution of the application program which then interacts with the user and with hardware devices, and detects and responds to various defined interrupts. Also, operating system 118 provides device drivers, and a variety of common services such as those that facilitate interfacing with peripherals and networking, that provide abstraction for application programs so that the applications do not need to be responsible for handling the details of such common operations. Operating system 118 additionally provides a graphical user interface (GUI) that facilitates interaction with the user via peripheral devices such as a monitor, keyboard, mouse, microphone, video camera, touchscreen, and the like. Runtime system 120 implements portions of an execution model, including such operations as putting parameters onto the stack before a function call, the behavior of disk input / output (I / O), and parallel execution-related behaviors. Runtime system 120 may also perform support services such as type checking, debugging, or code generation and optimization.

[0067] Libraries 122 include collections of program functions that provide further abstraction for application programs. These include shared libraries, dynamic linked libraries (DLLs), for example. Libraries 122 may be integral to the operating system 118, runtime system 120, or may be added-on features, or even remotely-hosted. Libraries 122 define an application program interface (API) through which a variety of function calls may be made by application programs 121 to invoke the services provided by the operating system 118. Application programs 121 are those programs that perform useful tasks for users, beyond the tasks performed by lower-level system programs that coordinate the basis operability of the computer system itself.

[0068] System Architecture and Operation

[0069] FIG. 2 is a high-level diagram of ASE system 200 for facilitating an adaptive sensory environment for subject 202 according to various aspects. ASE system 200 includes ASE control system 204, a set of biosensors 206, XR human interface 208, XR system controller 210, additional actuators 212, and data stores 214-218. In related implementations, ASE system 200 may further include remote server or cloud-computing system 220, which may be communicatively coupled to ASE control system 204 via network 222, such as the Internet.

[0070] ASE control system 204 is communicatively coupled, via suitable interfaces, to biosensors 206, XR system controller 210, and actuators 212, as well as to data stores 214- 218. ASE control system 204 may be realized as a computer system such as the example described above with reference to FIG. 1, which runs an application program 121 and device drivers that cause the computer system to execute the functionality described herein.

[0071] In operation, ASE control system 204 receives sensory information from biosensors 206 which indicate various physiologic measurements of subject 202, such as those described above. ASE control system 204 infers the psychological or emotional state of subject 202 based on state-assessment criteria, which may be retrieved from data store 214. Further, ASE control system 204 determines the imagery and other stimuli to be provided to the subject via XR system controller 210 or actuators 212. This determination may be based on the inferred state of subject 202, and determined based on environment-variation criteria, which may be retrieved from data store 216. ASE control system 204 may also execute a task-coordination process such as specified operations during various phases (e.g., preparation, prior to dosing, during dosing, after dosing, and during post-dosing integration), in which objectives and the corresponding environment-variation criteria, will vary according to phase. The task coordination process may be retrieved from data store 218. Subject information and session data logs may be stored in data store 224.

[0072] Cloud computing system 220 may host one or more of data stores 214-218, and it may also host ML systems that may train ML classifiers or generative systems used as part of the algorithms of ASE control system 204. In related embodiments, ML-trained classifiers may be used to assess the subject’s openness and absorption abilities, preparedness state, momentary mental status, mental health outcome for indication, and mental health outcome for relapse indicators. In other related embodiments, generative Al systems, such as specially-trained chatbots may be employed to interact with subject 202 in order to ascertain the subject’s mood, impressions, feelings, and the like, as well as to ascertain effectiveness of the preparatory activities, and administered therapy, as well as post-therapy state of subject 202 (e.g., side effects, anxiety, relaxation, focus states, etc.). Such assessment may be performed in conjunction with the biomarkers measured using biometric sensing.

[0073] User interface (UI) 230 includes display and input devices, and an interface with ASE control system 204, along with an interactive user-interface system, which provides real-time or periodic data on the functioning and state of ASE system 200 and all of its components, workflow, and the monitored and inferred patient state or condition as measured and interpreted via biometric sensing. The user may be a clinician responsible for providing therapy to subject 202 via ASE system 200.

[0074] FIG. 3 is a simplified example illustrating virtual environment 300 which may be implemented by ASE control system 204 as one of a plurality of virtual environments designed to provide a pleasant or soothing experience for subjects. Notably, different subjects with varying traits may respond more or less favorably in different types of virtual environments. Accordingly, one aspect of ASE control system 204 is to assess subject 202 to determine a most suitable virtual environment from among available virtual environments to provide an optimal experience for subject 202. FIG. 4 is a diagram illustrating an extra-pharmacological (EP) model of drug action. The state component 402 refers to the quality of the subjective experience during the acute phase of the dosing session. Research shows the quality of the subjective experience is predictive of subsequent therapeutic outcomes. Three aspects of the acute psychedelic experience have been found to predict long-term psychological outcomes of psychedelic assisted therapy; they are: “peak” or “mystical-type experiences” as measured by the Mystical Experience Questionnaire (MEQ), “challenging experiences” as measured by the Challenging Experience Questionnaire (CEQ), and emotional breakthrough as measured by the Emotional Breakthrough Inventory (EBI).

[0075] Predictors of the acute psychedelic experience fall into 4 categories: traits 404, prestate factors 406, medication dosage 408, and environment / stimuli 410.

