Companion application for a luminescent hair growth management device

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 light-emitting hair growth management devices. Specifically, the present invention relates to calculating the skin coverage rate achieved during one or more use sessions and / or dynamically scheduling one or more use sessions.

Background Art

[0002] Light-emitting hair growth management devices are becoming increasingly popular, especially in environments such as salons or homes. In such situations, it is common for operators of the devices not to have received intensive training courses regarding the use of the devices. That is, the operators are reasonably proficient in operating the devices but are not experts. 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 elapsed between a use session of a 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 characteristic of light-based hair growth management technology is that it is most effective when used according to a recommended schedule. While a recommended schedule can be provided to the operator, in reality, operators may not adhere to it precisely. This can happen, for example, when a client misses or reschedules a salon appointment, making the device unavailable, or when the operator forgets to use the device at the scheduled time. Recommended schedules are 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 the inability to adjust the schedule accordingly, which can lead to suboptimal results and user dissatisfaction. [Overview of the project] [Problems that the invention aims to solve]

[0004] Therefore, it is desirable to provide a mechanism that enables untrained individuals to objectively assess the effectiveness of a given use of a luminescent hair growth management device. Preferably, this mechanism is user-friendly and easy for untrained individuals to understand.

[0005] It is also desirable to provide a dynamic schedule for the use of a luminescent hair growth management device, and a mechanism to enable the generation of a dynamic usage schedule that is adjusted based on the 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 skin coverage achieved during a hair growth management device usage session. Skin coverage is automatically estimated using sensor data collected by one or more sensors of the hair growth management device during the usage session. The skin coverage 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 automated evaluation allows the operator of the 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 for a body hair growth management device. A usage plan containing one or more usage sessions is generated for the person to whom the usage sessions are to be performed. The usage plan may contain two or more usage sessions, for example, 2, 5, 10, or 20 usage sessions. The usage plan is presented by a scheduling application running on the electronic device, for example, in the form of a calendar in a calendar application. After each use of the body hair growth management device, the operator or user inputs information about the actually observed hair reduction effect, or alternatively, this information is automatically collected by processing the image(s) of the body part to which the usage session was to be performed. The scheduling application is configured to adjust the usage plan based on the information provided by the user or automatically collected. In this way, a dynamic usage plan is created that adjusts the scheduled usage sessions according to the actual use of the device. This can lead to improved user satisfaction because the user can better understand how to use the device effectively.

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

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

[0010] A second embodiment can be implemented by a computer implementation method for coordinating a usage plan for a luminescent body hair growth management device, the method comprising: providing a usage plan in a scheduling application for an electronic device that includes one or more planned usage events, wherein the planned usage event or each planned usage event has a corresponding date associated with the planned usage event or each planned usage event; obtaining an indication by the electronic device that shows the hair density of body hair present on the body part associated with the usage plan; and coordinating the date of at least one of the one or more planned usage events based on the indication by the electronic device.

[0011] The second embodiment can also be implemented by a non-temporary computer-readable medium for storing instructions, which, when executed by the processor of an electronic device, cause the electronic device to provide a usage plan in the electronic device's scheduling application, which includes one or more planned usage events, each of which has a corresponding date associated with that planned usage event or each of which has a corresponding date; to obtain an indication of the hair density of body hair present on a body part associated with the usage plan; and to cause the electronic device to adjust 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 embodiments are described in the appended dependent claims and / or embodiments for carrying out the invention. [Brief explanation of the drawing]

[0013] Embodiments of the present invention are described below with reference to the accompanying drawings, and are merely illustrative examples. [Figure 1] This figure schematically shows a light-emitting hair growth management device suitable for use with embodiments of the present invention. [Figure 2] This figure schematically illustrates a system suitable for carrying out embodiments of the present invention. [Figure 3] This is a flowchart showing a method for monitoring the use of a body hair growth management device according to one embodiment. [Figure 4] This flowchart shows a method for automatically adjusting the usage plan of a body hair growth management device based on user feedback, according to one embodiment. [Modes for carrying out the invention]

[0014] As used herein, the term "user" may refer to a person to whom the luminescent hair growth management device is used, or the operator of the device, who is different from the person to whom the device is used.

