Operation of oral treatment devices

Abstract

An oral cavity processing apparatus is provided for processing a user's oral cavity. The oral cavity processing apparatus includes: an image sensor device operable to generate an image dataset representing a portion of a user's oral cavity; a processing delivery system operable to deliver processing to interproximal spaces in the user's oral cavity in response to receiving a processing control signal; and a controller. The controller is configured to: receive a sequence of generated image datasets from the image sensor device; upon receiving each image dataset, apply a gap detection algorithm to the image dataset, the gap detection algorithm being configured to detect interproximal spaces in the portion of the oral cavity represented in the image dataset; and output a processing control signal to the processing delivery system based on the continuous detection of interproximal spaces for N image datasets in the image dataset. A computer-implemented method for operating the oral cavity processing apparatus is also provided, as well as a computer program including instructions that, when executed by the controller, cause the controller to perform the computer-implemented method.

Description

Background Technology

[0001] Oral treatment devices are used to provide treatment to a user's oral cavity (i.e., mouth). Examples of such devices include toothbrushes (which can be manual or electric), oral irrigators, interdental cleaning devices, dental floss devices, etc.

[0002] In some known cases, oral processing devices (also known as "oral care devices" or "oral processing appliances") may provide flossing functionality in addition to brushing. For example, a fluid delivery system may be incorporated into an electric toothbrush and may be used to deliver a jet of working fluid for interdental (or interproximal) cleaning. Such a fluid delivery system may include a nozzle disposed on the head of the device for spraying working fluid into the interdental spaces between teeth, for example to expel food matter from the spaces, and a fluid reservoir for storing the working fluid on the device.

[0003] However, the flexibility and / or versatility of known oral treatment devices are limited. This, in turn, may limit the ability of known devices to deliver treatment in the best manner. For example, known oral treatment devices often rely on the user to use the device correctly, and this may not always be the case.

[0004] In dental treatment devices that include onboard fluid reservoirs with a fixed capacity, the efficient use of working fluid becomes a particular consideration. Using and / or wasting more working fluid necessitates more frequent replenishment of the fluid reservoir. In some cases, effective treatment cannot be achieved even with repeated attempts. Summary of the Invention

[0005] According to a first aspect of this disclosure, an oral cavity processing apparatus is provided for processing a user's oral cavity, the oral cavity processing apparatus comprising: an image sensor device operable to generate an image dataset representing a portion of a user's oral cavity; a processing delivery system operable to deliver processing to interproximal spaces in the user's oral cavity in response to receiving a processing control signal; and a controller configured to: receive a sequence of generated image datasets from the image sensor device; upon receiving each image dataset, apply a gap detection algorithm to the image dataset, the gap detection algorithm being configured to detect interproximal spaces in the portion of the oral cavity represented in the image dataset; and output a processing control signal to the processing delivery system based on the continuous detection of interproximal spaces for N image datasets in the image dataset.

[0006] It should be understood that "interdental" can refer to the space between teeth, or the space between two adjacent teeth.

[0007] It should also be understood that "N" can represent an integer greater than 1. By outputting a processing control signal based on the continuous detection of neighbor gaps for N image datasets, processing can be delivered more accurately to the neighbor gaps, and / or inaccurate delivery of processing can be suppressed.

[0008] Each image dataset can correspond to a specific image frame.

[0009] The output of the gap detection algorithm can indicate the presence of adjacent gaps within a portion of the oral cavity represented in an image dataset. Thus, the controller can be configured to use the output of the gap detection algorithm to identify the presence of adjacent gaps within a portion of the oral cavity.

[0010] Gap detection algorithms can include object detection algorithms, such as trained object detection algorithms. Object detection algorithms can include machine learning algorithms, such as convolutional neural networks (CNNs).

[0011] A gap detection algorithm can be configured to detect adjacent gaps within a region of interest (ROI) of a portion of the oral cavity. That is, the output of the gap detection algorithm can indicate the presence of adjacent gaps within an ROI representing a portion of the oral cavity in an image dataset. The ROI can correspond to the target of a processing delivery system. In other words, the processing delivery system can be operable to deliver processing to adjacent gaps within the ROI. Therefore, the consecutive detection of adjacent gaps in N image datasets may correspond to the consecutive detection of adjacent gaps within an ROI of N image datasets. In this way, processing can be accurately delivered to the detected adjacent gaps.

