Adaptive breath controller

Abstract

Apparatus and associated methods relate to an adaptive breath controller (ABC) configured to select therapeutic digital assets in response to a user's breath input. In an illustrative example, the breath signal interface converts a breath input into a signal indicative of one or more breath parameters, and a therapeutic selection engine analyzes the signal and selects at least one therapeutic digital asset from a data store in response to the signal. Various embodiments may advantageously enable personalized delivery of therapeutic digital assets tailored to the user's realtime breath characteristics, thereby improving the effectiveness of digital therapy interventions.

Description

TPL Docket No.: 400-36ADAPTIVE BREATH CONTROLLERCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Application Serial No. 63 / 696,757, titled “ADAPTIVE BREATH CONTOLLER,” filed by Ryan J. Douglas on September 19, 2024.

[0002] This application also claims the benefit of U.S. Application Serial No. 63 / 699,031, titled “DIGITAL CARRIER OF THERAPEUTICS,” filed by Ryan J. Douglas on September 25, 2024.

[0003] This application incorporates the entire contents of the foregoing application(s) herein by reference.

[0004] The subject matter of this application may have common inventorship with and / or may be related to the subject matter of the following:• U.S. Application Serial No. 17 / 653,125, titled "FDA COMPLIANT QUALITY SYSTEM TO RISK-MITIGATE, DEVELOP, AND MAINTAIN SOFTWARE-BASED MEDICAL SYSTEMS," filed by Ryan J. Douglas on March 1, 2022, and issued as U.S. Patent No. 11531949 on December 20, 2022;• U.S. Application Serial No. 18 / 172,111, titled "IMMERSIVE MEDICINE TRANSLATIONAL ENGINE FOR DEVELOPMENT AND REPURPOSING OF NONVERIFIED AND VALIDATED CODE," filed by Ryan J. Douglas, et al., on February 21, 2023, and issued as U.S. Patent No. 11955232 on April 9, 2024;• U.S. Application Serial No. 16 / 423,981 , titled "FDA COMPLIANT QUALITY SYSTEM TO RISK-MITIGATE, DEVELOP, AND MAINTAIN SOFTWARE-BASED MEDICAL SYSTEMS," filed by Ryan J. Douglas on May 28, 2019, and issued as U.S. Patent No. 11295261 on April 5, 2022;• U.S. Application Serial No. 18 / 055,754, titled "FDA COMPLIANT QUALITY SYSTEM TO RISK-MITIGATE, DEVELOP, AND MAINTAIN SOFTWARE-BASED MEDICAL SYSTEMS," filed by Ryan J. Douglas on November 15, 2022, and issued as U.S. Patent No. 11790298 on October 17, 2023;• U.S. Application Serial No. 18 / 462,050, titled "FDA COMPLIANT QUALITY SYSTEM TO RISK-MITIGATE, DEVELOP, AND MAINTAIN SOFTWARE-BASED MEDICAL SYSTEMS," filed by Ryan J. Douglas on September 6, 2023;• U.S. Application Serial No. PCT / US2023 / 069442, titled "Dynamically Neuro-harmonized Audible Signal Feedback Generation," filed by Michael S. Wilson, et al., on June 29, 2023;• U.S. Application Serial No. PCT / US2023 / 063720, titled "TREATMENT CONTENT DELIVERY AND PROGRESS TRACKING SYSTEM," filed by Ryan J. Douglas on March 3, 2023;TPL Docket No.: 400-36• U.S. Application Serial No. 63 / 090,000, titled "IMMERSIVE MEDICINE PLATFORM AND MEDIA FOR EFFICIENT DELIVERY OF THERAPEUTIC LIGHT," filed by Ryan J. Douglas, et al., on October 9, 2020;• U.S. Application Serial No. 63 / 172,379, titled "DEEPWELL DIGITAL THERAPEUTIC CONCEPT," filed by Ryan J. Douglas, et al., on April 8, 2021;• U.S. Application Serial No. 63 / 260,128, titled "Patient Compliance-Inducing Digital Therapeutic Game Mechanics," filed by Ryan J. Douglas, et al., on August 10, 2021;• U.S. Application Serial No. 63 / 181,213, titled "WEARABLE AUDIO BIOFEEDBACK RESPIRATORY DETERMINATION FOR HYPERTENSION THERAPY," filed by Ryan J. Douglas on April 28, 2021;• U.S. Application Serial No. 63 / 202,881, titled "Immersive Digital Therapy," filed by Ryan J. Douglas on June 28, 2021 ;• U.S. Application Serial No. 63 / 203,058, titled "Management and Validation of Distributed Implementation of Treatment Modules," filed by Ryan J. Douglas, et al., on July 6, 2021;• U.S. Application Serial No. 63 / 198,030, titled "IMMERSIVE MEDICINE TRANSLATIONAL ENGINE FOR THE DEVELOPMENT AND REPURPOSING OF NON- VERIFIED AND VALIDATED CODE," filed by Ryan J. Douglas on September 24, 2020;• U.S. Application Serial No. 18 / 595,088, titled "IMMERSIVE MEDICINE TRANSLATIONAL ENGINE FOR DEVELOPMENT AND REPURPOSING OF NONVERIFIED AND VALIDATED CODE," filed by Ryan J. Douglas, et al., on March 4, 2024;• U.S. Application Serial No. PCT / US21 / 71585, titled "IMMERSIVE MEDICINE TRANSLATIONAL ENGINE FOR DEVELOPMENT AND REPURPOSING OF NONVERIFIED AND VALIDATED CODE," filed by Ryan J. Douglas, et al., on September 24, 2021;• U.S. Application Serial No. 63 / 203,673, titled "Deepwell Immersive Therapeutic Digital Media," filed by Ryan J. Douglas, et al., on July 27, 2021;• U.S. Application Serial No. 63 / 268,905, titled "FDA-Compliant Therapeutic Game Selection and Delivery Platform," filed by Ryan J. Douglas on March 4, 2022;• U.S. Application Serial No. 63 / 362,497, titled "DIGITAL PLATFORM FOR DELIVERY & TRACKING OF DTX VIDEO GAMES," filed by Ryan J. Douglas on April 5, 2022;• U.S. Application Serial No. 63 / 363,639, titled "Media Delivery Tool for Self Assessment of Physical and Mental State," filed by Ryan J. Douglas on April 26, 2022;TPL Docket No.: 400-36• U.S. Application Serial No. 63 / 366,521, titled "THERAPEUTIC GAME SELECTION AND DELIVERY ENGINE," filed by Ryan J. Douglas on June 16, 2022;• U.S. Application Serial No. 18 / 842,938, titled "TREATMENT CONTENT DELIVERY AND PROGRESS TRACKING SYSTEM," filed by Ryan J. Douglas on August 30, 2024;• U.S. Application Serial No. 63 / 367,235, titled "DEEPWELL MUSIC EXPERIENCE," filed by Michael S. Wilson, et al., on June 29, 2022; and• U.S. Application Serial No. 63 / 485,626, titled "Computer-Implemented Engagement and Therapeutic Mechanisms," filed by Ryan J. Douglas, et al., on February 17, 2023.

