Steady state resistance estimation for over-temperature protection of nicotine e-vaping devices

Abstract

A nicotine e-vaping device (60) is provided that includes a reservoir containing a nicotine pre-vapor formulation, a heating element configured to heat nicotine pre-vapor formulation drawn from the reservoir, and control circuitry. The control circuitry is configured to monitor a resistance value of the heating element for a first period of time after a first application of negative pressure to the nicotine e-vaping device (60), determine an estimated steady state resistance value of the heating element based on the monitored resistance value using a trained neural network, and control or disable power to the heating element based on the estimated steady state resistance value. The invention detects dry puff conditions and protects the device from overheating.

Description

Technical Field

[0001] This disclosure relates to systems, apparatus, methods, and / or non-transitory computer-readable media relating to estimating and / or predicting the steady-state resistance of a nicotine electronic vaping device (or e-vaping device) in order to prevent overheating of the nicotine electronic vaping device. Background Technology

[0002] Nicotine electronic vaporizers (nicotine e-vaporizers, nicotine EVD, nicotine vaporization devices, nicotine vapor generators, etc.) generate nicotine vapor by heating a nicotine vapor pre-formulation carried by a core to a heater (e.g., a resistance heating coil, an induction heater, etc.), thereby heating the nicotine vapor pre-formulation into nicotine vapor. The nicotine vapor pre-formulation may be a liquid, solid, and / or gel formulation, including but not limited to water, beads, solvents, active ingredients, ethanol, plant extracts, natural or artificial flavorings, and / or at least one nicotine vapor-forming agent, such as glycerin and propylene glycol. The heater heats the nicotine vapor pre-formulation to a desired temperature (e.g., 100°C to 200°C), causing the nicotine vapor pre-formulation to evaporate into nicotine vapor. However, when the amount of nicotine pre-mixed filling stored in the nicotine cartridge, reservoir, or container of the nicotine e-vaping device begins to run out, the coil may begin to dry out (e.g., not being fully moistened, not fully absorbing the nicotine pre-mixed filling, etc.). This can then lead to the heater overheating the coil and / or the nicotine pre-mixed filling. For example, overheating of the coil and / or the nicotine pre-mixed filling may introduce a "burnt," "sour," and / or "bitter" odor or taste into the nicotine vapor inhaled by an adult smoker. This phenomenon can be referred to as "dry vaping" and / or "dry wick vaping" events. Summary of the Invention

[0003] Various example implementations relate to systems, apparatus, methods, and / or non-transitory computer-readable media for detecting dry-vaping events by estimating the steady-state resistance value of the heater of a nicotine e-vapor device.

[0004] In at least one example embodiment, a nicotine electronic vaporizer (EVD) may include a reservoir for containing a nicotine vapor pre-mix, a heating element configured to heat the nicotine vapor pre-mix drawn from the reservoir, and a control circuitry. The control circuitry may be configured to monitor the resistance value of the heating element for a first time period following the initial application of negative pressure to the nicotine EVD, determine an estimated steady-state resistance value of the heating element using a trained neural network based on the monitored resistance value, and control the power supply to the heating element based on the estimated steady-state resistance value.

[0005] Some example implementations of the nicotine EVD provide that the control circuitry is further configured to detect dry-draft conditions at the nicotine EVD based on the estimated steady-state resistance value of the heating element, and to disable power supply to the heating element in response to the detected dry-draft conditions.

[0006] Some example implementations of the nicotine EVD provide that the control circuitry is also configured to prevent the application of power to the heating element in response to detecting a second application of negative pressure to the nicotine EVD.

[0007] Some example implementations of the nicotine EVD provide that the control circuitry is configured to monitor the resistance value of the heating element by determining a peak resistance value of the heating element during a first time period, and determining at least one additional resistance value of the heating element at a time after the determined peak resistance value during the first time period. The control circuitry may also be configured to determine the estimated steady-state resistance value of the heating element by using a trained neural network based on the peak resistance value and the at least one additional resistance value.

[0008] Some example implementations of the nicotine EVD provide that the trained neural network is a function fitting network configured to receive the peak resistance value and the at least one additional resistance value as input values, determine the decay of the input values ​​during the first time period, and output the estimated steady-state resistance value of the heating element based on the determined decay of the resistance value of the heating element during the first time period.

[0009] Some example implementations of the nicotine EVD provide that, after the first application of negative pressure to the nicotine EVD, at the time when the application of power to the heating element is stopped, the peak resistance value is determined.

[0010] Some example implementations of the nicotine EVD provide that the at least one additional resistance value includes at least a second resistance value and a third resistance value, the second resistance value is determined at a time after the time the peak resistance value is determined and before the time the third resistance value is determined, and the third resistance value is determined at a time after the time the second resistance value is determined and before the second negative pressure is detected.

[0011] Some example implementations of the nicotine EVD provide that the heating element is connected to a Wheatstone bridge circuit, and the control circuitry is further configured to detect a variable resistance value corresponding to the heating element during a first time period, detect a resistance value corresponding to the Wheatstone bridge circuit during the first time period, and use a trained neural network to estimate the estimated steady-state resistance value of the heating element based on the detected variable resistance value corresponding to the heating element and the detected resistance value corresponding to the Wheatstone bridge circuit.

[0012] In at least one example embodiment, a method of operating a nicotine electronic vaporizer (EVD) may include: during a first time period following the initial application of negative pressure to the nicotine EVD, using a control circuitry system of the nicotine EVD, monitoring the resistance value of a heating element included in the nicotine EVD; using the control circuitry system, using a trained neural network, determining an estimated steady-state resistance value of the heating element based on the monitored resistance value; and using the control circuitry system, controlling the power supply to the heating element based on the estimated steady-state resistance value.

[0013] In some example implementations, the method may further include using the control circuitry to detect dry-draft conditions at the nicotine EVD based on the estimated steady-state resistance of the heating element, and disabling power supply to the heating element in response to the detected dry-draft conditions.

[0014] In some example implementations, the method may further include using the control circuitry system to detect a second negative pressure being applied to the nicotine EVD, and, in response to detecting the second negative pressure being applied to the nicotine EVD, using the control circuitry system to prevent the application of electricity to the heating element.

[0015] In some example implementations, monitoring the resistance value of the heating element includes determining a peak resistance value of the heating element during the first time period, and determining at least one additional resistance value of the heating element at a time after the determined peak resistance value during the first time period. Determining the estimated steady-state resistance value of the heating element includes estimating the estimated steady-state resistance value of the heating element using a trained neural network based on the peak resistance value and the at least one additional resistance value.

[0016] In some example implementations, the trained neural network is a function fitting network, and the method further includes using the control circuit system to receive the peak resistance value and the at least one additional resistance value as input values, using the control circuit system to determine the decay of the resistance value of the heating element during the first time period, and using the control circuit system to output the estimated steady-state resistance value of the heating element based on the determined decay of the resistance value of the heating element during the first time period.

[0017] In some example implementations, the peak resistance value is determined at the time when the power is stopped being applied to the heating element after the first application of negative pressure to the nicotine EVD.

[0018] In some example implementations, the at least one additional resistance value includes at least a second resistance value and a third resistance value, wherein the second resistance value is determined after the time at which the peak resistance value is determined and before the time at which the third resistance value is determined, and the third resistance value is determined after the time at which the second resistance value is determined and before the second application of negative pressure is detected.

[0019] In some example implementations, the method may further include using the control circuit system to detect a variable resistance value corresponding to the heating element during the first time period, using the control circuit system to detect a resistance value corresponding to the Wheatstone bridge circuit during the first time period, and using the control circuit system to estimate the estimated steady-state resistance value of the heating element using a trained neural network based on the detected variable resistance value corresponding to the heating element and the detected resistance value corresponding to the Wheatstone bridge circuit.

[0020] In at least one example embodiment, the nicotine electronic vaporizer (EVD) may include a reservoir containing nicotine vapor pre-mixed material; a heating element configured to heat the nicotine vapor pre-mixed material drawn from the reservoir; a heater resistance monitoring circuitry configured to determine a peak resistance value of the heating element during a first time period following the initial application of negative pressure to the nicotine EVD, and to determine at least one additional resistance value of the heating element during the first time period; a trained neural network configured to estimate a steady-state resistance value of the heating element based on the determined peak resistance value and the determined at least one additional resistance value during the first time period; and a control circuitry configured to disable power supply to the heating element based on the estimated steady-state resistance value.

[0021] In some example implementations, the trained neural network is also configured to detect dry-draft conditions at the nicotine EVD based on the estimated steady-state resistance value of the heating element, and the control circuitry is also configured to disable the power supply to the heating element in response to the detected dry-draft conditions.

[0022] In some example implementations, the trained neural network is a function fitting network configured to receive the peak resistance value and the at least one additional resistance value as input values, determine the decay of the input values ​​during the first time period, and output the estimated steady-state resistance value of the heating element based on the determined decay of the resistance value of the heating element during the first time period.

[0023] In some example implementations, the peak resistance value is determined at the time when the power is stopped being applied to the heating element after the first application of negative pressure to the nicotine EVD. Attached Figure Description

[0024] The various features and advantages of the non-limiting embodiments described herein will become more apparent upon review of the detailed description in conjunction with the accompanying drawings. The drawings are provided for illustrative purposes only and should not be construed as limiting the scope of the claims. Unless explicitly stated otherwise, the drawings should not be considered to be drawn to scale. Various dimensions of the drawings may be enlarged for clarity.

