Droplet delivery device implementing ai

Abstract

A droplet delivery device that implements artificial intelligence (AI) to provide a drug reduction therapy, such as nicotine, that is specifically suitable for a user.

Description

(19) State Intellectual Property Office (12) Invention Patent Application (10) Application Publication Number (43) Application Publication Date (21) Application Number 202480042738.6 (22) Application Date 2024.06.26 (30) Priority Data 63 / 523,194 2023.06.26 US 63 / 658,765 2024.06.11 US (85) PCT International Application Entering National Phase Date 2025.12.25 (86) PCT International Application Application Data PCT / US2024 / 035647 2024.06.26 (87) PCT International Application Publication Data WO2025 / 006626 EN 2025.01.02 (71) Applicant: Precision Breathing Inc. Address: USA (72) Inventors: C.E. Hunter, G. Rapp, J. Miller, C. Modlin (74) Patent Agency: China Council for the Promotion of International Trade Patent & Trademark Office Co., Ltd. 11038 Patent Attorney: Zhang Ronghai (51) Int.Cl. A61M 11 / 00 (2006.01) A61M 15 / 00 (2006.01) G16H 20 / 13 (2018.01) (54) Invention Title: Droplet Delivery Device Implementing AI (57) Abstract: A droplet delivery device that implements artificial intelligence (AI) to provide drug reduction therapies, such as nicotine, specifically tailored to the user. Claims (2 pages), Description (6 pages), Drawings (7 pages), CN 121419805 A, 2026.01.27, CN 1 21 41 98 05 A. 1. A droplet delivery device control system comprising an artificial intelligence engine remotely and operably coupled via a computer network to implement a drug intake reduction plan on a droplet delivery device for dispensing drugs based on one or more of user habits, user physical characteristics, and user test doses. 2. The droplet delivery device control system of claim 1, wherein the drug is nicotine. 3. The droplet delivery device control system of claim 2, wherein the droplet delivery device includes a push-mode injector. 4. The droplet delivery device control system of claim 1, wherein the droplet delivery device includes a push-mode injector. 5. The droplet delivery device control system of claim 4, further comprising a portable electronic diagnostic device operably coupled to the computer network and providing disease diagnostic data to the artificial intelligence engine. 6. The droplet delivery device control system according to claim 3 further includes a portable electronic diagnostic device operatively coupled to the computer network and providing disease diagnostic data to the artificial intelligence engine.7. The droplet delivery device control system of claim 2 further includes a portable electronic diagnostic device operatively coupled to the computer network and providing disease diagnostic data to the artificial intelligence engine. 8. The droplet delivery device control system of claim 1 further includes a portable electronic diagnostic device operatively coupled to the computer network and providing disease diagnostic data to the artificial intelligence engine. 9. The droplet delivery device control system of claim 9 further includes a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan. 10. The droplet delivery device control system of claim 8 further includes a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan. 11. The droplet delivery device control system of claim 7 further includes a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan. 12. The droplet delivery device control system of claim 6 further includes a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan. 13. The droplet delivery device control system of claim 5 further includes a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan. 14. The droplet delivery device control system of claim 4 further includes a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan. 15. The droplet delivery device control system according to claim 3 further includes a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan.16. The droplet delivery device control system of claim 2, further comprising a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan. 17. The droplet delivery device control system of claim 1, further comprising a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan. 18. A mobile computing device configured to display a user interface via a mobile application connected to a remote server having an artificial intelligence engine and a drug droplet delivery device via a computer network, wherein the user interface presents information about a drug intake reduction plan determined by the artificial intelligence engine. 19. The mobile computing device of claim 18, wherein the drug intake reduction plan is a reduction in nicotine use. 20. The mobile computing device of claim 18, further operatively coupled to a portable electronic diagnostic device via the network. Claims 2 / 2 Page 3 CN 121419805 A Droplet Delivery Device for Implementing AI

[0001] Cross-Reference to Related Applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 658,765, filed June 11, 2024, and U.S. Provisional Patent Application No. 63 / 523,194, filed June 26, 2023, which are incorporated herein by reference in their entirety. Technical Field

