User feedback system and method

Abstract

To provide a user feedback system and method for a user of a delivery device in a delivery ecosystem.SOLUTION: The user feedback system comprises an obtaining processor configured to obtain one or more user factors indicative of a state of the user, an estimation processor configured to calculate an estimate of the user state based on one or more of the obtained user factors, and a feedback processor configured to select, in response to the estimate of the user state, for at least a first device in the delivery ecosystem, a feedback action that is expected to change the estimated state of the user.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 overview of the 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 are not admitted expressly 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 22.

[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 disclosure but are not restrictive of the disclosure.

[0013] The present disclosure and many of the attendant advantages thereof will be more fully appreciated as the same 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. DETAILED DESCRIPTION OF THE INVENTION

[0015] 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 energizing 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 an energy source and a controller. The energy source may be an electrical energy source or a heat-generating energy source. In one embodiment, the heat-generating energy source includes a carbon substrate that can be energized to provide energy in the form of heat to an aerosolizable material or a heat transfer material in proximity to the heat-generating energy source. In one embodiment, the energy source, such as a heat-generating energy source, is provided in an 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 energy 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 inlet 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). An air path is provided from the air inlet hole 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.

[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] As an example of historical information, a purchase history may be indicative of a user's state, which may be indicative of 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 may still be considered). Information about purchases may include time of day, frequency, location, any associated behavior detectable by user factors, and delays (individual or average) between purchase and usage.

[0073] Thus, a purchasing history that may be indicative of a user's status 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 status 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] Non-limiting examples of medium- to long-term data include the user's hormone levels or hormone cycles, such as estrogen, testosterone, dopamine, cortisol, any acute conditions or illnesses, and activity / health levels, for example, on the order of weeks to months.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] As with indirect or historical data, any suitable combination of data types and / or data from multiple sources may be used.

[0088] 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.

[0089] 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).

[0090] 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 the heating / aerosol generator output and the 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 the depth / duration of inhalation 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] Such information may be obtained by or for the acquisition processor from user surveys, social media data, etc.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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, photos, music, and other similar content available for acquisition 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, news targeted to users in a particular country or region, or any political, social, economic, or religious events related to that country or region. Other content may have a different impact on an individual, such as the performance of a user's favorite sports team, and may be individually assessed, for example, based on the results of a user survey.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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).

[0114] 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.

[0115] 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., between weekdays and weekends and / or between work hours and nighttime hours on weekdays), 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, the 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.

[0116] 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.

[0117] Vaping / consumption-based interactions may relate to the number, frequency, and / or distribution / pattern of puffs / consumption events within one or more selected time periods, which 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 in the body 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.

[0118] Vaping-based interactions may also relate to statistical descriptions of individual vaping behaviors or cohorts thereof (e.g., including, but not limited to, cohorts during one of the selected time periods described above), such as duration, volume, average airflow, airflow profile, active ingredient ratio, heater temperature, etc.

[0119] Data relating to vaping and vaping behavior (or more generally consumption) as described above may be acquired by or for an acquisition processor from the delivery device itself, for example via a Wi-Fi connection to a 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 a Bluetooth connection to form the delivery system.

[0120] 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 characteristics, and raw data regarding vaping / consumption events may be stored in a memory of the delivery device or transmitted to a companion mobile phone. The data may then be used by a processor in the delivery device and / or mobile phone to determine the number, frequency, and / or distribution / pattern of puffs / consumption events within one or more selected time periods, and / or characteristics such as the duration, volume, average airflow, airflow profile, average component ratios, heater temperature values, etc., of one or more vaping / consumption events.

[0121] Optionally, at least one sensor may be configured to detect at least two of the puff profile, puff frequency, puff duration, number of puffs, session length, and peak puff pressure, and determine the user's mood from the detected information.

