Determining the wear level of the treatment head of the personal care device.

Abstract

According to one aspect, a computer-implemented method 100 for determining the lifespan of a treatment head of a personal care device is provided, the method comprising step 102 receiving a plurality of images of a treatment head, each of the plurality of images being captured at a different time during the lifespan of the treatment head; step 104 determining, for each image of the plurality of images, a wear level indicative of the wear level of the treatment head at the time the image was captured; and step 106 determining an estimated time when the treatment head should be replaced based on the plurality of wear levels.

Description

Technical Field

[0001] The present invention relates to a personal care device including a treatment head, and more particularly to determining how quickly the treatment head wears due to use.

Background Art

[0002] Personal care devices are used by people to perform personal care activities such as hair care activities (such as beard shaving or trimming), skin treatment activities (such as skin brushing), oral care activities (such as toothbrushing), etc. For each personal care activity, different types of personal care devices can be used, and each personal care device can include a treatment portion, sometimes called a treatment head. The treatment head of a personal care device is the part that performs the intended treatment (e.g., hair cutting, skin brushing, or tooth brushing). Over time, with use, one or more parts of the treatment head wear (e.g., wear down or deteriorate), and as a result, the effectiveness of the treatment head decreases, and the ability to perform the intended treatment may decrease. Therefore, the treatment head can be made replaceable, which means that the user can replace only the treatment head portion of the personal care device.

Summary of the Invention

Problems to be Solved by the Invention

[0003] The treatment head can include a visual marker indicating how worn the treatment head is, which can help the user replace the treatment head at an appropriate time. However, the wear of the treatment head occurs at different rates for different users depending on the frequency of use and the nature of the use, and thus the visual marker may change very slowly over time. It can be difficult for the user to notice the change in the visual marker over time and thus difficult to determine the optimal time to order a replacement head so that it arrives at the appropriate time.

[0004] Therefore, a mechanism is needed that can quantify the degree of wear on the treatment head so that the estimated remaining lifespan of the treatment head can be indicated to the user. [Means for solving the problem]

[0005] The present invention provides a mechanism that enables quantitative evaluation of a treatment head, thereby estimating the degree of wear of the treatment head and communicating this to the user of the personal care device in which the treatment head is used. In this way, users who may not be able to perceive changes in the visual markers of the treatment head can be informed about the degree of wear, and as a result, a replacement treatment head can be ordered and installed before the worn treatment head becomes ineffective. The inventors of this disclosure have recognized that by using computer vision-based analysis techniques (e.g., image processing algorithms) applied to images of the treatment head, it is possible to determine the wear level of the treatment head corresponding to each image and predict the estimated remaining life of the treatment head based on how the wear level has changed over time, thereby enabling timely ordering of a replacement treatment head.

[0006] According to a first particular embodiment, a computer implementation method is provided for determining the lifespan of a treatment head of a personal care device, which includes the steps of: receiving a plurality of images of the treatment head, each of which is captured at a different time during the lifespan of the treatment head; determining a wear level for each of the plurality of images, indicating the wear level of the treatment head at the time the image was captured; and determining an estimated time when the treatment head should be replaced based on the plurality of wear levels.

[0007] By using image processing technology to evaluate images of the treatment head and estimate its remaining lifespan, and therefore when it should be replaced, treatment head wear can be evaluated more quantitatively than when a user manually evaluates the treatment head, for example, by determining the level of wear based on visual indicators included in the treatment head. In this way, the treatment head can be replaced at the appropriate time before the level of wear becomes too high, resulting in a performance degradation that could lead to a poor user experience or even ineffective treatment during personal care activities.

[0008] In some embodiments, the method may further include the steps of determining an indication time at which the receiving device should be provided with an indication that the treatment head should be replaced, based on an estimated time at which the treatment head should be replaced, and generating an instruction signal to be provided to the receiving device for supply at the indication time.

[0009] Determining each wear level may involve applying an edge detection algorithm to detect visible edges in the pattern displayed on the treatment head.

[0010] In other embodiments, determining each wear level may involve providing each image of a plurality of images as input to a predictive model trained to classify images based on the estimated wear level of the treatment head visible in the images.

