WO2024183049A1 - Control method of aerosol-generating device using histograms - Google Patents

Control method of aerosol-generating device using histograms Download PDF

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Publication number
WO2024183049A1
WO2024183049A1 PCT/CN2023/080445 CN2023080445W WO2024183049A1 WO 2024183049 A1 WO2024183049 A1 WO 2024183049A1 CN 2023080445 W CN2023080445 W CN 2023080445W WO 2024183049 A1 WO2024183049 A1 WO 2024183049A1
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WO
WIPO (PCT)
Prior art keywords
aerosol
generating device
usage
histogram
battery
Prior art date
Application number
PCT/CN2023/080445
Other languages
French (fr)
Inventor
Zhuowen LIN
Hung Bun Choi
Yiu Chi CHEUNG
Original Assignee
Philip Morris Products S.A.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Philip Morris Products S.A. filed Critical Philip Morris Products S.A.
Priority to PCT/CN2023/080445 priority Critical patent/WO2024183049A1/en
Publication of WO2024183049A1 publication Critical patent/WO2024183049A1/en

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G16H20/13—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered from dispensers
    • A—HUMAN NECESSITIES
    • A24—TOBACCO; CIGARS; CIGARETTES; SIMULATED SMOKING DEVICES; SMOKERS' REQUISITES
    • A24F—SMOKERS' REQUISITES; MATCH BOXES; SIMULATED SMOKING DEVICES
    • A24F40/00—Electrically operated smoking devices; Component parts thereof; Manufacture thereof; Maintenance or testing thereof; Charging means specially adapted therefor
    • A24F40/50—Control or monitoring
    • A24F40/53—Monitoring, e.g. fault detection
    • A—HUMAN NECESSITIES
    • A24—TOBACCO; CIGARS; CIGARETTES; SIMULATED SMOKING DEVICES; SMOKERS' REQUISITES
    • A24F—SMOKERS' REQUISITES; MATCH BOXES; SIMULATED SMOKING DEVICES
    • A24F40/00—Electrically operated smoking devices; Component parts thereof; Manufacture thereof; Maintenance or testing thereof; Charging means specially adapted therefor
    • A24F40/50—Control or monitoring
    • A24F40/57—Temperature control
    • A—HUMAN NECESSITIES
    • A24—TOBACCO; CIGARS; CIGARETTES; SIMULATED SMOKING DEVICES; SMOKERS' REQUISITES
    • A24F—SMOKERS' REQUISITES; MATCH BOXES; SIMULATED SMOKING DEVICES
    • A24F40/00—Electrically operated smoking devices; Component parts thereof; Manufacture thereof; Maintenance or testing thereof; Charging means specially adapted therefor
    • A24F40/90—Arrangements or methods specially adapted for charging batteries thereof
    • A24F40/95—Arrangements or methods specially adapted for charging batteries thereof structurally associated with cases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation

Definitions

  • the present disclosure relates to a computer-implemented method of controlling an aerosol-generating device.
  • the present disclosure further relates to an aerosol-generating device, an aerosol-generating system comprising an aerosol-generating device and a companion device, a computer program and a computer readable medium.
  • Aerosol-generating devices are typically designed as handheld devices that can be used by a user for consuming or experiencing, for instance in one or more usage sessions, aerosol generated by heating an aerosol-generating substrate or an aerosol-generating article.
  • the aerosol-generating devices the present disclosure pertains to are commonly referred to as heated tobacco products (HTP) , heat-not-burn devices, electronic cigarettes and/or vaporiser.
  • HTP heated tobacco products
  • Exemplary aerosol-generating substrates may comprise solid substrate material, such as tobacco material or tobacco cast leaves (TCL) material.
  • the substrate material may, for example, be assembled, often with other elements or components, to form a substantially stick-shaped aerosol-generating article.
  • a stick or aerosol-generating article may be configured in shape and size to be inserted at least partially into the aerosol-generating device, which, for example, may comprise a heating element or heater device for heating the aerosol-generating article and/or the aerosol-generating substrate.
  • aerosol-generating substrates may comprise one or more liquids and/or solids, which may, for example, be supplied to the aerosol-generating device in the form of a cartridge or container.
  • Corresponding exemplary aerosol-generating articles may, for example, comprise a cartridge containing or fillable with the liquid and/or solid substrate, which may be vaporized during aerosol consumption by the user based on heating the substrate and/or liquid.
  • a cartridge or container may be coupled to, attached to or at least partially inserted into the aerosol-generating device.
  • the cartridge may be fixedly mounted to the aerosol-generating device and refilled by inserting liquid and/or solid into the cartridge.
  • heat may be supplied by a heating element, heater device or heat source to heat at least a portion or part of the aerosol-generating substrate.
  • the heating element, heater device or heat source may be arranged in the handheld device or a handheld part of the aerosol-generating device.
  • at least a part of or the entire heating element or heater device or heat source may be fixedly associated with or arranged within an aerosol-generating article, for instance in the form of a stick or cartridge, which may be attached to and/or powered by the handheld device or handheld part of the aerosol-generating device.
  • Exemplary heating elements or heater devices can be based on one or more of resistive heating, inductive heating and microwave heating using electrical energy supplied via, drawn from or stored in battery of the aerosol-generating device.
  • a battery of the aerosol-generating device can generally refer to an energy storage of the aerosol-generating device configured to store electrical energy. Accordingly, the term battery can include one or more capacitors, one or more accumulators or other types of energy storage. Also, any reference to a battery herein can include a plurality of batteries.
  • aerosol-generating devices may comprise a battery providing the electrical energy needed to operate the aerosol-generating device and especially for heating the aerosol-generating substrate and/or article, for example to generate aerosol in one or more usage sessions using one or more aerosol-generating articles.
  • the battery may, for example, be a lithium-ion battery.
  • a battery capacity may typically be chosen so that the aerosol-generating device may provide a user with at least a minimum number, for example at least two or more, consecutive usage sessions or experiences without having to recharge the battery or the aerosol-generating device in between.
  • aerosol-generating devices may usually be designed to only allow a user to start a usage session if the battery contains enough electrical energy to fully complete the usage session.
  • the energy needed for a usage session may be highly variable and may depend on many factors from external parameters like ambient temperature to user habits.
  • Conventional statistical models used in controlling aerosol-generating devices may be hampered by the limited computational power typically available on these devices.
  • taking an average value, for example of energy consumption per usage session may be too much of a generalisation in some cases or for some parameters and may therefore not lead to an accurate estimation of the individual use case at hand. It may therefore happen that a user may be denied a further usage session despite there being a good chance that such a session could be completely provided under the specific circumstances.
  • a computer-implemented method of controlling an aerosol-generating device comprising: collecting a plurality of values of at least one usage pattern parameter related to a usage of the aerosol-generating device; creating a histogram by classifying each collected value of the usage pattern parameter into a bin of the histogram; extracting histogram feature information from the histogram; and controlling the aerosol-generating device with operational constraints that are based on the histogram feature information.
  • the method may include controlling the aerosol-generating device based on said histogram feature information by adapting operational constraints of the aerosol-generating device.
  • Usage pattern parameters may be indicative of different usage and/or operational characteristics of the aerosol-generating device by the user.
  • usage pattern parameters may pertain to parameters describing or related to the use or operation of an aerosol-generating device by a user, in particular how and/or how often and/or when and/or how long and/or in what state the aerosol-generating device is and/or has been used or operated by the user.
  • the usage pattern parameters may pertain or relate to one or both the usage sessions of the aerosol-generating device and the times between usage sessions, for example resting time or recharging events of the aerosol-generating device.
  • the usage pattern parameters described herein may characterise the different preferences and/or habits of each individual user, which may differ between users.
  • the method may thus provide a highly individual control of the aerosol-generating device.
  • At least one of the usage pattern parameters may, in an example, be indicative of the usage or operation of the aerosol-generating device by the user to generate aerosol in one or more usage sessions.
  • the puff volume may be measured during one or more usage sessions and collected as usage pattern parameter.
  • An average or a mean value may be calculated from multiple, for example all, puffs from one usage session.
  • puff volume may be measured during more than one usage sessions.
  • An average or a mean value may be calculated from multiple, for example all, puffs from all usage sessions.
  • Another example may be the frequency of at least two usage sessions in a row, in particular without recharging of the aerosol-generating device in between. This parameter can also only be determined by observing more than one usage sessions.
  • a value of a usage pattern parameter may be a numerical value corresponding to a degree or extent or count of a usage pattern parameter.
  • a histogram may be an approximate or simplified representation of the distribution of numerical data, for example the values of the at least one usage pattern parameter.
  • each value may be classified into a bin, the bin representing a range or interval of the numerical values. Values falling outside the range or interval of one bin may be classified into a different bin or optionally disregarded.
  • the bins of the histogram may represent consecutive, adjacent and non-overlapping intervals or ranges of the values of the usage pattern parameters. Overlap of at least some of the bins, however, can be possible.
  • the bins may but need not have ranges or intervals of the same size or width. As such, the definition of histograms in the context of the present disclosure may be the same as conventionally used.
  • histograms may approximate and therefore simplify the distribution of the usage pattern parameter values. They may thus need a lot less computational power to manipulate and extract information from than other statistical approaches. Simultaneously, histograms may offer a more detailed resolution of the underlying data than for example conventionally applied averaging of values. The inventors found that especially when observing usage pattern parameters of users of aerosol-generating devices, there may often be bi-or multimodal patterns, representing additional information in the data that would be lost on averaging.
  • a user using the aerosol- generating device to experience usage sessions of 2 minutes and of 6 minutes with equal frequency would be assumed to have an average duration of usage sessions of 4 minutes, which would not correctly represent a single one of the real usage sessions experienced by that user.
  • the histogram may clearly show this bimodal distribution which may then be taken into account in controlling the aerosol-generating device.
  • a key feature of the present invention may be extracting histogram feature information from the histogram.
  • Histogram feature information may correspond to or relate to or be indicative of one or more histogram features.
  • the histogram feature information may be representative of a probability that a usage pattern parameter will have a certain value or will lie in a certain range or interval of values in the future, preferably the immediate future.
  • the histogram feature information may be representative of a probability that a usage pattern parameter will have a certain value or will lie in a certain range or interval of values in the next, meaning the upcoming or just started, usage session or resting period. This probability may be empirically determined from the data in the histogram as will be explained in more detail below.
  • the aerosol-generating device may be controlled to accommodate and/or anticipate this future value and especially the upcoming usage session or resting period.
  • Controlling the aerosol-generating device based on histogram feature information includes adapting operational constraints or operation constraints of the aerosol-generating device in a way so that the operational constraints of the aerosol-generating device fit the histogram feature information and thus fit the individual usage preferences of the user of the aerosol-generating device.
  • the operational constraints of the aerosol-generating device may be increased or tightened or decreased or loosened. For example, if the currently implemented operational constraints of the aerosol-generating device indicate that no further usage session is to be provided to the user, but the histogram feature information indicates that according to the individual habits of the user, another usage session can actually be provided, then the currently implemented operational constraints may be changed, in this case loosened, to allow the device the provision of another usage session to the user.
  • the currently implemented operational constraints of the aerosol-generating device indicate that, for example, three usage sessions may be provided to the user with one fully charged battery, but the histogram feature information indicates that according to the individual habits of the user only two usage sessions may be provided, the currently implemented operational constraints may be changed, in this case tightened, so that only two usage sessions are provided to the user with one fully charged battery. They may pertain to usage sessions and/or charging of the aerosol-generating device.
  • the operational constraints may include limitations of battery and/or power consumption management of the aerosol-generating device. They may also include limitations on the length/duration and/or number of usage sessions provided to a user before recharging the battery.
  • an operational constraint of the aerosol-generating device may be a duration, as in a length of time, a usage session lasts. If the histogram feature information extracted from the histogram indicates that the capacity of the battery is enough to support one or more usage sessions of longer duration as currently set, the maximum duration of one or more, for example all, usage sessions may be increased. Inversely, if the histogram feature information extracted from the histogram indicates that the capacity of the battery is not enough to support one or more usage sessions of the duration as currently set, the maximum duration of one or more, for example all, usage sessions may be decreased. In this way, an individual maximum duration of usage sessions may be found for each individual user.
  • the usage pattern parameters may be collected over a predetermined period of time.
  • the predetermined period of time may be, for example, a fixed amount of hours, days, weeks, or months after the first use of the aerosol-generating device. Alternatively, the predetermined period of time may be the whole time since the first use of the aerosol-generating device.
  • the aerosol-generating device may be designed or configured to collect the usage pattern parameters, preferably automatically, periodically and/or continuously.
  • Collecting the usage pattern parameters may include storing corresponding data indicative of the one or more usage pattern parameters, for example in a data storage of the aerosol-generating device or other device communicatively coupleable thereto.
  • the aerosol-generating device may comprise means for determining the usage pattern parameters, preferably as numerical values, and/or for storing data indicative of the usage pattern parameters or corresponding values thereof.
  • These means may be or may comprise, for example, counters and/or timers and/or sensors, such as temperature sensors, volume sensors, humidity sensors and others.
  • the usage pattern parameters may be collected over the entire life of the aerosol-generating device, which may mean from a first to a last usage session of the aerosol-generating device.
  • the aerosol-generating device may comprise a storage or memory in which the collected parameters, parameter values and/or corresponding data may be stored.
  • the collected usage pattern parameters may also be stored in a user profile and/or transferred to another aerosol-generating device or other device communicatively coupleable to the aerosol-generating device, such as a companion device, a server, a smartphone or other computing device.
  • the at least one usage pattern parameter may be selected from the parameters:
  • the aerosol-generating device has been operated to generate aerosol, preferably per predefined time interval, for example per day,
  • the usage pattern parameter value pertaining to the resting time between consecutive usage sessions only varies for resting times between subsequent usage sessions of from 0 to 40 minutes
  • the energy consumption per usage session may describe the amount of electrical energy drained from the battery of the aerosol-generating device to provide or grant a usage session, for example from the start of the usage session to the end of the usage session. This may be represented in units of the capacity of the battery, for example as a percentage of the state of charge (SOC) of the battery drained to provide the usage session. It may also be represented as a total amount of battery capacity needed to provide the usage session, for example in mAh, which is the standard representation of battery capacity.
  • SOC state of charge
  • the number of usage sessions of the aerosol-generating device may be a relevant parameter because it may characterise the intensity of use of the device by the user. It therefore may allow to differentiate casual from heavy users and may be used to describe the progression through the lifetime of the device and/or the battery.
  • the number of usage sessions may alternatively be related to a different reference than a predefined time interval. For example, the number of usage sessions between recharging the device may be collected. For this value, the amount of time between the two consecutive recharging events of the device may be irrelevant.
  • the method according to this disclosure may include any one or any combination of the parameters listed, it is especially preferred that the at least one usage pattern parameter may be selected from the parameters energy consumption per usage session and/or number of usage sessions the aerosol-generating device has been operated to generate aerosol, preferably per predefined time interval.
  • the duration of one or more usage sessions may vary from user to user and may have an impact on the strain on the battery.
  • the amount of energy required for a usage session may be highly correlated to its duration, as the aerosol-generating device should preferably maintain the heating temperature during this period.
  • typical aerosol-generating devices allow usage sessions of up to 6 minutes.
  • the resting time between consecutive usage sessions may be related to the temperature of the device, a heating element of the device and the battery.
  • the heating element, the device and the battery may be heated up by heating the aerosol-generating substrate or article.
  • the device and the battery start to cool off or cool down until the device and the battery reach the ambient temperature. This time duration can be referred to as the resting time.
  • the battery typically reaches ambient temperature, which may mean that different resting times of 40 minutes or more may have the same effect, from the point of view of temperature.
  • only resting times between 0 and 40 minutes may result in different values for the corresponding usage pattern parameter, whereas times of 40 minutes and more may have the same value. Short resting times that are not long enough for the device to reach ambient temperature may be less strainful for the battery and therefore cause less battery degradation.
  • the frequency of at least two usage sessions in a row, especially without recharging of the aerosol-generating device or the battery in between, may also be referred to as back-to-back regime.
  • This parameter may, for example, be described by the percentage of two consecutive usage sessions which occur without the aerosol-generating device or the battery having been recharged before initiation of the second usage session. For example, in an aerosol-generating device designed or configured to provide two usage sessions after fully charging the battery, recharging the aerosol-generating device after each usage session would result in a back-to-back regime of 0 %, whereas recharging the device only after two usage sessions have been performed would result in a back-to-back regime of 100 %.
  • a back-to-back regime of 50 % would then describe recharging the device after one usage session half the time and only after two usage sessions the rest of the time.
  • the frequency of at least two usage sessions in a row may be determined by dividing the number of consecutive usage sessions by the total number of usage sessions.
  • a puff in the sense of the present disclosure may describe a pull and/or draw on the aerosol-generating device while inhaling a mixture of air and aerosol by a user.
  • the puff volume may describe the volume of said mixture inhaled in one pull or inhalation.
  • Puff frequency and rhythm may describe corresponding patterns in the occurrence of puffs characteristic for individual users.
  • typical aerosol-generating devices are designed to allow a maximum of 14 puffs per aerosol-generating article.
  • a pause mode may refer to a special mode of the aerosol-generating device allowing a pause during a usage session. Pause mode therefore may not pertain to and may be distinct from resting times between usage sessions.
  • the aerosol-generating device may be operated in at least two operation modes, an aerosol-releasing mode and a pause mode.
  • the aerosol-generating device may be configured to heat the heating element, the aerosol-generating article and/or the substrate at a first temperature level in the aerosol-releasing mode.
  • the first temperature level may correspond to a predetermined heating temperature or a temperature above, which may be sufficient to generate aerosol.
  • the aerosol-generating device may further be configured to heat the heating element, the aerosol-generating article and/or the substrate at a second temperature level below the first temperature level in a pause mode of the aerosol-generating device.
  • the second temperature level may, for example, refer to a temperature above room temperature and below the first temperature level.
  • a user experience also referred to as usage session or experience of an aerosol-generating article herein, may be interrupted, for example by switching the device into the pause mode, and resumed by a user at a later, wherein the aerosol-generating article or substrate may be kept in pause mode of the aerosol-generating device at a temperature below the first temperature level and/or below the predetermined heating temperature used during normal use of the device (in particular during a user experience or usage session) , but still above or well above room temperature. That is, the second temperature level preferably may be chosen such as to avoid degradation of the non-depleted substrate or aerosol-generating article.
  • the second temperature level may be chosen such as to be sufficiently low in order to minimize depletion of the substrate or article during the pause mode, and at the same time to be sufficiently high in order to avoid vapor to condensate in the device which otherwise could affect the quality of the non-depleted aerosol-generating substrate or article.
  • the aerosol-generating device may be operated in the aerosol-releasing mode, whereas during a use pause of the device, that is, when no user experience or usage session is to take place and/or when a usage session is interrupted by a pause, the aerosol-generating device may be operated in the pause mode.
