Illuminated hair growth management device associated application

Abstract

Techniques are provided for estimating skin coverage in a use session of a luminescent hair growth management device. Sensor data from one or more sensors of the illuminated hair growth management device is used by a rule-based algorithm and / or a machine learning algorithm to estimate coverage of one or more target body parts achieved in the use session. The 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 capable of dynamically adjusting a usage plan of one or more usage sessions including a luminescent hair growth management device based on information about a hair growth management level implemented in a given usage session.

Description

Technical Field

[0001] The present invention generally relates to monitoring the use of a luminous hair growth management device. In particular, the present invention relates to calculation of skin coverage achieved during one or more usage sessions and / or dynamic scheduling of one or more usage sessions. Background Art

[0002] Illuminated hair growth management devices are becoming increasingly popular, particularly in environments such as hair salons or homes. In such environments, operators of the devices have typically not received an intensive training course on the use of the devices. That is, the operators are reasonably proficient in the operation of the devices, but are not experts. As a result, the operators 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 so because a period of time typically elapses between a usage session of a hair growth management device and the effects of the device being visible, which means that unlike, for example, a shaver or razor, the operator is unable to receive immediate (visual) feedback on the effectiveness of the usage session.

[0003] A feature of light-based hair growth management technologies is that they are most effective when used according to a recommended schedule. An operator may be provided with a recommended schedule, but in practice, the operator may not follow the recommended schedule correctly, for example due to failure to use the device at the scheduled time, such as when a customer misses or reschedules a salon appointment, or because the operator forgets to use the device at the scheduled time. Recommended schedules are typically provided in a static manner, for example, as instructions in a leaflet or brochure provided with the device. A static format like this fails to take into account actual use of the device and to adjust the schedule accordingly, which can lead to suboptimal results and user dissatisfaction.

[0004] Therefore, it is desirable to provide a mechanism for enabling an untrained person to objectively judge the effectiveness of a given use of a light emitting hair growth management device. Preferably, the mechanism will be user-friendly and easy to understand for an untrained person.

[0005] It would also be desirable to provide a mechanism for enabling generation of a dynamic schedule of usage of a lighted hair growth management device, wherein the usage schedule is adjusted based on actual usage of the hair growth management device. Summary of the invention

[0006] The present invention has two main aspects. The first aspect relates to an estimation of skin coverage achieved during a usage session of a hair growth management device. 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 may be provided during the usage session (i.e., in real time or near real time), or may be provided after the usage session is completed. Such an objective and automatic assessment allows an operator of the hair growth management device to easily understand the effectiveness of a given use, which may result in improved user satisfaction.

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

[0008] The first aspect can be achieved by a computer-implemented method for monitoring the use of a luminous hair growth management device during a usage session of the hair growth management device, the method comprising: receiving, by a processor, sensor data from one or more sensors of the hair growth management device, the sensor data being collected during the usage session; performing, by the processor, a calculation of skin coverage achieved in the usage session using the sensor data; and displaying information related to the calculated skin coverage on a display of a 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 hair growth management system being configured to perform the method of the first aspect.

[0010] The second aspect can be achieved by a computer-implemented method for adjusting a usage plan for a luminous hair growth management device, the method comprising: providing a usage plan including one or more planned usage events in a scheduling application of an electronic device, the planned usage event or each planned usage event having a corresponding date associated with the planned usage event or each planned usage event; obtaining, by the electronic device, an indication of the 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 can also be implemented by a non-transitory computer-readable medium having instructions stored thereon, which instructions, when executed by a processor of an electronic device, cause the electronic device to: provide a usage plan including one or more planned usage events in a scheduling application of the electronic device, the planned usage event or each planned usage event having a corresponding date associated with the planned usage event or each planned usage event; obtain an indication of the hair density of hair present on a body part associated with the usage plan; and adjust the date of at least one of the one or more planned usage events based on the indication by the electronic device.

[0012] Further preferred features of the first and second aspects are set out in the accompanying dependent claims and / or in the detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Embodiments of the invention are described below, by way of example only, with reference to the accompanying drawings, in which:

[0014] Figure 1 A light emitting hair growth management device suitable for use with embodiments of the present invention is shown in schematic form;

[0015] Figure 2 A system suitable for implementing an embodiment of the present invention is shown in schematic form;

[0016] Figure 3 is a flow chart depicting a method for monitoring use of a hair growth management device according to one embodiment; and

[0017] Figure 4 is a flow chart depicting a method for automatically adjusting a usage plan of a hair growth management device based on user feedback, according to one embodiment. DETAILED DESCRIPTION

[0018] As used herein, the term "user" may be a person on whom the lighted hair growth management device is being used or an operator of the device, which may be a different person from the person on whom the device is being used.

[0019] The term "skin coverage" refers to a parameter that indicates the proportion of skin area that is exposed to one or more light pulses from a hair growth management device during a use session. Skin coverage may be calculated from data provided by the device sensors, as described below. For example, skin coverage may be expressed as a percentage or fraction of a target body part. Since the hair growth management effect of the device derives from exposure of the skin to the light pulses, skin coverage is a relevant parameter when evaluating the effectiveness of a use session. Effectiveness herein refers to the level of hair growth management resulting from a use session.

[0020] A first aspect of the invention provides techniques for estimating skin coverage in a use 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 coverage of one or more target body parts achieved in the use session. This information is fed back to a user of the hair growth management device via a graphical user interface. The graphical user interface may be displayed on a display of a user device or on a display of the hair growth management device. Further details on this first aspect are provided directly below.

