User feedback system and method

Abstract

To provide a user feedback system which can improve the responsiveness of a delivery device to a user.SOLUTION: A user feedback system for a user of a first device includes: an acquisition processor configured to acquire one or more user factors showing a user state; an estimation processor configured to identify at least a first feedback operation based on one or more of at least a subset of the acquired user factors; and a feedback processor which selects at least the identified first feedback operation and corrects one or more operations of the at least first device according to the or each of the selected feedback operation. The first device is not an aerosol delivery device.SELECTED DRAWING: Figure 7

Description

[Technical Field]

[0001] The present invention relates to a user feedback system and method for a user of a delivery device. [Background technology]

[0002] The "Background" discussion provided herein is intended to provide a general background to the present disclosure. The work of the inventors and aspects of the present disclosure that may not be admitted as prior art at the time of filing to the extent described in this Background section are not admitted explicitly or implicitly as prior art to the present disclosure.

[0003] Aerosol delivery systems have become popular with users because they allow the active ingredient (such as nicotine) to be delivered to the user conveniently and on demand as needed.

[0004] As an example of an aerosol delivery system, an electronic cigarette (e-cigarette) typically includes a reservoir of liquid feedstock containing a formulation (typically containing nicotine) from which an aerosol is generated, e.g., by thermal vaporization. Thus, the aerosol source of the aerosol delivery system may include a heater having a heating element configured to receive the liquid feedstock from the reservoir, e.g., by wicking / capillary action. Other feedstocks may also be similarly heated to generate aerosols, such as botanicals or gels containing active ingredients and / or flavorings. Thus, e-cigarettes are more generally considered to contain or receive a payload for thermal vaporization.

[0005] While a user inhales on the device, power is supplied to the heating element, causing an aerosol source (a portion of the payload) adjacent to the heating element to vaporize, generating an aerosol that is inhaled by the user. Such devices typically have one or more intake holes located away from the mouthpiece end of the system. When a user inhales on a mouthpiece connected to the mouthpiece end of the system, air is drawn in through the inlet hole and passes through the aerosol source. Because a flow path exists connecting the aerosol source to the mouthpiece opening, the drawn-in air passes through the aerosol source along the flow path to the mouthpiece opening, carrying a portion of the aerosol from the aerosol source. The aerosol-carrying air exits the aerosol delivery system through the mouthpiece opening, which is inhaled by the user.

[0006] Typically, current is supplied to the heater upon inhalation / puffing by the user on the device. Typically, current is supplied to the heater (e.g., a resistive heating element) in response to activation of an airflow sensor along the flow path during inhalation / inhalation / puffing by the user or activation of a button by the user. Heat generated by the heating element is used to vaporize the formulation. The released vapor mixes with air drawn into the device by the puffing consumer to form an aerosol. Alternatively or additionally, the heating element is typically used to heat, rather than burn, botanical material, such as tobacco, to release its active ingredient as a vapor / aerosol.

[0007] The manner in which a user interacts with an e-cigarette (e.g., the amount of vaporized / aerosolized payload consumed by the user and / or their respective usage patterns) and the actual or perceived utility of the interaction are believed to be influenced by the user's state, which may be expressed at least in part colloquially as their respective mood(s) and / or subjective need(s).

[0008] As a result, it would be useful to provide a delivery mechanism that is more responsive to the user's condition. Summary of the Invention

[0009] In a first aspect, there is provided a user feedback system for a user of a delivery device in a delivery ecosystem according to claim 1.

[0010] In another aspect, there is provided a method of user feedback to a user of a delivery device in a delivery ecosystem according to claim 27.

[0011] Other aspects and features of the present invention are defined in the accompanying claims.

[0012] It is to be understood that both the foregoing general summary of the disclosure and the following detailed description are illustrative of the present disclosure but are not restrictive of the present disclosure.

[0013] The present disclosure and many of the attendant advantages thereof will be readily appreciated as they become better understood by reference to the following detailed description when considered in connection with the accompanying drawings. [Brief explanation of the drawings]

[0014] [Figure 1] FIG. 1 is a schematic diagram of a delivery device according to an embodiment herein. [Figure 2] FIG. 1 is a schematic diagram of a body of a delivery device according to an embodiment herein. [Figure 3] FIG. 1 is a schematic diagram of a cartomizer of a delivery device, according to an embodiment herein. [Figure 4] FIG. 1 is a schematic diagram of a body of a delivery device according to an embodiment herein. [Figure 5] FIG. 1 is a schematic diagram of a delivery ecosystem, according to embodiments herein. [Figure 6] FIG. 1 is a schematic diagram of a user feedback system, according to an embodiment herein. [Figure 7] FIG. 1 is a flow diagram of a method for user feedback to a user of a delivery device in a delivery ecosystem, according to an embodiment herein. [Figure 8] FIG. 1 is a schematic diagram of a non-delivery ecosystem of a feedback device, according to an embodiment herein. DETAILED DESCRIPTION OF THE INVENTION

[0015] [Description of the embodiment] A user feedback system and method are disclosed. In the following description, numerous specific details are provided to enable a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to those skilled in the art that these specific details are not necessary to realize the embodiments of the present disclosure. Conversely, specific details that would be understood by those skilled in the art are omitted, where necessary, for the sake of clarity.

[0016] As noted above, the present disclosure relates to a user feedback system for improving the responsiveness of a delivery device to a user.

[0017] The term "delivery device" may encompass systems that deliver at least one substance to a user, and may include non-combustible aerosol delivery systems that release compounds from an aerosol-forming material without combustion of the aerosol-forming material, such as electronic cigarettes, tobacco heating products, and hybrid systems that generate an aerosol using a combination of aerosol-forming materials, and aerosol-free delivery systems that deliver at least one substance to a user orally, nasally, transdermally, or otherwise, without the formation of an aerosol (including, but not limited to, oral products such as lozenges, gums, patches, articles containing inhalable powders, and oral tobacco products including snus or moist snuff) (the at least one substance may or may not contain nicotine).

[0018] The substance to be delivered may be an aerosol-generating material or a material not subject to aerosolization, either of which may optionally include one or more active ingredients, one or more flavoring agents, one or more aerosol-forming materials, and / or one or more other functional materials.

[0019] Currently, the most common examples of such delivery devices are aerosol delivery systems (e.g., non-combustible aerosol delivery systems) or electronic vapor delivery systems (EVPS), such as e-cigarettes. Throughout the following description, the term "e-cigarette" may be used interchangeably with delivery device, unless otherwise stated or the context dictates. Similarly, the terms "vapour" and "aerosol" are referred to equivalently herein.

[0020] Generally, the electronic vapor / aerosol delivery system may be a vaping device or an electronic cigarette, also known as an electronic nicotine delivery system (END), although it is noted that the presence of nicotine in the aerosol-generating (e.g., aerosolizable) material is not a requirement. In some embodiments, the non-combustion aerosol delivery system is a tobacco heating system, also known as a non-combustion heating system. An example of such a system is a tobacco heating system. In some embodiments, the non-combustion aerosol delivery system is a hybrid system that generates aerosol through a combination of aerosol-generating materials (one or more of which may be heated). Each aerosol-generating material may be, for example, in solid, liquid, or gel form, and may or may not contain nicotine. In some embodiments, the hybrid system includes a liquid or gel aerosol-generating material as well as a solid aerosol-generating material. The solid aerosol-generating material may include, for example, tobacco or a non-tobacco product. In contrast, in some embodiments, the non-combustion aerosol delivery system generates vapor / aerosol from one or more such aerosol-generating materials.

[0021] Typically, a non-combustible aerosol delivery system may include a non-combustible aerosol delivery device and articles (sometimes referred to as consumables) for use with the non-combustible aerosol delivery system. However, it is contemplated that an article that itself includes a means for powering an aerosol generating component (e.g., an aerosol generator, such as a heater or a vibrating mesh) may itself constitute a non-combustible aerosol delivery system. In one embodiment, the non-combustible aerosol delivery device may include a power source and a controller. The power source may be an electrical source or a heat-generating power source. In one embodiment, the heat-generating power source comprises a carbon substrate that can be energized to provide power in the form of heat to an aerosolizable material or a heat transfer material in proximity to the heat-generating power source. In one embodiment, a power source, such as a heat-generating power source, is provided in the article to enable non-combustible aerosol delivery. In one embodiment, an article for use with a non-combustible aerosol delivery device may include an aerosolizable material.

[0022] In some embodiments, the aerosol-generating component is a heater capable of interacting with the aerosolizable material to release one or more volatile substances from the aerosolizable material to form an aerosol. In one embodiment, the aerosol-generating component is capable of generating an aerosol from the aerosolizable material without the application of heat. For example, the aerosol-generating component may be capable of generating an aerosol from the aerosolizable material without the application of heat, such as by one or more of vibrational, mechanical, pressure, or electrostatic means.

[0023] In some embodiments, the aerosolizable material may include an active material, an aerosol-forming material, and optionally one or more functional materials. The active material may include nicotine (optionally contained in tobacco or a tobacco derivative) or one or more other non-olfactory physiologically active materials. The non-olfactory physiologically active materials are materials that are included in the aerosolizable material to achieve a physiological response other than the sense of smell. The aerosol-forming material may include one or more of glycerin, glycerol, propylene glycol, diethylene glycol, triethylene glycol, tetraethylene glycol, 1,3-butylene glycol, erythritol, meso-erythritol, ethyl vanillate, ethyl laurate, diethyl sulfate, triethyl citrate, triacetin, diacetin mixtures, benzyl benzoate, benzyl phenylacetate, tributyrin, lauryl acetate, lauric acid, myristic acid, and propylene carbonate. The one or more functional ingredients may include one or more of a fragrance, a carrier, a pH adjuster, a stabilizer, and / or an antioxidant.

[0024] In some embodiments, an article for use with a non-burning aerosol delivery device may include an aerosolizable material or an area for receiving an aerosolizable material. In one embodiment, an article for use with a non-burning aerosol delivery device may include a mouthpiece. The area for receiving an aerosolizable material may be a storage area that stores the aerosolizable material. For example, the storage area may be a reservoir. In one embodiment, the area for receiving an aerosolizable material may be separate from the aerosol-generation area or may be coupled to the aerosol-generation area.

[0025] As an alternative or in addition to an aerosol delivery system, the delivery device may include any device that introduces / allows for the introduction of an active ingredient into the user's body so that the active ingredient can take effect.

[0026] Thus, exemplary delivery devices include, for example, devices that dispense an aerosol into a receptacle, after which a user can remove the receptacle from the device to inhale or smoke the aerosol, and thus do not necessarily require direct user involvement at the point of consumption.

[0027] In this regard, alternatively or additionally, the delivery device may provide reminders or usage regimes for the user (e.g., reminding the user when to use a snus pouch or other active delivery such as a tablet), and the delivery device may optionally store and dispense such consumables in accordance with the reminders or usage regimes.

[0028] Similarly, an exemplary delivery device may be a home refill station that mixes e-liquid ingredients for a user and uses the mixture to fill the reservoir of an e-cigarette, thereby determining the type, blend, and / or concentration (all things being equal) of active ingredients that the user consumes. Such a home refill station may be referred to as a "dock," a power charging station, or a device that combines both functions.

[0029] In this regard, delivery devices operating as vending machines can similarly provide consumable refills or disposable devices based on a mixture and / or selection of e-liquid components, either mixed on demand or equivalently selected from a variety of pre-made mixtures. Similarly, in other embodiments, vending machines may provide oral products (e.g., snus, snuff, gum, gels, sprays, and other delivery systems such as patches) or other consumable products, for example, containing active ingredients and / or flavorings.

[0030] In each case, the delivery device is operable to affect one or more of the amount, timing, type, blend, and / or concentration of the active ingredient that the user consumes.

[0031] More generally, therefore, the delivery device is operable to affect the properties of the active ingredient that the user consumes.

[0032] Of course, multiple delivery devices may operate in tandem to have such an effect. For example, a home refill station or vending machine may actually operate in conjunction with an e-cigarette to provide active ingredient changes or other feedback to the user. Similarly, a mobile phone may operate in parallel with an e-cigarette to provide information or analysis regarding such changes or other feedback.

[0033] In this sense, a delivery device may actually be a delivery system comprising multiple devices operating in sequence and / or in parallel to exert the desired influence / feedback, and therefore references herein to a delivery device or a delivery system are to be considered interchangeable unless otherwise stated.

[0034] Referring now to the drawings, wherein like reference numbers represent the same or corresponding parts throughout the several views, FIG. 1 is a schematic diagram (not to scale) of a vapor / aerosol delivery system, such as an e-cigarette 10, providing one non-limiting example of a delivery device according to some embodiments of the present disclosure.

[0035] The e-cigarette has a generally cylindrical shape extending along a longitudinal axis indicated by dashed line LA and comprises two major components: a body 20 and a cartomizer 30. The cartomizer comprises an internal chamber containing a reservoir of payload, e.g., a liquid containing nicotine, a vaporizer (e.g., a heater), and a mouthpiece 35. References hereinafter to "nicotine" are by way of example only and are understood to be substituted with any suitable active ingredient. References hereinafter to "liquid" as the payload are by way of example only and are understood to be substituted with any suitable payload, such as botanicals (e.g., tobacco that is heated rather than burned) or a gel containing an active ingredient and / or flavoring. The reservoir may be a foam matrix or any other structure that holds the liquid until it is needed for delivery to the vaporizer. In the case of liquid / flowable payloads, the vaporizer is for vaporizing the liquid, and the cartomizer 30 may further include a wick or similar mechanism for transporting a small amount of liquid from a reservoir to a vaporization location on or adjacent to the vaporizer. Hereinafter, a heater will be used as an example of a vaporizer. However, it will be appreciated that other forms of vaporizers (e.g., ultrasonic vaporizers) can also be used, and it will be appreciated that the type of vaporizer used may also depend on the type of payload being vaporized.

[0036] The main body 20 includes a rechargeable cell or battery that powers the e-cigarette 10 and a circuit board that controls the entire e-cigarette. The heater receives power from the battery and, when controlled by the circuit board, vaporizes the liquid. This vapor is then inhaled by the user through the mouthpiece 35. In some specific embodiments, the main body is further provided with a manual activation device 265 (e.g., a button, switch, or touch sensor located on the outside of the main body).

[0037] Although the main body 20 and the cartomizer 30 may be detachable from each other by separation in a direction parallel to the longitudinal axis LA, as shown in Fig. 1, when the device 10 is in use, mechanical and electrical connection is established between the main body 20 and the cartomizer 30 by integral joining via connectors schematically shown as 25A and 25B in Fig. 1. The electrical connector 25B of the main body 20, which is used to connect to the cartomizer 30, also functions as a socket for connecting a charging device (not shown) when the main body 20 is detached from the cartomizer 30. The battery in the main body 20 of the e-cigarette 10 can be charged by plugging the other end of the charging device into a USB socket. In other embodiments, a cable may be provided for directly connecting the electrical connector 25B of the main body 20 to the USB socket.

[0038] The e-cigarette 10 is provided with one or more intake holes (not shown in FIG. 1 ). These holes lead to an air passageway through the e-cigarette 10 and into the mouthpiece 35. When a user draws on the mouthpiece 35, air is drawn into this air passageway through one or more intake holes, preferably located on the exterior of the e-cigarette. When the heater is activated to vaporize nicotine from the cartridge, an airflow passes through and combines with the resulting vapor, and the combination of airflow and resulting vapor then exits the mouthpiece 35 for the user to draw on. Except for disposable devices, the cartomizer 30 may be detached from the body 20 and discarded (and replaced with another cartomizer, if desired) when the liquid supply is depleted.

[0039] It should be understood that the e-cigarette 10 shown in Figure 1 is provided by way of example, and various other implementations are possible. For example, in some embodiments, the cartomizer 30 is provided as two separate components: a cartridge with a liquid reservoir and a mouthpiece (which can be replaced when the reservoir is depleted), and a vaporizer with a (typically retained) heater. In another example, the charging mechanism may be connected to an additional or alternative power source, such as a car's cigarette lighter socket.

[0040] Figure 2 is a schematic (simplified) diagram of the body 20 of the e-cigarette 10 of Figure 1, according to some embodiments of the present disclosure. Figure 2 can generally be considered a cross-section taken along a plane passing through the longitudinal axis LA of the e-cigarette 10. Note that various components and details of the body (e.g., wiring and more complex moldings) have been omitted from Figure 2 for clarity.

[0041] The main body 20 includes a battery or cell 210 that powers the e-cigarette 10 in response to activation of the device by a user. The main body 20 also includes a control unit (not shown in FIG. 2 ) (e.g., a chip such as an application specific integrated circuit (ASIC) or microcontroller) that controls the e-cigarette 10. The microcontroller or ASIC includes a CPU or microprocessor. The operation of electronic components such as the CPU is typically controlled, at least in part, by a software program running on the CPU (or other component). Such software programs may be stored in non-volatile memory such as ROM, which may be embedded in the microcontroller itself or provided as a separate component. The CPU may access the ROM as needed to load and execute individual software programs. The microcontroller also includes appropriate communication interfaces (and control software) for communicating with other devices in the main body 10 as needed.

[0042] The main body 20 further includes a cap 225 that seals and protects the distal end of the e-cigarette 10. Typically, an air intake hole is provided in or adjacent to the cap 225 to allow air to enter the main body 20 when a user draws on the mouthpiece 35. A control unit or ASIC may be located next to or at one end of the battery 210. In some embodiments, the ASIC is attached to a sensor unit 215 to detect draws on the mouthpiece 35 (or, alternatively, the sensor unit 215 may be provided on the ASIC itself). In either case, the sensor unit 215, with or without an ASIC, may be understood as an example of a sensor platform. An air path is provided from the air intake, through the e-cigarette, via the airflow sensor 215 and the heater (of the vaporizer or cartomizer 30), to the mouthpiece 35. Thus, when a user draws on the e-cigarette's mouthpiece, the CPU detects such draws based on information from the airflow sensor 215.

[0043] At the end of the body 20 opposite the cap 225 is a connector 25B for joining the body 20 to the cartomizer 30. The connector 25B provides both mechanical and electrical connections between the body 20 and the cartomizer 30. The connector 25B is metallic (in some embodiments, silver-plated) and includes a body connector 240 that serves as a terminal for electrical connection (positive or negative) to the cartomizer 30. The connector 25B further includes an electrical contact 250 that provides a second terminal for electrical connection to the cartomizer 30 of opposite polarity to the first terminal, i.e., the body connector 240. The electrical contact 250 is mounted to a coil spring 255. When the body 20 is attached to the cartomizer 30, the connector 25A of the cartomizer 30 presses against the electrical contact 250, compressing the coil spring in an axial direction, i.e., a direction parallel to the longitudinal axis LA (collinear direction). Considering the resilience of the spring 255, this compression tends to expand the spring 255, which has the effect of pressing the electrical contact 250 firmly against the connector 25A of the cartomizer 30, thereby helping to ensure a good electrical connection between the main body 20 and the cartomizer 30. The main body connector 240 and the electrical contact 250 are separated by a trestle 260 which is made of a non-conductor (such as plastic) and therefore provides good insulation between the two electrical terminals. The trestle 260 is shaped to aid in the mechanical engagement of the connectors 25A and 25B with each other.

[0044] As mentioned above, a button 265, which represents one form of manual activation device 265, may be located on the outer housing of the main body 20. The button 265 may be implemented using any suitable mechanism operable to be manually activated by a user, such as, for example, a mechanical button or switch, a capacitive or resistive touch sensor, etc. It should also be appreciated that the manual activation device 265 may be located on the outer housing of the cartomizer 30 rather than on the outer housing of the main body 20, in which case the manual activation device 265 may be attached to the ASIC via connections 25A, 25B. The button 265 may also be located at the end of the main body 20 in place of (or in addition to) the cap 225.

[0045] Figure 3 is a schematic diagram of the cartomizer 30 of the e-cigarette 10 of Figure 1, according to some embodiments of the present disclosure. Figure 3 can generally be considered a cross-section in a plane passing through the longitudinal axis LA of the e-cigarette 10. Note that various components and details of the cartomizer 30 (e.g., wiring and more complex moldings) have been omitted from Figure 3 for clarity.

[0046] The cartomizer 30 includes an air passageway 355 extending along the central (longitudinal) axis of the cartomizer 30 from the mouthpiece 35 to a connector 25A that connects the cartomizer 30 to the main body 20. A liquid reservoir 360 is provided around the air passageway 355. The reservoir 360 may be realized, for example, by providing cotton or foam impregnated with a liquid. The cartomizer 30 also includes a heater 365 that heats liquid from the reservoir 360 in response to a user drawing on the e-cigarette 10, thereby generating vapor that flows through the air passageway 355 and exits the mouthpiece 35. The heater 365 is powered through lines 366 and 367, which are connected via a connector 25A to opposite poles (positive and negative, or vice versa) of the battery 210 in the main body 20 (details of the wiring between the wires 366 and 367 and the connector 25A are omitted from FIG. 3 ).

[0047] Connector 25A includes an inner electrode 375, which may be silver plated or comprised of any other suitable metal or conductive material. When cartomizer 30 is connected to body 20, inner electrode 375 contacts electrical contact 250 on body 20, providing a first electrical path between cartomizer 30 and body 20. In particular, when connectors 25A and 25B are engaged, inner electrode 375 presses against electrical contact 250, compressing coil spring 255 and helping to ensure good electrical contact between inner electrode 375 and electrical contact 250.

[0048] The inner electrode 375 is surrounded by an insulating ring 372, which may be constructed of plastic, rubber, silicone, or any other suitable material. The insulating ring is surrounded by a cartomizer connector 370, which may be silver-plated or constructed of any other suitable metal or conductive material. When the cartomizer 30 is connected to the main body 20, the cartomizer connector 370 contacts the main body connector 240 of the main body 20, providing a second electrical path between the cartomizer 30 and the main body 20. In other words, the inner electrode 375 and the cartomizer connector 370 function as positive and negative terminals (or vice versa) for supplying power from the battery 210 of the main body 20 to the heater 365 of the cartomizer 30 via supply lines 366 and 367 as needed.

[0049] The cartomizer connector 370 is provided with two lugs or tabs 380A, 380B that extend in opposite directions away from the longitudinal axis of the e-cigarette 10. These tabs are used to connect the cartomizer 30 to the body 20 by a bayonet fit with the body connector 240. This bayonet fit provides a secure and robust connection between the cartomizer 30 and the body 20, holding the cartomizer and body in a fixed position relative to each other with minimal wobble or flexing, greatly reducing the likelihood of any accidental separation. At the same time, the bayonet fit allows for easy and quick connection and separation by inserting and rotating to connect, and rotating (in the opposite direction) and then withdrawing to separate. Of course, in other embodiments, different forms of connection may be used between the body 20 and the cartomizer 30, such as a snap fit or a threaded connection.