[0003] This disclosure relates to droplet delivery devices, and more particularly, to droplet delivery devices for delivering fluids inhaled into the mouth, throat, nasal cavity, and / or lungs. While the invention particularly relates to the delivery of therapeutic compositions, in some embodiments, non-therapeutic compositions capable of being atomized may also be used. Background Art

[0004] The contents of WO 2020 / 264501 (describing “ring-mode” spray), PCT / US2022 / 034552 (describing “push-mode” spray), U.S. Patent No. 10,449,314 (describing dose verification) and U.S. Patent Application Publication No. 20190134330 (describing user feedback and instructions) are incorporated herein by reference in their entirety.

[0005] There is a need for droplet delivery devices, such as those described in the patent publications cited by the applicant, to implement artificial intelligence (AI) to improve the administration and personalization of aerosol therapy.Specifically, the present invention can use AI to determine the effectiveness of droplet delivery therapy for a user, the optimal dosing plan for the user, adjustments to the dosing plan and medication, and how aggregated data associated with the use and outcomes of droplet delivery device therapy can be applied to others, such as those with similar lifestyles, physical characteristics, and / or demographic features, based on numerous parameters associated with the user, daytime, dosing habits, inhalation data, etc.

[0006] In various embodiments, AI is implemented to deliver nicotine reduction therapy via droplet delivery devices. In particular, "push-mode" jet devices may be particularly well-suited for implementing AI compared to existing jet technologies that do not use "push-mode" technology. Furthermore, the combination of AI with "push-mode" jet devices appears to be particularly advantageous for nicotine reduction therapy. Summary of the Invention

[0007] Brief Description of the Drawings

[0008] Figures 1A and 1B show block diagrams of a network-based system in an embodiment of the present invention that encourages the withdrawal of medication delivered to the lungs via droplet delivery devices.

[0009] Figure 2 shows a display screen of an electronic device in an embodiment of the present invention, including an exemplary response received by a user when communicating with a Generative Pre-trained Transformer (GPT) chatbot.

[0010] Figure 3 shows a display screen of an electronic device in an embodiment of the present invention, including an exemplary inspirational story provided to the user by the GPT chatbot.

[0011] Figure 4 shows a display screen of an electronic device in an embodiment of the present invention, including prompts and responses provided to the user regarding facts in a scientific article. Specification 1 / 6 pages 4 CN 121419805 A

[0012] Figure 5 shows a flowchart of a process for creating a plan to gradually reduce a user's drug intake by using artificial intelligence (AI) through an electronic portable drug droplet delivery device in an embodiment of the present invention.

[0013] Figure 6 shows a flowchart of a process for generating a drug withdrawal or reduction plan by using AI through an electronic portable drug droplet delivery device in an embodiment of the present invention. Detailed Description

[0014] Embodiments of the present invention feature AI used in conjunction with a mobile application paired with a droplet delivery device to support treatment protocols, treatment personalization, and connectivity.

[0015] In embodiments combining AI with droplet delivery devices for nicotine reduction therapy, the AI ​​can consider a wide range of factors influencing the amount of nicotine needed to satisfy a user. These factors may include, but are not limited to, attributes such as height, weight, and gender. The user can input this data into a paired mobile application. The AI ​​and the application can then use this data to calculate the amount of nicotine needed to satisfy a nicotine craving based on a predicted blood concentration for a given dose. This can help develop personalized nicotine intake control or reduction plans for users through the AI ​​and the application.In some embodiments, this can be used to develop a smoking cessation plan to help users quit smoking.

[0016] In other embodiments, the mobile application may prompt smokers to enter, either alone or in addition to the exemplary data described herein, their current cigarette brand and smoking amount. AI embedded in the application can search a database to determine the nicotine content of the cigarette brand entered by the user. This data can be used to provide a recommended dose of nicotine to satisfy the user's nicotine cravings. In some cases, this dose can serve as the starting dose for developing a nicotine reduction plan based on the application. In other cases, this can serve as a controlled dose used during the smoking cessation process to help the user quit smoking.