[0012] The gap detection algorithm can be configured to determine location data indicating the position of adjacent gaps. The gap detection algorithm can also be configured to output location data. Therefore, the gap detection algorithm can be additionally or alternatively referred to as a gap locator algorithm. In this way, the gap detection algorithm can be configured to detect whether adjacent gaps are within a region of interest (ROI) of the oral cavity and / or the position of adjacent gaps within the ROI of the oral cavity as x, y coordinates.

[0013] In some examples, gap detection algorithms can be configured to use a sliding window to determine location data. In such examples, location data can be determined by detecting the presence of adjacent gaps within the sliding window. The sliding window can traverse the image, defining sub-regions of the image, and can determine whether a gap exists in each sub-region of the image. This allows not only the detection of adjacent gaps, but also, for example, the localization of adjacent gaps by identifying image sub-regions containing the gaps.

[0014] In some examples, the oral treatment device may also include an inertial measurement unit (IMU), and the controller may be configured to receive one or more IMU datasets from the IMU, each IMU dataset including data indicating the position and / or movement of the oral treatment device. Each IMU dataset may be associated with a corresponding image dataset; for example, such a dataset may include data that is sensed or received substantially simultaneously.

[0015] The gap detection algorithm can be configured, for example, to determine the location data of adjacent gaps from an IMU dataset. The IMU dataset can be associated with an image dataset representing the portion of the oral cavity where adjacent gaps were detected.

[0016] The oral treatment device may also include a user interface operable to output an alarm to a user in response to receiving an alarm control signal. In such an example, the controller may be configured to output an alarm control signal to the user interface based on the continuous detection of adjacent gaps for M image datasets in an image dataset, where N may be greater than M. As discussed in further detail below, M may be equal to 1. Therefore, the controller may be configured to output an alarm control signal to the user interface based on the detection of adjacent gaps. Alternatively or additionally, in such an example, the controller may be configured to output an alarm control signal based on the continuous detection of adjacent gaps for fewer than N image datasets. For example, the controller may be configured to output an alarm control signal only when adjacent gaps are continuously detected for fewer than N image datasets (therefore, the controller may be configured to stop outputting the alarm control signal when adjacent gaps are detected for N or more image datasets).

[0017] Therefore, when the oral processing device is positioned over the interproximal interspace, the user can be alerted in real time. The alert can prompt the user to stop the oral processing device over the interproximal interspace, or to slow down or maintain the movement of the oral processing device, allowing treatment to be delivered more accurately to the interproximal interspace. Without an alert, the user may often move the oral processing device too quickly, failing to accurately or effectively process the interproximal interspace. The user may move the oral processing device too quickly, causing the device to fail to detect the interproximal interspace and subsequently deliver treatment to it. Therefore, when delivering treatment, the oral processing device may be misaligned with the interproximal interspace, and / or the treatment may be delivered too late. Therefore, according to this disclosure, alerting the user before delivering treatment (e.g., by indicating N is greater than M) can result in more accurate delivery of treatment to the interproximal interspace.

[0018] In one or more examples, if the oral cavity processing device moves at an appropriate speed, each gap in the ROI can be detected from five or more consecutive image datasets.

[0019] Outputting alarms can better ensure that users use the oral treatment device effectively. Furthermore, outputting alarms can train users to use the oral treatment device effectively, allowing them to continue using the device effectively even when it is operating in a mode without alarm control signals.

[0020] In one or more examples, the controller can be configured to output an alarm control signal based on the continuous detection of at least M neighbor gaps in an image dataset. For example, the controller can be configured to output an alarm control signal based on the continuous detection of neighbor gaps in at least M and fewer than N image datasets. As an example, the controller can be configured to output an alarm control signal only after neighbor gaps have been continuously detected in more than one (in other words, M>1) image datasets. This alarm control signal can be output before the output processing control signal.