[0011] Determining each wear level, in some embodiments, involves the steps of: applying at least one of an object detection algorithm and an image segmentation algorithm to detect regions in each of a plurality of images that are expected to contain a predetermined marker; and providing each of the plurality of images as input to a predictive model trained to detect the predetermined marker. The predictive model can generate a score indicating the wear level of the treatment head.

[0012] In other embodiments, determining the estimated time when the treatment head should be replaced includes the steps of fitting a function to a plurality of determined wear levels, and determining the estimated time when the treatment head should be replaced based on the fitted function.

[0013] Determining the estimated time when a treatment head should be replaced involves the steps of: estimating a function that represents how the wear level of the treatment head changes over time using a regression algorithm applied to a set of determined wear levels; and determining the estimated time when the treatment head should be replaced based on the estimated function.

[0014] In some embodiments, the method may further include the step of generating a notification prompting the user of the personal care device to capture additional images showing the treatment head for supply to a receiving device.

[0015] The method may further include the step of generating a command prompting the user to operate at least one of the image capture device and the treatment head so that the image of the treatment head captured using the image capture device meets a set of predetermined criteria, for presentation to the user of the personal care device via a receiving device.

[0016] In some embodiments, the method includes the steps of determining that the remaining lifespan of a treatment head is less than a predetermined duration, based on an estimated time when the treatment head should be replaced, and generating a notification prompting the user of the personal care device to purchase a replacement treatment head for supply to the receiving device.

[0017] In some embodiments, the method further comprises the steps of receiving device data indicating how the personal care device was used during a previous treatment session, and determining an estimated time when the treatment head should be replaced based on the received data.

[0018] According to a second specific aspect, there is provided a computer program product including a non-transitory computer-readable medium, the computer-readable medium having computer-readable code embodied therein, the computer-readable code being configured to cause a computer or a processor to execute any of the methods recited in the claims when executed by a suitable computer or processor.

[0019] According to a third specific aspect, there is provided an apparatus for determining the lifespan of a treatment head of a personal care device, the apparatus having a processor configured to execute the steps of the method disclosed herein.

[0020] According to a fourth specific aspect, there is provided a personal care device having a main body portion and a replaceable treatment head removably coupled to the main body portion; and a system having an apparatus as disclosed herein.

[0021] In some embodiments, the system may further include an image capture device for capturing a plurality of images.

[0022] These and other aspects will become apparent from the embodiments described below and will be described with reference to the embodiments.

Brief Description of the Drawings

[0023] [Figure 1] It is a flowchart showing an example of a method for determining the lifespan of a treatment head of a personal care device. [Figure 2] It is a set of images showing an example of edge detection technology. [Figure 3] It is a graph showing an example of an estimated time when the treatment head should be replaced. [Figure 4] It is a flowchart of a further example of a method for determining the lifespan of a treatment head of a personal care device. [Figure 5] It is a schematic diagram of an example of a processor communicating with a computer-readable medium. [Figure 6]It is a schematic diagram of an example of a device for determining the lifespan of a treatment head of a personal care device. [Figure 7] It is a schematic diagram of a system for determining the lifespan of a treatment head of a personal care device. [Figure 8] It is a graph showing a confusion matrix of an implementation example of a prediction model in the method according to the present invention.

Embodiments for Carrying Out the Invention

[0024] Hereinafter, exemplary embodiments will be described with reference to the following drawings, which are merely illustrative.

[0025] The embodiments disclosed in this document provide a mechanism by which the wear level of a treatment head of a personal care device can be determined based on the analysis of a plurality of images of the treatment head at various stages during its lifespan. As will be described later, various techniques can be used to analyze the images, and based on the analysis, it is possible to estimate the time when the treatment head should be replaced in order to reduce the possibility that the user of the treatment head suffers from ineffective treatments.

[0026] Referring to the drawings, FIG. 1 is a flowchart of an example of a method 100 such as a computer-implemented method for determining the lifespan of a treatment head of a personal care device. The personal care device can have a device or instrument used to perform personal care activities, and can include, for example, a hair cutting device such as a hair trimmer or shaving device, a facial cleansing brush, a skin rejuvenation device or a skin treatment device such as an intense pulsed light (IPL) device, an oral care device such as an electric toothbrush or an air floss device, etc. Each personal care device can include a treatment head that performs the intended treatment on the subject during use. For example, the treatment head of a shaving device may have a cutting head or blade that cuts the subject's hair during use, and the treatment head of an electric toothbrush may have a brush head attachment with bristles that clean the subject's teeth during use.

