Companion application for light-emitting hair growth management devices

Abstract

The present invention provides techniques for estimating skin coverage in a use session of an illuminating hair growth management device. Sensor data from one or more sensors in the illuminating hair growth management device is used by rule-based and / or machine learning algorithms to estimate the coverage of a target body part(s) achieved in a use session. This information is fed back to a user of the hair growth management device via a graphical user interface. In another aspect, the present invention provides a scheduling application that can dynamically adjust a use plan including one or more use sessions of an illuminating hair growth management device based on information about the level of hair growth management achieved in a given use session.

Description

Technical Field

[0001] The present invention generally relates to monitoring the use of a light-emitting body hair growth management device. Specifically, the present invention relates to the calculation of the skin coverage rate achieved during one or more use sessions and / or the dynamic scheduling of one or more use sessions.

Background Art

[0002] Light-emitting body hair growth management devices are becoming increasingly popular, especially in environments such as salons or homes. In such situations, it is common for the operator of the device not to have received an intensive training course regarding the use of the device. That is, the operator is reasonably proficient in operating the device but is not an expert. As a result, the operator may not be able to objectively judge the effectiveness of a given use of the device, which can lead to suboptimal results and user dissatisfaction. This is particularly because there is usually some time that elapses between a use session of the body hair growth management device and the device's effects becoming visible, which means that, unlike, for example, a shaver or a razor, the operator cannot receive immediate (visual) feedback regarding the effectiveness of the use session.

[0003] One feature of light-based hair growth management technologies is that they are most effective when used according to a recommended schedule. While a recommended schedule can be provided to an operator, in practice, the operator may not follow it exactly. This can occur because, for example, a customer misses or reschedules a salon appointment, resulting in an inability to use the device when scheduled, or because the operator forgets to use the device when scheduled. The recommended schedule is often provided in a static format, for example, as instructions in a leaflet or brochure provided with the device. Such a static format fails to take into account the actual use of the device and adjust the schedule accordingly, which can lead to suboptimal results and user dissatisfaction. Summary of the Invention [Problem to be solved by the invention]

[0004] It is therefore desirable to provide a mechanism that allows an untrained person to objectively judge the effectiveness of a given use of a light emitting hair growth management device, preferably one that is user friendly and easy to understand for an untrained person.

[0005] It is also desirable to provide a mechanism to allow for the generation of a dynamic schedule for use of an illuminating hair growth management device, the dynamic usage schedule being adjusted based on actual use of the hair growth management device. [Means for solving the problem]

[0006] The present invention has two main aspects. The first aspect relates to the estimation of the skin coverage rate achieved during a usage session of a body hair growth management device. The skin coverage rate is automatically estimated using sensor data collected by one or more sensors of the body hair growth management device during the usage session. The skin coverage rate estimate can be provided during the usage session (i.e., in real-time or near real-time), or after the usage session is completed. This objective and automatic evaluation allows the operator of the body hair growth management device to easily understand how effective a given usage was, thereby improving user satisfaction.

[0007] The second aspect relates to the dynamic scheduling of usage sessions in a usage plan of a body hair growth management device. A usage plan including one or more usage sessions is generated for a person who is the subject of the usage session. The usage plan can include two or more usage sessions, for example, 2, 5, 10, 20 usage sessions. The usage plan is presented, for example, in the form of a calendar within a calendar application, by a scheduling application executed on an electronic device. After each use of the body hair growth management device, the operator or user inputs information regarding the actually observed body hair reduction effect, or alternatively, this information is automatically collected by processing an image(s) of the body part that is the subject of the usage session. The scheduling application is configured to adjust the usage plan based on the information supplied by the user or automatically collected. In this way, a dynamic usage plan is created that adjusts the usage sessions scheduled according to the actual use of the device. This can lead to an improvement in user satisfaction as the user can better understand how to effectively use the device.

[0008] A first aspect may be implemented by a computer-implemented method for monitoring use of a light-emitting hair growth management device during a use session of the hair growth management device, the method comprising: receiving, by a processor, sensor data collected during the use session from one or more sensors of the hair growth management device; performing, by the processor, a calculation of skin coverage achieved in the use session using the sensor data; and displaying information regarding the calculated skin coverage on a display of the user device or on a display of the hair growth management device.

[0009] The first aspect may also be implemented by a hair growth management system comprising a hair growth management device, a processor, and a display, the system being configured to perform the method of the first aspect.

[0010] A second aspect may be implemented by a computer-implemented method for adjusting a usage plan of an illuminating hair growth management device, the method including: providing, in a scheduling application of an electronic device, a usage plan including one or more planned usage events, the or each planned usage event having a corresponding date associated with the or each planned usage event; obtaining, by the electronic device, an indication indicative of a hair density of hair present on a body part associated with the usage plan; and adjusting, by the electronic device, the date of at least one of the one or more planned usage events based on the indication.

[0011] The second aspect may also be implemented by a non-transitory computer-readable medium storing instructions that, when executed by a processor of an electronic device, cause the electronic device to provide, in a scheduling application of the electronic device, a usage plan including one or more planned usage events, the or each planned usage event having a corresponding date associated with the or each planned usage event; obtain an indication indicative of a hair density of body hair present on a body part associated with the usage plan; and adjust, by the electronic device, the date of at least one of the one or more planned usage events based on the indication.

[0012] Further preferred features of the first and second aspects are set out in the accompanying dependent claims and / or detailed description. [Brief explanation of the drawings]

[0013] Embodiments of the present invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

Figure 1

Figure 2

Figure 3

Figure 4

[0014] As used herein, the term "user" refers to the person for whom the light-emitting hair growth management device is being used, or the operator of the device, who may be a different person from the person for whom the device is being used.

[0015] The term "skin coverage rate" refers to a parameter that indicates the proportion of the area of the skin that is exposed to one or more light pulses from the hair growth management device during a usage session. The skin coverage rate can be calculated from data provided by the device sensor as described below. The skin coverage rate can be expressed, for example, as a percentage or proportion of the target body part. Since the hair growth management effect of the device is obtained from exposing the skin to light pulses, the skin coverage rate is a relevant parameter when evaluating the effectiveness of a usage session. Here, the effectiveness refers to the level of hair growth management resulting from the usage session.