[0017] In embodiments where nicotine dosage is recommended via AI and an application, the application may prompt the user to provide feedback on their nicotine craving satisfaction. Based on this feedback, the AI ​​will learn and improve its calculations and recommendations over time. In other embodiments where AI is combined with a droplet delivery device for nicotine reduction therapy, the AI ​​may consider several other factors that influence the amount of nicotine needed to satisfy the user. These factors may include time of day, heart rate, daily steps, duration and frequency of exercise, and sleep quality. For example, most users may need less nicotine when they wake up in the morning and are already in a nicotine-free state. The amount of nicotine needed may vary after eating or exercising. Over time, the AI ​​will learn the user's habits and needs and customize the amount of nicotine delivered. Users can take advantage of this feature to reduce their nicotine intake over time.

[0018] In another embodiment, the droplet delivery system is used in conjunction with a mobile application to generate data to support model predictions of whether a user will achieve or fail to complete their plan to quit or reduce their nicotine intake. Data collected regarding the user's health data (weight, height, BMI, gender, etc.), their nicotine intake data (nicotine intake amount, time of daytime intake, location of use, inspiratory flow rate at intake, etc.), lung function data (forced expiratory volume in one second, forced vital capacity, peak expiratory flow rate, FEV1 / FVC, etc.), and their impact on the user's nicotine reduction and quit rate can all 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 intake.

[0019] To learn user habits, the device manages test doses based on multiple factors (i.e., time of day, time before / after meals, cardiovascular exercise, etc.). The user's inhalation intensity, topography, and frequency are recorded during the test dose period. Information recorded from the test dose is used to determine the minimum amount of nicotine required to meet the user's needs. This information is used to reduce the amount of nicotine used over time and to personalize the device to the specific needs of each user.

[0020] One embodiment includes implementing AI via a droplet delivery device to include connectivity with the user's phone, smartwatch, and / or health tracker and health tracking app. These connections provide the AI ​​with further data to learn about the user's activities and other factors related to the user's nicotine usage needs. Additionally, when the droplet delivery device is paired with its own mobile app, the app can provide alerts or prompts via the smartwatch, phone, or health tracking app regarding usage (i.e., above or below average dosage) and treatment progress. For example, in the case of nicotine tapering therapy, the app paired with the AI ​​will track the user's nicotine intake via the device. If the app / AI records an abnormally high intake or an intake higher than the recommended amount in the user's established tapering plan, the app can send an alert to the user's phone or smartwatch / health tracker. The app can then prompt the user for insights regarding whether there are specific triggers leading to the increased intake (e.g., stress, low mood, social activity).

[0021] In the above embodiments in which the droplet delivery device is connected to a mobile application with embedded AI, the application communicates with the device and can be used to set the amount of nicotine delivered through the device.

[0022] In some embodiments, the AI ​​is used in conjunction with a mobile application connected to the droplet delivery device to act as a virtual advisor throughout the treatment program. The application can act as an advisor, providing personalized support, such as cognitive behavioral therapy, by periodically following up with prompts to the user. The application / AI can monitor the user's treatment progress; if the treatment plan is not progressing well for the user, the application / AI can provide suggestions for modifying the treatment plan. For example, in the case of nicotine reduction, the application / AI can suggest a slower, gradual reduction of nicotine. As another example, in the case of smoking cessation, the application / AI can suggest a higher dose of nicotine from a paired device to help the user quit smoking.

[0023] In some embodiments, the AI ​​is used to personalize the treatment by communicating with the user through a mobile application or device. Over time, the AI ​​will learn the user's speech patterns and incorporate their speech habits into voice prompts and communications. This may make the user feel more comfortable and at ease throughout their treatment program.

[0024] In other embodiments, AI can be used to help monitor a smoking cessation program. External data tracking when a user is likely to smoke can help the user understand the situation. This could be a specific location, a particular time of day, or when the user's heart rate reaches a certain level. This can be used as an early intervention to ensure the user is aware of what is happening. This can be combined with AI-based therapies.