[0027] One or more parts of the treatment head of a personal care device may wear down over time due to repeated engagement with the object (e.g., the hair or teeth) during use. For example, the blades of a shaving device may become dull, resulting in less effective hair cutting, or the bristles of an electric toothbrush may wear down and become misaligned, leading to a reduced cleaning effect. Each treatment head has a lifespan, which is considered to be over when the treatment head wears down to the point where its ability to perform the intended treatment is significantly reduced.

[0028] Method 100 includes the step in step 102 of receiving a plurality of images of the treatment head, each of which is captured at a different time during the lifespan of the treatment head. Each image, for example, has a photograph of the treatment head, which can be captured using an image capture device such as a camera. For example, a user of the treatment head, or a user of a personal care device in which the treatment head is used, can take a photograph of the treatment head using, for example, a smartphone camera, and the image (e.g., image data representing the image) can be received by a device (e.g., one or more processors) performing Method 100. Each image of the plurality shows the treatment head at a different time during its lifespan. For example, the first image of the plurality may be captured on a first date after the first treatment session, and the second image of the plurality may be captured on a second date after the second treatment session.

[0029] Each image of the treatment head may be intended to show a specific part of the treatment head from which the level of wear can be determined. For example, each image of the treatment head of a shaving device may show the blade, and each image of the treatment head of an electric toothbrush may show the bristles. As will be described later, a mechanism may be provided to prompt the user to capture a photograph that more preferably shows the relevant part of the treatment head following the evaluation of the images received in step 102.

[0030] The user may be prompted or requested to upload an image of the treatment head, or to take a photograph of the treatment head showing a relevant part of the treatment head in its current state (e.g., a part of the treatment head where the level of wear can be determined). In some embodiments, the user may be guided through the image capture process by a set of instructions that inform the user how to capture a suitable image (e.g., with respect to field of view, resolution, zoom level, lighting conditions, etc.). In other embodiments, an augmented reality (AR) stencil or template may be provided to guide the user on how to capture a suitable image of the treatment head. In such examples, the guidance may be provided to the user via a display on a mobile device (e.g., via an application on the user's smartphone). The mobile device may provide an indication (e.g., visual or auditory) that informs the user when the conditions (e.g., field of view, lighting conditions, etc.) are suitable for capturing an image of the treatment device where the level of wear can be determined.

[0031] In step 104, method 100 includes the step of determining a wear level for each image in a plurality of images, indicating the wear level of the treatment head at the time the image was captured. One or more computer vision algorithms (e.g., image processing techniques) can be used to determine the wear level of the treatment head for each image. In the examples disclosed herein, the determination of the wear level can be performed using edge detection techniques, machine learning techniques, or object detection / segmentation techniques.

[0032] In the first example, determining each wear level (step 104) may involve applying an edge detection algorithm to detect visible edges in a pattern displayed on the treatment head. The treatment head may include patterns, symbols, or markings, the appearance of which changes over time as the treatment head wears down. In one example, the skin engagement portion of the treatment head may include patterns such as grids formed on the skin engagement portion by printing, painting, or other means. The skin engagement portion of the treatment head of a shaving device may have, for example, one or more blades. As the treatment head (e.g., the skin engagement portion of the treatment head) wears down with use, the pattern formed on it may become less visible, or its appearance, indicating the level of wear, may change in some other way. In another example, as the wear level of the treatment head increases, the pattern formed beneath the skin engagement portion may become more visible.

[0033] When a treatment head is new, the pattern is complete and fully visible to an observer (e.g., a user). In one example, the pattern may have a grid (e.g., hatching) formed over at least a portion of the treatment head, and in a new, unused treatment head, the grid pattern may contain a predetermined number (e.g., 500) of lines or edges that can be detected using an edge detection algorithm. Figure 2 illustrates an example of a pattern formed on a treatment head, such as the cutting section of a shaving device. Figure 2A shows an example of a new treatment head, where a grid pattern 202, formed by a series of hatched diagonal lines, is fully visible across the surface of the treatment head. Figure 2B shows an example image representing the edges detected in the image of Figure 2A by an edge detection algorithm. As can be seen, most of the edges (lines, etc.) present in the grid pattern 202 can be detected and counted. In this example, the edge detection algorithm detected 430 edges, which is considered to be associated with a new or nearly new treatment head.