[0016] A first aspect of the present invention provides a technique for estimating the skin coverage rate in a usage session of a light-emitting hair growth management device. Sensor data from one or more sensors of the light-emitting hair growth management device is used by a rule-based algorithm and / or a machine learning algorithm to estimate the coverage rate of the target body part(s) achieved in the usage session. This information is fed back to the user of the hair growth management device via a graphical user interface. The graphical user interface can be displayed on the display of the user device or on the display of the hair growth management device. Further details regarding this first aspect are provided immediately following this.

[0017] FIG. 1 is a diagram showing a light-emitting hair growth management device 100 in a schematic form. The device 100 can be, for example, a type of photoepilator known per se in the art. Since the configuration and operating principle of the photoepilator are known, for the sake of brevity, they will not be described in detail here.

[0018] Device 100 includes a housing 102 and a head 104 that contains, encloses, or is otherwise associated with a light-emitting component (not shown), typically a flashlamp housed within an optical window transparent to light of at least wavelengths known to have hair growth management effects.

[0019] Head 104 is preferably removably attached to housing 102 to allow an operator to attach different heads to housing 102. Each head may, for example, be particularly suited for use on a particular body part or parts.

[0020] A power source 106 is also present. This may include a battery and / or a port that allows for wired electrical coupling to an electrical outlet. Inductive charging techniques may also be used. The power source 106 provides power to operate the various components of the device 100, including, for example, the light-emitting components, the processor, and the transmitter / transceiver.

[0021] A power button 108 is provided on the housing 102, allowing an operator of the device 100 to turn the device 100 on and off. The device 100 may be configured to automatically turn off after a predetermined period of inactivity (e.g., after 5 minutes) and / or enter a power-saving sleep mode to avoid wasting power. The power button 108 may be, for example, a push button or a slider coupled to a switch.

[0022] The activation button 110 (or "trigger") is also provided within the housing 102 of the housing, enabling the operator of the device 100 to trigger a pulse of light (often referred to as a "flash") from the light-emitting component. The activation button 110 can be, for example, a push button coupled to a switch. The activation button 110 can be coupled to a skin sensor (not shown), which enables the device 100 to determine whether the head 104 is currently close to the skin. Such a skin sensor is known in the art. For safety reasons, the activation button 110 can be disabled, for example, by ignoring the input from the activation button 110 when it is determined that the head 104 is not currently close to the skin.

[0023] The device 100 preferably includes a skin tone sensor (not shown) that can automatically determine the skin tone. This may be the same sensor as the skin sensor described in the previous paragraph or a different sensor. Skin tone sensors are known per se.

[0024] The device 100 can also include a parameter setting mechanism (not shown) for setting the operating parameters of the device 100. This mechanism can include one or more sliders, wheels, etc., and each slider or wheel corresponds to a specific parameter. The parameters can include pulse duration, pulse fluence, skin tone (e.g., according to Fitzpatrick skin type classification), hair color, body part, etc.

[0025] Although physical buttons, sliders, wheels, etc. have been described above, it will be understood that any of these activation or selection components can be implemented digitally, for example, via a user input device coupled to a display showing a suitably configured user interface.

[0026] Device 100 also includes a processor (not shown), which can be, for example, a microcontroller. The processor is configured to control the operation of device 100 as described herein. The processor is communicatively coupled to a memory (not shown) that can store data such as sensor data as described below.

[0027] Device 100 further includes a transmitter / receiver (not shown) that enables device 100 to wirelessly communicate with other devices (see FIG. 2). The transmitter / receiver can be a Bluetooth transmitter / receiver, preferably a Bluetooth Low Energy (BLE) transmitter / receiver, a WiFi transmitter / receiver, a Near Field Communication (NFC) module, etc. In some cases, the transmitter / receiver can support multiple different communication technologies, such as both Bluetooth and WiFi, for example. In these cases, multiple antennas may be present.

[0028] Device 100 further includes one or more sensors (not shown). The sensor(s) function to collect information about the operation of device 100, particularly as related to the use of device 100. The one or more sensors can be any one or more of the following types of sensors: A) A flash counter configured to count the number of flashes emitted by device 100. The flash counter may use a button press sensor (not shown) that provides a signal each time a button (e.g., button 110) that triggers a flash is pressed. The flash counter may, for example, store the flash count in memory of device 100. The flash count may be reset to 0, for example, each time device 100 is turned off and / or each time a period of inactivity longer than a predetermined time is detected. The flash count may additionally or alternatively be reset to 0 after a skin contact sensor (not shown) determines that device 100 is no longer in contact with the skin or has not been in contact with the skin for longer than a predetermined time. For example, user input to indicate the completion of a usage session may additionally or alternatively be used to trigger the flash counter to reset to 0.

[0029] The flash counter may be configured to store the time at which the flash occurred, which can advantageously enhance the flash count data, as it can determine both the number of flashes and time-based parameters such as, for example, flash frequency, the time between adjacent flashes, and / or an average of these parameters.

[0030] Counting the number of flashes, especially when combined with knowledge of the body part(s) exposed to light during a use session, allows an objective measure of the skin coverage achieved in a corresponding device use session to be obtained. This information can be fed back to the user by displaying the skin coverage on the display of the user device. More information on this is provided later in this specification.

[0031] Advantageously, the processing resources consumed to perform the count are relatively low. Thus, this type of processing can be performed in real time by a relatively low-power electronic device such as a user device (e.g., smartphone, tablet, etc.). In some cases, the device 100 itself can perform the number of flashes, and only summary information such as the total number of flashes is transmitted from the device 100 to the user device 202. This further enhances the ability of the present invention to provide a real-time estimation of the skin coverage rate.

[0032] B) An accelerometer and / or a gyroscope. (When both are present, this can be referred to as an inertial measurement unit (IMU)). These sensors can each detect and quantify the linear and rotational movements of the device 100. In particular, the accelerometer and gyroscope generate time-series data that enables linear acceleration or rotational acceleration to be calculated as a function of time. The use of such sensors can be referred to as tracking the movement of the device 100.

[0033] By tracking the movement of the device 100, an objective estimation of the skin coverage rate of the device can be made. The estimation of the skin coverage rate is preferably performed using a trained machine learning model that accepts accelerometer and / or gyroscope data as input and outputs a value for the skin coverage rate.