  • the heating element, a heating circuitry and/or a heating arrangement may be in operation, in particular in heating operation, yet at different temperature levels, namely, at a first temperature level during the aerosol-releasing mode, which may be chosen to be sufficiently high in order to generate an aerosol, and at a second temperature level below the first temperature level during the pause mode, which may be chosen to be sufficiently low in order to minimize depletion of the substrate, whilst avoiding degradation.
  • the first temperature level may be in a range between 250 degree Celsius and 450 degree Celsius, particularly between 270 degree Celsius and 430 degree Celsius, more particularly between 315 degree Celsius and 355 degree Celsius. These temperatures may be suitable operating or heating temperatures sufficient to allow volatile compounds to be released from the aerosol-generating article or substrate, for example during one or more usage sessions and/or when operating the device in the aerosol releasing mode.
  • the first temperature level and/or heating temperature for liquid aerosol-generating articles or substrates may be lower than the first temperature level for solid aerosol-generating articles or substrates.
  • the second temperature level may be chosen to maintain a usability of the aerosol-generating article or substrate for a prolonged time.
  • the second temperature level may also depend on the type and composition of the aerosol-generating article or substrate to be used with the device. Accordingly, the second temperature level may be in a range between 175 degree Celsius and 225 degree Celsius, particularly between 185 degree Celsius to 215 degree Celsius, more particularly between 195 degree Celsius and 205 degree Celsius. These temperatures may be sufficiently low in order to minimize depletion of the substrate during the pause mode but at the same time sufficiently high in order to avoid vapor to condensate in the device, which could lead to degradation of the aerosol-generating article or substrate.
  • the second temperature level may be at least 150 degree Celsius, in particular at least 175 degree Celsius, preferably at least 185 degree Celsius, more preferably at least 195 degree Celsius.
  • the second temperature level may be at most 220 degree Celsius, in particular at most 225 degree Celsius, preferably at most 215 degree Celsius, more preferably at least 205 degree Celsius.
  • the second temperature level may be chosen such as to reduce the formation of aerosols by at least 50 percent compared to the aerosol-releasing mode.
  • the second temperature level may be lower than the first temperature level, for example by at least 50 degree Celsius, in particular at least 75 degree Celsius, more particularly at least 100 degree Celsius.
  • the temperature values given above preferably may be average temperatures of the aerosol-generating article or substrate during operation of the device.
  • the temperature values may depend, inter alia, on the type and composition of the aerosol-generating article or substrate to be used with the device.
  • the pause mode may refer to a first operational mode of aerosol-generating device, in which the heating element, the heating circuitry and/or a heating arrangement may be operated during an operation pause, that is, a use pause of the aerosol-generating device, that is, when a user experience or usage session is paused and aerosol generation may not take place, or at least may be reduced to a minimum level. That is, in the pause mode the aerosol-generating device is in a use pause.
  • the aerosol-releasing mode may refer to a second operational mode of the aerosol-generating device, which is the normal heating operational mode of the heating element, circuitry, and/or arrangement for aerosol generation, in which heating element, the heating circuitry and/or a heating arrangement may be operated during use of the device by a user, that is, when a user experience or usage session takes place, in particular when aerosol generation takes place.
  • aerosol generation may take place continuously or on demand, in particular on a puff basis, that is, on demand of a user when taking a puff.
  • the density, weight, type and/or humidity of an aerosol-generating substrate or aerosol-generating article may be detected by the aerosol-generating device recognising, sensing and/or identifying the stick or cartridge for example through RFID or other means. As these factors may influence the energy needed for aerosol generation from the substrate or article, they also influence battery degradation.
  • the method may also comprise collecting two or more usage pattern parameters and creating a histogram for each of the usage pattern parameters, and controlling the aerosol-generating device based on histogram feature information extracted from a plurality of the histograms.
  • the two or more usage pattern parameters may be selected from the list as described above. In cases in which the method described herein pertains to more than one usage pattern parameter, for example at least two usage pattern parameters, these usage pattern parameters may differ from each other. Each of the parameters may thus be one of the parameters listed above, wherein each parameter may be different from the others.
  • two usage pattern parameters as used herein may not describe or refer to different values, for example numerical values, of the same parameter, but rather values of different parameters.
  • Each usage pattern parameter may be used to create at least one distinct histogram. In this way, information from an arbitrary number or all of the collected usage pattern parameters may be extracted from the histograms.
  • the method according to the present disclosure may therefore provide detailed and highly individualised information based on which the aerosol-generating device may be controlled.
  • At least one histogram feature information extracted from the histograms is differently weighted than the others. Additionally or alternatively, it may be provided that histogram feature information extracted from different histograms is weighted differently.
  • at least one or each histogram feature information may be provided with a weighing or scaling factor, for example a multiplicative factor, thereby increasing or decreasing the numerical value provided by the histogram feature information and used in controlling the aerosol-generating device. In this way, the method may take into account that different histogram feature information and/or different histograms and/or different usage pattern parameters may have a different importance for controlling the aerosol-generating device.
  • the numerical value of more important histogram feature information and/or histograms and/or usage pattern parameters may be increased and thus have a bigger impact on controlling the aerosol-generating device then less important histogram feature information and/or histograms and/or usage pattern parameters and vice versa.
  • the extracted histogram feature information or histogram features may relate to any information that is available in or can be derived from the data related to the one or more usage pattern parameters that is provided as a histogram.
  • providing data in one or more histograms may make the further processing of the data easier and may necessitate less computational power than conventional statistical methods, while also potentially increasing the informational content derivable or obtainable from the one or more histograms.
  • the histogram feature information includes at least one of:
  • the bin or bins preferably corresponding to one or more local maxima of the histogram
  • a local maximum of a histogram may, for example, be a bin or a group of adjacent bins that have a greater height than neighbouring bins or groups of neighbouring bins.
  • a local maximum may be defined as the bin with the biggest height of all bins of a histogram.
  • the heights of neighbouring or adjacent bins may also be summed together.
  • a local maximum may therefore represent or be indicative of a bin or a group of bins (and therefore a range of values of usage pattern parameters represented by the bin or bins) in which more values of usage pattern parameters may be classified than in other areas of the histogram.
  • One histogram may have one or more local maxima, for example in bi-or multimodal distributions of values of usage pattern parameters.
  • the identity of one or more bins may correspond to the range of values of usage pattern parameters classified into this bin or bins. By identifying the bin or the bins of a local maximum, a range of values of the usage pattern parameter which occurred often during the collection of the values of the usage pattern parameter or parameters may be identified.
  • the height of a bin may correspond to or may be proportional to the number of values of the usage pattern parameter classified into this bin. For example, if 3 values of a specific usage pattern parameter fall into the range of a specific bin and are therefore classified into this bin, the height of this bin may be 3. It is therefore immediately apparent that the height of one or more bins may be summed together. For instance, the height of neighbouring or adjacent bins may be summed.
  • the summed heights of two or more groups of bins of one or more histograms may be compared to the summed heights of all other bins, specifically of all other bins not associated with the at least one local maximum.
  • the probability that an upcoming event, for example a usage session or a resting period, again falls into the range of values represented by the summed bins may be quickly and easily calculated, as will be explained in more detail below. That an upcoming event again falls into the range of values represented by a bin or by more than one bins may mean that if a value of the usage pattern parameter in question was determined for this event, the value would be classified into this bin or this group of bins.
  • the method may comprise, for example, determining from the histogram feature information a number of usage sessions that can be provided to a user to generate aerosol by the maximum capacity of a battery of the aerosol-generating device, for example within a predetermined degree of certainty.
  • Many of the usage pattern parameters mentioned above have an influence on the energy consumption during usage sessions and may therefore be used for this determination.
  • suitable parameters may be the number of usage sessions the aerosol-generating device has been operated to generate aerosol between recharging the device and/or the energy consumption per usage session. For example, it may be determined that during the time in which usage pattern parameters were collected, X usage sessions could be provided between two consecutive recharge events. It may therefore be assumed that this number of usage sessions can again be provided to the user. Alternatively, it may be determined that during the time in which usage pattern parameters were collected, the energy consumption per usage session was so high that X usage sessions can be provided when taking into account the maximum capacity of the battery of the aerosol-generating device. Then it may be assumed that this number of usage sessions can again be provided to the user.
  • the maximum capacity of the battery may be a nominal, initial or original maximum capacity or a current maximum capacity of the battery of the aerosol-generating device. During the lifetime of an aerosol-generating device, the battery capacity may degrade with use. The current maximum capacity of the battery may therefore describe or pertain to the actual state of the battery as currently in use in the device. The current maximum capacity of the battery may thus be smaller than the nominal, initial or original maximum capacity. There are several methods to determine the current battery capacity or state of degradation of the battery which can be employed and which therefore do not need to be explained in detail.
  • a predetermined degree of certainty may mean with a certainty of 95%or 90%or 85%or 80%or 75%or 70%. For example, it may be determined from the collected values of a usage pattern parameter that X%of the values fall within a certain numerical range, the numerical range being represented by one or more bins of the histogram. It may then be determined from this that an upcoming value of the usage pattern parameter may again lie in this numerical range with a certainty of X%. The predetermined degree of certainty may then be X%.
  • the predetermined degree of certainty may also be a fixed value, for example Y%.It may then be provided that only bins representing up to Y%of values of a usage pattern parameter are considered and/or that there may be a comparison between this Y%and the X%determined as described above. The result of this comparison, for example, that X%lies above or below Y%, may then be used in controlling the aerosol-generating device.
  • the method may additionally or alternatively comprise determining from the histogram feature information whether or not an additional usage session can be provided considering the current state of charge of a battery of the aerosol-generating device, for example within a predetermined degree of certainty. For instance, if the average energy consumption per usage session determined from a histogram is higher than the current state of charge of the battery, it may be determined that no additional usage session can be provided. Inversely, if the average energy consumption per usage session determined from a histogram is lower than the current state of charge of the battery, it may be determined that an additional usage session can be provided. Instead of the average energy consumption, it could be determined from the histogram what fraction of the total usage sessions used less energy than the current state of charge of the battery.
  • the heights of all bins representing an energy consumption below the current state of charge of the battery may be summed together and divided by the summed heights of all other bins of the histogram pertaining to energy consumption per usage session.
  • the result may be equal to the probability that the current state of charge of the battery may be sufficient for the upcoming usage session and therefore whether or not an additional usage session can be provided.
  • controlling the aerosol-generating device based on said histogram feature information may pertain to adjusting operational constraints directed to or including the total number of usage sessions provided to a user to generate aerosol before recharging of a battery of the aerosol-generating device.
  • the operational constraints may include limitations of battery and/or power consumption management.
  • controlling the aerosol-generating device based on said histogram feature information may pertain to limiting a total number of usage sessions provided to a user to generate aerosol before recharging of a battery of the aerosol-generating device to a number of usage sessions that can be provided within a predetermined degree of certainty.
  • controlling the aerosol-generating device based on said histogram feature information may pertain to allowing or prohibiting an additional usage session before recharging of a battery of the aerosol-generating device. This may also be achieved by adjusting the operational constraints pertaining to or including limitations of battery and/or power consumption management. Again additionally or alternatively, controlling the aerosol-generating device based on said histogram feature information may pertain to forcing and/or requesting a recharge of the battery.
  • the aerosol-generating device may therefore be designed so that the user cannot start a usage session when a predetermined number of usage sessions before recharging is reached or when it has been determined that no additional usage session can be fully provided. In this way, the user experience is improved and waste of aerosol-generating articles by an incomplete usage session can be avoided.
  • Forcing and/or requesting a recharge of the battery may pertain to putting the aerosol-generating device in a state in which no usage session can be initiated. Additionally, a notification or message to the user may be presented informing the user of the necessary recharge of the battery of the aerosol-generating device.
  • the aerosol-generating device may comprise a light, a display, a loudspeaker or a vibration device to deliver a visual, acoustic or haptic notification or message to the user.
  • the method may further comprise determining a total amount of battery capacity required for operating the aerosol-generating device between two consecutive recharging events of a battery of the aerosol-generating device, for example within a predetermined degree of certainty, from the histogram feature information. For instance, it may be determined what percentage of the capacity of the fully charged battery is required for operating the aerosol-generating device between two consecutive recharging events. This percentage may pertain to the state of charge (SOC) of the battery. Alternatively, the total amount of battery capacity required for operating the aerosol-generating device between two consecutive recharging events may also be expressed as an absolute numerical value, for example in mAh.
  • SOC state of charge
  • Consecutive recharging events may pertain to recharging events that directly follow each other with only a variable amount of resting time and/or a variable number of usage sessions in between the recharging events. Therefore, from the perspective of one recharging event, a consecutive recharging event may be either the next recharge event or the previous one. For example by counting the number of usage sessions between two consecutive recharging events, it may be determined how often the user operates the aerosol-generating device to provide a usage session before recharging the device again. The energy consumption per usage session may then be summed to determine the total amount of battery capacity required before the device is recharged again. Alternatively, the state of charge of the battery right before recharging or when recharging is initiated may be collected.
  • the aerosol-generating device may be controlled in a way that minimizes battery degradation. For example, it may be provided that controlling the aerosol-generating device based on said histogram feature information includes adjusting operational constraints of the aerosol-generating device including limitations of battery and/or power and/or charging management.
  • controlling the aerosol-generating device based on said histogram feature information includes terminating a recharging event of a battery of the aerosol-generating device at a state of charge below 100%, preferably below 95%or below 90%or below 85%or below 80%. Recharging the battery until it is fully charged is known to cause an accelerated degradation. Similarly, it is known that recharging the battery only to a state of charge below the maximum slows battery degradation. If it is determined through the method of the present disclosure that the user only needs a fraction of the total or maximum capacity of the battery, for example of the current total capacity, this information can then be used to slow down the degradation of the battery by avoiding fully charging the battery. It is similarly known that fully discharging the battery also accelerates battery degradation.
  • the aerosol-generating device may therefore be controlled so that the battery is not fully discharged during use and at least a residual state of charge of the battery is conserved, for example 5%or 10%or 15%or 20%of the state of charge of the battery.
  • This residual state of charge of the battery may also be taken into account when determining when to terminate a recharging event of the battery so that the battery capacity between the residual state of charge and the state of charge the battery is recharged to before terminating a recharging event corresponds to the total amount of battery capacity required for operating the aerosol-generating device between two consecutive recharging events.
  • the method may further comprise determining an amount of time a recharging event of a battery of the aerosol-generating device is going to last, for example within a predetermined degree of certainty, from the histogram feature information.
  • the histogram may, for example, pertain to the usage pattern parameter duration of a recharge event of the aerosol-generating device. In other words, it may be determined how long the aerosol-generating device is typically connected to a power source so as to recharge its battery according to the individual habits of the user.
  • controlling the aerosol-generating device based on said histogram feature information includes adjusting operational constraints of the aerosol-generating device including limitations of battery and/or power and/or charging management. This may mean that controlling the aerosol-generating device based on said histogram feature information includes limiting a charging rate during a recharging event of a battery of the aerosol-generating device. High charging rates for fast charging are known to be detrimental in terms of battery degradation. Therefore, it may be advantageous to limit the charging rate when it is known that the duration of the recharging event is long enough to recharge the battery to the desired state of charge even with a limited charging rate. For example, a user may habitually recharge the aerosol-generating device overnight, which provides ample time to charge the battery slowly with a limited charging rate. This will result in slower degradation and a longer lifetime of the battery.
  • the user’s habits this is the habits of one particular user, may differ greatly for different locations and/or different times at which the aerosol-generating device is operated, for example operated to generate aerosol in a usage session or to recharge the battery.
  • the present invention may therefore comprise collecting the at least one usage pattern parameter along with location information and/or time information pertaining to where and/or when the aerosol-generating device is operated.
  • the location information may pertain to the geographic location.
  • the location information may be determined through means like global positioning system (GPS) or a different global navigation satellite system (GNSS) system, through connections of the aerosol-generating device to local area networks, for example by Wifi or WLAN, for example a user’s home Wifi, office Wifi or a public Wifi, mobile cellphone network information or information from geolocator tags.
  • GPS global positioning system
  • GNSS global navigation satellite system
  • the time information may pertain to the time of day and/or the date of the calendar, for example determined by an internal timepiece and/or calendar of the aerosol-generating device. All of the usage pattern parameters mentioned herein may be collected with corresponding location information and/or time information. Such information may be collected, for example, for every usage session, recharging event and/or resting time, for example for each of the beginning and/or the end of the usage session, recharging event and/or resting time.
  • Collecting one or both the time and location information may enable a more detailed analysis of the data pertaining to the usage pattern parameters.
  • usage of the aerosol-generating device may differ greatly when the user is at work from when the user is at home. Usage may also differ for work days or holidays or for work hours and free hours. The method may therefore comprise identifying different usage patterns of the aerosol-generating device at different locations and/or in different periods of time.
  • a usage pattern in this sense may describe a set of values of a usage pattern parameter collected with location information and/or time information that differ from another set of values of the same usage pattern parameter collected with different location information and/or time information. If, for example, a usage pattern parameter has different values when collected at different locations and/or at different times, then it may be determined that this usage pattern parameter has different usage patterns depending on the location and/or time at which the values of the usage pattern parameter are collected. It may then be provided that these differences in the values of the usage pattern parameters are taken into account by creating a separate histogram for each usage pattern of the usage pattern parameter.
  • Usage patterns may be considered different when the histogram feature information extracted from histograms pertaining to at least two usage patterns would lead to controlling the aerosol-generating device differently based on the extracted histogram feature information from either one of these histograms.
  • the histograms pertaining to different usage patterns may comprise different or a different number of local maxima or any other histogram feature information or histogram features mentioned herein.
  • the aerosol-generating device may be designed to collect current location information and/or time information.
  • Current location information and/or time information may pertain to the present situation of the aerosol-generating device and/or the user, for example to the place where the aerosol-generating device and/or the user is at the moment and/or what time it is at the moment. In this way, information may be available as to where and/or when a current usage of the aerosol-generating device by the user occurs.
  • the method may comprise extracting histogram feature information from the histogram corresponding to the location and/or time of the current use of the aerosol-generating device and controlling the aerosol-generating device based on the histogram feature information extracted from said histogram.
  • control of the aerosol-generating device may be based on data pertinent to the location and/or time at which the user actually uses the device. Differences in usage, for example resulting in different usage patterns, may therefore be taken into account when controlling the aerosol-generating device.
  • the values of the usage pattern parameters may have an infinite range of numerical values. Different values of usage pattern parameters may therefore be classified into the same bin of the histogram when they have the same meaning despite being numerically different from each other.
  • the location information and/or time information collected along with the usage pattern parameter may, in principle, have an infinite number of numerical values. Therefore, to identify meaningful locations and/or times at which different usage patterns occur in such a way that each usage pattern actually results in controlling the aerosol-generating device differently from the other usage patterns, the location information and/or time information may be divided into meaningful groups as well. For this reason, the method may optionally comprise stratifying the collected values of the at least one usage pattern parameter in one or more categories according to the location information and/or time information. Each category may therefore be indicative or representative of a location and/or time that stratifies the movement and/or life rhythm of the user into meaningful subunits.