[0076] The psychological trait factors 404, notably trait absorption and openness, are an important predictor of the quality of the acute psychedelic experience. However, traits are notoriously stable over time and difficult to change, particularly in adulthood. Pre-state factors 406 can be grouped into 3 categories: “set,” “rapport,” and “intention.” “Set” refers to present mood and mental state including feelings of surrender, openness, preparedness and comfort. “Rapport” refers to inter-personal feelings towards the group of people who will be there during the dosing session. “Intention” is about the clarity of intentions regarding the upcoming dosing session. The individual’s mindset or “set” before the dosing session, is an important modulator of response to psychedelics. “Set” is a cluster of interrelated elements thought to be predictive of the quality of a psychedelic experience and subsequent outcomes.

[0077] The medication dosage factor 408 has been reliably correlated with peak experiences and other acute subjective outcomes. Environment / stimuli factors play a role during all 3 phases of the psychedelic therapy experience - preparation, dosing, and integration. Environment / stimuli 410 factors are linked to “setting” and “matrix,” which are two of the three important modulator or response to psychedelics. Modifiable environmental stimuli include, music listening, movie watching, or being immersed in XR experiences. State 402, together with environment / stimuli 410, affect long-term outcomes 412.

[0078] Use of ASE system 200 according to various embodiments impacts all of the EP components of psychedelic therapies, namely, traits 404, pre-state factors 406, medication dosage 408, and environment / stimuli 410. FIG. 5 is a diagram illustrating phases of psychedelic therapy that are facilitated by ASE system 200 according to various embodiments. They include screening 502, preparation 504, dosing: pre-dosing 506, acute dosing 508, dosing: post-dosing 510, integration 512, and maintenance 514. In screening 502, subjects who are most likely to benefit from treatment are identified for advancement to the subsequent phases, whereas subjects who score low on openness and absorption traits, high on avoidance and low on trust, or low on agreeableness, may likewise be identified to be de-prioritized or excluded from the subsequent phases as unlikely to benefit from therapy.

[0079] Screening phase 502 may be performed by, or aided by, ASE system 200, which can monitor various biometric (sensed and interpreted) indicia such as physiological activity (e.g., cardio, respiration, brain, perspiration, facial tension, temperature, voice, eye state, eye movements), or inferred psychological & physiological states (e.g., stress, anxiety, depression, well-being, emotions, focus, openness, absorption, relaxation, altered states of consciousness, pain, sleep). Stimuli may be provided by ASE system 200, a clinician, or a combination thereof, with biometric sensing performed by ASE system 200. Inference of condition or activity (i.e., state assessment) based on the biometric sensing may be performed ASE control system 204 based on the state-assessment criteria of data store 214, or by a trained machine-learning system that uses one or more ML engines that use supervised learning, reinforcement learning, transfer learning, semi-supervised learning, active learning, multi-task learning, or the like, which may be hosted on cloudcomputing system 220. The subject 202 may be scored according to a trait absorption and openness scale, such as based on TIPI openness questions, modified tellegen absorption scale (MODTAS); highly sensitive person scale, or according to avoidant attachment, e.g., psychedelic predictor scale (PPS) questions on trust attachment style scale.

[0080] In preparation phase 504, ASE system 200 facilitates XR sessions and data gathering to enable the pre-state components of set, rapport, and intention in the subject 202. Preparation phase 504 may span multiple sessions, which may be scheduled over several days or weeks. In addition, ASE system 200 performs XR priming for altered states of consciousness (ASC) of the subject 202. These operations may be performed in conjunction with guidance from a human therapist. Mechanisms of action include, PSI, mindfulness, relaxation, acceptance, mindset, intention-setting, psychoeducation, distraction, biofeedback, and the like. The condition of subject 202 may be scored on a psychedelic predictor scale (PPS). In pre-dosing phase 506, ASE system 200 facilitates a XR session and data gathering to maximize or enhance the subject’s mindset, and to refresh the subject’s intention. Pre-dosing phase 506 may be a single session, e.g., of approximately 30 minutes, preceding the subsequent dosing phase. The subject 202 may be placed in a familiar XR environment, and biometric data may be gathered and compared against previously- collected biometric data, from preparation phase 504 to ensure that the subject 202 is in a familiar and relaxed state before proceeding to the next phase, to facilitate the subject’s transition to ASC.

[0081] In acute dosing phase 508, ASE system 200 facilitates a XR session and data gathering to produce a desired effect as part of the therapy session. This may involve modulating the audio-visual stimuli in the XR environment to soothe the subject 202, as may be needed, or to enhance stimulation to achieve the therapeutic objectives. The sensory modulation may be performed in a controlled manner based on continuously-gathered biosensing feedback. This may be of particular importance in cases where the subject 202 is in a non-verbal state and unable to effectively communicate with the clinician. Thus, the biometric monitoring of subject 202 may be the most effective means for assessing the state of subject 202.

[0082] Accordingly, reports of the subject’s measured and inferred state, in real-time, or periodically during the therapy session, may be output to the clinician, which can assist the clinician with medicine titration decisions, and whether to override or adjust the programmed XR activities via UI 230, or conduct behavioral intervention. The objective for sensory this modulation is to minimize unpleasant psychedelic experience while maintaining the subject 202 in a proper stimulated and receptive state for therapy.

[0083] In post-dosing phase 510, which immediately follows acute dosing 508, ASE system 200 facilitates the continuation of the XR session and data gathering to provide and process immediate post-acute psychedelic experience. The subject stimulus provided by the XR environment may be modulated to help the clinician control the subject’s arousal level. In some instances, the clinician may choose to keep subject 202 at a moderate arousal level, whereas in other instances, the clinician may choose to soothe the subject during post-dosing phase 510.