[0005] This application incorporates the entire contents of the foregoing application(s) herein by reference.TECHNICAL FIELD

[0006] Various embodiments relate generally to adaptive therapeutics.BACKGROUND

[0007] Breathing is a fundamental physiological process that sustains life by regulating the exchange of oxygen and carbon dioxide. It plays a central role in cellular metabolism and energy production. Beyond its biological function, breathing is also linked to rhythms of the body, coordination with other physiological systems, and patterns of activity across daily life.SUMMARY

[0008] Apparatus and associated methods relate to an adaptive breath controller (ABC) configured to select therapeutic digital assets in response to a user’s breath input. In an illustrative example, the breath signal interface converts a breath input into a signal indicative of one or more breath parameters, and a therapeutic selection engine analyzes the signal and selects at least one therapeutic digital asset from a data store in response to the signal. Various embodiments may advantageously enable personalized delivery of therapeutic digital assets tailored to the user’s realtime breath characteristics, thereby improving the effectiveness of digital therapy interventions.

[0009] Various embodiments may achieve one or more advantages. For example, some embodiments may advantageously enable personalized delivery of therapeutic digital assets tailored to a user’s real-time breath characteristics. In some implementations, the ABC may, for example, advantageously analyze multiple breath parameters to select digital assets that are most appropriate for the user’s current physiological state. In some embodiments, the ABC may, for example, advantageously improve therapeutic outcomes by continuously optimizing asset selection based on recorded user responses. In some implementations, the ABC may facilitate dynamic adjustment of asset delivery parameters such as volume or display characteristics. InTPL Docket No.: 400-36 some implementations, the ABC may enhance user engagement through interactive and adaptive digital experiences.

[0010] The details of various embodiments are set forth in the accompanying drawings and the description below. Other features and advantages will be apparent from the description and drawings, and from the claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] FIG. 1 depicts an exemplary adaptive breath controller (ABC) employed in an illustrative use-case scenario.

[0012] FIG. 2 is a block diagram depicting an exemplary ABC.

[0013] FIG. 3 is a flowchart illustrating an exemplary method of operations performed by the ABC.

[0014] FIG. 4 is a flowchart illustrating an exemplary method of operations performed by the ABC to refine a selection algorithm of a therapeutic digital asset.

[0015] Like reference symbols in the various drawings indicate like elements.DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

[0016] To aid understanding, this document is organized as follows. First, to help introduce discussion of various embodiments, an adaptive breath controller (ABC) system is introduced with reference to FIGS. 1-2. Second, that introduction leads into a description with reference to FIGS. 3-4 of some exemplary embodiments of methods of operations performed by the ABC. Finally, the document discusses further embodiments, exemplary applications and aspects relating to the ABC.

[0017] FIG. 1 depicts an exemplary adaptive breath controller (ABC) 100 employed in an illustrative use-case scenario. The ABC 100 may, for example, include a breath sensor 105. The breath sensor 105 may, for example, be configured to detect a breath input from a user 110. The user 110 may interact with the system via a user device 115. The breath sensor 105 may, for example, include a microphone configured to detect breath sounds. The breath sensor 105 may, for example, include an accelerometer configured to sense chest movement. The breath sensor 105 may, for example, include a chest belt configured to measure expansion during breathing. The breath sensor 105 may, for example, include a camera configured to capture visual data indicative of breathing. The breath sensor 105 may, for example, include an infrared sensor configured to detect thermal changes associated with the user’s breath. The breath sensor 105 may, for example, advantageously enable non-invasive, real-time monitoring of the user’s breathing, facilitate accurate detection of breath parameters, and support a wide range of sensor modalities for flexibleTPL Docket No.: 400-36 deployment. The breath sensor 105 may, for example, be operatively coupled to a breath signal interface 120.