[0122] A user's puffing behavior provides a useful indicator of stressed versus non-stressed states. In particular, it has been found that a user's puffing intensity and frequency change from normal levels (e.g., stronger, shorter puffs, more frequent puffs) during moments of stress. Therefore, the delivery device, another device in the delivery ecosystem (e.g., the user's mobile phone), or a backend server may optionally establish a baseline profile, frequency, pattern, number of puffs, peak pressure, etc., as described elsewhere herein, and then detect deviations from this baseline (e.g., above a predetermined threshold) that indicate stress. Furthermore, different baselines can optionally be established for different situations and contexts, such as time of day, day of the week, and location. For example, a user may experience more stress at work than at home, but this partial increase in stress would be considered a baseline for the work context, and only if further stress were to occur would a deviation be considered indicative of a change in the user's stress that could prompt a response.

[0123] In any event, as discussed above, such information may then be packaged as one or more user factors and sent to an acquisition processor.

[0124] 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.

[0125] 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 handshakes, 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, adrenaline, autonomic nervous system function, user fatigue, user concentration, and deviations from a suitable baseline amount of the active ingredient in the user's body.

[0126] In particular, micromovements or vibrations between 1 and 15 Hz, more preferably between 2 and 11 Hz, and even more preferably between 3 and 9 Hz, are believed to indicate elevated stress or arousal, and detection of such movements may be used as an input indicative of stress. Conversely, for example, infrequent manipulations may optionally be considered to indicate low stress. Similarly, movements that are not "micro" movements (i.e., intentional shaking of the device as opposed to holding it in a trembling hand) may be ignored for the above purposes or analyzed in other ways (e.g., as movements characteristic of imminent use or associated with a broader context in which user state has a clear correlation).

[0127] Therefore, in some embodiments, a user feedback system for a user of a delivery device in a delivery ecosystem may include: an acquisition processor configured to acquire one or more user factors indicative of a state of the user, the acquisition processor including an output from a motion sensor configured to detect micromovements or vibrations of the user; an estimation processor configured to calculate an estimate of the user state based on one or more of the acquired user factors including at least the output from the motion sensor; and a feedback processor configured 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. In some embodiments, the acquisition processor acquires only the output of the motion sensor as the one or more user factors, and the estimation processor calculates the estimate of the user state based only on the output of the motion sensor. In some embodiments, the motion sensor is provided in or on an aerosol delivery device of the delivery ecosystem. In some embodiments, the feedback action includes a change in operation of the aerosol delivery device.

[0128] Also, for example, independent of micromotions, a given user may tend to hold a delivery device more frequently and / or for longer periods during stress than during relaxed or neutral scenarios because the user consciously or unconsciously associates the device with stress relief through vaping with it. Such holding may be detected using one or more accelerometers and / or touch sensors, as described elsewhere herein.

[0129] Optionally, the delivery device, another device in the delivery ecosystem (such as the user's mobile phone), or a backend server may establish a baseline level of delivery device holding and / or physical interaction for the purpose of detecting when increased holding and / or physical interaction occurs (e.g., above a predetermined threshold), and may also optionally correlate such normal or elevated levels with other indicators or correlates of the user's state and / or directly with the user's state, so that holding and / or physical interaction can act as a proxy for (another) designation of user state and / or another cause of such designation that is normally used / preferred but currently unavailable. As with puffing behavior, further optional, different baselines can be established for different situations and contexts, such as time of day, day of the week, and location. For example, a user may be more stressed at work than at home, but this partial elevated stress would be considered a baseline for the work context, and only if further stress is reached would this be considered an aberration indicative of a change in user stress that could prompt a response.