[0050] FIG. 4 is a schematic diagram of certain details of connector 25B at the end of body 20, according to some embodiments of the present disclosure (although for clarity, much of the internal structure of the connector, such as trestle 260, as shown in FIG. 2 has been omitted). In particular, FIG. 4 shows outer housing 201 of body 20 having a generally cylindrical tube form. This outer housing 201 may comprise, for example, a metal inner tube with an outer covering such as paper. Outer housing 201 may also include a manual activation device 265 (not shown in FIG. 4) for easy user access.

[0051] A body connector 240 extends from the outer housing 201 of the body 20. As shown in Figure 4, the body connector 240 includes two main portions: a shaft portion 241 in the shape of a hollow cylindrical tube sized to fit snugly within the outer housing 201 of the body 20, and a lip portion 242 facing radially outward, away from the e-cigarette's primary longitudinal axis (LA). Surrounding the shaft portion 241 of the body connector 240, but not overlapping with the outer housing 201, is a collar or sleeve 290, also in the shape of a cylindrical tube. The collar 290 is retained between the lip portion 242 of the body connector 240 and the body's outer housing 201, which together prevent movement of the collar 290 in the axial direction (i.e., in a direction parallel to the axis LA), although the collar 290 is free to rotate about the shaft portion 241 (and thus the axis LA).

[0052] As previously mentioned, cap 225 is provided with an intake hole through which air can flow when a user inhales on mouthpiece 35. However, in some embodiments, the majority of the air that enters the device when a user inhales flows through collar 290 and body connector 240, as shown by the two arrows in FIG.

[0053] 5, an e-cigarette 10 (or more generally any delivery device as described elsewhere herein) may be adapted to operate within a broader delivery ecosystem 1. In the broader delivery ecosystem, many devices may be in communication with each other, either directly (as indicated by the solid arrows) or indirectly (as indicated by the dashed arrows).

[0054] 5, exemplary delivery devices include an e-cigarette 10 that may communicate directly (e.g., by using Bluetooth® or WiFi Direct®) with one or more other classes of devices, including, but not limited to, a smartphone 100, a dock 200 (e.g., a home refill and / or charging station), a vending machine 300, or a wearable 400. As noted above, these devices may cooperate in any suitable configuration to form a delivery system.

[0055] Alternatively or additionally, a delivery device such as e-cigarette 10 may be adapted to communicate indirectly with one or more of these classes of devices via a network such as the Internet 500, for example using Wi-Fi, near field communication, a wired link, or integrated mobile data. Again, as described above, these devices may cooperate in any suitable configuration to form a delivery system.

[0056] Alternatively or additionally, a delivery device, such as the e-cigarette 10, may communicate indirectly with the server 1000 over a network such as the Internet 500, e.g., by itself using Wi-Fi, or through another device in the delivery ecosystem, such as a smartphone 100, a dock 200, a vending machine 300, or a wearable 400, e.g., by using Bluetooth or Wi-Fi Direct, which then communicates with the server to relay the e-cigarette's communications or report on communications with the e-cigarette 10. Thus, other devices in the delivery ecosystem, such as a smartphone, a dock, or a point-of-sale / vending machine, may optionally act as a hub for one or more delivery devices that only support short-range transmissions. Such a hub may thus extend the battery life of delivery devices that do not need to continuously maintain a Wi-Fi or mobile data link. It should also be appreciated that different types of data may be transmitted with different priorities. For example, data relating to a user feedback system (such as user factor data or feedback behavior data as discussed herein) may be transmitted with a higher priority than more general usage statistics, and similarly, some user factor data relating to shorter-term variables (such as current physiological data) may be transmitted with a higher priority than user factor data relating to longer-term variables (such as current weather or day of the week). A non-limiting example of a transmission scheme that allows for high and low priority transmission is LoRaWAN.

[0057] However, other classes of devices in the ecosystem, such as smartphones, docks, vending machines (or any other point of sale system), and / or wearables, may also communicate indirectly with server 1000 over a network such as the Internet 500 to fulfill an aspect of their own functionality or on behalf of the delivery system (e.g., as a relay or co-processing unit). These devices may also communicate with each other, either directly or indirectly.

[0058] In one embodiment herein, to form a user feedback system as described below, the server 1000, a delivery device such as the e-cigarette 10, and / or any other device in the delivery ecosystem may utilize one or more information sources within the delivery ecosystem or accessible by one or more devices thereof to more accurately respond to the user's condition. These may include information sources such as a wearable or mobile phone (or any other information source such as a dock or vending machine) or the server's storage system 1012. The delivery device may also provide information (such as data regarding interaction with the e-cigarette) to one or more data receivers in the ecosystem, which may again include one or more of the wearable, mobile phone, dock or vending machine, or server.

[0059] To configure a user feedback system as described below in this specification, devices in the delivery ecosystem, such as delivery device 10, may utilize one or more processors to analyze or process this information to estimate the state of a user (whether of a normal / default user, a user with attributes similar to the current user, or specifically the current user) and / or a form of feedback action determined to change the estimated state of the user, for example, by modifying one or more actions of the delivery device or another device in the delivery ecosystem.

[0060] Of course, a delivery ecosystem may include multiple delivery devices (10), for example, because a user owns multiple devices (e.g., to facilitate switching between different active ingredients or fragrances), or because multiple users share at least a portion of the same delivery ecosystem (e.g., users living in the same household may share a charging dock while having their own phones or wearables). Optionally, such devices may also communicate directly or indirectly with each other, with devices in the shared delivery ecosystem, and / or with a server. In such cases, a PIN, ID, or account may be associated with each delivery device so that the device can be associated with the correct user, especially when multiple users share the same delivery ecosystem.

[0061] It should be understood that reference to a "user state" includes one of many states of the user, or equivalently, an aspect of the user's overall state. Thus, for example, a user's stress level, which could also be a combination of social context and cortisol levels, as a non-limiting example, is an example of a "user state," but does not fully define the user. In other words, a user state is a state associated with the potential intervention of one or more feedback actions, as described elsewhere herein.

[0062] User Feedback System Referring now to FIG. 6 , in one embodiment herein, a user feedback system 2 for a user of a delivery device in a delivery ecosystem 1 comprises an acquisition processor 1010 operable to acquire one or more user factors indicative of a user state, an estimation processor 1020 operable to calculate an estimate of the user state based on one or more of the acquired user factors, and a feedback processor 1030 operable to select a feedback action for at least a first device in the delivery ecosystem in response to the estimation of the user state such that a change in the estimated state of the user is expected.

[0063] FIG. 6 shows, by way of non-limiting example, one possible embodiment of such a user feedback system.

[0064] In this embodiment, the acquisition processor 1010, the estimation processor 1020, and the feedback processor 1030 are located within the server 1000. However, it will be appreciated that any one or more of these processors may be located elsewhere within the ecosystem 1, or their roles may be shared by two or more processors of the server and / or ecosystem. For example, the acquisition processor may be located in the e-cigarette or the mobile phone, and the feedback processor may be located in the vending machine or the e-cigarette, or the functionality of these processors may be shared between the server and such devices. In other examples, these processors may be available only in the delivery device (e.g., the e-cigarette) or only in the delivery system that includes the delivery device and the mobile phone.

[0065] Retrieval Processor The acquisition processor 1010 acquires or receives one or more user factors contained in one or more data classes from one or more sources.

[0066] Such user factors may have a causal and / or correlative relationship with the user's state, or some other predictable relationship. Such states may be associated with what is colloquially referred to as the user's "mood," although the user's subjective mood itself is not a primary consideration of the feedback system. Rather, the feedback system is concerned with the correspondence between the captured user factor(s) and the user state, the user state, and forms of feedback action that may alter such state of the user in a predetermined manner that is typically beneficial to the user.

[0067] Furthermore, it should be appreciated that if there is a correspondence between user factor(s) and state, state, and feedback, then in principle there is also a correspondence between user factor(s) and feedback without necessarily having to explicitly infer an intermediary state.

[0068] Classes of data acquired by or for the acquisition processor include, but are not limited to, indirect or historical data, neurological or physiological data, contextual data, environmental or deterministic data, and usage-based data.

[0069] Indirect or historical data Indirect or historical data provides background information about the user that is not necessarily related to the immediate situation (eg, not the immediate environment or context), but that may affect the user's state.

[0070] Examples of indirect or historical data include, but are not limited to, a user's purchasing history, previously entered user preference data, or normal behavioral patterns. Thus, more generally, user choices or actions are typically related to the delivery device, but typically do not result directly from use of the delivery device itself.

[0071] Optionally, such information (or indeed any persistent information, such as preferred user settings, user state and / or feedback behavior model data as described elsewhere herein, account details, or other stored user factor data) may be transferred between devices when a given user purchases or uses different delivery devices, thereby eliminating the need to re-acquire such information for each new or each device. Such information may be transferred or shared, for example, by direct data transfer over a Bluetooth link between the old and new devices. However, since a potential reason for purchasing a new device is loss of the old device, the information may alternatively or additionally be transferred or shared by (similarly) maintaining it remotely in association with an account / user ID and subsequently associating it with the user's different delivery devices / systems. Thus, a system containing indirect or historical data learned / acquired on the old device may be transferred or shared to the new device either directly between devices or through a centralized user account.

[0072] It will also be appreciated that such historical data may be accumulated by any device in the delivery ecosystem, and may similarly be shared with replacement or complementary devices and / or stored in association with a user ID for purposes of such sharing, and / or used by a feedback system.

[0073] As an example of historical information, purchase history may indicate the user's state, which may indicate the user's general state over time (e.g., in terms of significant or recurring purchases) and / or the user's recent state (e.g., in terms of recent purchases or purchases whose impact on the user is still considered).

[0074] Thus, a purchasing history that may be indicative of a user's state may include the type(s) of product(s) purchased, the frequency of purchases, etc. (not necessarily limited to products directly related to the delivery device or its consumables), the purchasing method (e.g., online vs. in-store), and the amount purchased over a period of time. Correspondence between purchasing methods (and purchased products or services) that affect a user's state may initially be determined on a population basis (e.g., to allow for matching of a statistically significant amount of data), or on a subset of a population and / or individual user basis that are similar in attributes to the user. For example, marking purchases as associated with a particular state, whether through the use of human-readable or machine-readable markings (e.g., QR codes), may aid in this process. If a purchase, such as a consumable, includes a machine-readable mark, this may be registered as an indicator of mood. Similarly, a consumable may be equipped with a means for being recognized as indicating mood when inserted or loaded into a delivery device. For example, a microchip containing a code or another uniquely identifiable means of electronically detecting the type of payload (e.g., a binary pattern of conductive dots on the surface of the consumable that can be detected by corresponding contacts on the delivery device) may be used. Such identifiable types may differ by composition (e.g., fragrance, active ingredient, or concentration of either) or by pre-defined control (e.g., two types may be identical except that they exhibit different heating profiles to the device resulting in different inhalation effects).

[0075] The acquisition processor may obtain indirect or historical data from a number of sources, including, for example, previously entered user preference data and / or similar logs of interaction and / or usage patterns, web or internet-based data 110 such as purchase records received from partners such as vendors, user profile data maintained in server storage 1012, information collected with consent by the user's mobile phone 100 relating to various aspects of entered user preference data, online purchases, interaction / usage data (e.g., when the phone operates in tandem with a delivery device such as an e-cigarette as a delivery system available only to the user), user surveys, etc. Similarly, alternatively or additionally, the acquisition processor may obtain such data from the delivery device itself.

[0076] Neurological and / or physiological data Neurological and / or physiological data describes the user's physical state, with respect to mind and / or body. This data may describe the user's state over various time scales, such as immediate status or state changes (e.g., heart rate), long-term status or state changes (e.g., hormone cycles), or chronic status such as fitness level.

[0077] Non-limiting examples of long-term data include indicators of a user's metabolism, body type (e.g., lean, average, obese) or body mass index, chronic illness, pregnancy, or any other long-term condition, as well as activity / fitness levels, for example, on the order of months to years.

[0078] Such data may be obtained by or to the acquisition processor from one or more user surveys (e.g., surveys completed specifically to support the user feedback system and / or surveys completed for any third party partners (e.g., fitness wearable devices or social media providers)), consented medical or insurance records, or at least in part from other devices such as fitness wearable 400 and / or other devices in the broader ecosystem 1, such as smart scales.

[0079] Non-limiting examples of medium- to long-term data include the user's hormone levels or hormone cycles, such as estrogen, testosterone, dopamine, cortisol, etc., on the order of weeks to months, any acute conditions or illnesses, and activity / health levels.

[0080] Non-limiting examples of medium-term data include, for example, on the order of days to weeks, the user's sleep cycles, acute conditions or illnesses, and the user's hormone levels or cycles, such as estrogen, testosterone, dopamine, and cortisol.

[0081] Non-limiting examples of medium to short term data include, for example, on the order of hours to days, the user's alertness, activity, appetite or satiety, blood pressure, body temperature, and again, acute conditions or diseases, and / or hormones.

[0082] Additionally, such medium-term data (long or short term) may be obtained by or to the acquisition processor from questionnaires, medical or other records, fitness or other smart devices. Thus, for example, hormone levels may be obtained or inferred from questionnaires, medical or other records, consented diary or calendar entries, and / or fitness or other smart devices (e.g., pinprick blood tests, etc.). Similarly, blood pressure, temperature, activity levels, etc. may be obtained from smart devices (typically wearable) or user input.

[0083] Non-limiting examples of short-term data include, for example, on the order of minutes to hours, the user's sweat response, galvanic skin response (phasic and / or tonic), activity level, appetite or satiety, blood pressure, respiratory rate, body temperature, muscle tension, heart rate and / or heart rate variability, and again, any acute conditions or diseases and / or hormones.

[0084] The acquisition processor may also acquire neurological and / or physiological information specific to the delivery device, such as the cumulative amount of vapor generated over a short period of time (e.g., a period of time equivalent to one, two, or more of the pharmacological half-life of the active ingredient in the user's body).

[0085] Non-limiting examples of recent data include, for example, on the order of seconds to minutes, the user's body position, blink rate, respiration rate, heart rate, heart rate variability, brain wave patterns, galvanic skin response (e.g., phasic), muscle tension, skin temperature, voice (e.g., volume, pitch, quality of breathing, etc.), and activity level.

[0086] Short-term and recent data may also typically be acquired by or to an acquisition processor, for example, by biosensing using a smart device or any suitable technique described herein. For example, galvanic skin response may be measured by electrodes on a delivery device, and heart rate may be acquired by optically scanning blood vessels in the wrist with a wearable device or by using an electrocardiogram (ECG) or other dedicated strap-type device. Similarly, brain wave patterns may be detected by an electroencephalogram (EEG), and muscle tension may be detected by an electromyogram (EMG). Meanwhile, body posture, eye blinks, etc. may be captured by, for example, a camera on a phone or vending machine.

[0087] It will be appreciated that in the above description, the same example may have shorter and longer term characteristics, e.g., different hormones, hormone cycles, fitness levels, etc., to the extent that they span different time frames. It will also be appreciated that if an example of data is included in one list but not another, this does not preclude the collection / use of that data over different time frames. For example, blood pressure may be listed as an example of short term data, but may also clearly be part of long term data, e.g., due to persistent high blood pressure.

[0088] As with indirect or historical data, any suitable combination of data types and / or data from multiple sources may be used.

[0089] In addition to directly measured neurological or physiological data, any suitable analysis or data fusion may be performed to obtain data regarding the user's condition that is particularly relevant to the delivery device.

[0090] For example, the feedback system may be operable to estimate the current nicotine concentration (as one non-limiting example of an active ingredient) or the concentration of an active or inactive compound that breaks down in the user's body from the consumed ingredient (and then delivers the nicotine / active ingredient accordingly).

[0091] Thus, in principle, a feedback system (e.g., in a preprocessor or subsystem of the acquisition processor) may estimate a user's nicotine concentration based on monitoring the amount of nicotine consumed, the duration of consumption, and the value of the nicotine's half-life in the body (around 2 hours, but this value can be refined based on personal information such as height, weight, etc.). Such monitoring can be performed based on usage data from the delivery device. Thus, for example, based on the original active ingredient concentration and a predetermined relationship between heating / aerosol generator power and aerosol mass output, the mass of active ingredient per unit volume of inhalation may be estimated, from which the amount of active ingredient absorbed may be determined using a predetermined absorption relationship (optionally based on analysis of inhalation depth / duration using airflow data). Finally, the user's obesity level and potentially other factors such as age and gender may be used to determine the concentration of the active ingredient and / or degradation products in the user over time. Again, nicotine is a non-limiting example of an active ingredient.

[0092] It is recognized that users typically seek to maintain nicotine levels that lie between upper and lower thresholds (which may vary from user to user), which collectively may define a "baseline" level. A feedback system can establish such a baseline (e.g., by monitoring the user over time) and, as described in more detail below, select one or more operations of the delivery device to deliver nicotine and, optionally, modify the operations to match the baseline. The baseline may be a stable value or may vary, for example, by time of day or day of the week. The baseline may be initially estimated based on a user profile obtained, for example, from a questionnaire, and / or may be established or refined with information from the user (measurements and / or self-report).

[0093] Because it has been found that nicotine levels closer to an individual's baseline or threshold range increase the chances that a user will experience a positive mood, such modifications can be expected to positively alter the user's estimated state.

[0094] If a user consumes multiple different active ingredients, each may have its own baseline threshold. Optionally, the feedback system may monitor whether there is overlap with another active ingredient to the extent that consumption of one active ingredient may affect the baseline of another active ingredient, and if so, may modify these accordingly, for example, based on stored pharmacokinetic data associated with such overlap.

[0095] As noted above, in these situations, a user may interact with multiple delivery devices to consume different active ingredients, and usage from each device may be combined for the associated user. Alternatively, if a single device is capable of switching payloads (e.g., dispensing different gels) or has a mixed payload of active agents, the currently heated payload or payload mixture can be communicated to a feedback system for purposes of tracking consumption.

[0096] Context Data Contextual data relates to situational factors other than environmental factors (see elsewhere herein) that may affect a user's state of mind. Typically, such situational factors affect a user's psychological state or disposition toward stress, neutrality, happiness, sadness, or toward a particular behavioral pattern, and therefore may also affect and / or correlate with neurological and physiological user factors such as dopamine or cortisone levels, blood pressure, heart rate, etc., as described elsewhere herein.

[0097] Examples of contextual data include, broadly, place of residence, religion (if any), and, more narrowly, the user's culture, such as work and / or employment status and educational background, as well as socio-economic factors that may interact with these, such as gender and relationship status.

[0098] Such information may be obtained by or for the acquisition processor from user surveys, social media data, etc.

[0099] Other contexts include seasons (e.g., winter, spring, summer, fall) or months, as well as any particular events or periods within those seasons or months (Lent, Easter, Ramadan, Christmas, etc.) For example, users may be more likely to view consuming below their personal baseline positively during Lent or the first few weeks of January.

[0100] Such information may be obtained by or for the acquisition processor from calendars and databases of events suitably filtered as needed according to other contexts such as country, religion, employment, gender, etc. as described above.

[0101] Other contexts include a user's agenda or calendar, which may indicate sources of stress or relaxation, as well as how busy the user is at a given time. Thus, for example, a social event may be associated with a positive effect on a user's state, such as increased dopamine levels, while a doctor's appointment or driving test may be associated with stressors, such as increased cortisol and heart rate. Similarly, a rapid succession of events, appointments, and / or reminders may have a negative effect on a user's state.

[0102] The user's itinerary or calendar may also indicate the user's likely location, which may affect the user's state or ability to use the delivery device to potentially modify that state. For example, a user may have different typical states and different abilities to use the delivery device depending on whether they are at home, at work, in an outdoor or indoor public space, in an urban or rural environment, or commuting. The relationship between user state and location may be based, at least initially, on data from the user's corpus. Alternatively or additionally, the relationship may be established or refined based on data from the user (e.g., measurements or self-reports). Of course, the user's location may also be determined from GPS signals acquired by the delivery device or an associated device such as a smartphone, or from the registered location of a vending machine or point-of-sale unit.

[0103] For modes of travel such as commuting, the type of travel can affect a user's state. For example, walking may have a more favorable effect on a user's state than driving in terms of heart rate, blood pressure, etc. Of course, this context illustrates the potential importance of combining contexts, as walking in the sun may have a different effect on a user's state than walking in the rain. The type of travel can be inferred, for example, from GPS data on the user's phone, pairing of the phone or delivery device with a vehicle, purchasing a public transport ticket, or a questionnaire indicating travel habits / times.

[0104] Such information may be obtained by or for the acquisition processor, for example, from a work or personal digital calendar on the user's phone, and it will be appreciated that the user's phone or other smart wearable may also directly indicate the user's location and / or historical location patterns, for example, corresponding to the user's home and work locations and average commute times.

[0105] Other contexts include the weather at the user's location or upcoming weather at the user's location or future locations. For some users, good weather may improve the user's mood and sociability, while bad weather may lower the user's mood and make them less sociable, or affect their sociability. For example, some users may behave in a way that reflects their weather-implied mood expectations, optionally in conjunction with other contextual and user factors, as described herein.

[0106] Such information may be obtained by or to the acquisition processor from a weather app that may be present on the user's smartphone 100 or that may be directly accessible, for example, by server 1000. More generally, weather data may be obtained in response to GPS data (e.g., by the smartphone) and / or using a local weather measurement system such as a barometer.

[0107] Other contexts include the user's proximity to others, generally with respect to crowds or social environments, and specifically with respect to other individuals for whom there is in principle a measurable correlation with user behavior. For example, a user may be in a different state depending on whether they are close to their boss, coworkers, friends, partner, children, or parents. Thus, for example, a user may be in a different state when in a crowd or social environment than when they are alone or with their partner or family.

[0108] Such proximity can be inferred from a user's schedule or calendar, mobile phone, delivery device, or location. Specifically for purposes of the user feedback system herein, users may generally self-report their social status, for example, on social media. Alternatively, for example, a phone and / or delivery device may detect signals from other phones and / or delivery devices to indicate that they remain in each other's presence for more than a predetermined period of time. Optionally, detection of the other may be achieved through the use of a phone's camera, which may be unavailable if the phone is in a pocket or bag. The feedback system can also determine, for a user of a delivery device, the proximity of other users of such delivery devices (e.g., any suitable delivery device whose location can be determined by the feedback system (e.g., directly or via an associated mobile phone)), regardless of whether such other delivery devices are part of the feedback system itself. Similarly, the feedback system can determine the proximity of particular people to the feedback system that the user has authorized and identified, for example, by providing the feedback system with a phone number or by associating a detected Bluetooth or other identifier with the user.

[0109] Users may also indicate (e.g., via a questionnaire) their typical state in response to various social situations, groups, or individuals, whether at a broad level such as "introvert" or "extrovert," or at a more specific level.

[0110] Of course, there are other contexts that may affect the user's state, such as recently consumed information (social media content, news articles, streaming video, e-books, e-magazines, photographs, and other similar content that may be obtained by or to the acquisition processor. Some content may be expected to have a universal impact on the user's state, such as news of natural disasters, while other content may have a different impact on individuals, such as the performance of a user's favorite sports team, and may be individually assessed based on, for example, the results of a user survey.

