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WO2025006626A2 - Dispositif d'administration de gouttelettes mettant en oeuvre l'ia - Google Patents

Dispositif d'administration de gouttelettes mettant en oeuvre l'ia Download PDF

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Publication number
WO2025006626A2
WO2025006626A2 PCT/US2024/035647 US2024035647W WO2025006626A2 WO 2025006626 A2 WO2025006626 A2 WO 2025006626A2 US 2024035647 W US2024035647 W US 2024035647W WO 2025006626 A2 WO2025006626 A2 WO 2025006626A2
Authority
WO
WIPO (PCT)
Prior art keywords
user interface
droplet delivery
control system
artificial intelligence
user
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/US2024/035647
Other languages
English (en)
Other versions
WO2025006626A3 (fr
Inventor
Charles Eric Hunter
Greg RAPP
Jeffrey Miller
Caley MODLIN
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Pneuma Respiratory Inc
Original Assignee
Pneuma Respiratory Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Pneuma Respiratory Inc filed Critical Pneuma Respiratory Inc
Publication of WO2025006626A2 publication Critical patent/WO2025006626A2/fr
Publication of WO2025006626A3 publication Critical patent/WO2025006626A3/fr
Anticipated expiration legal-status Critical
Pending legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G16H20/13—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered from dispensers
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • This disclosure relates to droplet delivery 7 devices and more specifically to droplet delivery devices for the delivery of fluids that are inhaled into mouth, throat, nose, and/or lungs. While the present invention particularly pertains to the delivery of therapeutic compositions, in some embodiments non-therapeutic compositions that are capable of being aerosolized can also be used.
  • the present invention can determine through Al based on numerous parameters associated with the user, time of day, dosing habits, inhalation data, and the like, how effective droplet delivery 7 treatments are for a user, optimal dosing plans for the user, adjustments to dosing plans and medications, and how aggregated data associated with use and results of droplet delivery' device treatments can be applied to others, such as those having similar lifestyles, physical characteristics and/or demographics.
  • Al is implemented to provide nicotine reduction therapies with droplet delivery devices.
  • “Push Mode” ejection devices may be particularly well-suited for implementing Al as compared to prior ejection technologies that do not use “push mode” technologies. Further, Al in combination with “Push Mode” ejection devices appear particularly advantageous for nicotine reduction therapy.
  • FIGS 1A and IB show block diagrams of a network-based system that encourages cessation of a drug delivered to the lungs via a droplet delivery device in an embodiment of the invention.
  • FIG. 2 shows a display screen of an electronic device including exemplary responses the user receives when communicating with a Generative Pre-trained Transformer (GPT) chat-bot in an embodiment of the invention.
  • GPT Generative Pre-trained Transformer
  • FIG. 3 shows a display screen of an electronic device including an exemplary motivational story provided by a GPT chat-bot to a user in an embodiment of the invention.
  • FIG. 4 shows a display screen of an electronic device including a prompt and response for a fact from a scientific article provided to a user in an embodiment of the invention.
  • FIG. 5. shows a flow chart for a process to create a plan to gradually reduce a user's consumption of a drug by implementing artificial intelligence (Al) with an electronoic portable drug droplet delivery device in an embodiment of the invention.
  • Al artificial intelligence
  • FIG. 6 shows a flow chart for a process of generating a drug cessation or reduction plan implementing Al with an electronic portable drug droplet delivery device in an embodiment of the invention.
  • An embodiment of the invention features Al that is used in conjunction with a mobile app that is paired to a droplet delivery device to support treatment protocols, treatment personalization, and connectivity.
  • Al can consider a large number of factors that affect the amount of nicotine required to satisfy a user. These factors can include, but are not limited to, attributes such as body height, body weight, and gender.
  • This data can be input into a paired mobile app by the user. Al and the app can then use the data to calculate the amount of nicotine required to satisfy cravings based on predicted blood concentration for a given dose. Via Al and the app, this can aid in developing a personalized nicotine consumption control or reduction plan for the user. In some embodiments this can be used to develop a smoking cessation plan to aid in getting the user off cigarettes.
  • the mobile app can prompt cigarette smokers to enter their current cigarette brand and smoking usage, alone or in addition to exemplary data described in this disclosure.
  • the embedded Al in the app can search through databases to find the nicotine content of the user’s entered cigarette brand. This data can be used to provide a recommended dose of nicotine that should satisfy the user’s cravings. In some cases, this dose can be the starting dose from which a nicotine reduction plan is developed through the app. In other cases, this can be a controlled dose that is used in smoking cessation to get the user off cigarettes.
  • the app can prompt users to provide feedback on their level of craving satisfaction. Based on this feedback, the Al will learn and improve its calculations and recommendations over time.
  • Al can consider an additional number of factors that affect the amount of nicotine required to satisfy a user. These factors can include time of day, heart rate, steps taken in a day, length and frequency of workouts, and sleep quality. For example, most users may require less nicotine when they wake up in the morning and are already in nicotine withdrawal. After eating or working out, the amount of nicotine required may vary’. Over time, the Al will learn the user’s habits and needs and tailor the amount of nicotine delivered. The user can utilize this functionality to decrease their nicotine intake over time.
  • a droplet delivery system is used in conjunction with a mobile application to generate data for supporting a model to predict whether the user will succeed or fail a plan to quit or reduce their nicotine consumption.
  • Data collected on the user’s health data (weight, height, BMI, gender, etc.), their nicotine consumption data (nicotine intake, time of day of intake, location of usage, inspiratory flow during intake, etc.), and Pulmonary function data (forced expiratory volume in 1 second, forced vital capacity 7 , peak expiratory flow, FEVi/FVC, etc.) and their effects on the user’s nicotine reduction and cessation rates can be used in a supervised learning model.
  • the supervised learning model can then be used to predict the likelihood of a user quitting or reducing their nicotine consumption.
  • the device administers test doses based on a number of factors (i.e., time of day, time after/before eating, cardiovascular workouts, etc.).