[0130] Therefore, in some embodiments, a user feedback system for a user of a delivery device in a delivery ecosystem includes: an acquisition processor configured to acquire one or more user factors indicative of a user state, the user factors including output from one or more sensors configured to detect when and / or how a user holds a device in the delivery ecosystem; an estimation processor configured to calculate an estimate of the user state based on one or more of the acquired user factors including at least the output from the one or more sensors; and a feedback processor configured 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 user state is expected. In some embodiments, the acquisition processor acquires only the output of the one or more sensors as the one or more user factors, and the estimation processor calculates the estimate of the user state based only on the output of the one or more sensors. In some embodiments, the one or more sensors are provided in or on an aerosol delivery device of the delivery ecosystem. In some embodiments, the feedback action includes a change in operation of the aerosol delivery device.

[0131] More generally, in some embodiments, for a user feedback system, the one or more user factors comprise one or more user factors selected from the group comprising one or more user draw characteristics, output from one or more motion sensors configured to detect micromovements or vibrations of the user, and output from one or more sensors configured to detect when and / or how the user is holding the device in the delivery ecosystem. The inventors have appreciated that there is a strong correlation between data obtained from these user factors and a determination that the user is in a stressed state.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] The acquisition processor may include a pre-processor or sub-processor (not shown) configured to parse and / or convert acquired information into user factors, which may not be 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 chance of rain, 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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, for generating an output that reflects the user's state prior to generating suggested feedback actions (considered a two-step process), or implicit, for generating suggested feedback actions expected to change the user's state (considered a one-step process).

[0150] 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 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 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.

[0151] 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.

[0152] 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:

[0153] 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.

[0154] 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:

[0155] Using these examples, the operation of the estimation processor can be illustrated in a non-limiting manner as follows.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] An example of a correlation map is a neural network, which may take any suitable form.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] Examples of such machine learning systems will be known to those skilled in the art in the form of algorithms and / or neural networks.

[0172] 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.

[0173] Similarly, as a preliminary step in the estimation processor and / or acquisition processor, dimension 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.

[0174] 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.

[0175] 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.

[0176] On the other hand, the operation of the estimation processor when generating implicit estimates of the user state is described later in this specification.

[0177] 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.

[0178] 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.

[0179] For purposes of explanation, the second stage is described herein as occurring in the estimation processor.

[0180] 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.

[0181] 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.).

[0182] 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.

[0183] 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.

[0184] As mentioned above, this may include predefined rules, algorithms, and / or heuristics that convert estimated states into suggested feedback actions.

[0185] 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.

[0186] Alternatively, for example, a single category state may have a corresponding suggested feedback action.

[0187] 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.

[0188] As mentioned above, any one of these may be accompanied by one or more inputs to the first stage.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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 are a better fit to the available underlying empirical data set used to model the correspondence / correlation.

[0199] 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.

[0200] 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 components of the estimate 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 change the estimate as a result. The revised estimate of the user state can then be used in a second step, in addition to or instead of the initially generated estimate, to generate suggested feedback actions that are considered more accurate than suggestions based on the original estimate of the user state.

[0201] 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.

[0202] 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). 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.

[0203] 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 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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 generated by running the two-stage process using those inputs.

[0210] 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 behaviors generated by running the two-stage process using those inputs.

[0211] Optionally, a one-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 two-stage approach.

[0212] 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.

[0213] 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 estimate of the user state (whether explicitly or implicitly modeled) in a manner that is expected to change the estimated state of the user. Training data relating user factors to suggested feedback actions may be obtained as well.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] Possible feedback actions may differ qualitatively and / or quantitatively.

[0223] 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).

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] In particular, the feedback processor may optionally be located in a device in the delivery ecosystem having suitable computing power, such as a server and / or vending machine, or a mobile phone, 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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).

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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 from 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 send a notification to the user, 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, notify the user via a text or other similar mechanism to be delivered when the user is contactable again.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] Pharmaceutical feedback actions focus on pharmaceutical interventions that alter a user's state, typically based on active ingredient-based interventions such as amount or type, the 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).

[0252] 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.

[0253] 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.

[0254] 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.