  • the method may comprise creating different categories for collected values of the at least one usage pattern parameter which pertain to values of usage pattern parameters or to usage sessions of the aerosol-generating device which were collected or occurred
  • the method may comprise receiving information from the user identifying locations and/or times, for example current locations and/or current times.
  • the user may therefore provide the aerosol-generating device with the information that the location the user is at at the moment is his work place or his home or another location that he frequents.
  • the user may provide the aerosol-generating device with the information that he works on specific days and/or to specific times or that he will be on holiday on specific days and/or weeks.
  • the method may comprise the aerosol-generating device determining this information on its own by continually collecting the underlying data and identifying the mentioned categories.
  • the category pertaining to travel of the user may pertain both to travel away from locations the user normally frequents, for example to travel abroad, and to travel between locations the user normally frequents, for example to travel between the home and the workplace or a recreational location of the user.
  • the method may comprise creating a separate histogram for values of the usage pattern parameters pertaining to at least two or multiple of or all of the categories mentioned above.
  • controlling the aerosol-generating device through the method of the present disclosure may take different usage patterns or user habits connected to different locations and/or times into account.
  • the user may recharge the aerosol-generating device more often at home than at work, so that the battery may not need to be fully recharged when the user is at home, but may need to be fully recharged when the user is at work according to the principles outlined above.
  • the method according to the disclosure may provide an adaptive, smart control of the aerosol-generating device requiring only a minimum of computational power.
  • the histograms used in the method may have any number of bins necessary to represent the underlying data in a meaningful way. Different histograms may have different numbers of bins. The number of bins of each histogram may be changed when more values of the represented usage pattern parameter are collected over time.
  • the method may comprise dynamically adjusting the number of bins of the histogram used to classify the values of the at least one usage pattern parameter. A different number of bins may be necessary when, over time, the number of values collected and/or the distribution of values of usage pattern parameters changes.
  • the method may also comprise limiting the number of values of the at least one usage pattern parameter or all of the usage pattern parameters to a certain number of values or to values collected during a predetermined period of time.
  • the number of values of the at least one usage pattern parameter may be limited to the last 50 or 40 or 30 or 20 or 10 collected values of the at least one usage pattern parameter. Any value exceeding this number, pertaining to values that have been collected previously, may be deleted.
  • the method may comprise limiting the collected values of the at least one usage pattern parameter to values collected during a predetermined period of time, for example the last 12 months or 6 months or 3 months or month or 2 weeks or week or day. Any value collected prior to this period may be deleted. In this way, the data relied upon to control the aerosol-generating device may always be up-to-date. Simultaneously, the computational power required for the method according to the present disclosure is further reduced. As the data represented by each histogram may therefore change over time, how this data may be organized in the histogram may also change by dynamically adjusting the number of bins.
  • the method may therefore optionally comprise increasing the number of bins of the histogram used to classify the values of the at least one usage pattern parameter when the percentage of values of the at least one usage pattern parameter in one bin exceeds a predetermined threshold value.
  • the predetermined threshold value may for example be 50%or 60%or 70%or 80%or 90%of all the values of the at least one usage pattern parameter pertaining to the histogram.
  • a number of bins of the histogram may for example be increased by splitting the range of numerical values being classified into the bin, preferably the bin with the most values classified into it, in two or more ranges or subranges, each of the new ranges or subranges being represented by a new bin and re-classifying the values of the at least one usage pattern parameter accordingly.
  • the method may comprise increasing the number of bins of the histogram used to classify the values of the at least one usage pattern parameter when a collected value of the at least one usage pattern parameter does not fit any available bin.
  • the new bin may be created representing a numerical range of values into which the collected value of the at least one usage pattern parameter fits. The new bin may be added to the histogram so that collected values may then be classified into this bin in the future.
  • the method may also comprise decreasing the number of bins of the histogram used to classify the values of the at least one usage pattern parameter when the percentage of values of the at least one usage pattern parameter in all bins falls below a predetermined threshold value.
  • the predetermined threshold value may for example be 10%or 20%of 30%to 40%of all the values of the at least one usage pattern parameter pertaining to the histogram.
  • neighbouring bins may be joined together or merged so that a new bin may be created representing the numerical range of values of both bins together. Accordingly, also the height the merged bins may be summed to determine the height of the new bin.
  • bins that have no values classified into them may be deleted. Such empty bins may arise due to older values of usage pattern parameters being deleted, for example.
  • the method may comprise using an (artificial) intelligence engine or network or machine learning in extracting histogram feature information from the histogram and/or controlling the aerosol-generating device based on said histogram feature information.
  • an (artificial) intelligence engine or network or machine learning in extracting histogram feature information from the histogram and/or controlling the aerosol-generating device based on said histogram feature information.
  • a convolutional neural network (CNN) random forest, decision forest, decision tree etc.
  • CNN convolutional neural network
  • These engines or networks may be trained on large data sets beforehand so that they enable precise decision-making based on the data available on each and every one of the aerosol-generating devices. Organizing such data in histograms may increase performance of (artificial) intelligence engines or intelligence networks or machine learning.
  • an aerosol-generating device configured to perform steps, for example at least a subset or all of the steps, of the method according to the present disclosure. All of the features, effects and advantages described for the method are also valid for and equally apply to the aerosol-generating device and vice versa.
  • the aerosol-generating device may comprise a battery for storing electrical energy and processing circuitry or control circuitry with one or more processors configured to perform steps, for example at least a subset or all of the steps, of the method as disclosed herein.
  • an aerosol-generating system comprising a control arrangement, wherein the control arrangement is configured to perform steps, for example at least a subset or all of the steps, of the method according to the present disclosure.
  • the control arrangement may, for example, comprise processing circuitry with one or more processors.
  • the aerosol-generating system may comprise an aerosol-generating device and a companion device communicatively coupleable to the aerosol-generating device, wherein the companion device is configured to perform steps, for example at least a subset or all of the steps, of the method according to the present disclosure. All of the features, effects and advantages described for the method are also valid for and equally apply to the aerosol-generating system and vice versa.
  • the companion device may be, for example, a smartphone, a tablet computer, a personal computer, a computing device, a server, or a device configured to charge the aerosol-generating device. It may be advantageous to perform the method according to the present disclosure on the companion device especially in cases in which the companion device may have more computational power than the aerosol-generating device. Also, in cases in which the user owns and/or operates more than one aerosol-generating device, all of these may be communicatively coupleable to the companion device so that the companion device can collect data, for example values of usage pattern parameters, from a plurality of aerosol-generating devices. In this way, control of all of the aerosol-generating devices may be improved regardless of which aerosol-generating device the user uses at what time or in what location.
  • a computer program or software or computer executable code which when executed on a processor of an aerosol-generating device and/or a companion device performs steps, for example at least a subset or all of the steps, of the method according to the present disclosure. All of the features, effects and advantages described for the method are also valid for and equally apply to the computer program or software or computer executable code and vice versa.
  • a computer readable medium having stored thereon a computer program or software or computer executable code which when executed on a processor of an aerosol-generating device and/or a companion device performs steps, for example at least a subset or all of the steps, of the method according to the present disclosure. All of the features, effects and advantages described for the method are also valid for and equally apply to the computer readable medium and vice versa.
  • Example 1 A computer-implemented method of controlling an aerosol-generating device, the method comprising:
  • Example 2 The method according to Example 1, wherein the at least one usage pattern parameter is selected from the parameters:
  • the aerosol-generating device has been operated to generate aerosol, preferably per predefined time interval
  • the usage pattern parameter value pertaining to the resting time between consecutive usage sessions only varies for resting times between subsequent usage sessions of from 0 to 40 minutes
  • Example 3 The method according to any of the preceding Examples, comprising collecting two or more usage pattern parameters and creating a histogram for each of the usage pattern parameters, and controlling the aerosol-generating device based on histogram feature information extracted from a plurality of the histograms.
  • Example 4 The method according to the preceding Example, wherein at least one histogram feature information extracted from the histograms is differently weighted than the others; and/or
  • histogram feature information extracted from different histograms is weighted differently.
  • Example 5 The method according to any of the preceding Examples, wherein the extracted histogram feature information includes at least one of:
  • Example 6 The method according to any of the preceding Examples, further comprising determining at least one of the following from the histogram feature information:
  • the maximum capacity of a battery of the aerosol-generating device preferably wherein the maximum capacity of the battery is a nominal maximum capacity or a current maximum capacity of the battery of the aerosol-generating device
  • Example 7 The method according to the preceding Example, the method preferably further comprising a step of changing the operational constraints of the aerosol-generating device including limitations of battery and/or power consumption management based on the histogram feature information and/or wherein controlling the aerosol-generating device based on said histogram feature information preferably includes at least one of:
  • Example 8 The method according to any of the preceding Examples, further comprising determining a total amount of battery capacity required for operating the aerosol-generating device between two consecutive recharging events of a battery of the aerosol-generating device from the histogram feature information.
  • Example 9 The method according to the preceding Example, wherein controlling the aerosol-generating device based on said histogram feature information includes adjusting operational constraints of the aerosol-generating device including limitations of battery and/or power and/or charging management and/or preferably includes terminating a recharging event of a battery of the aerosol-generating device at a state of charge below 100%, preferably below 95%or below 90%or below 85%or below 80%.
  • Example 10 The method according to any of the preceding Examples, further comprising determining an amount of time a recharging event of a battery of the aerosol-generating device is going to last from the histogram feature information.
  • Example 11 The method according to the preceding Example, wherein controlling the aerosol-generating device based on said histogram feature information includes adjusting operational constraints of the aerosol-generating device including limitations of battery and/or power and/or charging management and/or preferably includes limiting a charging rate during a recharging event of a battery of the aerosol-generating device.
  • Example 12 The method according to any of the preceding Examples, comprising collecting the at least one usage pattern parameter along with location information and/or time information pertaining to where and/or when the aerosol-generating device is operated.
  • Example 13 The method according to the preceding Example, comprising identifying different usage patterns of the aerosol-generating device at different locations and/or in different periods of time and preferably creating separate histograms and/or bins for said locations and/or periods of time.
  • Example 14 The method according to the preceding Example, comprising extracting histogram feature information from the histogram corresponding to the location and/or time of the current use of the aerosol-generating device and controlling the aerosol-generating device based on the histogram feature information extracted from said histogram.
  • Example 15 The method according to any of the Examples 12 to 14, comprising stratifying the collected values of the at least one usage pattern parameter in one or more categories according to the location information and/or time information.
  • Example 16 The method according to the preceding Example, comprising creating different categories for collected values of the at least one usage pattern parameter which pertain to usage sessions of the aerosol-generating device which occurred
  • Example 17 The method according to any of the preceding Examples, comprising dynamically adjusting the number of bins of the histogram used to classify the values of the at least one usage pattern parameter.
  • Example 18 The method according to any of the preceding Examples, comprising increasing the number of bins of the histogram used to classify the values of the at least one usage pattern parameter when the percentage of values of the at least one usage pattern parameter in one bin exceeds a predetermined threshold value.
  • Example 19 The method according to any of the preceding Examples, comprising increasing the number of bins of the histogram used to classify the values of the at least one usage pattern parameter when a collected value of the at least one usage pattern parameter does not fit any available bin.
  • Example 20 The method according to any of the preceding Examples, comprising decreasing the number of bins of the histogram used to classify the values of the at least one usage pattern parameter when the percentage of values of the at least one usage pattern parameter in all bins falls below a predetermined threshold value.
  • Example 21 The method according to any of the preceding Examples, wherein an intelligence engine or network or machine learning is used in extracting histogram feature information from the histogram and/or controlling the aerosol-generating device based on said histogram feature information.
  • Example 22 An aerosol-generating device configured to perform steps of the method according to any one of the preceding Examples.
  • Example 23 The aerosol-generating device according to Example 22, comprising:
  • processing circuitry with one or more processors configured to perform steps of the method according to any one of Examples 1 to 21.
  • Example 24 Aerosol-generating system, comprising an aerosol-generating device and a companion device communicatively coupleable to the aerosol-generating device, wherein the companion device is configured to perform steps of the method according to any one of Examples 1 to 21.
  • Example 25 The aerosol-generating system according to Example 24, wherein the companion device is a smartphone, a tablet computer, a personal computer, a server, or a device configured to charge the aerosol-generating device.
  • the companion device is a smartphone, a tablet computer, a personal computer, a server, or a device configured to charge the aerosol-generating device.
  • Example 26 Computer program which when executed on a processor of an aerosol-generating device and/or a companion device performs steps of the method according to any one of Examples 1 to 21.
  • Example 27 Computer readable medium having stored thereon a computer program which when executed on a processor of an aerosol-generating device and/or a companion device performs steps of the method according to any one of Examples 1 to 21.
  • Example 28 Aerosol-generating system comprising a control arrangement configured to
  • control the aerosol-generating device with operational constraints that are based on the histogram feature information.
  • Example 29 The aerosol-generating system according to the preceding Example, wherein the at least one usage pattern parameter is selected from the parameters:
  • the aerosol-generating device has been operated to generate aerosol, preferably per predefined time interval
  • the usage pattern parameter value pertaining to the resting time between consecutive usage sessions only varies for resting times between subsequent usage sessions of from 0 to 40 minutes
  • Example 30 The aerosol-generating system according to any of Examples 28 to 29, wherein two or more usage pattern parameters are collected and a histogram is created for each of the usage pattern parameters, and wherein the aerosol-generating device is controlled based on histogram feature information extracted from a plurality of the histograms.
  • Example 31 The aerosol-generating system according to the preceding Example, wherein at least one histogram feature information extracted from the histograms is differently weighted than the others; and/or
  • histogram feature information extracted from different histograms is weighted differently.
  • Example 32 The aerosol-generating system according to any of Examples 28 to 31, wherein the extracted histogram feature information includes at least one of:
  • Example 33 The aerosol-generating system according to any of Examples 28 to 32, wherein at least one of the following is determined from the histogram feature information:
  • the maximum capacity of a battery of the aerosol-generating device preferably wherein the maximum capacity of the battery is a nominal maximum capacity or a current maximum capacity of the battery of the aerosol-generating device
  • Example 34 The aerosol-generating system according to the preceding Example, the method preferably further comprising a step of changing the operational constraints of the aerosol-generating device including limitations of battery and/or power consumption management based on the histogram feature information and/or wherein controlling the aerosol-generating device based on said histogram feature information preferably includes at least one of:
  • Example 35 The aerosol-generating system according to any of Examples 28 to 34, wherein a total amount of battery capacity required for operating the aerosol-generating device between two consecutive recharging events of a battery of the aerosol-generating device is determined from the histogram feature information.
  • Example 36 The aerosol-generating system according to the preceding Example, wherein controlling the aerosol-generating device based on said histogram feature information includes adjusting operational constraints of the aerosol-generating device including limitations of battery and/or power and/or charging management and/or preferably includes terminating a recharging event of a battery of the aerosol-generating device at a state of charge below 100%, preferably below 95%or below 90%or below 85%or below 80%.
  • Example 37 The aerosol-generating system according to any of Examples 28 to 36, further comprising determining an amount of time a recharging event of a battery of the aerosol-generating device is going to last from the histogram feature information.
  • Example 38 The aerosol-generating system according to the preceding Example, wherein controlling the aerosol-generating device based on said histogram feature information includes adjusting operational constraints of the aerosol-generating device including limitations of battery and/or power and/or charging management and/or preferably includes limiting a charging rate during a recharging event of a battery of the aerosol-generating device.
  • Example 39 The aerosol-generating system according to any of Examples 28 to 38, wherein the at least one usage pattern parameter is collected along with location information and/or time information pertaining to where and/or when the aerosol-generating device is operated.
  • Example 40 The aerosol-generating system according to the preceding Example, wherein different usage patterns of the aerosol-generating device at different locations and/or in different periods of time are identified and wherein preferably separate histograms and/or bins are created for said locations and/or periods of time.
  • Example 41 The aerosol-generating system according to the preceding Example, wherein histogram feature information is extracted from the histogram corresponding to the location and/or time of the current use of the aerosol-generating device and wherein the aerosol-generating device is controlled based on the histogram feature information extracted from said histogram.
  • Example 42 The aerosol-generating system according to any of Examples 28 to 41, wherein the location information and/or time information is used to stratify the collected values of the at least one usage pattern parameter in one or more categories.
  • Example 43 The aerosol-generating system according to the preceding Example, wherein different categories are created for collected values of the at least one usage pattern parameter which pertain to usage sessions of the aerosol-generating device which occurred
  • Example 44 The aerosol-generating system according to any of Examples 28 to 43, wherein the number of bins of the histogram used to classify the values of the at least one usage pattern parameter is dynamically adjusted.
  • Example 45 The aerosol-generating system according to any of Examples 28 to 44, wherein the number of bins of the histogram used to classify the values of the at least one usage pattern parameter is increased when the percentage of values of the at least one usage pattern parameter in one bin exceeds a predetermined threshold value.
  • Example 46 The aerosol-generating system according to any of Examples 28 to 45, wherein the number of bins of the histogram used to classify the values of the at least one usage pattern parameter is increased when a collected value of the at least one usage pattern parameter does not fit any available bin.
  • Example 47 The aerosol-generating system according to any of Examples 28 to 46, wherein the number of bins of the histogram used to classify the values of the at least one usage pattern parameter is decreased when the percentage of values of the at least one usage pattern parameter in all bins falls below a predetermined threshold value.
  • Example 48 The aerosol-generating system according to any of Examples 28 to 47, wherein an intelligence engine or network or machine learning is used in extracting histogram feature information from the histogram and/or controlling the aerosol-generating device based on said histogram feature information.
  • Example 49 The aerosol-generating system according to any of Examples 28 to 48, comprising an aerosol-generating device and a companion device, wherein the control arrangement is arranged on the aerosol-generating device and/or the companion device.
  • Figure 1 shows an aerosol-generating system comprising an aerosol-generating device and a companion device
  • Figure 2 shows a histogram pertaining to the usage pattern parameter energy consumption per usage session
  • Figure 3 shows a histogram pertaining to the usage pattern parameter number of usage sessions the aerosol-generating device has been operated to generate aerosol per day
  • Figure 4 shows a flow diagram of the method.
  • Figure 1 shows an aerosol-generating system 1 for generating aerosol, for example for consumption by a user in one or more usage sessions.
  • the system 1 may comprise an aerosol-generating device 2 for generating aerosol and a companion device 3 for at least partially receiving the aerosol-generating device 2.
  • the companion device 3 may be a charging device for charging the aerosol-generating device 2 and/or an energy storage or battery thereof.
  • the aerosol-generating device 2 may comprise an insertion opening 4 for at least partially inserting an aerosol-generating article 17.