[0025] In some embodiments, when AI is used in a mobile application paired with a droplet delivery device, AI can generate educational or motivational content for the user.The content can be personalized based on the user's historical data or based on specific prompts entered by the user. Prompts can be given through a chat-like section of the application. In some embodiments, ChatGPT is used for this chat section.

[0026] The AI ​​can learn from a database of stories or educational materials to generate appropriate outputs for prompts. For example, during a smoking cessation program, users can prompt the application to tell stories about quitting smoking to motivate them. The application can then generate stories about someone quitting smoking and the positive impact of quitting smoking on their life. Continuously reinforcing the positive benefits of living a nicotine-free and addiction-free life can influence a user's willingness to quit smoking.

[0027] In addition to inspirational stories, the AI ​​can also provide users with educational materials and facts. For example, the AI ​​can be trained based on information from official websites such as the US CDC and use that information to educate users about the short-term and long-term effects of quitting smoking, such as the effects on extending life expectancy and improving lung function.

[0028] Key AI Parameters

[0029] While AI based on machine learning and application notifications is well known, this invention provides a new way to implement these AI technologies for nicotine reduction therapies.

[0030] Machine Learning Algorithms:

[0031] A database of smokers' behavior, smoking habits, and known smoking cessation models is acquired. (Instruction manual 3 / 6 pages 6 CN 121419805 A)

[0032] AI models can be trained based on the database to improve accuracy.

[0033] Algorithms can be continuously updated with new data to improve their performance and ensure they remain relevant.

[0034] Personalization:

[0035] AI models can collect users' smoking habits to provide personalized suggestions.

[0036] Customized forms are provided to display personal data.

[0037] Infographics are provided for users to visually view their smoking and cessation status.

[0038] Users' exhalations are measured, similar to spirometry, to collect more data about the user and provide feedback.

[0039] This personalization can improve the effectiveness of the device and make it more attractive to users.

[0040] Gaming:

[0041] People are particularly receptive to games, including those that can help change lifestyles and adjust people's activities to make healthier choices.

[0042] Droplet devices with AI can incorporate gamification elements, such as rewards and challenges, to motivate users to quit smoking and increase their motivation.

[0043] Games can be self-challenging, interpersonal, group-based, or community-based. Diverse game systems can enrich society and encourage sociologists, psychologists, and physicians to create new game systems or conduct experiments. "A game for making games."

[0044] Real-time feedback can be provided.

[0045] Real-time statistics – personal and macro data.

[0046] Real-time behavioral interference.

[0047] Integration with other health devices:

[0048] The device can be integrated with other health devices such as fitness trackers to provide a more comprehensive approach to smoking cessation and improve overall health.

[0049] Using the integrated data can help users know when they need to consume nicotine and when they have consumed enough. This can also adjust the amount of nicotine to be delivered. This can also change the user's smoking cessation plan.

[0050] Using the integrated data can help us understand when a user will quit smoking and whether a user will quit smoking.

[0051] Cross-platform application.

[0052] CO monitoring.

[0053] Predictive modeling:

[0054] Collect existing predictive models, including genetic or non-genetic models.

[0055] Machine learning algorithms can be used to develop predictive models that can identify users at high risk of relapse and provide targeted support to prevent relapse.

[0056] The algorithm can also predict the next smoking-related action and provide suggestions in advance. Advance notification may reduce overall nicotine intake.

[0057] Data can be collected to predict whether a user's lungs are improving or whether they have lung problems.

[0058] Referring to Figures 1A and 1B, these figures illustrate a system 100 designed to encourage withdrawal of medication delivered to the lungs via a droplet delivery device 105. The system includes several processing devices connected via a network 120 to display information to users about their medication use and health data. Data analysis includes using an AI engine 230 with a machine learning model 232 trained to support withdrawal of the delivered medication. The system interacts with the system 100 within a user interface 210 using a Generative Transformer (GPT) chatbot platform 215, as described in page 4 / 6 of the specification 7 CN 121419805 A.