[0015] The term "skin coverage" refers to a parameter that indicates the percentage of skin area exposed to one or more light pulses from a hair growth management device during a usage session. Skin coverage can be calculated from data provided by the device sensor, as described below. Skin coverage 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 derived from exposing the skin to light pulses, skin coverage is a relevant parameter when evaluating the effectiveness of a usage session. Effectiveness here 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 skin coverage during a usage session of a luminescent hair growth management device. Sensor data from one or more sensors of the luminescent hair growth management device is used by a rule-based algorithm and / or a machine learning algorithm to estimate the coverage of the target body part(s) achieved during 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 may be displayed on the user device's display or on the display of the hair growth management device. Further details relating to this first aspect are provided immediately below.

[0017] Figure 1 shows a schematic representation of the light-emitting hair growth management device 100. Device 100 could be, for example, a type of light-based hair removal device known in the art. Since the configuration and operating principle of light-based hair removal devices are known, they will not be described in detail here for the sake of brevity.

[0018] The device 100 includes a housing 102 and a head 104 which contains, encapsulates, or otherwise associates with a light-emitting component (not shown). The light-emitting component is typically a flash lamp housed in an optical window transparent to light of wavelengths known to have at least a hair growth management effect.

[0019] The head 104 is preferably detachably mounted to the housing 102 so that an operator can attach different heads to the housing 102. Each head may be particularly suitable for use on a specific body part(s), for example.

[0020] A power supply 106 is also present. This may include a battery and / or port that enables wired electrical coupling to an electrical outlet. Inductive charging technology can also be used. Power supply 106 provides power to operate various components of device 100, including, for example, light-emitting components, a processor, and a transmitter / receiver.

[0021] A power button 108 is provided on the housing 102. This allows the 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., 5 minutes) and / or enter a power-saving sleep mode to avoid power waste. The power button 108 may be, for example, a push button or slider coupled to a switch.

[0022] The activation button 110 (or "trigger") is also provided within the housing 102 of the housing, enabling an 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 skin sensors are 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 via, for example, a user input device coupled to a display showing a suitably configured user interface.

[0026] Device 100 also includes a processor (not shown), which may 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 / transceiver (not shown) that enables device 100 to communicate wirelessly with other devices (see Figure 2). The transmitter / transceiver may be a Bluetooth transmitter / transceiver, preferably a Bluetooth Low Energy (BLE) transmitter / transceiver, a WiFi transmitter / transceiver, a Near Field Communication (NFC) module, etc. In some cases, the transmitter / transceiver may support multiple different communication technologies, such as both Bluetooth and WiFi. In these cases, multiple antennas may be present.

[0028] Device 100 further includes one or more sensors (not shown). The sensors(s) function to collect information about the operation of device 100, particularly relevant to the use of device 100. The one or more sensors may be any one or more of the following types: 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 that triggers a flash (e.g., button 110) is pressed. The flash counter may store the flash count in the memory of device 100, for example. The flash count may be reset to 0, for example, each time device 100 is switched off and / or each time an inactivity period 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 skin or has not been in contact with skin for a period longer than a predetermined time. For example, user input indicating the completion of a usage session may additionally or alternatively be used to trigger a reset of the flash counter to 0.

[0029] A flash counter can be configured to store the time when a flash occurred. This can be advantageous in improving flash count data because it is possible to determine both the number of flashes and time-based parameters such as flash frequency, the time between adjacent flashes, and / or the average of these parameters.

[0030] Counting the number of flashes, especially when combined with knowledge of the body part(s) exposed to light during the usage session, allows for an objective measurement of the skin coverage achieved during a corresponding device usage session. This information can be provided to the user by displaying the skin coverage on the user device's display. Further information regarding this will be provided later in this specification.

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

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

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

[0034] C) Skin contact sensor. The skin contact sensor can detect when the device 100, in particular the head 104, is in contact with the skin. The skin contact sensor can provide this data as a function of time, for example, as time-series data showing the moments when the device 100 is in contact with the skin during a usage session. Derived quantities such as the percentage of total usage sessions in 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 skin coverage rate during 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 to another electronic device continuously or semi-continuously. Semi-continuous means that the data can be buffered by device 100 for a period of 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 items A and B above, and even more preferably, also incorporates the sensors discussed under item C and optionally under item D. The combination of data from all of these sensors can, advantageously, yield a highly accurate calculation of skin coverage.