[0034] C) A skin contact sensor. The skin contact sensor can detect when the device 100, particularly the head 104, is in contact with the skin. The skin contact sensor can provide this data as a function of time, for example, during a usage session, as time-series data indicating the instants when the device 100 is in contact with the skin. Derived quantities such as the proportion of the total usage session during which the device 100 was in contact with the skin can also be calculated.

[0035] D) Barometric pressure sensor. Data from the barometric pressure sensor can be used to determine the height of device 100 as a function of time. This information can be used to track the movement of device 100 and provide an objective estimate of the skin coverage rate in a given usage session.

[0036] Device 100 can be configured to stream accelerometer data, gyroscope data, barometric pressure sensor data, and / or skin contact data as a data stream continuously or quasi - continuously to another electronic device. By quasi - continuous it is meant that the data is buffered by device 100 for some time and then transmitted when the buffer (e.g., the memory of device 100) reaches a predetermined level of fullness. The predetermined level of fullness is preferably less than the total capacity of the buffer.

[0037] In a preferred embodiment, device 100 incorporates at least the sensors discussed under item A above. In a more preferred embodiment, device 100 incorporates all of the sensors discussed under item A and item B above, and even more preferably also incorporates the sensors discussed under item C and optionally item D. The combination of data from all of these sensors can advantageously result in a very accurate calculated value of the skin coverage rate.

[0038] Device 100 can include a display (e.g., a touch screen) (not shown) that can also function as a user input device. The display can be used to display the skin coverage rate calculated as described herein. The calculation of the skin coverage rate can be performed by a processor or by device 100, or by another processor that is not part of device 100 and transmitted to device 100 for display. If present, the display of device 100 can additionally or alternatively be used to display a schedule of the type discussed later herein.

[0039] Device 100 can include an image capture module (not shown) such as a camera. The image capture module is preferably arranged to be able to easily capture images of the body part(s) that are the subject of the usage session. The images captured in this way can be processed to estimate the hair density. Further information regarding this will be provided later in this specification.

[0040] Referring now to FIG. 2, a system 200 suitable for implementing an embodiment of the present invention is shown. System 200 includes device 100 and user device 202, and optionally also includes cloud 206.

[0041] In FIG. 2, the arrows are used to indicate the communication paths between the various components of system 200. As can be seen from the figure, device 100 is communicatively coupled to user device 202 via the transmitter / receiver described above, for example, via a WiFi or Bluetooth connection. Although two-way communication is shown in FIG. 2, this does not limit the present invention, and in some embodiments, device 100 utilizes a transmitter that provides one-way communication from device 100 to user device 202.

[0042] User device 202 can be any electronic device capable of performing the functions attributable to user device 202 as described herein. User device 202 can be, for example, a smartphone, a tablet, a laptop computer, a wearable electronic device such as a smartwatch, a desktop computer, etc. User device 202 includes a display 204 of a type known in the art itself. User device 202 also includes one or more user input components (not shown) such as a touch screen, a keyboard, etc. User device 202 further includes a processor and a memory (not shown).

[0043] It is also contemplated that the function(s) of the user device 202 described herein may alternatively be performed by the device 100. Accordingly, the present invention is not limited to the system 200, and there are other embodiments in which the user device 202 is omitted. In these embodiments, all functions discussed herein in relation to the user device 202 are instead performed by the device 100 and / or the cloud 206 (if present).

[0044] The cloud 206 is a type of cloud computing environment known in the art. Briefly, the cloud 206 provides processing resources that are remote from the device 100 and to which a particular activity can be assigned as a task. It should be understood that the processing capabilities of the cloud 206, such as processor speed, the number of available processors, the amount of available memory, etc., tend to be significantly greater than the processing capabilities of the user device 202 and the device 100. Accordingly, the cloud 206 is more suitable for performing processor-intensive operations than the user device 202.

[0045] The device 100 and / or the user device 202 are communicatively coupled to the cloud 206 (if present), for example, via the Internet.

[0046] The cloud 206 includes a machine learning module 208. The machine learning module 208 is configured to perform aspects of the present invention as will be described in more detail later herein. The machine learning module 208 may be selected according to the complexity of the sensor data provided to the module as input. The module 208 preferably implements a deep learning algorithm, particularly when sensor data of type B), C) and / or D) is supplied as input to the module 208. However, the present invention is not limited thereto, and other machine learning algorithms can be used instead of deep learning.

[0047] One or more applications (the "apps") can be installed on user device 202 and / or device 100 using known installation techniques. The one or more apps can include a scheduling application and / or a companion application for the body hair growth management device. These may be provided as separate applications or as a single application that provides both a scheduling function and a feedback function. The following provides an explanation of the functions of these applications.

[0048] First, the companion application will be described. The companion application can be executed by user device 202 and / or device 100. The main function of the companion application is to provide feedback regarding the usage session based on data collected during the usage session by one or more sensors that are part of device 100. The feedback provided by the companion application includes the skin coverage rate. The skin coverage rate can be displayed using a graphical user interface displayed on display 204. The graphical user interface can include text and / or images. A percentage value may be displayed in text form indicating the skin coverage rate achieved in a given usage session. Additionally or alternatively, a graphical representation of the skin coverage rate, such as a shape shaded and / or colored in proportion to the achieved skin coverage rate, can be displayed. Alternative forms of graphical user interface elements will be apparent to those skilled in the art having the benefit of this disclosure.

[0049] In addition to the skin coverage rate, other information related to the usage session can be provided on the graphical user interface. Such other information includes, for example, one or more of the following: an indication showing the body part(s) exposed to light during the usage session, the total time of the usage session, an indication showing the head(s) used during the usage session, a motivational message and / or a suggestion for increasing the skin coverage rate in a future usage session (e.g., "Move the device more slowly"), etc., which are text feedbacks related to the usage session. This list is not exhaustive, and other information can be additionally or alternatively displayed.