  • the aerosol-generating article 17 may comprise an aerosol-forming substrate, such as a tobacco containing substrate, and/or a cartridge comprising a liquid, for example a liquid that can be aerosolized for inhalation.
  • the aerosol-generating device 2 may further include processing circuitry 5 or control circuitry 5 with one or more processors 6.
  • the aerosol-generating device 2 may comprise at least one heating element 7 or heater device for applying heat to at least a portion of the aerosol-generating article 17.
  • the processing circuitry 5 may be configured to control actuation, activation and/or deactivation of at least one heating element 7.
  • the processing circuitry 5 may further be configured to perform steps of the method described herein.
  • the aerosol-generating device 2 may further comprise at least one energy storage, for example in the form of a battery 15, for storing electrical energy or power.
  • the aerosol-generating device 2 may further comprise at least one electrical connector 12 for coupling to a corresponding at least one electrical connector 13 of the companion device 3.
  • the one or more electrical connectors 12 of the aerosol-generating device 2 may be coupled with the one or more electrical connectors 13 of the companion device 3 to charge the at least one battery 15 of the aerosol-generating device 2.
  • the aerosol-generating device 2 may further comprise user interface components, for example comprising an input element in the form of a pushbutton 8.
  • the pushbutton 8 may be used as a power button to activate or deactivate the heating element 7 for aerosol generation thereby to activate or deactivate the aerosol-generating device 2.
  • the heating element 7 may be activated and heat may be applied to at least a part of the aerosol-generating article 17, such that aerosol can be generated for consumption by the user, for example in a usage session.
  • the aerosol-generating device 2 may further comprise a communications arrangement 9 or communication circuitry 9 with one or more communications interfaces 10 for communicatively coupling the aerosol-generating device 2 with the companion device 3, for example, via an Internet connection, a wireless LAN connection, a WiFi connection, a Bluetooth connection, a mobile phone network, a mobile data connection for example but not limited to a 3G/4G/5G connection, an edge connection, an LTE connection, a BUS connection, a wireless connection, a wired connection, an optical data connection such as but not limited to IrDa, a radio connection, a near field connection, and/or an IoT connection.
  • a communications arrangement 9 or communication circuitry 9 with one or more communications interfaces 10 for communicatively coupling the aerosol-generating device 2 with the companion device 3, for example, via an Internet connection, a wireless LAN connection, a WiFi connection, a Bluetooth connection, a mobile phone network, a mobile data connection for example but not limited to a 3G/4G/5G connection, an edge connection
  • the aerosol-generating device 2 may further comprise a data storage 11 for storing information or data, such as collected values of usage pattern parameters, battery degradation data and/or one or more mathematical functions or formulas, and for storing computer instructions that can be executed by processing circuitry 5.
  • a data storage 11 for storing information or data, such as collected values of usage pattern parameters, battery degradation data and/or one or more mathematical functions or formulas, and for storing computer instructions that can be executed by processing circuitry 5.
  • the aerosol-generating device 2 is configured to collect, gather and/or store values of at least one usage pattern parameter related to a usage of the aerosol-generating device 2.
  • One or more sensors 16 may be arranged on the aerosol-generating device 2 to collect data, for example values of usage pattern parameters and/or battery capacity data and/or location information and/or time information.
  • Figure 2 shows an example of a histogram pertaining to the usage pattern parameter energy consumption per usage session.
  • the histogram may comprise a total of five bins.
  • the energy consumption per usage session may be measured in total capacity of the battery used per usage session.
  • the numerical range of the bins may be different for each bin.
  • the histogram may be designed to determine how many usage sessions can be provided to the user in a range from 1 to 5 usage sessions by the energy stored in the battery 15 with a current maximum capacity of an exemplary value of 235 mAh.
  • the numerical range of the bins may be calculated by dividing the current maximum capacity of the battery 15 by the number of usage sessions that could theoretically be provided. The resulting number may represent the upper end of the numerical range of the respective bin, whereas the lower end of the numerical range of the respective bin may be either 0 or be determined by the upper end of the numerical range of the neighbouring bin.
  • the current maximum capacity of the battery 15 of 235 mAh divided by the maximum number of 5 possible usage sessions equals 47 mAh. This may be the upper end of the numerical range of the first bin, with the lower end being 0 mAh.
  • the current maximum capacity of the battery 15 of 235 mAh divided by the next number of 4 possible usage sessions equals 59 mAh. This may be the upper end of the numerical range of the second bin, with the lower end beginning where the upper end of the previous bin ends.
  • the current maximum capacity of the battery 15 of 235 mAh divided by the next number of 3 possible usage sessions equals 78 mAh. This may be the upper end of the numerical range of the third bin, with the lower end beginning where the upper end of the previous bin ends.
  • the current maximum capacity of the battery 15 of 235 mAh divided by the next number of 2 possible usage sessions equals 117 mAh. This may be the upper end of the numerical range of the fourth bin, with the lower end beginning where the upper end of the previous bin ends.
  • the current maximum capacity of the battery 15 of 235 mAh divided by the next number of 1 possible usage sessions equals 235 mAh. This may be the upper end of the numerical range of the fifth bin, with the lower end beginning where the upper end of the previous bin ends.
  • the first bin may represent a numerical range of from 0 to 47 mAh
  • the second bin may represent a numerical range of from 48 to 59 mAh
  • the third bin may represent a numerical range of from 60 to 78 mAh
  • the fourth bin may represent a numerical range of from 79 to 117 mAh
  • the fifth bin may represent a numerical range of from 118 to 235 mAh.
  • values for the energy consumption per usage session may be collected and classified into the bins of the histogram. If the energy consumption of all of the usage sessions of the aerosol-generating device 2 were classified into the first bin, this would mean that very little energy is used per usage session and the full 5 usage sessions could be provided with one full charge of the battery 15. Similarly, if the energy consumption of all of the usage sessions of the aerosol-generating device 2 were classified into the second bin, this would mean that only 4 usage sessions could be provided with one full charge of the battery 15 and so on. In practice, the energy consumption per usage session may not be this homogeneous.
  • the histogram of figure 2 shows an exemplary, but more realistic distribution of the values of the usage pattern parameter.
  • the number n of values classified in the first bin is 6, the number n of values classified in the second bin is 3, the number n of values classified in the third bin is 8, the number n of values classified in the fourth bin is 3 and the number n of values classified in the fifth bin is 0. Only the 20 most recent values that have been collected may be represented in the histogram.
  • the histogram as represented by figure 2 may be used to calculate how many usage sessions can be provided to the user with one full charge of the battery 15 within a predetermined degree of certainty.
  • the histogram feature information extracted from the histogram for this determination is represented by the heights of the bins and sums of these heights.
  • the predetermined degree of certainty in this case may be expressed as a percentage of the number n of values classified into all of the bins of the histogram. For example, if the number n of values classified into the first bin represents a percentage of all the values of all the bins of the histogram that is equal to or greater than the percentage required for the predetermined degree of certainty, then a total of 5 usage sessions can be provided to the user.
  • bins have to be taken into account. For example, if the number n of values classified into the first and the second bin summed together represents a percentage of all the values of all the bins of the histogram that is equal to or greater than the percentage required for the predetermined degree of certainty, then a total of 4 usage sessions can be provided to the user. If this is also not the case, another bin may be taken into account. For example, the heights of the first, second and third bins may be summed and checked against the predetermined degree of certainty as explained above to check whether 3 usage sessions can be provided.
  • the heights of the first, second, third and fourth bins may be summed and checked against the predetermined degree of certainty as explained above to check whether 2 usage sessions can be provided. If this sum does not satisfy the predetermined degree of certainty, only one usage session may be provided.
  • the first, the second and the third bin together contain or represent 17 of 20 of the most recent usage sessions, 20 being an exemplary non-limiting value.
  • the method may therefore comprise limiting a total number of usage sessions provided to a user to generate aerosol before recharging of the battery 15 of the aerosol-generating device 2 to 3 usage sessions. This may also be used to decide whether or not to allow a further usage session depending on the number of usage sessions that have already occurred since the last recharging event.
  • the aerosol-generating device 2 If the user has just started using the aerosol-generating device 2, it may be that not enough data is available to make a meaningful decision based upon usage pattern parameters of the user. In this case, it may be provided that the aerosol-generating device 2 is controlled in a predetermined way until a sufficient dataset has been collected.
  • Figure 3 shows an example of a histogram pertaining to the usage pattern parameter number of usage sessions the aerosol-generating device 2 has been operated to generate aerosol per day.
  • the histogram may comprise a total of six bins, each bin representing one more usage session per day than the one before.
  • 1 value of the usage pattern parameter has been classified into the first bin, representing 1 usage session per day
  • 3 values of the usage pattern parameter have been classified into the second bin, representing 2 usage sessions per day
  • 16 values of the usage pattern parameter have been classified into the third bin, representing 3 usage sessions per day
  • 2 values of the usage pattern parameter have been classified into the fourth bin, representing 4 usage sessions per day
  • 0 usage sessions have each been classified into the fifth and sixth bin, representing 5 and 6 usage sessions per day, respectively.
  • the histogram feature information extracted from the histogram for this determination may again be represented by the heights of the bins and sums of these heights.
  • both histograms according to figures 2 and 3 pertain to the same user.
  • the aerosol-generating device 2 may therefore be controlled in a way so that the battery 15 is not recharged above a state of charge of for example 80%or 85%or 90%. This battery management may result in a lower battery degradation over time while still providing the user with the desired number of usage sessions within a high probability.
  • FIG 4 a shows a flowchart of the method 18 of the present disclosure.
  • the method 18 may begin with step 19, in which a plurality of values of at least one usage pattern parameter related to usage of the aerosol-generating device 2 may be collected, for example for a given number of usage sessions.
  • a histogram may be created by classifying each collected value of the usage pattern parameter into a bin of the histogram. This may result in histograms as for example shown in figures 2 and 3.
  • histogram feature information may be extracted from the histogram.
  • the histogram feature information may, for example, pertain to heights of different bins of the histogram or to sums of heights of adjacent bins.
  • the method 18 may comprise step 22, in which the aerosol-generating device 2 may be controlled based on said histogram feature information.
  • method 18 may allow for adapting the operational constraints of the aerosol-generating device 2 according to the histogram feature information.
  • the method 18 according to the present disclosure may allow for controlling the aerosol-generating device 2 taking into account individual user habits. Both battery management and/or availability or duration of usage sessions may be adapted in this way. Simultaneously, the method 18 requires only minimal memory or storage space and computational power and is therefore suitable for implementation on aerosol-generating devices 2.

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Abstract

A computer-implemented method of controlling an aerosol-generating device, the method comprising: collecting a plurality of values of at least one usage pattern parameter related to a usage of the aerosol-generating device; creating a histogram by classifying each collected value of the usage pattern parameter into a bin of the histogram; extracting histogram feature information from the histogram; and controlling the aerosol-generating device with operational constraints that are based on the histogram feature information.

Description

CONTROL METHOD OF AEROSOL-GENERATING DEVICE USING HISTOGRAMS
The present disclosure relates to a computer-implemented method of controlling an aerosol-generating device. The present disclosure further relates to an aerosol-generating device, an aerosol-generating system comprising an aerosol-generating device and a companion device, a computer program and a computer readable medium.
Aerosol-generating devices are typically designed as handheld devices that can be used by a user for consuming or experiencing, for instance in one or more usage sessions, aerosol generated by heating an aerosol-generating substrate or an aerosol-generating article. The aerosol-generating devices the present disclosure pertains to are commonly referred to as heated tobacco products (HTP) , heat-not-burn devices, electronic cigarettes and/or vaporiser. The applicant has marketed such devices, for example, under the brand name
Exemplary aerosol-generating substrates may comprise solid substrate material, such as tobacco material or tobacco cast leaves (TCL) material. The substrate material may, for example, be assembled, often with other elements or components, to form a substantially stick-shaped aerosol-generating article. Such a stick or aerosol-generating article may be configured in shape and size to be inserted at least partially into the aerosol-generating device, which, for example, may comprise a heating element or heater device for heating the aerosol-generating article and/or the aerosol-generating substrate. Alternatively or additionally, aerosol-generating substrates may comprise one or more liquids and/or solids, which may, for example, be supplied to the aerosol-generating device in the form of a cartridge or container. Corresponding exemplary aerosol-generating articles may, for example, comprise a cartridge containing or fillable with the liquid and/or solid substrate, which may be vaporized during aerosol consumption by the user based on heating the substrate and/or liquid. Usually, such cartridge or container may be coupled to, attached to or at least partially inserted into the aerosol-generating device. Alternatively, the cartridge may be fixedly mounted to the aerosol-generating device and refilled by inserting liquid and/or solid into the cartridge.
For generating the aerosol during use or consumption, heat may be supplied by a heating element, heater device or heat source to heat at least a portion or part of the aerosol-generating substrate. The heating element, heater device or heat source may be arranged in the handheld device or a handheld part of the aerosol-generating device. Alternatively or additionally, at least a part of or the entire heating element or heater device or heat source may be fixedly associated with or arranged within an aerosol-generating article, for instance in the form of a stick or cartridge, which may be attached to and/or powered by the handheld device or handheld part of the aerosol-generating device.
Exemplary heating elements or heater devices can be based on one or more of resistive heating, inductive heating and microwave heating using electrical energy supplied via, drawn from or stored in battery of the aerosol-generating device. As used herein, a battery of the aerosol-generating device can generally refer to an energy storage of the aerosol-generating device  configured to store electrical energy. Accordingly, the term battery can include one or more capacitors, one or more accumulators or other types of energy storage. Also, any reference to a battery herein can include a plurality of batteries.
Typically, aerosol-generating devices may comprise a battery providing the electrical energy needed to operate the aerosol-generating device and especially for heating the aerosol-generating substrate and/or article, for example to generate aerosol in one or more usage sessions using one or more aerosol-generating articles. The battery may, for example, be a lithium-ion battery. A battery capacity may typically be chosen so that the aerosol-generating device may provide a user with at least a minimum number, for example at least two or more, consecutive usage sessions or experiences without having to recharge the battery or the aerosol-generating device in between. To improve user experience, aerosol-generating devices may usually be designed to only allow a user to start a usage session if the battery contains enough electrical energy to fully complete the usage session. However, the energy needed for a usage session may be highly variable and may depend on many factors from external parameters like ambient temperature to user habits. Conventional statistical models used in controlling aerosol-generating devices may be hampered by the limited computational power typically available on these devices. Additionally, taking an average value, for example of energy consumption per usage session, may be too much of a generalisation in some cases or for some parameters and may therefore not lead to an accurate estimation of the individual use case at hand. It may therefore happen that a user may be denied a further usage session despite there being a good chance that such a session could be completely provided under the specific circumstances.
It may therefore be desirable to provide for an improved control of aerosol-generating devices and/or aerosol-generating systems.
This is achieved by the subject-matter of the independent claims. Optional features are provided by the dependent claims and by the description.
According to an aspect of the present disclosure, there is provided a computer-implemented method of controlling an aerosol-generating device, the method comprising: collecting a plurality of values of at least one usage pattern parameter related to a usage of the aerosol-generating device; creating a histogram by classifying each collected value of the usage pattern parameter into a bin of the histogram; extracting histogram feature information from the histogram; and controlling the aerosol-generating device with operational constraints that are based on the histogram feature information. In other words, the method may include controlling the aerosol-generating device based on said histogram feature information by adapting operational constraints of the aerosol-generating device.
Usage pattern parameters may be indicative of different usage and/or operational characteristics of the aerosol-generating device by the user. For example, usage pattern parameters may pertain to parameters describing or related to the use or operation of an aerosol-generating device by a user, in particular how and/or how often and/or when and/or how long  and/or in what state the aerosol-generating device is and/or has been used or operated by the user. The usage pattern parameters may pertain or relate to one or both the usage sessions of the aerosol-generating device and the times between usage sessions, for example resting time or recharging events of the aerosol-generating device. Thus, the usage pattern parameters described herein, which may also be referred to as “parameters” herein, may characterise the different preferences and/or habits of each individual user, which may differ between users. The method may thus provide a highly individual control of the aerosol-generating device.
At least one of the usage pattern parameters may, in an example, be indicative of the usage or operation of the aerosol-generating device by the user to generate aerosol in one or more usage sessions. For example, the puff volume may be measured during one or more usage sessions and collected as usage pattern parameter. An average or a mean value may be calculated from multiple, for example all, puffs from one usage session. Additionally or alternatively, puff volume may be measured during more than one usage sessions. An average or a mean value may be calculated from multiple, for example all, puffs from all usage sessions. Additionally or alternatively, there may also be parameters that can only be determined by observing more than one usage sessions. For example, the resting time between usage sessions can only be determined when two usage sessions occur. Another example may be the frequency of at least two usage sessions in a row, in particular without recharging of the aerosol-generating device in between. This parameter can also only be determined by observing more than one usage sessions. A value of a usage pattern parameter may be a numerical value corresponding to a degree or extent or count of a usage pattern parameter.
A histogram may be an approximate or simplified representation of the distribution of numerical data, for example the values of the at least one usage pattern parameter. In a histogram, each value may be classified into a bin, the bin representing a range or interval of the numerical values. Values falling outside the range or interval of one bin may be classified into a different bin or optionally disregarded. The bins of the histogram may represent consecutive, adjacent and non-overlapping intervals or ranges of the values of the usage pattern parameters. Overlap of at least some of the bins, however, can be possible. The bins may but need not have ranges or intervals of the same size or width. As such, the definition of histograms in the context of the present disclosure may be the same as conventionally used.
One of the advantages of using histograms in the present invention lies in the fact that histograms may approximate and therefore simplify the distribution of the usage pattern parameter values. They may thus need a lot less computational power to manipulate and extract information from than other statistical approaches. Simultaneously, histograms may offer a more detailed resolution of the underlying data than for example conventionally applied averaging of values. The inventors found that especially when observing usage pattern parameters of users of aerosol-generating devices, there may often be bi-or multimodal patterns, representing additional information in the data that would be lost on averaging. For example, a user using the aerosol- generating device to experience usage sessions of 2 minutes and of 6 minutes with equal frequency would be assumed to have an average duration of usage sessions of 4 minutes, which would not correctly represent a single one of the real usage sessions experienced by that user. The histogram, in contrast, may clearly show this bimodal distribution which may then be taken into account in controlling the aerosol-generating device.
Thus, a key feature of the present invention may be extracting histogram feature information from the histogram. Histogram feature information may correspond to or relate to or be indicative of one or more histogram features. The histogram feature information may be representative of a probability that a usage pattern parameter will have a certain value or will lie in a certain range or interval of values in the future, preferably the immediate future. In an example, the histogram feature information may be representative of a probability that a usage pattern parameter will have a certain value or will lie in a certain range or interval of values in the next, meaning the upcoming or just started, usage session or resting period. This probability may be empirically determined from the data in the histogram as will be explained in more detail below. Based on the histogram feature information, for example based on the probability of the future value of the usage pattern parameter, the aerosol-generating device may be controlled to accommodate and/or anticipate this future value and especially the upcoming usage session or resting period.