[0025] Figure 1 It is a perspective view of a nicotine electronic vaping or e-vaping device according to at least one example embodiment.

[0026] Figure 2 A schematic diagram of an example device system according to at least one example embodiment is shown, the device system including an example nicotine e-vapor device body connected to an example nicotine container system.

[0027] Figure 3A and Figure 3B This is a block diagram illustrating various components of an example heater resistance monitoring circuit for a nicotine electronic vapor device according to some example embodiments.

[0028] Figures 4A to 4C This is a diagram illustrating a neural network for predicting the resistance value of the heating element of a nicotine e-vapor device according to at least one example embodiment.

[0029] Figure 5 It is a graph corresponding to the resistance value of the heating element of the nicotine e-vapor device during a single inhalation event, according to at least one example embodiment.

[0030] Figure 6 It is a graph showing the resistance decay after a single suction event according to at least one example embodiment.

[0031] Figures 7A to 7B This is a flowchart illustrating a method for detecting dry smoking events using the steady-state resistance value of a heating element of a nicotine e-vapor device, according to at least one example embodiment. Detailed Implementation

[0032] It should be noted that these figures are intended to illustrate the general characteristics of the methods and / or structures utilized in particular example embodiments and to supplement the written description provided below. However, these figures are not drawn to scale and may not accurately reflect the precise structural or performance characteristics of any given example embodiment, and should not be interpreted as limiting or restricting the range of values ​​or characteristics covered by the example embodiments.

[0033] This document discloses several detailed example implementations. However, for the purpose of describing the example implementations, the specific structural and functional details disclosed herein are only representative. The example implementations may be embodied in many alternative forms and should not be construed as being limited to the example implementations described herein.

[0034] Therefore, while the example embodiments can have various modifications and alternatives, they are illustrated in the figures by way of example and will be described in detail herein. However, it should be understood that the example embodiments are not intended to be limited to the specific forms disclosed; rather, the example embodiments will encompass all modifications, equivalents, and alternatives falling within the scope of the example embodiments. Throughout the description of the figures, similarity numbers refer to similar elements.

[0035] It should be understood that when an element or layer is referred to as being "on," "connected to," "attached to," or "covering" another element or layer, it may be directly on, connected to, attached to, or cover the other element or layer, or there may be intermediate elements or layers present. In contrast, when an element is referred to as being "directly" on, directly connected to, or directly attached to another element or layer, there are no intermediate elements or layers present. Throughout this specification, similar numbers refer to similar elements. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0036] It should be understood that although the terms first, second, third, etc., may be used herein to describe various elements, regions, layers, and / or sections, these elements, regions, layers, and / or sections should not be limited by these terms. These terms are used only to distinguish one element, region, layer, or section from another. Therefore, without departing from the teachings of the example embodiments, the first element, region, layer, or section discussed below may be referred to as the second element, region, layer, or section.

[0037] For ease of description, spatial relative terms (e.g., “below,” “under,” “lower,” “above,” “upper,” etc.) are used herein to describe the relationship between one element or feature and another, as shown in the figures. It should be understood that, in addition to the orientation depicted in the figures, spatial relative terms are also intended to cover different orientations of the device during use or operation. For example, if the device in the figures is flipped, an element described as “below” or “under” other elements or features will be oriented “above” other elements or features. Therefore, the term “below” can encompass both above and below orientations. The device may be oriented in other ways (rotated 90 degrees or in other orientations), and the spatial relative descriptive terms used herein are interpreted accordingly.

[0038] The terminology used herein is for the purpose of describing various exemplary embodiments only and is not intended to limit the exemplary embodiments. As used herein, the singular forms “a” and “described” are intended to also include the plural forms unless the context clearly indicates otherwise. It will also be understood that, when used in this specification, the terms “includes / including,” “comprises,” and / or “comprising” specify the presence of the stated features, integrals, steps, operations, and / or elements, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, and / or groups thereof.

[0039] This document describes example embodiments with reference to cross-sectional diagrams, which are schematic illustrations of idealized embodiments (and intermediate structures) of the example embodiments. Therefore, variations in shape from the diagrams should be expected due to factors such as manufacturing techniques and / or tolerances. Consequently, the example embodiments should not be construed as limited to the shapes of the areas shown herein, but should include, for example, shape deviations caused by manufacturing processes.

[0040] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the example embodiments pertain. It will also be understood that terms (including those as defined in common dictionaries) shall be interpreted to have a meaning consistent with their meaning in the relevant field, and shall not be interpreted in an idealized or overly formalized sense unless so explicitly defined herein.

[0041] Figure 1 This is a perspective view of a nicotine e-vaporizer according to at least one example embodiment; however, the example embodiments are not limited thereto, and the nicotine e-vaporizer may take other forms. References Figure 1 The nicotine e-vaping device 60 includes a device body 10 configured to house a nicotine container assembly 30 (e.g., a nicotine e-vaping cartridge). The nicotine container assembly 30 is a modular article of manufacture configured to contain a nicotine vapor pre-formulation and may be replaceable. A “nicotine vapor pre-formulation” is a material or combination of materials that can be converted into nicotine vapor. For example, a nicotine vapor pre-formulation can be a liquid, solid, and / or gel formulation, including but not limited to water, beads, solvents, active ingredients, ethanol, plant extracts, natural or artificial flavorings, and / or nicotine vapor-forming agents such as glycerol and propylene glycol. During nicotine vaporization, the nicotine e-vaping device 60 is configured to heat the nicotine vapor pre-formulation to produce nicotine vapor. As mentioned herein, “vapor” refers to any substance produced or output from any nicotine e-vaping device according to any of the disclosed, claimed exemplary embodiments and / or their equivalents, wherein such substance contains nicotine. The nicotine electronic vapor device 60 can be considered as an electronic nicotine delivery system (ENDS).

[0042] The device body 10 includes a front cover 104, a frame 106, and a rear cover 108. The front cover 104, frame 106, and rear cover 108 form a device housing that encloses the mechanical, electronic, and / or circuitry associated with the operation of the nicotine e-vaping device 60. For example, the device housing of the device body 10 may enclose a power supply (e.g., a power source, battery, etc.) configured to power the nicotine e-vaping device 60, which may include supplying current to the nicotine container assembly 30. Additionally, when assembled, the front cover 104, frame 106, and rear cover 108 may constitute a large portion of the visible portion of the device body 10, but the example embodiments are not limited thereto.

[0043] A front cover 104 (e.g., a first cover) defines a primary opening configured to receive a frame structure 112. The frame structure 112 defines a through-hole 150 configured to receive a nicotine container assembly 30.

[0044] The front cover 104 also defines secondary openings configured to receive a light guide arrangement. The secondary openings may resemble slots (e.g., segmented slots), but other shapes are possible depending on the shape of the light guide arrangement. In an example embodiment, the light guide arrangement includes a light guide lens 116. Furthermore, the front cover 104 defines tertiary and quaternary openings configured to receive a first button 118 and a second button 120. Each of the tertiary and quaternary openings may resemble a rounded square, but other shapes are possible depending on the shape of the button. The first button housing 122 is configured to expose the first button lens 124, while the second button housing 123 is configured to expose the second button lens 126.

[0045] The operation of the nicotine electronic vaporizer 60 can be controlled by a first button 118 and a second button 120. For example, the first button 118 may be a power button, and the second button 120 may be an intensity button. Although two buttons related to the light guide device are shown in the diagram, it should be understood that more (or fewer) buttons may be provided depending on the available features and the desired user interface.

[0046] Frame 106 (e.g., base frame) is the central support structure of the device body 10 (and the nicotine e-vapor device 60 as a whole). Frame 106 may be referred to as a chassis. Frame 106 includes a proximal end, a distal end, and a pair of side sections between the proximal and distal ends. The proximal and distal ends may also be referred to as the downstream end and the upstream end, respectively. As used herein, "proximal" (and conversely, "distal") is relative to the adult vaporizer during nicotine vaporization, and "downstream" (and conversely, "upstream") is relative to the flow of nicotine vapor. Bridging sections (e.g., approximately at the midpoint along the length of frame 106) may be provided between the opposing inner surfaces of the side sections for additional strength and stability. Frame 106 may be integrally formed to be a monolithic structure.

[0047] The rear cover 108 (e.g., a second cover) also defines an opening configured to receive the frame structure 112. The front cover 104 and the rear cover 108 may be configured to engage with the frame 106 via a snap-fit ​​arrangement.

[0048] The device body 10 also includes a mouthpiece 102. The mouthpiece 102 can be fixed to the proximal end of the frame 106. In addition, at least one end of the mouthpiece 102 may include a plurality of air outlets (not shown) through which nicotine vapor generated by the nicotine electronic vaporizer 60 can be drawn.