[0021] Figure 1 The light-emitting hair growth management device 100 is shown in schematic form. For example, the device 100 may be a light epilator of a type known per se in the art. The construction and operating principles of light epilators are known and therefore, for the sake of brevity, are not described in detail herein.

[0022] The device 100 includes a housing 102 and a head 104 that includes, encloses, or is otherwise associated with a light-emitting component (not shown). The light-emitting component is typically a flashlight housed in an optical window that is transparent to at least wavelengths of light known to have hair growth management effects.

[0023] The head 104 is preferably removably attached to the housing 102 to enable an operator to attach different heads to the housing 102. For example, each head may be specifically adapted for one or more specific body parts.

[0024] There is also a power supply 106. This may include a battery and / or a port that enables wired electrical coupling to an electrical outlet. Inductive charging techniques may also be used. The power supply 106 provides power for operating the various components of the device 100, including, for example, the lighting components, the processor, and the transmitter / transceiver.

[0025] A power button 108 is provided in the housing 102. This enables an operator of the device 100 to turn the device 100 on and off. The device 100 may be configured to automatically shut down and / or enter a power saving sleep mode after a predetermined period of inactivity (e.g., 5 minutes) to avoid wasting power. For example, the power button 108 may be a push button or a slider coupled to a switch.

[0026] An activation button 110 (or "trigger") is also provided in the housing 102 to enable an operator of the device 100 to trigger a light pulse (commonly referred to as a "flash") from the light emitting component. For example, the activation button 110 may be a push button coupled to a switch. The activation button 110 may be coupled to a skin sensor (not shown) that enables the device 100 to determine whether the head 104 is currently in proximity to the skin. Such skin sensors are known in the art. For safety reasons, in the event that it is determined that the head 104 is not currently in proximity to the skin, the activation button 110 may be disabled, for example by ignoring input from the activation button 110.

[0027] The device 100 preferably comprises a skin color sensor (not shown) capable of automatically determining skin color. This may be the same sensor as the skin sensor described in the previous paragraph, or a different sensor. Skin color sensors are known per se.

[0028] The device 100 may also include a parameter setting mechanism (not shown) for setting operating parameters of the device 100. The mechanism may include one or more sliders, wheels, etc., wherein each slider or wheel corresponds to a specific parameter. The parameters may include: pulse duration, pulse fluence, skin color (e.g., according to the Fitzpatrick skin type classification), hair color, body part, etc.

[0029] While physical buttons, sliders, wheels etc. have been described above, it will be appreciated that any of these activation or selection features may be implemented digitally, such as via a user input device coupled to a display showing a suitably configured user interface.

[0030] The device 100 also includes a processor (not shown), which may be, for example, a microcontroller. The processor is configured to control the operation of the device 100 as described herein. The processor is communicatively coupled to a memory (also not shown) that is capable of storing data, such as sensor data, as described below.

[0031] The device 100 also includes a method for enabling the device 100 to communicate with other devices (see Figure 2) A transmitter / transceiver (not shown) for wireless communication. 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 a variety of different communication technologies, such as both Bluetooth and WiFi. In these cases, multiple antennas may be present.

[0032] The device 100 also includes one or more sensors (not shown). The sensors are used to collect information about the operation of the device 100, which is particularly related to the use of the device 100. The one or more sensors can be any one or more of the following types of sensors:

[0033] 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. For example, the flash counter may store the flash count in a memory of device 100. For example, the flash count may be reset to zero each time device 100 is turned off and / or a period of inactivity greater than a predetermined time is detected. The flash count may additionally or alternatively be reset to zero 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 a period greater than a predetermined time. User input, such as indicating completion of a usage session, may additionally or alternatively be used to trigger the flash counter to be reset to zero.

[0034] The flash counter may be configured to store the time at which the flash occurred. This may advantageously enhance the flash count data, since the number of flashes and time-based parameters such as flash frequency, time between adjacent flashes and / or average values ​​of these parameters may then be determined.

[0035] Counting the number of flashes enables an objective measurement of the skin coverage achieved in a corresponding device usage session, particularly when combined with knowledge of the body parts exposed to light during the usage session. This information can be fed back to the user by displaying the skin coverage on a display of the user device. Further information on this is provided later in this specification.

[0036] Advantageously, the processing resources consumed to perform the counting 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., a smartphone, tablet, etc.). In some cases, the device 100 itself may perform the flash counting, with only aggregate information such as the total number of flashes being transmitted from the device 100 to the user device 202. This further enhances the ability of the present invention to provide skin coverage estimates in real time.

[0037] B) Accelerometer and / or gyroscope. (If both are present, this may be referred to as an inertial measurement unit (IMU). These sensors can detect and quantify linear and rotational movement of the device 100, respectively. Specifically, the accelerometer and gyroscope generate time series data that enables linear or rotational acceleration to be calculated as a function of time.

[0038] The use of such sensors may be referred to as tracking the motion of device 100 .

[0039] By tracking the motion of the device 100, an objective estimation of the skin coverage of the device can be made. The skin coverage estimation is preferably performed using a trained machine learning model that accepts accelerometer and / or gyroscope data as input and outputs a skin coverage value.

[0040] C) 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, time series data indicating those moments during a usage session when the device 100 was in contact with the skin. Derived quantities can also be calculated, such as the fraction of the total usage sessions in which the device 100 was in contact with the skin.

[0041] D) Air Pressure Sensor. Data from the air pressure sensor can be used to determine the altitude of the device 100 over time. This information can be used to track the movement of the device 100 and provide an objective estimate of skin coverage in a given usage session.