[0018] The breath signal interface 120 may, for example, be configured to convert the breath input into a signal indicative of one or more breath parameters. For example, the one or more breath parameters may include the volume of the breath. By way of example, and not limitation, one or more breath parameters may include breath rate. By way of example, and not limitation, one or more breath parameters may include breath period. By way of example, and not limitation, one or more breath parameters may include breath waveform data.

[0019] The breath signal interface 120 may, for example, process analog or digital signals. The breath signal interface 120 may, for example, filter noise, and extract relevant features from the breath input. The breath signal interface 120 may, for example, advantageously provide standardized, reliable data for downstream analysis. The breath signal interface 120 may, for example, advantageously support multiple sensor types. The breath signal interface 120 may, for example, advantageously enable seamless integration with the ABC 100. The breath signal interface 120 may, for example, be communicably coupled to a breath record data store 125.

[0020] The breath record data store 125 may, for example, be configured to store data and metadata relating to the signal indicative of the one or more breath parameters. The breath record data store 125 may, for example, store historical breath inputs, user responses, and effectiveness evaluations. The breath record data store 125 may, for example, advantageously enable longitudinal analysis. The breath record data store 125 may, for example, advantageously facilitate the ABC 100 refining the ABC’s 100 algorithms. The breath record data store 125 may, for example, advantageously facilitate personalized therapy via the ABC 100. The breath record data store 125 may, for example, be operatively coupled to a remote data store 130.

[0021] The remote data store 130 may, for example, be configured to store digital assets and user data in a cloud-based environment. The remote data store 130 may, for example, provide access to a wide variety of therapeutic digital assets. The remote data store 130 may, for example, enable synchronization across devices and support scalable storage solutions. The remote data store 130 may, for example, advantageously facilitate remote updates. The remote data store 130 may, for example, advantageously allow secure access to digital assets. The remote data store 130 may, for example, be communicably coupled to a therapeutic selection engine 135.

[0022] The therapeutic selection engine 135 may, for example, be configured to receive and analyze the signal indicative of the one or more breath parameters from the breath signal interface 120. The therapeutic selection engine 135 may, for example, be configured to select at least one therapeutic digital asset from a digital asset data store 140 in response to the analyzed signal. The therapeutic selection engine 135 may, for example, employ rule-based logic, machine learningTPL Docket No.: 400-36 algorithms, or adaptive strategies to optimize asset selection. The therapeutic selection engine 135 may, for example, be operatively coupled to the digital asset data store 140.

[0023] The digital asset data store 140 may, for example, contain one or more therapeutic digital assets. The one or more therapeutic digital assets may, for example, include videos. The one or more therapeutic digital assets may, for example, include audio files. The one or more therapeutic digital assets may, for example, include games or interactive media. The digital asset data store 140 may, for example, be configured to store assets locally. The digital asset data store 140 may, for example, be configured to store assets remotely. The digital asset data store 140 may, for example, advantageously enable rapid retrieval of digital assets. The digital asset data store 140 may, for example, advantageously support user-specific preferences and facilitate dynamic updates. The digital asset data store 140 may, for example, be communicably coupled to a selection optimization engine 145.

[0024] The selection optimization engine 145 may, for example, be configured to retrieve data and metadata relating to the signal indicative of the one or more breath parameters and one or more delivered digital assets in the data store. The selection optimization engine 145 may, for example, monitor the user’s response to the delivered digital asset. The selection optimization engine 145 may, for example, evaluate the effectiveness of the asset in relation to the breath parameters and user response. The selection optimization engine 145 may, for example, update the selection strategy of the therapeutic selection engine 135 and refine the selection of therapeutic digital assets from the data store. The selection optimization engine 145 may, for example, advantageously improve therapeutic outcomes. The selection optimization engine 145 may, for example, advantageously enable continuous learning. The selection optimization engine 145 may, for example, advantageously personalize asset selection over time.

[0025] The ABC may, for example, include an output interface 150. For example, the output interface 150 may operably couple the therapeutic selection engine 135. The output interface 150 may, for example, communicably couple the user device 115. The output interface 150 may, for example, be configured to deliver the selected therapeutic digital asset to the user 110 via the user device 115. The output interface 150 may, for example, display a video. The output interface 150 may, for example, output audio. The output interface 150 may, for example, present a video game interface. The output interface 150 may, for example, provide haptic feedback. The output interface 150 may, for example, advantageously support multimodal delivery. The output interface 150 may, for example, advantageously enhance user engagement. The output interface 150 may, for example, advantageously facilitate real-time interaction.

[0026] In a typical use-case scenario, the user 110 interacts with the ABC 100 through the user device 115, which is equipped with the breath sensor 105. As the user 110 breathes, the breathTPL Docket No.: 400-36 sensor 105 detects the breath input and transmits it to the breath signal interface 120, which converts the input into a signal indicative of one or more breath parameters. The breath record data store 125 stores the processed breath data, while the remote data store 130 provides access to a broad library of therapeutic digital assets. The therapeutic selection engine 135 analyzes the breath signal and selects an appropriate digital asset from the digital asset data store 140. The output interface 150 delivers the selected digital asset to the user 1 10 via the user device 115. The selection optimization engine 145 monitors the user’s response to the delivered asset, evaluates its effectiveness, and refines the selection strategy for future sessions.