[0084] Integration phase 512 is for assessing the treatment outcome on the subject’s condition being treated. One or more XR sessions may be used to place the subject 202 in a familiar sensory environment where the subject’s state can be assessed, including testing the subject’s responsiveness to certain stimuli, such as anxiety-causing stimuli in some cases. The clinician may use the biometric data gathered during integration phase 512 to obtain objective before-and-after measures of the subject’s states and behaviors prior to, and after, therapy. In addition, the XR session(s) and biometric assessments may assist the clinician in providing psychoeducation, intention setting, and other psychotherapies. Assessments may be made using an early prognostic predictor scale (EPS), psychological insight scale (PIS), or the like. In addition, XR-environment stimulus and subject monitoring may provide or assist with assessment of the subject’s health outcomes for the treated indication, e.g., MADRS-S, or PHQ-9 for Treatment Resistant Depression (TRD).

[0085] Maintenance phase 514 is used to consolidate therapeutic gains, prevent relapse of subject 202, and determine whether there is any need for more therapy, including the possible need for subsequent dosing session with psychedelic medicine. In maintenance phase 514 one or more XR sessions over weeks or months may be facilitated by ASE system 200, which works to place subject 202 in the familiar sensory environment in which subject 202 may be assessed in similar fashion as in integration phase 512.

[0086] In phases 502-514, subject-specific biometric data may be stored in data store 224 along with the corresponding session logs. This subject-specific data can be used as baseline data to be compared against subject data collected in later phases of the psychedelic therapy. The subject-specific biometric data may also be recorded for offline (i.e., outside of XR session) processing, e.g., as may be implemented in cloud-computing system 220.

[0087] The subject’s physiological and psychological states can be inferred by processing of the biometric data, whether in real time or offline. Examples of inferred states include, for example:

[0088] • Acute Stress (HF HRV, EEG, EDA)

[0089] • Chronic Stress / Anxiety (long term HRV)

[0090] • Depression (voice, long term HRV)

[0091] • Mental well-being (RSP, HF HRV)

[0092] • Relaxation (HF HRV)

[0093] • Emotion recognition (stimuli / response & EDA / HRV)

[0094] • Mindfulness, Focus, Attention (EEG, IBI, HF HRV, pupil dilation)

[0095] • Altered states of consciousness ( EEG)

[0096] • Pain • Sleep (overnight EEG, HF / LF HRV)

[0097] Processing of the biometric data may use time-domain, frequency-domain, wavelet domain analyses, and other known techniques, such as Kalman filtering, neural network, and the like. Some computations may be performed in near-real-time, while others may require offline execution on large data sets.

[0098] Moreover, the familiar sensory environment facilitated by ASE system 200 can help subjects enter and return to baseline conditions in which their emotional states can be assessed and adjusted by increasing or decreasing stimulus within the XR environment. Furthermore, the use of the familiar sensory environment that is facilitated by ASE system 200 allows, as much as possible, for each subject 202 to be uniformly treated and assessed by different individual clinicians.

[0099] FIG. 6 is a flow diagram illustrating a process executable by ASE system 200 to implement screening phase 502 to screen for potential treatment deterrents according to an example embodiment. At 602, ASE system 200 initiates a XR-based scenario to expose a vulnerability of the subject 202. Examples of such vulnerabilities are absorption (suggestibility), openness, agreeableness, avoidant attachment style. In related implementations, different scenarios may be needed to cover the range of traits being tested. Such scenarios may be presented sequentially to conduct an effective evaluation of subject 202. During each scenario, biometric data of subject 202 is gathered at 604. At 606, the biometric data is processed along with information about the contemporaneous activity of the XR-based scenario to compute inferences of the emotional state of subject 202. Inferred states may include, for instance, ritual breath (relaxation, well-being), attention focus, and mindful breathing (presence, mindfulness, absorption). A combination of biometric indicia may be considered for each inference. For instance, for assessing attention focus, eye movement, dilation, body movement, HF HRV, may all be considered in combination. A trained model, which may be specific to subject 202, may be employed.

[0100] At 607, ASE system 200 may also accept clinician input, such as the clinician’s own assessments based on concurrent interaction with subject 202. The clinician’s input may include expert assessment of question-and-answer sessions that may be performed concurrently with the XR scenario, using developed scoring scale(s).

[0101] At 608, the various scores for assessed deterrents are compiled. If further XR-based scenarios are to be carried out, the process may loop back to 602 to initiate a next scenario. At 610, the scores are compared to predefined criteria. Decision 612 determines, whether the scores are favorable, meaning that few, if any, deterrents are exposed. In the affirmative case, at 614, ASE system 200 outputs a recommendation for the clinician to recommend psychedelic treatment. Otherwise, if substantial deterrent(s) are identified based on the assessment criteria applied at 610, decision 612 outputs a recommendation opposing psychedelic treatment at 616.

[0102] FIG. 7 is a flow diagram illustrating a process executable by ASE system 200 to implement preparation phase 504 for initial XR-based assessment of preparedness. Preparation phase 504 can be spread out throughout a few days or weeks depending on the treatment setting (inpatient or outpatient). As an example, one protocol for preparation phase 504 includes six 30-minute sessions in XR human interface 208 that entail delivering a series of psychoeducation, relaxation, mindfulness, intention-setting, and acceptance activities all meant to prepare the user patient in immersive environments. At 702, ASE system 200 runs a XR program for subject 202 during which the subject is exposed to various levels of sensory stimulation for evaluation. The subject may be asked to answer questions from PPS VAS scale in the virtual environment to assess the subject’s state of preparedness for their upcoming psychedelic treatment. Besides assessment, the XR programs that may be run at 702 include psychoeducation, optimization of relaxation, mindfulness, acceptance, and intention-setting, as well as various skills practices.