[0042] The device 100 may be configured to stream the accelerometer data and / or the gyroscope data and / or the air pressure sensor data and / or the skin contact data to another electronic device continuously or quasi-continuously as a data stream. Quasi-continuously means that the data may be buffered by the device 100 for a period of time and then transmitted once the buffer (e.g., the memory of the device 100) has reached a predetermined fullness. The predetermined fullness is preferably less than the total capacity of the buffer.

[0043] In a preferred embodiment, device 100 incorporates at least the sensors discussed above under A. In a more preferred embodiment, device 100 incorporates all of the sensors discussed above under A and B, and even more preferably, also incorporates the sensors discussed above under C and optionally also under D. The combination of data from all of these sensors can advantageously produce a highly accurate calculated skin coverage.

[0044] Device 100 may include a display (not shown) that may also double as a user input device, such as a touch screen. The display may be used to display the skin coverage calculated as described herein. The calculation of the skin coverage may be performed by the processor or device 100, or the calculation may be performed 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 additionally or alternatively be used to display a schedule of the type discussed later in this specification.

[0045] The device 100 may include an image capture module (not shown) such as a camera. The image capture module is preferably positioned so that it can easily capture images of the body part that is the subject of the usage session. The images captured in this manner may be processed to estimate hair density. More information on this is provided later in this specification.

[0046] Reference now Figure 2 , a system 200 suitable for implementing an embodiment of the present invention is shown. The system 200 includes the device 100, a user device 202, and optionally also a cloud 206.

[0047] exist Figure 2 , arrows are used to illustrate communication paths between various components of system 200. As can be seen, device 100 is communicatively coupled to user device 202 via the transmitter / transceiver discussed above (e.g., via a WiFi or Bluetooth connection). Figure 2 Bidirectional communication is shown in , but this is not limiting of the present invention as in some embodiments, device 100 utilizes a transmitter that provides unidirectional communication from device 100 to user device 202.

[0048] 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, a wearable electronic device such as a smart watch, a desktop computer, etc. The user device 202 includes a display 204 of a type known per se in the art. The user device 202 also includes one or more user input components (not shown), such as a touch screen, a keyboard, etc. The user device 202 also includes a processor and a memory (not shown).

[0049] It is also contemplated that the functionality of user device 202 as discussed herein may alternatively be performed by device 100. Thus, the present invention is not limited to system 200, and other embodiments exist that omit user device 202. In these embodiments, all functionality discussed herein in conjunction with user device 202 is instead performed by device 100 and / or cloud 206 (if present).

[0050] Cloud 206 is a cloud computing environment of a type known in the art. In short, cloud 206 provides processing resources remote from device 100 that can be tasked with specific activities. It should be understood that the processing capabilities of cloud 206 (e.g., processor speed, number of available processors, amount of available memory, etc.) often significantly exceed the processing capabilities of user device 202 and device 100. Therefore, cloud 206 is better suited to perform more processor-intensive operations than user device 202.

[0051] The device 100 and / or the user device 202 are communicatively coupled to the cloud 206 (when present), for example, via the Internet.

[0052] The cloud 206 includes a machine learning module 208. The machine learning module 208 is configured to perform aspects of the present invention as described in more detail later in this specification. The machine learning module 208 may be selected based on the complexity of the sensor data provided as input to the module. The module 208 preferably implements a deep learning algorithm, particularly where 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 may be used instead of deep learning.

[0053] One or more applications ("apps") may be installed on the user device 202 and / or the device 100 using known installation techniques. The one or more applications may include a calendaring application and / or a hair growth management device companion application. These applications may be provided as standalone applications or as a single application that provides both a calendaring function and a feedback function. A description of the functionality of these applications is provided below.

[0054] First, a description of the companion application is provided. The companion application may be executed by the user device 202 and / or the device 100. The primary function of the companion application is to provide feedback about a usage session based on data collected during the usage session by sensors that are part of the device 100. The feedback provided by the companion application includes skin coverage. The skin coverage may be displayed using a graphical user interface displayed on the display 204. The graphical user interface may include text and / or images. A percentage value may be displayed in text form, indicating the skin coverage achieved in a given usage session. Additionally or alternatively, a graphical representation of the skin coverage may be displayed, such as a shape having a fraction corresponding to the skin coverage achieved in shade and / or color. Alternative forms of graphical user interface elements will be apparent to a technician having the benefit of this disclosure.

[0055] In addition to skin coverage, other information related to the usage session may be provided on the graphical user interface, such as any one or more of the following: an indication of the body parts exposed to light during the usage session; the total time of the usage session; an indication of the head used during the usage session; text feedback related to 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 additionally or alternatively be displayed.

[0056] In addition to data from the sensors of device 100, the companion application may collect other information related to the usage session. This information may be used to improve or supplement the skin coverage estimate. This information may include any one or more of the following:

[0057] - The skin tone of the skin to which the device 100 is applied during the use session. Skin tone information may be manually input 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 the device 100 includes a skin tone sensor, data collected by the skin tone sensor may be provided to a companion application to allow automatic determination of skin tone. Techniques for determining skin tone using skin tone sensors are known.

[0058] - The hair colour of the hair present on the skin to which the device 100 is applied during the usage session.

[0059] Hair colour information may be provided in the same manner as skin colour, ie manually or automatically. A hair colour sensor may be provided for automatically detecting hair colour. Hair colour sensors are known.