[0027] FIG. 2 is a block diagram depicting an exemplary ABC 100. The ABC 100 may, for example, include a processor 205. The processor 205 may, for example, operatively couple to a breath signal interface 120. The breath signal interface 120 may, for example, be configured to convert a breath input received from a breath sensor 105 into a signal indicative of one or more breath parameters. The breath sensor 105 may, for example, be configured to detect breath input from a user 110 and transmit the breath input to the breath signal interface 120.

[0028] The processor 205 may, for example, operatively couple to a communication module 210. The communication module 210 may, for example, be configured to transmit and receive data between the processor 205 and external systems. The communication module 210 may, for example, be operatively coupled to a remote data store 130, which may be configured to store digital assets and user data accessible by the ABC 100. The communication module 210 may, for example, also be operatively coupled to a user device 115, which may be configured to deliver selected digital assets to the user 110 and receive user responses.

[0029] The processor 205 may, for example, operatively couple to a storage 215. The storage 215 may, for example, be configured to store executable instructions and modules. The storage 215 may, for example, contain a therapeutic selection engine 135. The therapeutic selection engine 135 may be configured to analyze the signal indicative of one or more breath parameters and select at least one therapeutic digital asset from a digital asset data store 140 in response to the analyzed signal. The storage 215 may, for example, further contain a selection optimization engine 145, which may be configured to retrieve data and metadata relating to breath parameters and delivered digital assets, monitor user responses, evaluate effectiveness, update selection strategies, and refine asset selection.

[0030] The processor 205 may, for example, operatively couple to a memory module 220. The memory module 220 may, for example, be configured to store metadata and data generated or used by the ABC 100. The memory module 220 may, for example, contain a breath record data store 125, which may be configured to store historical breath input data and associated metadata.TPL Docket No.: 400-36 10031 1 The processor 205 may, for example, operatively couple to an output interface 150. The output interface 150 may, for example, be configured to deliver selected therapeutic digital assets to the user 110 via the user device 115. The processor 205 may, for example, operatively couple to a digital asset data store 140. The digital asset data store 140 may, for example, be configured to store one or more digital assets. For example, the one or more digital assets may include video assets 225. The video assets 225 may, for example, include instructional videos, guided exercises, or calming visual scenes. The one or more digital assets may, for example, include audio assets 230. The audio assets 230 may, for example, include music tracks, sound files, or guided meditations. The one or more digital assets may, for example, include interactive media assets 235. The interactive media assets 235 may, for example, include software modules, video games, or interactive exercises. The one or more digital assets may, for example, include haptic assets 240. The haptic assets 240 may, for example, include files or instructions for generating tactile feedback.

[0032] FIG. 3 is a flowchart illustrating an exemplary method of operations 300 performed by the ABC. In a step 305, the method of operations 300 begins by receiving a breath input from a user (e.g., breath sensor 105). The breath sensor 105 may be configured to detect various forms of breath input, such as breath sounds, chest movement, or thermal changes.

[0033] In a step 310, the method of operations 300 converts the breath input into a signal indicative of one or more breath parameters (e.g., breath signal interface 120). The breath signal interface 120 may process the raw breath input, filter noise, and extract relevant features such as breath volume, rate, period, or waveform data, resulting in a standardized signal suitable for analysis.

[0034] In a step 315, the method of operations 300 analyzes the signal indicative of the one or more breath parameters (e.g., therapeutic selection engine 135). The therapeutic selection engine 135 may, for example, analyze the breath parameters assess based on a predetermined set of criterion used to determine the user’s current state or therapeutic needs. The therapeutic selection engine 135 may, for example, employ rule-based logic to interpret the breath parameters and assess the user’s current state or therapeutic needs. The therapeutic selection engine 135 may employ machine learning algorithms to interpret the breath parameters and assess the user’s current state or therapeutic needs.

[0035] In a step 325, the method of operations 300 generates a selection of a therapeutic digital asset (e.g., therapeutic selection engine 135 and digital asset data store 140). The therapeutic selection engine 135 may select an appropriate digital asset — such as a video, audio file, interactive media, or haptic feedback — from the digital asset data store 140, based on the analyzed breath parameters and user-specific preferences.TPL Docket No.: 400-36 |0036| In a step 330, the ABC 100 delivers the therapeutic digital asset to the user (e.g., output interface 150 and user device 115). The output interface 150 may transmit the selected digital asset to the user device 115, enabling the user to experience the therapeutic intervention through visual, auditory, interactive, or tactile modalities.

[0037] In a step 335, the ABC 100 monitors the user’s response to the delivered therapeutic digital asset (e.g., selection optimization engine 145). The selection optimization engine 145 may track the user’s physiological and behavioral responses, such as changes in breath parameters, engagement level, or feedback provided.

[0038] In a step 340, the method of operations 300 records data on the signal, the delivered therapeutic digital asset, and the user’s response (e.g., breath record data store 125 and memory module 220). This step involves storing historical breath input data, details of the delivered digital asset, and user response metadata for longitudinal analysis and future reference.