[0111] The content of the consumption information may be rated, for example, by keywords, to generate a rating of its positive or negative impact on the user's condition. Optionally, the rating may only be obtained by or for the acquisition processor, or any suitable digest of keyword selections, etc. More generally, the acquisition processor may only receive a digest of user factors as needed, especially if the material itself does not list any user factor characteristics.

[0112] Similarly, use of devices other than the delivery device may affect the user's state. In particular, the user's selection of apps on their phone, their interactions with the apps, the type of interaction, and / or the duration of the interaction with the apps may correlate with the user's state. For example, playing a social media or gaming app may increase dopamine and / or cortisol levels, heart rate, etc., while listening to a music app may decrease heart rate and / or cortisol levels. The duration of the interaction may have a linear or nonlinear relationship with these state changes and may indicate different states over time. For example, playing a game for an extended period of time may indicate boredom.

[0113] Of course, for many user factors (not just context but other types as well), the situational response (e.g., expectation state) may, at least initially, be based on data from a cohort of users (e.g., a pre-test population of users), but may alternatively or additionally be constructed or refined by information obtained from the user (whether measured, received, or self-reported).

[0114] Environmental and deterministic data Environmental and deterministic data effectively relate to long-term contextual data outside of user preference or influence, and overlap with longer-term contextual influences such as culture (and thus, for example, a user's upbringing, genetics, gender, internal biome (e.g., gut biome) and / or external biome (e.g., arid or lush habitat), and age).

[0115] As with other data described herein, such environmental and deterministic data may be obtained by or to the acquisition processor from one or more user questionnaires (e.g., questionnaires filled out specifically to support the user feedback system and / or questionnaires filled out for any third party partners (e.g., fitness wearable device or social media provider)). Among other things, such questionnaires may ask for details such as gender, height, weight, ethnicity, age, etc. Such questionnaires may also include psychological test questions to estimate the user's psychological disposition and / or background (e.g., one or more of extrovert / introvert, active / passive, optimistic / pessimistic, neutral / anxious, independent / dependent, satisfied / oppressed, etc.). Such questionnaires may also ask questions related to the user's culture and beliefs (e.g., one or more of: country of origin of the user or their parents, religion (if any), political beliefs (if any), newspaper or news website subscriptions (if any), other media consumption (if any), etc.). Again, as with other data described herein, some of this environmental and deterministic data may be obtained by or to an acquisition processor from medical or insurance records with consent, and / or may be inferred from the user's location, as appropriate.

[0116] Not all environmental and deterministic data need be long-term; for example, time of day, day of the week, and month are also considered environmental and deterministic data. Thus, for example, user state may vary throughout the day or week (e.g., weekdays versus weekends and / or weekday work hours versus nighttime), or may vary during specific times of day. Similarly, there may be overlap with other contextual data, such as weather. Again, synergies between different user factors are possible. For example, time of year may affect the amount of sunlight (both in terms of duration and potentially weather patterns). The level and / or duration of sunlight, measured (e.g., using light sensors / cameras on devices in the delivery ecosystem) or inferred from the date, may have a detectable relationship to the user state. Light quality (e.g., color temperature, indoor / outdoor flicker) may also be treated as a user factor.

[0117] Usage-based data Usage-based data relates to a user's direct interactions with the delivery device and / or optionally any other device in the delivery ecosystem or device capable of reporting interactions to a feedback system (e.g., acquisition processor). These interactions may be related to vaping / consumption and / or device operation / handling and / or settings.

[0118] Vaping / consumption-based interactions may relate to inhale-to-inhale characteristics such as the number, frequency, and / or distribution / pattern of puffs / consumption events within one or more selected time periods. Such time periods may include daily, hourly, or any other time period that may be relevant to the user's condition as a function of location, pharmacokinetics (e.g., the half-life of one or more delivered active ingredients), and / or any other time period selected to increase the apparent correlation between the number, frequency, and / or distribution / pattern of puffs / consumption and the user's condition. For example, this time period may be equal to the average time it takes to smoke a conventional cigarette for an individual user or for the general population average.

[0119] Vaping-based interactions may also relate to intra-puff characteristics such as duration, volume, average airflow, airflow profile, active ingredient ratio, active ingredient delivery timing, heater temperature, etc., statistical descriptions of individual vaping actions or cohorts thereof (e.g., including, but not limited to, cohorts during one of the selected time periods described above).

[0120] Such data regarding vaping and vaping behavior (or, more generally, consumption) may be acquired by or for an acquisition processor from the delivery device itself, e.g., via a Wi-Fi connection to the server 1000, or via communication with a local computing device, such as a companion mobile phone 100 that pairs with the delivery device 10 via, e.g., a Bluetooth connection to form a delivery system. However, in principle, at least some data regarding consumption may be acquired from one or more other devices in the delivery ecosystem. For example, an associated mobile phone may record vaping events to collate frequency / distribution data. Similarly, a wearable sensor may determine the extent of inhalation based solely on movement. Thus, one or more sensors related to determining vaping-based interactions may be located external to the delivery device, although typically at least one will be internal to the vaping device. Most commonly, this is an airflow sensor, typically used to detect the onset of inhalation and activate the device's aerosolization mechanism (i.e., typically a heater, as described herein above). In any event, such internal or external sensors, alone or in combination, represent examples of sensor platforms.

[0121] In any event, the result is a user feedback system comprising at least a first sensor platform (internal and / or external to the delivery device 10) having at least a first sensor operable to detect at least a first physical characteristic associated with at least a first user inhalation action.

[0122] As noted above, the or each physical property may be one or more of an intra-suction property or an inter-suction property.

[0123] The delivery device may include one or more airflow sensors, such as those described herein above, to determine the timing and / or manner of vaping by a user, e.g., such features, and raw data regarding vaping / consumption events may be stored in a memory of the delivery device or transmitted to a companion mobile phone or any other suitable device in the delivery ecosystem. The data may then be used by a processor in the delivery device and / or any other device in the delivery ecosystem to determine characteristics such as the number, frequency, and / or distribution / pattern of puffs / consumption events within one or more selected time periods, and / or the duration, volume, average airflow, airflow profile, average component ratios, and / or heater temperature values ​​of one or more vaping / consumption events.

[0124] Optionally, at least two of the puff profile, puff frequency, puff duration, number of puffs, session length, and peak puff pressure may be detected by at least one sensor of the sensor platform, and the detected information may be used to determine the state / mood of the user.

[0125] For example, a puff profile may characterize the variation in puff intensity over the duration of a puff (or statistically, over a cohort of puffs), such that a relatively shallow, short, and intense puff or a relatively deep, short, and intense puff may indicate high stress or a user's feelings of needing more active ingredient, while a relatively shallow, slow, and long puff or a relatively deep, slow, and long puff may indicate low stress. Thus, for example, the airflow rate of a puff may be used to characterize a puff profile, with a higher airflow rate associated with a short, intense puff potentially indicating higher stress than a lower airflow rate.

[0126] Similarly, puff frequency is correlated with stress, so it is thought that when a user is under stress, the puff frequency will be higher than normal.

[0127] Puff duration can be considered a subset of the puff profile, where the variation in draw intensity (e.g., as indicated by a proxy measure of airflow) over the duration of the draw provides a profile, and by integration, the total draw of the puff. However, to a first approximation, duration also indicates the type of draw, typically correlating short puffs in stressful situations with longer puffs during the user's normal state.

[0128] The number of puffs within a session may also indicate the user's condition. A session is understood to be a fixed period of time, such as an hourly interval or a minutely interval, where N may be any suitable value, such as 1, 5, 10, 20, 30, or 45 minutes. Alternatively, a session may be functionally defined as a period of time that includes puffs separated by less than the predetermined period required to indicate that the session is over. This period may also be any suitable value, such as 1, 5, 10, 20, 30, or 45 minutes.

[0129] In any given session, all other things being equal, a user is likely to puff more when under stress than when the user is under normal circumstances.

[0130] Similarly, sessions, when functionally defined, may be shorter when the user is under more stress than normal.

[0131] Peak puff pressure is also considered a subset of the puff profile and indicates the intensity of the user's inhalation. Both the peak pressure and its relative position within the duration of the inhalation are considered characteristics of the inhalation the user performs during the puff. A high peak, especially early in the inhalation, may indicate user stress or a perceived desire by the user to increase the intake of the active ingredient, while a low peak, typically mid-inhalation, indicates that the user is not experiencing much stress and is simply maintaining an inhalation rate close to a preferred baseline level.

[0132] As an alternative or addition to the number of puffs within a session, the frequency of puffs within a predetermined period of time, such as 24 hours, one or more sessions as described above, or a time period at a given location (e.g., work / home), may follow a predictable pattern. As a non-limiting example, a user may experience significant increases in usage early in the day, during lunch breaks, and immediately after work, with a slightly higher frequency at night before going to bed. This frequency pattern can be learned and used to predict the user's state and / or as a factor in if the user's usage pattern deviates from the learned pattern. Of course, puff frequency is only one characteristic of a puff-based user's interactions that may be subject to pattern analysis. For example, the distribution of puffs within a predetermined period of time may have characteristics that can be used to predict the user's subsequent state and / or detect deviations from habitual behavior. Thus, for example, as a function of frequency and / or distribution, if a user is unable to vape during a work meeting, resulting in frequency dropping to effectively zero, and / or if the usage distribution shows long gaps compared to the user's learned normal distribution, this may indicate stress.

[0133] Of course, any other measurable characteristic described herein, such as average daily variability in suction depth, suction duration, etc., may be modeled as a pattern or distribution that can be used to make predictions or identify deviations from normal behavior or conditions. Such profiles may be constructed for a single assumed day, assumed work and rest days, or assumed individual days of the week.

[0134] It will be appreciated that the measurements may be obtained using one or more sensors on the sensor platform, such as an airflow sensor, an air velocity sensor, a dynamic pressure sensor, a microphone, etc., and the measurements may be correlated to the extent of the user's inhalation and thus may be used to provide the intra- and inter-inhalation data described above.

[0135] In any event, as discussed above, such information may then be packaged as one or more user factors and sent to an acquisition processor.

[0136] Manipulation / handling-based interaction can relate to how a user interacts with the delivery device when not actively vaping, characterizing, for example, whether the delivery device is stored in a bag until immediately prior to use, or whether the user plays with or manipulates the delivery device during use.

[0137] Thus, for example, the delivery device or any other handheld device in the delivery ecosystem (such as a user's mobile phone) may be equipped with a sensor that detects small involuntary movements (so-called micromovements), such as shaking hands, i.e., shaking of the user's hand. Such micromovements may be indicative of the user's state. For example, the amount, frequency, or incidence of such micromovements and / or the amplitude of such micromovements may correlate or correspond to one or more of user stress, user fatigue, user concentration, and deviations from a suitable baseline amount of the active ingredient in the user's body.

[0138] The delivery device may include one or more touch sensors or accelerometers to determine such interactions. Similarly, the device may include buttons or other environmental features that allow user interactions to be recorded. Interactions with buttons or other environmental features on the delivery device on a companion mobile phone may also be recorded. Such interaction data may then be packaged as one or more user factors and sent to a capture processor.

[0139] It should be understood that detecting touch is considered one of multiple functions of the sensors of the sensor platform. For example, such sensors may be used to obtain physiological data. Conversely, such physiological sensors may provide touch detection functionality. Thus, a galvanic skin response detector and / or a heart rate detector may simultaneously detect touch and other physiological characteristics of the user. Such sensors may be located, for example, in one or more of the user's fingers and / or in the grip of the delivery device where the device may be held for an extended period of time in the palm of the user's hand (as compared to, for example, contact with a user interface element such as the mouthpiece of the delivery device or any buttons on the delivery device).

[0140] Galvanic skin response detectors typically operate by measuring skin conductance or skin potential, which is typically a function of the user's perspiration (often small, typically by measuring variations in skin conductance (resistance) after applying a low, constant voltage to the user's skin (e.g., through the grip portion of a delivery device). There is typically a tonic or slow-varying component, on the order of seconds to minutes, and a fast-varying phase component, which varies within seconds. Either component can be indicative of the user's state and thus a physical characteristic that contributes to the user factor of a user feedback system. Notably, both positive and negative stimuli (e.g., pleasure or stress) increase galvanic skin response, so optionally, other contextual information can be useful in distinguishing between the signals. However, apart from this, there is a clear correlation or correspondence between galvanic skin response and the consumption of certain active ingredients, such as nicotine.

[0141] Meanwhile, the type of heart rate detector most often found in wearables, and also in delivery ecosystems (e.g., wearables or mobile phones or delivery devices), typically includes an LED light source and a sensor. The sensor detects reflection from the light source after passing through the user's skin and being at least partially reflected by the blood pulsating in the veins and arteries. The pulsating motion causes a characteristic change in the amount of reflected light, which is detected to determine the user's heart rate. Of course, similar electrode-based heart rate detectors (electrocardiograph or ECG sensors) are also available that detect changes in electrical properties associated with the heart's electrical activity or blood pulsation.

[0142] As described elsewhere herein, a user's heart rate (whether instantaneous or averaged over a predetermined period of time) can be indicative of their state and thus a physical characteristic that contributes to the user factor of a user feedback system. Similarly, the variability of a user's heart rate can be indicative of their state, with large variations being associated with stress. Of course, a heart rate monitor can in principle generate instantaneous, average, and / or variability-based data using the same sensor.

[0143] The delivery device or any other device in the delivery ecosystem with which the user may interact to enable physiological measurements may optionally include other sensors associated with such measurements as well, such as muscle tone and / or cortisol sensors.

[0144] Electromyography (EMG) can be used to detect muscle tension, again using surface electrodes. EMG data is typically based on the voltage difference between a recording site and a reference site, which is typically a bony, low-muscle point in the body. Thus, for a handheld device such as a delivery device, an appropriate location for the reference electrode might coincide with the knuckle crease of a finger or thumb. Such a location can be predicted based on the shape of the device (e.g., grip portion) and the location of the activation button or any other user interface elements.

[0145] Alternatively, cortisol can be detected using sensors known in the art located in the mouthpiece of the delivery device. Cortisol can be measured in saliva and therefore may be measured from the user's lips during inhalation. Alternatively or additionally, cortisol is also found in sweat and could, in principle, be detected using a sensor integrated into the body of the delivery device held by the user. As described elsewhere herein, there is a correlation between a user's cortisol levels and their stress levels.

[0146] It will be appreciated that electrodes incorporated into the delivery device (e.g., grip area) (or any other device in the delivery ecosystem, as described elsewhere herein) may be adapted to be used for more than one detection mode, such as galvanic skin conductance, heart rate, muscle tension, etc., in parallel or sequential cycles with each analysis of the same raw signal data.

[0147] Such sensors typically require two electrodes to measure skin conductance between them. Optionally, in smaller delivery devices, the electrodes may be concentric (e.g., inner and outer circles or disks / dots) to provide a compact sensor that can be used, for example, on a fingertip.

[0148] The delivery device itself and / or the delivery device in combination with any other suitable device in the delivery ecosystem may optionally include one or more of the above sensors in any combination.

[0149] Interaction with user interface elements, such as buttons, can also provide information about the user's state when using the delivery device. For example, in a delivery device that uses a UI interface, such as a button press, for activation, the delivery device may measure the time between such activation and the resulting inhalation. This time may correlate or correspond to one or more of user stress, user fatigue, user concentration, and deviation from a preferred baseline amount of the active ingredient in the user's body. Thus, for example, this time may be shorter when the user is stressed than when they are normal.

[0150] Similarly, the magnitude of force, e.g., peak force / pressure and / or force profile applied to a button or user interface element, may be measured and may indicate a user state. Thus, for example, a large force (e.g., above a predetermined threshold) and / or a short interaction with a user interface element such as a button may indicate user stress, and thus there may be a correlation or correspondence between the magnitude of force or shortness of actuation and the degree of user stress.

[0151] As mentioned above, the delivery device may include one or more accelerometers and / or similar gyroscopes or other motion sensors capable of determining the motion of the delivery device. Using telemetry from such one or more motion sensors in the delivery device, a user feedback system can detect, for example, accidental or unconscious manipulation of the device. For example, a change in orientation and / or slow or total horizontal movement while the overall position remains within a predetermined radius may indicate that the user is fiddling with the device in their hands while stationary or walking. Such fiddling may indicate a state of the user, for example, at least an unconscious desire to use the device or a desire to use the device more than they currently do. Thus, it may correlate with increased stress, lack of concentration, and / or deviations from a desirable baseline amount of the active ingredient in the user's body.

[0152] Similarly, such telemetry can be used to detect characteristic gestures associated with use, such as lifting the device into position for application to the user's mouth and then removing it from the mouth. The speed and / or fumbling of these movements may similarly correlate or correspond to the user's mood, for example, faster movements being associated with increased stress and slower movements being associated with the user being in a neutral state.

[0153] Similarly, such telemetry can be used to detect characteristic gestures not associated with use, such as a user's overall movements when climbing stairs or using a lift, or traveling at a speed and / or speed profile consistent with cycling, driving a car, traveling by bus, train, or plane. These actions may, in turn, indicate the user's state in terms of their internal state regarding shortness of breath or fatigue (related to overall movements), excitement or stress (related to gestures), or their external state regarding how easily they can use the delivery device, for example, when cycling or on public transport.

[0154] Similarly, the use of such telemetry can detect other movements, such as small pendulum movements associated with placing the device in a bag or large pendulum movements associated with holding the device in the user's hand as the user walks, or patterns of movement consistent with placing the device in the user's pocket.

[0155] In addition to physical manipulation, other interactions with the delivery device or devices in the delivery ecosystem may also optionally be evaluated. For example, a microphone on the delivery device or the user's mobile phone may be used to detect the user's voice (e.g., when specifically speaking to the device or nearby others, during a phone call, or optionally as ongoing background activity similar to a voice-activated personal digital assistant). Analysis of characteristics of the user's voice, such as volume, speech rate, timbre, tone, pitch, and / or inharmonic content, may optionally determine whether the user's vocalizations are neutral or stressed, for example, after calibration against the user's neutral voice. Similarly, devices in such a delivery ecosystem may optionally monitor keywords indicative of various user states (positive and / or negative).

[0156] As well as audio expressions, facial expressions may optionally be monitored alternatively or additionally. In this case, the delivery device, a device in the delivery ecosystem such as the user's mobile phone, or a vending machine may be equipped with a camera. In the case of a delivery device, it may be equipped with one or more cameras positioned to include the user's face in its field of view during inhalation and / or the act of raising the device (e.g., on the same side as the mouthpiece) to the user's face. Alternatively or additionally, there may be a camera facing away from the user during inhalation to capture details of the user's environment.

[0157] Images from such cameras can provide data about the user's state, such as the user's overall facial expression, which typically correlates strongly with the user's subjective mood, as well as facial muscle tension, which tends to correlate with stress, tension, or pain. Eye movements, on the other hand, can indicate the user's level of concentration and / or the nature of the user's activity (e.g., eye movement and / or blink patterns tend to be different when driving, reading, or socializing, and when alert versus drowsy). Similarly, if the camera allows resolution, micro-movements of the face or neck can indicate heart rate.

[0158] Such cameras may also be used to obtain other data, such as motion based on the relative movement of the scene to the camera or points of interest, detection of people important to the user (such as a partner or children), the extent or nature of the social situation (such as the number of people near the user), etc. Similarly, such cameras may be used to determine whether the user is indoors or outdoors based on detection of indoor features such as the sky, color temperature, flickering lights, windows or TV screens, etc.

[0159] It should also be appreciated that user interaction may include the user's specific designation of a user condition. In this case, a user interface is provided that allows the user to select a setting that is indicative of each condition. This indication may be explicit, such as by selecting a user condition and, optionally, providing a value (e.g., 1-100) indicating the severity of the condition, or the user may directly input a subjective assessment of their condition. As described elsewhere herein, this may be useful for training purposes of an evaluation processor and / or evaluation model or for constructing rules or lookup tables that associate user factors with user conditions. Alternatively, the user interface may be more indirect, e.g., offering a "normal" mode and a "boost" mode. The mode defaults to the user's normal state, while the "boost" mode may correlate with user stress by delivering more active ingredient per inhaled aerosol volume.

[0160] Similar to the indicators provided by the use of normal and boost modes, the selection of a particular consumable (e.g., a normal or normal concentration of the active ingredient, or a high or boost concentration of the active ingredient) may indicate a user's level of stress or normality (typically at the start of the day when such a consumable is selected), and therefore may indicate a more chronic stress level.

[0161] Of course, if a user has multiple delivery devices 10, usage may be aggregated across these devices by obtaining user factor data from each device, or usage may already be aggregated through an intermediary such as a phone app or one of the delivery devices acting as a hub for this purpose. Also, as a non-limiting example of pharmacokinetics, if different devices deliver different active ingredients (whether by type or concentration), this may be taken into account in usage modeling.

[0162] Sensor Location Although the above description generally refers to sensors being located in or on the user's delivery device for purposes of explanation, sensors for inhalation, user behavior, and physiological measurements may alternatively or additionally be located on devices other than the delivery device if preferred.

[0163] Typically, activation in response to inhalation is initiated through the use of an airflow sensor within the delivery device (some devices may also use button-based activation), which may be used for many inhalation-based physical characteristics that are then attributed to one or more user factors.

[0164] However, other sensors related to sucking actions may be located away from the delivery device itself. For example, a microphone may be located on a mobile phone or a wireless earpiece connected to the mobile phone, attached to clothing or jewelry, or located in a home hub voice-activated assistant. Such a microphone may detect sucking sounds, and the microphone signal may be processed to optionally determine the duration of the sucking action, the intensity of the sucking action, and / or the sucking profile, for example, based on the noise envelope of the sucking action heard. As described elsewhere herein, these physical characteristics correlate or correspond to degrees of stress or relaxation, so that, for example, a short, high-intensity suck typically corresponds to higher stress, while a gradually increasing intensity of sucking within the profile indicates satisfaction.

[0165] Notably, the microphone can also detect exhalation (which is typically not detected by the delivery device's airflow sensor because users typically do not blow back through the delivery device). As with inhalation, the duration, intensity, and / or profile of the exhalation may be determined based on the noise envelope of the exhalation action heard. These physical characteristics also correlate with the degree of stress or relaxation. The time elapsed between the completion of an inhalation (whether detected by an airflow sensor, microphone, or other sensor) and the start of the corresponding exhalation indicates the length of time the aerosol (and thus any active ingredient) was retained in the user's lungs, and is therefore also a physical characteristic indicative of the user's state. Again, there is a correlation between the length of lung retention and the user's stress or relaxation, and the duration of retention may optionally be used as an input for any pharmacokinetic modeling performed by the user feedback system.

[0166] The microphones will typically be directional microphones, but may also have fixed or steerable arrays to reduce extraneous environmental noise.