[0060] - The hair density of hair present on the skin to which the device 100 was applied during the use session. Hair density information may be provided by displaying two or more images representing different hair densities, wherein each image corresponds to a different hair density. The user may select the image that most closely corresponds to the hair density of the skin to which the device 100 was applied during the use session. Alternatively, the hair density may be detected automatically.

[0061] - An identifier of the body part to which the device 100 is to be 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) the device 100 is proximate to during a usage session.

[0062] -Identifiers of the heads 104 of the device 100 that have been / will be used during the use 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 the housing 102 during the use session. The companion application may also provide recommendations for which head(s) should be used during the use session based on the part of the body on which the device 100 will be used. The recommendation may be text indicating which head should be used and / or an image of the head that should be used. In this case, if a suboptimal head is detected to be attached to the device 100, the companion application may warn the user, for example by a text warning displayed on the display 204 and / or by a graphic such as a warning sign. The warning may also suggest the best head to use during the next use session of the device 100.

[0063] - Information specific to the person on whom the device is to be used in a usage session, such as: gender, name or some other identifier like an email address, username, phone number, etc., one or more goals for the person (e.g., 50% reduction in hair density, total hair removal, etc.), feedback from the person related to previous usage sessions (e.g., satisfied, dissatisfied), etc. This information can be used to create and maintain a profile that stores information such as the skin coverage achieved for each usage session for that person.

[0064] - The pulse energy or fluence applied to the skin during the usage session, either as a cumulative value or on a "per pulse" basis. This information may be entered manually, but is preferably obtained automatically by the device 100 by determining the pulse energy or fluence set during the usage session.

[0065] - Physiological information about the person on whom the device is to be used in a usage session, such as height and / or weight. This information can be used together with the sensor data discussed above as an input in the estimation of skin coverage to further enhance the accuracy of the skin coverage estimate.

[0066] The companion application can be configured to Figure 3 The method monitors usage of device 100 in a usage session.

[0067] In step 300, the processor receives sensor data from one or more sensors of the device 100 (e.g., sensors discussed above under 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. Selecting processing resources in this manner advantageously enables real-time or near real-time skin coverage estimates to be provided during a usage session, if desired.

[0068] The sensor data may be received by the processor when the sensor data is collected by the device 100, ie, when the usage session is ongoing. Alternatively, the sensor data may be received by the processor after the usage session is completed.

[0069] The sensor data may be transmitted to the processor by a transmitter or transceiver (e.g., a Bluetooth or WiFi transmitter / transceiver) of the device 100. In the case where the processor is in the cloud 206, the user device 202 may act as an intermediary device to route the sensor data to the cloud 206, or the device 100 may alternatively send the sensor data directly to the cloud 206. As described above, the device 100 may be configured to transmit the sensor data as a data stream continuously or quasi-continuously to another electronic device.

[0070] In step 302, the processor uses the sensor data to perform a calculation of the skin coverage achieved in the use session. In one embodiment where the sensor data includes a flash count, the processor can be a processor of the user device 202. In this embodiment, the user device 202 can use a rule-based algorithm to calculate the skin coverage based on the flash count. This embodiment requires relatively few processing resources to calculate the skin coverage, so the user device 202 can handle the calculation itself. Therefore, the user device 202 can perform the necessary calculations in real time or near real time, so that a graphical user interface that shows the progress of the use session in real time or near real time can be displayed on the display 206 as the use session progresses. For example, the graphical user interface can be a progress bar. The progress bar or equivalent can be displayed at the end of the use session, that is, after the use session is completed, or the progress bar or equivalent can be displayed and updated while the use session is ongoing.

[0071] 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 can stream the sensor data directly to the cloud 206, rather than introducing latency by sending the data via the user device 202. The output of the machine learning module 208 (i.e., the estimated skin coverage) can be transmitted from the cloud 206 to the user device 202.

[0072] The machine learning module 208 may take the accelerometer data as input and output an estimated skin coverage. Any of the other sensor data types previously discussed under A, B, C, and / or D in this specification may additionally or alternatively be provided as input to the machine learning module 208.

[0073] In a preferred form of this embodiment, the machine learning module 208 is supplemented by a pre-processing rule-based algorithm that works together with the machine learning module 208 to provide skin coverage. Specifically, some sensor data such as flash counts 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 along with other sensor data (e.g., accelerometer and / or gyroscope data). This can increase the accuracy of the skin coverage estimate provided by the machine learning module 208.

[0074] In addition to or as an alternative to pre-processing rule-based algorithms, post-processing rule-based algorithms may also be used. The post-processing rule-based algorithms may perform one or more so-called "sanity checks" on the output from the machine learning module 208. The sanity checks ensure that the output of the machine learning module 208 makes sense, for example, excluding negative skin coverage values ​​or values ​​above 100%.

[0075] It should be understood that multiple post-processing rule-based algorithms and / or multiple pre-processing rule-based algorithms may be used.

[0076] In step 304, the user device 202 displays information related to the calculated skin coverage on the display 204, and / or the information is displayed on the display of the device 100. The skin coverage information can be displayed in the manner discussed above, such as as a percentage, a progress bar, etc. At least the skin coverage is displayed, such as as a numerical percentage and / or an equivalent graphical representation. Other information as discussed above may be additionally displayed, such as the percentage of the target body part covered during the use session; a use session score and / or suggestions on how to increase the skin coverage of subsequent use sessions relative to the calculated skin coverage. The suggestions may be in text form, such as operator instructions, such as "move the device more slowly over the skin", "trigger more flashes", "use a different head in the next use session", etc. Information related to the operation of the device 100 may be additionally or alternatively displayed, such as a suggested energy level setting for a subsequent use session.