[0103] At 704, during the session, biometric data of subject 202 is monitored and saved for analysis. The subject’s amount of preparation needed may vary based on their own psychological readiness, prior experience with psychedelics and / or mindfulness practice. Continuous assessment of the subject’s preparedness through biosensing may help shorten the time and amount of preparation required of them before they can benefit from the psychedelic treatment.

[0104] At 706, the physical and psychological state of the subject is computed, including computation of inferred emotional state. For example, inferred states may include release, relaxation, well-being, openness, concentration, attention, focus, presence, absorption, mindfulness, and the like. Scores for these various states are computed at 708 based on predefined criteria, which may include machine-learning-based assessment. Clinician input may be also read at 709, which may include the clinician’s assessments or scores of observed indicia (e.g., based on concurrent question-and-answer sessions).

[0105] At 710, the result of the computation at block 708 is compared against criteria, which may be multi-faceted, e.g., a score threshold for a psychological assessment, or it may be a target range for a biometric measurement such as respiration or heart rate. If target criteria is not met, at 712, the XR parameters may be adjusted or the XR program may be changed. Accordingly, subject 202 receives direct biofeedback from the system based on information captured with the biosensors. For example, if the subject’s breathing rate is higher than a defined target rate, ASE system 200 will provide the subject with visual and verbal cues to help the subject lower their breathing rate. The activity will end when the subject has achieved the target breathing rate and desired relaxation state.

[0106] At 714, once biosensing data scores meet the criteria across all categories, ASE system 200 runs XR validation program to validate the measured states / performance of subject 202 as measured. The validation program may include XR-based assessment of preparedness (PRO: PPS) using VAS scale, for example.

[0107] FIG. 8 is a flow diagram illustrating a process executable by ASE system 200 to implement pre-dosing phase 506 to briefly assess subject preparedness just prior to administration of dosing. On the day of dosing, the subject who has successfully completely preparation phase 504 is placed in the XR environment to receive a momentary assessment of their mental state (focus on anxious measure) to ensure they are fully prepared right before their dosing of psychedelic therapy.

[0108] At 802, a XR program is run that assesses the subject’s preparedness (e.g., PRO: PPS). Concurrently, the subject’s biometrics are captured at 804 and used to corroborate outcome data from the anxiety VAS scale, with a focus on biometrics that measure anxiety and relaxation.

[0109] At 806, the physical and psychological state of the subject is computed, including computation of inferred emotional state. For example, inferred states may include release, openness, absorption, anxiety, stress, relaxation, and mood states. Scores for these various states are computed at 808 based on predefined criteria, which may include machinelearning-based assessment.

[0110] Clinician input may be also read at 809, which may include the clinician’s assessments or scores of observed indicia (e.g., based on concurrent question-and-answer sessions). Reported outcome & biometric data of subject 202 are provided by the clinician via UI 230.

[0111] At 810, the result of the computation at block 808 is compared against various criteria, which may be multi-faceted, e.g., a score threshold for a psychological assessment, or it may be a target range for a biometric measurement such as respiration or heart rate.

[0112] According to decision 810, if the scores computed at 808 show the subject has a state that meets the relevant criteria across all (or sufficient number of) categories, ASE system 200 may output a recommendation to proceed to acute dosing phase 508 at 812.

[0113] If, on the other hand, the criteria is not met at 810, such as if the subject is overly anxious, the ASE system 200 may recommend intervention by a clinician at 814. Also, the XR program or its parameters may be adjusted at 816 to help steer the subject’s emotional state towards the desired state. For instance, a specific XR program can be presented to the subject in the form of relaxation and mindfulness activities.

[0114] FIG. 9 is a flow diagram illustrating a process executable by ASE system 200 to implement acute dosing phase 508 establish and maintain the subject in a desired state(s) while the dosing is administered. During the active psychedelic experience, the subject 202 can experience a range of mental states: stressful states that can be extremely unpleasant and can be predictor of non response to treatment or adverse effects; emotional release that can be predictive of positive response to treatment; or more neutral states combining low stress and an absence of emotional release.

[0115] While under the influence of the psychedelic medicine, subject 202 may be unable to communicate stressful states or positive emotional release states. Accordingly, the attending clinician may not know how to best support the subject during their psychoactive trip. Using biometrics sensing, ASE system 200 can capture high level mental states such as the presence of stress and its duration, the presence of emotional release, and neutral states when neither stress nor emotional release are present. Measures are relative to the user patient’s baseline before the start of the psychedelic journey.

[0116] Accordingly, at 902, a XR program is run that maintains the achieved state from pre-dosing phase 506. Concurrently, the subject’s biometrics are captured at 904, with a focus on biometrics that measure indicia of stress and duration of stress and emotional release.

[0117] At 906, the physical and psychological state of the subject is computed, including computation of these inferred emotional states. Scores for these various states are computed at 908 based on predefined criteria, which may include machine-learning-based assessment.

[0118] Clinician input may be also read at 909, which may include the clinician’s assessments or scores of observed indicia (e.g., based on concurrent dialog with the subject). Reported outcome & biometric data of subject 202 are provided by the clinician via UI 230.

[0119] At 910, the result of the computation at block 908 is compared against various criteria, which may be multi-faceted, e.g., a score threshold for a psychological assessment, or it may be a target range for a biometric measurement such as respiration or heart rate. In some embodiments, the focus may be on prolonged stress and emotional release.