[0050] In addition to the data from the sensor(s) of the device 100, the companion application can collect other information related to the usage session. This information can be used to improve or complement the estimated value of the skin coverage rate. This information can include any one or more of the following: - The skin tone to which the device 100 is applied during the usage session. The skin tone information can be manually input by the user, for example, by comparing the skin tone with a skin tone sample that can be displayed on the display 204 of the user device 202. Alternatively, if the device 100 includes a skin tone sensor, the data collected by this skin tone sensor can be provided to the companion application, enabling an automatic determination of the skin tone. Techniques for using a skin tone sensor to determine the skin tone are known. - The hair color present on the skin to which the device 100 is applied during the usage session. The hair color information can be provided manually or automatically in the same way as the skin tone, i.e., by using a hair color sensor. A hair color sensor can be provided to automatically detect the hair color. Hair color sensors are known. - The hair density of the body hair present on the skin to which device 100 is applied during a usage session. The hair density information can be provided by two or more images representing different hair densities, where each image can be displayed by presenting two or more images corresponding to different hair densities. The user can select the image that most closely corresponds to the hair density of the skin to which device 100 is applied during the usage session. Alternatively, the hair density may be automatically detected. - Identifiers of the body part(s) to which device 100 is applied during a usage session. This information may be manually entered by the user, or a body part sensor (not shown) may be used to automatically detect which body part(s) device 100 is proximate to during the usage session. - Identifiers of the head(s) 104 of device 100 used / to be used during a usage session. This information may be manually entered by the user, or a device head sensor (not shown) may be used to automatically detect which head(s) 104 are attached to housing 102 during the usage session. The companion application may also provide a recommendation as to which head(s) should be used during the usage session based on the body part(s) to which device 100 is applied. The recommendation can be text indicating which head should be used and / or an image of the head to be used. In this case, if the companion application detects that a sub-optimal head is attached to device 100, it can warn the user, for example, by a text warning and / or a graphic such as a warning sign displayed on display 204. The warning can also propose the head that is optimal for use during the next usage session of device 100. - Information unique to the person for whom the device is to be used in a usage session (e.g., gender, name, or some other identifier such as an email address, username, phone number, etc.), one or more goals of the person (e.g., a 50% reduction in hair density, total hair removal, etc.), feedback from the same person regarding previous usage sessions (e.g., satisfaction, dissatisfaction), etc. This information can be used to create and maintain a profile that stores information such as the skin coverage rate achieved for each usage session of that person. - The pulse energy or fluence applied to the skin during a usage session, either as a cumulative value or on a "per pulse" basis. This information can be manually input, but preferably is automatically obtained by the device 100 by determining the pulse energy or fluence set during the usage session. - Physiological information regarding the person for whom the device is used in a usage session, such as height and / or weight. This information, together with the sensor data described above, is used as an input in the estimation of the skin coverage rate and can further improve the accuracy of the estimation of the skin coverage rate.

[0051] The companion application can be configured to monitor the use of the device 100 in a usage session according to the method of FIG. 3.

[0052] In step 300, the processor receives sensor data from one or more sensors of the device 100, such as the sensors discussed under items A, B, C, and / or D. The processor can be part of the system 200. In some embodiments where the processing complexity is on the lower side (e.g., embodiments that do not utilize machine learning), the processor is part of the user device 202 or the device 100. Conversely, in other embodiments where the processing complexity is on the higher side (e.g., embodiments that utilize machine learning), the processor is part of the cloud 206. By selecting the processing resources in this way, it is advantageously possible to provide an estimated value of the skin coverage rate in real time or substantially in real time during a usage session as needed.

[0053] The sensor data may be received by the processor while it is being collected by the device 100, i.e., while a use session is in progress. Alternatively, the sensor data may be received by the processor after a use session is completed.

[0054] The sensor data may be transmitted to the processor by a transmitter or transceiver of device 100, for example, a Bluetooth or WiFi transmitter / transceiver. If the processor is in the cloud 206, user device 202 may act as an intermediate device to route the sensor data to the cloud 206, or device 100 may alternatively transmit the sensor data directly to the cloud 206. As described above, device 100 may be configured to transmit the sensor data as a data stream to another electronic device continuously or quasi-continuously.

[0055] In step 302, the processor uses the sensor data to perform a calculation of the skin coverage rate achieved in the usage session. In one embodiment where the sensor data includes the count of flashes, the above-mentioned processor can be the processor of the user device 202. In this embodiment, a rule-based algorithm can be used by the user device 202 to calculate the skin coverage rate based on the count of flashes. This embodiment requires relatively few processing resources to calculate the skin coverage rate, and thus, the user device 202 can handle the calculation by itself. Therefore, the user device 202 can perform the necessary calculations in real time or near real time, and as the usage session progresses, a graphical user interface indicating the progress of the usage session can be displayed on the display 206 in real time or near real time. The graphical user interface can be, for example, a progress bar. The progress bar or the equivalent may be displayed at the end of the usage session, i.e., after the usage session is completed, or the progress bar or the equivalent may be displayed and updated while the usage session is in progress.

[0056] In another embodiment where the sensor data includes at least accelerometer data, the processor is part of the machine learning module 208 in the cloud 206. In this case, the device 100 may directly stream the sensor data to the cloud 206 without incurring a delay by sending the data via the user device 202. The output of the machine learning module 208, i.e., the estimated skin coverage rate, can be sent from the cloud 206 to the user device 202.

[0057] The machine learning module 208 can take in the accelerometer data as input and output the estimated skin coverage rate. Any of the other sensor data types discussed previously under items A, B, C, and / or D can additionally or alternatively be provided as input to the machine learning module 208.

[0058] In a preferred form of this embodiment, the machine learning module 208 is supplemented by a rule-based preprocessing algorithm that works in cooperation with the machine learning module 208 to provide a skin coverage rate. In particular, some sensor data, such as the number of flashes and / or skin contact sensor data, can be input into the rule-based algorithm, and the output of the rule-based algorithm can be input into the machine learning module 208 together with other sensor data, such as accelerometer and / or gyroscope data. This can improve the accuracy of the estimation of the skin coverage rate provided by the machine learning module 208.

[0059] In addition to or as an alternative to the rule-based preprocessing algorithm, a rule-based postprocessing algorithm can be used. The rule-based postprocessing algorithm can perform one or more so-called "sanity tests" on the output from the machine learning module 208. The sanity test(s) ensures that the output of the machine learning module 208 is reasonable, for example, excluding negative skin coverage rate values or values exceeding 100%.

[0060] It will be understood that multiple rule-based postprocessing algorithms and / or multiple rule-based preprocessing algorithms can be used.