[0167] Similar devices capable of measuring inhalation, exhalation, and intervening periods include chest movement meters, such as chest straps. For example, a pendant or similar jewelry that measures chest movement using an accelerometer or the like (optionally in conjunction with detecting physical contact with the chest to avoid false motion detection) may be worn around the user's neck and report such chest movement via, for example, a Bluetooth connection to the delivery device, a mobile phone, or any other device in the delivery ecosystem. Of course, such a pendant may also serve as a sensor platform for physiological sensors such as galvanic skin response, heart rate, muscle tension, etc., and may also include a microphone (e.g., a directional microphone) that listens for inhalation and exhalation movements from above the user's mouth. If both the motion detector and microphone are integrated into the device or provided by separate sensors to an analysis processor, such as a pre-processor of the acquisition processor, cross-referencing of data from both the motion sensor and microphone may reduce false detection of inhalation and / or exhalation.

[0168] Similarly, a camera may detect inhalation and exhalation. Such a camera may be located on any device in the delivery ecosystem, such as a mobile phone, a docking station, a home hub, a vending machine or point-of-sale device, or any other camera configured to participate in such data collection, for example, by a user electing to download a suitable app. Such other cameras include a webcam on a laptop or a camera associated with a video game console.

[0169] Camera images can be processed to detect inhalation, for example by detecting characteristic movements as the user brings the delivery device to their mouth, and similarly, such image processing can detect exhalation by detecting exhaled vapor.

[0170] Again, combining disparate data sources can improve detection; for example, combining camera and microphone signals can provide better discrimination between aspirating and dispensing actions. It should also be apparent that combining data from one or more sensors on the delivery device with data from one or more sensors not on the delivery device can improve detection and / or characterization of physical characteristics related to aspirating actions. Thus, combining data and / or analysis results from disparate and / or complementary sensors in one or more devices in the delivery ecosystem can provide a more complete picture and cross-validate detected features related to aspirating actions.

[0171] Similarly, while motion detectors and the like that detect user behavior related to the user's interaction with the non-suction-based delivery device may be incorporated into the delivery device, sensors related to such user interaction and user behavior in general may alternatively or additionally be located away from the delivery device itself.

[0172] In particular, motion detectors are also typically present in mobile phones and fitness wearables. Thus, while a motion detector in a delivery device may detect a user fiddling with the device or characteristic device motions related to a sucking action, more general user behaviors in connection with movements (walking, cycling, climbing stairs, etc.), gestures, or fiddling performed while holding the phone or wearing the fitness tracker may also be captured, and correlations between these actions and user state may also be identified. For example, an uncharacteristic gesture (relative to the average, i.e., the average of previously detected gestures) may indicate stress. For example, a gesture with a velocity, acceleration, or jerk value above an absolute or relative threshold (e.g., relative to the aforementioned average) may indicate stress, and optionally, in combination with detected audio stress or keywords, may indicate anger.

[0173] However, as noted above, any suitable device within the delivery ecosystem, such as a user's phone, fitness wearable, docking station, home hub, vending machine, or point-of-sale device, may incorporate a microphone to enable analysis of the user's voice and / or speech.

[0174] Similarly, as mentioned above, any suitable device in the delivery ecosystem, such as the user's phone, fitness wearable, docking station, home hub, vending machine, or point-of-sale device, may also be equipped with a camera to analyze one or more of the user's facial expression, facial tension, the user's eye movements, the user's gestures, the user's social environment, etc.

[0175] Additionally, sensors regarding user activity or behavior may be available in other devices that are not typically part of the delivery ecosystem for delivery devices such as aerosol delivery devices. Examples of such include gym equipment that can track a user's activity, heart rate, etc., such as fitness wearables, electronic scales that can provide physiological information (optionally including with a body mass index calculator), and vehicles driven by the user.

[0176] Thus, for example, gym equipment such as an exercise bike or rowing machine can track a user's exertion level (e.g., in terms of watts or calories) based on current exercise, and optionally, the user's heart rate. Such information can indicate not only the user's current behavior (e.g., that they are enjoying a fitness activity), but also other aspects of their health, such as the amount of exercise performed or the relationship between exercise and heart rate (such as the ratio of the two as a function of time).

[0177] Similarly, one or more force or pressure sensors may be integrated into a user interface element of the delivery device, although alternatively or additionally, such sensors may be integrated into any device in the delivery ecosystem, such as a user's mobile phone, a docking unit, a home hub, a vending machine, or other point of sale system. Similarly, any other connected device not typically considered part of the delivery ecosystem but which may have relevant data (such as a smart doorbell) may also be included.

[0178] Thus, for example, if a user touches an icon on a phone screen with a degree of pressure or force indicated by the area of ​​the finger pressed against the screen (the larger the area, the more pressure required), periods when the user applied greater force can be detected. Applying greater than average force generally correlates or corresponds to increased stress. Similarly, in this case, a characteristic pattern of pressure or force may be detected that indicates the user is using the pad or fingertip, with a transition from fingerprint to fingertip also correlating or corresponding to stress. Similarly, tapping speed tends to increase while tapping accuracy decreases under stress.

[0179] Similar metrics can be derived for physical buttons, such as those found on vending machines or other point-of-sale systems. The force with which the button is pressed indicates the user state, with greater force indicating stress. Similarly, the duration of the button press also indicates the user state, with shorter-than-average presses indicating stress.

[0180] Similar to the aspiration-related measurements described herein above, a combination of data and / or analytical results from disparate and / or complementary sensors in one or more devices in the delivery ecosystem may provide a more complete picture and cross-validate detected operations / behaviors.

[0181] Just as suction-based and behavioral criteria may alternatively or additionally be obtained from sensors on devices in the delivery ecosystem other than the delivery device, neurological and / or physiological criteria may alternatively or additionally be obtained from such sensors.

[0182] As noted above in this specification, devices such as fitness wearables (e.g., smart watches), chest straps, or other biofeedback mechanisms (e.g., integrated into any handheld device such as a cell phone or one or more pieces of gym equipment) may be used to collect neurological and / or physiological metrics of the types described elsewhere herein. Similarly, as described elsewhere herein, some metrics approximate instantaneous values ​​such as heart rate or skin conductance, while other metrics may represent averages or other statistical characteristics of data over longer periods of time, or may relate to characteristics that themselves change over longer periods of time.

[0183] As a result, in principle, some physiological measures could also be used for docking and / or reloading stations that replenish the payload of a delivery device containing one or more active ingredients, or similarly for devices with less frequent user interaction, such as vending machines or point-of-sale devices, etc. Such devices may be equipped with, for example, galvanic skin response detectors, heart rate detectors, muscle tension detectors, etc.

[0184] Similarly, sensors such as cortisol sensors may be provided by devices separate from the delivery device for saliva or sweat-based detection.

[0185] Again, a combination of data and / or analysis results from disparate and / or complementary sensors in one or more devices within the delivery ecosystem may provide a more complete picture and cross-validate neurological and / or physiological criteria.

[0186] As described elsewhere herein, acquisition of the physical property data (including any optional pre-processing, passing, or other analysis to obtain user factors) may be performed by an acquisition processor, which may in turn be a real or virtual processor located on one or more devices. Of course, regardless of whether the delivery device includes sensors that provide physical property data for one or more user factors, in principle the role of the acquisition processor may be performed entirely within the delivery device, partially within the delivery device, or entirely outside the delivery device (e.g., in one or more other devices and / or a server in the delivery ecosystem), with appropriate communication of the data to the associated processor(s) as described elsewhere herein. If processing occurs within the delivery ecosystem, location on the device with most of the sensors or on a device that acts as a natural intermediary to other devices in the delivery ecosystem may be advantageous. One possible example is a mobile phone, which may be in communication with the user's wearables, Bluetooth headset, home hub / assistant, charging station, etc., and potentially with the delivery device, and may be equipped with a microphone, camera, etc., and typically has appropriate processing power to process the data. Similarly, a smartwatch may analyze and package the acquired data. As described elsewhere herein, subsequent roles in the feedback system may likewise be located in any suitable device or devices and / or servers in the delivery ecosystem.

[0187] Multiple Data Sources As discussed above and shown in FIG. 6, the acquisition processor may receive a plurality of user factors of the type described herein from one or more data sources, such as data sources in the delivery ecosystem 1, data sources on the Internet 110, and records maintained by the feedback system 1012, such as on the server 1000.

[0188] As discussed above, these user factors may be variously categorized as indirect or historical data, neurological or physiological data, contextual data, environmental or deterministic data, and / or usage-based data.

[0189] In the case of usage-based data, it will be appreciated that some or all of such usage-based data may be obtained through the use of multiple sensors and / or sensors with multiple sensing capabilities in a sensor platform.

[0190] How the Capture Processor Works Referring again to FIG. 6, the acquisition processor 1010 is typically part of the remote server 1000 and may receive user factors from different data sources, such as the server's own storage / database 1012, online data sources 110, and devices in the user's delivery ecosystem 1, such as the delivery device 10 itself, the mobile phone 100, the fitness wearable 400, the docking unit 200, the vending machine 300, and any other suitable device that may provide information related to the user's condition (such as a voice-activated home assistant, smart thermostat, smart doorbell, or other Internet of Things (IoT) device).

[0191] The acquisition processor 1010 may comprise one or more physical and / or virtual processors, and may be located in a remote server and / or have functionality distributed or otherwise distributed across multiple devices, including, but not limited to, the user's mobile phone 100, the docking unit 200, the vending machine 300, and the delivery device 10 itself. The acquisition processor may also have one or more communication inputs, e.g., via a network connection and / or a local connection to local storage. The acquisition processor may also have one or more communication outputs, e.g., via a network connection and / or a local connection, e.g., to the estimation processor 1020.

[0192] The acquisition processor may include a pre-processor or sub-processor (not shown) configured to parse and / or convert acquired information into user factors, even if the information is not immediately usable as described above. Examples include keyword or sentiment analysis of consumed media to determine a net positive or negative impact on an aspect of the user's state as a user factor, or similarly, keyword analysis of a user's calendar to determine locations and events to determine a net positive or negative impact on an aspect of the user's state as a user factor. Other inputs, such as air temperature or rain probability, may similarly be converted to a scale appropriate for the user factors, e.g., normalized or categorized according to their impact on the user's state. Similarly, noisy data may be processed to remove statistical outliers, perform smoothing functions, calculate averages or other statistics, etc. Of course, such pre-processing or sub-processing may be performed on one or more devices in the user's delivery ecosystem instead of the acquisition processor.

[0193] In this manner, the acquisition processor may be operable to generate and / or relay user factors for input to the estimation processor at various levels of abstraction from the original material.

[0194] Thus, the original data can optionally be enumerated, coded, classified, formatted, or otherwise processed, or simply passed through, to provide as input to the inference processor, with potentially as many or more inputs as there are original data sources. As will be appreciated from the above discussion, this can result in a large number of inputs.

[0195] Thus, optionally, one, some, or all of the original data may simply be passed through to any evaluation, coding, classification, formatting, or other processing, or even an optional intermediate user factor generation stage of the acquisition processor as needed, so that the transmitted input may determine a positive or negative effect on a particular subset of user factors that are related to user state but not directly or easily measurable, such as effects on dopamine and / or cortisol, heart rate, satiety, etc.

[0196] Similarly, the intermediate user factor generation stage of such an acquisition processor may combine inputs from similar classes to generate class-level user factors for one or more of the data classes described herein.

[0197] Thus, by way of non-limiting example, indirect or historical data may be aggregated at a given scale as the manner in which a user actively modifies or updates their respective device or accepts such modifications. Neurological or physiological data may be aggregated at a given scale and / or trajectory on that scale as the user's apparent stress. Contextual data may be aggregated at a given scale as the socially desirable use of the current delivery device. Environmental or deterministic data may also be aggregated by the likelihood that a user will want to use the delivery device in a given time frame, and usage-based data may be aggregated as the frequency or depth of a user's recent delivery device use.

[0198] Of course, in practice, raw data from only some or one of the classes may be available, and even if data from one class is available, class-level user factors as in the above example may not be generated, or different types of class-level user factors (e.g., different subsets of individual user factors) may be generated depending on the type of data received within that class. Similarly, class-level user factors may be generated as input to an inference processor in parallel with the individual user factors.

[0199] Contribution values ​​and / or influences from different individual, subset, and / or class level user factors may then be presented as inputs to the estimation processor, with the selection of class, subset, and / or individual user factors being chosen to provide good discrimination between different user states.

[0200] For example, galvanic skin response can provide a good indicator of a user's condition and respond to nicotine as an active ingredient by suppressing the response. Therefore, it can optionally be a candidate for an individual data source to be used as input to the estimation processor. Other physiological measures that provide good discrimination include muscle tension (EMG), heart rate, skin temperature, electroencephalography (EEG), and respiratory rate. Any available of these could be considered for inclusion as individual data sources, optionally after any evaluation, coding, classification, formatting, or other processing, in place of or in addition to the above, in any combination with these or other user factors described elsewhere herein.

[0201] Similarly, location, social environment, time of day, and hormone levels are all good indicators of a user's state and may be candidates for use as individual sources of data to input into the inference processor.

[0202] Thus, more generally, the user factors may be acquired by or to the acquisition processor, for example as individual, subset, and / or class level user factors, and after any suitable parsing or processing, provided to the estimation processor as individual and / or subset or class values ​​combined with one or more other user factors (e.g., based on weighted contributions, statistical functions, trained machine learning outputs, look-up tables of pre-computed correspondences between values ​​of the acquired data and values ​​of the target user factors, etc.).

[0203] Estimation Processor The estimation processor 1020 is operable to calculate an estimate of the user state based on one or more of the inputs received from the acquisition processor, including the acquired user factors, or the acquired user factors. The calculation of the estimate of the user state can be explicit, to generate an output that reflects the user's state prior to generating a suggested feedback action (considered a two-step process), or implicit, to identify a suggested feedback action that is expected to change the user's state (considered a one-step process).

[0204] Like the acquisition processor, the estimation processor may comprise one or more physical and / or virtual processors, and may be located in a remote server and / or functionality within a device of the delivery ecosystem, such as the delivery device 10, or functionality may be distributed or otherwise distributed across multiple devices, including, but not limited to, the user's mobile phone 100, the docking unit 200, the vending machine 300, and the delivery device 10 itself. The estimation processor may also comprise one or more communication inputs, for example, for receiving data from the acquisition processor 1010. The estimation processor may also comprise one or more communication outputs, for example, for providing suggested feedback actions to the feedback processor 1030.

[0205] Explicit State Estimation In one embodiment herein, the inference processor first explicitly infers the user's state in a two-stage process, and then generates suggested feedback actions in a second stage in response to the inferred state, which itself may be in the form of a single value or category, or may be a multivariate description of the user's state.

[0206] As a non-limiting example of a single-valued state, the estimated state may be: i. the user's stress level; and ii. The degree of benefit that a user is expected to experience subjectively in response to consuming a unit of the proposed active ingredient; and iii. a social flexibility score indicating the ease with which the user's current use of the delivery device would allow them to change their respective state through delivery modifications; and It may also represent:

[0207] As non-limiting examples of state categories, the estimated state may be: i. One, all, some, or none of a plurality of state categories may correspond to what is colloquially referred to as mood (e.g., happy, sad, low cortisol, moderate cortisol, high cortisol, neutral, stressed, acceptance of change (e.g., willingness to alter state using the respective delivery device), or rejection of change). ii. One of the state classifications is selected to have a clear correlation with either the input from the acquisition processor and / or the available feedback actions, and these classifications do not necessarily fit into assumed categories such as "happiness" or "high cortisol," but have classification boundaries that are driven at least in part by responses to the available input from the acquisition processor or the output to the feedback processor.

[0208] As a non-limiting example of a multivariate description of a user's state, the estimated state may include: i. a user's stress level as a function of physiological as well as contextual indicators, and an indicator of current social flexibility based on time of day, location, and / or proximity to specific individuals; ii. Indicators of the user's physiological state based on galvanic skin response and heart rate, as well as their current position in their hormone cycle and mental state derived from surveys and / or social media analysis; may also include:

[0209] Using these examples, the operation of the estimation processor can be illustrated in a non-limiting manner as follows. The estimation processor may transform the input data from the acquisition processor into an estimated state through the use of predetermined rules, algorithms, and / or heuristics. For example, a single-valued state, such as a user's stress level, may be derived by applying a predetermined combination of multiple user factors, such as a weighted sum, with the result normalized according to the number of currently available inputs contributing to the weighted sum. Similarly, a single-value state, such as a user's expected degree of benefit, may be derived by estimating the user's positive or negative emotional state based on the sum of index values ​​for positive or negative keywords or emotions in recently consumed or created online media and positive or negative values ​​associated with the user's location classification. Similarly, the putative condition category may be selected by template matching of the user factor values ​​against predetermined values ​​indicative of a given category, or similarly by identifying the smallest mean squared error between the user factors and a template of user factor values ​​for each candidate category, optionally with different linear or non-linear weighting of different categories and large errors to reflect their relative importance in category discrimination. Finally, as an example, a multivariate condition may include deriving individual indicators of the condition according to any of the above examples. Thus, as described above, a single-valued stress level can be generated for each physiological and contextual indicator, and a social flexibility value can be determined based on scores pre-associated with different time periods, locations, and particular classes of individuals (e.g., partner vs. child). Alternatively, social flexibility classification may be based on template matching of such scores and / or values ​​for the underlying input data.

[0210] Alternatively or additionally, the estimation processor may convert input data from the acquisition processor into estimated states through the use of a look-up table.

[0211] In one example, these lookup tables may simply provide a pre-computed implementation of the above predetermined rules, algorithms, and / or heuristics to avoid repeating these calculations on a server or on a device in the delivery ecosystem that has limited processing power but can act as or share the role of an estimation processor, such as a delivery device 10, a dock 200, a vending machine 300, a wearable device 400, or an associated phone 100.

[0212] In another example, such lookup tables may provide associations between input values ​​from the acquisition processor and output values ​​of user states, state classifications, and / or multivariate states previously derived according to any suitable mechanism, such as feedback from extensive user testing, or, as described below in this specification, the output of a machine learning system. Again, in the latter case, the lookup tables may potentially provide simple replication of the operation of such machine learning systems by recording input and output pairs to common values ​​that can be easily implemented in devices in the delivery ecosystem with relatively low computing power.

[0213] Alternatively or additionally, the estimation processor may model correlations between the input data and the estimated user state. Such correlations may be due to causal relationships between the user factors and the user state, or due to the tendency of the user factors to accompany the causes of the user state, typically acting as proxies with a certain probability. Similarly, such correlations may be due to user factors and user states that are both responsive to distinct causes or circumstances, such that a correlation can be constructed with sufficient repetition. Similarly, such correlations may be due to the user state that gives rise to the user factor. Thus, more generally, correlations relate to measurably predictable correspondences between one or more user factors (whether individual, subset, or class-level user factors, as output by the acquisition processor) and the user state (whether single-valued, categorical, or multivariate), typically due to causal relationships (in either direction between the user factors and the state), common causes that result in user factor and user state responses that have a relationship that is reproducible at least at a statistical level, and / or measurable correspondences regardless of whether direct or indirect causality is known.

[0214] When modeling correlations, the estimation processor can be trained using a dataset that takes as input data corresponding to the above-mentioned outputs of the acquisition processor and includes as target outputs descriptors of the user's state (whether single-valued, categorical, or multivariate, e.g., based on direct measurements of the user's state and / or self-reports of the user's state).

[0215] Specific means by which such correlations may be derived include any suitable technique for estimating such correlations, such as a correlation map between inputs and outputs, in which the link between a particular input and output is strengthened (e.g., by incrementing connection weights) by simultaneous (or, if a temporal factor is included, within a predetermined time window) presentation of the input and output. When trained on a dataset, a new input will, via the connection weights, more or less activate one or more candidate states correlated with that input. The most strongly activated candidate state may then be selected as the user state, and such states may be ranked by activation strength. Of course, such a system may simultaneously provide multiple input values ​​corresponding to subsets of individual, class-level user factors, as described elsewhere herein, and the generated output may correspond to a single-valued state, a classification, or a multivariate state, with many values ​​representing different aspects of the output user state, as described elsewhere herein.

[0216] An example of a correlation map is a neural network, which may take any suitable form.

[0217] More generally, any suitable machine learning system capable of determining correlations or other predictable correspondences between one or more inputs and one or more outputs is contemplated.

[0218] Given the above-described dataset, such machine learning systems are typically supervised, e.g., supervised classification learning algorithms if the user state is a classification, or supervised regression learning algorithms if the user state is single-valued or multivariate. Other forms of machine learning, such as reinforcement learning, adversarial learning, or semi-supervised learning, are also suitable. Furthermore, multiple independent machine learning systems trained separately on different or overlapping individual, subset, or class-level outputs of acquisition processors can be ensembled to improve modeling results, addressing different configurations of the raw data, e.g., resulting from different users' different patterns of delivery ecosystem device ownership and different permissions and habits affecting the availability of online information sources. It should also be appreciated that a mixture of different machine learning systems can be used in parallel to generate, e.g., a multivariate state of a user, with one or more different elements of the multivariate description generated by each different machine learning system. Each of these machine learning systems can be implemented on separate hardware (e.g., based on dedicated neural processors), but more commonly, they are considered to be implemented on the same hardware (software-based machine learning systems that are loaded and executed as needed).

[0219] However, unsupervised learning algorithms are also possible, so that, for example, associative learning can determine the probability that a user will be in a given state in the presence of a certain input or input pattern.

[0220] Examples of such machine learning systems will be known to those skilled in the art in the form of algorithms and / or neural networks.

[0221] Alternatively, machine learning may optionally be used to prepare (e.g., pre-process) data in either the estimation processor and / or the acquisition processor. Thus, clustering (e.g., k-means clustering) may be used to classify a diverse set of inputs into class-level user factors of the type described herein above. Such techniques may be used to derive classifications for user states according to the second example of state category classification described herein above, for example, in response to inputs from the acquisition processor or available feedback operations of the feedback processor.

[0222] Similarly, as a preliminary step in the estimation processor and / or acquisition processor, dimensionality reduction such as principal component analysis may be employed to reduce the number of inputs while retaining information that significantly corresponds to the user state.

[0223] In summary, when the estimation processor generates an explicit estimate of the user state, it uses a repository of correspondence between available inputs from the acquisition processor and the estimated state, which may be embodied in algorithms, rules or heuristics, one or more lookup tables, and / or one or more trained machine learning systems.

[0224] The result in each case is an estimate of the user state, which may be in the form of a single value, a category, or a multivariate description / representation of the user state, as described herein above.

[0225] On the other hand, the operation of the estimation processor when generating implicit estimates of the user state is described later in this specification.

[0226] Feedback suggestions from estimated states As described herein above, the estimation processor may be adapted to operate in a two-stage process, in a first stage, by the acquisition processor, to estimate a user state from inputs including one or more user factors or data derived from such user factors, and in a second stage, as described below, to generate suggested feedback actions expected to change the user state.

[0227] In principle, the second stage may be implemented by the feedback processor rather than the estimation processor, or may be shared between the feedback processor and the estimation processor. Alternatively, the feedback processor may simply receive the suggested feedback actions. In either case, the feedback processor may then select a feedback action (defaulting if there is only one suggestion, or selecting one or more if there are multiple suggestions), and optionally act to cause the feedback action or actions suggested by the estimation processor to occur appropriately within the delivery ecosystem.