[0120] According to decision 910, the scores computed at 908 may show the subject has a state that calls for intervention or no intervention. If the target criteria is met at 910, ASE system 200 may recommend continuing the dosing protocol at 912. If the target criteria is not met at 910, such as if the subject has experienced prolonged stress, the ASE system 200 may recommend intervention by a clinician at 914. Also, the XR program or its parameters may be adjusted at 916 to help steer the subject’s emotional state towards the desired state. For instance, a specific XR program can be presented to the subject in the form of relaxation and mindfulness activities.

[0121] FIG. 10 is a decision tree diagram illustrating an example set of assessed subject states and recommended responses (including no intervention, or various interventions) according to an example implementation. Stress and emotional release indicators are shown on UI 230 in real time. Along with that information, is a CDSS that suggests interventions for the different scenarios. Some interventions may entail performing some XR-based activities to help reduce stress. Mindfulness and relaxation XR programs may be called up, to be paired with biofeedback. Medicine titration suggestions may involve complex titration algorithm using machine learning and targeted to individual patient characteristics.

[0122] FIG. 11 is a flow diagram illustrating a process executable by ASE system 200 to implement post-dosing phase 510 to assess the subject’s state immediately following administration of dosing. Right after the active psychedelic experience, the subject may experience stressful states that can be extremely unpleasant and sometimes scary. The subject may also experience emotional breakthroughs, often verbalized in the forms of insights. ASE system 200 evaluates those states in three ways: by asking the user patient to complete a VAS assessment in XR human interface 208 (based on the UPES and EBI scales); by using gathered biometric measures to detect stress level and duration; and by recording the subject’s narrative and analyzing voice and speech characteristics including sentiment, tone, and semantic. The resulting assessment is presented to the clinician via UI 230.

[0123] At 1102, a XR program is run that assesses the subject’s emotional breakthrough and unpleasant experiences (e.g., PRO: EBI and UPES). Concurrently, the subject’s biometrics are captured at 1104 and used to corroborate outcome data from the anxiety VAS scale, with a focus on biometrics that measure anxiety, relaxation, and mood states.

[0124] At 1106, the physical and psychological state of the subject is computed, including computation of inferred emotional state. At 1107, input from the subject 202 is read using captured audio and optionally other automated observations via XR human interface 208, and incorporated with the analysis at 1106. Scores for these various states are computed at 1108 based on predefined criteria, which may include machine-learning-based assessment.

[0125] At 1110, the result of the computation at block 1108 is compared against various criteria, which may be multi-faceted, e.g., a score threshold for a psychological assessment, or it may be a target range for a biometric measurement such as respiration or heart rate. The focus on the decision at 1110 for post-dosing phase 510 is whether subject 202 has a high stress score.

[0126] According to decision 1110, if the scores computed at 1108 show the subject has a state that meets the relevant criteria, ASE system 200 may output a recommendation to end the dosing session at 1112.

[0127] If, on the other hand, the criteria is not met at 1110, such as if the subject is overly anxious, the ASE system 200 may recommend intervention by a clinician at 1114. Also, the XR program or its parameters may be adjusted at 1116 to help steer the subject’s emotional state towards the desired state. For instance, a specific XR program can be presented to the subject in the form of relaxation and mindfulness, and acceptance activities.

[0128] FIG. 12 is a flow diagram illustrating a process executable by ASE system 200 to implement integration phase 512. Typically, a subject 202 may present with different integration treatment trajectories. ASE system 200 can assess those trajectories by asking the subj ect to complete VAS assessments in the XR human interface 208 based on validated treatment predictive scales (IPPS and PIS) within a few days and a few weeks of the dosing experience. The subject also completes VAS assessments related to specific outcomes for any given indication (for example, phq9 for depression, GAD 7 for anxiety). The resulting assessment is shared with the clinician via UI 230.

[0129] Based on the results, ASE system 200 helps support clinical decision including the level of in-person clinical intervention and the level of automatic integration support from ASE system 200. Notably, a hybrid approach is also possible with support from both, in- person clinical intervention, and ASE system 200, to varying degrees.

[0130] For the integration support that is handled by ASE system 200, at 1202, ASE system 200 runs a XR program for subject 202 during which the subject is exposed to various levels of sensory stimulation for evaluation. The subject may be asked to answer questions from EPS and PIS scales in the virtual environment to assess the subject’s outcomes for the indicated condition which was treated. At 1204-1208, ASE system 200 continues to capture the subject’s biometric measures, as well as the subject’s input at 1207, as during previous treatment phases including measures related to anxiety, relaxation, and stress. At 1210, a decision is made as to whether the subject’s buimetric measures meet applicable criteria.

[0131] In some embodiments, an additional intervention during integration phase 512 is that of a virtual human therapist which is presented as part of a XR program, which can be instantiated at 1212. The virtual therapist program helps guide the subject 202 through the processing of further insights using affective computing, NLP, and generative Al technologies.

[0132] The subject’s voice narrative continues to be recorded as part of subject input at 1207 during their interactions with the virtual therapist. ASE system 200 continues to analyze voice and speech characteristics (sentiment, tone, semantic) to help assess and monitor the subject’s mood states. This accomplishes several purposes: to guide virtual responses to the subject, to keep a log of integration insights for the subject, and to inform the clinician of the subject’s progress.

[0133] The virtual therapist may recommend further XR-based activities for the subject within each virtual session. ASE system 200 presents the advantage of the subject being able to have as many integration sessions as they need, and at their own pace.

[0134] After each session, ASE system 200 may administer VAS single item questions regarding the patient’s mood state. Results are combined with results from biosensing and voice biomarker to generate a single score to assess the progress of subject 202. If score is above a certain level, the subject is asked to complete PRO scale related to indication. Integration ends at 1214 once scores meet treatment goals.