[0021] In some cases, alarms can be repeatedly output to the user until processing is delivered, such as when M and N are non-sequential numbers. In other words, the controller can also be configured to output alarm control signals more than once before outputting processing control signals. As an example, if M is 2 and N is 5, three alarms can be output to the user, where the first alarm can be output after neighbor gaps are detected in 2 consecutive image datasets, the second alarm after neighbor gaps are detected in 3 consecutive image datasets, and the third alarm after neighbor gaps are detected in 4 consecutive image datasets. Processing can then be delivered after neighbor gaps have been detected in 5 consecutive image datasets.

[0022] In some cases, the delivery of the treatment can prompt the user to move the oral treatment device to the next inter-interspace.

[0023] It should be understood that "M" can represent an integer.

[0024] In one or more examples, N can be any integer greater than 1. For example, N can be greater than 2 or greater than 3. Therefore, for example, the controller can be configured to output processing control signals based on the continuous detection of neighbor gaps for three image datasets.

[0025] In one or more examples, M can be an integer less than 3. For example, M can be 1 or 2. Therefore, for example, the controller can be configured to output an alarm control signal based on the continuous detection of adjacent gaps for two image datasets in an image dataset. Continuously detecting adjacent gaps for one image dataset can refer to detecting adjacent gaps for one image dataset.

[0026] Specifically, in one or more embodiments, for example, M can be 1 and N can be 2. In other embodiments, for example, M can be 2 and N can be 3.

[0027] Such values ​​for N and / or M ensure that processing is applied more accurately to adjacent inter-interval spaces. For example, these values ​​ensure that the user holding or adjusting the movement of their oral delivery device results in processing being applied accurately to the adjacent inter-interval spaces. If N is too small, the user may not have time to adjust the movement of the oral delivery device before delivering the processing after an alarm is triggered. If N is too large, processing may only be delivered to a small number of adjacent inter-interval spaces because, for a dataset of N consecutive images, it may be unlikely that the user will hold the oral delivery device over the adjacent inter-interval spaces.

[0028] In one or more embodiments, the controller may be configured to stop outputting an alarm control signal in response to the absence of an interproximal gap or in response to the detection of the absence of an interproximal gap. The gap detection algorithm may be configured to detect the absence of an interproximal gap, for example, that corresponds to a tooth. Therefore, if the oral processing device is not located on an interproximal gap, no alarm may be output. In this way, the user can understand whether the oral processing device is on an interproximal gap. For example, a first alarm control signal may be output after a gap is detected in a first image dataset, and a second alarm control signal may be output after a gap is detected in a second image dataset. If no gap is detected in a third image dataset, no alarm control signal may be output. If a gap is detected in a subsequent image dataset, this subsequent image dataset may be considered the first image dataset for the purpose of determining when to output a disposal control signal to the processing delivery system.

[0029] In one or more examples, the controller can be configured to output a stop control signal to the user interface in response to the absence of an inter-interstitial space or in response to the detection of the absence of an inter-interstitial space. The user interface can be configured to stop outputting an alarm in response to receiving the stop control signal. Therefore, if the oral treatment device is not positioned on the inter-interstitial space, no alarm may be output. In this way, the user can understand whether the oral treatment device is on the inter-interstitial space.

[0030] In one or more examples, the oral cavity processing device may include a counter. The counter may be configured to count the number of image datasets in which adjacent gaps are detected. A controller may include the counter, or the controller may be configured to communicate with the counter. The controller may be configured to increment the counter based on the detection of adjacent gaps. For example, the controller may be configured to increment the counter by an integer, such as one, based on the detection of adjacent gaps. In this way, the controller may be configured to identify multiple image datasets in which adjacent gaps are detected.

[0031] The controller can be configured to identify whether the counter has a count of N. The controller can also be configured to identify whether the counter has a count of N or greater.

[0032] The controller can be configured to output alarm control signals and / or process control signals based on a counter.

[0033] The controller can be configured to output a processing control signal in response to recognizing that the counter has a count of N or more. In this way, the controller can be configured to output the processing control signal based on continuously detecting neighbor gaps for N image datasets.

[0034] The controller can be configured to output an alarm control signal in response to identifying that a counter has a count of M or at least M. In this way, the controller can be configured to output the alarm control signal based on continuously detecting M neighbor gaps in the image dataset.

[0035] The controller can be configured to output an alarm control signal in response to identifying that the counter has a count less than N. In this way, the controller can be configured to output an alarm control signal based on continuously detecting neighbor gaps for fewer than N image datasets.