[0038] Device 100 may include a display (e.g., a touchscreen) (not shown) that can also function as a user input device. The display may be used to display the skin coverage calculated as described herein. The skin coverage calculation may be performed by a processor or 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 may be used additionally or alternatively to display a schedule of the types discussed later herein.

[0039] Device 100 may include an image capture module (not shown), such as a camera. The image capture module is preferably arranged to easily capture images(or more) of the body(s) that are the subject of the session. The images thus captured may be processed to estimate hair density. Further information relating to this will be provided later herein.

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

[0041] In Figure 2, arrows are used to indicate communication paths between the various components of System 200. As can be seen from the figure, device 100 is communicably coupled to user device 202 via the aforementioned transmitter / receiver, for example, via a WiFi or Bluetooth connection. Although bidirectional communication is shown in Figure 2, this is not limiting to 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] The user device 202 may be any electronic device capable of performing the functions attributed to the user device 202 as described herein. The user device 202 may be, for example, a smartphone, a tablet, a laptop computer, a wearable electronic device such as a smartwatch, or a desktop computer. The user device 202 includes a display 204 of a type known in the art. The user device 202 also includes one or more user input components (not shown), such as a touchscreen or a keyboard. The user device 202 further includes a processor and memory (not shown).

[0043] It is also intended that the functions(s) of the user device 202 described herein can be performed by device 100 instead. Therefore, the present invention is not limited to system 200, and other embodiments exist 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 device 100 and / or cloud 206 (if any).

[0044] Cloud 206 is a type of cloud computing environment known in the relevant technical field. In short, Cloud 206 provides remote processing resources from device 100, which may be tasked with specific activities. It should be understood that the processing power of Cloud 206, such as processor speed, the number of available processors, and the amount of available memory, tends to significantly exceed the processing power of user device 202 and device 100. Therefore, Cloud 206 is better suited to performing processor-intensive operations than user device 202.

[0045] Device 100 and / or user device 202 are connected to a cloud 206 (if any) via, for example, the internet.

[0046] 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. Module 208 preferably implements a deep learning algorithm, particularly when sensor data of type B), C), and / or D) is supplied as input to module 208. However, the present invention is not limited thereto, and other machine learning algorithms may be used instead of deep learning.

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

[0048] First, a companion application will be described. The companion application may be performed by user device 202 and / or device 100. The main function of the companion application is to provide feedback on the usage session based on data collected during the usage session by sensors(s) that are part of device 100. The feedback provided by the companion application includes skin coverage. Skin coverage can be displayed using a graphical user interface displayed on display 204. The graphical user interface may include text and / or images. Percentage values ​​may be displayed in text format indicating the skin coverage achieved in a given usage session. Additionally or alternatively, a graphical representation of skin coverage may be displayed, such as a shaded and / or colored shape representing the percentage corresponding to the achieved skin coverage. Alternative forms of graphical user interface elements will be apparent to those skilled in the art who are interested in this disclosure.

[0049] In addition to skin coverage, other information related to the usage session may be provided on the graphical user interface. Such other information may include, for example, one or more of the following: an indication showing the body part(s) exposed to light during the usage session; the total duration of the usage session; an indication showing the head(s) used during the usage session; and text feedback regarding the usage session, such as motivational messages and / or suggestions for increasing skin coverage in future usage sessions (e.g., "move the device more slowly"). This list is not exhaustive, and other information may be displayed additionally or alternatively.