[0049] The distal end of the nicotine e-vaping device 60 includes a port 110 (e.g., a mini-USB connector). Port 110 is configured to receive current from an internal power source (e.g., via a mini-USB cable, USB cable, power cable, etc.) to charge a power supply (e.g., a power source, battery, etc.) within the nicotine e-vaping device 60 (not shown). In at least one example embodiment, the nicotine e-vaping device 60 may be configured to receive current from a wireless power source (e.g., a wireless charging pad, etc.). Additionally, port 110 may be configured to send data to and / or receive data from another nicotine e-vaping device or other electronic device (e.g., a telephone, tablet, computer, etc.) (e.g., via a mini-USB cable, USB cable, etc.). Furthermore, the nicotine e-vaping device 60 may be configured to wirelessly communicate with another electronic device (such as a telephone, tablet, computer, server, kiosk, wireless beacon, VR / AR device, etc.) via application software (application) (e.g., a nicotine e-vaping device application) installed on the device. In this scenario, adult vaporizers can control the nicotine e-vaping device 60 via an app or interact with it in other ways (e.g., locating the nicotine e-vaping device 60, checking the status information of the nicotine e-vaping device and / or nicotine container components, changing operating parameters, locking / unlocking the nicotine e-vaping device 60, etc.).

[0050] The nicotine e-vaping device 60 includes a nicotine container assembly 30 configured to contain a nicotine vapor pre-mixed substance. The nicotine container assembly 30 may be removable (e.g., replaceable) or permanently attached to the nicotine e-vaping device 60 and refillable with nicotine vapor pre-mixed substance. The nicotine container assembly 30 has an upstream end (arranged facing the light guide) and a downstream end (facing the mouthpiece 102). In a non-limiting example embodiment, the upstream end is the surface of the nicotine container assembly 30 opposite the downstream end. The nicotine container assembly 30 includes a connector module (not shown) disposed within the nicotine container body and exposed through an opening in the upstream end. The outer surface of the connector module includes at least one electrical contact. This at least one electrical contact may include a plurality of electrical contacts configured to electrically connect to at least one electrical contact (not shown) of the device body 10 (e.g., at least one electrical contact of port 110, etc.). Additionally, the at least one electrical contact of the nicotine container assembly 30 includes a plurality of data contacts. Multiple data contacts of the nicotine container assembly 30 are configured to be electrically connected to data contacts (not shown) of the device body 10 (e.g., at least one power contact of port 110).

[0051] The nicotine container assembly 30 may include a reservoir (not shown) located within the assembly and configured to contain a nicotine vapor pre-preparation. The reservoir may be configured to hermetically seal the nicotine vapor pre-preparation until the nicotine container assembly 30 is activated to release the nicotine vapor pre-preparation from the reservoir. As a result of the hermetically sealed design, the nicotine vapor pre-preparation may be isolated from the environment and from internal components of the nicotine container assembly 30 that may potentially react with the nicotine vapor pre-preparation, thereby reducing or preventing the possibility of adverse effects on the shelf life and / or sensory properties (e.g., taste) of the nicotine vapor pre-preparation. The nicotine container assembly 30 may also include structures configured to activate the nicotine container assembly 30 and, upon activation, contain and heat the nicotine vapor pre-preparation released from the reservoir.

[0052] The nicotine container assembly 30 can be manually activated by an adult vaporizer before being inserted into the device body 10. Alternatively, the nicotine container assembly 30 can be activated as part of the nicotine container assembly 30 inserted into the device body 10. In an example embodiment, the nicotine container body includes a perforator (e.g., a pin) configured to release a pre-mixed nicotine vapor from the reservoir during activation of the nicotine container assembly 30.

[0053] As shown, the device body 10 and nicotine container assembly 30 include mechanical, electronic, and / or circuitry components associated with the operation of the nicotine e-vapor device 60. For example, the nicotine container assembly 30 may include mechanical elements configured to actuate to release nicotine vapor pre-formulation from a sealed reservoir therein. The nicotine container assembly 30 may also have mechanical aspects configured to engage with the device body 10 to facilitate insertion and placement of the nicotine container assembly 30.

[0054] Additionally, the nicotine container assembly 30 can be a "smart container," comprising electronic components and / or circuitry configured to store, receive, and / or transmit information to / from the device body 10. This information can be used to authenticate the nicotine container assembly 30 used with the device body 10 (e.g., to reduce and / or prevent the use of unapproved / modified / counterfeit nicotine container assemblies). Furthermore, this information can be used to identify the type of nicotine container assembly 30, and subsequently, based on the identified type, to associate that type with a vaporization profile. The vaporization profile can be designed to illustrate general parameters for heating the pre-mixed nicotine vapor and can be adjusted, refined, or otherwise modified by the adult vaporizer before and / or during nicotine vaporization.

[0055] The nicotine container assembly 30 may also communicate with the device body 10 other information that may be relevant to the operation of the nicotine e-vapor device 60. Examples of such information may include the content of nicotine vapor pre-formulation within the nicotine container assembly 30 and / or the length of time that has elapsed since the nicotine container assembly 30 was inserted into the device body 10 and activated.

[0056] The device body 10 may include mechanical elements (e.g., complementary structures) configured to engage, accommodate, and / or activate the nicotine container assembly 30. Additionally, the device body 10 may include electronic elements and / or circuitry configured to receive current to charge an internal power source, which is further configured to supply power to the nicotine container assembly 30 during nicotine vaporization. Furthermore, the device body 10 may include electronic elements and / or circuitry configured to communicate with the nicotine container assembly 30, various nicotine e-vaping devices, non-nicotine e-vaping devices, other electronic devices (e.g., telephones, tablets, computers, etc.), and / or adult vaping users.

[0057] The device body 10 may also include a device electrical connector (not shown) configured to electrically engage with the nicotine container assembly 30 and supply power from the device body 10 to the nicotine container assembly 30 via the device electrical connector during nicotine vaporization. Additionally, data can be transmitted to and / or received from the device body 10 and the nicotine container assembly 30 via the device electrical connector.

[0058] According to some example embodiments, the nicotine container assembly 30 may include a core (not shown) configured to transfer a nicotine vapor pre-formulation to a heater (not shown). The heater is configured to heat the nicotine vapor pre-formulation during nicotine vaporization to produce nicotine vapor. The heater is electrically connected to at least one electrical contact of an electrical connector of the device. In example embodiments, the heater includes a folded heating element; however, the example embodiments are not limited thereto. In this case, the core may have a planar form configured to be held by the folded heating element; however, the example embodiments are not limited thereto. When the nicotine container assembly 30 is assembled, the core is configured to be in fluid communication with an absorbent material such that nicotine vapor pre-formulation located in the absorbent material (when the nicotine container assembly 30 is activated) is transferred to the core via capillary action. In this specification, the heater may also be referred to as a heating engine, heating coil, etc.

[0059] According to at least some example embodiments, the core can be a fiber pad, or other structure with pores / voids designed for capillary action. Additionally, the core can have a rectangular shape, but the example embodiments are not limited to this.

[0060] In an example implementation, the heater is configured to undergo Joule heating (also known as ohmic / resistance heating) when an electric current is applied thereto. More specifically, the heater may be formed of one or more conductors and configured to generate heat when an electric current passes through it. The current may be supplied by an electrical supply (e.g., a power source, battery, etc.) within the device body 10 and transmitted to the heater via electrical contacts.

[0061] The heater and associated structures are described in more detail in U.S. Application No. 15 / 729,909, filed October 11, 2017, entitled “Folded Heater For Electronic Vaping Device”.

[0062] Figure 2 A schematic diagram of an example device system according to at least one example embodiment is shown, the device system including an example nicotine e-vapor device body connected to an example nicotine container system.

[0063] Device system 2100 includes a controller 2105, a power supply 2110, an actuator controller 2115, a nicotine container electrical / data interface 2120, device sensors 2125, an input / output (I / O) interface 2130, a vapor indicator 2135, at least one antenna 2140, an on-product controller 2150, a storage medium 2145, and / or a heater resistance monitoring circuit 3000. However, device system 2100 is not limited to... Figure 2 The features shown in the document may include more or fewer constituent elements.

[0064] The controller 2105 can be hardware, firmware, hardware executing software, or any combination thereof. When the controller 2105 is hardware, such existing hardware may include one or more central processing units (CPUs), microprocessors, processor cores, multiprocessors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), computers configured as dedicated machines to perform the functions of the controller 2105, etc. CPUs, microprocessors, processor cores, multiprocessors, DSPs, ASICs, and FPGAs are generally referred to as processing devices.

[0065] In cases where the controller 2105 is or includes a processor executing software, the controller 2105 is configured as a dedicated machine (e.g., a processing device) to execute software stored in memory accessible to the controller 2105 (e.g., storage medium 2145 or another storage device) to perform the functions of the controller 2105. The software may be embodied in program code, including instructions for performing and / or controlling any or all operations described herein as being performed by the controller 2105.

[0066] As disclosed herein, the terms "storage medium," "computer-readable storage medium," or "non-transitory computer-readable storage medium" can refer to one or more means for storing data, including read-only memory (ROM), random access memory (RAM), magnetic RAM, magnetic core memory, disk storage media, optical storage media, flash memory devices, and / or other tangible machine-readable media for storing information. The term "computer-readable medium" may include, but is not limited to, portable or fixed storage devices, optical storage devices, and various other media capable of storing, containing, or carrying instructions and / or data.

[0067] According to the example implementation, controller 2105 may include at least one microprocessor, etc. Furthermore, controller 2105 may include input / output interfaces such as general purpose input / output (GPIO), internal integrated circuit (I2C) interfaces, serial peripheral interface bus (SPI) interfaces, etc.; multi-channel analog-to-digital converters (ADCs) and / or digital-to-analog converters (DACs); and / or clock input terminals, etc. However, the example implementation is not limited to this example. For example, controller 2105 may also include arithmetic circuitry or circuitry.