[0135] Additional Notes and Examples

[0136] Example 1 is an adaptive sensory environment (ASE) system, comprising: a control system; a set of biosensors operatively coupled to the control system; and a virtual / augmented-reality (XR) human interface system operatively coupled to the control system; wherein the ASE system, when operatively deployed, is interfaced with a subject via the biosensors and the XR human interface system, and the control system is operative to cause the ASE system to: present an XR-based program to the subject via the XR human interface system, the XR-based program providing a virtual environment and sensory stimulation to the subject to modulate an emotional state of the subject; monitor a plurality of physiologic measurements of the subject via the set of biosensors; process the physiologic measurements to infer an emotional state of the subject; apply predefined criteria to the inferred emotional state of the subject to determine if the subject is in a target emotional state; and in response to a determination that the subject is not in the target emotional state, adjust the XR-based program to further modulate the emotional state of the subject towards the target emotional state.

[0137] In Example 2, the subject matter of Example 1 includes, wherein the control system is further operative to cause the ASE system to recommend medicinal dosing of the subject based on the inferred emotional state of the subject corresponding to the target emotional state.

[0138] In Example 3, the subject matter of Examples 1-2 includes, wherein adjustment of the XR-based program to further modulate the emotional state of the subject by the control system includes presenting an interactive experience via the XR human interface system to the subject that reduce stress of the subject.

[0139] In Example 4, the subject matter of Examples 1-3 includes, wherein adjustment of the XR-based program to further modulate the emotional state of the subject by the control system includes presenting an interactive experience via the XR human interface system to the subject that increase sensory stimulation of the subject to expose a defined vulnerability of the subject. In Example 5, the subject matter of Examples 1-4 includes, wherein the control system is further operative to initiate machine-learning based assessment of the emotional state of the subject based on prior-gathered data of the subject.

[0140] In Example 6, the subject matter of Examples 1-5 includes, a user interface device operatively coupled with the control system, and operative to receive input from a clinician, the input from the clinician including observation of the subject during execution of the XR-based program, and wherein the control system is further operative to incorporate the observation of the subject in processing of the physiologic measurements to infer the emotional state of the subject.

[0141] In Example 7, the subject matter of Examples 1-6 includes, wherein the control system is further operative to process the physiologic measurements to infer an emotional state of the subject based on a patient-specific machine-learning-based model.

[0142] In Example 8, the subject matter of Examples 1-7 includes, wherein the control system is further operative to generate an assessment of whether the subject qualifies for psychoactive therapy according to a plurality of predefined factors indicative of human psychological traits.

[0143] In Example 9, the subject matter of Examples 1-8 includes, wherein the control system is further operative to track gathered data of emotional state of the subject over a course of XR-based sessions, and to determine assessments of emotional state of the subject in later ones of the sessions based on baseline assessments of the subject from earlier ones of the sessions.

[0144] Example 10 is a method for performing a psychological assessment of a subject using an adaptive sensory environment (ASE) system, the method comprising: interfacing the ASE system with a human subject via a set of biosensors and a virtual / augmented- reality (XR) human interface system; presenting an XR-based program to the subject via the XR human interface system, the XR-based program providing a virtual environment and sensory stimulation to the subject to modulate an emotional state of the subject; autonomously monitoring a plurality of physiologic measurements of the subject via the set of biosensors; processing the physiologic measurements to infer an emotional state of the subject; applying predefined criteria to the inferred emotional state of the subject to determine if the subject is in a target emotional state; in response to a determination that the subject is not in the target emotional state, adjusting the XR-based program to further modulate the emotional state of the subject towards the target emotional state; and generating an assessment of the subject based on a response of the subject to the adjusting of the XR-based program.

[0145] In Example 11, the subject matter of Example 10 includes, executing, by the control system, a set of operational phases, including: screening to monitor biometric indicia of the subject to ascertain qualification of the subject to psychoactive medicinal therapy; in response to a result of the screening, preparation of the subject for dosing of the medicinal therapy based on XR-based training of the subject via the XR-based program of the ASE system; following the preparation of the subject, pre-dosing assessment and modulation of an emotional state of the subject via the ASE system; and following the pre-dosing assessment and modulation, providing ASE-system-based acute dosing support of the subject, including determining if the emotional state of the subject during administration of the medicinal therapy, and modulating the emotional state of the subject via the XR-based program to facilitate a therapeutic effect of the medicinal therapy.

[0146] Example 12 is at least one non-transitory machine-readable storage medium comprising instructions that, when executed by an adaptive sensory environment (ASE) system that is interfaced with a subject via a set of biosensors and a virtual / augmented- reality (XR) human interface system, cause the ASE system to: present an XR-based program to the subject via the XR human interface system, the XR-based program providing a virtual environment and sensory stimulation to the subject to modulate an emotional state of the subject; monitor a plurality of physiologic measurements of the subject via the set of biosensors; process the physiologic measurements to infer an emotional state of the subject; apply predefined criteria to the inferred emotional state of the subject to determine if the subject is in a target emotional state; and in response to a determination that the subject is not in the target emotional state, adjust the XR-based program to further modulate the emotional state of the subject towards the target emotional state.

[0147] In Example 13, the subject matter of Example 12 includes, wherein the instructions further cause the ASE system to recommend medicinal dosing of the subject based on the inferred emotional state of the subject corresponding to the target emotional state.