[0050] In addition to data from the sensor(s) - The skin tone to which device 100 is applied during a usage session. Skin tone information can be manually entered by the user, for example, by comparing the skin to a skin tone sample that may be displayed on the display 204 of the user device 202. Alternatively, if device 100 includes a skin tone sensor, the data collected by this skin tone sensor may be provided to a companion application to enable automatic determination of the skin tone. Techniques using a skin tone sensor to determine skin tone are known. - The hair color of the body hair present on the skin to which device 100 is applied during the usage session. Hair color information can be provided in the same way as skin tone, i.e., manually or automatically. A hair color sensor can be provided to automatically detect hair color. A hair color sensor is known. - The hair density of body hair present on the skin to which device 100 is applied during the usage session. Hair density information can be provided by two or more images representing different hair densities, where each image displays two or more images corresponding to different hair densities. The user can select the image that best corresponds to the hair density of the skin to which device 100 is applied during the usage session. Alternatively, hair density may be detected automatically. - Identifiers of the body part(s) to which device 100 is applied during the usage session. This information may be entered manually by the user, or body part sensors (not shown) may be used to automatically detect which body part(s) device 100 is in close proximity to during the usage session. - Identifiers of the heads 104 of device 100 that were used or should be used during the usage session. This information may be entered manually by the user, or a device head sensor (not shown) may be used to automatically detect which heads 104 are mounted on the housing 102 during the usage session. The companion application may also provide recommendations on which heads should be used during the usage session based on the body part(s) on which device 100 is used. Recommendations may be text indicating which heads should be used, and / or images of the heads that should be used. If the companion application detects that a less-than-optimal head is mounted on device 100, it may warn the user, for example, by a text warning and / or graphic such as a warning sign displayed on display 204. The warning may also suggest the best head to use during the next usage session of device 100. - Information specific to the person who should be using the device during a usage session (e.g., gender, name, or any other identifier such as email address, username, phone number, etc.), one or more of the person's goals (e.g., 50% reduction in hair density, total hair removal, etc.), and feedback from the same person regarding previous usage sessions (e.g., satisfied, dissatisfied). This information may be used to create and maintain a profile that stores information such as the skin coverage 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 entered manually, but is preferably obtained automatically by the device 100 by determining the pulse energy or fluence set during the usage session. - Physiological information about the person using the device during the usage session, such as height and / or weight. This information, along with the sensor data mentioned above, can be used as input in the estimation of skin coverage to further improve the accuracy of the skin coverage estimation.

[0051] The companion application may be configured to monitor the use of device 100 during a usage session, according to the method shown in Figure 3.

[0052] In step 300, the processor receives sensor data from one or more sensors of device 100, e.g., the sensors discussed under items A, B, C, and / or D. The processor may be part of 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 user device 202 or 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 cloud 206. By selecting the processing resources in this way, it is advantageous to be able to provide estimates of skin coverage in real time or near real time during a usage session, as needed.

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

[0054] Sensor data may be transmitted to the processor by a transmitter or transceiver of device 100, such as a Bluetooth or WiFi transmitter / transceiver. If the processor is located in the cloud 206, the user device 202 may act as an intermediate device for routing 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 semi-continuously.

[0055] In step 302, the processor uses the sensor data to calculate the skin coverage achieved during the usage session. In one embodiment, where the sensor data includes the flash count, the processor may be the processor of the user device 202. In this embodiment, a rule-based algorithm may be used by the user device 202 to calculate the skin coverage based on the flash count. This embodiment requires relatively few processing resources to calculate the skin coverage, and therefore the user device 202 can handle the calculation itself. Thus, 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 showing the progress of the usage session in real time or near real time can be displayed on the display 206. The graphical user interface may be, for example, a progress bar. The progress bar or equivalent may be displayed at the end of the usage session, i.e., after the usage session is completed, or the progress bar or 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 a machine learning module 208 in the cloud 206. In this case, device 100 may stream the sensor data directly to the cloud 206 instead of incurring delays by transmitting 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 accelerometer data as input and output an estimated skin coverage rate. In this specification, any of the other sensor data types previously discussed under sections A, B, C, and / or D may be provided as input to the machine learning module 208, either additionally or alternatively.

[0058] In a preferred embodiment of this design, the machine learning module 208 is supplemented by a rule-based preprocessing algorithm that works in conjunction with the machine learning module 208 to provide skin coverage. In particular, several sensor data, such as flash counts and / or skin contact sensor data, can be input to the rule-based algorithm, and the output of the rule-based algorithm can be input to the machine learning module 208 along with other sensor data, such as accelerometer and / or gyroscope data. This can improve the accuracy of the skin coverage estimate provided by the machine learning module 208.

[0059] In addition to, or as an alternative to, rule-based preprocessing algorithms, rule-based postprocessing algorithms can be used. The rule-based postprocessing algorithm can perform one or more so-called "health tests" on the output from machine learning module 208. These health tests ensure that the output from machine learning module 208 is reasonable, for example, by excluding negative skin coverage values ​​or values ​​greater than 100%.