  • the inhalation strength, topography, and frequency of the user during the test doses is recorded.
  • the information recorded from the test doses is used to determine the minimum amount of nicotine required to satisfy the user. This information is used to reduce the amount of nicotine used over time and individualize the device to specific user’s needs.
  • One embodiment includes the implementation of Al with a droplet delivery device to include connection to a user’s phone, smartwatch and/or health trackers, and health tracking apps. These connections provide further data for the Al to learn a user’s activity and other factors in connection with the user’s nicotine use requirements. Additionally, when the droplet delivery device is paired with its own mobile app, the app can provide alerts or prompts through the smartwatch, phone, or health tracking apps on usage (i.e., higher or lower than average usage) and treatment progress. As an example, with nicotine reduction therapy, the paired app with Al will track a user’s nicotine consumption through the device.
  • the app can send an alert to the user’s phone or smartwatch/health tracker.
  • the app can then prompt the user to provide insight on whether something in particular is causing the increase in consumption (e.g., stress, feeling low, social event).
  • a droplet delivery device is connected to a mobile app with embedded Al
  • the app communicates with the device and can be used to set the amount of nicotine delivered through the device.
  • Al is used in conjunction with a mobile app connected to a droplet delivery device to function as a virtual counselor throughout treatment programs.
  • the app can act as a counselor, prompting the user with regular check-ins and providing personalized support, such as cognitive behavioral therapy.
  • the app/AI can monitor the progress of a user’s treatment; if a treatment plan is not going well for a user, the app/AI can provide suggestions for modifying the treatment plan. For example, in the case of nicotine reduction, the app/AI can suggest a slower taper off nicotine. As another example, in the case of smoking cessation, the app/AI can suggest a higher dosage of nicotine from the paired device to help the user get off cigarettes.
  • Al is used to personalize treatment through the way a mobile app or the device communicates with a user. Over time, Al will learn the way a user speaks and incorporate their speech mannerisms into voice prompts and communications. This may allow the user to feel more comfortable and connected in their treatment plan.
  • Al can be used to help monitor smoking cessation plans. Keeping track of external data of when a user is likely to smoke to help keep the user informed. This could be a specific location, a time of day, or when the user’s heart rate reaches a certain level. This can be used as an early interv ention to ensure the user is aware of what is happening. This can be in combination with the Al therapy.
  • Al when Al is used in a mobile app paired with a droplet delivery device, Al can generate educational or motivational content for a user. This content can be personalized based on historic data from the user or can be based on specific prompts input by a user. Prompts can be given through a chat-style section of the app. In some embodiments, ChatGPT is utilized for this chat section.
  • Al can learn from databases of stories or educational material to generate appropriate output for the prompt.
  • a user can prompt the app to tell a story about quitting cigarettes to provide them with motivation.
  • the app can then generate a story about someone quitting cigarettes and the positive impact it had on their life.
  • Continually reinforcing the positive benefits of living a nicotine-free and addiction -free life can affect the user’s willingness to quit.
  • Al can supply the user with educational materials and facts.
  • Al can be trained on information from official sites, such as the US CDC and use this information to provide the user with education on the short term and long term impacts of stopping smoking, such as the impact on increased life expectancy and improved lung functioning.
  • the Al model can be trained on the databases to improve the accuracy.
  • the algorithms can be continuously updated with new data to improve their performance and ensure they remain relevant.
  • the Al model can collect user's smoking habits to provide personalized recommendations.
  • a droplet device with Al can incorporate gamification elements, such as rewards and challenges to incentivize users to quit smoking and increase their motivation.
  • the device can be integrated with other health devices, such as fitness trackers, to provide a more holistic approach to quitting smoking and improve overall health outcomes.
  • Other health devices such as fitness trackers, to provide a more holistic approach to quitting smoking and improve overall health outcomes.
  • FIGS. 1A and IB the diagrams show a system 100 designed to encourage cessation of a drug delivered to the lungs via a droplet delivery device 105.
  • the system comprises several processing devices connected via a network 120 to display information to the user about their drug usage and health data.
  • the analyzation of the data includes using an Al engine 230 with a machine learning model 232 trained to support the cessation of the delivered drug.
  • the system utilizes a generative pre-trained transformer (GPT) chat-bot platform 215 to interact with the system 100 within the user interface 210.
  • GPS generative pre-trained transformer
  • the administration interface 205 is used to manage users and deliver insights on the performance of the system 100.
  • the administration 205 interface helps improve the system to increase the chances of user cessation.
  • FIG. 4 illustrates an example of prompt and response for a fact from a scientific article provided by the GPT chat-bot 215 in user interface 210.
  • FIG. 5 illustrates a flow chart describing a process to create a plan to gradually reduce the user’s consumption of the drug with Al on server 220.
  • server processor 270 initiates the process to create the plan.
  • data from the drug delivery system 134 and diagnostic system 117 are processed by processor 270 to create an initial dataset.
  • the artificial intelligence engine 230 generates an initial plan for reducing drug intake gradually over time based on drug usage data 114 and diagnostic data 107.
  • a message is transmitted to the user interface 210 displaying the plan.
  • FIG. 6 illustrates a flow chart describing a process of how a plan generated through Al on server 220 can control the portable electronic droplet delivery device 105. At portable drug delivery device 105 the process begins at step 600.
  • the delivery device 105 receives a gradual drug reduction plan from user interface 210, including receiving such plan from a mobile device that is running an application providing the user interface 210.
  • drug delivery device tracks the total drug consumption on time-determined basis, such daily and hourly, throughout the course of the plan.
  • the drug delivery device 105 will not dispense more drug until is reset be an elapsed time.