[0039] In a step 345, the method of operations 300 refines a selection algorithm of the therapeutic digital asset (e.g., selection optimization engine 145). The selection optimization engine 145 may utilize the recorded data to update and improve the therapeutic selection engine 135’s asset selection strategy, enabling continuous learning and personalization of future therapeutic interventions.

[0040] FIG. 4 is a flowchart illustrating an exemplary method of operations 345 performed by the ABC to refine a selection algorithm of a therapeutic digital asset. The method of operations 345 may, for example, correspond to operations performed at step 345 of FIG. 3. In a step 405, the method of operations 345 begins by retrieving recorded data on the signal, the therapeutic digital asset delivered, and the user’s response (e.g., from the breath record data store 125 and memory module 220).

[0041] In a step 410, the method of operations 345 retrieves metadata related to one or more digital assets (e.g., digital asset data store 140). The metadata may include asset type, delivery modality, usage frequency, user preferences, and effectiveness ratings.

[0042] In a step 415 , the user has reached a decision point, to decide whether the retrieved recorded data on signal, therapeutic digital asset delivered, and user's response indicate an improved therapeutic outcome (e.g., via the selection optimization engine 145). If the user decides yes, the method of operations 345 proceeds to step 420. If the user decides no, the method of operations 345 proceeds to step 425.

[0043] In a step 420, the ABC 100 increases the selection weight for the one or more digital assets (e.g., via the selection optimization engine 145). This step involves updating the selection algorithm to favor digital assets that have demonstrated improved therapeutic outcomes, thereby increasing their likelihood of being selected in future sessions.TPL Docket No.: 400-36 100441 In a step 425, the ABC 100 decreases the selection weight for the one or more digital assets (e.g., via the selection optimization engine 145). This step involves reducing the selection priority of digital assets that have not demonstrated improved outcomes, making them less likely to be chosen in subsequent interventions.

[0045] In a step 430, the method of operations 345 updates the digital asset selection strategy (e.g., via the therapeutic selection engine 135 or selection optimization engine 145). The selection strategy is refined based on the adjusted selection weights and accumulated data, enabling the ABC 100 to continuously learn and personalize asset selection for optimal therapeutic effect.

[0046] After step 430, the method of operations 345 reverts to step 405, thereby enabling ongoing refinement of the selection algorithm through iterative analysis and adjustment.

[0047] Although various embodiments have been described with reference to the figures, other embodiments are possible.

[0048] In addition to selecting a therapeutic digital asset for delivery, the ABC 100 system may, for example, be configured to dynamically modulate various parameters of the selected asset during the delivery process. These adjustments may include modifying the volume, playback speed, background color, color palette, brightness, contrast, or other settings associated with the asset. For example, the system may make music softer or louder, change the intensity of a game, or alter visual elements such as color schemes and brightness to better suit the user’s therapeutic needs. Such modulation can be performed in real time, either by software routines or through machine learning algorithms that analyze user responses and adapt the asset parameters accordingly. By enabling dynamic adjustment of asset characteristics, the ABC 100 may, for example, advantageously enhance both user experience and therapeutic effectiveness, allowing for a more personalized and responsive intervention.

[0049] Although an exemplary system has been described with reference to FIG. 1 , other implementations may be deployed in other industrial, scientific, medical, commercial, and / or residential applications.

[0050] In various embodiments, some bypass circuits implementations may be controlled in response to signals from analog or digital components, which may be discrete, integrated, or a combination of each. Some embodiments may include programmed, programmable devices, or some combination thereof (e.g., PLAs, PLDs, ASICs, microcontroller, microprocessor), and may include one or more data stores (e.g., cell, register, block, page) that provide single or multi-level digital data storage capability, and which may be volatile, non-volatile, or some combination thereof. Some control functions may be implemented in hardware, software, firmware, or a combination of any of them.TPL Docket No.: 400-36 10051 1 Computer program products may contain a set of instructions that, when executed by a processor device, cause the processor to perform prescribed functions. These functions may be performed in conjunction with controlled devices in operable communication with the processor. Computer program products, which may include software, may be stored in a data store tangibly embedded on a storage medium, such as an electronic, magnetic, or rotating storage device, and may be fixed or removable (e.g., hard disk, floppy disk, thumb drive, CD, DVD).

[0052] Although an example of a system, which may be portable, has been described with reference to the above figures, other implementations may be deployed in other processing applications, such as desktop and networked environments.

[0053] Temporary auxiliary energy inputs may be received, for example, from chargeable or single use batteries, which may enable use in portable or remote applications. Some embodiments may operate with other DC voltage sources, such as a 9V (nominal) batteries, for example. Alternating current (AC) inputs, which may be provided, for example from a 50 / 60 Hz power port, or from a portable electric generator, may be received via a rectifier and appropriate scaling. Provision for AC (e.g., sine wave, square wave, triangular wave) inputs may include a line frequency transformer to provide voltage step-up, voltage step-down, and / or isolation.