[0060] It will be understood that multiple rule-based post-processing algorithms and / or multiple rule-based pre-processing algorithms may be used.

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

[0062] The skin coverage value is advantageous because it allows users to gain an immediate understanding of the effectiveness of a given use of device 100. This is because it is generally understood by users that a higher skin coverage leads to a more effective use session. The skin coverage value is provided in a user-friendly manner, as the calculation is handled automatically, requiring only the user to use device 100. Furthermore, the single value (skin coverage) is easy for even untrained users to understand, and therefore, users can immediately obtain an objective outlook on the effectiveness of their use session of device 100 based on this single, easy-to-understand parameter.

[0063] The skin coverage value can be used to make predictions about the outlook for skin coverage in future use sessions, potentially providing the ability to make predictions about the overall efficacy outlook of a use plan that includes multiple use sessions. (Use plans will be described in more detail later in this specification.) This may include, for example, converting actual and / or predicted skin coverage to the efficacy of a hair growth management treatment by using clinical data or other such efficacy data that correlates skin coverage to the efficacy of a hair growth management treatment.

[0064] In embodiments utilizing machine learning, a processor (for example, a cloud-based processor which may be part of the machine learning module 208) may be configured to store both sensor data and calculated skin coverage in a training dataset. This training dataset may be used by the processor to train a machine learning model to calculate skin coverage.

[0065] 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 model. The trained or improved machine learning model can then be used by the processor to calculate the skin coverage rate for subsequent usage sessions of device 100. Training or retraining may be performed periodically (e.g., once a day, once a week, once a month), or (re)training may be a continuous process.

[0066] It will be understood that configuring the processor in this way is advantageous because it tends to improve the accuracy of the skin coverage calculation provided by the machine learning module 208. This is because the training dataset tends to grow over time, increasing the effectiveness of the training process. Furthermore, the training dataset can be complemented by data from many different users, resulting in a diverse and representative sample set for training.

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

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

[0069] In calendar applications, the scheduling user interface may include a calendar showing the currently displayed day, week, or month. In a week or month view, the calendar typically shows days in one or more rows, with each day labeled by its corresponding date. Visual indicators, such as a shape or shaded area, may be present to indicate the current day.

[0070] The day(s) on which a scheduled usage session currently exists may be indicated in some way, for example, by displaying their labels in bold, in a different color, and / or by using some visual indicator such as a dot displayed adjacent to the label of the relevant day(s).

[0071] The user can interact with the calendar application via the user input components of user device 202 or 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 extended view. This information may include whether or not a usage session is scheduled for the selected date. If a usage session is scheduled, details about the usage session, for example, the target body part(s), can be provided.

[0072] A scheduled use session can be part of a use plan, which is understood to refer to a group of use sessions. A use plan may focus on a specific body part and have a specific purpose associated with it. The duration of a use plan can be defined, which is the total time it will take to complete the plan (e.g., one month, two months). The dates on which individual use sessions of the use plan are scheduled to take place can be indicated as part of the use plan.

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

[0074] [Table 1]

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

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

[0077] Body parts are parameters (e.g., strings) that identify the body parts to which the usage plan relates. This information can be provided by the user during the 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 may be performed using a dropdown list of predetermined items, a free-text field with a linked search function, or the like.

[0078] Users can provide the purpose of their usage plan, which can be stored as text (a string). This purpose describes the results the user wants to achieve at the end of the usage plan. The purpose can be selected from a predefined set of goals, such as temporary hair loss, visible hair reduction, or slowing of hair growth. Allowing users to define goals is advantageous as it can help monitor the progress of the usage plan.

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

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

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

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

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

[0084] Table A is purely illustrative, and it will be understood that other information may be included additionally or alternatively in the data structure representing the usage plan. Such information would be obvious to a person skilled in the art who is interested in this disclosure and has been given the details of the circumstances.

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

[0086] User device 202 and / or device 100 can be configured to provide reminders about usage sessions based on a usage plan, for example, using a “next usage 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 may be used in addition to or instead of the techniques enumerated herein.