[0228] For purposes of explanation, the second stage is described herein as occurring in the estimation processor.

[0229] The second stage may be chosen for practical reasons, for example, a training set used to model the correspondence / correlation between the user factors or their derived values ​​by the acquisition processor and the user state may be easier to generate or obtain than a training set used to directly model the correspondence / correlation between the user factor-based input and the suggestion feedback behavior, since the user state may be directly measurable or easier to report by the user.

[0230] Similarly, it may be easier to generate training sets that determine correspondences / correlations between measurable and / or self-reported user states and suggested feedback actions based on, for example, user surveys ranking feedback actions for a given state and / or the subsequent effectiveness of implemented feedback actions in changing the user state toward a more desirable state, such as measured and / or reported by the user. Typically, a more desirable state is one that improves the user's subjective sense of well-being and / or brings physiological or neurological indicators of the user's state closer to a preferred baseline (e.g., increased heart rate, galvanic skin response, increased skin temperature, and / or reduced respiratory rate, etc.).

[0231] The input to the second stage will typically be an estimate of the user's state, expressed as a single value, a category, or a multivariate description as described above in this specification, or a plurality of such states if multiple states are estimated (e.g., different activity / correlation strengths in response to the first stage input). Optionally, the input to the second stage may also include one or more user factors and / or inputs as provided by the acquisition processor. For example, as described elsewhere herein, certain physiological measurements may be useful indicators / surrogates of user state, such as galvanic skin response, heart rate, respiratory rate, skin temperature, etc. Thus, one or more of these inputs, or any other input to the first stage, may optionally be provided to the second stage along with the or each estimated state.

[0232] In any event, similar to the estimation of the user state, the generation of the suggested feedback actions may use any suitable mechanism that embodies a correspondence / correlation between the estimated user state and the suggested feedback actions.

[0233] As mentioned above, this may include predefined rules, algorithms, and / or heuristics that convert estimated states into suggested feedback actions. For example, a single-valued state (e.g., degree of stress) may drive a corresponding suggested feedback action, such as increasing the percentage of active ingredient in a unit inhaled volume of generated aerosol, which may be achieved by modifying heater, airflow, reservoir, and / or other payload storage settings, and may be managed by a feedback processor, as described later in this specification. The relationship between the degree of stress and the change in active ingredient may be linear or nonlinear, or may vary qualitatively at different values, e.g., no change at all at low levels of stress, a linear relationship at medium levels of stress, and an asymptotic relationship at high levels of stress up to a maximum percentage of active ingredient. At or near this maximum, the behavior of the user interface of the delivery device or other devices in the ecosystem may also be modified, e.g., issuing a warning or calming message to the user's mobile phone. Alternatively, for example, a single category state may have a corresponding suggested feedback action. Finally, a multivariate state may result in a corresponding suggested feedback action based, for example, on weighted or non-weighted contributions from different elements of the state description, and / or different feedback actions may be suggested based on overlapping or non-overlapping subsets of elements of the state description. Thus, for example, if a state description suggests that a user is stressed and is in a work environment, a feedback action may assume that the user is experiencing implicit stress due to being in a work environment, but is currently unable to increase their intake of the active ingredient, and issue a message to the UI of the delivery device or other devices in the ecosystem, such as the user's phone, suggesting that the user take a break. On the other hand, if the user is stressed but is not in a work environment, the feedback action may be similar to the example stress level described above, resulting in an increase in the rate of active ingredient delivered to the user.

[0234] As mentioned above, any one of these may be accompanied by one or more inputs to the first stage.

[0235] Again, as with estimating the user state, the estimation processor may alternatively or additionally convert the state estimation data into suggested feedback actions through the use of a look-up table.

[0236] Alternatively or additionally, similar to the estimation of the user state, the estimation processor may model the correlation between the estimated user state and the suggested feedback actions and use similar techniques to do so.

[0237] If the inference processor models correspondence / correlation, it can be trained using a dataset that includes as input data corresponding to inferred user states (e.g., in the form of single values, classifications, or multivariate descriptions, or combinations thereof), optionally inputs from an acquisition processor as described herein above, and suggested feedback actions as target outputs.

[0238] Suggested feedback actions, discussed in more detail below, typically include at least one type of action and, optionally, one or more variables that characterize the performance of that action. Thus, for example, a change in vaporization temperature would be a type of action, and an increase / decrease or amount of increase / decrease would represent a variable that characterizes the performance of that action. Similarly, modifying the concentration of an active ingredient in an aerosol would be a type of action, and an increase / decrease or amount of increase / decrease in concentration would represent a variable that characterizes the performance of that action.

[0239] Thus, as a non-limiting example in the context of a machine learning system, different output nodes may represent different types of behavior, and the values ​​of these nodes may represent flags indicating the selection of that feedback behavior or values ​​for variables of that feedback behavior depending on how the system is trained. It should also be appreciated that in a machine learning system, multiple output nodes may be associated with one or more types of behavior depending on the training format.

[0240] Of course, potentially multiple feedback actions may be indicated in response to the estimated user state. In such a situation, the feedback processor may then decide whether to select only one feedback action or to execute multiple feedback actions in parallel or sequentially, for example based on the degree of change that action causes as implied by the associated variable or variables, optionally ordered according to a predetermined order, also in response to the strength of activation of the flag output node for each feedback action and / or the degree of change implied by each action's associated variable or variables.

[0241] It will also be appreciated that for training such machine learning systems, measured and / or reported user states can be provided as inputs, with respective suggested feedback actions provided as targets, with actions and values ​​selected according to their reported effects in user trials for users with the corresponding user states, where again, effect or effectiveness typically relates to an improvement in user-perceived state and / or a change in neurological and / or physiological state towards a predetermined baseline or preferred state.

[0242] Optionally, the use of simulated conditions and corresponding feedback behavior as an initial training phase can provide initial training (e.g., based on survey results as described above), after which the model can be refined using a relatively small cohort of real-world training data.

[0243] Optionally, the user's own feedback regarding the effectiveness and / or suitability, desirability, usefulness, etc. of any feedback actions may be further used to refine the model and effectively customize it for the user. This feedback may likewise be reported by the user based on measurements of the user interface of devices in the delivery ecosystem, such as the delivery device or phone, and / or neurological and / or physiological responses. If multiple feedback actions are performed or specified, the user may optionally rank each in order of preference.

[0244] In summary, a two-stage process involving explicit estimation of user state as a first or interim stage, whether performed by rule-based techniques or machine learning, may be used when these stages better fit the available underlying empirical data set used to model correspondence / correlation.

[0245] Objectively, in this mode, the estimation processor operates as described above by taking input from the acquisition processor, typically in the form of different individual, subset, and / or class-level user factors, and outputting one or more suggested feedback actions, including simply identifying the actions in a manner similar to flags, identifying the relevance of the actions to the estimated state based on the activation levels and outputs corresponding to the suggested feedback actions, and / or specifying a change or amount of change in one or more variables that at least partially characterize the suggested feedback action.

[0246] Thus, explicit estimation of the user state is typically an internal, interim step. However, it should be understood that this estimate can also be relayed as information to the user, and optionally, the user can modify the estimate, especially if the estimate or its components in the multivariate description relate to subjective measures or proxies for subjective measures such as the user's sense of stress. Thus, for example, the estimate can be displayed in a user interface on the user's mobile phone, and the user can use this information to self-assess and consequently modify the estimate. Subsequently, in a second step, the revised estimate of the user state can be used in addition to or instead of the initially generated estimate to identify / generate suggested feedback actions that are considered more accurate than suggestions based on the original estimate of the user state.

[0247] Furthermore, any changes made to the user's state estimate can be used to update and refine the first-stage model; indeed, for certain machine learning techniques, the absence of user corrections can similarly be seen as a positive reinforcement of the estimate for training purposes.

[0248] As previously mentioned herein, if further training is not desired, the relationships between input and output values ​​resulting from the machine learning process may optionally be captured in one or more lookup tables, which may be computationally easier to use (although likely occupy more memory).

[0249] Implicit State Estimation In one embodiment herein, rather than using the two-stage process described above, the estimation processor performs a one-stage process that implicitly estimates the user's state as part of the relationship between the individual, subset, and / or class-level user factors provided as input by the acquisition processor and the suggested feedback actions generated as output, typically expected to change the user's state.

[0250] Thus, an estimation processor (1020) configured to calculate an estimate of a user state based on one or more of the acquired user factors may be equivalent to an estimation processor (1020) configured to identify / generate a suggested feedback action state based on one or more of the acquired user factors, where the user state is implicit in the relationship between the user factors and the suggested feedback action expected to change the user's implicit estimated state.

[0251] Similar to the two-stage process described herein above, the estimation processor may transform the input data from the acquisition processor into an estimated state through the use of predetermined rules, algorithms, and / or heuristics, which may combine the two separate stage processes of an explicit state estimation embodiment, for example, and / or refine some or all of the rules, diagrams, and / or heuristics in response to the one-stage nature of the implicit state estimation approach, or may be derived from scratch in the case of a one-stage process.

[0252] Again, as with the two-stage process, the estimation processor may alternatively or additionally use lookup tables to convert the input data into suggested feedback actions, which may similarly be concatenated lookup tables from the two-stage approach and / or processed separately to provide a one-stage lookup table, or derived from scratch in the case of a one-stage process.

[0253] Again, as with the two-stage process, the estimation processor may alternatively or additionally use machine learning, for example, by using inputs used in the first stage of explicit state estimation and goals used in the second stage of generating suggested feedback actions from the estimation stage to train a machine learning system to identify a measurable correspondence between the two.

[0254] Of course, to present corresponding inputs and goals for training, the training set must capture this correspondence. As described above in this specification, data sets may exist for inputs and user states, as well as for user states and effective feedback actions. As a result, inputs and feedback actions can be combined for training purposes, if necessary, based on common user state values, classes, or multivariate descriptors. Clearly, if the training data set was collected by users who measured and / or self-reported user factors, measured and / or self-reported user states, and measured and / or self-reported the effectiveness, suitability, desirability, usefulness, etc., of subsequent feedback actions, then a self-consistent set of input user factors and goal feedback actions (as provided by the acquisition processor) can be used for training.

[0255] Alternatively or additionally, a two-stage system of explicit state estimation trained on separate data sets, a two-stage system of explicit state estimation using each rule, algorithm, and / or heuristic from the two stages, and / or a two-stage system of explicit state estimation using lookup tables from the two stages can be used as data sources.

[0256] For example, a two-stage estimation lookup table or a one-stage lookup table created by running rules, algorithms, and / or heuristics and / or machine learning systems through the first and second stages may provide a lookup link between inputs as provided by the acquisition processor and the suggested feedback actions identified / generated by running the two-stage process using those inputs.

[0257] Alternatively or additionally, two-stage estimation lookup tables or rules, algorithms, and / or heuristics and / or training of a one-stage machine learning system by running the machine learning system through the first and second stages may provide inputs such as those provided by the acquisition processor and provide goals for training the suggested feedback actions identified / generated by running the two-stage process using those inputs.

[0258] Optionally, a single-stage machine learning system so trained may then refine its training using additional data, such as a composite training set as described above, and / or data received from one or more users during use of a user feedback system, similar to that described herein above for the staged approach.

[0259] It should also be appreciated that, for example, the training set may be based directly on capturing desired input and target values ​​rather than using an amalgamation of data sets or processes.

[0260] Of course, for either the two-stage or one-stage approach, a training set relating user factors to user states may be constructed by collecting training data using one or more devices in the delivery ecosystem. Such a training set may be generated by a version of the user feedback system that does not generate suggested feedback actions but simply collects user factor and user state information. Similarly, a training set relating user states to suggested feedback actions may initially be based on questioning users whose respective states are known (e.g., measured / reported) to evaluate suggested feedback actions via a telephone user interface, for example, as part of a user testing regime. Thus, in this case, the feedback system may suggest feedback actions and select one or more of the suggested actions, but in a different version or mode, may present the selected suggested feedback action(s) to the user (e.g., via a user interface) for evaluation, e.g., during a training data collection phase or a calibration phase (e.g., characterizing users in subgroups whose responses can be better tailored, as described elsewhere herein), and may modify one or more operations of at least a first device in the delivery ecosystem by executing the selected suggested feedback action(s) in response to an estimation of the user state (whether explicit or implicit modeling) in a manner expected to change the estimated state of the user. Training data relating user factors to suggested feedback actions may be obtained as well.

[0261] Thus, such data sets may be obtained using a version or mode of a user feedback system that, as described above, does not actually result in modification of the operation of one or more devices in the ecosystem (optionally, other than to elicit a response from the user, e.g., for training data purposes).

[0262] Thus, a pre-generation or training / refinement mode of such a user feedback system may include an acquisition processor (1010) operable to acquire one or more user factors indicative of a user state, and to acquire user state data (e.g., based on measurements similar to the user factors and / or self-reports by the user) and / or feedback action preference / effectiveness data. The estimation processor would then include a training or development phase, e.g., once a sufficient corpus of data has been accumulated, in which correspondences / relationships / correlations between inputs based on the user factors as described above and goals based on the user state (in a two-phase approach) or suggested feedback actions (in a one-phase approach) are modeled as described above.

[0263] Alternatively or additionally to the above, in a pre-generation and / or training mode of such a feedback system, the delivery device and / or other participating devices in the delivery ecosystem may end up only uploading data to the acquisition processor and not downloading feedback operations (or optionally any other data) from the feedback system.

[0264] Similarly, the pre-generation and / or training modes of such feedback systems and / or providing refinement or supplemental input to the feedback system may involve direct input of the user's state as reported by the user, as described elsewhere herein, to user factors such as neurological / physiological data (e.g., via biosensing), movement and / or location user factors (e.g., via touch, accelerometer, or GPS sensors), contextual user factors, and / or any of the other user factors disclosed herein. This can be used to generate a training set, as described above, but alternatively or additionally, the user's reported state may be processed as a user factor directly by the acquisition or estimation processor. In principle, the user's reported state may optionally be used in place of an explicit state estimate by the estimation processor, but in at least some cases it may be approximate compared to what can be derived or estimated from several measurements (if available), and the user may not be informed of all the facts available to the feedback system. Furthermore, some users may normalize and self-report their state with preconceived notions, especially in the case of pathological conditions such as depression. Thus, the direct user input regarding the state may optionally be used as input to the estimation processor in the first stage (or only in the first stage) as described above, in conjunction with one or more other user factors from the acquisition processor as described above. Optionally, alternatively or additionally, when using a two-stage technique, the direct user input regarding the state may be used as input to the second stage of the estimation processor, in conjunction with the estimate of the respective state.

[0265] Other variables in the training and input are also contemplated. For example, it will be appreciated that, as noted herein above, different user factors will operate or vary over different time frames. As a result, in the case of a two-stage or one-stage approach to the estimation processor as described herein, user factors that are not expected to change within the interval between successive operations of the estimation processor may be stored (e.g., in storage 1012) and reused rather than being reacquired.

[0266] Furthermore, some of the estimation models for these long-term factors may not need to be re-run if the results of these factors are not expected to change. While this may be straightforward for rules, algorithms, and / or heuristics and / or lookup tables, for machine learning systems, it may require architectural changes. For example, a two-stage ML or multi-layer system may be trained on all inputs, but then run with clamped inputs or outputs for long-term user factors, and the computed intermediate results of that part of the ML system may be fed to other parts of the ML system along with newly generated intermediate results from user factors over a shorter time frame.

[0267] It should also be appreciated that, as discussed above, different users may have different combinations of devices in their delivery ecosystems and / or may have different combinations of these devices active at any one time. Similarly, different users may have varying degrees of social media presence or may utilize digital calendars to varying degrees. As a result, the user factors available to the acquisition processor, and therefore the inputs available to the inference processor, may vary from user to user and / or from time to time. Thus, the inference processor may suggest feedback actions by using different models (explicit or implicit, as discussed above) depending on the available inputs. Alternatively or additionally, if an input to a model is missing, a neutral input value may be provided to reduce or eliminate the impact of the missing input on the suggested feedback action. Thus, the number of different models provided by / to the inference processor may depend on the number of data sources assumed by the model (more or more diverse data sources potentially making the model more vulnerable) and the robustness of the model to the substitution of an input with a placebo / neutral value when the input is currently unavailable. In the latter case, it is understood that some inputs may be deemed more important than others, and therefore at least some individual inputs may be required to run the model. Thus, depending on the complexity and robustness of the model, only one model may be needed, or a family of models may be needed that consider different scenarios. Optionally, a subset of all available models may be selected for a user according to the devices known to be in the delivery ecosystem. However, new models may be added when new devices join the delivery ecosystem, whether permanently, such as when a user purchases a new dock 200, or temporarily, such as when a user interacts with a vending machine or point-of-sale device.

[0268] Estimated Processor Output Whether a one-stage or two-stage process is used, and whether the estimation of any stage is based on rules, algorithms, and / or heuristics, look-up tables, and / or machine learning, the output of the estimation processor is a suggested feedback action.

[0269] Possible feedback actions may differ qualitatively and / or quantitatively.

[0270] Thus, for example, feedback actions may vary qualitatively based on modifying aerosol production for the user (whether in response to or in advance of the current situation), modifying the user's interaction with the delivery device or system during or between inhalations, modifying the user interface of the delivery device or system, prompting the user to use or modify the use of the delivery device or system, recommending the operation or selection of the delivery device or delivery device consumables, and / or recommending / activating / modifying the operation of a device that is not directly related to the delivery of the active ingredient but can directly change the user's state (e.g., through biofeedback) or indirectly change it (e.g., by activating noise cancellation on the user's headphones).

[0271] Thus, more generally, feedback actions can be categorized into behavioral categories that focus on altering a user's behavior and / or habits to alter their state; pharmaceutical categories that focus on how one or more active ingredients delivered to a user alter their respective state; and non-consumable intervention categories that focus on alternative first or third party options for altering a user's state (i.e., related to the delivery device, other devices in the delivery ecosystem, or elsewhere).

[0272] On the other hand, suggested feedback actions may vary qualitatively depending on the degree to which the effect of the feedback action is desired to bring about a positive change in the user's condition. Thus, for example, in a delivery device, changes in heater temperature, payload aerosolization, payload composition, etc. may include quantitative values ​​indicating the degree or class of change, as appropriate. Similarly, user interface modifications in a delivery device or another device in the delivery ecosystem may include incremental steps regarding the number of user interactions required or prompted with the delivery system and the nature of these user interactions. For example, these may be implemented through five categories, with the first category having no notifications to minimize user interruption, the second category having only critical notifications such as low battery or low payload, the third category corresponding to a default where critical and non-critical notifications are provided, the fourth category further including recommendations and / or prompts to engage the user with other features of the user interface, and the fifth category additionally including an audible tone. These five categories may be selected on a scale of stress (e.g., minimal notifications for high stress) and / or boredom (e.g., loud notifications for high boredom) depending on the user's state.

[0273] As discussed above, the type of feedback action and / or amount or class of change may be identified according to rules, algorithms, and / or heuristics, look-up tables, and / or machine learning, as appropriate.

[0274] Similarly, as mentioned above, if multiple types of feedback actions and / or multiple change amounts or change classes are calculated / estimated as appropriate responses to a user factor / user state, then optionally multiple feedback actions may be suggested accordingly, or the top N feedback actions may be selected, for example based on activation intensity, where N may be 1 or more.

[0275] Feedback Processor The feedback processor 1030 is operable to modify one or more operations of devices in the delivery ecosystem in response to the estimation of the user state by executing one or more suggested feedback actions.

[0276] The feedback processor may therefore act to cause one or more feedback actions suggested by the estimation processor to occur appropriately within the delivery ecosystem.

[0277] The feedback action(s) are typically performed in a manner that is expected to change the estimated state of a user, who is considered a typical, average, or expected user. Of course, the model(s) underlying the generation of the suggested feedback action(s) are typically developed or trained using data from a corpus of users and therefore relate to changes in the state of a typical, average, or expected user.

[0278] However, most users are likely to react similarly to these changes, so typically each delivery device will result in a similar change in state for a particular user.

[0279] However, as described elsewhere herein, if the feedback system is capable of receiving separate feedback from individual users (e.g., by measurement or self-report) regarding the effectiveness of the proposed feedback action, then the feedback action may optionally be performed in response to the estimation of the user state in a manner that is expected to alter the particular user's estimated state, allowing further adjustment for the particular user, e.g., through supplemental training and / or parameter refinement. Similarly, separate rules, algorithms, and / or heuristics, lookup tables, or machine learning systems may be generated for different groups of users, e.g., based on attributes and / or patterns of response to the feedback action, so that the proposed feedback action is further tailored to a particular user in one of these groups, even if no measured or reported evaluation of the effectiveness of the feedback is available from the particular user, even if the machine learning system is too small to effectively refine its training or modify parameters of the algorithm, etc., to customize the response.

[0280] Like the acquisition processor and estimation processor, the feedback processor 1030 may comprise one or more physical and / or virtual processors and may be located in a remote server 1000 and / or functionality may be distributed or otherwise distributed across multiple devices in the delivery ecosystem, including, but not limited to, the user's mobile phone 100, the docking unit 200, the vending machine 300, and the delivery device 10 itself. The feedback processor may comprise one or more communication inputs, for example, for receiving data from the estimation processor 1010, and one or more communication outputs, for communicating with, for example, the delivery device 10 and / or another device in the delivery ecosystem 1 as listed above, or any other device that may participate in the feedback operation.

[0281] In particular, the feedback processor may optionally be located on a server and / or a device in the delivery ecosystem having suitable computing power, such as a vending machine, a mobile phone, or a suitable actual delivery device, and may comprise a selection and notification sub-processor (not shown) that can optionally select one or more feedback actions and select one or more respective devices in the ecosystem to execute the one or more feedback actions, and optionally an action execution sub-processor (not shown) that manages the execution of the feedback actions at one or more respective devices in the ecosystem. Optionally, the action execution sub-processor may be considered a separate processor from the feedback processor.

[0282] References herein to a selection and notification sub-processor and a feedback processor, or to an action-performing sub-processor and a feedback processor, respectively, are considered to be synonymous. It will be appreciated that these sub-processors may be complementary hardware to the feedback processor and / or may effectively share the role of the feedback processor, while also being functionally equivalent to the feedback processor operating under suitable software instructions. However, as noted above, at least the action-performing sub-processor may optionally be a processor separate from the feedback processor, communicating with the feedback processor, for example, via the Internet.

[0283] Choice and Notice Optionally, the selection and notification sub-processor may select one or more of the feedback actions generated by the inference processor as described herein above if the inference processor indicates that more than one feedback action may be appropriate. Obviously, if only one feedback action is proposed, this will be selected as the default.

[0284] For a selected feedback action, the selection and notification sub-processor may then select one or more devices in the delivery ecosystem on which to perform the feedback action and formulate a command / notification / instruction for the or each device characterizing the type and / or amount of the feedback action. Of course, if only one feedback action is possible for a device, the type may be implicit in the notification action; similarly, if only one amount of feedback action is possible for a device, the amount may be implicit in the notification action. Any device in the delivery ecosystem may potentially be equipped with feedback means. Thus, of course, the device or devices that provide user factor data to the acquisition processor in the delivery ecosystem may potentially be different from the device or devices that perform the or each feedback action.