[0034] Figure 2C shows an example of a well-used treatment head, where a portion of the grid pattern 202 is worn. Figure 2D shows an example image representing the edges detected in the image of Figure 2C by the edge detection algorithm. In this case, 160 edges (e.g., lines) are detected, which is far fewer than in Figure 2B. In one example, the percentage of lines or edges detected using the edge detection algorithm can indicate the level of wear on the treatment head. For example, if half the number of edges are detected compared to when the treatment head was new, the treatment head is considered to be 50% worn and / or at 50% of its lifespan. An advantage of using the edge detection algorithm in this embodiment is that no training of the algorithm is required.

[0035] In the examples shown in Figures 2C and 2D, an image of symbol 204 is also visible on the treatment head. Symbol 204 may be placed (e.g., printed or painted) on the surface beneath the wearable portion of the treatment head. As a result, symbol 204 becomes more visible as the treatment head wears down and the grid pattern 202 wears away. The gradual appearance of symbol 204 can provide the user with a visual indication that the treatment head is wearing down and nearing the end of its lifespan. In some examples, an object detection algorithm can be used to search for and detect symbol 204 in the received image of the treatment head. In such examples, the confidence level (e.g., confidence score) at which the object detection algorithm can detect symbol 204 can indicate the level of wear on the treatment head. For example, if the object detection algorithm detects symbol 204 with only 10% confidence (e.g., because the grid pattern 202 covers most of the symbol), it can be determined that the treatment head is 10% worn. However, if the object detection algorithm detects symbol 204 with 90% confidence, it can be considered that a large portion of the grid pattern 202 is likely worn, and therefore it can be determined that the treatment head is 90% worn.

[0036] In some cases, an object detection algorithm can have a "you only look once" (YOLO) algorithm, which can determine a score (e.g., a probability score) for each of several classes for each image. For example, the algorithm can consider 10, 100, or other numbers of classes, and for each class, the algorithm can determine a score for the image. As an example, the algorithm can determine a score for every 10 classes. The score for each class can be thought of as the probability or likelihood that the image belongs to that class, and each class can correspond to a different wear level. The algorithm can determine that the image belongs to the class corresponding to the highest score determined. Therefore, the treatment head shown in that image is considered to have a wear level corresponding to the wear level associated with the highest-scoring class.

[0037] In the second example, determining each wear level (step 104) may involve providing each image of a group of images as input to a predictive model trained to classify images based on the estimated wear level of the treatment heads visible in the images. Many different types of predictive models or algorithms can be used, including, for example, artificial neural networks (e.g., deep learning algorithms such as convolutional neural networks). The predictive model can be trained with training data having images of treatment heads at different wear levels. In some examples, the predictive model may focus on textures present in the images, such as when the treatment head has a cutting blade for a shaving device. A new cutting blade may have a rough surface, while a used cutting blade may have a smoother surface. As shown in the examples in Figures 2C and 2D, one or more visual markers or symbols may become visible or gradually become visible as the blade wears down. In such examples, the predictive model can be trained to detect the visual markers or symbols and determine the wear level based on how clearly the markers or symbols are visible. In some examples, a given marker may have a pattern, such as a grid pattern as described in this document. The predictive model can provide an output classifying the treatment head into one of a set of categories indicating its estimated remaining lifespan. For example, the predictive model could classify the treatment head as having 0-10% wear or 80-90% wear.

[0038] In a third example, an object detection algorithm and / or an image segmentation algorithm can be used to determine the wear level of a treatment head based on an image of the treatment head. In this example, determining each wear level (step 104) involves applying at least one of the object detection algorithm and / or an image segmentation algorithm to detect in each of a plurality of images a region that is expected to contain a given marker. For example, the given marker is symbol 204 shown in Figure 2. An object detection algorithm or an image segmentation algorithm can be used to analyze an image of the treatment head and determine whether the marker (e.g., symbol 204) is present in the image. The object detection algorithm or image segmentation algorithm can be trained using a set of training images annotated by drawing a bounding box around the marker / symbol to be identified or by indicating the region in the image corresponding to the marker / symbol. A predictive model trained on a YOLO (i.e., you only look once) dataset and / or a COCO (i.e., common objects in context) dataset can also be used to determine whether the symbol is present in the image. Therefore, each image in a set of images can be provided as input to a predictive model trained to detect a given marker. If the presence of the symbol is determined, it can be determined that the treatment head is worn. Generally, the predictive model can generate a score indicating the level of wear on the treatment head. The score generated by the predictive model may have a probability score or a confidence score, and / or may be based on a prediction probability, a conditional probability, confidence in the prediction, objectivity of the detection, or a combination thereof.