Controlling the aerosol-generating device based on histogram feature information includes adapting operational constraints or operation constraints of the aerosol-generating device in a way so that the operational constraints of the aerosol-generating device fit the histogram feature information and thus fit the individual usage preferences of the user of the aerosol-generating device. The operational constraints of the aerosol-generating device may be increased or tightened or decreased or loosened. For example, if the currently implemented operational constraints of the aerosol-generating device indicate that no further usage session is to be provided to the user, but the histogram feature information indicates that according to the individual habits of the user, another usage session can actually be provided, then the currently implemented operational constraints may be changed, in this case loosened, to allow the device the provision of another usage session to the user. Inversely, if the currently implemented operational constraints of the aerosol-generating device indicate that, for example, three usage sessions may be provided to the user with one fully charged battery, but the histogram feature information indicates that according to the individual habits of the user only two usage sessions may be provided, the currently implemented operational constraints may be changed, in this case tightened, so that only two usage sessions are provided to the user with one fully charged battery. They may pertain to usage sessions and/or charging of the aerosol-generating device. The operational constraints may include limitations of battery and/or power consumption management of the aerosol-generating device. They may also include limitations on the length/duration and/or number of usage sessions provided to a user before recharging the battery. For example, an operational constraint of the aerosol-generating device may be a duration, as in a length of time,  a usage session lasts. If the histogram feature information extracted from the histogram indicates that the capacity of the battery is enough to support one or more usage sessions of longer duration as currently set, the maximum duration of one or more, for example all, usage sessions may be increased. Inversely, if the histogram feature information extracted from the histogram indicates that the capacity of the battery is not enough to support one or more usage sessions of the duration as currently set, the maximum duration of one or more, for example all, usage sessions may be decreased. In this way, an individual maximum duration of usage sessions may be found for each individual user. Other operational constraints may, for example, include the total number of usage sessions provided to a user before a recharge of the battery, whether or not a further usage session is allowed, the state of charge the battery is charged to when charging or the charging rate at which the battery is charged. These constraints will be further explained below.
The usage pattern parameters may be collected over a predetermined period of time. The predetermined period of time may be, for example, a fixed amount of hours, days, weeks, or months after the first use of the aerosol-generating device. Alternatively, the predetermined period of time may be the whole time since the first use of the aerosol-generating device. Optionally, the aerosol-generating device may be designed or configured to collect the usage pattern parameters, preferably automatically, periodically and/or continuously.
Collecting the usage pattern parameters may include storing corresponding data indicative of the one or more usage pattern parameters, for example in a data storage of the aerosol-generating device or other device communicatively coupleable thereto. Alternatively or additionally, the aerosol-generating device may comprise means for determining the usage pattern parameters, preferably as numerical values, and/or for storing data indicative of the usage pattern parameters or corresponding values thereof. These means may be or may comprise, for example, counters and/or timers and/or sensors, such as temperature sensors, volume sensors, humidity sensors and others. In an example, the usage pattern parameters may be collected over the entire life of the aerosol-generating device, which may mean from a first to a last usage session of the aerosol-generating device.
The aerosol-generating device may comprise a storage or memory in which the collected parameters, parameter values and/or corresponding data may be stored. The collected usage pattern parameters may also be stored in a user profile and/or transferred to another aerosol-generating device or other device communicatively coupleable to the aerosol-generating device, such as a companion device, a server, a smartphone or other computing device.
The at least one usage pattern parameter may be selected from the parameters:
- energy consumption per usage session,
- number of usage sessions the aerosol-generating device has been operated to generate aerosol, preferably per predefined time interval, for example per day,
- duration of a usage session,
- resting time between consecutive usage sessions, preferably wherein the usage pattern parameter value pertaining to the resting time between consecutive usage sessions only varies for resting times between subsequent usage sessions of from 0 to 40 minutes,
- frequency of at least two usage sessions in a row, in particular without recharging of the aerosol-generating device in between (also referred to as back-to-back regime) ,
- ambient temperature during a usage session,
- ambient air pressure during a usage session,
- ambient humidity during a usage session,
- ambient temperature during recharging of a battery of the aerosol-generating device,
- temperature of a battery of the aerosol-generating device during a usage session,
- temperature of a heating element or heater device of the aerosol-generating device within a predefined period of time before start of a usage session,
- number of puffs per usage session,
- puff volume,
- puff frequency,
- puff rhythm,
- time of initiation of a pause mode at the aerosol-generating device,
- time of termination of a pause mode at the aerosol-generating device,
- duration of a pause mode at the aerosol-generating device,
- resting time after recharging the aerosol-generating device,
- resting time with a battery state of charge of less than 10 %,
- resting time with a battery state of charge of more than 90 %,
- density of an aerosol-generating substrate or aerosol-generating article used with the aerosol-generating device to generate aerosol,
- weight of an aerosol-generating substrate or aerosol-generating article used with the aerosol-generating device to generate aerosol,
- type of an aerosol-generating substrate or aerosol-generating article used with the aerosol-generating device to generate aerosol,
- humidity of an aerosol-generating substrate or aerosol-generating article used in the aerosol-generating device,
- temperature profile selected by a user.
The energy consumption per usage session may describe the amount of electrical energy drained from the battery of the aerosol-generating device to provide or grant a usage session, for example from the start of the usage session to the end of the usage session. This may be  represented in units of the capacity of the battery, for example as a percentage of the state of charge (SOC) of the battery drained to provide the usage session. It may also be represented as a total amount of battery capacity needed to provide the usage session, for example in mAh, which is the standard representation of battery capacity.
The number of usage sessions of the aerosol-generating device, respectively, the number of usage sessions the aerosol-generating device has been operated, may be a relevant parameter because it may characterise the intensity of use of the device by the user. It therefore may allow to differentiate casual from heavy users and may be used to describe the progression through the lifetime of the device and/or the battery. The number of usage sessions may alternatively be related to a different reference than a predefined time interval. For example, the number of usage sessions between recharging the device may be collected. For this value, the amount of time between the two consecutive recharging events of the device may be irrelevant.
Although the method according to this disclosure may include any one or any combination of the parameters listed, it is especially preferred that the at least one usage pattern parameter may be selected from the parameters energy consumption per usage session and/or number of usage sessions the aerosol-generating device has been operated to generate aerosol, preferably per predefined time interval.
The duration of one or more usage sessions may vary from user to user and may have an impact on the strain on the battery. The amount of energy required for a usage session may be highly correlated to its duration, as the aerosol-generating device should preferably maintain the heating temperature during this period. Merely as an example, typical aerosol-generating devices allow usage sessions of up to 6 minutes.
The resting time between consecutive usage sessions may be related to the temperature of the device, a heating element of the device and the battery. During a usage session, the heating element, the device and the battery may be heated up by heating the aerosol-generating substrate or article. After a usage session, the device and the battery start to cool off or cool down until the device and the battery reach the ambient temperature. This time duration can be referred to as the resting time. As a non-limiting example, after around 40 minutes, the battery typically reaches ambient temperature, which may mean that different resting times of 40 minutes or more may have the same effect, from the point of view of temperature. For this reason, optionally, only resting times between 0 and 40 minutes may result in different values for the corresponding usage pattern parameter, whereas times of 40 minutes and more may have the same value. Short resting times that are not long enough for the device to reach ambient temperature may be less strainful for the battery and therefore cause less battery degradation.
The frequency of at least two usage sessions in a row, especially without recharging of the aerosol-generating device or the battery in between, may also be referred to as back-to-back regime. This parameter may, for example, be described by the percentage of two consecutive usage sessions which occur without the aerosol-generating device or the battery having been  recharged before initiation of the second usage session. For example, in an aerosol-generating device designed or configured to provide two usage sessions after fully charging the battery, recharging the aerosol-generating device after each usage session would result in a back-to-back regime of 0 %, whereas recharging the device only after two usage sessions have been performed would result in a back-to-back regime of 100 %. A back-to-back regime of 50 %would then describe recharging the device after one usage session half the time and only after two usage sessions the rest of the time. Generally, the frequency of at least two usage sessions in a row may be determined by dividing the number of consecutive usage sessions by the total number of usage sessions.
A puff in the sense of the present disclosure may describe a pull and/or draw on the aerosol-generating device while inhaling a mixture of air and aerosol by a user. The puff volume may describe the volume of said mixture inhaled in one pull or inhalation. Puff frequency and rhythm may describe corresponding patterns in the occurrence of puffs characteristic for individual users. Merely as an example, typical aerosol-generating devices are designed to allow a maximum of 14 puffs per aerosol-generating article.
A pause mode may refer to a special mode of the aerosol-generating device allowing a pause during a usage session. Pause mode therefore may not pertain to and may be distinct from resting times between usage sessions.
The aerosol-generating device may be operated in at least two operation modes, an aerosol-releasing mode and a pause mode. The aerosol-generating device may be configured to heat the heating element, the aerosol-generating article and/or the substrate at a first temperature level in the aerosol-releasing mode. Therein, the first temperature level may correspond to a predetermined heating temperature or a temperature above, which may be sufficient to generate aerosol. The aerosol-generating device may further be configured to heat the heating element, the aerosol-generating article and/or the substrate at a second temperature level below the first temperature level in a pause mode of the aerosol-generating device. The second temperature level may, for example, refer to a temperature above room temperature and below the first temperature level.
A user experience, also referred to as usage session or experience of an aerosol-generating article herein, may be interrupted, for example by switching the device into the pause mode, and resumed by a user at a later, wherein the aerosol-generating article or substrate may be kept in pause mode of the aerosol-generating device at a temperature below the first temperature level and/or below the predetermined heating temperature used during normal use of the device (in particular during a user experience or usage session) , but still above or well above room temperature. That is, the second temperature level preferably may be chosen such as to avoid degradation of the non-depleted substrate or aerosol-generating article. In particular, the second temperature level may be chosen such as to be sufficiently low in order to minimize depletion of the substrate or article during the pause mode, and at the same time to be sufficiently high in  order to avoid vapor to condensate in the device which otherwise could affect the quality of the non-depleted aerosol-generating substrate or article.
During use of the device, in particular when a user experience or usage session is to take place, the aerosol-generating device may be operated in the aerosol-releasing mode, whereas during a use pause of the device, that is, when no user experience or usage session is to take place and/or when a usage session is interrupted by a pause, the aerosol-generating device may be operated in the pause mode. During both, the aerosol-releasing mode and the pause mode of the aerosol-generating device, the heating element, a heating circuitry and/or a heating arrangement may be in operation, in particular in heating operation, yet at different temperature levels, namely, at a first temperature level during the aerosol-releasing mode, which may be chosen to be sufficiently high in order to generate an aerosol, and at a second temperature level below the first temperature level during the pause mode, which may be chosen to be sufficiently low in order to minimize depletion of the substrate, whilst avoiding degradation.
Depending on the type and composition of the specific aerosol-generating article or substrate to be used with the device, the first temperature level may be in a range between 250 degree Celsius and 450 degree Celsius, particularly between 270 degree Celsius and 430 degree Celsius, more particularly between 315 degree Celsius and 355 degree Celsius. These temperatures may be suitable operating or heating temperatures sufficient to allow volatile compounds to be released from the aerosol-generating article or substrate, for example during one or more usage sessions and/or when operating the device in the aerosol releasing mode. For example, the first temperature level and/or heating temperature for liquid aerosol-generating articles or substrates may be lower than the first temperature level for solid aerosol-generating articles or substrates.
In general, the second temperature level may be chosen to maintain a usability of the aerosol-generating article or substrate for a prolonged time. The second temperature level may also depend on the type and composition of the aerosol-generating article or substrate to be used with the device. Accordingly, the second temperature level may be in a range between 175 degree Celsius and 225 degree Celsius, particularly between 185 degree Celsius to 215 degree Celsius, more particularly between 195 degree Celsius and 205 degree Celsius. These temperatures may be sufficiently low in order to minimize depletion of the substrate during the pause mode but at the same time sufficiently high in order to avoid vapor to condensate in the device, which could lead to degradation of the aerosol-generating article or substrate.
In order to avoid condensation effects in the device, in particular to avoid condensation of substances in the aerosol-generating article or substrate, the second temperature level may be at least 150 degree Celsius, in particular at least 175 degree Celsius, preferably at least 185 degree Celsius, more preferably at least 195 degree Celsius.
Vice versa, in order to minimize depletion of the substrate or article during the pause mode the second temperature level may be at most 220 degree Celsius, in particular at most 225 degree  Celsius, preferably at most 215 degree Celsius, more preferably at least 205 degree Celsius. In particular, the second temperature level may be chosen such as to reduce the formation of aerosols by at least 50 percent compared to the aerosol-releasing mode.
In relative terms, the second temperature level may be lower than the first temperature level, for example by at least 50 degree Celsius, in particular at least 75 degree Celsius, more particularly at least 100 degree Celsius.
The temperature values given above preferably may be average temperatures of the aerosol-generating article or substrate during operation of the device. In addition, as already mentioned, the temperature values may depend, inter alia, on the type and composition of the aerosol-generating article or substrate to be used with the device.
As used herein, the pause mode may refer to a first operational mode of aerosol-generating device, in which the heating element, the heating circuitry and/or a heating arrangement may be operated during an operation pause, that is, a use pause of the aerosol-generating device, that is, when a user experience or usage session is paused and aerosol generation may not take place, or at least may be reduced to a minimum level. That is, in the pause mode the aerosol-generating device is in a use pause.
Vice versa, the aerosol-releasing mode may refer to a second operational mode of the aerosol-generating device, which is the normal heating operational mode of the heating element, circuitry, and/or arrangement for aerosol generation, in which heating element, the heating circuitry and/or a heating arrangement may be operated during use of the device by a user, that is, when a user experience or usage session takes place, in particular when aerosol generation takes place. In general, aerosol generation may take place continuously or on demand, in particular on a puff basis, that is, on demand of a user when taking a puff.
The density, weight, type and/or humidity of an aerosol-generating substrate or aerosol-generating article may be detected by the aerosol-generating device recognising, sensing and/or identifying the stick or cartridge for example through RFID or other means. As these factors may influence the energy needed for aerosol generation from the substrate or article, they also influence battery degradation.
The method may also comprise collecting two or more usage pattern parameters and creating a histogram for each of the usage pattern parameters, and controlling the aerosol-generating device based on histogram feature information extracted from a plurality of the histograms. The two or more usage pattern parameters may be selected from the list as described above. In cases in which the method described herein pertains to more than one usage pattern parameter, for example at least two usage pattern parameters, these usage pattern parameters may differ from each other. Each of the parameters may thus be one of the parameters listed above, wherein each parameter may be different from the others. In particular, it is noted that two usage pattern parameters as used herein may not describe or refer to different values, for example numerical values, of the same parameter, but rather values of different parameters. Each  usage pattern parameter may be used to create at least one distinct histogram. In this way, information from an arbitrary number or all of the collected usage pattern parameters may be extracted from the histograms. The method according to the present disclosure may therefore provide detailed and highly individualised information based on which the aerosol-generating device may be controlled.
Both in the case that one histogram or that two or more histograms are used in the invention, it may be provided that at least one histogram feature information extracted from the histograms is differently weighted than the others. Additionally or alternatively, it may be provided that histogram feature information extracted from different histograms is weighted differently. For example, at least one or each histogram feature information may be provided with a weighing or scaling factor, for example a multiplicative factor, thereby increasing or decreasing the numerical value provided by the histogram feature information and used in controlling the aerosol-generating device. In this way, the method may take into account that different histogram feature information and/or different histograms and/or different usage pattern parameters may have a different importance for controlling the aerosol-generating device. Therefore, the numerical value of more important histogram feature information and/or histograms and/or usage pattern parameters may be increased and thus have a bigger impact on controlling the aerosol-generating device then less important histogram feature information and/or histograms and/or usage pattern parameters and vice versa.
Generally, the extracted histogram feature information or histogram features may relate to any information that is available in or can be derived from the data related to the one or more usage pattern parameters that is provided as a histogram. As explained above, providing data in one or more histograms may make the further processing of the data easier and may necessitate less computational power than conventional statistical methods, while also potentially increasing the informational content derivable or obtainable from the one or more histograms.
In an example, the histogram feature information includes at least one of:
- one or more local maxima of the histogram,
- the identity of one or more bins of the histogram corresponding to one or more local maxima of the histogram,
- one or more heights of the bins of the histogram, the bin or bins preferably corresponding to one or more local maxima of the histogram,
- a sum of heights of two or more bins of the histogram, preferably wherein the bins the heights of which are summed are adjacent to each other,
- an average of the values of the usage pattern parameter classified into one or more bins of the histogram,
- a median of the values of the usage pattern parameter classified into one or more bins of the histogram,
- a maximum of the values of the usage pattern parameter classified into one or more bins of the histogram,
- a minimum of the values of the usage pattern parameter classified into one or more bins of the histogram.
A local maximum of a histogram may, for example, be a bin or a group of adjacent bins that have a greater height than neighbouring bins or groups of neighbouring bins. For example, a local maximum may be defined as the bin with the biggest height of all bins of a histogram. For the purpose of finding a local maximum, the heights of neighbouring or adjacent bins may also be summed together. A local maximum may therefore represent or be indicative of a bin or a group of bins (and therefore a range of values of usage pattern parameters represented by the bin or bins) in which more values of usage pattern parameters may be classified than in other areas of the histogram. One histogram may have one or more local maxima, for example in bi-or multimodal distributions of values of usage pattern parameters.
The identity of one or more bins may correspond to the range of values of usage pattern parameters classified into this bin or bins. By identifying the bin or the bins of a local maximum, a range of values of the usage pattern parameter which occurred often during the collection of the values of the usage pattern parameter or parameters may be identified.
Preferably, the height of a bin may correspond to or may be proportional to the number of values of the usage pattern parameter classified into this bin. For example, if 3 values of a specific usage pattern parameter fall into the range of a specific bin and are therefore classified into this bin, the height of this bin may be 3. It is therefore immediately apparent that the height of one or more bins may be summed together. For instance, the height of neighbouring or adjacent bins may be summed.
There may be provided a comparison between the summed heights of two or more groups of bins of one or more histograms. For example, the summed heights of all bins associated with at least one local maximum may be compared to the summed heights of all other bins, specifically of all other bins not associated with the at least one local maximum. In this way, the probability that an upcoming event, for example a usage session or a resting period, again falls into the range of values represented by the summed bins may be quickly and easily calculated, as will be explained in more detail below. That an upcoming event again falls into the range of values represented by a bin or by more than one bins may mean that if a value of the usage pattern parameter in question was determined for this event, the value would be classified into this bin or this group of bins.
In general, there are many different conclusions that may be drawn from the extracted histogram feature information and that may be used in controlling the aerosol-generating device. The method may comprise, for example, determining from the histogram feature information a number of usage sessions that can be provided to a user to generate aerosol by the maximum capacity of a battery of the aerosol-generating device, for example within a predetermined degree  of certainty. Many of the usage pattern parameters mentioned above have an influence on the energy consumption during usage sessions and may therefore be used for this determination.