[0077] The skin coverage value advantageously allows a user to gain an immediate understanding of the effectiveness of a given use of the device 100. This is because users generally perceive that greater skin coverage results in a more effective use session. The skin coverage value is provided in a user-friendly manner because all the user has to do is use the device 100 and the calculation is automatically performed. Furthermore, the single value (skin coverage) is easy to understand for an untrained user, so that they can immediately gain an objective view of the effectiveness of a use session of the device 100 based on this single, easy to understand parameter.

[0078] The skin coverage value may additionally be used to make predictions about likely skin coverage in future usage sessions, thereby potentially yielding the ability to predict likely overall efficacy of a usage plan that includes multiple usage sessions. (Usage plans are discussed in more detail later in this specification.) This may involve translating actual and / or predicted skin coverage into hair growth management efficacy, such as by using clinical data or other such efficacy data that correlates skin coverage to hair growth management efficacy.

[0079] In embodiments utilizing machine learning, a processor (e.g., a cloud-based processor that may be part of the machine learning module 208) may be configured to store both the sensor data and the calculated skin coverage in a training dataset. The training dataset may be used by the processor to train a machine learning model to calculate skin coverage.

[0080] Alternatively, where a trained machine learning model already exists, the training data set may be used to improve the trained machine learning model to obtain a more accurate machine learning model. The trained or improved machine learning model may then be used by the processor to calculate skin coverage for subsequent use sessions of the 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.

[0081] It will be appreciated that configuring the processor in this manner advantageously tends to increase the accuracy of the skin coverage calculations provided by the machine learning module 208. This is because over time, the training data set grows and has a tendency to increase the effectiveness of the training process. In addition, the training data set can be supplemented with data from many different users, thereby generating a diverse and representative set of samples for training.

[0082] In a second aspect, the present invention provides a scheduling application that is capable of dynamically adjusting a usage plan for one or more usage sessions including a light-emitting hair growth management device based on information related to the hair density of a target body part. The information may be provided as feedback received from a user about 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 may be automatically estimated. Further information on the second aspect is provided directly below. The second aspect may be combined with the first aspect, i.e. both the first and second aspects of the present invention may be provided by a single application or by separate companion applications and a scheduling application. The scheduling application may be executed by the user device 202 and / or the device 100.

[0083] The scheduling application of the second aspect is used to provide a mechanism for dynamically scheduling usage sessions. The scheduling application has a graphical user interface, which includes a scheduling type user interface that allows scheduling events. For example, this can be a calendar of a type known per se in the art. The following description refers to a "calendar application" and a "calendar", but this should be understood as a specific example of a scheduling application, so that the present invention is not particularly limited to calendars and calendar applications.

[0084] In the case of a calendar application, the scheduling user interface may include a calendar indicating the day, week, or month currently being shown. In a week or month view, the calendar typically shows the days in one or more rows, with each day labeled by its corresponding date. Visual indicators such as shapes, shaded areas, etc. may be presented to show the current day.

[0085] The day or days that currently have scheduled usage sessions may be indicated in some manner, such as by showing their markers in bold, a different color, and / or with some visual indicator, such as a dot displayed near the marker of the associated day(s).

[0086] The user may interact with the calendar application via the user input component of the user device 202 or device 100. The user may select a particular date, and in response, the calendar application provides more information about the selected date, for example in an expanded view. The information may include whether a usage session is scheduled for the selected date. If a usage session is scheduled, details related to the usage session, such as the target body part, may be provided.

[0087] The scheduled usage sessions may be part of a usage plan, which is understood to refer to multiple usage sessions grouped together. A usage plan may focus on a specific body part and may have specific goals associated with it. A usage plan duration may be defined, which is the total time it will take to complete the plan (e.g., 1 month, 2 months). The dates on which the individual usage sessions of the usage plan are scheduled to occur may be shown as part of the usage plan.

[0088] Some exemplary usage plans are listed directly below in Table A. It should be understood that the information shown in Table A may be captured by the user device 202 and stored as a data structure, such as in an XML format or a JSON format data structure.

[0089] Table A - Example Usage Plan

[0090]

[0091] The parameters shown in Table A are discussed in detail directly below.

[0092] A plan ID is a unique identifier assigned to a usage plan. This is typically a string, such as "Plan 1" above. A plan ID may be automatically assigned by the user device as part of creating a new program. The user may edit or specify a plan ID.

[0093] Body part is a parameter (e.g., a string) that identifies one or more body parts that are involved in the use plan. This information can be provided by the user during the creation of the use plan. For example, the user can select one or more body parts on an image of the body. Alternatively, the body part selection can be performed using a drop-down list of predefined items, a free text field with a linked search function, etc.

[0094] The user may provide a goal for the use plan, and this may be stored as text (a string). The goal describes the result the user wishes to achieve at the end of the use plan. The goal may be selected from a predetermined set of goals, such as temporary hair removal, visible hair reduction, slowed hair growth, etc. Allowing the user to define goals may advantageously help monitor the progress of the use plan.

[0095] A start date for a usage plan may be set. For example, a user may 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 may be the date on which the usage plan was created, or a future date.

[0096] The usage count may be maintained by the user device 202. The usage count is a number that indicates the total number of usage sessions that have occurred within the usage plan.