[0285] Optionally, the selection and notification sub-processor may poll devices in the delivery ecosystem to determine their availability for purposes of providing feedback. For example, for devices accessible by the processor via the internet, devices registered in association with the user or the user's delivery device (e.g., delivery device 10, mobile phone 100, wearable device 400, docking device 200) may be polled directly.

[0286] For devices that are only accessible via an intermediary device (e.g., a Bluetooth connection to the accessible device), the accessible device may be required to poll such indirect devices. Thus, for example, the selection and notification sub-processor may cause the user's mobile phone 100 to poll / request the delivery device 10, the wearable device 400, or the docking device 200 (if accessible only via a local wired or wireless connection).

[0287] For devices not formally associated with a user, such as a vending machine 300 or other point-of-sale system, or devices only intermittently associated with a user, the selection and notification sub-processor may receive location data from devices in the delivery ecosystem associated with the user, such as a mobile phone 100 or delivery device 10, and compare it to the registered or reported location of the vending machine 300. If the locations are within a threshold distance of each other, the vending machine is considered part of the delivery ecosystem while that condition applies. Alternatively or additionally, the selection and notification sub-processor may instruct the accessible device to poll any compatible vending machines or broadcast a Bluetooth beacon identifying the accessible device, for example, by using a disposable ID to identify the accessible device without revealing details of the user or its associated device. Such an ID may include a component identifying the purpose of the ID to allow detection by a vending machine, followed by a disposable component unique to the user or their associated device. A compatible vending machine according to embodiments of the present invention may then optionally recognize and relay the single-use ID to a selection and notification sub-processor, thereby notifying the user that they have an accessible device within local wireless range of the vending machine. Of course, while the above references to vending machines are intended as an illustrative example only, these techniques may also apply to any device that is not formally associated with a user or that is only intermittently associated with a user, such as a car or train, a Wi-Fi or Bluetooth hotspot in a store, a smart TV, etc.

[0288] Optionally, devices outside the user's own delivery ecosystem may be selected. For example, friends or family may be able to intervene by notifying them of the user's status using the delivery device and / or their associated devices, such as their phones (e.g., following the user's registration of these people). Optionally, the user can set the conditions under which this occurs and / or under which friends or family are notified. Similarly, devices within a predetermined proximity of the user may be selected. For example, if the user is in a good mood, all compatible devices within a predetermined radius of the user may synchronize characteristics such as light color to notify the user of a fun social encounter.

[0289] By using one or more of these techniques, the selection and notification sub-processor may determine the devices currently available for delivery of feedback actions.

[0290] Typically, feedback actions are specific to a particular device or pair of devices that cooperate to perform a function within the delivery ecosystem. As a result, for a suggested or selected feedback action, the selection and notification sub-processor may optionally only poll one or more devices within the delivery ecosystem that are associated with that feedback action.

[0291] More generally, however, feedback actions are considered specific to the particular function required to deliver them. Thus, for example, a feedback action including a message prompting a user to perform a particular action, such as breathing more slowly / calmly between uses of the delivery device or inhaling more slowly / calmly while using the delivery device, may be performed by any device in the delivery ecosystem capable of displaying such a message, such as provided by one or more of the delivery device itself (if equipped with a display), the user's mobile phone, a fitness wearable, or a suitably equipped docking unit for the delivery device. In principle, such messages may also be provided to the user by a vending machine or other point-of-sale device.

[0292] Similarly, it will be appreciated that certain devices within the delivery ecosystem may provide input data to the feedback system that is used to generate the suggested feedback actions. As a result, input actions from such devices may be recorded as indicative of their respective accessibility, and / or the suggested feedback actions may imply that a particular device is currently accessible to the feedback system. In either case, polling of devices may not be necessary, or, if a polling scheme is present, receipt of the input data may be treated as an effective polling result.

[0293] If one or more devices associated with a feedback action are unavailable (e.g., do not respond to polling), the feedback processor / selection and notification sub-processor may optionally select the next suggested feedback action among the top N feedback actions if multiple feedback actions were suggested by the inference processor. If no relevant devices are available for a feedback action, the feedback processor may not perform any feedback action and / or may notify the user thereof, for example, via a user interface on the user's phone, or, if the user's phone is not available as an accessible device for linking to other devices in the ecosystem, may notify the user via a text or other similar mechanism to be delivered once the user is contactable again. Similarly, if there is no currently available valid communication between the feedback processor and one or more associated devices, or if there is no active communication between the feedback processor in one or more associated devices and the inference processor or other parts of the feedback system (depending at least in part on where the feedback processor is located), the associated one or more devices may default to normal or other default delivery or other default behavior appropriate for that device.

[0294] If one or more devices associated with the feedback operation are available (i.e., if they respond to polling, if they respond to polling within a predetermined period of time prior to the device being assumed to still be accessible, or if they provide input data within a predetermined period of time), the feedback processor will send one or more commands to the one or more devices to perform the feedback operation as suggested by the estimation processor.

[0295] As noted above, the nature of the command may depend on the proposed action as well as the target device or devices. In some cases, the mere presence of a command will be sufficient to specify the proposed action (e.g., turning on a device that is off). In other cases, the command will need to specify the type of feedback action, e.g., with respect to changing heater function in the delivery system, payload type, user interface behavior, etc. In any of these cases, the command may need to specify the amount of feedback action, e.g., changing the temperature, concentration of active ingredients or fragrances in the payload, or selected parameters for the user interface.

[0296] As noted above, commands may be passed directly to the accessible device, or the accessible device may request that the accessible device relay the command to another device in the ecosystem or itself issue the command to such a device. For example, the feedback processor may instruct the user's mobile phone 100 to issue a command to the delivery device 10. Other degrees of indirection are possible, such as where the user's mobile phone issues a command to the dock 200, which may modify settings on the delivery device when the dock 200 is docked (e.g., for charging or to recharge a payload). Likewise, it should be appreciated that the feedback processor may issue different types of commands to different devices. Thus, for example, a command to change aspects of a user interface may be issued directly to the mobile phone and then to the dock 200 (directly, if possible, or via a telephone call), causing the dock 200 to change the composition of the payload provided to the delivery device and, when docked, to change one or more settings on the delivery device. It should be appreciated that other permutations of such commands (whether direct, indirect, or a mixture of the two) are possible within the delivery ecosystem.

[0297] As described elsewhere herein, it will be appreciated that various feedback actions may relate to the behavior of the delivery ecosystem, the drug, and / or non-consumable aspects.

[0298] Behavioral feedback actions typically focus on modifying user behavior and habits regarding operation and / or interaction with devices in the delivery ecosystem other than actions related to the amount or nature of the active ingredient delivered by the delivery device itself, although this can occur in parallel. Examples may relate to modifying the flavor or flavor concentration, modifying inhalation behavior by changing the delivered vapor volume, modifying scheduling schemes or reminders associated with or correlated to delivery device use, providing information, modifying the user interface (whether on the delivery device or another device in the delivery ecosystem) in terms of feedback modes (e.g., haptic and / or visual, such as colored lights, graphic themes, and / or messages) (e.g., providing a traffic light UI display on the delivery device, such as an LED, to alert the user to a mode of device use), etc.

[0299] Thus, for example, selecting a fragrance may include selecting a fragrance that promotes behavior complementary to the user's current state. Thus, for example, if the user is tired, a peppermint fragrance may be energizing, while a lavender fragrance may promote hope or hypnotic effects if the user is stressed. The relationship between fragrances and user states may be empirically determined. Furthermore, fragrance selection may affect user behavior based on the user's fragrance preference, with higher or lower fragrance preferences resulting in increased or decreased consumption. Changing fragrances may also serve to prompt the user to change behavior in a predetermined manner. For example, different fragrances may be marketed with imagery that corresponds to different moods, behaviors, or user states, so that when the feedback processor prompts a particular fragrance selection, the user is prompted according to the associated marketing / imagery.

[0300] Flavor switching may be achieved, for example, by using a gel patch for each flavor and selectively heating the appropriate patch, or similarly by selectively heating or delivering alternate flavors in the aerosol generation process. Other techniques include the use of multiple reservoirs of liquid flavor and selective delivery.

[0301] Fragrance concentration may similarly modify user behavior. For example, completely disabling fragrance may encourage users to reduce consumption. On the other hand, patterning fragrance concentration (e.g., starting with a high fragrance concentration and gradually decreasing it over a usage session separated by a one-hour period, a twenty-minute period, or a predetermined period of non-use) may give the user an initial sense of intervention from the delivery device while also giving them a sense of diminishing returns, encouraging them to discontinue use more quickly within a period / session. More generally, users will associate stronger fragrances with a stronger placebo effect. Thus, if the behavior of using a delivery device is part of modifying a user's state, a stronger fragrance may enhance the effect of this behavior. This allows fragrance concentration to optionally be used as a modifier for other feedback actions, including, in particular, pharmaceutical feedback actions, as discussed elsewhere herein.

[0302] Varying the delivered vapor volume independently of the active ingredient delivery has a similar effect to changing the flavoring in that it gives the user the impression of inhaling more or less aerosol / vapor. Increasing the delivered vapor volume gives the user the impression that they are inhaling more active ingredient than they actually are, and conversely, decreasing the delivered vapor volume gives the user the impression that they are inhaling less.

[0303] Thus, for example, increasing the delivered vapor volume can encourage users to use less.

[0304] Alternatively or additionally to the above, changes in frequency or pattern of use can be corrected by changing scheduling or reminders directly through the delivery device or any other device in the delivery ecosystem (e.g., docking unit or user's mobile phone, etc.).

[0305] Other forms of feedback may be provided by devices in the delivery ecosystem, for example, by changing the color of a user interface component (whether a single LED, a full display, or something in between), or through any other user interface medium, such as actual touch or audio. Thus, for example, a traffic light system could be used with a single LED to prompt a user to, for example, modify their behavior in a predetermined manner. For example, as discussed elsewhere herein, an LED may progress from green to yellow to red (or directly from green to red) in a feedback action responsive to user factors such as physiological signs of stress, such as heart rate, respiratory rate, galvanic skin response, and / or other stress indicators, such as keywords in the user's social media or text posts, or calendared situations, such as particularly stressful locations.

[0306] A more capable user interface allows for more detailed and / or personalized feedback to the user. Thus, if a device in the delivery ecosystem has a text-capable display, it can provide specific messages to the user. Examples include encouraging the user to take slow or steady breaths between uses of the delivery device and / or to take slow or steady inhalations while using the delivery device, as described herein above, or advising the user to take longer intervals between inhalation orientation sessions or to change one or more settings on the device (especially if these are not done automatically).

[0307] In addition to advising the user on how to modify their use of the delivery device or any other device in the delivery ecosystem, such feedback actions can also advise modifying their behavior more generally. For example, the user may be encouraged to take time out from stressful situations, engage in energizing exercise, or conversely, engage in meditative activities such as yoga. Such recommendations may be selected according to previously received user preferences. Thus, for example, yoga may not be suggested to someone who does not attend yoga classes.

[0308] The advice or prompt may be related to physiological or situational factors that may be contributing to the user's current state. Thus, for example, if the user is experiencing physical signs of stress and the background environment is detected to be noisy, the advice may be to put on headphones and listen to relaxing music. Thus, the advice need not be directly related to the use of the delivery device or the consumption of materials through the delivery device. Thus, more generally, the wording of any message provided to the user may be modified according to one or more user factors.

[0309] Thus, optionally, a device within the delivery ecosystem may prompt a user to use another device within the delivery ecosystem in a particular manner, or to use other devices whether or not associated with vaping or similar activities.

[0310] Therefore, devices in the delivery ecosystem may prompt users to use a particular product that is appropriate for their current state. For example, the device may recommend that instead of using an e-cigarette, the user switch to a snus pouch. This may occur, for example, when a user appears stressed but is in an environment where use of an e-cigarette delivery device is not possible (e.g., the user appears indoors and their calendar indicates they are at a restaurant).

[0311] Of course, other forms of feedback behavior described above may also interact: for example, a user may have two separate delivery devices providing separate fragrances (or independent of the separate active ingredients or active ingredient concentrations described above), and the devices in the delivery ecosystem will advise the user on the best one to currently use based on feedback behavior identified depending on currently available user factors related to the user.

[0312] More generally, feedback actions may provide prompts that reinforce a user's current positive state by complementing it, or prompts that are intended to restore a user's current negative state to a better state. Thus, feedback actions that are expected to change a user's state may involve actions that change a user's negative state to a new state in a repair situation. Conversely, feedback actions that maintain a user's current positive state, even if it would otherwise change in a complementary or supportive situation, may involve actions that maintain a user's current positive state.

[0313] Similar use of such user interfaces may provide additional feedback to the user after a feedback action has occurred, following an action that changes the user state. This additional feedback may provide positive reinforcement of the intended user state following the action, or may prompt the user to measure the effectiveness of the action relative to each state (e.g., for purposes of training the feedback system and / or for self-assessment to recognize / understand the effects of the feedback action).

[0314] Pharmaceutical feedback actions, in turn, focus on pharmaceutical interventions that alter the user's state, typically based on active ingredient-based interventions such as amount or type, timing of changing these (e.g., as a response or preemption based on correlations between current user factors and future user state or feedback actions), etc. Such actions may also relate to the selection of alternative consumption modes (e.g., switching from vaping to snus or vice versa).

[0315] As a result, the estimation processor may be adapted to identify at least a first feedback action based on one or more user factors as described elsewhere herein, the at least first feedback action relating to the amount or nature of the active ingredient delivered by the delivery device.

[0316] With respect to quantity, the discriminatory feedback operation may involve a binary decision to not deliver any active ingredient (e.g., switch to a placebo output). This may occur if the active ingredient is known to have a particular physiological effect that is considered harmful, given the user's current physiological state as inferred from available user factors. Thus, for example, an active ingredient that may increase heart rate may be stopped if the user's heart rate is detected to be high. At least in the short term, a placebo effect may be at work, as the user will still consume from the delivery device with the belief that they are ingesting the active ingredient (or, if consent was nevertheless given, in the midst of a previous positively connoted act).

[0317] Of course, this can be accomplished by completely disabling the delivery function of the delivery device, or by simply not including the active ingredient in the delivery vehicle provided by the delivery device, in which case a message may be provided to the user indicating that a feedback action has occurred, so that the user does not believe that the delivery device has failed.

[0318] As an alternative to a binary decision, the discriminatory feedback operation may modify the concentration of the active ingredient delivered to the user, for example, the concentration of the active ingredient may be increased or, if desired, decreased from a predetermined level according to the output of the estimation processor, such as the degree of activation of the feedback operation or an output value associated with the feedback operation, as described elsewhere herein.

[0319] Thus, the feedback processor may be responsive to a quantitative value indicative of the degree of change or class of change, and may be responsive to discriminatory feedback action, if desired.

[0320] It will be appreciated that the feedback action may modify the amount of active ingredient from zero to a predetermined value, or may be a sliding modification, for a subsequent puff, for a subsequent period of puffs, or for a predetermined number of puffs, or for a predetermined total amount of puffs estimated, for example, from measurements of the airflow and duration of puffing of the delivery device.

[0321] Alternatively or additionally to such modifications, the feedback system may also manage the distribution of delivery of the active ingredient over a period of time, for example to provide a rapid or habitual delivery regime that is relatively independent of the user's consumption patterns and allows the user to consume from the delivery device relatively frequently compared to that period. Thus, for example, if a given period normally includes 20 puffs, the feedback may provide a habitual or rapid delivery regime, for example by delivering the same total amount of active ingredient in an average manner with similar overall puffs, or by concentrating some, most, or all of the active ingredient in a small number of the 20 puffs.

[0322] Such delivery regimen modification may be reactive, such that if the user appears particularly stressed, an emergency delivery regimen may be implemented, followed by an optional return to a lower intensity habitual delivery regimen corresponding to the intensity of the emergency phase. Conversely, such delivery regimen modification may be predictive, such that if the user indicates an increased stress level, or if situational or other user factors indicate an impending high-stress situation (e.g., an about-to-start driving test), the feedback action may include, for example, providing an emergency delivery regimen in response to the increased stress, or an emergency delivery regimen that blocks the increase in stress in advance of the anticipated high-stress situation. Thereafter, optionally, a transition to a lower habitual delivery regimen may occur in response to user factors indicating a decrease in stress level and / or situational user factors indicating the end of the high-stress situation.

[0323] The above discussion of variations in the amount of active ingredient on an inhalation basis or over a predetermined period of time assumes that a single active ingredient is available in the same delivery device or multiple delivery devices, although more than one active ingredient may be available and the feedback action may include switching from one active ingredient to another, or mixing or modifying the mixture of two or more active ingredients.

[0324] Active ingredients may include any composition that provides a physiological effect to the user, for example, altering heart rate, dopamine and / or cortisol levels, and / or affecting brain chemistry and / or the subjective user experience, as described elsewhere herein.

[0325] The most common active ingredient is nicotine, but any suitable active ingredient is contemplated.

[0326] Thus, there is a feedback action such that a delivery device that has already delivered active ingredient X to a user introduces additional active ingredient Y during use, while the concentration of active ingredient X may remain the same or may increase or decrease as needed, depending, for example, on whether the desired change in the user's condition benefits from a transition from X to Y or from the complementarity of X and Y.

[0327] As an alternative or in addition to introducing a second active ingredient as part of a complementary composition or transition from one to the other, the feedback action may involve simply switching from one active ingredient to another. This may be accomplished within a single delivery device, for example, by selectively heating a gel or other carrier medium for each active ingredient, or by encouraging the user to replace a consumable cartridge in the same delivery device or switch to another device if they own more than one delivery device. In the latter case, the user will typically have registered the delivery device with the feedback system, and optionally the delivery device will have reported the type of payload it is currently carrying to the feedback system as a user factor.

[0328] Feedback actions involving switching from one active ingredient to another may also include switching from one form of the active ingredient to a different form of the active ingredient.

[0329] For example, feedback action may include selecting whether to provide protonated nicotine as the active ingredient, or adjusting the proportion of protonated nicotine delivered within the overall mixture of nicotine or another active ingredient provided en bloc as the active ingredient.

[0330] Protonated nicotine is absorbed by the lungs more quickly than unprotonated nicotine and may be advantageous, for example, when user factors indicate a sudden onset of stress.

[0331] The delivery device may include reservoirs, gels, or other delivery mechanisms for protonated and unprotonated nicotine, or may include means for generating protonated nicotine on demand.

[0332] Thus, more generally, feedback actions may involve the selection of a stronger version of the same active ingredient or a more effective version of the same active ingredient from a pharmacokinetic / user response perspective.

[0333] Of course, where the strength, efficacy, and / or concentration of an active ingredient can be changed by adjusting the mixture of active and inactive aerosol ingredients or the mixture of two active ingredients in the aerosol, or by substituting one active ingredient for another (whether a different active ingredient or a different version of the same active ingredient), such changes by feedback action can be reactive or preventative.

[0334] For example, responsive feedback actions may be identified if user factors indicate the presence of physiological stress and / or situational and / or environmental stressors.

[0335] Meanwhile, before the presence of, for example, a situational and / or environmental stressor, a preventative or predictive feedback action may be identified based on information in the user's calendar, texts, and / or social media posts indicating a stressful event, such as a dentist visit or a driving test, or comments made by the user in texts or social media posts indicating anticipated stress. Similarly, user factors related to the user's location or the people they are with may also be associated with elevated stress levels. For example, the feedback system may learn correspondences between physiological stress levels and other aspects of the situation, such as the user's current situation or environmental circumstances, so that if the user appears to be moving toward a particular location, begins to be surrounded by certain people, or enters another situation previously associated with high physiological stress, a preventative feedback action may modify the active ingredient as described above to be more effective against stress.

[0336] Thus, for example, increasing the amount of nicotine and / or protonated nicotine in a given aerosol volume before a user encounters a stressful situation will reduce the impact of the stressor on the user. Optionally, the feedback system can use a pharmacokinetic model to determine whether a given puff prior to an anticipated stressful situation should be modified in this manner so that the adjusted active ingredient (e.g., nicotine) has the desired effect at the time the stressful event is expected to occur.

[0337] Of course, although the above examples relate to stress relief, similar techniques may be used to promote or maintain a positive state in a user.

[0338] Finally, non-consumption feedback actions typically relate to activating / controlling or simply recommending the use of devices that are not specifically related to the consumption of an active ingredient, such as aromatherapy systems / steamers, biofeedback devices, headphones (e.g., activating noise cancellation or modifying volume or music selection), vehicle use (e.g., stress warnings or selecting / reselecting a longer but less congested or slower route), etc.

[0339] The Selection and Notification Sub-Processor may consist of one or more real or virtual processors, and its functionality may be located or distributed within one or more devices in the server and / or delivery ecosystem as appropriate.

[0340] Where to get feedback The above description generally assumes for purposes of explanation that the feedback operations are performed by one or more delivery devices themselves, a user's mobile phone, a user's wearable device, a delivery device docking unit, and / or a device within the delivery ecosystem, such as a vending machine or point-of-sale device.

[0341] However, and referring now also to Figure 8, other devices may be used to provide feedback operations not directly related to the delivery device or its operation, or optionally to the broader delivery ecosystem, but these devices may share network or other functional connections with one or more devices in the delivery ecosystem. These devices are considered to occupy a non-delivery ecosystem (3) of feedback devices that coexists with the delivery ecosystem (1) described elsewhere herein (although some devices that provide communication or processing capabilities to the feedback processor may be shared, such as the user's phone or delivery device, or a docking unit (not shown), or any other device in the delivery ecosystem that provides communication or processing services, as described elsewhere).

[0342] These other devices may provide one or more of the following basic classes of mechanisms that may be used in feedback actions that include sensory and / or neurological stimulation, or that affect a user's situation or environment, for example by modifying the user's plan:

[0343] Sensory stimulation mechanisms include environmental olfactory feedback mechanisms (other than the delivery device itself) 810, visual feedback mechanisms 820, audio feedback mechanisms 830, and / or tactile feedback mechanisms 840. Neurological stimulation mechanisms include electrical stimulation feedback mechanisms 840. Similarly, other devices may affect the user's situation or environment by modifying the user's plans (e.g., changing the user's schedule 100, modifying the user's driving route 100, 850, recommendations or options provided to the user 860, and / or modifying other aspects of the user's day).

[0344] Thus, more generally, these other devices, while operable to stimulate or otherwise modify the user's environment or situation, are not related to the consumption of the active ingredient by the delivery device, or more generally, to the use of or interaction with the delivery device.

[0345] A first class of devices may be considered as ambient olfactory feedback devices, and include aromatherapy devices, steamers, and atomizers (810) that introduce scents into the user's general environment, such as the room or vehicle they are currently in. Such devices may also be able to select one or more pre-created scents, or may contain a selection of ingredients from which scents can be synthesized according to received specifications.