[0068] return Figure 2 The controller 2105 communicates with the power supply 2110, actuator controller 2115, nicotine container electrical / data interface 2120, device sensor 2125, input / output (I / O) interface 2130, vapor indicator 2135, product controller 2150, and at least one antenna 2140.

[0069] The controller 2105 can also communicate with the non-volatile memory 2205b (NVM), heater resistance monitoring circuitry 3000, and / or nicotine container sensor 2220 in the nicotine container assembly 30 via the nicotine container electrical / data interface 2120 and the main electrical / data interface 2210. According to at least one example embodiment, the NVM 2205b may be a cryptographic coprocessor and a non-volatile memory package (CC-NVM) (not shown), but the example embodiments are not limited thereto. More specifically, the controller 2105 may utilize encryption to authenticate the nicotine container assembly 30. As will be described, the controller 2105 communicates with the NVM or CC-NVM package to authenticate the nicotine container assembly 30. More specifically, the non-volatile memory is encoded during manufacturing using product and other information for authentication.

[0070] When the nicotine container assembly 30 is inserted into the device body 10, the storage device can be coded using an electronic identifier to authorize at least one of a pair of nicotine container assembly 30 authentication and operating parameters specific to the type (or physical construction, such as heating engine type) of the nicotine container assembly 30. In addition to authentication based on the electronic identifier of the nicotine container assembly 30, the controller 2105 can also authorize the use of the nicotine container assembly 30 based on the expiration date of the nicotine vapor pre-preparation and / or heater stored in the non-volatile memory encoded in the NVM or CC-NVM. If the controller 2105 determines that the expiration date encoded in the non-volatile memory has expired, the controller 2105 may not authorize the use of the nicotine container assembly 30 and disable the nicotine e-vapor device 60.

[0071] Controller 2105 (or storage medium 2145) stores key materials and proprietary algorithm software for encryption. For example, the encryption algorithm relies on the use of random numbers. The security of these algorithms depends on the genuine randomness of these numbers. These numbers are typically pre-generated and encoded into a processor or storage device. Example implementations can increase the randomness of the numbers used for encryption by generating numbers that are more random and have greater inter-individual variation than pre-generated random numbers using nicotine vapor extraction parameters (e.g., the duration of nicotine vapor extraction instances, the intervals between nicotine vapor extraction instances, or combinations thereof). All communication between controller 2105 and nicotine container assembly 30 is encrypted.

[0072] The controller 2105 may also include an encryption accelerator to allow the resources of the controller 2105 to perform functions other than the encoding and decoding involved in authentication. The controller 2105 may also include other security features, such as preventing unauthorized use of the communication channel and unauthorized access to data if the nicotine container or adult vapor user is not authenticated.

[0073] In addition to the encryption accelerator, the controller 2105 may also include other hardware accelerators. For example, the controller 2105 may include a floating-point unit (FPU), a standalone DSP core, digital filters, and a Fast Fourier Transform (FFT) module.

[0074] Controller 2105 is configured to operate a real-time operating system (RTOS), control device system 2100, and can be updated via communication with NVM or CC-NVM or when device system 2100 is connected to other devices (e.g., smartphones) through I / O interface 2130 and / or antenna 2140. I / O interface 2130 and antenna 2140 allow device system 2100 to connect to various external devices, such as smartphones, tablets, PCs, etc. For example, I / O interface 2130 may include, but is not limited to, a micro-USB connector. The micro-USB connector can be used by device system 2100 to charge power supply 2110b.

[0075] Controller 2105 may include onboard RAM and flash memory to store and execute code, including for analytics, diagnostics, and software upgrades. Alternatively, storage medium 2145 may store code. Additionally, in another example embodiment, storage medium 2145 may be on the controller 2105 board.

[0076] The controller 2105 may also include on-board clock, reset, and power management modules to reduce the area covered by the PCB in the main body 10 of the device.

[0077] Device sensor 2125 may include multiple sensor transducers that provide measurement information to controller 2105. Device sensor 2125 may include, but is not limited to, a power supply temperature sensor, an external nicotine container temperature sensor, a current sensor for the heater, a power supply current sensor, an airflow sensor, and an accelerometer for monitoring motion and orientation, etc. The power supply temperature sensor and the external nicotine container temperature sensor may be thermistors or thermocouples, and the current sensor for the heater and the power supply current sensor may be resistance-based sensors, or another type of sensor configured to measure current. The airflow sensor may be a microelectromechanical system (MEMS) flow sensor, or another type of sensor configured to measure airflow, such as a hot-wire anemometer, etc. In addition, instead of using the flow sensor included in device sensor 2125 of device system 2100 of device body 100 to measure airflow, one or more sensors located in nicotine container assembly 30, etc., may be used to measure airflow.

[0078] Data generated from one or more of the device sensors in device sensor 2125 can be sampled at a sampling rate suitable for parameters measured using a discrete multichannel analog-to-digital converter (ADC).

[0079] The controller 2105 can adjust the heater profile and other profiles of the nicotine vapor pre-formulation based on measurement information received from the controller 2105. For convenience, these profiles are generally referred to as vaporization or vapor profiles. The heater profile identifies the power supplied to the heater during the few seconds that occur when nicotine vapor extraction occurs. For example, when a nicotine vapor extraction instance is initiated, the heater profile may deliver maximum power to the heater, but immediately reduce the power to half or a quarter after a desired time period (e.g., about one second). According to at least some example embodiments, the modulation of the electrical power supplied to the heater can be achieved using pulse width modulation.

[0080] Additionally, the heater profile can be modified based on the negative pressure applied to the nicotine e-vapor device 60. The use of a MEMS flow sensor allows for the measurement of nicotine vapor inhalation intensity and serves as feedback to the controller 2105 to adjust the power of the heater delivered to the nicotine container, which can be referred to as heating or energy delivery.

[0081] According to at least some example embodiments, when controller 2105 identifies a currently installed nicotine container (e.g., via SKU, serial number, unique identifier, public encryption key corresponding to the individual nicotine container, etc.), controller 2105 matches the associated heating profile designed for that specific nicotine container. Controller 2105 and storage medium 2145 store data and algorithms that allow the generation of heating profiles for various nicotine container types, nicotine vapor pre-mixings, etc. In another example embodiment, controller 2105 can read the heating profile from the nicotine container. Adult vapor users can also adjust the heating profile to suit their personal preferences.

[0082] like Figure 2 As shown, controller 2105 sends data to power supply 2110 and receives data from power supply 2110. Power supply 2110 includes power source 2110b and power controller 2110a to manage the power output by power source 2110b.

[0083] The power source 2110b can be a lithium-ion battery or a variant thereof, such as a lithium-ion polymer battery. Alternatively, the power source 2110b can be a nickel-metal hydride battery, a nickel-cadmium battery, a lithium-manganese battery, a lithium-cobalt battery, or a fuel cell unit. The power source 2110b can be rechargeable and includes a circuitry that allows the battery to be charged by an external charging device. In this case, when charging, the circuitry provides power for the required (or alternatively, predetermined) number of nicotine vapor inhalation instances, after which the circuitry must be reconnected to the external charging device.

[0084] The power controller 2110a provides commands to the power supply 2110b based on instructions from the controller 2105. For example, when the nicotine container is certified and the adult vapor user activates the device system 2100 (e.g., by activating a switch such as a toggle button, a capacitive sensor, an IR sensor, or applying negative pressure to the mouthpiece), the power supply 2110 may receive a command from the controller 2105 to provide power to the nicotine container (via the nicotine container electrical / data interface 2120). When the nicotine container is not certified, the controller 2105 may not send commands to the power supply 2110, or may send a command to the power supply 2110 not to provide power. In another example embodiment, if the nicotine container is not certified, the controller 2105 may disable all operations of the device system 2100.

[0085] In addition to supplying power to the nicotine container, power supply 2110 also supplies power to controller 2105. Furthermore, power controller 2110a can provide feedback to controller 2105 indicating the performance of power supply 2110b.

[0086] The controller 2105 transmits data to and receives data from at least one antenna 2140. The at least one antenna 2140 may include a Near Field Communication (NFC) modem and a Bluetooth Low Energy (LE) modem, and / or other modems for other wireless technologies such as Wi-Fi. In an example implementation, the communication stack is located within the modem, but the modem is controlled by the controller 2105. The Bluetooth LE modem is used for data and control communication with applications on external devices such as smartphones, tablets, computers, wireless beacons, etc. The NFC modem can be used to pair the nicotine e-vaping device 60 with applications for diagnostic information and retrieval. Furthermore, the Bluetooth LE modem can be used to provide location information (for adult vapers to locate the nicotine e-vaping device 60) or authentication during purchase.

[0087] The controller 2105 supplies information to the vapor user indicator 2135 to indicate the status and ongoing operation to the adult vapor user. The vapor user indicator 2135 includes a power indicator (e.g., an LED) that can be activated when the controller 2105 senses a button pressed by the adult vapor user. The vapor user indicator 2135 may also include a vibrator, a speaker, an indicator of the current status of nicotine vaporization parameters (e.g., nicotine vapor volume) controlled by the adult vapor user, and other feedback mechanisms.