[0036] The controller can be configured to output an alarm control signal and / or increment the counter's count in response to recognizing that the counter has a count less than N. The count can be incremented by an integer, such as 1.

[0037] In one or more examples, the controller can be configured to reset the counter to zero in response to recognizing that the counter has a count of N or a count of N or greater. In this way, the oral delivery device can be prevented from delivering the process twice to a given inter-interproximal space. Furthermore, when the oral delivery device is positioned on another inter-interproximal space, the oral delivery device can output an alarm after delivering the process.

[0038] The counter can be configured to count the number of consecutive image datasets in which adjacent gaps are detected. In one or more examples, the controller can be configured to reset the counter to zero in response to the absence of an adjacent gap or in response to the detection of the absence of an adjacent gap. In this way, for example, delivery processing can be disabled once the oral delivery device detects another adjacent gap. This allows for more accurate delivery of processing to the adjacent gaps and / or can suppress inaccurate delivery of processing.

[0039] In one or more examples, the controller may be configured to, in response to detecting a neighbor gap for an image dataset, identify whether the detected neighbor gap is the same as a previously detected neighbor gap from a previous image dataset or represented in a previous image dataset.

[0040] In one or more examples, the previously detected neighbor gaps come from the immediately preceding or consecutive image dataset. That is, in one or more examples, the controller is configured to, in response to detecting a neighbor gap for an image dataset, identify whether the detected neighbor gap is the same neighbor gap as a previously detected neighbor gap from the immediately preceding or consecutive image dataset.

[0041] This ensures that the neighbor gaps detected for continuous image datasets are the same. In this way, inaccurate delivery of processing can be suppressed.

[0042] In one or more examples, the controller can be configured to output a processing control signal based on the recognition of N consecutively detected adjacent gaps in an image dataset. Therefore, processing can only be delivered when the same adjacent gap is detected consecutively in N image datasets. In this way, inaccurate delivery of processing can be suppressed. For example, if the user moves the oral processing device at a speed that would result in different adjacent gaps being detected from consecutive image datasets, processing may not be delivered. This lack of processing can be used as a form of feedback to the user to train the user to move the oral processing device more slowly.

[0043] In one or more examples, the controller can be configured to output an alarm control signal based on the recognition of M consecutively detected identical adjacent gaps in an image dataset. Alternatively, the controller can be configured to output an alarm control signal based on the recognition of fewer than N consecutively detected identical adjacent gaps in an image dataset. Thus, if at least two consecutive identical adjacent gaps are detected in an image dataset, only an alarm can be output. In this way, misalignment of the user of the oral cavity processing device with the adjacent gaps can be suppressed. Therefore, inaccurate delivery of processing can be suppressed. For example, if the user moves the oral cavity processing device at a speed that results in two different adjacent gaps being detected from two consecutive image datasets, no alarm can be output, and this can be used as a form of feedback to the user to train the user to move the oral cavity processing device more slowly.

[0044] In an example where the oral delivery device includes a counter, the controller can be configured to increment the counter based on the recognition that a detected adjacent gap is the same as a previously detected adjacent gap (e.g., from an immediately preceding image dataset). The count can be incremented by an integer, such as 1. Therefore, the counter can be configured to count the number of consecutive image datasets in which the same adjacent gap is detected. In this way, the controller can be configured to operate based on the number of consecutive image datasets in which the same adjacent gap is detected (e.g., outputting signals such as processing control signals and alarm signals).

[0045] The controller can be configured to, in response to identifying that a detected adjacent gap is the same adjacent gap as a previously detected adjacent gap (e.g., from an immediately preceding image dataset), determine whether the count of the counter is N or N or greater. In this way, the controller can be configured to output a processing control signal based on the continuous detection of adjacent gaps for N image datasets, and / or the controller can be configured to output an alarm control signal based on the continuous detection of adjacent gaps for fewer than N image datasets.

[0046] As described above, the controller can be configured to output a processing control signal and / or reset the counter count in response to identifying that the counter count is N or N or more. As further mentioned above, the controller can be configured to output an alarm control signal and / or increment the counter count, for example, by 1, in response to identifying that the counter has a count less than N.