[0059] Network 100 receives and transmits data to and from all processing devices via wired or wireless connections (e.g., Bluetooth, Zigbee, Wi-Fi, cellular data). Network 100 is enabled by a data communication system 150 on the processing devices.

[0060] Drug usage data 107 collected from the portable electronic droplet delivery device 105 includes information about the time of daytime dosing from a timing system 132 operatively coupled to a microcontroller 130. This information can be used to approximate the plasma concentration of the drug in the user's body in real time. Data 107 is collected and stored locally in a memory 111 on the portable electronic droplet delivery device until the device is connected to network 100 to transmit the data to the cloud for permanent storage and analysis.Instructions 113 for the operation and control of the droplet delivery device 105 (including the drug delivery system 134) are also included in the memory 113.

[0061] The portable electronic diagnostic device 110 can be used to collect disease diagnostic data 114 about a user's health via a disease diagnostic system 117 operably coupled to a microcontroller 118 operably coupled to a timing system 119. This data 114 can be used in conjunction with drug use data 107 collected from a portable drug delivery device 150 to encourage drug discontinuation. This data is collected and stored locally in the memory 112 on the portable electronic diagnostic device 110 until the device is connected to a network to transmit the data to the cloud for permanent storage and analysis. Instructions 116 for the operation and control of the diagnostic device 1110 are also included in the memory 112.

[0062] Server 220 connected to network 120 includes computer processor 270 and AI engine 230 supporting machine learning model 232, which is trained to determine the likelihood of a user discontinuing medication and generate new treatment plans to be displayed to the user and loaded onto droplet delivery device 105. Engine 230 uses medication use data 107 collected from portable electronic droplet delivery device 105 and disease diagnosis data 114 collected from portable electronic diagnostic device 110 (these data are stored in memory 260) to help the user discontinue medication. Server memory 260 also includes user data 108 from user interface 210. Memory 260 also includes instructions 280 for collaboratively processing diagnostic data 114, usage data 107, and user 108 with other devices connected to network 100. Training engine 240 is used to further improve the machine learning model on server 220, thereby producing more accurate predictions and generating more appropriate treatment plans.

[0063] User interface 210, such as that contained in a mobile application on a user's mobile computing device (e.g., a mobile phone, tablet, etc.), or that may be integrated with droplet delivery device 105 as a user interface for computing and display devices, is connected to server 220 via network 100. A device having an application providing user interface 210 includes a computer processor 203 operatively coupled to GPT chatbot 215 for receiving input and providing output from AI engine 230 on server 220 back to the user. The device including user interface 210 also includes memory 270 having instructions 275 for storing and retrieving drug use data 114, disease diagnosis data 107, and user data 108 in memory 270 associated with user interface 210.

[0064] Management interface 205 is used to manage users and provide insights into the performance of system 100. Management interface 205 helps improve the system to increase the chances of users discontinuing medication.

[0065] Figure 2 illustrates examples of the types of responses a user receives when communicating with the GPT chatbot 215 in user interface 210.

[0066] Figure 3 illustrates examples of inspirational stories provided by the GPT chatbot 215 in user interface 210.

[0067] Figure 4 illustrates examples of prompts and responses provided by the GPT chatbot 215 in user interface 210 regarding facts in scientific articles.

[0068] Figure 5 illustrates a flowchart describing the process of creating a plan to gradually reduce a user's medication intake using AI on server 220. In step 500, server processor 270 initiates the process to create the plan. In step 510, processor 270 processes data from drug delivery system 134 and diagnostic system 117 to create an initial dataset. In step 515, AI engine 230 generates an initial plan for gradually reducing medication intake over time based on medication use data 114 and diagnostic data 107. In step 525, a message is sent to user interface 210 to display the plan. In step 530, further data is received during the drug intake reduction process, including user compliance, drug concentration levels, daytime dosing times, and similar relevant data from drug delivery system 134 and diagnostic system 117, to determine adjustments to the plan. In step 535, a modified plan is generated based on the collected data to improve user compliance, and the modified plan is sent to the user interface by returning to step 525. The plan is completed when the user is fully compliant.