[0088] Furthermore, the device system 2100 may include multiple on-product controllers 2150 that provide commands from adult vapers to controller 2105. For example, on-product controllers 2150 may include a switch button, a capacitive sensor, or an IR sensor. On-product controllers 2150 may also include a nicotine vaporization control button (in case the adult vaper wishes to override the buttonless nicotine vaporization feature to power the heater), a hard reset button, a touch-based slider (for controlling the setting of nicotine vaporization parameters, such as nicotine vapor inhalation volume), a nicotine vaporization control button for activating the slider, and a mechanical adjustment for the air inlet. Hand-to-mouth gesture (HMG) detection is another example of buttonless nicotine vaporization. Additionally, combinations of keystrokes (e.g., keystrokes input by the adult vaper via on-product controller 2150) can be used to lock the nicotine e-vaporizer 60 and prevent the device from operating to generate nicotine vapor. According to at least some example embodiments, the keystroke combination can be set by the manufacturer of the nicotine e-vaping device 60 and / or device system 2100. According to at least some example embodiments, the keystroke combination can be set or changed by an adult vaper (e.g., by keystrokes input by the adult vaper via a controller 2150 on the product).

[0089] According to at least one example embodiment, the nicotine container system 2200 may include a heater 2215, a non-volatile memory 2205b, a main electrical / data interface 2210, one or more nicotine container sensors 2220 and / or heater resistance monitoring circuitry 3000, but the example embodiments are not limited thereto. The nicotine container system 2200 communicates with the device system 2100 via the main electrical / data interface 2210 and the nicotine container electrical / data interface 2120.

[0090] Heater 2215 can be actuated by controller 2105 and can transfer heat to at least a portion of the nicotine vapor pre-preparation in the nicotine container assembly 30, for example, based on command curves (volume, temperature (based on power curves), and flavor) from controller 2105, so as to vaporize the nicotine vapor pre-preparation into nicotine vapor. Controller 2105 can determine the amount of nicotine vapor pre-preparation to be heated based on feedback from the nicotine container sensor or heater 2215. The flow of the nicotine vapor pre-preparation can be regulated by microcapillary action or wicking. Furthermore, controller 2105 can send commands to heater 2215 to adjust the air inlet of heater 2215.

[0091] For example, heater 2215 may be a planar body, a ceramic body, a monofilament, a cage of resistance wire, a coil of wire surrounding a core, a mesh, a surface, or any other suitable form. Examples of suitable resistive materials include titanium, zirconium, tantalum, and platinum group metals. Examples of suitable metal alloys include stainless steel, nickel-containing, cobalt-containing, chromium-containing, aluminum-containing, titanium-containing, zirconium-containing, hafnium-containing, niobium-containing, molybdenum-containing, tantalum-containing, tungsten-containing, tin-containing, gallium-containing, manganese-containing, and iron-containing alloys, as well as superalloys based on nickel, iron, cobalt, and stainless steel. For example, the heater may be formed from nickel aluminum compounds, materials with a layer of alumina on their surface, iron aluminum compounds, and other composite materials. The resistive material may optionally be embedded in an insulating material, encapsulated or coated with an insulating material, or vice versa, depending on the energy transfer kinetics and desired external physicochemical properties. In one embodiment, heater 2215 comprises at least one material selected from the group consisting of stainless steel, copper, copper alloys, nickel-chromium alloys, superalloys, and combinations thereof. In an example embodiment, heater 2215 is formed from a nickel-chromium alloy or an iron-chromium alloy. In at least one example embodiment, heater 2215 may be a ceramic heater having a resistance layer on its outer surface.

[0092] In another example embodiment, heater 2215 may be made of aluminized iron (e.g., FeAl or Fe3Al). Additionally, according to some example embodiments, heater 2215 may be included in device system 2100 but not in nicotine container system 2200.

[0093] exist Figure 2 In an example implementation, the nicotine container system 2200 may include non-volatile memory 2205b instead of the CC-NVM, and the cryptographic coprocessor is omitted. When the cryptographic coprocessor is absent in the nicotine container system 2200, the controller 2105 may read data from the non-volatile memory 2205b without using the cryptographic coprocessor to control / define the heating curve. However, when the nicotine container system 2200 includes a cryptographic coprocessor, the cryptographic coprocessor may control the transmission (e.g., reading) of information encoded on the NVM 2205b to the controller 2105, and / or receive (e.g., write) information to be stored on the NVM 2205b from the controller 2105.

[0094] In addition, the non-volatile memory 2205b can store information such as stock units (SKUs) of nicotine vapor pre-preparations (including nicotine vapor pre-preparation compositions) in the nicotine vapor pre-preparation compartment, software patches for the device system 2100, and product usage information such as nicotine vapor extraction instance counts, nicotine vapor extraction instance durations, and nicotine vapor pre-preparation content. The non-volatile memory 2205b can store operating parameters specific to the type of nicotine container and nicotine vapor pre-preparation composition. For example, the non-volatile memory 2205b can store the electrical and mechanical designs of the nicotine container used by the controller 2105 to determine commands corresponding to the desired nicotine vaporization profile. Furthermore, the non-volatile memory 2205b can store dedicated computer-readable instructions corresponding to a trained neural network. Figures 4A to 7B The trained neural network is discussed in more detail.

[0095] The nicotine vapor pre-conditioning content can be an approximate measurement of the nicotine vapor pre-conditioning content in the nicotine container, and can be determined, for example, by using a nicotine container sensor in nicotine container sensor 2220 to directly measure the nicotine vapor pre-conditioning content in the nicotine container, and / or by using controller 2105 to count the number of nicotine vapor extraction instances corresponding to the nicotine container in non-volatile memory 2205b, wherein the count of nicotine vapor extraction instances is used as a substitute for the amount of vaporized nicotine vapor pre-conditioning.

[0096] The controller 2105 and / or storage medium 2145 may store nicotine vapor pre-formulation calibration data, which identifies the operating point of the nicotine vapor pre-formulation composition. The nicotine vapor pre-formulation calibration data includes data describing how the nicotine vapor pre-formulation flow rate varies with the remaining nicotine vapor pre-formulation content or how volatility varies with the age of the nicotine vapor pre-formulation, and can be used for calibration by the controller 2105. The nicotine vapor pre-formulation calibration data may be stored in tabular form by the controller 2105 and / or storage medium 2145. The nicotine vapor pre-formulation calibration data allows the controller 2105 to make the nicotine vapor extraction instance count equal to the amount of nicotine vapor pre-formulation evaporated.

[0097] The controller 2105 writes the nicotine vapor pre-conditioning content and the nicotine vapor extraction instance count back to the non-volatile memory 2205b in the nicotine container, so that if the nicotine container is removed from the device body 10 and later reinstalled, the controller 2105 will still know the accurate nicotine vapor pre-conditioning content of the nicotine container.

[0098] Operating parameters (such as power supply parameters, power duration parameters, air channel control parameters, etc.) are referred to as vaporization profiles. Furthermore, non-volatile memory 2205b can record information transmitted by controller 2105. Even when the device body 10 is disconnected from the nicotine container, the non-volatile memory 2205b retains the recorded information.

[0099] In an example implementation, the non-volatile memory 2205b may be a programmable read-only memory.

[0100] Data generated by the nicotine container sensor 2220 can be sampled using a discrete multichannel analog-to-digital converter (ADC) at a sampling rate suitable for the measured parameter. The nicotine container sensor 2220 may include, for example, a heater temperature sensor, a nicotine vapor premix flow rate monitor, an airflow sensor, an ohmmeter for measuring the resistance of the heater, and / or a suction detector. According to at least one example embodiment, the heater temperature sensor may be a thermistor or a thermocouple, and the nicotine vapor premix flow rate sensing may be performed by the nicotine container system 2200 using electrostatic interference or a vapor premix in-molecular rotator.

[0101] Additionally, according to at least one example embodiment, the nicotine container system 2200 also includes a heater resistance monitoring circuit 3000 for measuring the resistance of the heater 2215. The heater resistance monitoring circuit will be combined with... Figure 3A and Figure 3B Further details will be provided. Additionally, according to other example embodiments, the heater resistance monitoring circuit 3000 may be included in the device system 2100.

[0102] Although Figures 1 to 2 Example embodiments of a nicotine electronic vaporizer are depicted, but the nicotine electronic vaporizer is not limited thereto and may include additional and / or alternative hardware configurations suitable for the illustrated purpose. For example, the nicotine electronic vaporizer may include multiple additional or alternative elements, such as additional or alternative heating elements, a storage unit, a battery, etc. Additionally, although... Figures 1 to 2 Example embodiments of nicotine e-vapor devices are depicted as being embodied in two separate housing elements, but additional example embodiments may be for nicotine e-vapor devices arranged in a single housing and / or more than two housing elements.

[0103] Figure 3A and Figure 3B This is a block diagram illustrating various components of an example heater resistance monitoring circuit for a nicotine electronic vapor device according to some example embodiments.