[0054] Although particular features of an architecture have been described, other features may be incorporated to improve performance. For example, caching (e.g., LI, L2, . . .) techniques may be used. Random access memory may be included, for example, to provide scratch pad memory and or to load executable code or parameter information stored for use during runtime operations. Other hardware and software may be provided to perform operations, such as network or other communications using one or more protocols, wireless (e.g., infrared) communications, stored operational energy and power supplies (e.g., batteries), switching and / or linear power supply circuits, software maintenance (e.g., self-test, upgrades), and the like. One or more communication interfaces may be provided in support of data storage and related operations.

[0055] Some systems may be implemented as a computer system that can be used with various implementations. For example, various implementations may include digital circuitry, analog circuitry, computer hardware, firmware, software, or combinations thereof. Apparatus can be implemented in a computer program product tangibly embodied in an information carrier, e.g., in a machine-readable storage device, for execution by a programmable processor; and methods can be performed by a programmable processor executing a program of instructions to perform functions of various embodiments by operating on input data and generating an output. Various embodiments can be implemented advantageously in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system,TPL Docket No.: 400-36 at least one input device, and / or at least one output device. A computer program is a set of instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result. A computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0056] Suitable processors for the execution of a program of instructions include, by way of example, both general and special purpose microprocessors, which may include a single processor or one of multiple processors of any kind of computer. Generally, a processor will receive instructions and data from a read-only memory or a random-access memory or both. The essential elements of a computer are a processor for executing instructions and one or more memories for storing instructions and data. Generally, a computer will also include, or be operatively coupled to communicate with, one or more mass storage devices for storing data files; such devices include magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including, by way of example, semiconductor memory devices, such as EPROM, EEPROM, and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, ASICs (applicationspecific integrated circuits).

[0057] In some implementations, each system may be programmed with the same or similar information and / or initialized with substantially identical information stored in volatile and / or nonvolatile memory. For example, one data interface may be configured to perform auto configuration, auto download, and / or auto update functions when coupled to an appropriate host device, such as a desktop computer or a server.

[0058] In some implementations, one or more user-interface features may be custom configured to perform specific functions. Various embodiments may be implemented in a computer system that includes a graphical user interface and / or an Internet browser. To provide for interaction with a user, some implementations may be implemented on a computer having a display device. The display device may, for example, include an LED (light-emitting diode) display. In some implementations, a display device may, for example, include a CRT (cathode ray tube). In some implementations, a display device may include, for example, an LCD (liquid crystal display). A display device (e.g., monitor) may, for example, be used for displaying information to the user. Some implementations may, for example, include a keyboard and / or pointing device (e.g., mouse, trackpad, trackball, joystick), such as by which the user can provide input to the computer.TPL Docket No.: 400-36 |0059| In various implementations, the system may communicate using suitable communication methods, equipment, and techniques. For example, the system may communicate with compatible devices (e.g., devices capable of transferring data to and / or from the system) using point-to-point communication in which a message is transported directly from the source to the receiver over a dedicated physical link (e.g., fiber optic link, point-to-point wiring, daisy-chain). The components of the system may exchange information by any form or medium of analog or digital data communication, including packet-based messages on a communication network. Examples of communication networks include, e.g., a LAN (local area network), a WAN (wide area network), MAN (metropolitan area network), wireless and / or optical networks, the computers and networks forming the Internet, or some combination thereof. Other implementations may transport messages by broadcasting to all or substantially all devices that are coupled together by a communication network, for example, by using omni-directional radio frequency (RF) signals. Still other implementations may transport messages characterized by high directivity, such as RF signals transmitted using directional (i.e., narrow beam) antennas or infrared signals that may optionally be used with focusing optics. Still other implementations are possible using appropriate interfaces and protocols such as, by way of example and not intended to be limiting, USB 2.0, Firewire, ATA / IDE, RS-232, RS-422, RS-485, 802.11 a / b / g, Wi-Fi, Ethernet, IrDA, FDDI (fiber distributed data interface), token-ring networks, multiplexing techniques based on frequency, time, or code division, or some combination thereof. Some implementations may optionally incorporate features such as error checking and correction (ECC) for data integrity, or security measures, such as encryption (e.g., WEP) and password protection.

[0060] In various embodiments, the computer system may include Internet of Things (loT) devices. loT devices may include objects embedded with electronics, software, sensors, actuators, and network connectivity which enable these objects to collect and exchange data. loT devices may be in-use with wired or wireless devices by sending data through an interface to another device. loT devices may collect useful data and then autonomously flow the data between other devices.

[0061] Various examples of modules may be implemented using circuitry, including various electronic hardware. By way of example and not limitation, the hardware may include transistors, resistors, capacitors, switches, integrated circuits, other modules, or some combination thereof. In various examples, the modules may include analog logic, digital logic, discrete components, traces and / or memory circuits fabricated on a silicon substrate including various integrated circuits (e.g., FPGAs, ASICs), or some combination thereof. In some embodiments, the module(s) may involve execution of preprogrammed instructions, software executed by a processor, or some combination thereof. For example, various modules may involve both hardware and software.TPL Docket No.: 400-36 100621 In some aspects, the techniques described herein relate to an adaptive breath controller including: a breath signal interface configured to convert a breath input into a signal indicative of one or more breath parameters; and, a therapeutic selection engine communicably coupled to the breath signal interface and configured to analyze the signal indicative of the one or more breath parameters and communicably coupled to a data store, wherein the therapeutic selection engine is configured to select at least one therapeutic digital asset from the data store in response to the signal indicative of the one or more breath parameter.