[0087] Users can configure reminders generated using appropriate graphical user interface elements, for example, by setting the reminder type (audio / visual / both / other), setting some reminders to be provided prior to the session, and / or setting the period before the session in which the reminders should be generated (e.g., one day before the scheduled session, one hour before the scheduled session, ten minutes before the scheduled session, etc.).

[0088] A calendar application may also provide a progress checker user interface that allows the user to check the progress of a given usage plan. The progress checker user interface may show the percentage of the usage plan that has been completed, 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 been done. In addition to the percentage of completion, text and / or images may be included on the progress checker user interface to allow the user to receive feedback on the current status of the usage plan. The text may include motivational messages, comments on changes in skin and / or body hair density that the user can expect to see at relevant points in the usage plan, etc. The images may indicate expected progress, for example, a graphic of clumps of body hair that become progressively more transparent as the user progresses through the usage plan to represent hair loss.

[0089] It will be understood 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 therefore be defined by the manufacturer or retailer of device 100 and downloaded to user devices 202 and / or device 100 that are ready for use.

[0090] The usage plan described above is a dynamic entity; that is, a usage plan can be modified after one or more usage sessions of that 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 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 effect that previously performed usage sessions have had on the body hair of the person to whom device 100 is applied. For example, if it is found that the reduction in body hair growth is occurring faster than initially expected, the usage plan may be adjusted so that there is more time between adjacent future scheduled usage sessions, and / or one or more scheduled usage sessions that have not yet taken place may be canceled. Alternatively, if it is found that the reduction in body hair growth is occurring slower than initially expected, or if no reduction is found and (re)growth of body hair is actually occurring, the usage plan may be adjusted so that there is less time between adjacent future scheduled usage sessions, and / or one or more additional usage sessions may be added to the usage plan.

[0092] Adjustments to the usage plan can be performed automatically according to the method shown in Figure 4. The adjustments can be performed using a machine learning algorithm, preferably a recurrent neural network (RNN) or 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 cloud 206 selected for more complex algorithms and user device 202 selected for less complex algorithms.

[0093] Adjustments to usage plans can also be made manually by the user. For example, a user may manually skip or reschedule a usage session if they cannot foresee that the session will be unavailable. A calendar application may include a graphical user interface that allows usage sessions in a usage plan to be skipped or rescheduled. The calendar application may also be able to automatically detect skipped usage sessions, for example, based on a lack of user input regarding such 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 pre-processing algorithm and / or a rule-based post-processing algorithm. The rule-based pre-processing algorithm can be configured to receive scheduling-related information, such as skipped, manually rescheduled, or overlooked usage sessions. This can be used by the rule-based pre-processing algorithm to compute one or more parameters that can be supplied as input to a machine learning model, along with an indication of hair density, as described below.

[0095] Rule-based post-processing algorithms can be used in addition to, or as an alternative to, rule-based pre-processing algorithms. A rule-based post-processing algorithm can perform one or more health tests on the output from a machine learning model. These health tests ensure that the machine learning model's output is reasonable, for example, that the values ​​output by the machine learning model are valid dates and not past dates. The rule-based post-processing algorithm may also include safety-based elements, such as preventing the time between adjacent usage sessions from falling below a threshold "safety limit." Other post-processing rules may be user-convenient, for example, preventing excessively frequent changes to the usage plan that could frustrate or inconvenience the user.

[0096] It will be understood that multiple rule-based post-processing algorithms and / or multiple rule-based pre-processing algorithms may be used.

[0097] Referring now to Figure 4, the adjustment process is shown. The following explanation will focus on user device 202 performing various aspects of the steps in Figure 4, but please understand that these operations can also be performed by device 100.

[0098] In step 400, the user device 202 provides a usage plan in the calendar application of the type described above. The usage plan includes one or more planned usage events. Each planned usage event has a corresponding date associated with it, as also described above. The usage plan may be downloaded to the user device 202 from, for example, a manufacturer's or retailer's computer. Alternatively, the usage plan may 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 for whom device 100 will be used, the purpose of device 100 in relation to the use of device 100, etc. Based on this information, a “default” or “initial” usage plan can be created, which is modified as described below in relation to Figure 3.

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

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

[0101] To assist the user in providing objective indications of 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, each image corresponding to a different hair density. The user can then examine the skin on which the device 100 is used and compare the hair density observed with the eye with the images presented on the user interface.