[0148] In Example 14, the subject matter of Examples 12-13 includes, wherein the instructions cause the ASE system to adjust adjustment the XR-based program to further modulate the emotional state of the subject by presenting an interactive experience via the XR human interface system to the subject that reduce stress of the subject. In Example 15, the subject matter of Examples 12-14 includes, wherein the instructions cause the ASE system to adjust of the XR-based program to further modulate the emotional state of the subject by causing the ASE system to present an interactive experience via the XR human interface system to the subject that increase sensory stimulation of the subject to expose a defined vulnerability of the subject.

[0149] In Example 16, the subject matter of Examples 12-15 includes, wherein the instructions cause the ASE system to initiate machine-learning based assessment of the emotional state of the subject based on prior-gathered data of the subject.

[0150] In Example 17, the subject matter of Examples 12-16 includes, wherein the ASE system further comprises a user interface device operatively coupled with the control system, wherein the instructions cause the ASE system to receive input from a clinician, the input from the clinician including observation of the subject during execution of the XR-based program, and wherein the control system is further operative to incorporate the observation of the subject in processing of the physiologic measurements to infer the emotional state of the subject.

[0151] In Example 18, the subject matter of Examples 12-17 includes, wherein the instructions cause the ASE system to process the physiologic measurements to infer an emotional state of the subject based on a patient-specific machine-learning -based model.

[0152] In Example 19, the subject matter of Examples 12-18 includes, wherein the instructions cause the ASE system to generate an assessment of whether the subject qualifies for psychoactive therapy according to a plurality of predefined factors indicative of human psychological traits.

[0153] In Example 20, the subject matter of Examples 12-19 includes, wherein the instructions cause the ASE system to track gathered data of emotional state of the subject over a course of XR-based sessions, and to determine assessments of emotional state of the subject in later ones of the sessions based on baseline assessments of the subject from earlier ones of the sessions.

[0154] In Example 21, the subject matter of Examples 12-20 includes, wherein the instructions cause the ASE system to execute, by the control system, a set of operational phases, including: screening to monitor biometric indicia of the subject to ascertain qualification of the subject to psychoactive medicinal therapy; in response to a result of the screening, preparation of the subject for dosing of the medicinal therapy based on XR- based training of the subject via the XR-based program of the ASE system; following the preparation of the subject, pre-dosing assessment and modulation of an emotional state of the subject via the ASE system; and following the pre-dosing assessment and modulation, providing ASE-sy stem -based acute dosing support of the subject, including determining if the emotional state of the subject during administration of the medicinal therapy, and modulating the emotional state of the subject via the XR-based program to facilitate a therapeutic effect of the medicinal therapy.

[0155] Example 23 is an apparatus comprising means to implement of any of Examples 1- 21.

[0156] Conclusion

[0157] The embodiments above are intended to be illustrative and not limiting. In addition, although aspects of the present disclosure have been described with reference to particular embodiments, those skilled in the art will recognize that changes may be made in form and detail without departing from the scope of the inventive concepts as described herein.

[0158] Persons of ordinary skill in the relevant arts will recognize that the invention may comprise fewer features than illustrated in any individual embodiment described above. The embodiments described herein are not meant to be an exhaustive presentation of the ways in which the various features of the inventive concepts may be combined. Accordingly, the embodiments are not mutually exclusive combinations of features; rather, the inventive concepts may comprise a combination of different individual features selected from different individual embodiments, as will be understood by persons of ordinary skill in the art.

[0159] Any incorporation by reference of documents above is limited such that no subject matter is incorporated that is contrary to the explicit disclosure herein. Any incorporation by reference of documents above is further limited such that no claims that are included in the documents are incorporated by reference into the claims of the present Application. The claims of any of the documents are, however, incorporated as part of the disclosure herein, unless specifically excluded. Any incorporation by reference of documents above is yet further limited such that any definitions provided in the documents are not incorporated by reference herein unless expressly included herein.

Claims

What is claimed is:

1. An adaptive sensory environment (ASE) system, comprising: a control system; a set of biosensors operatively coupled to the control system; and a virtual / augmented-reality (XR) human interface system operatively coupled to the control system; wherein the ASE system, when operatively deployed, is interfaced with a subject via the biosensors and the XR human interface system, and the control system is operative to cause the ASE system to: present an XR-based program to the subject via the XR human interface system, the XR-based program providing a virtual environment and sensory stimulation to the subject to modulate an emotional state of the subject; monitor a plurality of physiologic measurements of the subject via the set of biosensors; process the physiologic measurements to infer an emotional state of the subject; apply predefined criteria to the inferred emotional state of the subject to determine if the subject is in a target emotional state; and in response to a determination that the subject is not in the target emotional state, adjust the XR-based program to further modulate the emotional state of the subject towards the target emotional state.

2. The ASE system of claim 1, wherein the control system is further operative to cause the ASE system to recommend medicinal dosing of the subject based on the inferred emotional state of the subject corresponding to the target emotional state.

3. The ASE system of claim 1, wherein adjustment of the XR-based program to further modulate the emotional state of the subject by the control system includes presenting an interactive experience via the XR human interface system to the subject that reduce stress of the subject.

4. The ASE system of claim 1, wherein adjustment of the XR-based program to further modulate the emotional state of the subject by the control system includes presenting an interactive experience via the XR human interface system to the subject that increase sensory stimulation of the subject to expose a defined vulnerability of the subject.

5. The ASE system of claim 1, wherein the control system is further operative to initiate machine-learning based assessment of the emotional state of the subject based on prior- gathered data of the subject.

6. The ASE system of claim 1, further comprising a user interface device operatively coupled with the control system, and operative to receive input from a clinician, the input from the clinician including observation of the subject during execution of the XR-based program, and wherein the control system is further operative to incorporate the observation of the subject in processing of the physiologic measurements to infer the emotional state of the subject.