[0063] In some aspects, the techniques described herein relate to an adaptive breath controller, further including a selection optimization engine configured to: retrieve data and metadata relating to the signal indicative of the one or more breath parameters and one or more delivered digital assets in the data store; monitor a user's response to the one or more delivered digital assets; evaluate an effectiveness of the delivered digital asset in relation to the signal indicative of the one or more breath parameters and the monitored user response; update a selection strategy of the therapeutic selection engine based on the retrieved data, metadata, and effectiveness evaluations; and, refine the selection of therapeutic digital assets from the data store in response to the updated selection strategy of the therapeutic selection engine.

[0064] In some aspects, the techniques described herein relate to an adaptive breath controller, wherein the at least one therapeutic digital asset further includes a plurality of digital assets.

[0065] In some aspects, the techniques described herein relate to an adaptive breath controller, wherein the data store includes a remote data store via a wide area network.

[0066] In some aspects, the techniques described herein relate to an adaptive breath controller, wherein the data store includes a local data store.

[0067] In some aspects, the techniques described herein relate to an adaptive breath controller system including: a breath sensor configured to receive a breath input from a user; a breath signal interface operatively coupled to the breath sensor and configured to convert the breath input into a signal indicative of one or more breath parameters; a digital asset data store containing one or more digital assets; a therapeutic selection engine communicably coupled to the breath signal interface and configured to analyze the signal indicative of the one or more breath parameter and operably coupled to the data store, wherein the therapeutic selection engine is configured to select at least one therapeutic digital asset from the data store in response to the analyzed signal indicative of the one or more breath parameter; an output interface communicably coupled to the therapeutic selection engine and configured to deliver the selected therapeutic digital asset to the user; a processor operatively coupled to the breath signal interface, the digital asset data store, the therapeutic selection engine, and the output interface; and, a data store operatively coupled to the processor and containing instructions that, when operated by the processor cause operations toTPL Docket No.: 400-36 deliver at least one therapeutic digital asset to the user based on an analysis of the signal indicative of the one or more breath parameters.

[0068] In some aspects, the techniques described herein relate to an adaptive breath controller system, wherein the operations further include: retrieve data and metadata relating to the signal indicative of the one or more breath parameters and the delivered digital asset in the data store; monitor the user's response to the delivered digital asset; evaluate an effectiveness of the delivered digital asset in relation to the signal indicative of the one or more breath parameters and the monitored user response; update a selection strategy of the therapeutic selection engine based on the retrieved data, metadata, and effectiveness evaluations; and, refine the selection of therapeutic digital assets from the data store in response to the updated selection strategy of the therapeutic selection engine.

[0069] In some aspects, the techniques described herein relate to an adaptive breath controller system, further including a selection optimization engine operatively couple to the processor and the data store and configured to: receive recorded data and metadata relating to the breath input and the delivered digital asset in the data store; monitor the user's response to the delivered digital asset; evaluate an effectiveness of the delivered digital asset in relation to the at least one breath parameters and the monitored user response; update a selection strategy of the therapeutic selection engine based on the received recorded data and effectiveness evaluations; and, refine the selection of therapeutic digital assets from the data store in response to the updated selection strategy of the therapeutic selection engine.

[0070] In some aspects, the techniques described herein relate to an adaptive breath controller system, wherein the operations further include: updating the digital asset data store with at least one digital asset preferentially selected based on association with the user, such that the selection optimization engine refines the digital asset data store according to one or more user preferences and one or more user responses.

[0071] In some aspects, the techniques described herein relate to an adaptive breath controller system, wherein the signal indicative of the one or more breath parameters includes breath period.

[0072] In some aspects, the techniques described herein relate to an adaptive breath controller system, wherein the signal indicative of the one or more breath parameters includes breath volume.

[0073] In some aspects, the techniques described herein relate to an adaptive breath controller system, wherein the signal indicative of the one or more breath parameters includes breath rate.

[0074] In some aspects, the techniques described herein relate to an adaptive breath controller system wherein the breath sensor includes a microphone configured to detect breath input from the user.TPL Docket No.: 400-36 |0075 | In some aspects, the techniques described herein relate to an adaptive breath controller system, wherein the breath sensor includes an accelerometer configured to detect movement associated with the user's breathing.

[0076] In some aspects, the techniques described herein relate to an adaptive breath controller system, wherein the breath sensor includes a chest belt configured to measure expansion of a user's chest during breathing.

[0077] In some aspects, the techniques described herein relate to an adaptive breath controller system, wherein the breath sensor includes a camera configured to capture visual data indicative of the user’s breathing.

[0078] In some aspects, the techniques described herein relate to an adaptive breath controller system, wherein the breath sensor includes an infrared sensor configured to detect thermal changes associated with the user's breath.

[0079] In some aspects, the techniques described herein relate to an adaptive breath controller, wherein the output interface is configured to display a video to the user.

[0080] In some aspects, the techniques described herein relate to an adaptive breath controller system, wherein the output interface is configured to output audio to the user.

[0081] In some aspects, the techniques described herein relate to an adaptive breath controller system, wherein the output interface is configured to present a video game interface to the user.