[0102] Each image could be a photograph of skin with a specific hair density. Alternatively, each image could be a graphical representation of skin with a specific hair density, such as a diagram, sketch, or animation.

[0103] The user can select an image that best resembles the current hair density of the skin on which device 100 is being used. The user device 202 can register this selection and thus receive an indication showing the hair density of the body part relevant to the usage plan.

[0104] One or more images of the skin on which the device is used may also be displayed in a way that allows for comparison with the images displayed within the hair density graphical user interface element, side by side. This may be more intuitive for the user. These images(s) may be captured by the camera of the user device 202 or other such imaging module.

[0105] If the usage plan relates to multiple body parts, step 402 may be repeated for each body part, and the user's selection may be recorded separately for each body part.

[0106] It will be understood that the device 100 may be used one or more times between steps 400 and 402, and that the hair density shown in step 402 does not have to be the starting hair density of the body part on which the device 100 is used.

[0107] If an indication of hair density is to be automatically acquired, one or more images of the skin on which the device is used are acquired, for example, by the camera of the user device 202. These images are processed to enable the estimation of hair density. This image processing can be performed by an image processing algorithm, for example, a machine learning algorithm trained to detect image pixels that are likely to correspond to body hair. Detection of body hair in the images can enable the estimation of hair density.

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

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

[0110] The usage plan can be adjusted by providing the indications 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 indications received from the user. The machine learning module outputs the adjusted usage plan. The machine learning module may be, for example, module 208 shown in Figure 2. The machine learning module may utilize an RNN or preferably an HMM, but the present invention is not limited thereto, and other types of machine learning algorithms may 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 to the usage plan in step 404. 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 adjustments have been made.

[0112] Following step 404, in step 406, one or more subsequent uses of device 100 can be performed according to the adjusted usage plan. As shown in Figure 4, steps 402 and 404 can be repeated after subsequent uses of device 100 to further adjust the usage plan.

[0113] In this way, the usage plan becomes dynamic, responding to the actual effect that device 100 has on the hair of the target body part(s). This can, advantageously, lead to an improved user experience and / or improved user satisfaction.

[0114] Dynamic adjustments to the use plan can take safety-related factors into consideration. For example, safety rules may be incorporated into the logic to adjust the use plan so that it does not suggest practices that are considered unsafe. For instance, one or more safety rules may prevent the use plan from adjusting adjacent use sessions to be closer together in time than recommended. This would prevent the use plan from suggesting a use pattern that could cause harm to the skin of the person using the device 100.

[0115] Similar to the skin coverage parameter, the processor (e.g., a cloud-based processor which may be part of machine learning module 208) may be configured to store the usage plan and hair density indications within a training dataset. This training dataset may 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 compute adjustments to the usage plan for subsequent iterations, at least steps 402 and 404 in Figure 4.

[0116] It will be understood that configuring the processor in this way is advantageous because it tends to improve the suitability of the tailored usage plan provided by the machine learning module 208. This is because the training dataset tends to grow over time, increasing the effectiveness of the training process. Furthermore, the training dataset can be complemented by data from many different users, resulting in a diverse and representative sample set 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 may be non-temporary. Therefore, 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 implementation method for coordinating a usage plan for a luminescent body hair growth management device, comprising: providing a usage plan in an electronic device scheduling application that includes one or more planned usage events, wherein the planned usage event or each planned usage event has a corresponding date associated with the planned usage event or each planned usage event; obtaining an indication by the electronic device that shows the hair density of body hair present on a body part associated with the usage plan; and coordinating the date of at least one of the one or more planned usage events based on the indication by the electronic device.

[0120] Clause 2: The computer implementation method described in Clause 1, which includes adjusting the date of at least one of one or more planned usage events based on user input by an electronic device, providing the indication and usage plan to a machine learning module configured to adjust the usage plan based on user input, and receiving the adjusted usage plan as output of a machine learning model.

[0121] Clause 3: A computer implementation method according to Clause 1 or 2, further comprising displaying a graphical user interface element for hair density on the display of an electronic device, the element comprising at least two images representing different hair densities, each of which includes at least two images corresponding to different hair densities, wherein the electronic device obtains an indication of the hair density of body hair present on a body part, which includes receiving a user selection of one of the at least two images representing different hair densities.