[0061] In step 304, the user device 202 displays information regarding the calculated skin coverage rate on the display 204 and / or displays this information on the display of the device 100. The information on the skin coverage rate can be displayed in the above-described manner, for example, as a percentage, a progress bar, etc. At least the skin coverage rate is displayed as, for example, a numerical percentage and / or an equivalent graphical representation. Other information as described above may be additionally displayed. Examples include the percentage of the target body part covered during the use session, the rating of the use session, and / or a proposal on how to increase the skin coverage rate of subsequent use sessions with respect to the calculated skin coverage rate. The proposal may be in text form, for example, instructions to the operator such as "move the device more slowly on the skin", "trigger more flashes", "use a different head in the next use session", etc. Information regarding the operation of the device 100, for example, the proposed energy level setting for subsequent use sessions, may be additionally or alternatively displayed.

[0062] The value of the skin coverage rate advantageously enables the user to obtain an immediate understanding of the effectiveness of a given use of the device 100. This is because it is generally understood by the user that a larger skin coverage rate leads to a more effective use session. The value of the skin coverage rate is provided in a user-friendly manner since all the user needs to do is use the device 100 and the calculation is automatically processed. Furthermore, a single value (the skin coverage rate) is easy for untrained users to understand, and thus the user can immediately obtain an objective perspective on the effectiveness of the use session of the device 100 based on this single, easy-to-understand parameter.

[0063] The value of the skin coverage rate can additionally be used to make predictions regarding the outlook of the skin coverage rate in future use session(s), potentially providing the ability to make predictions regarding the overall effectiveness outlook of a usage plan that includes multiple use sessions. (The usage plan will be described in more detail later in this specification). This can include, for example, converting the actual and / or predicted skin coverage rate into the effectiveness of hair growth management by using clinical data or other such effectiveness data that associates the skin coverage rate with the effectiveness of hair growth management.

[0064] In embodiments where machine learning is utilized, a processor (e.g., a cloud-based processor that can be part of the machine learning module 208) can be configured to store both the sensor data and the calculated skin coverage rate in a training data set. This training data set can be used by the processor to train a machine learning model to calculate the skin coverage rate.

[0065] Alternatively, if a trained machine learning model already exists, the training data set can be used to improve the trained machine learning model and reach a more accurate machine learning model. The trained or improved machine learning model can then be used by the processor to calculate the skin coverage rate for subsequent use sessions of the device 100. Training or retraining can be performed periodically (e.g., once a day, once a week, once a month), or the (re)training can be a continuous process.

[0066] It will be understood that configuring the processor in this way advantageously tends to increase the accuracy of the skin coverage rate calculation provided by the machine learning module 208. This is because the training data set tends to grow over time and increase the effectiveness of the training process. Additionally, the training data set can be supplemented with data from many different users, providing a diverse and representative sample set for training.

[0067] In a second aspect, the present invention provides a scheduling application that can dynamically adjust a usage plan including one or more usage sessions of a light-emitting body hair growth management device based on information regarding the hair density of a target body part(s). This information can be provided as feedback received from the user regarding the level of body hair growth management achieved in a given usage session. Alternatively, the level of body hair growth management achieved in a given usage session can be automatically estimated. Further information regarding the second aspect is provided immediately following. The second aspect can be combined with the first aspect, i.e., both the first and second aspects of the present invention can be provided by a single application or by separate companion and scheduling applications. The scheduling application can be executed by the user device 202 and / or the device 100.

[0068] The scheduling application of the second aspect functions to provide a mechanism for dynamically scheduling usage sessions. The scheduling application has a graphical user interface including a scheduling type user interface that enables events to be scheduled. This can be, for example, a calendar of a type known in the art itself. The following description refers to a "calendar application" and a "calendar", which should be understood as one specific example of a scheduling application, and thus the present invention is not particularly limited to calendars and calendar applications.

[0069] In the case of a calendar application, the scheduling user interface can include a calendar showing the currently displayed day, week, or month. In the week or month view, the calendar typically shows the days in one or more rows, and each day is labeled by its corresponding date. There may be a visual indicator such as a shape or a shaded area indicating the current day.

[0070] The day(s) currently having a scheduled usage session can be indicated in some way, for example, by making their labels bold, in a different color, and / or by some visual indicator such as dots displayed close to the labels of the related day(s).

[0071] The user can interact with the calendar application via the user input components of the user device 202 or the device 100. The user can select a specific date, and in response, the calendar application provides more information about the selected date, for example, in an expanded view. The information can include whether a usage session is scheduled for the selected date. If a usage session is scheduled, details about the usage session, such as the target body part(s), can be provided.

[0072] Scheduled usage sessions can be part of a usage plan, which is understood to refer to a plurality of usage sessions grouped together. The usage plan may focus on a specific body part and may have a specific purpose associated therewith. The duration of the usage plan can be defined, which is the total time taken to complete the plan (e.g., 1 month, 2 months). The dates on which the individual usage sessions of the usage plan are scheduled can be shown as part of the usage plan.

[0073] Some exemplary usage plans are shown in Table A immediately following this. It should be understood that the information shown in Table A can be captured and stored by the user device 202 as a data structure, for example, in a data structure in XML format or JSON format.

[0074] [Table 1]

[0075] The parameters shown in Table A will be discussed in detail immediately following this.

[0076] The plan ID is a unique identifier assigned to the usage plan. This is typically a string, such as "Plan 1" as described above. The plan ID can be automatically assigned by the user device as part of creating a new program. The user may be allowed to edit or specify the plan ID.

[0077] The body part(s) is a parameter (e.g., a string) that identifies the body part(s) to which the usage plan pertains. This information can be provided by the user during creation of the usage plan. The user may, for example, select one or more body parts on an image of the body. Alternatively, body part selection can be performed using a drop-down list of predefined items, a free text field with a linked search function, etc.

[0078] The user can provide the purpose of the usage plan, which can be stored as text (a string). This purpose describes the result the user wishes to achieve at the end of the usage plan. The purpose can be selected from a predefined set of goals, such as temporary hair removal, reduction of visible hair, slowing of hair growth, etc. Allowing the user to define the goal can advantageously assist in monitoring the progress of the usage plan.

[0079] The start date of the usage plan can be set. The user can, for example, use a date picker user interface element to set the start date. The start date indicates the date on which the first usage session of the plan is scheduled to occur. This can be the date the usage plan was created, or a future date.