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  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Public Health (AREA)
  • Medical Informatics (AREA)
  • Primary Health Care (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Medicinal Chemistry (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Chemical & Material Sciences (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Pathology (AREA)
  • Business, Economics & Management (AREA)
  • General Business, Economics & Management (AREA)
  • Medical Treatment And Welfare Office Work (AREA)
  • Pharmaceuticals Containing Other Organic And Inorganic Compounds (AREA)
  • Medicinal Preparation (AREA)

Abstract

Dispositif d'administration de gouttelettes mettant en œuvre l'intelligence artificielle (IA) pour assurer une thérapie de réduction de la consommation de substances toxiques, telles que la nicotine, spécifiquement adaptée à un utilisateur.
PCT/US2024/035647 2023-06-26 2024-06-26 Dispositif d'administration de gouttelettes mettant en oeuvre l'ia Pending WO2025006626A2 (fr)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US202363523194P 2023-06-26 2023-06-26
US63/523,194 2023-06-26
US202463658765P 2024-06-11 2024-06-11
US63/658,765 2024-06-11

Publications (2)

Publication Number Publication Date
WO2025006626A2 true WO2025006626A2 (fr) 2025-01-02
WO2025006626A3 WO2025006626A3 (fr) 2025-05-01

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PCT/US2024/035647 Pending WO2025006626A2 (fr) 2023-06-26 2024-06-26 Dispositif d'administration de gouttelettes mettant en oeuvre l'ia

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Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016161416A1 (fr) * 2015-04-02 2016-10-06 Click Therapeutics, Inc. Système thérapeutique de désaccoutumance du tabac et dispositif de surveillance de patient à distance
US11633554B1 (en) * 2019-06-11 2023-04-25 Luca Puviani Adaptive systems and methods for delivery of a medicament
CN113674513B (zh) * 2020-05-15 2025-07-22 麦克尼尔有限公司 基于用户吸烟行为记录的干预提醒的触发方法及触发装置
EP4370181A4 (fr) * 2021-07-16 2025-05-07 Predictably Human, Inc. Systèmes, dispositifs et procédés d'administration de médicament

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Publication number Publication date
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