[0122] Clause 4: The computer implementation method according to Clause 1 or 2, wherein an electronic device obtains an indication of hair density of body hair present on a body part, the method comprising obtaining one or more images, each of which includes at least a portion of the body part, and automatically processing one or more images to estimate hair density.

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

[0124] The dimensions and values ​​disclosed herein should not be understood as being strictly limited to the exact numerical values ​​listed. Instead, unless otherwise stated, each such dimension is intended to mean both the listed value and the functionally equivalent range encompassing that value. For example, a dimension disclosed as "40 mm" is intended to mean "approximately 40 mm."

Claims

1. A computer implementation method for monitoring the use of a light-emitting hair growth management device during a session of using the device, The processor receives sensor data collected during the usage session from one or more sensors of the hair growth management device, wherein the sensor data includes at least the number of flashes of light emitted by the hair growth management device during the usage session. The aforementioned processor uses the sensor data to: (a) The number of flashes, and (b) Known irradiation area of ​​the hair growth management device Based on this, the calculation of the skin coverage achieved in the aforementioned usage session is performed, The processor displays the calculated information regarding skin coverage on the display of the user device or on the display of the hair growth management device. Methods that include...

2. The computer implementation method according to claim 1, wherein the processor is the processor of the user device or the hair growth management device.

3. Displaying the information relating to the calculated skin coverage rate includes displaying a graphical user interface showing the percentage of the target body part covered in the usage session, and the method is The computer implementation method according to claim 1 or 2, further comprising updating the display of the graphical user interface by the processor during or after the usage session.

4. The computer implementation method according to claim 1, wherein at least a portion of the processor is a cloud-based processor, and the calculation of the achieved skin coverage rate is performed using a machine learning algorithm.

5. The aforementioned sensor data is as follows: Accelerometer data from the accelerometer sensor of the hair growth management device, wherein the accelerometer data was collected during the usage session. The rotational data of the gyroscope of the hair growth management device, wherein the rotational data collected during the usage session, and / or Skin contact sensor data of the skin contact sensor collected during the aforementioned usage session, The computer implementation method according to claim 1, further comprising one or more of the above.

6. The computer implementation method according to claim 1, further comprising transmitting the sensor data to the processor by a transmitter or transceiver of the hair growth management device.

7. The transmitter or transceiver of the hair growth management device transmits the sensor data to the user device, The user device transmits the sensor data to the processor via a transmitter or transceiver. The computer implementation method according to claim 1, further comprising:

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

9. The processor stores the sensor data and the calculated skin coverage rate in a training dataset. The processor trains a machine learning model using the training dataset to produce a trained machine learning model, or improves an existing machine learning model. The computer implementation method according to claim 1, further comprising: the processor using the trained or improved machine learning model to calculate the skin coverage rate for subsequent use sessions of the hair growth management device.

10. Displaying the information related to the calculated skin coverage rate is, Display the percentage of the target body part covered during the aforementioned usage session. Display the rating of the session being used, and / or To present suggestions on how the skin coverage rate for subsequent usage sessions can be increased relative to the calculated skin coverage rate, The computer implementation method according to claim 1, comprising any one or more of the following.

11. The hair growth management device includes a head sensor configured to detect the type of head currently detachably attached to the hair growth management device, The above method is performed by the processor, Based on the data from the head sensor, the type of head currently attached to the hair growth management device is detected, Determining whether the currently attached head is the optimal head for the next usage session of the hair growth management device, If the currently attached head is not optimal, a notification will be displayed on the user device's display indicating that the currently attached head is not optimal for the next usage session of the hair growth management device. The computer implementation method according to claim 1, further comprising:

12. The computer implementation method according to claim 11, wherein the notification also includes a suggestion of an optimal head for the next use session of the hair growth management device.

13. The computer implementation method according to claim 1, further comprising displaying suggestions for energy level settings for the next usage session of the hair growth management device on the display of the user device.

14. A computer program that, when executed by one or more processors, causes the one or more processors to perform the method according to claim 1.

15. A hair growth management system comprising a hair growth management device, a processor, and a display, configured to perform the method according to claim 1.

Images