[0080] The usage count can be maintained by the user device 202. The usage count is a number indicating the total number of usage sessions within the performed usage plan.

[0081] The next use date can be provided by the user device 202. This indicates the date on which the next use session in the usage plan is scheduled. The next use date can include only the next session (i.e., one day), or can include all remaining use session dates (i.e., multiple days). This information can be graphically displayed to the user on the calendar graphical user interface element described above.

[0082] The duration of the usage plan can be set. The duration can be defined in terms of the total number of use sessions within the usage plan, or can be defined in terms of the time expected to complete the usage plan (e.g., x days, x weeks, x months, etc.).

[0083] When calculated, the value of the skin coverage rate as described above in relation to the first aspect of the present invention can be automatically added to the calendar entry of the usage session that has already been performed. The skin coverage rate can be viewed by the user when the date corresponding to the usage session is selected.

[0084] It will be understood that Table A is purely illustrative and other information can be additionally or alternatively included in the data structure representing the usage plan. Such information will be apparent to those skilled in the art having the benefit of this disclosure and given the details of the situation.

[0085] Any one or more of the parameters described above can be viewable by the user in an appropriate graphical user interface. For example, a user selection of a particular usage plan can cause the user device 202 or device 100 to display a graphical user interface showing the body part(s) associated with the usage plan, and a schedule showing one or more next use sessions (e.g., by date, day of the week, time, etc.). The estimated completion date of the usage plan can be shown on the graphical user interface.

[0086] User device 202 and / or device 100 can be configured to provide reminders for usage sessions based on a usage plan, for example, using a "next use date" parameter. Reminders can be generated by user device 202 and / or device 100 in the form of audio reminders, such as alarms, visual reminders, such as notification messages, or a combination of both audio and visual reminders. This list is not exhaustive, and other reminder techniques can be used in addition to or instead of the techniques listed herein.

[0087] The user can configure the reminders to be generated using appropriate graphical user interface elements, for example, to set the reminder type (audio / visual / both / other), to set how many reminders should be provided in advance of the usage session, and / or to set the period of time before the usage session that a reminder should be generated (e.g., one day before the usage session is scheduled, one hour before the usage session is scheduled, ten minutes before the usage session is scheduled, etc.).

[0088] The calendar application may also provide a progress checker user interface that enables a user to check the progress of a given usage plan. The progress checker user interface can indicate the percentage of the usage plan that is complete, for example, as a percentage or as a progress bar. When the user completes another usage session, the progress checker user interface is updated to indicate that this has occurred. In addition to the percentage complete, text and / or images may be included on the progress checker user interface, which may enable the user to receive feedback regarding the current status of the usage plan. The text can include motivational messages, comments regarding changes in skin and / or hair density that the user may expect to see at relevant points in the usage plan, and the like. The image can indicate the expected progress and, for example, be a graphic of a tuft of hair that becomes increasingly transparent as the user progresses through the usage plan to represent hair loss.

[0089] It will be appreciated that the usage plan can be defined in a computer-readable format, for example, as a data structure of the type described above. The usage plan can thus be defined by the manufacturer or retailer of device 100 and downloaded to user device 202 and / or device 100 that is ready for use.

[0090] The usage plan described above is a dynamic entity. That is, the usage plan can be changed after one or more usage sessions of the plan have been executed. In particular, the dates of one or more usage sessions that have not yet been executed can be rescheduled after the usage plan has been started. Additionally or alternatively, one or more of the usage sessions that have not yet been executed can be skipped (cancelled), and / or one or more additional usage sessions can be added to the usage plan.

[0091] Adjustments to the usage plan are made based on the effects that the usage session(s) already conducted had on the body hair of the person to whom the device 100 is applied. For example, if it is found that the reduction of hair growth is occurring faster than initially expected, the usage plan can be adjusted such that there is more time between adjacent future scheduled usage sessions and / or one or more of the yet-to-be-conducted scheduled usage sessions can be cancelled. Alternatively, if it is found that the reduction of hair growth is occurring more slowly than initially expected, or no reduction is found and actual (re)growth of body hair is occurring, the usage plan can be adjusted such that the amount of time between adjacent future scheduled usage sessions is less and / or one or more additional usage sessions can be added to the usage plan.

[0092] Adjustments to the usage plan can be automatically performed according to the method shown in FIG. 4. The adjustments can be performed using a machine learning algorithm, preferably a recurrent neural network (RNN) or an HMM. The adjustments can be performed in the cloud, for example, by the machine learning module 208, or by the user device 202. Preferably, the location where the processing is performed (user device 202 or cloud 206) is selected based on the complexity of the machine learning algorithm, with the cloud 206 being selected for more complex algorithms and the user device 202 being selected for less complex algorithms.

[0093] Adjustments to the usage plan can also be made manually by the user. For example, a person may not be able to anticipate that a particular usage session will be unavailable, and the usage session may be manually skipped or rescheduled. The calendar application can include a graphical user interface that enables usage sessions within the usage plan to be skipped or rescheduled. The calendar application can also automatically detect skipped usage sessions, for example, based on the lack of user input regarding such usage sessions.

[0094] In a preferred embodiment, the adjustment is performed by a combination of a rule-based algorithm and a machine learning algorithm. The rule-based algorithm can be a rule-based preprocessing algorithm and / or a rule-based postprocessing algorithm. The rule-based preprocessing algorithm can be configured to receive information related to scheduling, such as skipped, manually rescheduled, or missed usage session(s). This can be used by the rule-based preprocessing algorithm to calculate one or more parameters that can be supplied as input to the machine learning model, along with an indication of hair density, as described below.

[0095] In addition to, or as an alternative to, a rule-based preprocessing algorithm, a rule-based postprocessing algorithm can be used. The rule-based postprocessing algorithm can perform one or more sanity tests on the output from the machine learning model. The sanity test(s) can ensure that the output of the machine learning model makes sense, e.g., that the value output by the machine learning model is a valid date that is not a past date. The rule-based postprocessing algorithm may include preventing safety-based elements, e.g., that the time between adjacent usage sessions is less than a threshold "safety limit" value. Other postprocessing rules can be based on user convenience, e.g., preventing overly frequent changes to the usage schedule that could annoy or inconvenience the user.