7. The ASE system of claim 1, wherein the control system is further operative to process the physiologic measurements to infer an emotional state of the subject based on a patientspecific machine-learning-based model.

8. The ASE system of claim 1, wherein the control system is further operative to generate an assessment of whether the subject qualifies for psychoactive therapy according to a plurality of predefined factors indicative of human psychological traits.

9. The ASE system of claim 1, wherein the control system is further operative to track gathered data of emotional state of the subject over a course of XR-based sessions, and to determine assessments of emotional state of the subject in later ones of the sessions based on baseline assessments of the subject from earlier ones of the sessions.

10. A method for performing a psychological assessment of a subject using an adaptive sensory environment (ASE) system, the method comprising: interfacing the ASE system with a human subject via a set of biosensors and a virtual / augmented-reality (XR) human interface system;presenting an XR-based program to the subject via the XR human interface system, the XR-based program providing a virtual environment and sensory stimulation to the subject to modulate an emotional state of the subject; autonomously monitoring a plurality of physiologic measurements of the subject via the set of biosensors; processing the physiologic measurements to infer an emotional state of the subject; applying predefined criteria to the inferred emotional state of the subject to determine if the subject is in a target emotional state; in response to a determination that the subject is not in the target emotional state, adjusting the XR-based program to further modulate the emotional state of the subject towards the target emotional state; and generating an assessment of the subject based on a response of the subject to the adjusting of the XR-based program.

11. The method of claim 10, further comprising: executing, by the control system, a set of operational phases, including: screening to monitor biometric indicia of the subject to ascertain qualification of the subject to psychoactive medicinal therapy; in response to a result of the screening, preparation of the subject for dosing of the medicinal therapy based on XR-based training of the subject via the XR-based program of the ASE system; following the preparation of the subject, pre-dosing assessment and modulation of an emotional state of the subject via the ASE system; and following the pre-dosing assessment and modulation, providing ASE-system- based acute dosing support of the subject, including determining if the emotional state of the subject during administration of the medicinal therapy, and modulating the emotional state of the subject via the XR-based program to facilitate a therapeutic effect of the medicinal therapy.

12. At least one non-transitory machine-readable storage medium comprising instructions that, when executed by an adaptive sensory environment (ASE) system that is interfaced with a subject via a set of biosensors and a virtual / augmented-reality (XR) human interface system, cause the ASE system to:present an XR-based program to the subject via the XR human interface system, the XR-based program providing a virtual environment and sensory stimulation to the subject to modulate an emotional state of the subject; monitor a plurality of physiologic measurements of the subject via the set of biosensors; process the physiologic measurements to infer an emotional state of the subject; apply predefined criteria to the inferred emotional state of the subject to determine if the subject is in a target emotional state; and in response to a determination that the subject is not in the target emotional state, adjust the XR-based program to further modulate the emotional state of the subject towards the target emotional state.

13. The at least one machine-readable storage medium of claim 12, wherein the instructions further cause the ASE system to recommend medicinal dosing of the subject based on the inferred emotional state of the subject corresponding to the target emotional state.

14. The at least one machine-readable storage medium of claim 12, wherein the instructions cause the ASE system to adjust adjustment the XR-based program to further modulate the emotional state of the subject by presenting an interactive experience via the XR human interface system to the subject that reduce stress of the subject.

15. The at least one machine-readable storage medium of claim 12, wherein the instructions cause the ASE system to adjust of the XR-based program to further modulate the emotional state of the subject by causing the ASE system to present an interactive experience via the XR human interface system to the subject that increase sensory stimulation of the subject to expose a defined vulnerability of the subject.

16. The at least one machine-readable storage medium of claim 12, wherein the instructions cause the ASE system to initiate machine-learning based assessment of the emotional state of the subject based on prior-gathered data of the subject.

17. The at least one machine-readable storage medium of claim 12, wherein the ASE system further comprises a user interface device operatively coupled with the control system, wherein the instructions cause the ASE system to receive input from a clinician, the input from the clinician including observation of the subject during execution of the XR-based program, and wherein the control system is further operative to incorporate the observation of the subject in processing of the physiologic measurements to infer the emotional state of the subject.

18. The at least one machine-readable storage medium of claim 12, wherein the instructions cause the ASE system to process the physiologic measurements to infer an emotional state of the subject based on a patient-specific machine-learning -based model.

19. The at least one machine-readable storage medium of claim 12, wherein the instructions cause the ASE system to generate an assessment of whether the subject qualifies for psychoactive therapy according to a plurality of predefined factors indicative of human psychological traits.

20. The at least one machine-readable storage medium of claim 12, wherein the instructions cause the ASE system to track gathered data of emotional state of the subject over a course of XR-based sessions, and to determine assessments of emotional state of the subject in later ones of the sessions based on baseline assessments of the subject from earlier ones of the sessions.

21. The at least one machine-readable storage medium of claim 12, wherein the instructions cause the ASE system to execute, by the control system, a set of operational phases, including: screening to monitor biometric indicia of the subject to ascertain qualification of the subject to psychoactive medicinal therapy; in response to a result of the screening, preparation of the subject for dosing of the medicinal therapy based on XR-based training of the subject via the XR-based program of the ASE system; following the preparation of the subject, pre-dosing assessment and modulation of an emotional state of the subject via the ASE system; andfollowing the pre-dosing assessment and modulation, providing ASE-system-based acute dosing support of the subject, including determining if the emotional state of the subject during administration of the medicinal therapy, and modulating the emotional state of the subject via the XR-based program to facilitate a therapeutic effect of the medicinal therapy.

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