In an example, suitable parameters may be the number of usage sessions the aerosol-generating device has been operated to generate aerosol between recharging the device and/or the energy consumption per usage session. For example, it may be determined that during the time in which usage pattern parameters were collected, X usage sessions could be provided between two consecutive recharge events. It may therefore be assumed that this number of usage sessions can again be provided to the user. Alternatively, it may be determined that during the time in which usage pattern parameters were collected, the energy consumption per usage session was so high that X usage sessions can be provided when taking into account the maximum capacity of the battery of the aerosol-generating device. Then it may be assumed that this number of usage sessions can again be provided to the user. The maximum capacity of the battery may be a nominal, initial or original maximum capacity or a current maximum capacity of the battery of the aerosol-generating device. During the lifetime of an aerosol-generating device, the battery capacity may degrade with use. The current maximum capacity of the battery may therefore describe or pertain to the actual state of the battery as currently in use in the device. The current maximum capacity of the battery may thus be smaller than the nominal, initial or original maximum capacity. There are several methods to determine the current battery capacity or state of degradation of the battery which can be employed and which therefore do not need to be explained in detail.
In the whole present disclosure, within a predetermined degree of certainty may mean with a certainty of 95%or 90%or 85%or 80%or 75%or 70%. For example, it may be determined from the collected values of a usage pattern parameter that X%of the values fall within a certain numerical range, the numerical range being represented by one or more bins of the histogram. It may then be determined from this that an upcoming value of the usage pattern parameter may again lie in this numerical range with a certainty of X%. The predetermined degree of certainty may then be X%. The predetermined degree of certainty may also be a fixed value, for example Y%.It may then be provided that only bins representing up to Y%of values of a usage pattern parameter are considered and/or that there may be a comparison between this Y%and the X%determined as described above. The result of this comparison, for example, that X%lies above or below Y%, may then be used in controlling the aerosol-generating device.
The method may additionally or alternatively comprise determining from the histogram feature information whether or not an additional usage session can be provided considering the current state of charge of a battery of the aerosol-generating device, for example within a predetermined degree of certainty. For instance, if the average energy consumption per usage session determined from a histogram is higher than the current state of charge of the battery, it may be determined that no additional usage session can be provided. Inversely, if the average energy consumption per usage session determined from a histogram is lower than the current  state of charge of the battery, it may be determined that an additional usage session can be provided. Instead of the average energy consumption, it could be determined from the histogram what fraction of the total usage sessions used less energy than the current state of charge of the battery. For this, for example the heights of all bins representing an energy consumption below the current state of charge of the battery may be summed together and divided by the summed heights of all other bins of the histogram pertaining to energy consumption per usage session. The result may be equal to the probability that the current state of charge of the battery may be sufficient for the upcoming usage session and therefore whether or not an additional usage session can be provided.
Considering the results as determined from the histogram feature information in the examples explained above, controlling the aerosol-generating device based on said histogram feature information may pertain to adjusting operational constraints directed to or including the total number of usage sessions provided to a user to generate aerosol before recharging of a battery of the aerosol-generating device. In other words, the operational constraints may include limitations of battery and/or power consumption management. Thus, controlling the aerosol-generating device based on said histogram feature information may pertain to limiting a total number of usage sessions provided to a user to generate aerosol before recharging of a battery of the aerosol-generating device to a number of usage sessions that can be provided within a predetermined degree of certainty. Additionally or alternatively, controlling the aerosol-generating device based on said histogram feature information may pertain to allowing or prohibiting an additional usage session before recharging of a battery of the aerosol-generating device. This may also be achieved by adjusting the operational constraints pertaining to or including limitations of battery and/or power consumption management. Again additionally or alternatively, controlling the aerosol-generating device based on said histogram feature information may pertain to forcing and/or requesting a recharge of the battery. The aerosol-generating device may therefore be designed so that the user cannot start a usage session when a predetermined number of usage sessions before recharging is reached or when it has been determined that no additional usage session can be fully provided. In this way, the user experience is improved and waste of aerosol-generating articles by an incomplete usage session can be avoided. Forcing and/or requesting a recharge of the battery may pertain to putting the aerosol-generating device in a state in which no usage session can be initiated. Additionally, a notification or message to the user may be presented informing the user of the necessary recharge of the battery of the aerosol-generating device. For this purpose, the aerosol-generating device may comprise a light, a display, a loudspeaker or a vibration device to deliver a visual, acoustic or haptic notification or message to the user.
The method may further comprise determining a total amount of battery capacity required for operating the aerosol-generating device between two consecutive recharging events of a battery of the aerosol-generating device, for example within a predetermined degree of certainty,  from the histogram feature information. For instance, it may be determined what percentage of the capacity of the fully charged battery is required for operating the aerosol-generating device between two consecutive recharging events. This percentage may pertain to the state of charge (SOC) of the battery. Alternatively, the total amount of battery capacity required for operating the aerosol-generating device between two consecutive recharging events may also be expressed as an absolute numerical value, for example in mAh. Consecutive recharging events may pertain to recharging events that directly follow each other with only a variable amount of resting time and/or a variable number of usage sessions in between the recharging events. Therefore, from the perspective of one recharging event, a consecutive recharging event may be either the next recharge event or the previous one. For example by counting the number of usage sessions between two consecutive recharging events, it may be determined how often the user operates the aerosol-generating device to provide a usage session before recharging the device again. The energy consumption per usage session may then be summed to determine the total amount of battery capacity required before the device is recharged again. Alternatively, the state of charge of the battery right before recharging or when recharging is initiated may be collected.
From this information, it may be determined that the user, according to his individual user habits, does not require the whole capacity of the battery between two consecutive recharging events. For example, the number of usage sessions the user requires between two consecutive recharging events may be low, so that not the entire capacity of the battery is used. It may also be that the individual user habits of the user lead to energy conservative usage sessions so that not the whole capacity of the battery is used between two consecutive recharging events. In this case, the aerosol-generating device may be controlled in a way that minimizes battery degradation. For example, it may be provided that controlling the aerosol-generating device based on said histogram feature information includes adjusting operational constraints of the aerosol-generating device including limitations of battery and/or power and/or charging management. This may mean that controlling the aerosol-generating device based on said histogram feature information includes terminating a recharging event of a battery of the aerosol-generating device at a state of charge below 100%, preferably below 95%or below 90%or below 85%or below 80%. Recharging the battery until it is fully charged is known to cause an accelerated degradation. Similarly, it is known that recharging the battery only to a state of charge below the maximum slows battery degradation. If it is determined through the method of the present disclosure that the user only needs a fraction of the total or maximum capacity of the battery, for example of the current total capacity, this information can then be used to slow down the degradation of the battery by avoiding fully charging the battery. It is similarly known that fully discharging the battery also accelerates battery degradation. The aerosol-generating device may therefore be controlled so that the battery is not fully discharged during use and at least a residual state of charge of the battery is conserved, for example 5%or 10%or 15%or 20%of the state of charge of the battery. This residual state of charge of the battery may also be taken into account  when determining when to terminate a recharging event of the battery so that the battery capacity between the residual state of charge and the state of charge the battery is recharged to before terminating a recharging event corresponds to the total amount of battery capacity required for operating the aerosol-generating device between two consecutive recharging events.
The method may further comprise determining an amount of time a recharging event of a battery of the aerosol-generating device is going to last, for example within a predetermined degree of certainty, from the histogram feature information. The histogram may, for example, pertain to the usage pattern parameter duration of a recharge event of the aerosol-generating device. In other words, it may be determined how long the aerosol-generating device is typically connected to a power source so as to recharge its battery according to the individual habits of the user.
Taking this information into account, it may be provided that controlling the aerosol-generating device based on said histogram feature information includes adjusting operational constraints of the aerosol-generating device including limitations of battery and/or power and/or charging management. This may mean that controlling the aerosol-generating device based on said histogram feature information includes limiting a charging rate during a recharging event of a battery of the aerosol-generating device. High charging rates for fast charging are known to be detrimental in terms of battery degradation. Therefore, it may be advantageous to limit the charging rate when it is known that the duration of the recharging event is long enough to recharge the battery to the desired state of charge even with a limited charging rate. For example, a user may habitually recharge the aerosol-generating device overnight, which provides ample time to charge the battery slowly with a limited charging rate. This will result in slower degradation and a longer lifetime of the battery.
The user’s habits, this is the habits of one particular user, may differ greatly for different locations and/or different times at which the aerosol-generating device is operated, for example operated to generate aerosol in a usage session or to recharge the battery. The present invention may therefore comprise collecting the at least one usage pattern parameter along with location information and/or time information pertaining to where and/or when the aerosol-generating device is operated. The location information may pertain to the geographic location.
The location information may be determined through means like global positioning system (GPS) or a different global navigation satellite system (GNSS) system, through connections of the aerosol-generating device to local area networks, for example by Wifi or WLAN, for example a user’s home Wifi, office Wifi or a public Wifi, mobile cellphone network information or information from geolocator tags.
The time information may pertain to the time of day and/or the date of the calendar, for example determined by an internal timepiece and/or calendar of the aerosol-generating device. All of the usage pattern parameters mentioned herein may be collected with corresponding location information and/or time information. Such information may be collected, for example, for  every usage session, recharging event and/or resting time, for example for each of the beginning and/or the end of the usage session, recharging event and/or resting time.
Collecting one or both the time and location information may enable a more detailed analysis of the data pertaining to the usage pattern parameters. For example, usage of the aerosol-generating device may differ greatly when the user is at work from when the user is at home. Usage may also differ for work days or holidays or for work hours and free hours. The method may therefore comprise identifying different usage patterns of the aerosol-generating device at different locations and/or in different periods of time.
For instance, separate histograms and/or bins may be created for said locations and/or periods of time. A usage pattern in this sense may describe a set of values of a usage pattern parameter collected with location information and/or time information that differ from another set of values of the same usage pattern parameter collected with different location information and/or time information. If, for example, a usage pattern parameter has different values when collected at different locations and/or at different times, then it may be determined that this usage pattern parameter has different usage patterns depending on the location and/or time at which the values of the usage pattern parameter are collected. It may then be provided that these differences in the values of the usage pattern parameters are taken into account by creating a separate histogram for each usage pattern of the usage pattern parameter. Usage patterns may be considered different when the histogram feature information extracted from histograms pertaining to at least two usage patterns would lead to controlling the aerosol-generating device differently based on the extracted histogram feature information from either one of these histograms. For example, the histograms pertaining to different usage patterns may comprise different or a different number of local maxima or any other histogram feature information or histogram features mentioned herein.
To make use of this additional information provided by the location information and/or time information, the aerosol-generating device may be designed to collect current location information and/or time information. Current location information and/or time information may pertain to the present situation of the aerosol-generating device and/or the user, for example to the place where the aerosol-generating device and/or the user is at the moment and/or what time it is at the moment. In this way, information may be available as to where and/or when a current usage of the aerosol-generating device by the user occurs. The method may comprise extracting histogram feature information from the histogram corresponding to the location and/or time of the current use of the aerosol-generating device and controlling the aerosol-generating device based on the histogram feature information extracted from said histogram. In this way, control of the aerosol-generating device may be based on data pertinent to the location and/or time at which the user actually uses the device. Differences in usage, for example resulting in different usage patterns, may therefore be taken into account when controlling the aerosol-generating device.
The values of the usage pattern parameters may have an infinite range of numerical values. Different values of usage pattern parameters may therefore be classified into the same bin of the histogram when they have the same meaning despite being numerically different from each other. Similarly, the location information and/or time information collected along with the usage pattern parameter may, in principle, have an infinite number of numerical values. Therefore, to identify meaningful locations and/or times at which different usage patterns occur in such a way that each usage pattern actually results in controlling the aerosol-generating device differently from the other usage patterns, the location information and/or time information may be divided into meaningful groups as well. For this reason, the method may optionally comprise stratifying the collected values of the at least one usage pattern parameter in one or more categories according to the location information and/or time information. Each category may therefore be indicative or representative of a location and/or time that stratifies the movement and/or life rhythm of the user into meaningful subunits.
For example, the method may comprise creating different categories for collected values of the at least one usage pattern parameter which pertain to values of usage pattern parameters or to usage sessions of the aerosol-generating device which were collected or occurred
- at a work place of a user,
- at a home of a user,
- during travel of a user,
- at a recreational location of a user,
- during work hours of a user,
- during free time of a user,
- during a holiday of a user, or
- during spring or summer or autumn or winter.
The method may comprise receiving information from the user identifying locations and/or times, for example current locations and/or current times. The user may therefore provide the aerosol-generating device with the information that the location the user is at at the moment is his work place or his home or another location that he frequents. Similarly, the user may provide the aerosol-generating device with the information that he works on specific days and/or to specific times or that he will be on holiday on specific days and/or weeks. Additionally or alternatively, the method may comprise the aerosol-generating device determining this information on its own by continually collecting the underlying data and identifying the mentioned categories.
The category pertaining to travel of the user may pertain both to travel away from locations the user normally frequents, for example to travel abroad, and to travel between locations the user normally frequents, for example to travel between the home and the workplace or a recreational location of the user.
The method may comprise creating a separate histogram for values of the usage pattern parameters pertaining to at least two or multiple of or all of the categories mentioned above. In  this way, controlling the aerosol-generating device through the method of the present disclosure may take different usage patterns or user habits connected to different locations and/or times into account. For example, the user may recharge the aerosol-generating device more often at home than at work, so that the battery may not need to be fully recharged when the user is at home, but may need to be fully recharged when the user is at work according to the principles outlined above. By splitting the available data into different histograms corresponding to the location information and/or time information, the method according to the disclosure may provide an adaptive, smart control of the aerosol-generating device requiring only a minimum of computational power.
The histograms used in the method may have any number of bins necessary to represent the underlying data in a meaningful way. Different histograms may have different numbers of bins. The number of bins of each histogram may be changed when more values of the represented usage pattern parameter are collected over time. For example, the method may comprise dynamically adjusting the number of bins of the histogram used to classify the values of the at least one usage pattern parameter. A different number of bins may be necessary when, over time, the number of values collected and/or the distribution of values of usage pattern parameters changes. The method may also comprise limiting the number of values of the at least one usage pattern parameter or all of the usage pattern parameters to a certain number of values or to values collected during a predetermined period of time. For example, the number of values of the at least one usage pattern parameter may be limited to the last 50 or 40 or 30 or 20 or 10 collected values of the at least one usage pattern parameter. Any value exceeding this number, pertaining to values that have been collected previously, may be deleted. Similarly, the method may comprise limiting the collected values of the at least one usage pattern parameter to values collected during a predetermined period of time, for example the last 12 months or 6 months or 3 months or month or 2 weeks or week or day. Any value collected prior to this period may be deleted. In this way, the data relied upon to control the aerosol-generating device may always be up-to-date. Simultaneously, the computational power required for the method according to the present disclosure is further reduced. As the data represented by each histogram may therefore change over time, how this data may be organized in the histogram may also change by dynamically adjusting the number of bins.
For example, it may be determined that the number of bins of the histogram is too small to extract any meaningful histogram feature information. In other words, the bins available in the histogram may provide a data resolution that is too low for extracting meaningful histogram feature information from the histogram. This is usually the case when too many values of a usage pattern parameter are classified into a single bin of the histogram. The method may therefore optionally comprise increasing the number of bins of the histogram used to classify the values of the at least one usage pattern parameter when the percentage of values of the at least one usage pattern parameter in one bin exceeds a predetermined threshold value. The predetermined threshold value may for example be 50%or 60%or 70%or 80%or 90%of all the values of the at least one  usage pattern parameter pertaining to the histogram. A number of bins of the histogram may for example be increased by splitting the range of numerical values being classified into the bin, preferably the bin with the most values classified into it, in two or more ranges or subranges, each of the new ranges or subranges being represented by a new bin and re-classifying the values of the at least one usage pattern parameter accordingly.
Additionally or alternatively, the method may comprise increasing the number of bins of the histogram used to classify the values of the at least one usage pattern parameter when a collected value of the at least one usage pattern parameter does not fit any available bin. In this case, the new bin may be created representing a numerical range of values into which the collected value of the at least one usage pattern parameter fits. The new bin may be added to the histogram so that collected values may then be classified into this bin in the future.
The method may also comprise decreasing the number of bins of the histogram used to classify the values of the at least one usage pattern parameter when the percentage of values of the at least one usage pattern parameter in all bins falls below a predetermined threshold value. The predetermined threshold value may for example be 10%or 20%of 30%to 40%of all the values of the at least one usage pattern parameter pertaining to the histogram. In this case, neighbouring bins may be joined together or merged so that a new bin may be created representing the numerical range of values of both bins together. Accordingly, also the height the merged bins may be summed to determine the height of the new bin. Additionally or alternatively, bins that have no values classified into them may be deleted. Such empty bins may arise due to older values of usage pattern parameters being deleted, for example.
Apart from the ways of extracting histogram feature information as outlined above, there are other methods that may be employed for this purpose. For example, the method may comprise using an (artificial) intelligence engine or network or machine learning in extracting histogram feature information from the histogram and/or controlling the aerosol-generating device based on said histogram feature information. For example, a convolutional neural network (CNN) , random forest, decision forest, decision tree etc. may be employed in extracting histogram feature information and/or controlling the aerosol-generating device. These engines or networks may be trained on large data sets beforehand so that they enable precise decision-making based on the data available on each and every one of the aerosol-generating devices. Organizing such data in histograms may increase performance of (artificial) intelligence engines or intelligence networks or machine learning.
According to another aspect of the present disclosure, there is provided an aerosol-generating device configured to perform steps, for example at least a subset or all of the steps, of the method according to the present disclosure. All of the features, effects and advantages described for the method are also valid for and equally apply to the aerosol-generating device and vice versa.
The aerosol-generating device may comprise a battery for storing electrical energy and processing circuitry or control circuitry with one or more processors configured to perform steps, for example at least a subset or all of the steps, of the method as disclosed herein.
According to another aspect of the present disclosure, there is provided an aerosol-generating system comprising a control arrangement, wherein the control arrangement is configured to perform steps, for example at least a subset or all of the steps, of the method according to the present disclosure. The control arrangement may, for example, comprise processing circuitry with one or more processors. Additionally or alternatively, the aerosol-generating system may comprise an aerosol-generating device and a companion device communicatively coupleable to the aerosol-generating device, wherein the companion device is configured to perform steps, for example at least a subset or all of the steps, of the method according to the present disclosure. All of the features, effects and advantages described for the method are also valid for and equally apply to the aerosol-generating system and vice versa.