[0097] The next usage date may be provided by the user device 202. This indicates the date when the next usage session in the usage plan is scheduled. The next usage date may include only the next session (i.e., one date), or it may include all remaining usage session dates, i.e., multiple dates. This information may be displayed graphically to the user on the calendar graphical user interface element discussed above.

[0098] The duration of the usage plan can be set. The duration can be defined based on the total number of usage sessions within the usage plan, or can be defined based on the time it is expected to complete the usage plan (e.g., x days, weeks, months, etc.).

[0099] If calculated, the skin coverage value as previously discussed in relation to the first aspect of the invention may be automatically added to the calendar entry for the usage session that has occurred.When selecting a date corresponding to a usage session, the user may view the skin coverage.

[0100] It should be understood that Table A is purely exemplary and that other information may additionally or alternatively be included in the data structure representing the usage plan. Such information will be apparent to the skilled person having the benefit of this disclosure and given the details of the situation at hand.

[0101] Any one or more of the parameters discussed above may be viewed by the user in a suitable graphical user interface. For example, user selection of a particular usage plan may cause the user device 202 or device 100 to display a graphical user interface showing the body part associated with the usage plan, a schedule indicating one or more upcoming usage sessions (e.g., by date, day, time, etc.). An estimated completion date for the usage plan may be displayed on the graphical user interface.

[0102] The user device 202 and / or the device 100 may be configured to provide reminders related to the usage session based on the usage plan, for example using a "next usage date" parameter. The reminders may be generated by the user device 202 and / or the device 100 in the form of an audio reminder such as an alarm, a visual reminder such as a notification message, or a combination of both audio reminders and visual reminders. This list is not exhaustive, and other reminder techniques may be used in addition to or instead of the techniques listed here.

[0103] The user can configure the generated reminders using appropriate graphical user interface elements, such as setting the reminder type (audio / visual / both / other), setting the number of reminders that should be provided before a usage session, and / or setting the time period before a usage session that reminders should be generated (e.g., 1 day before a usage session is scheduled, 1 hour before a usage session is scheduled, 10 minutes before a usage session is scheduled, etc.).

[0104] The calendar application may also provide a progress checker user interface that allows the user to check the progress of a given use plan. The progress checker user interface may indicate the portion of the use plan that has been completed, such as as a percentage or as a progress bar. When the user completes another use session, the progress checker user interface is updated to show that this has occurred. In addition to the completed portion, text and / or images may also be included on the progress checker user interface to enable the user to receive feedback on the current state of the use plan. The text may include motivational messages, comments on changes in their skin and / or hair density that the user may expect to see at relevant points in the use plan, etc. An image may indicate the expected progress, such as a graphic of a wisp of hair that gradually becomes more transparent to represent hair reduction as the user progresses in the use plan.

[0105] It should be understood that the use plan can be defined in a computer-readable format, such as a data structure of the type discussed above. Therefore, the use plan can be defined by the manufacturer or retailer of the device 100 and downloaded to the user device 202 and / or the device 100 for use.

[0106] The usage plan as described above is a dynamic entity. That is, the usage plan may change after one or more of the planned usage sessions have been conducted. Specifically, after the usage plan has been started, the date of one or more of the usage sessions that have not yet been conducted may be rescheduled. Additionally or alternatively, one or more of the usage sessions that have not yet been conducted may be skipped (canceled), and / or one or more additional usage sessions may be added to the usage plan.

[0107] Adjustments to the usage plan are made based on the effects of usage sessions that have already occurred on the hair of the person to whom the device 100 is being applied. For example, if a reduction in hair growth is found to occur more quickly than initially expected, the usage plan may be adjusted so that there is a greater amount of time between adjacent future scheduled usage sessions, and / or one or more scheduled usage sessions that have not yet occurred may be cancelled. Alternatively, if a reduction in hair growth is found to occur more slowly than initially expected, or no reduction is found and in fact hair (regrowth) is occurring, the usage plan may be adjusted so that there is a lesser amount of time between adjacent future scheduled usage sessions, and / or one or additional usage sessions may be added to the usage plan.

[0108] According to Figure 4 The method shown automatically performs adjustments to the usage plan. 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, where the cloud 206 is selected for more complex algorithms and the user device 202 is selected for less complex algorithms.

[0109] Adjustments to the usage plan may also be made manually by the user. For example, a person may unexpectedly be unavailable for a particular usage session, resulting in manually skipping or rescheduling the usage session. The calendar application may include a graphical user interface that allows for skipping or rescheduling of usage sessions within the usage plan. The calendar application may also automatically detect skipped usage sessions, such as based on a lack of user input related to such usage sessions.

[0110] 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 pre-processing rule-based algorithm and / or a post-processing rule-based algorithm. The pre-processing rule-based algorithm can be configured to receive information related to the schedule, such as usage sessions that have been skipped, manually rescheduled, or missed. This can be used by the pre-processing rule-based algorithm to calculate one or more parameters, which can be fed into the machine learning model as input along with the hair density indication, as discussed below.

[0111] In addition to or as an alternative to pre-processing rule-based algorithms, post-processing rule-based algorithms may also be used. Post-processing rule-based algorithms may perform one or more sanity checks on the output from the machine learning model. The sanity checks ensure that the output of the machine learning model makes sense, for example, the value output by the machine learning model is not in the past validity date. Post-processing rule-based algorithms may include security-based elements, such as preventing the time between adjacent usage sessions from being less than a threshold "safety limit" value. Other post-processing rules may be based on user convenience, such as preventing usage plans from being changed too frequently, which may disturb or otherwise inconvenience the user.