[0346] Thus, for example, if a user factor corresponding to stress identifies a corresponding feedback action, the ambient olfactory feedback device may introduce sandalwood, lavender, chamomile, bergamot, and / or ylang-ylang into the environment, or any other similar aroma that has a calming effect.

[0347] Similarly, if a corresponding feedback action is identified by a user factor corresponding to fatigue or lack of concentration other than a sleep or wake transition period, the ambient olfactory feedback device may introduce lemon, eucalyptus, and / or peppermint, or any other similar stimulating aroma, into the user's environment.

[0348] It will be appreciated that the ambient olfactory feedback device may introduce scents into the user's general environment in accordance with standard aromatherapy principles, according to indications of the user's state given by the acquired user factors and at least implicitly identified by the selection of appropriate feedback action(s).

[0349] Although the above references aromatherapy, it will be appreciated that the ambient olfactory heating device need not operate according to such aromatherapy principles, but may operate in any manner that is deemed beneficial / promising towards modifying the user's condition, typically to introduce a better condition or maintain a positive current condition.

[0350] Another class of visual environment feedback devices may include, for example, a virtual reality headset (820) that ultimately allows the inference processor to respond to user factors and immerse the user in an environment or stimulus that corresponds to a particular feedback action. Thus, if the user is stressed, a VR headset may provide a calming environment and / or music, while the same can be provided if the user wishes to benefit from stimulation.

[0351] Similarly, audio environmental feedback devices are another class of devices that may be considered: it will be appreciated that the virtual reality headsets mentioned above may combine visual and audio feedback devices, while other devices may be audio only.

[0352] Thus, for example, a feedback action may include activating a noise cancellation feature on a pair of wireless headphones (830), for example, when a microphone on a delivery ecosystem device detects elevated background noise and / or other user factors indicate elevated user stress.

[0353] Alternatively or additionally, the feedback action may modify the musical selections provided to the user to provide calming or stimulating music as desired, and / or to provide upbeat or downbeat music as desired.

[0354] Similarly, feedback actions may adjust the presence or level of audible notifications from one or more devices in the user's environment if these may be distracting or irritating to the user, such as the notification sound typically played on a mobile phone when a message arrives.

[0355] Another class of devices is contemplated as haptic environmental feedback devices (840), which can provide one or more touch-based interventions, such as activation / control of massage functions in a chair, footrest, or dedicated unit such as a head-mounted or handheld massage device. Other haptic devices include the user's mobile phone (100).

[0356] Thus, for example, feedback actions may include activating and / or controlling such haptic feedback devices to provide a massage function to the user, for example to relieve stress.

[0357] Similarly, feedback actions may adjust the presence or level of haptic notifications from one or more devices in the user's environment if these may be distracting or irritating to the user, such as a notification buzzer typically activated when a message arrives on a mobile phone.

[0358] Yet another class of devices indirectly modifies a user's situation or environment by modifying the user's plans. Thus, for example, a feedback action may be to cancel or postpone a stressful event on a calendar on the user's phone (100) or to encourage the user to avoid such an event if user factors indicate that the user is already stressed or is likely to be stressed by such an event, as indicated by identification of an appropriate feedback action by the inference processor.

[0359] Similarly, feedback actions may modify routing parameters of the car's (850) or phone's (100) satellite navigation functions to proactively avoid route characteristics deemed particularly stressful, such as congested areas or detours or highways, or, if the user appears to be stressed, to promote a less stressful trip or commute for the user by setting a preferred maximum speed and routing accordingly. In such situations, the device may optionally notify the user that this is an option at the outset, requiring the user to decide between options, so that the user is not additionally stressed by the device appearing to take an unexpected route.

[0360] Similarly, the feedback action, whether for restaurant meals, food and groceries, general merchandise, merchandise related to the consumption of active ingredients as described herein, services associated with any of these, or any other service provision or plan selection of the user, such as a song selection, such as the audio environment feedback described above, may modify the user's selection selection, for example by selecting an option, selecting a candidate, promoting, or demoting, in an online menu, such as provided by the third party server 860 or any other device / interface operable to receive dates related to the feedback action.

[0361] As an alternative or addition to the above class of non-vaping / non-delivery devices operable to modify a user's environment or situation, it will be appreciated that one or more user factors may be sufficiently distinctive for each user to allow for their respective identification. Thus, as an alternative or addition to the techniques described herein, the feedback system may operate as an ID system (910) that identifies one or more users based on one or more user factors, respectively. In this case, the feedback action corresponds to an action appropriate to the presence or absence of user recognition, or, optionally, to low-confidence user recognition and a request for additional user factor input.

[0362] Yet another class of non-vaping / non-delivery devices are electrical stimulation feedback devices (920), such as neuromodulation devices, one example of which is a transcranial direct current stimulation (tDCS) device. Such tDCS devices deliver a low electrical current to the user's scalp with the intent of increasing the resting potential of neurons in the brain, making them more likely to fire. The intent is to improve attention and focus, as well as assist in modifying habitual behaviors and reducing inhibitions.

[0363] Thus, for example, if a user factor corresponding to lethargy or reduced attention is obtained, or if a situation is indicated in which increased alertness may be required, for example due to an upcoming meeting on the user's calendar, this may cause a corresponding feedback action to be identified by the inference processor, such as turning on a tDCS device or providing a message to the user to consider using a tDCS device.

[0364] Electrical stimulation feedback devices are also considered to be one example of a broader class of biofeedback devices, which may alternatively or additionally include visual and / or audio feedback to the user.

[0365] Of course, similar to devices in the delivery ecosystem, the feedback processor can determine whether other non-vaping / non-delivery devices in the non-delivery ecosystem are currently available by polling them as described above, and / or can assume the availability of a device if its presence is signaled by a user, such as in the case of a non-networked device. If the feedback processor cannot directly or indirectly command the non-vaping / non-delivery device itself, the feedback action may include providing a message to the user that activates the non-vaping / non-delivery device, and optionally may include instructions regarding the appropriate settings for the feedback action.

[0366] Finally, it should be appreciated that devices may be provided whose primary function is to provide some form of feedback for the feedback system (optionally in conjunction with collecting data on one or more user factors). By way of example, jewelry such as pendants, as described elsewhere herein as an example of an auxiliary sensor platform, may be provided. Such devices may thus be auxiliary feedback platforms, providing, for example, audio, optical, and / or haptic feedback (depending on their capabilities) as part of the feedback operation. Such jewelry may advertise its feedback capabilities to the feedback system (e.g., via a Bluetooth link to the user's phone or delivery device), allowing different jewelry pieces with different feedback (and / or sensor) modes to be available and worn without the user having to consider how they interact within the delivery ecosystem. That is, they may be selected primarily for aesthetic reasons and then integrated with the current set of available input / output devices within the delivery ecosystem.

[0367] Executing an action The action execution sub-processor may be optional. For example, some devices are capable of accepting commands directly without further interpretation or processing. In this case, the action execution sub-processor may not be necessary, and its role may be fulfilled by the feedback processor / selection and notification sub-processor.

[0368] On the other hand, in some cases the role of the action execution sub-processor may actually reside within the device, for example being able to interpret user interface commands (wireless remote control commands) and modify the operation of the device, in which case the commands from the feedback processor may optionally simply replicate such user interface commands.

[0369] In other cases, the operation-performing sub-processor may be provided separately, for example, by employing a conventional processor in response to suitable software instructions. An example of such may be an app on a mobile phone operable to receive commands and modify one or more aspects of the user's mobile phone and / or an app on the mobile phone, the delivery device, and / or one or more other devices in the delivery ecosystem, or other non-vaping / non-delivery devices in the non-delivery ecosystem as described herein above. Similarly, the delivery device dock 200 may include such an operation-performing sub-processor, as may multiple types of delivery devices.

[0370] The action execution sub-processor is operative to perform feedback actions on the or each associated device. Thus, for example, if a command for a feedback action describes changing a heater temperature of a delivery device, the action execution sub-processor may effect the specified change by changing the heater power source and / or the heater duty cycle.

[0371] Similarly, for example, if a command relating to a feedback action describes reducing environmental noise levels for a user, the action execution sub-processor may cause a pair of noise cancelling headphones to activate a noise cancelling function, while the action execution sub-processor may cause the user's mobile phone to lower the volume of music playing in the headphones and display a message to the user encouraging them to avoid noise sources in their environment.

[0372] The particular actions each sub-processor performs may thus depend on the nature of the suggestion feedback action and the nature of the devices within the delivery ecosystem, but will typically represent a direct translation of the suggestion feedback action into mechanisms that can be implemented within the device.

[0373] As discussed above in this specification, a feedback action may be accompanied by or followed by a request or opportunity for the user to report on its effectiveness and / or the welcome of the feedback action at that time. Alternatively or additionally, a feedback action may be accompanied by or followed by positive reinforcement of an expected state change, for example, through a message on a UI, a color change in an interface, a haptic response, etc. Or, a feedback action may be accompanied by or followed by a positive goal to be achieved in a wearable app. This reinforcement may be a simple message indicating that feedback has occurred, or it may be based on measurements to report, for example, that the user's heart rate has decreased, or it may be confirmation that an action has worked (usually by changing the user's state, typically as evidenced by a change in one or more user factors or as self-reported by the user, or conversely, by maintaining the user's state when desired, for example, under adverse conditions). The awareness and / or expectation of a state change brought about by such positive reinforcement may increase the effectiveness of at least some feedback actions.

[0374] The operation-performing sub-processors may consist of one or more real or virtual processors, the functionality of which may be located or distributed within a server and / or one or more devices within the delivery ecosystem as appropriate.

[0375] The autonomy of the action execution sub-processor (and more generally the feedback processor and / or feedback system) may be set globally or may vary depending on the type of feedback action or on individual feedback actions, where autonomy refers to the degree to which the action execution sub-processor proceeds to execute a feedback action without notifying or seeking user consent, either as initial permission or for each execution of the feedback action.

[0376] As a result, optionally, associated devices in the delivery ecosystem or non-vaping / non-delivery devices in the non-delivery ecosystem are configured to automatically perform that portion of the feedback operation, such as automatically selecting a fragrance or adjusting fragrance concentration, automatically adjusting the delivered vapor volume rate, automatically providing feedback or a text message, etc.

[0377] As a result, the feedback system automatically performs feedback actions that are expected to change the user's state.

[0378] Such automatic adjustments may be applied globally, e.g., set for all feedback actions at the time of manufacture. Alternatively, such automatic adjustments may be applied only for certain feedback actions (e.g., feedback actions that are deemed unlikely to prompt a user to reject) and / or for certain user situations (e.g., where the automatic adjustment is deemed likely to be received positively). Alternatively, such automatic adjustments may be selected by the user, e.g., during an initial setup phase, so that the user's settings are initial preferences and do not need to be prompted again. Optionally, in this case, the user can reconfirm their respective preferences to change whether or not certain feedback actions are automatically applied.

[0379] Alternatively, optionally, the associated device in the delivery ecosystem or the non-vaping / non-delivery device in the non-delivery ecosystem is configured to prompt the user prior to performing an action that may adjust or affect the user state, thereby allowing the user to control whether the device performs some of the feedback actions.

[0380] In this case, depending on the device's user interface capabilities, the prompt may be a text or speech prompt, a tactile prompt, an audio prompt, or the activation of an LED or the selection of a particular LED color. The user's response (most simply a yes / no response, or a yes by inaction, or a no by inaction response) may then similarly be determined by the device's user interface capabilities. For example, the user may display an icon indicating acceptance or denial on a touchscreen, or press a button indicating acceptance or denial. It should also be appreciated that one or more buttons may be repurposed for providing consent for a predetermined period of time after the user has been prompted. For example, any other device may be temporarily repurposed, such as the "+" and "-" buttons used to change the temperature or volume of a heater, where "+" means acceptance and "-" means denial. It should also be appreciated that any suitable button may be repurposed in this manner.

[0381] As with automatic feedback actions, prompts may be configured to apply globally or may be configured depending on the feedback action and / or the source or target user state.

[0382] Similarly, the prompt may relate to the type of feedback action, the type of change in user state expected as a result of the feedback action, or any mixture of the two. Thus, for example, a prompt may indicate to the user that the user is in a particular mood, has an elevated heart rate, or any other condition discussed elsewhere herein, and may ask whether the user would like to change an aspect of the delivery process, change their mood, or change their heart rate, as appropriate. Similarly, for example, a prompt may suggest that a particular feedback action will result in a different user state, or maintain a current user state that may change.

[0383] Thus, as an alternative or in addition to requesting consent to a particular feedback action, the prompt may offer the user a choice of feedback actions. As described elsewhere herein, the feedback processor may make an automatic selection from among multiple identified feedback actions, although alternatively, this function may be configured to involve the user. Optionally, the feedback processor may make a preliminary or candidate selection of identified feedback options, for example, based on devices currently available in the delivery ecosystem or elsewhere that may fulfill the identified feedback action or portions thereof, before providing the user with a final selection. Similarly, the feedback processor may make a preliminary or candidate selection of identified feedback options based on frequency of use and / or selection by individual users or cohorts of users and / or effectiveness of the feedback actions reported by individual users or cohorts of users.

[0384] Typically, as described elsewhere herein, discriminative feedback actions are identified in response to some or all of the user factors obtained by the user feedback system and are therefore likely to be directed toward achieving a similar effect, although in different ways, and some ways may be more preferred by the user. Thus, the user may be prompted to select one (or more) of the suggested discriminative feedback actions. The feedback system may optionally modify any of these feedback actions if implementing more than one would change the intended result, and / or similarly dynamically gray out certain options if another, incompatible option is selected by the user.

[0385] As an alternative or in addition to prompting the user to select from alternative feedback actions that tend to achieve a similar effect, the delivery ecosystem devices may suggest different feedback actions for various user states that may be associated with the same user factor or each subset of received user factors. Thus, for example, a user may be calm while subjectively feeling focused or lethargic. Depending on the available user factors, these aspects of the user state may or may not be distinguished. If the user then appears calm, different feedback actions may be provided to the user regarding whether they are focused, drowsy, or lethargic, allowing the user to make their own selection. Thus, for example, it may be possible to provide a different fragrance if the user is focused when drowsy and / or to modify the delivery of fragrances and / or ingredients during the inhalation process by using a different heating profile.

[0386] Of course, optionally, if the user feedback system is initially based on average or cohort data of user behavior, but is capable of learning about individual users, such a system may present more feedback actions to the user during initial and early use, but thereafter reduce the number of options as it learns which feedback actions the user prefers and / or responds to most. Thus, optionally, once a clear preference is determined for a given user factor, the user feedback system may only request confirmation from the user to proceed with that feedback action, or may automatically perform the feedback action, as discussed elsewhere herein.

[0387] As an alternative to automatically performing one or more discriminatory feedback actions, requesting permission to perform one or more discriminatory feedback actions, or selecting from suggested discriminatory feedback actions, the prompt may optionally include instructions on how the user can perform the discriminatory feedback action themselves (e.g., a prompt to manually change a setting on a device in the delivery ecosystem, a prompt to increase the heater temperature on the delivery device).

[0388] In any event, it will be appreciated that the prompts may optionally be provided on a device with a more sophisticated user interface than the device on which the feedback action is performed.

[0389] Consent or denial, whether indicated by explicit action, inaction, or selection in response to a selected user interface, can be used to train the feedback system to better determine when to select a feedback action in the future.

[0390] Processor As noted above, the acquisition processor, estimation processor, and feedback processor (and any sub-processors) may comprise one or more real or virtual processors located in one or more servers and / or delivery ecosystems. Furthermore, it should be understood that the division of roles described herein is not rigid. For example, the acquisition processor may receive information directly indicative of the user's state (e.g., via the user's self-report), so that the first stage of the estimation processor's two-stage process can be bypassed or supplemented by the acquisition processor. Similarly, in this case, the feedback processor may, for example, search for a corresponding suggested feedback action. Thus, in this example, the role of the estimation processor is performed by the acquisition processor and feedback processor. Thus, more generally, these processors are considered to be representative of tasks that may be implemented by any processor under suitable software instructions, and equivalently include data collection tasks, feedback suggestion tasks (whether or not based on explicit estimation of the user's state), and feedback learning or feedback provision tasks.

[0391] Alternative Platforms and Services While the above discussion describes the ability to identify feedback actions that modify one or more components of the delivery ecosystem for an aerosol delivery device in response to an explicit or implicit model of the user state derived from acquired user factors, it will be appreciated that this approach is not limited to the user of the aerosol delivery device or to the modification of components of the delivery ecosystem for such an aerosol delivery device.

[0392] As described elsewhere herein, feedback behavior may also apply to non-delivery ecosystem components, such as those shown in FIG.

[0393] More generally, however, the first device may be any device (e.g., other than the aerosol delivery device described above) that would benefit from modifying one or more of its operations in response to an explicit or implicit model of the user state derived from the acquired user factors.

[0394] Furthermore, such first device may provide at least a portion of one or more functions selected from the list consisting of an acquisition processor, an estimation processor, and a feedback processor, as described elsewhere herein, with any other functions being provided by one of the other devices, such as a remote server or the delivery ecosystem or non-delivery ecosystem.

[0395] The acquisition processor may acquire one or more user factors from the first device, whether some of them are based within the first device or located elsewhere. Typically, these will be related to the current relationship between the user and the first device and may include sensor data from the first device's motion sensors, camera, microphone, and / or pressure / force sensors, as described elsewhere herein, which may typically be used to characterize the user's mood and behavior. Alternatively or additionally, the first device may include sensors, such as galvanic skin response sensors, heart rate sensors, muscle tension sensors, and / or touch sensors, as described elsewhere herein, which may typically be used to characterize the user's physiological state. Meanwhile, other user factors, such as those related to other aspects of the user's environment or situation or history, may be acquired from records associated with the user, as described elsewhere herein.

[0396] A first device that is owned or used exclusively by a particular user is considered to be easily associated with the user.

[0397] In contrast, for a first device with which the user interacts only occasionally or once, acquisition of the user's identity may be required. For such non-exclusive first devices, the acquisition processor may be operable to acquire the user's identity, such that the acquisition processor can acquire user factors related to the identified user. This may be achieved, for example, by one or more selected from the list consisting of facial recognition, voice recognition (e.g., using only voice and / or a password or passphrase), wireless communication with the user's registered mobile phone (e.g., via near field communication or Bluetooth), and wireless communication with the user's registered aerosol delivery device (e.g., via near field communication or Bluetooth).

[0398] In particular, it will be appreciated that the provision of a payment card may typically enable clear identification of a user for interactions with many non-exclusive devices, with payment typically representing the final part of that interaction. In contrast, it is typically desirable to identify and perform feedback actions early in a user's interaction with a first device. Thus, the user's identity is typically obtained by or for the first device before the device presents options to the user via a user interface, or indeed before it requests or receives payment from the user.

[0399] Examples of first devices include any device that provides a user interface for user interaction. A non-limiting list of examples includes electronic menus (e.g., in fast food restaurants, libraries, hotels, department stores, and other locations that offer users disparate choices), automated teller machines, gym equipment, point-of-sale devices (e.g., self-service kiosks, vending machines, etc.), medical equipment, or other devices that may provide access to products and / or services.

[0400] In these cases, the discriminatory feedback action may include modifying one or more operations of the first device with respect to user interface complexity and / or number of user interface options. Thus, for example, if the acquired user factor correlates with stress, the modification may be to modify the trade-off between ease of use and degree of control in favor of ease of use by reducing the complexity of the user interface, for example, by flattening portions of a menu tree or emphasizing or reordering commonly selected options. Similarly and relatedly, if the acquired user factor correlates with stress, the modification may be to reduce the number of user interface options, for example, by removing portions of a menu tree or combining options in common combinations.

[0401] Similarly, if the acquired user factors correlate with stress, the feedback actions may include shortlisting options or selecting some or all of the options on behalf of the user, e.g., by automatically selecting or equivalently bypassing options that the user would always select or skip. These options may relate to aspects of navigating the user interface or to accessing products or services accessible through the user interface. Alternatively or additionally, these options may relate directly to individual products or services, such as consumables, that the user purchases. Again, the feedback actions may include shortlisting or selecting such products or services for the user based on their being the correct option for the majority of users (or, if the system has learned the preferences of individual users, their typically correct option for that user), thereby typically minimizing the user's interaction with the device unless a different selection is desired. In such cases, if options, services, or products (e.g., consumables) are shortlisted, the number of product options in the shortlist may be reduced as a function of the user's apparent stress. Of course, the stress here may be actual physiological / neurological stress, or alternatively or additionally, it may be situational stress, for example, when a user is late for a meeting and is quickly navigating a user interface.

[0402] In contrast, if the user factors indicate that the user is calm, satisfied, happy, and / or has free time, the feedback action may not modify the user interface of the first device or may modify it to provide more options for the user to view (e.g., adding optional surveys to a menu based on the likelihood that such a person would like to complete a survey). Thus, in this case, the feedback action may optionally enrich the user interface by modifying the complexity of the user interface or the number of interface options. Similarly, the feedback action may increase the number of options or products offered to the user, for example, by lowering a relevance criterion cutoff threshold and / or increasing a price point cutoff threshold.

[0403] Of course, such an approach may be particularly valuable for point-of-sale systems, but it can equally be appreciated that it can be used for any device that a user may need to navigate to achieve a goal, especially for non-exclusive devices where navigating the user interface can be an unfamiliar process for the user and can therefore be a source of potential frustration.

[0404] As described elsewhere herein, in a specific example of a point-of-sale system that may be (e.g., temporarily) included in the delivery ecosystem of an aerosol delivery device, if the inference processor identifies a feedback action based on one or more of at least a subset of the acquired user factors that optionally indicate that the user is feeling stressed (in other words, a feedback action that is suitable for a person who feels stressed), the corresponding modification of one or more operations of the first device for that feedback action may relate to one or more selected from the list consisting of: supplying a payload whose composition or concentration of active ingredients is selected as suitable for consumption during stress; and modifying one or more settings of an aerosol delivery device that wirelessly communicates with the point-of-sale device to deliver a modified aerosol that is suitable for consumption during stress.

[0405] Thus, in effect, the point of sale system has the opportunity to provide the user with a payload more suited to their stress level or to modify the settings of the aerosol delivery device to, for example, increase aerosol production per given volume of inhaled air. Such provision may be automatic by the first device, or the first device may allow the user to make a final selection, for example, by prompting or shortlisting payloads for selection that achieve such an effect and / or recommending settings of the aerosol delivery device that achieve such an effect.

[0406] Of course, the point of sale system may also promote / provide payloads or settings suitable for low stress levels (e.g., a normal, happy user) when feedback behavior is identified by user factors.

[0407] A similar approach may be provided by a similar first device for refilling an aerosol delivery device, such as a home dock for the aerosol delivery device.

[0408] Thus, in this case, if the estimation processor identifies a feedback action based on one or more of at least a subset of the acquired user factors that indicates the user is feeling stressed, the corresponding modification of one or more actions of the first device may be related to one or more selected from the list consisting of: supplying the aerosol delivery device with a payload having an active ingredient composition or concentration selected as suitable for consumption during stress (e.g., if the dock provides automatic refilling and is capable of selecting or prompting refill options); and modifying one or more settings of the docked aerosol delivery device to deliver a modified aerosol suitable for consumption during stress.