[0039] Referring again to Figure 1, Method 100 has a step in step 106 to determine an estimated time when the treatment head should be replaced based on multiple wear levels. Once Method 100 determines (e.g., estimates) the wear level of the treatment head in each image (i.e., the amount of wear on the treatment head at various times corresponding to the time the image was captured), an estimate can be made regarding how much useful life the treatment head has left. In one example, determining an estimated time when the treatment head should be replaced may include the steps of fitting a function to the determined multiple wear levels and determining an estimated time when the treatment head should be replaced based on the fitted function. The determined wear levels can be time-stamped wear levels because they represent the wear level at a specific time.

[0040] Figure 3 is a graph showing an example of the determined wear level of a treatment head plotted against time for each of three images captured at three different dates over the lifespan of the treatment head. In this example, point 302 represents the wear level of the treatment head determined from the image captured at the first date after the first period of use, point 304 represents the wear level of the treatment head determined from the image captured at the second date after the second period of use, and point 306 represents the wear level of the treatment head determined from the image captured at the third date after the third time period. With multiple wear levels plotted on the graph, a function is fitted to the data, as shown by the straight line 308. It will be clear that the more data points included in the graph, the better the fit of the function will be. Based on the fitted function, it is possible to determine a future time / date 310 at which the treatment head will reach a predetermined wear level that is considered to represent 100% wear or the end of the useful life of the treatment head. The determined end date 310 for the treatment head's lifespan is the date on which the treatment head is most likely to be completely worn out, based on its past usage. Therefore, it may be recommended that the treatment head be replaced on the determined end date.

[0041] In some embodiments, determining the estimated time when the treatment head should be replaced (step 106) may involve the step of estimating a function that represents how the wear level of the treatment head changes over time, using a regression algorithm applied to a plurality of determined wear levels. In some examples, multiple algorithms can be used, including, for example, simple linear models such as linear regression models, nonlinear models, support vector machines (SVMs), artificial neural networks, and autoregressive integral moving average (ARIMA) models. The estimated time when the treatment head should be replaced may be determined based on the estimated function.

[0042] The embodiments disclosed above allow for the estimation of the likely date on which a treatment head will reach the end of its useful life. However, it would be even more useful if the user of the treatment head could be given advance notice that the end of its life is approaching, and arrangements could be made for a timely replacement of the treatment head. For example, the user might want to purchase a replacement treatment head one to two weeks before the estimated end of its life to ensure that a replacement treatment head arrives in time. Embodiments described below with reference to Figure 4 offer these additional advantages.

[0043] Figure 4 is a flowchart of a further example of Method 400 (e.g., a computer-implemented method) for determining the lifespan of a treatment head of a personal care device. Method 400 may include the steps of Method 100 described above. In some embodiments, Method 400 may further include, in step 402, determining an indication time at which an indication that the treatment head should be replaced should be provided to the receiving device, based on an estimated time at which the treatment head should be replaced. The indication time may be determined so that the recipient (e.g., the user of the receiving device) is provided with sufficient notice to replace the treatment head before or at the estimated time at which the treatment head should be replaced. For example, the indication time may be determined based on the expected delivery time if the user needs to order a replacement treatment head.

[0044] In step 404, method 400 may further include the step of generating an instruction signal to be supplied to a receiving device at an indication time. The instruction signal may have a signal that instructs a device (e.g., a personal care device, a computing device, a smartphone, a tablet computer, a wearable device, an interactive mirror, etc.) to display a message or notification informing the user of the device that the treatment head should be replaced.