[0255] On the other hand, in some cases the role of the action execution sub-processor may actually reside within the device, e.g., capable of interpreting user interface commands and modifying the operation of the device, in which case the commands from the feedback processor may optionally simply replicate such user interface commands.

[0256] In other cases, the operation-performing sub-processor may be provided separately, for example, by employing a conventional processor responsive 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 apps on the mobile phone, the delivery device, and / or one or more other devices in the delivery ecosystem. Similarly, the delivery device dock 200 may include such an operation-performing sub-processor, as may multiple types of delivery devices.

[0257] The action execution sub-processor is operative to execute a feedback action 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 energy supply and / or the heater duty cycle.

[0258] Similarly, for example, if the feedback action command describes reducing environmental noise levels for the user, the action execution sub-processor may cause a pair of noise cancelling headphones to activate a noise cancelling function, and 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 the environment.

[0259] The particular operations performed by each sub-processor may thus depend on the nature of the suggestion feedback operation and the nature of the devices within the delivery ecosystem, but will typically represent a direct translation of the suggestion feedback operation into mechanisms that can be implemented within the device.

[0260] 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. 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 an app for the wearable. 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 (usually as evidenced by a change in one or more user factors or as self-reported by the user) that an action (by changing the user's state) worked successfully. 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.

[0261] 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.

[0262] 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.

[0263] Overview of embodiment In one general embodiment of the present specification, a user feedback system for a user of a delivery device in a delivery ecosystem (1) comprises an acquisition processor (1010) configured to acquire (e.g., by suitable software instructions) one or more user factors indicative of a user's status, e.g., as described elsewhere herein, as described above in this specification.

[0264] Also in this general embodiment, the user feedback system comprises an estimation processor (1020) configured to calculate (e.g., by suitable software instructions) an estimate of the user state based on one or more of the user factors obtained, e.g., as described elsewhere herein, e.g., as described above in this specification.

[0265] Similarly, in this general embodiment, the user feedback system comprises a feedback processor (1030) configured, in response to an estimation of the user state, e.g., as described elsewhere herein (either by an explicit estimation of the user's state and subsequent generation of a suggested action, or by generating a suggested action in response to the user's implicit state embodied in a computational process, as described above herein), to select (e.g., by suitable software instructions) a feedback action, e.g., for at least a first device in the delivery ecosystem as described above herein, that is expected to change the user's estimated state, also as described elsewhere herein.

[0266] In one example of this summary embodiment, the feedback processor is optionally configured to selectively modify one or more operations of at least a first device in the delivery ecosystem according to the selected feedback actions, as described elsewhere herein.

[0267] In this case, the device in the delivery ecosystem whose operation or operations are modified is optionally a delivery device, also as described elsewhere herein.

[0268] In one example of this general embodiment, each of the one or more obtained user factors is, as described herein above: i. historical data that provides context about the user; ii. neurological data about the user; and iii. physiological data about the user; and iv. Contextual data about the user; and v. Environmental data about the user; vi. deterministic data about the user; vii. usage-based data about users; and associated with at least one class selected from the list consisting of:

[0269] In one example of this general embodiment, the acquisition processor, as described herein above, i. one or more individual values ​​each based on one or more user factors; ii. one or more combined values ​​each based on two or more user factors; and iii. one or more values ​​each based on a user factor from a single data class; and and generating an input for the estimation processor that includes one or more selected from the list consisting of:

[0270] In one example of this general embodiment, the estimation processor: i. a single representative value; ii. Representative categories; iii. Multivariate representation; The method is operable to calculate an estimate of the user state as one or more selected from the list consisting of:

[0271] In one instance of this general embodiment, the estimation processor is operable to generate one or more suggested feedback actions based on the calculated estimate of the user state, as described elsewhere herein.

[0272] In this case, the estimation processor is optionally operable to output an estimate of the calculated user state, which may be used for feedback to the user, e.g., for training purposes, positive reinforcement, etc., as also described elsewhere herein.