[0047] In one or more examples, the controller can be configured to stop outputting an alarm control signal and / or output a stop control signal to the user interface in response to recognizing that a detected adjacent gap is different from a previously detected adjacent gap (e.g., from an immediately preceding image dataset). Thus, if the same adjacent gap is detected consecutively for at least two in the image dataset, only an alarm can be output. In this way, misalignment of the oral processing device with the adjacent gaps by the user can be suppressed. Therefore, inaccurate delivery of processing can be suppressed. For example, if the user moves the oral processing device at a speed that would result in two different adjacent gaps being detected from two consecutive image datasets, no alarm can be output, and this can be used as a form of feedback to the user to train the user to move the oral processing device more slowly.

[0048] In one or more examples, the controller can be configured to reset a counter to zero in response to recognizing that a detected adjacent gap differs from a previously detected adjacent gap (e.g., from an immediately preceding image dataset). In this way, the controller can be configured to recognize multiple consecutive image datasets in which the same adjacent gap is detected. Therefore, processing can only be delivered if the same adjacent gap is detected consecutively for N image datasets. In this way, inaccurate delivery of processing can be suppressed. For example, if the user moves the oral processing device at a speed that would result in two different adjacent gaps being detected from two consecutive image datasets, processing may not be delivered, and thus this can be used as feedback to the user to train the user to move the oral processing device more slowly.

[0049] In one or more examples, the controller may be configured to identify whether the detected neighbor gap is a new neighbor gap, that is, whether the detected neighbor gap is different from one or more previously detected neighbor gaps for which control signals have been processed for its output. In one or more examples, such a previous image dataset containing these previously detected and processed neighbor gaps may be discontinuous with the current image dataset. That is, such a previous image dataset may not immediately precede the current image dataset.

[0050] The controller can be configured to output processing control signals and / or alarm control signals only when a detected interproximal space is identified as a new interproximal space (in other words, identified as different from a previously processed interproximal space). Therefore, repeated processing of the same space during a single oral treatment session can be reduced and / or avoided. This allows for more efficient use of the oral treatment device.

[0051] For example, the controller can be configured to, in response to detecting an adjacent gap, identify whether the detected adjacent gap is a new adjacent gap. The controller can be configured to, in response to the identification that the detected adjacent gap is a new adjacent gap, identify whether the detected adjacent gap is the same as any of the previously detected and processed adjacent gaps. The controller can be configured to, in response to the identification that the detected adjacent gap is not a new adjacent gap, reset the counter to zero and / or output a stop control signal.

[0052] The controller can be configured to store one or more image datasets from which neighbor gaps are detected in memory for subsequent comparison of neighbor gaps.

[0053] In one or more examples, the controller may be configured to apply a motion direction detection algorithm to each image dataset upon receipt. The motion direction detection algorithm may be configured to compute motion parameters of the image dataset or motion parameters associated with the image dataset. The motion direction detection algorithm may also be configured to compute motion parameters of the image dataset relative to a previous image dataset (e.g., the immediately preceding image dataset) or motion parameters associated with the image dataset. The motion parameters may correspond to motion vectors, such as motion trajectories.

[0054] Identifying whether a detected neighboring gap is the same as a previously detected neighboring gap can be based on a movement parameter. That is, identifying whether a detected neighboring gap is the same as a previously detected neighboring gap can include identifying whether the detected neighboring gap is the same as a previously detected neighboring gap based on a movement parameter. In this way, the identification of whether a detected neighboring gap is the same as a gap from the immediately preceding image dataset can be performed. Similarly, in this way, the identification of whether a detected neighboring gap is a new gap can be performed.

[0055] For example, if the calculated movement vector corresponds essentially to zero, this can indicate that the detected neighbor gap is the same neighbor gap as the previously detected neighbor gap.

[0056] In one or more examples, the motion direction detection algorithm may include an optical flow algorithm. Therefore, the motion direction detection algorithm can be applied to both image datasets and previous image datasets.

[0057] As described above, in one or more examples, the oral processing device may also include an inertial measurement unit (IMU), and the controller may be configured to receive one or more IMU datasets from the IMU, each IMU dataset including data indicating the position and / or movement of the oral processing device. Each IMU dataset may be associated with a corresponding image dataset; for example, such a dataset may include data that is sensed or received substantially simultaneously.