[0045] In some embodiments, a user of a personal care device to which a treatment head is attached may be offered the opportunity to purchase a replacement treatment (tree) head at an appropriate time based on a determined estimated end date of the treatment head's lifespan. For example, Method 400 may further include in step 406 the step of determining that the remaining lifespan of the treatment head is less than a predetermined duration, based on the estimated time when the treatment head should be replaced. The predetermined duration may be, for example, one week, two weeks, one month, etc., and may be selected based on the nature of the personal care device and / or the treatment head, and / or the manner in which the personal care device was used. In step 408, Method 400 may further include the step of generating a notification prompting the user of the personal care device to purchase a replacement treatment head for supply to a receiving device. This notification may be supplied, for example, to the user's smartphone via an application, which allows the user to order a replacement treatment head so that it arrives before the estimated time when the treatment head should be replaced.

[0046] Users of the personal care device may be prompted or required to take a photograph of the treatment head used with the personal care device and / or upload the image after each treatment session (e.g., after each use of the personal care device). Thus, regularly taken images of the treatment head can be used to determine an accurate estimate of the treatment head's end-of-life date. In some embodiments, Method 400 may include, in step 410, generating a notification prompting the user of the personal care device to capture further images showing the treatment head for supply to a receiving device. For example, a message suggesting that the user take a photograph of the treatment head after use for uploading for wear level analysis may be supplied to the user's smartphone.

[0047] In some cases, the image of the treatment head received in step 102 may be considered to be of poor quality for an accurate assessment of its wear level. For example, the image of the treatment head may not be captured under optimal lighting conditions, or it may be captured from an angle that makes analysis by the edge detection algorithm difficult. In such cases, the user may be prompted or requested to capture and upload a new, higher-quality image. The Method 400 may further include in step 412 a step of generating instructions prompting the user to operate at least one of the image capture device and the treatment head so that the image of the treatment head captured using the image capture device meets a set of predetermined criteria for presentation to the user of the personal care device via the receiving device. The instructions may include, for example, text and / or audio instructions instructing the user to move the camera (e.g., a smartphone camera) and / or the treatment head to a position where an image that meets the predetermined set of criteria can be captured. The predetermined criteria may include criteria relating to lighting conditions, orientation of the treatment head and / or camera, image resolution, etc.

[0048] According to methods 100 and 400, it is possible to determine an estimated time when a treatment head should be replaced based solely on multiple photographs of the treatment head. However, in some embodiments, additional information may be available in addition to the images to determine the estimated end-of-life date of the treatment head. Thus, in some embodiments, method 400 may further include, in step 414, receiving device data indicating how the personal care device was used during a previous treatment session. For example, data or information indicating the frequency of use of the personal care device and the duration of each use may be provided, as well as data from one or more sensors located inside, on, or around the personal care device, or data otherwise associated with the personal care device. In some examples, data from an inertial measuring unit (IMU), pressure sensors, motor force sensors, and / or optical sensors may be used, for example, to determine the intensity of personal care activity, and data from one or more sensors may be used when determining estimates. The data may be received via a wired or wireless connection. Thus, determining an estimated time when the treatment head should be replaced (step 106) may be further performed based on the received data. For example, if the received device data indicates that the personal care device is used roughly each time it is used, the treatment head may wear out faster than if the personal care device were used gently and with little force.

[0049] The present invention also provides computer program products. Figure 5 is a schematic diagram of an example of a processor 502 communicating with a computer-readable medium 504. According to an embodiment, a computer program product is provided which includes a non-transient computer-readable medium 504, the computer-readable medium having computer-readable code embodied therein, the computer-readable code being configured, when executed by a suitable computer or processor 502, to cause the computer or processor to perform the steps of methods 100, 400 disclosed herein. Thus, the steps of methods 100, 400 may be performed using one or more computing devices and / or one or more processors included in a computing environment such as a server located in, for example, a cloud computing environment.

[0050] The present invention also provides an apparatus. Figure 6 is a schematic diagram of an example apparatus 600 for determining the lifespan of a treatment head of a personal care device. The apparatus has a processor 602 (e.g., processor 502) configured to perform the steps of methods 100, 400 disclosed herein. The apparatus 600 may have, for example, a personal care device in which the treatment head is used, and a computing device such as a smartphone, a wearable device, or an interactive mirror. In some embodiments, the processor 602 is located within the apparatus 600. However, in other embodiments, the processor that performs the steps of the method may be located away from the apparatus 600 and / or the personal care device.