The companion device may be, for example, a smartphone, a tablet computer, a personal computer, a computing device, a server, or a device configured to charge the aerosol-generating device. It may be advantageous to perform the method according to the present disclosure on the companion device especially in cases in which the companion device may have more computational power than the aerosol-generating device. Also, in cases in which the user owns and/or operates more than one aerosol-generating device, all of these may be communicatively coupleable to the companion device so that the companion device can collect data, for example values of usage pattern parameters, from a plurality of aerosol-generating devices. In this way, control of all of the aerosol-generating devices may be improved regardless of which aerosol-generating device the user uses at what time or in what location.
According to another aspect of the present disclosure, there is provided a computer program or software or computer executable code which when executed on a processor of an aerosol-generating device and/or a companion device performs steps, for example at least a subset or all of the steps, of the method according to the present disclosure. All of the features, effects and advantages described for the method are also valid for and equally apply to the computer program or software or computer executable code and vice versa.
According to another aspect of the present disclosure, there is provided a computer readable medium having stored thereon a computer program or software or computer executable code which when executed on a processor of an aerosol-generating device and/or a companion device performs steps, for example at least a subset or all of the steps, of the method according to the present disclosure. All of the features, effects and advantages described for the method are also valid for and equally apply to the computer readable medium and vice versa.
The invention is defined in the claims. However, below, there is provided a non-exhaustive list of non-limiting examples. Any one or more of the features of these examples may be combined with any one or more features of another example, embodiment, or aspect described herein.
Example 1. A computer-implemented method of controlling an aerosol-generating device, the method comprising:
collecting a plurality of values of at least one usage pattern parameter related to a usage of the aerosol-generating device;
creating a histogram by classifying each collected value of the usage pattern parameter into a bin of the histogram;
extracting histogram feature information from the histogram; and
controlling the aerosol-generating device with operational constraints that are based on the histogram feature information.
Example 2. The method according to Example 1, wherein the at least one usage pattern parameter is selected from the parameters:
- an energy consumption per usage session,
- a number of usage sessions the aerosol-generating device has been operated to generate aerosol, preferably per predefined time interval,
- a duration of a usage session,
- a resting time between consecutive usage sessions, preferably wherein the usage pattern parameter value pertaining to the resting time between consecutive usage sessions only varies for resting times between subsequent usage sessions of from 0 to 40 minutes,
- a frequency of at least two usage sessions in a row, in particular without recharging of the aerosol-generating device in between,
- an ambient temperature during a usage session,
- an ambient air pressure during a usage session,
- an ambient humidity during a usage session,
- an ambient temperature during recharging of a battery of the aerosol-generating device,
- a temperature of a battery of the aerosol-generating device during a usage session,
- a temperature of a heating element or heater device of the aerosol-generating device within a predefined period of time before start of a usage session,
- a number of puffs per usage session,
- a puff volume,
- a puff frequency,
- a puff rhythm,
- a time of initiation of a pause mode at the aerosol-generating device,
- a time of termination of a pause mode at the aerosol-generating device,
- a duration of a pause mode at the aerosol-generating device,
- a duration of a recharge event of the aerosol-generating device,
- a resting time after recharging the aerosol-generating device,
- a resting time with a battery state of charge of less than 10 %,
- a resting time with a battery state of charge of more than 90 %,
- a density of an aerosol-generating substrate or aerosol-generating article used with the aerosol-generating device to generate aerosol,
- a weight of an aerosol-generating substrate or aerosol-generating article used with the aerosol-generating device to generate aerosol,
- a type of an aerosol-generating substrate or aerosol-generating article used with the aerosol-generating device to generate aerosol,
- a humidity of an aerosol-generating substrate or aerosol-generating article used in the aerosol-generating device,
- a temperature profile selected by a user.
Example 3. The method according to any of the preceding Examples, comprising collecting two or more usage pattern parameters and creating a histogram for each of the usage pattern parameters, and controlling the aerosol-generating device based on histogram feature information extracted from a plurality of the histograms.
Example 4. The method according to the preceding Example, wherein at least one histogram feature information extracted from the histograms is differently weighted than the others; and/or
wherein histogram feature information extracted from different histograms is weighted differently.
Example 5. The method according to any of the preceding Examples, wherein the extracted histogram feature information includes at least one of:
- one or more local maxima of the histogram,
- the identity of one or more bins of the histogram corresponding to one or more local maxima of the histogram,
- one or more heights of the bins of the histogram corresponding to one or more local maxima of the histogram, preferably wherein the height of a bin corresponds to or is proportional to the number of values of the usage pattern parameter classified into this bin,
- a sum of heights of two or more bins of the histogram, preferably wherein the height of a bin corresponds to or is proportional to the number of values of the usage pattern parameter classified into this bin,
- an average of the values of the usage pattern parameter classified into one or more bins of the histogram,
- a median of the values of the usage pattern parameter classified into one or more bins of the histogram,
- a maximum of the values of the usage pattern parameter classified into one or more bins of the histogram,
- a minimum of the values of the usage pattern parameter classified into one or more bins of the histogram.
Example 6. The method according to any of the preceding Examples, further comprising determining at least one of the following from the histogram feature information:
- a number of usage sessions that can be provided to a user to generate aerosol by the maximum capacity of a battery of the aerosol-generating device, preferably wherein the maximum capacity of the battery is a nominal maximum capacity or a current maximum capacity of the battery of the aerosol-generating device,
- whether or not an additional usage session can be provided considering the current state of charge of a battery of the aerosol-generating device.
Example 7. The method according to the preceding Example, the method preferably further comprising a step of changing the operational constraints of the aerosol-generating device including limitations of battery and/or power consumption management based on the histogram feature information and/or wherein controlling the aerosol-generating device based on said histogram feature information preferably includes at least one of:
- limiting a total number of usage sessions provided to a user to generate aerosol before recharging of a battery of the aerosol-generating device to a number of usage sessions that can be provided within a predetermined degree of certainty,
- allowing or prohibiting an additional usage session before recharging of a battery of the aerosol-generating device; and/or
- forcing and/or requesting a recharge of the battery.
Example 8. The method according to any of the preceding Examples, further comprising determining a total amount of battery capacity required for operating the aerosol-generating device between two consecutive recharging events of a battery of the aerosol-generating device from the histogram feature information.
Example 9. The method according to the preceding Example, wherein controlling the aerosol-generating device based on said histogram feature information includes adjusting operational constraints of the aerosol-generating device including limitations of battery and/or power and/or charging management and/or preferably includes terminating a recharging event of a battery of the aerosol-generating device at a state of charge below 100%, preferably below 95%or below 90%or below 85%or below 80%.
Example 10. The method according to any of the preceding Examples, further comprising determining an amount of time a recharging event of a battery of the aerosol-generating device is going to last from the histogram feature information.
Example 11. The method according to the preceding Example, wherein controlling the aerosol-generating device based on said histogram feature information includes adjusting operational constraints of the aerosol-generating device including limitations of battery and/or power and/or charging management and/or preferably includes limiting a charging rate during a recharging event of a battery of the aerosol-generating device.
Example 12. The method according to any of the preceding Examples, comprising collecting the at least one usage pattern parameter along with location information and/or time information pertaining to where and/or when the aerosol-generating device is operated.
Example 13. The method according to the preceding Example, comprising identifying different usage patterns of the aerosol-generating device at different locations and/or in different periods of time and preferably creating separate histograms and/or bins for said locations and/or periods of time.
Example 14. The method according to the preceding Example, comprising extracting histogram feature information from the histogram corresponding to the location and/or time of the current use of the aerosol-generating device and controlling the aerosol-generating device based on the histogram feature information extracted from said histogram.
Example 15. The method according to any of the Examples 12 to 14, comprising stratifying the collected values of the at least one usage pattern parameter in one or more categories according to the location information and/or time information.
Example 16. The method according to the preceding Example, comprising creating different categories for collected values of the at least one usage pattern parameter which pertain to usage sessions of the aerosol-generating device which occurred
- at a work place of a user,
- at a home of a user,
- during travel of a user,
- at a recreational location of a user,
- during work hours of a user,
- during free time of a user,
- during a holiday of a user, or
- during spring or summer or autumn or winter.
Example 17. The method according to any of the preceding Examples, comprising dynamically adjusting the number of bins of the histogram used to classify the values of the at least one usage pattern parameter.
Example 18. The method according to any of the preceding Examples, comprising increasing the number of bins of the histogram used to classify the values of the at least one usage pattern parameter when the percentage of values of the at least one usage pattern parameter in one bin exceeds a predetermined threshold value.
Example 19. The method according to any of the preceding Examples, comprising increasing the number of bins of the histogram used to classify the values of the at least one usage pattern parameter when a collected value of the at least one usage pattern parameter does not fit any available bin.
Example 20. The method according to any of the preceding Examples, comprising decreasing the number of bins of the histogram used to classify the values of the at least one usage pattern parameter when the percentage of values of the at least one usage pattern parameter in all bins falls below a predetermined threshold value.
Example 21. The method according to any of the preceding Examples, wherein an intelligence engine or network or machine learning is used in extracting histogram feature information from the histogram and/or controlling the aerosol-generating device based on said histogram feature information.
Example 22. An aerosol-generating device configured to perform steps of the method according to any one of the preceding Examples.
Example 23. The aerosol-generating device according to Example 22, comprising:
a battery for storing electrical energy; and
processing circuitry with one or more processors configured to perform steps of the method according to any one of Examples 1 to 21.
Example 24. Aerosol-generating system, comprising an aerosol-generating device and a companion device communicatively coupleable to the aerosol-generating device, wherein the companion device is configured to perform steps of the method according to any one of Examples 1 to 21.
Example 25. The aerosol-generating system according to Example 24, wherein the companion device is a smartphone, a tablet computer, a personal computer, a server, or a device configured to charge the aerosol-generating device.
Example 26. Computer program which when executed on a processor of an aerosol-generating device and/or a companion device performs steps of the method according to any one of Examples 1 to 21.
Example 27. Computer readable medium having stored thereon a computer program which when executed on a processor of an aerosol-generating device and/or a companion device performs steps of the method according to any one of Examples 1 to 21.
Example 28. Aerosol-generating system comprising a control arrangement configured to
collect a plurality of values of at least one usage pattern parameter related to a usage of the aerosol-generating device;
create a histogram by classifying each collected value of the usage pattern parameter into a bin of the histogram;
extract histogram feature information from the histogram; and
control the aerosol-generating device with operational constraints that are based on the histogram feature information.
Example 29. The aerosol-generating system according to the preceding Example, wherein the at least one usage pattern parameter is selected from the parameters:
- an energy consumption per usage session,
- a number of usage sessions the aerosol-generating device has been operated to generate aerosol, preferably per predefined time interval,
- a duration of a usage session,
- a resting time between consecutive usage sessions, preferably wherein the usage pattern parameter value pertaining to the resting time between consecutive usage sessions only varies for resting times between subsequent usage sessions of from 0 to 40 minutes,
- a frequency of at least two usage sessions in a row, in particular without recharging of the aerosol-generating device in between,
- an ambient temperature during a usage session,
- an ambient air pressure during a usage session,
- an ambient humidity during a usage session,
- an ambient temperature during recharging of a battery of the aerosol-generating device,
- a temperature of a battery of the aerosol-generating device during a usage session,
- a temperature of a heating element or heater device of the aerosol-generating device within a predefined period of time before start of a usage session,
- a number of puffs per usage session,
- a puff volume,
- a puff frequency,
- a puff rhythm,
- a time of initiation of a pause mode at the aerosol-generating device,
- a time of termination of a pause mode at the aerosol-generating device,
- a duration of a pause mode at the aerosol-generating device,
- a duration of a recharge event of the aerosol-generating device,
- a resting time after recharging the aerosol-generating device,
- a resting time with a battery state of charge of less than 10 %,
- a resting time with a battery state of charge of more than 90 %,
- a density of an aerosol-generating substrate or aerosol-generating article used with the aerosol-generating device to generate aerosol,
- a weight of an aerosol-generating substrate or aerosol-generating article used with the aerosol-generating device to generate aerosol,
- a type of an aerosol-generating substrate or aerosol-generating article used with the aerosol-generating device to generate aerosol,
- a humidity of an aerosol-generating substrate or aerosol-generating article used in the aerosol-generating device,
- a temperature profile selected by a user.
Example 30. The aerosol-generating system according to any of Examples 28 to 29, wherein two or more usage pattern parameters are collected and a histogram is created for each of the usage pattern parameters, and wherein the aerosol-generating device is controlled based on histogram feature information extracted from a plurality of the histograms.
Example 31. The aerosol-generating system according to the preceding Example, wherein at least one histogram feature information extracted from the histograms is differently weighted than the others; and/or
wherein histogram feature information extracted from different histograms is weighted differently.
Example 32. The aerosol-generating system according to any of Examples 28 to 31, wherein the extracted histogram feature information includes at least one of:
- one or more local maxima of the histogram,
- the identity of one or more bins of the histogram corresponding to one or more local maxima of the histogram,
- one or more heights of the bins of the histogram corresponding to one or more local maxima of the histogram, preferably wherein the height of a bin corresponds to or is proportional to the number of values of the usage pattern parameter classified into this bin,
- a sum of heights of two or more bins of the histogram, preferably wherein the height of a bin corresponds to or is proportional to the number of values of the usage pattern parameter classified into this bin,
- an average of the values of the usage pattern parameter classified into one or more bins of the histogram,
- a median of the values of the usage pattern parameter classified into one or more bins of the histogram,
- a maximum of the values of the usage pattern parameter classified into one or more bins of the histogram,
- a minimum of the values of the usage pattern parameter classified into one or more bins of the histogram.
Example 33. The aerosol-generating system according to any of Examples 28 to 32, wherein at least one of the following is determined from the histogram feature information:
- a number of usage sessions that can be provided to a user to generate aerosol by the maximum capacity of a battery of the aerosol-generating device,  preferably wherein the maximum capacity of the battery is a nominal maximum capacity or a current maximum capacity of the battery of the aerosol-generating device,
- whether or not an additional usage session can be provided considering the current state of charge of a battery of the aerosol-generating device.
Example 34. The aerosol-generating system according to the preceding Example, the method preferably further comprising a step of changing the operational constraints of the aerosol-generating device including limitations of battery and/or power consumption management based on the histogram feature information and/or wherein controlling the aerosol-generating device based on said histogram feature information preferably includes at least one of:
- limiting a total number of usage sessions provided to a user to generate aerosol before recharging of a battery of the aerosol-generating device to a number of usage sessions that can be provided within a predetermined degree of certainty,
- allowing or prohibiting an additional usage session before recharging of a battery of the aerosol-generating device; and/or
- forcing and/or requesting a recharge of the battery.
Example 35. The aerosol-generating system according to any of Examples 28 to 34, wherein a total amount of battery capacity required for operating the aerosol-generating device between two consecutive recharging events of a battery of the aerosol-generating device is determined from the histogram feature information.
Example 36. The aerosol-generating system according to the preceding Example, wherein controlling the aerosol-generating device based on said histogram feature information includes adjusting operational constraints of the aerosol-generating device including limitations of battery and/or power and/or charging management and/or preferably includes terminating a recharging event of a battery of the aerosol-generating device at a state of charge below 100%, preferably below 95%or below 90%or below 85%or below 80%.
Example 37. The aerosol-generating system according to any of Examples 28 to 36, further comprising determining an amount of time a recharging event of a battery of the aerosol-generating device is going to last from the histogram feature information.
Example 38. The aerosol-generating system according to the preceding Example, wherein controlling the aerosol-generating device based on said histogram feature information includes adjusting operational constraints of the aerosol-generating device including limitations of battery and/or power and/or charging management and/or preferably includes limiting a charging rate during a recharging event of a battery of the aerosol-generating device.
Example 39. The aerosol-generating system according to any of Examples 28 to 38, wherein the at least one usage pattern parameter is collected along with location information and/or time information pertaining to where and/or when the aerosol-generating device is operated.
Example 40. The aerosol-generating system according to the preceding Example, wherein different usage patterns of the aerosol-generating device at different locations and/or in different periods of time are identified and wherein preferably separate histograms and/or bins are created for said locations and/or periods of time.
Example 41. The aerosol-generating system according to the preceding Example, wherein histogram feature information is extracted from the histogram corresponding to the location and/or time of the current use of the aerosol-generating device and wherein the aerosol-generating device is controlled based on the histogram feature information extracted from said histogram.
Example 42. The aerosol-generating system according to any of Examples 28 to 41, wherein the location information and/or time information is used to stratify the collected values of the at least one usage pattern parameter in one or more categories.
Example 43. The aerosol-generating system according to the preceding Example, wherein different categories are created for collected values of the at least one usage pattern parameter which pertain to usage sessions of the aerosol-generating device which occurred
- at a work place of a user,
- at a home of a user,
- during travel of a user,
- at a recreational location of a user,
- during work hours of a user,
- during free time of a user,
- during a holiday of a user, or
- during spring or summer or autumn or winter.
Example 44. The aerosol-generating system according to any of Examples 28 to 43, wherein the number of bins of the histogram used to classify the values of the at least one usage pattern parameter is dynamically adjusted.
Example 45. The aerosol-generating system according to any of Examples 28 to 44, wherein the number of bins of the histogram used to classify the values of the at least one usage pattern parameter is increased when the percentage of values of the at least one usage pattern parameter in one bin exceeds a predetermined threshold value.
Example 46. The aerosol-generating system according to any of Examples 28 to 45, wherein the number of bins of the histogram used to classify the values of the at least one usage pattern parameter is increased when a collected value of the at least one usage pattern parameter does not fit any available bin.
Example 47. The aerosol-generating system according to any of Examples 28 to 46, wherein the number of bins of the histogram used to classify the values of the at least one usage pattern parameter is decreased when the percentage of values of the at least one usage pattern parameter in all bins falls below a predetermined threshold value.
Example 48. The aerosol-generating system according to any of Examples 28 to 47, wherein an intelligence engine or network or machine learning is used in extracting histogram feature information from the histogram and/or controlling the aerosol-generating device based on said histogram feature information.
Example 49. The aerosol-generating system according to any of Examples 28 to 48, comprising an aerosol-generating device and a companion device, wherein the control arrangement is arranged on the aerosol-generating device and/or the companion device.
Examples will now be further described with reference to the figures in which:
Figure 1 shows an aerosol-generating system comprising an aerosol-generating device and a companion device;
Figure 2 shows a histogram pertaining to the usage pattern parameter energy consumption per usage session;
Figure 3 shows a histogram pertaining to the usage pattern parameter number of usage sessions the aerosol-generating device has been operated to generate aerosol per day; and
Figure 4 shows a flow diagram of the method.
The figures are schematic only and not to scale.
Figure 1 shows an aerosol-generating system 1 for generating aerosol, for example for consumption by a user in one or more usage sessions. The system 1 may comprise an aerosol-generating device 2 for generating aerosol and a companion device 3 for at least partially receiving the aerosol-generating device 2. The companion device 3 may be a charging device for charging the aerosol-generating device 2 and/or an energy storage or battery thereof.