[0069] Figure 6 illustrates a flowchart describing how a plan generated by AI on server 220 can control a portable electronic droplet delivery device 105. In portable drug delivery device 105, the process begins at step 600. In step 610, delivery device 105 receives a progressive drug reduction plan from user interface 210, including receiving such a plan from a mobile device running an application providing user interface 210. In step 620, the drug delivery device tracks the total drug intake based on determined time (e.g., daily and hourly) throughout the plan. In step 630, when the user reaches a time-based limit, the drug delivery device 105 will not dispense more drug until it is reset after a certain period of time.

[0070] Various embodiments of the invention have been described. However, it will be apparent that various modifications and alterations can be made thereto, and other embodiments can be implemented without departing from the broader scope of the invention set forth in this disclosure. This specification should be regarded as illustrative rather than restrictive.Instruction manual page 6 / 6 9 CN 121419805 A Figure 1A Instruction manual figure 1 / 7 page 10 CN 121419805 A Figure 1B Instruction manual figure 2 / 7 page 11 CN 121419805 A Figure 2 Instruction manual figure 3 / 7 page 12 CN 121419805 A Figure 3 Instruction manual figure 4 / 7 page 13 CN 121419805 A Figure 4 Instruction manual figure 5 / 7 page 14 CN 121419805 A Figure 5 Instruction manual figure 6 / 7 page 15 CN 121419805 A Figure 6 Instruction manual figure 7 / 7 page 16 CN 121419805 A.

Claims

1. A droplet delivery device control system, comprising an artificial intelligence engine remotely and operably coupled via a computer network to implement a drug intake reduction plan on a droplet delivery device for dispensing drugs based on one or more of the user's habits, the user's physical characteristics, and the user's test dose.

2. The droplet delivery device control system according to claim 1, wherein the drug is nicotine.

3. The droplet delivery device control system according to claim 2, wherein the droplet delivery device includes a push-mode ejector.

4. The droplet delivery device control system according to claim 1, wherein the droplet delivery device includes a push-mode ejector.

5. The droplet delivery device control system according to claim 4 further includes a portable electronic diagnostic device operatively coupled to the computer network and providing disease diagnostic data to the artificial intelligence engine.

6. The droplet delivery device control system according to claim 3 further includes a portable electronic diagnostic device operatively coupled to the computer network and providing disease diagnostic data to the artificial intelligence engine.

7. The droplet delivery device control system according to claim 2 further includes a portable electronic diagnostic device operatively coupled to the computer network and providing disease diagnostic data to the artificial intelligence engine.

8. The droplet delivery device control system according to claim 1 further includes a portable electronic diagnostic device operatively coupled to the computer network and providing disease diagnostic data to the artificial intelligence engine.

9. The droplet delivery device control system of claim 9 further includes a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan.

10. The droplet delivery device control system of claim 8 further includes a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan.

11. The droplet delivery device control system of claim 7 further includes a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan.

12. The droplet delivery device control system of claim 6 further includes a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan.

13. The droplet delivery device control system of claim 5 further includes a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan.

14. The droplet delivery device control system of claim 4 further includes a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan.

15. The droplet delivery device control system of claim 3 further includes a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan.

16. The droplet delivery device control system of claim 2 further includes a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan.

17. The droplet delivery device control system of claim 1 further includes a mobile computing device configured to display a user interface via a mobile application connected to the artificial intelligence engine via the network, wherein the user interface presents a treatment plan based on the drug intake reduction plan.

18. A mobile computing device configured to display a user interface via a mobile application connected via a computer network to a remote server having an artificial intelligence engine and a drug droplet delivery device, wherein the user interface presents information on a drug intake reduction plan determined by the artificial intelligence engine.

19. The mobile computing device according to claim 18, wherein the drug intake reduction program is to reduce nicotine use.

20. The mobile computing device according to claim 18 is also operatively coupled to a portable electronic diagnostic device via the network.

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