[0051] The present invention also provides a system. Figure 7 is a schematic diagram of an example of a system 700 for determining the lifespan of a treatment head of a personal care device. System 700 has a personal care device 702 having a main body 704 and a replaceable treatment head 706 detachably coupled to the main body. As discussed herein, the personal care device 702 may include a hair cutting device, a shaving device, an oral care device, a skin treatment device, and the like. System 700 further includes a device 600. The device 600 can operably communicate with the personal care device 702. As a result, it can communicate with the personal care device and receive data from the personal care device.

[0052] In some embodiments, the system 700 may further include an image capture device 708 for capturing multiple images. The image capture device 708 may have a camera in a mobile device such as a smartphone. In some embodiments, the image capture device 708 may form all of the components housed within the device 600.

[0053] Figure 8 is a chart showing an example output of a test using the predictive model in Method 100 disclosed herein. The chart is a confusion matrix, which is a table showing the performance of the classification task (i.e., determining the wear level of the treatment head, which is the cutting element of the shaving device). The x-axis of the confusion matrix visualizes the true labels (ground truth), and the y-axis visualizes the predicted labels. Within each cell of the confusion matrix, a value from 0.0 to 1.0 indicates the confidence score of the predictive model. The labels "WO_B1"..."WO_B10" represent the wear level classes of the treatment head (i.e., the cutting element), increasing in steps from an unworn treatment head ("WO_B1") to a fully worn treatment head ("WO_B10"). The confusion matrix shows that some incorrect predictions are made at a + / -1 wear class degree (e.g., the square cell areas in "WO_B4" and "WO_B5"). The absence of a value in the "backgroundFN" row indicates that all treatment heads were detected, while the value in the "backgroundFP" column with a low confidence score indicates that the predictive model detected some erroneous bounding boxes, which are subsequently filtered out due to their low confidence scores. The absence of any value in the "WO_B2" row or column is due to the fact that there were no "WO_B2" samples used in the test set. The test set contained a set of 36 images. The output shown in the confusion matrix indicates that the predictive model can accurately determine the wear level of treatment heads from the images.

[0054] The processors 502, 602 may have one or more processors, processing units, multicore processors, or modules configured or programmed to control the apparatus 600 in the manner described herein. In a particular implementation, the processors 502, 602 may include a plurality of software and / or hardware modules, each configured or intended to perform or to perform one or more steps of the method described herein.

[0055] As used in this book, the term "module" is intended to include hardware elements such as a processor or processor component configured to perform a specific function, or software elements such as a set of instruction data that has a specific function when executed by a processor.

[0056] It should be understood that embodiments of the present invention also apply to computer programs adapted to carry out the present invention, particularly computer programs on or within a carrier. The program may be source code, object code, intermediate code of source and object code such as a partially compiled form, or any other form suitable for use in implementing the methods according to embodiments of the present invention. It should also be understood that such programs may have many different architectural designs. For example, program code that implements the functionality of a method or system according to the present invention may be subdivided into one or more subroutines. Many different ways of distributing functionality among these subroutines will be apparent to those skilled in the art. The subroutines may be stored together in a single executable file to form a self-contained program. Such an executable file may contain computer-executable instructions, such as processor instructions and / or interpreter instructions (e.g., Java interpreter instructions). Alternatively, one, one, or all of the subroutines may be stored in at least one external library file and linked statically or dynamically, for example at runtime, to the main program. The main program includes at least one call to at least one subroutine. Subroutines may also have function calls to one another. Embodiments relating to a computer program product have computer-executable instructions corresponding to at least one processing stage of the method defined herein. These instructions may be subdivided into subroutines and / or stored in one or more files that can be statically or dynamically linked. Another embodiment relating to a computer program product has computer-executable instructions corresponding to at least one means of the system and / or product defined herein. These instructions may be subdivided into subroutines and / or stored in one or more files that can be statically or dynamically linked.

[0057] The carrier of a computer program can be any entity or device capable of carrying the program. For example, the carrier may include data storage devices such as ROMs, which are CD-ROMs or semiconductor ROMs, or magnetic recording media, such as hard disks. Furthermore, the carrier may be a carrier capable of transmitting electrical or optical signals, which can be transmitted via electrical or optical cables, or wirelessly or by other means. If the program is embodied in such signals, the carrier may consist of such cables or other devices or means. Alternatively, the carrier may be an integrated circuit in which the program is embedded, which is configured to perform or used to perform the relevant methods.