[0096] It will be understood that multiple rule-based postprocessing algorithms and / or multiple rule-based preprocessing algorithms may be used.

[0097] Referring now to FIG. 4, an adjustment process is shown. The following description focuses on user device 202 that performs various aspects of the steps of FIG. 4, but it should be understood that these operations may alternatively be performed by device 100.

[0098] In step 400, the user device 202 provides a usage plan in a calendar application of the type described above. The usage plan includes one or more planned usage events. Each of the planned usage events has a corresponding date associated therewith. This is also as described above. The usage plan can be downloaded, for example, from a manufacturer's or retailer's computer to the user device 202. Alternatively, the usage plan can be created by the user device 202 by asking the user a series of questions, such as which body part(s) the usage plan relates to, the skin tone and / or hair color of the person using the device 100, the purpose of the person with respect to the use of the device 100, etc. A "default" or "initial" usage plan can be created based on this information, and this usage plan is modified as described below in connection with FIG. 3.

[0099] In step 402, the user device 202 obtains an indication of the hair density of the body hair present on the body part associated with the usage plan. This indication can be obtained manually or automatically.

[0100] In the manual case, the user device 202 receives user input via the user interface of the usage device 204. The user input includes an indication of the hair density on the body part(s) associated with the usage plan.

[0101] To assist the user in providing an objective indication of the hair density, the user device 202 can display a hair density graphical user interface element on the display 206. The hair density graphical user interface element includes at least two images representing different hair densities, with each image corresponding to a different hair density. The user can then examine the skin where the device 100 is used and compare the visually observed hair density with the images presented on the user interface.

[0102] Each image can be a photograph of skin having a particular hair density. Alternatively, each image can be a graphical representation of skin having a particular hair density, such as a figure, sketch, animation, etc.

[0103] The user can select the image that most closely resembles the current hair density of the skin where the device 100 is being used. The user device 202 can register this selection and thus receive an indication indicating the hair density of the body part related to the usage plan.

[0104] One or more images of the skin where the device is being used may also be displayed in a state of being "lined up" horizontally with each other for comparison with the images displayed within the hair density graphical user interface element. This can be more intuitive for the user. This / These image(s) can be captured by the camera of the user device 202 or other such imaging module.

[0105] When the usage plan is related to multiple body parts, step 402 is repeated for each body part, and the user's selection can be recorded separately for each body part.

[0106] It will be understood that one or more uses of the device 100 can be performed between steps 400 and 402, and the hair density shown in step 402 may not be the starting hair density of the body part where the device 100 is being used.

[0107] When an indication indicating the hair density is automatically obtained, one or more images of the skin where the device is being used are obtained, for example, by the camera of the user device 202. This / These image(s) are processed to enable the hair density to be estimated. The image processing can be performed by an image processing algorithm, such as a machine learning algorithm trained to detect the pixels of an image that are likely to correspond to body hair. Detecting body hair in the image can enable the hair density to be estimated.

[0108] In step 404, based on the indication obtained in step 402, the usage plan is adjusted. Adjusting includes changing the data of at least one of the planned usage events that form part of the usage plan. The date can be rescheduled by advancing it (i.e., bringing it closer to the current date) or delaying it (i.e., moving it further away from the current date). Two or more of the planned usage events can be rescheduled at once, for example, all the planned usage events can be moved forward or backward by one day, two days, one week, etc. Alternatively, the planned usage events may be rescheduled so that there is a specific amount of time (e.g., one week) between adjacent events.

[0109] In addition to date adjustment, in step 404, the usage plan can be adjusted by adding one or more additional planned usage events and / or skipping / deleting one or more existing planned usage events.

[0110] The usage plan can be adjusted by providing the indication received in step 402 and the usage plan itself to a machine learning module. The machine learning module is trained to adjust the usage plan based on the indication received from the user. The machine learning module outputs the adjusted usage plan. The machine learning module can be, for example, module 208 shown in FIG. 2. The machine learning module can utilize an RNN or preferably an HMM, but the present invention is not limited thereto, and other types of machine learning algorithms can be used instead.

[0111] The calendar within the calendar application can be automatically updated by the user device 202 to take into account the adjustments made in step 404 with respect to the usage plan. Any alarms, reminders, etc. set by the user device 202 can also be automatically adjusted. A notification may be displayed on the display 206 so that the user can confirm that the adjustment has been made.

[0112] Following step 404, in step 406, one or more subsequent usages of the device 100 can be performed according to the adjusted usage plan. As shown in FIG. 4, it is possible to repeat steps 402 and 404 after the subsequent usage of the device 100 in order to further adjust the usage plan.

[0113] In this way, the usage plan becomes a dynamic state as it responds to the actual effects that the device 100 has on the body hair of the target body part(s). This can advantageously lead to an improvement in the user experience and / or user satisfaction.

[0114] The dynamic adjustment of the usage plan can take into account safety-related factors. For example, safety rules for adjusting the usage plan may be logically incorporated so as to prevent the usage plan from proposing practices that are considered unsafe. For example, the usage plan may be prevented from being adjusted by one or more safety rules to bring adjacent usage sessions closer to each other in time than recommended. This prevents the usage plan from proposing usage patterns that could harm the skin of the person using the device 100.

[0115] Similar to the case of the skin coverage rate parameter, a processor (e.g., a cloud-based processor that can be part of the machine learning module 208) can be configured to store usage plans and hair density indications within a training dataset. This training dataset can be used by the processor to train a machine learning model to determine recommended adjustments to the usage plan. Alternatively, if a trained machine learning model already exists, the training dataset can be used to improve the trained machine learning model and arrive at a more accurate machine learning model. The trained or improved machine learning model can then be used by the processor to calculate adjustments to the usage plan for subsequent iterations of at least steps 402 and 404 of FIG. 4.

[0116] It will be appreciated that configuring the processor in this manner advantageously tends to enhance the suitability of the adjusted usage plan provided by the machine learning module 208. This is because the training dataset tends to grow over time and increase the effectiveness of the training process. Additionally, the training dataset can be supplemented with data from many different users, providing a diverse and representative set of samples for training.

[0117] It will be understood that the operations described herein can be performed by a processor in accordance with computer-readable instructions stored on a computer-readable medium. The computer-readable medium can be non-transitory. Thus, one or more computer-readable media storing such instructions also form part of the present invention.