[0082] A number of implementations have been described. Nevertheless, it will be understood that various modifications may be made. For example, advantageous results may be achieved if the steps of the disclosed techniques were performed in a different sequence, or if components of the disclosed systems were combined in a different manner, or if the components were supplemented with other components. Accordingly, other implementations are contemplated within the scope of the following claims.

Claims

TPL Docket No.: 400-36CLAIMSWhat is claimed is:

1. An adaptive breath controller comprising: a breath signal interface configured to convert a breath input into a signal indicative of one or more breath parameters; and, a therapeutic selection engine communicably coupled to the breath signal interface and configured to analyze the signal indicative of the one or more breath parameters and communicably coupled to a data store, wherein the therapeutic selection engine is configured to select at least one therapeutic digital asset from the data store in response to the signal indicative of the one or more breath parameter.

2. The adaptive breath controller of claim 1 , further comprising a selection optimization engine configured to: retrieve data and metadata relating to the signal indicative of the one or more breath parameters and one or more delivered digital assets in the data store; monitor a user’s response to the one or more delivered digital assets; evaluate an effectiveness of the delivered digital asset in relation to the signal indicative of the one or more breath parameters and the monitored user response; update a selection strategy of the therapeutic selection engine based on the retrieved data, metadata, and effectiveness evaluations; and, refine the selection of therapeutic digital assets from the data store in response to the updated selection strategy of the therapeutic selection engine.

3. The adaptive breath controller of claim 1, wherein the at least one therapeutic digital asset further comprises a plurality of digital assets.

4. The adaptive breath controller of claim 1 , wherein the data store comprises a remote data store.

5. The adaptive breath controller of claim 1 , wherein the data store comprises a local data store.TPL Docket No.: 400-366. An adaptive breath controller system comprising: a breath sensor configured to receive a breath input from a user; a breath signal interface operatively coupled to the breath sensor and configured to convert the breath input into a signal indicative of one or more breath parameters; a digital asset data store containing one or more digital assets; a therapeutic selection engine communicably coupled to the breath signal interface and configured to analyze the signal indicative of the one or more breath parameter and operably coupled to the data store, wherein the therapeutic selection engine is configured to select at least one therapeutic digital asset from the data store in response to the analyzed signal indicative of the one or more breath parameter; an output interface communicably coupled to the therapeutic selection engine and configured to deliver the selected therapeutic digital asset to the user; a processor operatively coupled to the breath signal interface, the digital asset data store, the therapeutic selection engine, and the output interface; and, a data store operatively coupled to the processor and containing instructions that, when operated by the processor cause operations to deliver at least one therapeutic digital asset to the user based on an analysis of the signal indicative of the one or more breath parameters.

7. The adaptive breath controller system of claim 6, wherein the operations further comprise: retrieve data and metadata relating to the signal indicative of the one or more breath parameters and the delivered digital asset in the data store; monitor the user’ s response to the delivered digital asset; evaluate an effectiveness of the delivered digital asset in relation to the signal indicative of the one or more breath parameters and the monitored user response; update a selection strategy of the therapeutic selection engine based on the retrieved data, metadata, and effectiveness evaluations; and, refine the selection of therapeutic digital assets from the data store in response to the updated selection strategy of the therapeutic selection engine.

8. The adaptive breath controller system of claim 6, further comprising a selection optimization engine operatively couple to the processor and the data store and configured to: receive recorded data and metadata relating to the breath input and the delivered digital asset in the data store; monitor the user’ s response to the delivered digital asset; evaluate an effectiveness of the delivered digital asset in relation to the at least one breath parameters and the monitored user response;TPL Docket No.: 400-36 update a selection strategy of the therapeutic selection engine based on the received recorded data and effectiveness evaluations; and, refine the selection of therapeutic digital assets from the data store in response to the updated selection strategy of the therapeutic selection engine.

9. The adaptive breath controller system of claim 8, wherein the operations further comprise: updating the digital asset data store with at least one digital asset preferentially selected based on association with the user, such that the selection optimization engine refines the digital asset data store according to one or more user preferences and one or more user responses.

10. The adaptive breath controller system of claim 6, wherein the signal indicative of the one or more breath parameters comprises breath period.

11. The adaptive breath controller system of claim 6, wherein the signal indicative of the one or more breath parameters comprises breath volume.

12. The adaptive breath controller system of claim 6, wherein the signal indicative of the one or more breath parameters comprises breath rate.

13. The adaptive breath controller system of claim 6 wherein the breath sensor comprises a microphone configured to detect breath input from the user.

14. The adaptive breath controller system of claim 6, wherein the breath sensor comprises an accelerometer configured to detect movement associated with the user’s breathing.

15. The adaptive breath controller system of claim 6, wherein the breath sensor comprises a chest belt configured to measure expansion of a user’s chest during breathing.

16. The adaptive breath controller system of claim 6, wherein the breath sensor comprises a camera configured to capture visual data indicative of the user’s breathing.

17. The adaptive breath controller system of claim 6, wherein the breath sensor comprises an infrared sensor configured to detect thermal changes associated with the user’s breath.

18. The adaptive breath controller of claim 6, wherein the output interface is configured to display a video to the user.

19. The adaptive breath controller system of claim 6, wherein the output interface is configured to output audio to the user.TPL Docket No.: 400-3620. The adaptive breath controller system of claim 6, wherein the output interface is configured to present a video game interface to the user.

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