[0104] refer to Figure 3AAccording to at least one example embodiment, the nicotine e-vaping device may include a heater resistance monitoring circuit 3000A to detect the resistance of a heater (e.g., a heating coil) (e.g., heater 2215) in real time or at desired time points controlled by a controller of the nicotine e-vaping device (e.g., controller 2105, but not limited thereto). The heater resistance monitoring circuit 3000A may include a voltage meter 2221 (e.g., a voltmeter) connected at least to the controller 2105, the power supply 2110, and the heater 2215, but the example embodiment is not limited thereto. For example, the example embodiment may also include one or more reference resistors connected in series between the power supply 2110 and the heater 2215, the reference resistors having known resistance values ​​to aid in the calculation of the resistance of the heater 2215; a second dedicated controller for measuring the resistance of the heater and executing a trained neural network for estimating the steady-state resistance of the heater, etc. Power supply 2110 can be configured to output at least two power signals to heater 2215 based on a trigger signal (e.g., command signal, instruction, etc.) output from controller 2105: a first power signal during normal operation of the nicotine e-vaping device 60, and a second power signal during heater resistance measurement operation, but the example embodiment is not limited thereto. During normal operation of the nicotine e-vaping device, normal operation power from power supply 2110 flows to heater 2215. In response to controller 2105 outputting a trigger signal indicating the start of heater resistance measurement operation, power supply 2110 can output a second power signal with a known current value. Voltmeter 2221 is connected in parallel with heater 2215 to power supply 2110 and controller 2105. Voltmeter 2221 measures the voltage drop across heater 2215 and outputs the measured voltage drop to controller 2105. The controller 2105 then uses Ohm's law to calculate the resistance of the heater 2215 based on the known current value output from the power supply 2110 and the voltage drop measured by the voltmeter 2221. After a short period of time (e.g., from about 50 ms to about 100 ms), the controller 2105 stops outputting the trigger signal to the power supply 2110, and normal power from the power supply 2110 can flow to the heater 2215 again.

[0105] refer to Figure 3BAccording to at least one other example embodiment, the heater resistance monitoring circuit may be configured to detect the resistance of the heater in real time or at desired time points controlled by the controller 2105 (e.g., continuous monitoring and / or dynamic monitoring of the heater resistance, etc.). The heater resistance monitoring circuit 3000B may include multiple MOSFETs, a load switch 3130, at least one controller 2105, a voltage divider 3120, and / or a Wheatstone bridge 3140, but the example embodiments are not limited thereto. For example, according to other example embodiments, the heater resistance monitoring circuit 3000B may also include a second dedicated controller for measuring the resistance of the heater and executing a trained neural network for estimating the steady-state resistance of the heater, etc. The plurality of MOSFETs may include at least a first PMOSFET 3151 and a second PMOSFET 3152 connected in a back-to-back configuration and connected between a power source (e.g., power source 2110) and a heater 2215, and at least one NMOSFET 3153, wherein the drain D of the NMOSFET 3153 is connected to the gate G of the PMOSFETs 3151 and 3152, and the gate G of the NMOSFET 3153 is connected to a controller 2105. During normal operation of the nicotine e-vapor device, power from the power source 2110 flows to the heater 2215 through the closed PMOSFETs 3151 and 3152.

[0106] The Wheatstone bridge may include, but is not limited to, a first resistor R1, a second resistor R3, and a third resistor R5, and the resistors may all have fixed resistance values ​​(e.g., known non-variable resistance values). The Wheatstone bridge may be connected to heater 2215, and heater 2215 may be used as a variable resistor in conjunction with the fixed-value resistor R1, while resistors R3 and R5 may form the fixed resistance of the Wheatstone bridge. The Wheatstone bridge may also be connected in series with load switch 3130. Load switch 3130 may output a signal R_SENSE_nEN to controller 2105, which causes controller 2105 to begin sensing / monitoring the heater resistance by outputting a COIL_LOCKOUT_nEN signal to PMOSFETs 3151 and 3152. In response to the COIL_LOCKOUT_nEN signal, PMOSFETs 3151 and 3152 are turned off, and the power supply to heater 2215 is cut off (e.g., stopped). The controller 2105 then uses the voltage V_BRIDGE from the load switch 3130 to sense the variable resistor COIL_RES and the fixed resistor BRIDGE_REF. After a short period of time (e.g., about 50ms to about 100ms), the controller 2105 stops outputting the COIL_LOCKOUT_nEN signal, and the power from the power supply 2110 can flow back to the heater 2215 through the PMOSFETs 3151 and 3152.

[0107] The controller 2105 can determine the resistance value of the heater 2215 by calculating the difference between the measured variable resistor COIL_RES and the known resistance of the resistor R1, so as to determine the resistance of the heater 2215 during the resistance monitoring period.

[0108] Although Figure 3A and Figure 3B An example implementation of a heater resistance monitoring circuit is depicted, but this example implementation is not limited thereto, and other heater resistance monitoring circuits may include additional and / or alternative hardware configurations that may be suitable for the purposes shown.

[0109] Figures 4A to 4C It is a diagram illustrating a neural network for predicting and / or estimating the steady-state resistance value of the heating element of a nicotine e-vapor device according to at least one example embodiment. Figure 5 It is a graph showing the resistance value of the heating element of a nicotine e-vapor device during a single inhalation event according to at least one example embodiment. Figure 6 It is a graph showing the resistance decay after a single suction event according to at least one example embodiment.

[0110] According to at least one example embodiment, a neural network implemented on a nicotine e-vaping device can be used to determine the steady-state resistance (e.g., baseline resistance value, final resistance value, etc.) of a heating element (e.g., heater 2215) included in the nicotine e-vaping device after an inhalation event by an adult vaper, and the steady-state resistance can be used to detect dry inhalation events (e.g., dry wicking events, etc.) of the nicotine e-vaping device.

[0111] First refer to Figure 5 The resistance of heater 2215 depends on the heater's temperature and metallurgy, and the resistance value of heater 2215 can vary with increasing or decreasing heater temperature, such as when electricity is applied to heater 2215 to vaporize the nicotine vapor pre-formulation stored on the core. For example, if the heater is made of nickel-chromium alloy-60 wire, the temperature-dependent resistance of the heater may change by only about 2% due to the heater's temperature, but the temperature-dependent resistance of a heater made of stainless steel may change by as much as about 20% based on the temperature of the stainless steel heater, etc.

[0112] During a vaping event (e.g., when an adult vaper applies negative pressure to the mouthpiece of a nicotine e-vaping device), electricity is supplied from power source 2110 to heater 2215, raising its temperature to a level sufficient to vaporize the nicotine vapor pre-formulation. After the vaping event concludes (and assuming no further vaping event occurs), controller 2105 stops supplying power from power source 2110 to heater 2215, and the temperature of heater 2215, and correspondingly its resistance, decays until a steady-state temperature / resistance value is reached.

[0113] like Figure 5 As shown in the figure, this diagram illustrates the resistance values ​​of an example heater of a nicotine e-vapor device over time, corresponding to multiple inhalation events (e.g., a training set of inhalation events), and as... Figure 6 As shown in the figure, the resistance value decays over time after a single suction event. The initial resistance measurement during the suction event reaches a local maximum resistance value (e.g., approximately 3.67 ohms), and then decays to a local minimum resistance value (e.g., approximately 3.6 ohms) over a decay period of approximately 30 to 60 seconds. The local maximum resistance value can be considered as the peak resistance value of heater 2215 for the suction event, and the local minimum resistance value can be considered as the steady-state resistance value (e.g., the final resistance value) of heater 2215 for the suction event. According to at least one example embodiment, it can be used... Figure 3A or Figure 3B The heater resistance monitoring circuit measures the resistance value of heater 2215 in real time, but the example implementation is not limited to this and other real-time heater resistance monitoring circuits can be used.

[0114] Furthermore, the steady-state resistance of heater 2215 increases as the amount of nicotine vapor pre-formulation stored on the wick decreases, and therefore, the steady-state resistance can be used to detect dry-vaping events by comparing the steady-state resistance with a dry-vaping detection threshold. Additionally, according to some example embodiments, when the nicotine e-vaping device does not include a wick, the steady-state resistance also increases as the amount of nicotine vapor pre-formulation heated and / or vaporized by the heating element decreases. The dry-vaping detection threshold can be determined based on experimental data (e.g., laboratory tests) regarding the steady-state resistance observed for the heater's metallurgical composition, the heater design type, and the known power supplied to the heater for each specific nicotine e-vaping device.

[0115] However, while heater resistance monitoring circuitry can be used to accurately measure the steady-state resistance of the heater after a single vaping event, it may not provide an accurate measurement of the heater's steady-state resistance when multiple vaping events occur before the decay period is complete. For example, the behavior of a typical adult vaper may include two or more vaping events occurring within a short period of approximately 30 seconds or less (e.g., an adult vaper applies a first negative pressure at t0 and subsequently applies a second negative pressure at t1, where t1 <= t0 + 30 seconds). Therefore, because the heater of the nicotine e-vaping device is not de-energized throughout the decay period (e.g., from approximately 30 seconds to approximately 60 seconds), the steady-state resistance value for the first vaping event is not reached, as power is supplied to the heater again for the second vaping event.

[0116] The example implementation provides a method for determining a more accurate estimate of steady-state resistance values ​​that does not require adult vapor users to wait approximately 30 to 60 seconds between puff events to detect whether a dry puff event has occurred.