[0409] Again, such a dock may promote / provide payloads or settings suitable for low stress levels (e.g., a normal, happy user) when feedback behavior is identified by user factors.

[0410] For example, if the feedback system is implemented on a backend server and the dock itself is associated with a user account, or conversely, if the feedback system is implemented at least partially within the dock or within a device paired with the dock, such as the user's phone, then such a home dock may not need to explicitly identify the user when interacting with it.

[0411] However, since a user may have multiple aerosol delivery devices and use different devices for different situations, and similarly a home dock may be used by multiple residents each with their own aerosol delivery device, in this case it may be optionally preferable to treat each aerosol delivery device as representing a different user, some of whom may in fact be the same user in different situations, each with their own user profile.

[0412] Improving product and service navigation via a user interface is a suitable application for a user feedback system, but it is not the only one. For example, the first device may be fitness equipment (e.g., gym equipment such as a treadmill or a cycling machine). In this case, the feedback action derived in response to the user factor may relate to modifying the fitness equipment's fitness program. This may be in response to physiological user factors, but alternatively or additionally, it may be in response to other user factors, such as the environment, situation, etc. For example, a user may take a longer ride on a cycling machine at the gym if it is raining outside, but may shorten their workout if they have an upcoming meeting on their calendar. The fitness equipment may anticipate the user's fitness efforts and modify their cycling program accordingly.

[0413] Similar modifications to the operation of such a first device may be considered, for example, a robot vacuum cleaner may not initiate patrols when the user is at home if the user factors indicate stress, as the robot may be perceived as a irritant.

[0414] Thus, more generally, the feedback operation of the first device may result in any suitable modification of one or more operations of the first device that is synchronized with the user's state as indicated by the obtained user factors (e.g., via the model of the estimation processor). This synchronized modification may typically serve to mitigate a negative user state or promote a positive user state, as described elsewhere herein.

[0415] Overview of embodiment In one general embodiment of the present specification, a user feedback system (1) for a user of a first device comprises: an acquisition processor (1010) configured to acquire one or more user factors indicative of a state of the user, as described elsewhere herein; an estimation processor (1020) configured to identify at least a first feedback action based on one or more of at least a subset of the acquired user factors, as described elsewhere herein; and a feedback processor (1030) configured to select the identified at least a first feedback action, as described elsewhere herein, and to modify one or more actions of the at least first device in accordance with the or each selected feedback action, as described elsewhere herein, wherein the first device is not an aerosol delivery device (10) as described elsewhere herein (but instead is, for example, an alternative platform).

[0416] In one example of this summary embodiment, the processor of the first device provides at least a portion of one or more functions selected from the list consisting of an acquisition processor, an estimation processor, and a feedback processor, as described elsewhere herein.

[0417] In one instance of this general embodiment, the acquisition processor acquires one or more user factors from the first device, as described elsewhere herein.

[0418] In one example of this summary embodiment, the first device comprises one or more selected from the list consisting of a motion sensor, a camera, a microphone, and a pressure or force sensor, as described elsewhere herein.

[0419] In one example of this summary embodiment, the first device comprises one or more selected from the list consisting of a galvanic skin response sensor, a heart rate sensor, a muscle tension sensor, and a touch sensor, as described elsewhere herein.

[0420] In one instance of this summary embodiment, the first device is operable to obtain an identification of the identified user, as described elsewhere herein, such that the obtaining processor can obtain user factors related to the identified user.

[0421] In this example, the user's identification information is optionally obtained by one or more selected from the list consisting of facial recognition, voice recognition, communication (e.g., wireless) with the user's registered terminal (e.g., phone, tablet, PDA, laptop, smartwatch), and communication (e.g., wireless) with the user's registered aerosol delivery device, as described elsewhere in this specification.

[0422] Similarly, in this example, the user's identification information is obtained by the first device prior to one or more selected from the list consisting of providing options to the user via a user interface, requesting payment from the user, and receiving payment from the user, as described elsewhere in this specification.

[0423] In one instance of this general embodiment, the first device provides a user interface for interaction with a user, as described elsewhere herein.

[0424] In this example, the modification of the one or more behaviors of the first device optionally relates to one or more selected from the list consisting of modifying the complexity of the user interface and modifying the number of user interface options, as described elsewhere herein. In this case, optionally, if the inference processor (1020) identifies a feedback behavior based on one or more of at least a subset of the obtained user factors that indicates the user is experiencing stress, the corresponding modification of the one or more behaviors of the first device optionally relates to one or more selected from the list consisting of reducing the complexity of the user interface and reducing the number of user interface options, as described elsewhere herein.

[0425] In this example, the modification of one or more operations of the first device is optionally related to one or more selected from the list consisting of: a candidate selection or selection of options for the user; and a candidate selection or selection of products for the user, as described elsewhere herein. In this case, optionally, if the inference processor (1020) identifies a feedback behavior based on one or more of at least a subset of the obtained user factors that indicates the user is experiencing stress, the corresponding modification of one or more operations of the first device is related to one or more selected from the list consisting of: a reduction in the number of candidate selection options for the user; and a reduction in the number of candidate selection products for the user, as described elsewhere herein.

[0426] In one instance of this general embodiment, the first device is a point of sale device, as described elsewhere herein.

[0427] In this example, the first device is optionally a point-of-sale device (e.g., any point-of-sale device, such as a kiosk, a cash register or vending machine, an automated teller machine, a digital restaurant menu, etc.), as described elsewhere in this specification, and optionally a point-of-sale device operable to be included in the delivery ecosystem of the user's aerosol delivery device, as described elsewhere in this specification.

[0428] In this case, if the point-of-sale device is operable to be included in the delivery ecosystem of the user's aerosol delivery device and the inference processor (1020) identifies a feedback action based on one or more of at least a subset of the acquired user factors indicating that the user is experiencing stress, the corresponding modification of one or more actions of the first device relates to one or more selected from the list consisting of: delivering a payload having a composition or concentration of active ingredients selected as suitable for consumption during stress, as described elsewhere herein, and modifying one or more settings of an aerosol delivery device that wirelessly communicates with the point-of-sale device to deliver a modified aerosol suitable for consumption during stress.

[0429] Alternatively, in this case, if the inference processor identifies a feedback action based on one or more of at least a subset of the obtained user factors that indicates the user is experiencing stress, the corresponding modification of one or more actions of the first device relates to one or more selected from the list consisting of: providing an oral product having a composition or concentration of active ingredients selected as suitable for consumption during times of stress; and modifying one or more settings of an oral product provision device in communication with the point-of-sale device to deliver the modified oral product suitable for consumption during times of stress.

[0430] In one example of this general embodiment, the first device is a dock for an aerosol delivery device, as described elsewhere herein.

[0431] In this example, optionally, if the estimation processor (1020) identifies a feedback action based on one or more of at least a subset of the acquired user factors that indicate the user is experiencing stress, the corresponding modification of one or more actions of the first device relates to one or more selected from the list consisting of: supplying a payload to the aerosol delivery device having a composition or concentration of active ingredients selected as suitable for consumption during stress, as described elsewhere herein, and modifying one or more settings of the docked aerosol delivery device to deliver a modified aerosol suitable for consumption during stress.

[0432] In this example, different user profiles and respective user factor data are optionally associated with different aerosol delivery devices, as described elsewhere herein.

[0433] In one example of this general embodiment, the first device is fitness equipment, as described elsewhere herein.

[0434] In this example, the identified at least first feedback action optionally includes modifying a fitness program of the fitness equipment, as described elsewhere herein.

[0435] In one example of this summary embodiment, the estimation processor (1020) is configured to identify a one-stage or two-stage correlation between one or more user factors indicative of the obtained user state and at least a first feedback action, as described elsewhere herein.

[0436] In this example, the one-stage correlation optionally includes a first correlation between one or more user factors indicative of the obtained user state and at least a first feedback action, as described elsewhere herein, in which case the estimation processor is optionally operable to identify the at least first feedback action based on the obtained one or more user factors by using a model including correlation data between the one or more feedback actions and the obtained one or more user factors (e.g., by generating / calculating an output corresponding to the feedback action), as described elsewhere herein.

[0437] In this example, the two-stage correlation optionally includes a first correlation between one or more user factors indicative of the acquired user state and at least a first state of the user, and a second correlation between the at least first state of the user and at least a first feedback action, as described elsewhere herein. In this case, the estimation processor is optionally operable to calculate an estimate of the at least first state of the user based on the acquired one or more user factors by using a model including correlation data between the one or more user factors and the one or more user states, as described elsewhere herein. Similarly, in this case, the estimation processor is optionally operable to identify at least a first feedback action based on the calculated estimate of the user state by using a model including correlation data between one or more user states and one or more feedback actions (e.g., by generating / calculating an output also corresponding to the feedback action), as described elsewhere herein.

[0438] In one example of this summary embodiment, the inference processor is operable to identify one or more further suggested feedback actions for one or more selected from the list consisting of: a behavioral feedback action that affects at least a first behavior of the user, and a medication feedback action that affects consumption of an active ingredient by the user, as described elsewhere herein.

[0439] In one instance of this general embodiment, the feedback processor is configured to automatically cause the identified at least a first feedback action to be performed, as described elsewhere herein.

[0440] In one example of this summary embodiment, the feedback processor is configured to prompt the user for consent to causing at least some of the identified at least first feedback actions to be performed, as described elsewhere herein, and, if consent is determined, to cause only at least some of the identified at least first feedback actions to be performed.

[0441] Turning now to FIG. 7, in one general embodiment herein, a method for user feedback to a user of a first device includes the following steps. First, it includes an obtaining step s710 of obtaining one or more user factors indicative of the user's state, as described elsewhere herein. Second, it includes an estimating step s720 of identifying at least a first feedback action based on one or more of at least a subset of the obtained user factors, as described elsewhere herein. Third, there is a feedback step s730 of selecting at least a first feedback action identified, as described elsewhere herein. And fourth, a correcting step s740 is included, in which one or more operations of at least the first device are corrected in accordance with the or each selected feedback operation, as described elsewhere herein, and the first device is not an aerosol delivery device (10).

[0442] Thus, the principle of modifying the operation of a device responsive to a model of a user's state based on user factors indicative of such a state is applicable not only to aerosol delivery systems and their delivery ecosystems (or non-delivery ecosystems associated with a user), but also to any device or system whose modification of its operation may benefit a user by, for example, alleviating a negative user state, assisting in the transition to a more positive user state, or maintaining a positive user state, whether the state is related in whole or in part to physiological, neurological, psychological, situational, environmental, or historical influences.

[0443] Variations of the above methods corresponding to the operation of various embodiments of the methods and / or apparatus as described and claimed herein are considered to be within the scope of this disclosure and will be apparent to those skilled in the art, including, but not limited to: The processor of the first device performs at least a portion of one or more steps selected from the list consisting of an acquisition step, an estimation step, a feedback step, and a correction step, as described elsewhere herein. The obtaining step includes obtaining one or more user factors from the first device, as described elsewhere herein. The first device provides a user interface for interaction with a user, as described elsewhere herein. The modification of one or more operations of the first device relates to one or more selected from the list consisting of modifying the complexity of the user interface and modifying the number of user interface options, as described elsewhere in this specification. The modification of one or more operations of the first device is associated with one or more selected from a list consisting of candidate selections or choices of options for the user and candidate selections or choices of consumables for the user, as described elsewhere herein. As described elsewhere in this specification, the first device is operable to acquire identification information of the identified user so that the acquisition processor can acquire user factors related to the identified user, the user's identification information being acquired by one or more selected from the list consisting of facial recognition, voice recognition, communication with the user's registered terminal, and communication with the user's registered aerosol delivery device. As described elsewhere in this specification, the first device is a point-of-sale device (which may be any point-of-sale device), optionally a point-of-sale device operable to be included in the delivery ecosystem of the user's aerosol delivery device. The first device is a dock for an aerosol delivery device, as described elsewhere herein. The first device is fitness equipment, as described elsewhere herein. The method may include any steps corresponding to the operation of the user feedback system of the above general embodiment or enumerated herein.

[0444] It will be appreciated that the above methods may be implemented on conventional hardware (such as the acquisition processor, estimation processor, feedback processor, server and / or first device) suitably adapted to be applicable by the inclusion or substitution of software instructions or dedicated hardware.

[0445] Thus, any necessary adaptations to existing portions of a conventional equivalent device may be implemented in the form of a computer program product including processor-executable instructions stored on a non-transitory machine-readable medium such as a floppy disk, optical disk, hard disk, semiconductor disk, PROM, RAM, flash memory, or any combination thereof, or other storage medium, or may be realized in hardware as an ASIC (application-specific integrated circuit), FPGA (field-programmable gate array), or other configurable circuitry suitable for use in adapting a conventional equivalent device. Alternatively, such a computer program may be transmitted via data signals over a network such as Ethernet, a wireless network, the Internet, or any combination thereof, or other network.

[0446] The following provisions are included in this specification: [Article 1] 1. A user feedback system for a user of a first device, comprising: an acquisition processor configured to acquire one or more user factors indicative of a state of the user; an inference processor configured to identify at least a first feedback action based on one or more of the at least a subset of the obtained user factors; a feedback processor configured to at least select an identified first feedback action and to modify one or more actions of at least the first device in accordance with the or each selected feedback action; Equipped with The user feedback system, wherein the first device is not an aerosol delivery device. [Clause 2] a processor of the first device, i. said acquisition processor; ii. said estimation processor; iii. said feedback processor; 2. The user feedback system of claim 1, providing at least a portion of one or more functions selected from the list consisting of: [Article 3] 3. The user feedback system of claim 1 or 2, wherein the acquisition processor acquires one or more user factors from the first device. [Article 4] The first device i. a motion sensor; ii. a camera; iii. a microphone; iv. a pressure or force sensor; 4. The user feedback system of any one of clauses 1 to 3, comprising one or more selected from the list consisting of: [Article 5] The first device i. a galvanic skin response sensor; ii. a heart rate sensor; iii. a muscle tension sensor; iv. a touch sensor; 5. The user feedback system of any one of clauses 1 to 4, comprising one or more selected from the list consisting of: [Article 6] 6. The user feedback system of any one of clauses 1 to 5, wherein the first device is operable to acquire identification information of the user so that the acquisition processor can acquire user factors related to the identified user. [Article 7] The identification information of the user is i. Facial recognition; ii. Speech recognition; iii. communicating with the user's registered terminal; iv. communicating with the user's registered aerosol delivery device; and 7. The user feedback system of claim 6, wherein the user feedback is obtained by one or more selected from the list consisting of: [Article 8] The identification information of the user is i. providing options to the user via a user interface; ii. requesting payment from said user; and iii. receiving payments from said users; and 8. The user feedback system of claim 6 or 7, wherein the first device acquires one or more of the following prior to the selection of the first device from the list consisting of: [Article 9] 9. The user feedback system of any one of clauses 1 to 8, wherein the first device provides a user interface for interaction with the user. [Article 10] the modifying of one or more operations of the first device includes: i. Modifying the complexity of the user interface; ii. Modifying the number of user interface options; and 10. The user feedback system of clause 9, relating to one or more selected from the list consisting of: [Article 11] If the inference processor identifies a feedback behavior based on one or more of at least a subset of the obtained user factors that indicates that the user is experiencing stress, a corresponding modification of one or more behaviors of the first device is performed by: i. Reduced user interface complexity; ii. A reduction in the number of user interface options; and 11. The user feedback system of clause 10, relating to one or more selected from the list consisting of: [Article 12] the modifying of one or more operations of the first device includes: i. a candidate selection or selection of an option for said user; ii. a candidate selection or selection of a product for said user; and 12. The user feedback system of any one of clauses 9 to 11, relating to one or more selected from the list consisting of: [Article 13] If the inference processor identifies a feedback behavior based on one or more of at least a subset of the obtained user factors that indicates that the user is experiencing stress, a corresponding modification of one or more behaviors of the first device is performed by: i. reducing the number of candidate selection options for the user; ii. reducing the number of products for the user to select; 13. The user feedback system of clause 12, relating to one or more selected from the list consisting of: [Article 14] 14. The user feedback system of any one of clauses 1 to 13, wherein the first device is a point-of-sale device. [Article 15] 15. The user feedback system of clause 14, wherein the first device is a point-of-sale device. [Article 16] the point of sale device is operable to be included in a delivery ecosystem of a user's aerosol delivery device; If the inference processor identifies a feedback behavior based on one or more of at least a subset of the obtained user factors that indicates that the user is experiencing stress, a corresponding modification of one or more behaviors of the first device is performed by: i. providing a payload selected for its composition or concentration of active ingredients suitable for consumption during times of stress; ii. modifying one or more settings of an aerosol delivery device in wireless communication with said point of sale device to deliver a modified aerosol suitable for consumption during times of stress; 16. The user feedback system of clause 15, relating to one or more selected from the list consisting of: [Article 17] If the inference processor identifies a feedback behavior based on one or more of at least a subset of the obtained user factors that indicates that the user is experiencing stress, a corresponding modification of one or more behaviors of the first device is performed by: i. providing an oral product having a composition or concentration of active ingredients selected to be suitable for consumption during times of stress; ii. modifying one or more settings of an oral product delivery device in communication with said point of sale device to deliver a modified oral product suitable for consumption during times of stress; 16. The user feedback system of clause 15, relating to one or more selected from the list consisting of: [Article 18] 14. The user feedback system of any one of clauses 1 to 13, wherein the first device is a dock for an aerosol delivery device. [Article 19] If the inference processor identifies a feedback behavior based on one or more of at least a subset of the obtained user factors that indicates that the user is experiencing stress, a corresponding modification of one or more behaviors of the first device is performed by: i. providing said aerosol delivery device with a payload selected for its composition or concentration of active ingredients suitable for consumption during times of stress; ii. modifying one or more settings of the docked aerosol delivery device to deliver a modified aerosol suitable for consumption during times of stress; and 19. The user feedback system of clause 18, relating to one or more selected from the list consisting of: [Article 20] 20. The user feedback system of clause 18 or 19, wherein different user profiles and respective user factor data are associated with different aerosol delivery devices. [Article 21] 14. The user feedback system of any one of clauses 1 to 13, wherein the first device is a piece of fitness equipment. [Article 22] 22. The user feedback system of claim 21, wherein the identified at least a first feedback action includes modifying a fitness program of the fitness equipment. [Article 23] The user feedback system of any one of clauses 1 to 22, wherein the estimation processor (1020) is configured to identify a one-stage or two-stage correlation between one or more user factors indicative of the acquired user state and at least a first feedback action. [Article 24] 24. The user feedback system of claim 23, wherein the one-stage correlation includes a first correlation between one or more user factors indicative of the acquired user state and at least a first feedback action. [Article 25] 24. The user feedback system of claim 23, wherein the estimation processor is operable to identify at least a first feedback action based on the acquired one or more user factors by using a model including correlation data between one or more feedback actions and the acquired one or more user factors. [Article 26] The two-stage correlation is a first correlation between one or more user factors indicative of the obtained user state and at least a first state of the user; a second correlation between at least a first state of the user and at least a first feedback action; 24. A user feedback system as defined in clause 23, including: [Article 27] 27. The user feedback system of claim 26, wherein the estimation processor is operable to calculate an estimate of at least a first state of the user based on the acquired one or more user factors by using a model including correlation data between one or more user factors and one or more user states. [Article 28] 28. The user feedback system of clause 26 or 27, wherein the estimation processor is operable to identify at least a first feedback action based on the calculated estimate of the user state by using a model including correlation data between one or more user states and one or more feedback actions. [Article 29] the estimation processor: i. a behavior feedback action that affects at least a first behavior of the user; ii. a drug feedback action that affects the consumption of an active ingredient by said user; 29. A user feedback system as claimed in any one of clauses 1 to 28, operable to identify one or more further suggested feedback actions relating to one or more selected from the list consisting of: [Article 30] 30. A user feedback system according to any one of clauses 1 to 29, wherein the feedback processor is configured to automatically perform the identified at least a first feedback action. [Article 31] A user feedback system as described in any one of clauses 1 to 29, wherein the feedback processor is configured to prompt the user for consent to having at least some of the identified at least first feedback actions executed, and if consent is determined, to have only the at least some of the identified at least first feedback actions executed. [Article 32] 1. A method of user feedback to a user of a first device, comprising: acquiring one or more user factors indicative of the user's state; an estimating step of identifying at least a first feedback action based on one or more of at least a subset of the obtained user factors; a feedback step of selecting at least the identified first feedback action; modifying one or more operations of at least said first device in accordance with the or each selected feedback operation; Including, The user feedback method wherein the first device is not an aerosol delivery device. [Article 33] a processor of the first device, i. the obtaining step; ii. the estimation step; iii. said feedback step; iv. the modifying step; 33. The user feedback method according to claim 32, wherein the method performs at least part of one or more selected from the list consisting of: [Article 34] 34. The user feedback method of clause 32 or 33, wherein the obtaining step includes obtaining one or more user factors from the first device. [Article 35] 35. The user feedback method of any one of clauses 32 to 34, wherein the first device provides a user interface for interaction with the user. [Article 36] the modifying of one or more operations of the first device includes: i. Modifying the complexity of the user interface; ii. Modifying the number of user interface options; and 36. The user feedback method according to any one of clauses 32 to 35, relating to one or more selected from the list consisting of: [Article 37] the modifying of one or more operations of the first device includes: i. a candidate selection or selection of an option for said user; ii. a candidate selection or selection of consumable items for said user; 37. The user feedback method of any one of clauses 32 to 36, relating to one or more selected from the list consisting of: [Article 38] The first device is operable to obtain an identification of the user such that a obtaining processor can obtain a user factor relating to the identified user, the identification of the user comprising: i. Facial recognition; ii. Speech recognition; iii. communicating with the user's registered terminal; iv. communicating with the user's registered aerosol delivery device; and 38. The user feedback method according to any one of clauses 32 to 37, wherein the user feedback is obtained by one or more selected from the list consisting of: [Article 39] 39. The user feedback method of any one of clauses 32 to 38, wherein the first device is a point of sale device. [Article 40] 39. The user feedback method of any one of clauses 32 to 38, wherein the first device is a dock for an aerosol delivery device. [Article 41] The user feedback method of any one of clauses 32 to 38, wherein the first device is fitness equipment. [Article 42] A computer program comprising computer executable instructions configured to cause a computer system to perform the method of any one of clauses 32 to 41. [Article 43] 42. A computer program product comprising the computer program of clause 42 stored on a non-transitory machine-readable medium.

Claims

[Claim 1] 1. A user feedback system for a user of a first device, comprising: an acquisition processor configured to acquire one or more user factors indicative of a state of the user; an estimation processor configured to identify at least a first feedback action based on one or more of the at least a subset of the obtained user factors; a feedback processor configured to at least select an identified first feedback action and to modify one or more actions of at least the first device in accordance with the or each selected feedback action; Equipped with The user feedback system, wherein the first device is not an aerosol delivery device.

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