[0273] In one instance of this summary embodiment, the estimation processor is operable to generate one or more suggested feedback actions based on one or more of the obtained user factors, as described elsewhere herein.

[0274] In this case, the estimation processor optionally does not generate an explicit estimation of the user state as an interim step in generating one or more suggested feedback actions, also as described elsewhere herein.

[0275] Combining the above two examples, the estimation processor may generate one or more suggested feedback actions, for example as an interim step, as described elsewhere in this specification, with or without explicitly calculating an estimate of the user state.

[0276] In one instance of this summary embodiment, the inference processor uses one or more machine learning systems, as described elsewhere herein, to model a one-stage or two-stage relationship between one or more of the user factors and one or more generated suggested feedback actions.

[0277] In one instance of this summary embodiment, the estimation processor uses different machine learning systems (whether hardware or software) in response to a configuration of one or more user factors provided as input to the estimation processor, as described elsewhere herein.

[0278] In one example of this general embodiment, the estimation processor, as described elsewhere herein, i. a behavioral feedback action that affects at least a first behavior of the user; ii. Drug feedback actions that affect the consumption of the active ingredient by the user; and iii. a non-consumptive feedback action that affects one or more non-consumptive actions of the delivery ecosystem; and the method is operable to generate one or more suggested feedback actions for one or more selected from the list consisting of:

[0279] In one instance of this general embodiment, the feedback processor is operable to select at least a first suggested feedback action generated by the estimation processor, as described elsewhere herein.

[0280] In one instance of this general embodiment, the feedback processor is operable to determine devices within the delivery ecosystem that are currently available for performing feedback operations, as described elsewhere herein.

[0281] In one instance of this summary embodiment, the feedback processor is operable to select devices within the delivery ecosystem that will perform each selected suggested feedback action, as described elsewhere herein.

[0282] In one example of this summary embodiment, the feedback processor is operable to send commands to a selected device in the delivery ecosystem that cause the selected device to perform at least a portion of the selected suggested feedback action, as described elsewhere herein.

[0283] In this case, the feedback processor is optionally operable to send a command to an intermediate device in the delivery ecosystem instructing the intermediate device to send a command to the selected device that causes the selected device in the delivery ecosystem to perform at least a portion of the selected suggested feedback action.

[0284] In one example of this general embodiment, the delivery ecosystem includes: i. one or more delivery devices (10); ii. one or more mobile terminals (100); iii. one or more wearable devices (400); iv. one or more docking units (200) for the or each delivery device; The method comprises one or more selected from the list consisting of:

[0285] In one example of this general embodiment, at least a portion of the functionality of one or more of the acquisition processor, estimation processor, and feedback processor is provided by a remote server (1000), as described elsewhere herein.

[0286] In one example of this general embodiment, at least a portion of the functionality of one or more of the acquisition processor, estimation processor, and feedback processor, as described elsewhere herein, is provided by one or more processors located within one or more devices (10, 100, 200, 300, 400) of the delivery ecosystem (1).

[0287] Referring now to FIG. 7, in one general embodiment herein, a method for user feedback to a user of a delivery device in a delivery ecosystem includes the following steps.

[0288] The first step s710 is to acquire one or more user factors indicative of the user's state. As described herein above, this may be a function of the acquisition processor (1010), or may be performed by one or more devices / processors / data sources operating integrally or in parallel with or to the acquisition processor, or may be performed by one or more devices / processors / data sources that may share such functionality with the acquisition processor.

[0289] In a second step s720, an estimate of the user state is calculated based on one or more of the obtained user factors. As described herein above, this may be the function of the estimation processor (1020), or more generally, of a processor of the feedback system, typically located in a server remote to the delivery ecosystem, but optionally operating integrally or in parallel with or to the estimation processor, or sharing such functionality with the estimation processor.