[0117] Now for reference Figures 4A to 4C According to at least one example embodiment, a neural network can be provided to estimate the steady-state resistance value of the heater of the nicotine e-vaping device based on at least two measured resistance values ​​of the heater during and / or after a vaping event. According to at least one example embodiment, the two or more measured resistance values ​​of the heater can be used to estimate (and / or predict) the heater resistance value 30 to 60 seconds after the end of the vaping event, which corresponds to an estimate of the steady-state resistance value (e.g., the estimated final resistance value), and therefore eliminates the need for the adult vaper to wait for the decay period (e.g., about 30 seconds to about 60 seconds) to complete accurate detection of a dry vaping event. For example, a first measured heater resistance value (to measure the peak resistance value of the heater) can be observed when power to heater 2215 is cut off, and a second measured heater resistance value can be observed shortly afterward at the beginning of the decay slope of the resistance value (e.g., about 0.5 seconds, etc.). However, the example implementation is not limited to this, and for example, the number of heater resistance values ​​measured to estimate the steady-state heater resistance value can be three or more, and for example, a third heater resistance value can be observed after the second measured heater resistance value, such as at the beginning of the ankle of the decay curve (e.g., about 2.0 seconds after the power supply to heater 2215 is cut off), and a fourth heater resistance value can be observed after the third measured heater resistance value and before the second inhalation event occurs, and so on. Furthermore, the timing of measuring the heater resistance can be adjusted, and the decay period can be adjusted to a suitable time period based on factors such as the temperature / resistance characteristics of the heater included in the nicotine e-vapor device.

[0118] According to at least one example embodiment, the neural network can be implemented as dedicated program code (e.g., dedicated computer-readable instructions) loaded onto the controller (such as controller 2105) of the nicotine e-vapor device, but the example embodiment is not limited thereto, and the neural network can be implemented in a separate dedicated processor (e.g., dedicated programmable FPGA, dedicated ASIC, dedicated SoC, etc.) included in the nicotine e-vapor device, etc., and / or the neural network can be provided to a specially programmed external computing device, etc., via wired and / or wireless network connections.

[0119] Now for more specific reference Figure 4A , Figure 4A The overall topology of a neural network according to at least one example embodiment is shown. The neural network itself can be a function-fitting network that approximates the "most reasonable" function corresponding to the decay process of the heater's resistance, and according to at least one example embodiment, the neural network can compute the following function:

[0120]

[0121] In the above equation, R(t) refers to the resistance function over time (e.g., a function covering the peak resistance to the steady-state resistance value), A and B are the first and second decay rates, t1 refers to the first decay rate factor corresponding to the first (e.g., rapid) temperature decay rate observed in the nicotine e-vaporizer, t2 refers to the second decay rate factor corresponding to the second (e.g., slow) temperature decay rate observed in the nicotine e-vaporizer, and R f This refers to the original resistance value of heater 2215 (e.g., the resistance value of the heater when no power is applied or when the heater is "cold"). The attenuation magnitudes A and B, as well as the attenuation rates t1 and t2, are constant values ​​that vary based on the composition of the specific nicotine container (e.g., based on / affected by the composition of the materials forming the heater, core, and / or nicotine vapor pre-formulation, etc.). These constant values ​​can be obtained from experimental data.

[0122] According to at least one example embodiment, the topology of the neural network may include at least one input stage, wherein a measured resistance value of the heater of the nicotine e-vapor device is input into at least one hidden layer of the neural network, the at least one hidden layer outputs a vector to at least one output layer, and the output layer may output a single scalar value as an estimated steady-state (e.g., final) resistance value of the heater of the nicotine e-vapor device. However, the example embodiment is not limited thereto, and may include more or fewer layers, inputs, and / or outputs in the neural network.

[0123] Figure 4BThe illustration shows at least one hidden layer (e.g., a first layer, intermediate layer, activation layer, etc.) of a neural network according to at least one example embodiment, but the example embodiments are not limited thereto. While for convenience, this disclosure refers to the first layer (e.g., an intermediate layer) of a neural network as a "hidden" layer, the example embodiments are not limited thereto, and in some example embodiments, the first layer may be connected to external connections along with the input layer, and therefore may not be a truly "hidden" layer. Figure 4B In this embodiment, at least one hidden layer may include three neurons (e.g., activation nodes, etc.) in a single hidden layer, but the example implementation is not limited thereto, and the number of hidden layers may be greater than one, and the number of neurons in one or more hidden layers may be greater than or less than three, etc. Each of the three neurons may receive an input vector including a measured heater resistance value (e.g., R0, R1, R2, etc.) and a weight matrix. Each neuron in at least one hidden layer may take a single-row dot product of the input vector and the weight matrix to obtain a 3-tuple vector. The 3-tuple vector may then be supplemented with a bias value vector to produce a vector "n", which is applied element-wise to the transfer function to produce a vector "a".

[0124] According to at least one example implementation, the transfer function used can be tangent sigmoid or tansig, as shown below; however, the example implementation is not limited to this.

[0125]

[0126] However, according to some example implementations, the Taylor expansion of the tansig function (Equation 3) and / or the application of Horner's rule (Equation 4) can be used as the transfer function instead of the tansig function, especially for low-processing controllers (e.g., 8-bit controllers with or without floating-point units) in order to perform the computation of the hidden layer more efficiently.

[0127]

[0128]

[0129] However, the example implementation is not limited to this.

[0130] Now for reference Figure 4C , Figure 4CThe diagram illustrates the output layer of a neural network according to at least one example embodiment. According to at least one example embodiment, the output layer of the neural network may include a single neuron, and this single neuron may include an output weight vector, an input vector of the hidden layer output, a bias value, and a transfer function; however, the example embodiments are not limited thereto. The neuron in the output layer may take the dot product of the hidden layer's output vector "a" and the output weight vector. The resulting output may be added to the bias value, and the result may be input to the transfer function. Figure 4C As shown in the example, according to at least one example implementation, the bias value can be R. f It is the asymptotic value to which the resistance of the heater decays over time (e.g., R used in Equation 1 above). f The value is specified, and the transfer function is a pass-through function, such as y = x, however the example implementation is not limited to this. The output layer then outputs the estimated steady-state heater resistance value.

[0131] According to at least one example embodiment, the weight matrix and bias values ​​of at least one hidden layer of the neural network, and the weight vector and bias values ​​of at least one output layer, can be determined by training the neural network on a training dataset corresponding to: an actual measured resistance value corresponding to the heater of a nicotine e-vapor device (or an equivalent heater exhibiting similar temperature / electrical characteristics to the heater included in a nicotine e-vapor device); a measured resistance value corresponding to the time when the measured resistance value used as input to the neural network will be measured (e.g., at the time when power to the heater is cut off, at the beginning of the decay slope, at the ankle of the decay curve, etc.); and a measured value of the actual steady-state resistance value (e.g., a measurement obtained at the end of the decay period, such as 30 to 60 seconds after the heater has been turned off). During the neural network training phase, measured resistance values ​​of the training dataset, excluding the measured steady-state resistance values, are input into the neural network, and the mean square error (MSE) between the estimated steady-state resistance value and the actual steady-state resistance value is measured to generate an estimation algorithm for the neural network. The neural network parameters (e.g., weight matrix values ​​for hidden and output layers, bias values, etc.) are then adjusted using estimation algorithms, and training is repeated until the neural network outputs an estimated steady-state resistance value (and / or an estimated asymptotic resistance value) within the required error margin of the actual steady-state resistance value. Based on the experiments conducted, the training dataset may include 20 to 100 sucking / decaying events, and five rounds of training may be performed to accurately train the neural network. However, the example implementation is not limited to this.

[0132] Figures 7A to 7BThis is a flowchart illustrating a method for detecting dry smoking events using the steady-state resistance value of a heating element of a nicotine e-vapor device, according to at least one example embodiment.

[0133] According to at least one example embodiment, in operation S710, the nicotine e-vaping device can detect the application of negative pressure (e.g., a puffing event) by an adult vaper. In operation S720, after the application of negative pressure ends and the power between the nicotine e-vaping device and the heater is subsequently cut off, the nicotine e-vaping device can perform at least one real-time measurement of the resistance value of the heater. According to some example embodiments, three or more measurements of the heater's resistance value can be obtained, including measurements at the time when the power supply to the heater is cut off, measurements at the time when the start of the decay slope is observed, and / or measurements at the time when the start of the ankle of the decay curve is observed, etc., however, the example embodiments are not limited to this. In operation S725, the measured resistance value of the heater is then input into a trained neural network, and the nicotine e-vaping device uses the trained neural network to estimate the steady-state resistance value of the heater (e.g., estimate the final resistance value). Figure 7B The example calculations for estimating steady-state resistance values ​​are discussed in more detail.

[0134] In operation S730, the nicotine e-vapor device determines whether a dry-vaping condition exists at the heater based on the estimated steady-state resistance value and the required threshold resistance value of the heater. If the nicotine e-vapor device determines that a dry-vaping condition does not exist, the nicotine e-vapor device continues its normal operation (S740) and returns to operation S710.

[0135] Returning to operation S730, if the nicotine e-vaping device determines that a dry-vaping condition exists, the nicotine e-vaping device disables power supply to the heater (S750). According to some example embodiments, information indicating that the nicotine container assembly is empty may be stored in the memory of the nicotine e-vaping device and / or the memory of the nicotine container assembly containing the nicotine vapor pre-preparation (e.g., a nicotine container, a storage container containing the nicotine vapor pre-preparation, etc.). This information may include a unique identifier used to identify the nicotine container assembly. Furthermore, the heater power may remain disabled until a new nicotine container assembly (e.g., a non-empty nicotine container assembly) is inserted into the nicotine e-vaping device. The nicotine e-vaping device may determine whether the newly inserted nicotine container assembly is a new nicotine container assembly or an empty nicotine container assembly based on the information stored in the memory of the nicotine e-vaping device and / or the nicotine container assembly.