[0118] In addition to the embodiments described above, the following clauses describe further embodiments of the present invention.

[0119] Clause 1: A computer-implemented method for adjusting a usage plan of a light-emitting body hair growth management device, comprising: providing, in a scheduling application of an electronic device, a usage plan including one or more scheduled usage events, wherein the scheduled usage event or each scheduled usage event has a corresponding date associated therewith; obtaining, by the electronic device, an indication of the hair density of the body hair present on the body part associated with the usage plan; and adjusting, by the electronic device, at least one date of one or more scheduled usage events based on the indication.

[0120] Clause 2: Adjusting, by the electronic device, at least one date of one or more scheduled usage events based on user input includes: providing the indication and the usage plan to a machine learning module configured to adjust the usage plan based on user input; and receiving, as an output of the machine learning model, the adjusted usage plan. The computer-implemented method according to Clause 1.

[0121] Clause 3: Further comprising: displaying, on a display of the electronic device, a graphical user interface element of hair density including at least two images representing different hair densities, each of the images corresponding to a different hair density. Obtaining, by the electronic device, an indication of the hair density of the body hair present on the body part includes receiving a user selection of one of the at least two images representing different hair densities. The computer-implemented method according to Clause 1 or 2.

[0122] Clause 4: Obtaining, by the electronic device, an indication of the hair density of the body hair present on the body part includes: obtaining one or more images, each of the one or more images including at least a part of the body part; and automatically processing the one or more images to estimate the hair density. The computer-implemented method according to Clause 1 or 2.

[0123] Clause 5: A non-transitory computer-readable medium storing instructions that, when executed by a processor of an electronic device, cause the electronic device to provide, in a scheduling application of the electronic device, a usage plan including one or more scheduled usage events, wherein the scheduled usage event or each scheduled usage event has a corresponding date associated with the scheduled usage event or each scheduled usage event, obtain an indication of the hair density of hair present on a body part associated with the usage plan, and cause the electronic device to adjust at least one date of the one or more scheduled usage events based on the indication.

[0124] The dimensions and values disclosed herein are not to be understood as being strictly limited to the exact numerical values recited. Instead, unless otherwise specified, each such dimension is intended to mean both the recited value and a functionally equivalent range surrounding that value. For example, a dimension disclosed as "40 mm" is intended to mean "about 40 mm".

Claims

1. A computer-implemented method for monitoring the use of a light-emitting body hair growth management device during a use session of the body hair growth management device, the method comprising: receiving, by a processor, sensor data collected during the use session from one or more sensors of the body hair growth management device; executing, by the processor, a calculation of a skin coverage rate achieved in the use session using the sensor data; displaying information regarding the calculated skin coverage rate on a display of a user device or on a display of the body hair growth management device; A method comprising the steps of.

2. The computer-implemented method according to claim 1, wherein the processor is a processor of the user device or the body hair growth management device, and the sensor data includes the number of flashes of at least some light emitted by the body hair growth management device during the use session. 【Claim ③】 The displaying of the information regarding the calculated skin coverage rate includes displaying a graphical user interface indicating a percentage of a target body part covered in the use session, and the method further includes: updating, by the processor, the display of the graphical user interface during the use session or after the use session is completed. The computer-implemented method according to claim 1 or 2.

4. The computer-implemented method according to any one of claims 1 to 3, wherein at least a part of the processor is a cloud-based processor, and the calculation of the achieved skin coverage rate is executed using a machine learning algorithm.

5. The sensor data includes one or more of the following: accelerometer data of an accelerometer sensor of the body hair growth management device, the accelerometer data collected during the use session; rotation data of a gyroscope of the body hair growth management device, the rotation data collected during the use session; the number of flashes of some light emitted by the body hair growth management device during the use session; and / or skin contact sensor data of a skin contact sensor collected during the use session. The computer-implemented method according to any one of claims 1 to 4.

6. The computer-implemented method according to any one of claims 1 to 5, further comprising transmitting the sensor data to the processor by a transmitter or transceiver of the hair growth management device.

7. transmitting the sensor data to the user device by a transmitter or transceiver of the hair growth management device; transmitting the sensor data to the processor by a transmitter or transceiver of the user device; The computer-implemented method according to any one of claims 1 to 5, further comprising.

8. The computer-implemented method according to claim 6 or 7, wherein the sensor data is transmitted continuously or quasi-continuously as a data stream during the usage session.

9. storing, by the processor, the sensor data and the calculated skin coverage rate in a training data set; training, by the processor, a machine learning model using the training data set to generate a trained machine learning model or improving an existing machine learning model; calculating, by the processor, a skin coverage rate for a subsequent usage session of the hair growth management device using the trained or improved machine learning model; The computer-implemented method according to any one of claims 1 to 7, further comprising.

10. Displaying the information related to the calculated skin coverage rate includes displaying the percentage of the target body part covered during the usage session, displaying a rating of the usage session, and / or displaying a proposal on how to increase the skin coverage rate of a subsequent usage session relative to the calculated skin coverage rate. The computer-implemented method according to any one of claims 1 to 9, including any one or more of the above.

11. The hair growth management device includes a head sensor configured to detect the type of the head currently removably attached to the hair growth management device, and the method includes detecting, based on data from the head sensor, the type of the head currently attached to the hair growth management device; determining whether the currently attached head is the optimal head for the next usage session of the hair growth management device; When the currently attached head is not optimal, display a notification on the display of the user device indicating that the currently attached head is not optimal for the next usage session of the body hair growth management device. The computer-implemented method according to any one of claims 1 to 10, further comprising. **Claim 12** The computer-implemented method according to claim 11, wherein the notification also includes a proposal for an optimal head for the next usage session of the body hair growth management device. **Claim 13** The computer-implemented method according to any one of claims 1 to 12, further comprising displaying a proposal for an energy level setting for the next usage session of the body hair growth management device on the display of the user device. **Claim 14** A computer program that, when executed by one or more processors, causes the one or more processors to execute the method according to any one of claims 1 to 13. **Claim 15** A body hair growth management system comprising a body hair growth management device, a processor, and a display, the body hair growth management system being configured to execute the method according to any one of claims 1 to 13.

Images