[0112] It should be understood that multiple post-processing rule-based algorithms and / or multiple pre-processing rule-based algorithms may be used.

[0113] Now turn to Figure 4 , showing the adjustment process. The following description focuses on executing Figure 4 The user device 202 may perform various aspects of the steps, but it should be understood that these operations may alternatively be performed by the device 100.

[0114] In step 400, the user device 202 provides a usage plan in a calendar application of the type discussed above. The usage plan includes one or more planned usage events. The planned usage event or each planned usage event has a corresponding date associated with it. This is also as discussed above. The usage plan may be downloaded to the user device 202 from, for example, a manufacturer or retailer 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 should relate to, the skin color and / or hair color of the person on whom the device 100 is to be used, the goals of the person of the device 100 regarding the use of the device 100, etc. A "default" or "initial" usage plan may be created based on this information, wherein this usage plan is subject to the following in conjunction with Figure 3 The modifications described.

[0115] In step 402, the user device 202 obtains an indication of the hair density of hair present on a body part associated with the usage plan. Such an indication may be obtained manually or automatically.

[0116] In the manual case, the user device 202 receives user input via a user interface of the usage device 204. The user input includes an indication of the hair density on the body part associated with the usage plan.

[0117] To assist the user in providing an objective indication of hair density, the user device 202 may 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, where each image corresponds to a different hair density. The user can then examine the skin on which the device 100 will be used and compare the hair density observed by eye with the images presented on the user interface.

[0118] Each image can be a photograph of skin with a specific hair density. Alternatively, each image can be a graphical representation of skin with a specific hair density, such as a drawing, sketch, animation, etc.

[0119] The user may select an image that most closely resembles the current hair density of the skin on which the device 100 is being used. The user device 202 may register the selection and thereby receive an indication of the hair density on the body part associated with the usage plan.

[0120] One or more images of the skin on which the device is to be used may also be displayed to allow a "side by side" comparison with the image displayed in the hair density graphical user interface element. This may be more intuitive for the user. The image / s may be captured by a camera or other such imaging module of the user device 202.

[0121] In the event that the usage plan involves multiple body parts, step 402 may be repeated for each body part, and the user's selections may be recorded separately for each body part.

[0122] It should be understood that one or more uses of the device 100 may be performed between steps 400 and 402, such that the hair density indicated in step 402 may not be the starting hair density of the body part on which the device 100 is used.

[0123] In the case of automatically obtaining an indication of hair density, one or more images of the skin on which the device is to be used are obtained, for example by a camera of the user device 202. The image / s are processed to enable an estimation of hair density. The image processing may be performed by an image processing algorithm (e.g., a machine learning algorithm trained to detect pixels in an image that may correspond to hair). Detection of hair in an image may allow an estimation of hair density.

[0124] In step 404, the usage plan is adjusted based on the indication obtained in step 402. Adjusting comprises changing the data of at least one planned usage event forming part of the usage plan. The 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). More than one planned usage event may be rescheduled at a time, for example, moving all planned usage events forward or backward by one day, two days, a week, etc. Alternatively, the planned usage events may be rescheduled so that there is a specific amount of time, such as a week, between adjacent events.

[0125] In addition to the date adjustment, the usage plan may also be adjusted in step 404 by adding one or more additional planned usage events and / or skipping / deleting one or more existing planned usage events.

[0126] The usage plan may be adjusted by providing the instructions 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 instructions received from the user. The machine learning module outputs an adjusted usage plan. For example, the machine learning module may be Figure 2 Module 208 is shown. The machine learning module may utilize RNN or preferably HMM, but the present invention is not limited thereto, and other types of machine learning algorithms may be used.

[0127] The calendar in the calendar application may be automatically updated by the user device 202 to take into account the adjustments to the usage plan made in step 404. Any alarms, reminders, etc. that have been set by the user device 202 may also be automatically adjusted. A notification may be displayed on the display 206 to enable the user to confirm that the adjustments have been made.

[0128] After step 404, in step 406, one or more subsequent uses of the device 100 may be performed according to the adjusted usage plan. Figure 4 As shown, steps 402 and 404 may be repeated after subsequent uses of device 100 to further adjust the usage plan.

[0129] In this way, the usage plan is dynamically formulated because it responds to the actual effect of the device 100 on the hair of the target body part. This can advantageously result in an improved user experience and / or improved user satisfaction.

[0130] Dynamic adjustment of the usage plan may take into account safety-related factors. For example, safety rules may be built into the logic for adjusting the usage plan to prevent the usage plan from suggesting practices that are considered unsafe. For example, one or more safety rules may prevent the usage plan from being adjusted in a manner that places adjacent usage sessions closer together in time than is recommended. This prevents the usage plan from suggesting usage patterns that may cause harm to the skin of the person on whom the device 100 is to be used.

[0131] As in the case of the skin coverage parameter, a processor (e.g., a cloud-based processor that may be part of the machine learning module 208) may be configured to store the usage plan and the hair density indication in a training data set. The training data set may be used by the processor to train a machine learning model to determine recommended adjustments to the usage plan. Alternatively, where a trained machine learning model already exists, the training data set may be used to improve the trained machine learning model to obtain a more accurate machine learning model. The trained or improved machine learning model may then be used by the processor to calculate adjustments to the usage plan. Figure 4 Subsequent iterations of at least steps 402 and 404 use the planned adjustments.