[0136] As mentioned above, Figure 7B This illustrates a method for estimating the steady-state resistance of a heater using a trained neural network. (Reference) Figure 7B According to at least one example embodiment, at operation S726, the nicotine e-vaping device uses a heater resistance measuring circuit to measure the peak resistance value of the heater (e.g., the resistance value at the time when the power supply to the heater is cut off) during a decay period (e.g., a first time period). At operation S727, the nicotine e-vaping device uses the heater resistance measuring circuit to measure at least one additional resistance value of the heater during the decay period (e.g., the first time period). In operation S728, the nicotine e-vaping device may input the measured peak resistance value and the measured at least one additional resistance value into a trained neural network. In operation S729, the nicotine e-vaping device performs calculations on the trained neural network and outputs an estimated steady-state resistance value of the heater, which is... Figure 7A It is used in the S730 operation.

[0137] The described example embodiments provide methods, systems, apparatus, and / or non-transitory computer-readable media for detecting dry-vaping events based on estimated steady-state resistance values ​​of the heater in a nicotine e-vaping device. One or more example embodiments may reduce the size and / or manufacturing cost of a nicotine e-vaping device and / or provide more accurate temperature readings.

[0138] While exemplary embodiments have been disclosed herein, it should be understood that other variations are possible. Such variations should not be considered as departing from the scope of this disclosure, and as will be apparent to those skilled in the art, all such modifications are intended to be included within the scope of the following claims.

Claims

1. A nicotine electronic vaporizer, comprising: A storage container that holds the pre-formulation of nicotine vapor; A heating element configured to heat the nicotine vapor pre-formulation drawn from the reservoir; as well as The control circuit system is configured as follows: The resistance value of the heating element was monitored during a first time period after the negative pressure was first applied to the nicotine electronic vaporizer. The trained neural network is used to determine the estimated minimum steady-state resistance value of the heating element for the first application of negative pressure, based on the monitored resistance value. as well as The power supply to the heating element is controlled based on the estimated minimum steady-state resistance value for the first application of negative pressure.

2. The nicotine electronic vaporizer as claimed in claim 1, wherein the control circuit system is further configured to: The dry draw condition at the nicotine e-vaporizer is detected based on the estimated minimum steady-state resistance value of the heating element at the first application of negative pressure; and In response to the detected dry suction condition, power supply to the heating element is disabled.

3. The nicotine electronic vaporizer as claimed in claim 2, wherein the control circuit system is further configured to: In response to the detection of a second application of negative pressure to the nicotine electronic vaporizer, power is prevented from being applied to the heating element.

4. The nicotine electronic vaporizer as claimed in claim 1, 2, or 3, wherein the control circuit system is configured to: The resistance value of the heating element is monitored by the following steps. During the first time period, the peak resistance value of the heating element is determined, and During the first time period, at a time following the determined peak resistance value, at least one additional resistance value of the heating element is determined; and The estimated minimum steady-state resistance value of the heating element for the first application of negative pressure is determined by the following steps. Based on the peak resistance value and the at least one additional resistance value, the trained neural network is used to estimate the estimated minimum steady-state resistance value of the heating element for the first application of negative pressure.

5. The nicotine electronic vaporizer of claim 4, wherein the trained neural network is a function fitting network, the function fitting network being configured to: Receive the peak resistance value and the at least one additional resistance value as input values; Determine the decay of the input value during the first time period; as well as Based on the determined decay of the resistance value of the heating element during the first time period, the estimated minimum steady-state resistance value of the heating element for the first application of negative pressure is output.

6. The nicotine electronic vaporizer as claimed in claim 4, wherein... The peak resistance value is determined at the time when power is stopped after the first application of negative pressure to the nicotine electronic vaporizer.

7. The nicotine electronic vaporizer as claimed in claim 6, wherein... The at least one additional resistance value includes at least a second resistance value and a third resistance value; The second resistance value is determined at a time after the peak resistance value is determined and before the third resistance value is determined; and The third resistance value is determined at the time after the second resistance value is determined and at the time before the second negative pressure is detected.

8. The nicotine electronic vaporizer as claimed in claim 1, 2 or 3, wherein... The heating element is connected to a Wheatstone bridge circuit; and The control circuit system is also configured to The variable resistance value corresponding to the heating element is detected during the first time period; During the first time period, the resistance value corresponding to the Wheatstone bridge circuit is detected; and Based on the detected variable resistance value corresponding to the heating element and the detected resistance value corresponding to the Wheatstone bridge circuit, the trained neural network is used to estimate the estimated minimum steady-state resistance value of the heating element for the first application of negative pressure.

9. A method of operating a nicotine electronic vaporizer, the method comprising: Using the control circuit system of the nicotine electronic vaporizer, the resistance value of the heating element included in the nicotine electronic vaporizer is monitored during the first time period after the negative pressure is first applied to the nicotine electronic vaporizer. Using the control circuit system, a trained neural network is used to determine the estimated minimum steady-state resistance value of the heating element for the first application of negative pressure, based on the monitored resistance value. as well as The power supply to the heating element is controlled using the control circuit system based on the estimated minimum steady-state resistance value for the first application of negative pressure.

10. The method of claim 9, further comprising: Using the control circuit system, the dry-vaping condition at the nicotine e-vapor device is detected based on the estimated minimum steady-state resistance of the heating element for the first application of negative pressure; as well as In response to a detected dry suction condition, the control circuitry system disables the power supply to the heating element.

11. The method of claim 10, further comprising: The control circuit system is used to detect the second application of negative pressure to the nicotine electronic vaporizer; as well as In response to the detection of a second application of negative pressure to the nicotine e-vapor device, the control circuitry system prevents the application of electricity to the heating element.

12. The method of claim 9, 10 or 11, wherein Monitoring the resistance value of the heating element includes During the first time period, the peak resistance value of the heating element is determined, and During the first time period, at a point after the determined peak resistance value, at least one additional resistance value of the heating element is determined; and Determining the estimated minimum steady-state resistance value of the heating element for the first application of negative pressure includes using the trained neural network to estimate the estimated minimum steady-state resistance value of the heating element for the first application of negative pressure, based on the peak resistance value and the at least one additional resistance value.

13. The method of claim 12, wherein The trained neural network is a function fitting network; and The method also includes The control circuit system receives the peak resistance value and the at least one additional resistance value as input values. The control circuit system is used to determine the attenuation of the resistance value of the heating element during the first time period; as well as Using the control circuit system, based on the determined decay of the resistance value of the heating element during the first time period when a negative pressure is first applied, the estimated minimum steady-state resistance value of the heating element is output.

14. The method of claim 12, wherein The peak resistance value is determined at the time when power is stopped after the first application of negative pressure to the nicotine electronic vaporizer.

15. The method of claim 14, wherein The at least one additional resistance value includes at least a second resistance value and a third resistance value; The second resistance value is determined at a time after the peak resistance value is determined and before the third resistance value is determined; and The third resistance value is determined at the time after the second resistance value is determined and at the time before the second negative pressure is detected.

16. The method of claim 9, 10, or 11, further comprising: During the first time period, the control circuit system is used to detect the variable resistance value corresponding to the heating element; During the first time period, the control circuit system is used to detect the resistance value corresponding to the Wheatstone bridge circuit; as well as Based on the detected variable resistance value corresponding to the heating element and the detected resistance value corresponding to the Wheatstone bridge circuit, the control circuit system uses the trained neural network to estimate the estimated minimum steady-state resistance value of the heating element for the first application of negative pressure.

17. A nicotine electronic vaporizer, comprising: A storage container that holds the pre-formulation of nicotine vapor; A heating element configured to heat the nicotine vapor pre-formulation drawn from the reservoir; The heater resistance monitoring circuit system is configured as follows: During a first time period following the initial application of negative pressure to the nicotine e-vaporizer, the peak resistance value of the heating element is determined, and During the first time period, at least one additional resistance value of the heating element is determined; The trained neural network is configured to During the first time period, based on the determined peak resistance value and at least one determined additional resistance value, the minimum steady-state resistance value of the heating element for the first application of negative pressure is estimated. as well as A control circuit system is configured to disable power supply to the heating element based on the estimated minimum steady-state resistance value for the first application of negative pressure.

18. The nicotine electronic vaporizer as claimed in claim 17, wherein... The trained neural network is also configured to detect dry puffing at the nicotine vaporizer based on the estimated minimum steady-state resistance value of the heating element at the first application of negative pressure; and The control circuit system is also configured to disable the power supply to the heating element in response to a detected dry suction condition.

19. The nicotine electronic vaporizer of claim 17 or 18, wherein the trained neural network is a function fitting network, the function fitting network being configured to: Receive the peak resistance value and the at least one additional resistance value as input values; Determine the decay of the input value during the first time period; as well as Based on the determined decay of the resistance value of the heating element when the negative pressure is first applied during the first time period, the estimated minimum steady-state resistance value of the heating element is output.

20. The nicotine electronic vaporizer as claimed in claim 17 or 18, wherein... The peak resistance value is determined at the time when power is stopped after the first application of negative pressure to the nicotine electronic vaporizer.

Images