The aerosol-generating device 2 may comprise an insertion opening 4 for at least partially inserting an aerosol-generating article 17. The aerosol-generating article 17 may comprise an aerosol-forming substrate, such as a tobacco containing substrate, and/or a cartridge comprising a liquid, for example a liquid that can be aerosolized for inhalation.
The aerosol-generating device 2 may further include processing circuitry 5 or control circuitry 5 with one or more processors 6. For generating the aerosol during use or consumption of the aerosol-generating article 17, the aerosol-generating device 2 may comprise at least one heating element 7 or heater device for applying heat to at least a portion of the aerosol-generating article 17. The processing circuitry 5 may be configured to control actuation, activation and/or deactivation of at least one heating element 7. The processing circuitry 5 may further be configured to perform steps of the method described herein.
For powering the at least one heating element 7 with electrical power, the aerosol-generating device 2 may further comprise at least one energy storage, for example in the form of a battery 15, for storing electrical energy or power. The aerosol-generating device 2 may further comprise at least one electrical connector 12 for coupling to a corresponding at least one electrical connector 13 of the companion device 3. For example, when the aerosol-generating device 2 is at least partially inserted into the opening 14 of the companion device 3, the one or more electrical  connectors 12 of the aerosol-generating device 2 may be coupled with the one or more electrical connectors 13 of the companion device 3 to charge the at least one battery 15 of the aerosol-generating device 2.
The aerosol-generating device 2 may further comprise user interface components, for example comprising an input element in the form of a pushbutton 8. The pushbutton 8 may be used as a power button to activate or deactivate the heating element 7 for aerosol generation thereby to activate or deactivate the aerosol-generating device 2. Upon activation of the aerosol-generating device 2, the heating element 7 may be activated and heat may be applied to at least a part of the aerosol-generating article 17, such that aerosol can be generated for consumption by the user, for example in a usage session.
The aerosol-generating device 2 may further comprise a communications arrangement 9 or communication circuitry 9 with one or more communications interfaces 10 for communicatively coupling the aerosol-generating device 2 with the companion device 3, for example, via an Internet connection, a wireless LAN connection, a WiFi connection, a Bluetooth connection, a mobile phone network, a mobile data connection for example but not limited to a 3G/4G/5G connection, an edge connection, an LTE connection, a BUS connection, a wireless connection, a wired connection, an optical data connection such as but not limited to IrDa, a radio connection, a near field connection, and/or an IoT connection.
The aerosol-generating device 2 may further comprise a data storage 11 for storing information or data, such as collected values of usage pattern parameters, battery degradation data and/or one or more mathematical functions or formulas, and for storing computer instructions that can be executed by processing circuitry 5.
As described in detail hereinabove and hereinbelow, the aerosol-generating device 2 is configured to collect, gather and/or store values of at least one usage pattern parameter related to a usage of the aerosol-generating device 2. One or more sensors 16 may be arranged on the aerosol-generating device 2 to collect data, for example values of usage pattern parameters and/or battery capacity data and/or location information and/or time information.
Figure 2 shows an example of a histogram pertaining to the usage pattern parameter energy consumption per usage session. The histogram may comprise a total of five bins. The energy consumption per usage session may be measured in total capacity of the battery used per usage session. The numerical range of the bins may be different for each bin. In the example shown in figure 2, the histogram may be designed to determine how many usage sessions can be provided to the user in a range from 1 to 5 usage sessions by the energy stored in the battery 15 with a current maximum capacity of an exemplary value of 235 mAh. The numerical range of the bins may be calculated by dividing the current maximum capacity of the battery 15 by the number of usage sessions that could theoretically be provided. The resulting number may represent the upper end of the numerical range of the respective bin, whereas the lower end of the numerical  range of the respective bin may be either 0 or be determined by the upper end of the numerical range of the neighbouring bin.
For example, the current maximum capacity of the battery 15 of 235 mAh divided by the maximum number of 5 possible usage sessions equals 47 mAh. This may be the upper end of the numerical range of the first bin, with the lower end being 0 mAh. The current maximum capacity of the battery 15 of 235 mAh divided by the next number of 4 possible usage sessions equals 59 mAh. This may be the upper end of the numerical range of the second bin, with the lower end beginning where the upper end of the previous bin ends. The current maximum capacity of the battery 15 of 235 mAh divided by the next number of 3 possible usage sessions equals 78 mAh. This may be the upper end of the numerical range of the third bin, with the lower end beginning where the upper end of the previous bin ends. The current maximum capacity of the battery 15 of 235 mAh divided by the next number of 2 possible usage sessions equals 117 mAh. This may be the upper end of the numerical range of the fourth bin, with the lower end beginning where the upper end of the previous bin ends. The current maximum capacity of the battery 15 of 235 mAh divided by the next number of 1 possible usage sessions equals 235 mAh. This may be the upper end of the numerical range of the fifth bin, with the lower end beginning where the upper end of the previous bin ends. Accordingly, the first bin may represent a numerical range of from 0 to 47 mAh, the second bin may represent a numerical range of from 48 to 59 mAh, the third bin may represent a numerical range of from 60 to 78 mAh, the fourth bin may represent a numerical range of from 79 to 117 mAh, and the fifth bin may represent a numerical range of from 118 to 235 mAh.
During operation of the aerosol-generating device 2, values for the energy consumption per usage session may be collected and classified into the bins of the histogram. If the energy consumption of all of the usage sessions of the aerosol-generating device 2 were classified into the first bin, this would mean that very little energy is used per usage session and the full 5 usage sessions could be provided with one full charge of the battery 15. Similarly, if the energy consumption of all of the usage sessions of the aerosol-generating device 2 were classified into the second bin, this would mean that only 4 usage sessions could be provided with one full charge of the battery 15 and so on. In practice, the energy consumption per usage session may not be this homogeneous. The histogram of figure 2 shows an exemplary, but more realistic distribution of the values of the usage pattern parameter. Specifically, the number n of values classified in the first bin is 6, the number n of values classified in the second bin is 3, the number n of values classified in the third bin is 8, the number n of values classified in the fourth bin is 3 and the number n of values classified in the fifth bin is 0. Only the 20 most recent values that have been collected may be represented in the histogram.
The histogram as represented by figure 2 may be used to calculate how many usage sessions can be provided to the user with one full charge of the battery 15 within a predetermined degree of certainty. The histogram feature information extracted from the histogram for this  determination is represented by the heights of the bins and sums of these heights. The predetermined degree of certainty in this case may be expressed as a percentage of the number n of values classified into all of the bins of the histogram. For example, if the number n of values classified into the first bin represents a percentage of all the values of all the bins of the histogram that is equal to or greater than the percentage required for the predetermined degree of certainty, then a total of 5 usage sessions can be provided to the user. If this is not the case, further bins have to be taken into account. For example, if the number n of values classified into the first and the second bin summed together represents a percentage of all the values of all the bins of the histogram that is equal to or greater than the percentage required for the predetermined degree of certainty, then a total of 4 usage sessions can be provided to the user. If this is also not the case, another bin may be taken into account. For example, the heights of the first, second and third bins may be summed and checked against the predetermined degree of certainty as explained above to check whether 3 usage sessions can be provided. Similarly, the heights of the first, second, third and fourth bins may be summed and checked against the predetermined degree of certainty as explained above to check whether 2 usage sessions can be provided. If this sum does not satisfy the predetermined degree of certainty, only one usage session may be provided.
In the example of figure 2, the first, the second and the third bin together contain or represent 17 of 20 of the most recent usage sessions, 20 being an exemplary non-limiting value. This means that within a degree of certainty of 17/20*100%=85%, 3 usage sessions can be provided to the user with one full charge of the battery 15. The method may therefore comprise limiting a total number of usage sessions provided to a user to generate aerosol before recharging of the battery 15 of the aerosol-generating device 2 to 3 usage sessions. This may also be used to decide whether or not to allow a further usage session depending on the number of usage sessions that have already occurred since the last recharging event.
If the user has just started using the aerosol-generating device 2, it may be that not enough data is available to make a meaningful decision based upon usage pattern parameters of the user. In this case, it may be provided that the aerosol-generating device 2 is controlled in a predetermined way until a sufficient dataset has been collected.
Figure 3 shows an example of a histogram pertaining to the usage pattern parameter number of usage sessions the aerosol-generating device 2 has been operated to generate aerosol per day. The histogram may comprise a total of six bins, each bin representing one more usage session per day than the one before. In the exemplary case of figure 3, 1 value of the usage pattern parameter has been classified into the first bin, representing 1 usage session per day, 3 values of the usage pattern parameter have been classified into the second bin, representing 2 usage sessions per day, 16 values of the usage pattern parameter have been classified into the third bin, representing 3 usage sessions per day, 2 values of the usage pattern parameter have been classified into the fourth bin, representing 4 usage sessions per day, and 0  usage sessions have each been classified into the fifth and sixth bin, representing 5 and 6 usage sessions per day, respectively.
From the histogram shown in figure 3, it may be determined how many usage sessions the user will need per day within a predetermined degree of certainty. The histogram feature information extracted from the histogram for this determination may again be represented by the heights of the bins and sums of these heights. For example, the first, the second and the third bin together may contain or represent 20 of a total of 22 collected values of the usage pattern parameter. It may therefore be determined that the user will need a maximum of 3 usage sessions per day within a degree of certainty of 20/22*100%=90, 9%. If the user recharges the aerosol-generating device 2 once per day, then it can also be determined that the user will need 3 usage sessions between two consecutive recharging events of the aerosol-generating device 2 within that same degree of certainty.
As an example, it may be assumed that both histograms according to figures 2 and 3 pertain to the same user. As explained above, it may be determined from the histogram in figure 2 that the user will need a maximum of 78 mAh of battery capacity per usage session within a degree of certainty of 85%. This means that for the 3 usage sessions the user is probably going to need between recharge events according to the histogram of figure 3, the user will need a total battery capacity of 3*78mAh=234mAh. If the current maximum battery capacity of the battery 15 is 300 mAh, this needed capacity could be provided with a state of charge of the battery 15 of 234/300*100%=78%. The aerosol-generating device 2 may therefore be controlled in a way so that the battery 15 is not recharged above a state of charge of for example 80%or 85%or 90%. This battery management may result in a lower battery degradation over time while still providing the user with the desired number of usage sessions within a high probability.
Figure 4 a shows a flowchart of the method 18 of the present disclosure. The method 18 may begin with step 19, in which a plurality of values of at least one usage pattern parameter related to usage of the aerosol-generating device 2 may be collected, for example for a given number of usage sessions. In step 20, a histogram may be created by classifying each collected value of the usage pattern parameter into a bin of the histogram. This may result in histograms as for example shown in figures 2 and 3. In step 21, histogram feature information may be extracted from the histogram. The histogram feature information may, for example, pertain to heights of different bins of the histogram or to sums of heights of adjacent bins. Finally, the method 18 may comprise step 22, in which the aerosol-generating device 2 may be controlled based on said histogram feature information. Specifically, method 18 may allow for adapting the operational constraints of the aerosol-generating device 2 according to the histogram feature information. The method 18 according to the present disclosure may allow for controlling the aerosol-generating device 2 taking into account individual user habits. Both battery management and/or availability or duration of usage sessions may be adapted in this way. Simultaneously, the method 18  requires only minimal memory or storage space and computational power and is therefore suitable for implementation on aerosol-generating devices 2.
For the purpose of the present description and of the appended claims, except where otherwise indicated, all numbers expressing amounts, quantities, percentages, and so forth, are to be understood as being modified in all instances by the term "about" . Also, all ranges include the maximum and minimum points disclosed and include any intermediate ranges therein, which may or may not be specifically enumerated herein. In this context, therefore, a number A is understood as A ± 10%of A. Within this context, a number A may be considered to include numerical values that are within general standard error for the measurement of the property that the number A modifies. The number A, in some instances as used in the appended claims, may deviate by the percentages enumerated above provided that the amount by which A deviates does not materially affect the basic and novel characteristic (s) of the claimed invention. Also, all ranges include the maximum and minimum points disclosed and include any intermediate ranges therein, which may or may not be specifically enumerated herein.

Claims (15)

  1. A computer-implemented method of controlling an aerosol-generating device, the method comprising:
    collecting a plurality of values of at least one usage pattern parameter related to a usage of the aerosol-generating device;
    creating a histogram by classifying each collected value of the usage pattern parameter into a bin of the histogram;
    extracting histogram feature information from the histogram; and
    controlling the aerosol-generating device with operational constraints that are based on the histogram feature information.
  2. The method according to claim 1, wherein the at least one usage pattern parameter is selected from the parameters:
    - an energy consumption per usage session,
    - a number of usage sessions the aerosol-generating device has been operated to generate aerosol, preferably per predefined time interval,
    - a duration of a usage session,
    - a resting time between consecutive usage sessions, preferably wherein the usage pattern parameter value pertaining to the resting time between consecutive usage sessions only varies for resting times between subsequent usage sessions of from 0 to 40 minutes,
    - a frequency of at least two usage sessions in a row, in particular without recharging of the aerosol-generating device in between,
    - an ambient temperature during a usage session,
    - an ambient air pressure during a usage session,
    - an ambient humidity during a usage session,
    - an ambient temperature during recharging of a battery of the aerosol-generating device,
    - a temperature of a battery of the aerosol-generating device during a usage session,
    - a temperature of a heating element or heater device of the aerosol-generating device within a predefined period of time before start of a usage session,
    - a number of puffs per usage session,
    - a puff volume,
    - a puff frequency,
    - a puff rhythm,
    - a time of initiation of a pause mode at the aerosol-generating device,
    - a time of termination of a pause mode at the aerosol-generating device,
    - a duration of a pause mode at the aerosol-generating device,
    - a duration of a recharge event of the aerosol-generating device,
    - a resting time after recharging the aerosol-generating device,
    - a resting time with a battery state of charge of less than 10 %,
    - a resting time with a battery state of charge of more than 90 %,
    - a density of an aerosol-generating substrate or aerosol-generating article used with the aerosol-generating device to generate aerosol,
    - a weight of an aerosol-generating substrate or aerosol-generating article used with the aerosol-generating device to generate aerosol,
    - a type of an aerosol-generating substrate or aerosol-generating article used with the aerosol-generating device to generate aerosol,
    - a humidity of an aerosol-generating substrate or aerosol-generating article used in the aerosol-generating device,
    - a temperature profile selected by a user.
  3. The method according to claim 1, wherein the at least one usage pattern parameter is selected from the parameters:
    - an energy consumption per usage session,
    - a number of usage sessions the aerosol-generating device has been operated to generate aerosol, preferably per predefined time interval.
  4. The method according to any of the preceding claims, comprising collecting two or more usage pattern parameters and creating a histogram for each of the usage pattern parameters, and controlling the aerosol-generating device based on histogram feature information extracted from a plurality of the histograms.
  5. The method according to the preceding claim, wherein at least one histogram feature information extracted from the histograms is differently weighted than the others; and/or wherein histogram feature information extracted from different histograms is weighted differently.
  6. The method according to any of the preceding claims, wherein the extracted histogram feature information includes at least one of:
    - one or more local maxima of the histogram,
    - the identity of one or more bins of the histogram corresponding to one or more local maxima of the histogram,
    - one or more heights of the bins of the histogram corresponding to one or more local maxima of the histogram, preferably wherein the height of a bin corresponds  to or is proportional to the number of values of the usage pattern parameter classified into this bin,
    - a sum of heights of two or more bins of the histogram, preferably wherein the height of a bin corresponds to or is proportional to the number of values of the usage pattern parameter classified into this bin,
    - an average of the values of the usage pattern parameter classified into one or more bins of the histogram,
    - a median of the values of the usage pattern parameter classified into one or more bins of the histogram,
    - a maximum of the values of the usage pattern parameter classified into one or more bins of the histogram,
    - a minimum of the values of the usage pattern parameter classified into one or more bins of the histogram.
  7. The method according to any of the preceding claims, further comprising determining at least one of the following from the histogram feature information:
    - a number of usage sessions that can be provided to a user to generate aerosol by the maximum capacity of a battery of the aerosol-generating device, preferably wherein the maximum capacity of the battery is a nominal maximum capacity or a current maximum capacity of the battery of the aerosol-generating device,
    - whether or not an additional usage session can be provided considering the current state of charge of a battery of the aerosol-generating device,
    the method preferably further comprising a step of changing the operational constraints of the aerosol-generating device including limitations of battery and/or power consumption management based on the histogram feature information and/or wherein controlling the aerosol-generating device based on said histogram feature information preferably includes at least one of:
    - limiting a total number of usage sessions provided to a user to generate aerosol before recharging of a battery of the aerosol-generating device to a number of usage sessions that can be provided within a predetermined degree of certainty,
    - allowing or prohibiting an additional usage session before recharging of a battery of the aerosol-generating device; and/or
    - forcing and/or requesting a recharge of the battery.
  8. The method according to any of the preceding claims, further comprising determining a total amount of battery capacity required for operating the aerosol-generating device between two  consecutive recharging events of a battery of the aerosol-generating device from the histogram feature information,
    preferably wherein controlling the aerosol-generating device based on said histogram feature information includes adjusting operational constraints of the aerosol-generating device including limitations of battery and/or power and/or charging management and/or preferably includes terminating a recharging event of a battery of the aerosol-generating device at a state of charge below 100%, preferably below 95%or below 90%or below 85%or below 80%.
  9. The method according to any of the preceding claims, further comprising determining an amount of time a recharging event of a battery of the aerosol-generating device is going to last from the histogram feature information,
    preferably wherein controlling the aerosol-generating device based on said histogram feature information includes adjusting operational constraints of the aerosol-generating device including limitations of battery and/or power and/or charging management and/or preferably includes limiting a charging rate during a recharging event of a battery of the aerosol-generating device.
  10. The method according to any of the preceding claims, comprising collecting the at least one usage pattern parameter along with location information and/or time information pertaining to where and/or when the aerosol-generating device is operated.
  11. The method according to the preceding claim, comprising identifying different usage patterns of the aerosol-generating device at different locations and/or in different periods of time and preferably creating separate histograms and/or bins for said locations and/or periods of time, preferably comprising extracting histogram feature information from the histogram corresponding to the location and/or time of the current use of the aerosol-generating device and controlling the aerosol-generating device based on the histogram feature information extracted from said histogram.
  12. The method according to any of the claims 10 to 11, comprising stratifying the collected values of the at least one usage pattern parameter in one or more categories according to the location information and/or time information.
  13. The method according to any of the preceding claims, comprising dynamically adjusting the number of bins of the histogram used to classify the values of the at least one usage pattern parameter.
  14. An aerosol-generating device configured to perform steps of the method according to any one of the preceding claims.
  15. Aerosol-generating system, comprising an aerosol-generating device and a companion device communicatively coupleable to the aerosol-generating device, wherein the companion device is configured to perform steps of the method according to any one of claims 1 to 13.
PCT/CN2023/080445 2023-03-09 2023-03-09 Control method of aerosol-generating device using histograms WO2024183049A1 (en)

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