[0058] Modifications to the disclosed embodiments can be understood and implemented by those skilled in the art who practice the principles and techniques described herein, based on a consideration of the figures, disclosures, and appended claims. In the claims, the word “has” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude plurality. One processor or other unit can perform the functions of multiple items described in the claims. The mere fact that certain means are described in different dependent claims does not imply that combinations of these means cannot be used advantageously. Computer programs can be stored or distributed on suitable media such as optical storage media or solid-state media supplied together with or as part of other hardware, but they can also be distributed in other forms, such as via the Internet or other wired or wireless communication systems. Any reference numerals in the claims should not be construed as limiting the scope of the invention.

Claims

1. In a computer implementation method for determining the lifespan of a treatment head of a personal care device, A step of receiving a plurality of images of the treatment head, wherein each of the plurality of images is captured at a different time during the lifespan of the treatment head. For each of the plurality of images, the step of determining a wear level indicating the wear level of the treatment head at the time the image was captured, A method comprising the step of determining an estimated time when the treatment head should be replaced based on the plurality of wear levels.

2. The steps include determining an indication time at which an indication that the treatment head should be replaced is provided to the receiving device, based on the estimated time at which the treatment head should be replaced, The computer implementation method according to claim 1, further comprising the step of generating an instruction signal to be supplied to the receiving device during the indication time.

3. The computer implementation method according to claim 1, wherein the step of determining each wear level includes the step of applying an edge detection algorithm to detect edges visible in the pattern displayed on the treatment head.

4. The computer implementation method according to claim 1, wherein the step of determining each wear level includes providing each of the plurality of images as input to a predictive model trained to classify images based on the estimated wear level of the treatment head visible in the images.

5. The step of determining each wear level is, The steps include: applying at least one of an object detection algorithm and an image segmentation algorithm to detect regions in each of the plurality of images that are expected to contain a predetermined marker; The step of providing each of the plurality of images as input to a predictive model trained to detect the predetermined marker, The computer implementation method according to claim 1, wherein the predictive model generates a score indicating the wear level of the treatment head.

6. The step of determining the estimated time when the treatment head should be replaced is: The steps include fitting a function to the plurality of determined wear levels, A computer implementation method according to any one of claims 1 to 5, comprising the step of determining an estimated time at which the treatment head should be replaced based on the fitted function.

7. The step of determining the estimated time when the treatment head should be replaced is: The steps include: estimating a function that represents how the wear level of the treatment head changes over time using a regression algorithm applied to the plurality of determined wear levels; A computer implementation method according to any one of claims 1 to 5, comprising the step of determining an estimated time at which the treatment head should be replaced based on the estimated function.

8. The computer implementation method according to any one of claims 1 to 5, further comprising the step of generating a notification prompting the user of the personal care device to capture further images showing the treatment head for supply to a receiving device.

9. The computer implementation method according to any one of claims 1 to 5, further comprising the step of generating a command prompting a user to operate at least one of the image capture device and the treatment head so that an image of the treatment head captured using the image capture device satisfies a set of predetermined criteria, for presentation to the user of the personal care device via a receiving device.

10. Based on the estimated time when the treatment head will be replaced, the step of determining that the remaining lifespan of the treatment head is less than a predetermined duration, The computer implementation method according to any one of claims 1 to 5, further comprising the step of generating a notification prompting the user of the personal care device to purchase a replacement treatment head for supply to a receiving device.

11. The steps include receiving device data indicating how the personal care device was used during a previous treatment session, The computer implementation method according to any one of claims 1 to 5, further comprising the step of determining an estimated time when the treatment head should be replaced based on the received data.

12. A computer program having computer-readable code that causes a suitable computer or processor to perform the method described in any one of claims 1 to 5 when executed by the computer or processor.

13. A device for determining the lifespan of the treatment head of a personal care device, An apparatus having a processor that performs the method according to any one of claims 1 to 5.

14. It is a system, A personal care device having a main body and a replaceable treatment head detachably coupled to the main body, A system comprising the apparatus described in claim 13.

15. The system according to claim 14, further comprising an image capture device for capturing multiple images.

Images