[0132] It will be appreciated that configuring the processor in this manner advantageously tends to increase the applicability of the adjusted usage plan provided by the machine learning module 208. This is because over time, the training data set grows and has a tendency to increase the effectiveness of the training process. In addition, the training data set can be supplemented with data from many different users, thereby generating a diverse and representative sample set for training.

[0133] It should be understood that the operations described herein may be performed by a processor according to computer-readable instructions stored on a computer-readable medium. The computer-readable medium may be non-transient. Therefore, one or more computer-readable media storing such instructions also form a part of the present invention.

[0134] In addition to the embodiments described above, the following clauses set forth further embodiments of the present invention.

[0135] Item 1: A computer-implemented method for adjusting a usage plan for a luminous hair growth management device, the method comprising: providing a usage plan including one or more planned usage events in a scheduling application of an electronic device, the planned usage event or each planned usage event having a corresponding date associated with the planned usage event or each planned usage event; obtaining, by the electronic device, an indication of the 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.

[0136] Clause 2: A computer-implemented method according to Clause 1, wherein adjusting, by the electronic device, the date of at least one of the one or more planned usage events based on user input comprises: providing the indication and the usage plan to a machine learning module, the machine learning module being configured to adjust the usage plan based on the user input; and receiving the adjusted usage plan as an output of the machine learning model.

[0137] Clause 3: A computer-implemented method according to clause 1 or clause 2, the method further comprising: displaying a hair density graphical user interface element on a display of the electronic device, the hair density graphical user interface element comprising at least two images representing different hair densities, each of the images corresponding to a different hair density; wherein the step of obtaining by the electronic device an indication of the hair density of hair present on a body part comprises: receiving a user selection of one of the at least two images representing different hair densities.

[0138] Clause 4: A computer-implemented method according to clause 1 or clause 2, wherein the step of obtaining, by the electronic device, an indication of hair density of hair present on a body part comprises: obtaining one or more images, each of the one or more images including at least a portion of the body part; and automatically processing the one or more images to estimate the hair density.

[0139] Item 5: A non-transitory computer-readable medium having instructions stored thereon, which instructions, when executed by a processor of an electronic device, cause the electronic device to: provide a usage plan including one or more planned usage events in a scheduling application of the electronic device, the planned usage event or each planned usage event having a corresponding date associated with the planned usage event or each planned usage event; obtain an indication of the hair density of 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.

[0140] The dimensions and values ​​disclosed herein should not be understood as being strictly limited to the exact numerical values ​​cited. Instead, unless otherwise indicated, each such dimension is intended to represent the stated value and a functionally equivalent range around 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 use of a light-emitting hair growth management device during a use session of the light-emitting hair growth management device, the method comprising: receiving, by a processor, sensor data from one or more sensors of the hair growth management device, the sensor data being collected during the usage session; performing, by the processor, a calculation of skin coverage achieved in the usage session using the sensor data; as well as Information related to the calculated skin coverage is displayed on a display of a user device or on a display of the hair growth management device.

2. The computer-implemented method of claim 1, wherein the processor is a processor of the user device or the hair growth management device, and the sensor data comprises at least a number of flashes emitted by the hair growth management device during the usage session.

3. The computer-implemented method of claim 1 or claim 2, wherein the displaying of the information related to the calculated skin coverage comprises displaying a graphical user interface indicating a fraction of the target body part covered in the usage session, the method further comprising: The display of the graphical user interface is updated, by the processor, during the usage session or after the usage session is completed.

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

5. The computer-implemented method of one of claims 1 to 4, wherein the sensor data comprises one or more of: accelerometer data of an accelerometer sensor of the hair growth management device, the accelerometer data collected during the usage session; rotation data of a gyroscope of the hair growth management device, the rotation data collected during the usage session; a count of the number of flashes emitted by the hair growth management device during the usage session; and / or Skin contact sensor data of the skin contact sensor collected during the usage session.

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

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

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

9. The computer-implemented method of any one of claims 1 to 7, further comprising: storing, by the processor, the sensor data and the calculated skin coverage in a training data set; Using the training data set to train a machine learning model by the processor to generate a trained machine learning model or improve an existing machine learning model; as well as The trained or improved machine learning model is used by the processor to calculate skin coverage for subsequent usage sessions of the hair growth management device.

10. The computer-implemented method of any preceding claim, wherein the display of the information related to the calculated skin coverage comprises any one or more of the following: displaying the percentage of the target body part covered during said usage session; Display usage session ratings; and / or Suggestions are displayed on how the skin coverage for a subsequent usage session can be increased relative to the calculated skin coverage.

11. The computer-implemented method of any preceding claim, wherein the hair growth management device comprises a head sensor configured to detect a type of head currently removably attached to the hair growth management device, the method further comprising: detecting a type of head currently attached to the hair growth management device based on data from the head sensor; determining whether a currently attached head is an optimal head for a next usage session of the hair growth management device; as well as In the event that the currently attached head is suboptimal, a notification is displayed on the display of the user device indicating that the currently attached head is suboptimal for a next usage session of the hair growth management device.

12. The computer-implemented method of claim 11, wherein the notification further comprises a suggestion of an optimal head for a next usage session of the hair growth management device.

13. The computer-implemented method of any preceding claim, further comprising: A suggestion for an energy level setting for a next usage session of the hair growth management device is displayed on the display of the user device.

14. A computer program which, when executed by one or more processors, causes the one or more processors to perform the method according to any one of claims 1 to 13.

15. A hair growth management system comprising a hair growth management device, a processor and a display, the hair growth management system being configured to perform the method according to any one of claims 1 to 13.

Images