[0290] In a third step s730, in response to the estimation of the user state, a feedback action is selected for at least a first device in the delivery ecosystem that is expected to change the estimated state of the user. As described herein above, this may be a function of the feedback processor (1030), which may comprise one or more of a selection and notification sub-processor and an action execution sub-processor. More generally, it may be a function of a processor of the feedback system, typically located in a server remote to the delivery ecosystem, but optionally operating as or in parallel with the feedback processor or its sub-processors, or sharing such functionality with the feedback processor or its sub-processors.

[0291] It will be apparent to one skilled in the art that variations of the above methods corresponding to the operation of various embodiments of the apparatus / system as described and claimed herein include, but are not limited to, the following and are considered to be within the scope of this disclosure.

[0292] An example of this general embodiment includes modifying one or more operations of at least a first device in the delivery ecosystem according to the selected feedback action.

[0293] In this example, the device in the delivery ecosystem whose operation or operations are modified is optionally a delivery device.

[0294] In one example of this summary embodiment, the one or more obtained user factors are each associated with at least one class selected from the list consisting of historical data providing background information about the user, neurological data about the user, physiological data about the user, contextual data about the user, environmental data about the user, deterministic data about the user, and usage-based data about the user.

[0295] In one example of this summary embodiment, the obtaining step includes generating inputs for the estimating step that include one or more selected from a list consisting of one or more individual values ​​each based on one or more user factors, one or more combined values ​​each based on two or more user factors, and one or more values ​​each based on user factors from a single data class.

[0296] In one instance of this summary embodiment, the estimating step includes calculating an estimate of the user state as one or more selected from the list consisting of a single representative value, a representative category, and a multivariate representation.

[0297] In one instance of this general embodiment, the estimating step includes generating one or more suggested feedback actions based on the calculated estimate of the user state.

[0298] In one instance of this summary embodiment, the estimating step includes generating one or more suggested feedback actions based on one or more of the obtained user factors.

[0299] In one instance of this summary embodiment, the estimating step includes using one or more machine learning systems to model a one- or two-stage relationship between one or more of the user factors and one or more generated suggested feedback actions.

[0300] In this example, the estimation steps optionally use different machine learning systems in response to a configuration of one or more user factors provided as input to the estimation processor.

[0301] In one example of this summary embodiment, the estimating step includes generating one or more suggested feedback actions regarding one or more selected from the list consisting of: a behavioral feedback action affecting at least a first behavior of the user; a drug feedback action affecting consumption of the active ingredient by the user; and a non-consummatory feedback action affecting one or more non-consummatory actions of the delivery ecosystem.

[0302] In one instance of this general embodiment, the feedback step includes selecting at least a first suggested feedback action generated by the estimation step.

[0303] In one instance of this summary embodiment, the feedback step includes determining which devices within the delivery ecosystem are currently available to perform the feedback action.

[0304] In one instance of this summary embodiment, the feedback step includes selecting devices within the delivery ecosystem that will each perform the selected suggested feedback action.

[0305] In one example of this summary embodiment, the delivery ecosystem comprises one or more selected from the list consisting of one or more delivery devices (10), one or more mobile terminals (100), one or more wearable devices (400), and one or more docking units (200) for the or each delivery device.

[0306] In one example of this summary embodiment, the one or more user factors include one or more user factors selected from the group including one or more user draw characteristics, output from one or more motion sensors configured to detect micromovements or vibrations of the user, and output from one or more sensors configured to detect when and / or how the user is holding the device in the delivery ecosystem, as described elsewhere herein.

[0307] It will be appreciated that the methods described above may be adapted to be performed on conventional hardware suitably adapted to be applied by the inclusion or substitution of software instructions or dedicated hardware, examples of which have been described herein, for example, in connection with server 1000 and devices of delivery ecosystem 1.

[0308] Thus, any adaptations required to existing portions of a conventional equivalent device may be implemented in the form of a computer program product including processor-executable (computer-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.

Claims

[Claim 1] The invention described in the specification.

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