[0058] Motion direction detection algorithms can be applied to IMU datasets associated with image datasets. As an example, motion direction detection algorithms can be applied to both IMU datasets associated with image datasets and image datasets.

[0059] In some examples, the motion direction detection algorithm can be configured to extract one or more image features from an image dataset and use the extracted one or more image features to determine motion parameters. For example, this can improve the accuracy of the calculated motion vector or trajectory. In some examples, the motion direction detection algorithm can be configured to use a feature extraction algorithm to extract one or more image features. Features extracted using a feature extraction algorithm can be used to more accurately detect and locate neighbor gaps from an image dataset. Feature extraction algorithms can include one or more of the following algorithms: Shi-Tomasi corner detection algorithm, Harris corner detection, scale-invariant feature transform, BRIEF (Binary Robust Independent Basic Features), or ORB (Oriented FAST and Rotated BRIEF). For example, the Shi-Tomasi corner detection algorithm can provide good feature extraction. The motion direction detection algorithm can be configured to use at least one of the following to extract one or more image features: edge detector, corner detector, and blob extractor. Compared to other methods, using such methods to extract image features can provide a more accurate motion trajectory or motion vector for neighbor gaps.

[0060] As discussed, the controller can be configured to perform steps such as receiving each image dataset, applying a gap detection algorithm, and outputting a processing control signal, which may correspond to the steps of the method. The controller can be configured to execute the steps of the method cyclically, such that after some endpoint steps, the method starts again from the first step. The first step may include receiving the image dataset. The endpoint steps may include resetting a counter to zero, outputting a processing control signal, outputting an alarm control signal, and / or outputting a stop control signal.

[0061] In one or more embodiments, the controller may be further configured to log information, including an indication of whether processing was delivered to the adjacent gap for each detected adjacent gap. The controller may also be configured to output a processing profile containing information from the log.

[0062] It should be understood that "each detected neighbor gap" can refer to each distinct neighbor gap detected. For example, the controller can be configured to record an indication of the neighbor gap for each newly identified neighbor gap, and this indication can be included in the log. The controller can also be configured to record an indication of the processing delivery each time a processing delivery signal is output, and this indication can be included in the log.

[0063] This allows the system to output a treatment profile to the user, providing information about the delivery of the treatment. In this way, the user can gain an understanding of how their oral cavity and / or interproximal spaces are being effectively treated. For example, the treatment profile could indicate how many interproximal spaces were treated and / or which interproximal spaces were treated.

[0064] In one or more examples, for each detected inter-adjacent gap, the log may also include the gap's location. As described above, the gap detection algorithm can be configured to determine location data indicating the location of inter-adjacent gaps. The gap detection algorithm can be configured to output location data of inter-adjacent gaps, which can indicate regions within a user's oral cavity. Thus, for example, a user can gain an understanding of areas within their oral cavity that may require additional processing.

[0065] The treatment profile can correspond to a 3D treatment profile. The treatment profile can relate to a given oral treatment procedure.

[0066] The controller can be configured to output the processing profile to a display, such as that of an oral processing device, and / or to an external device, such as a mobile device.

[0067] In one or more examples, the oral processing device may include a toothbrush.

[0068] Image sensor devices can include cameras, such as intraoral cameras.

[0069] The oral cavity processing device may include a head, and an image sensor device may be at least partially included in the head. Since the head of the device can be used to deliver processing inside a user's oral cavity, arranging the image sensor device at least partially in the head allows for imaging of the oral cavity without the need for a separately mounted camera (i.e., a camera mounted separately from the head).

[0070] The oral processing device may include a handle, and an image sensor device may optionally be at least partially included in the handle. By arranging the image sensor device at least partially in the handle of the device, space on the device can be managed more efficiently. Compared to the handle, the head of the device can be relatively small, and including the image sensor device in the head may require architectural and / or structural changes to the head, which can be relatively complex and / or expensive. Furthermore, the head of the device may be detachable from the handle and disposable, and the user may wish to replace the head periodically after use. Therefore, arranging the image sensor device at least partially in the handle rather than entirely in the head can reduce the cost of replacing parts.

[0071] The user interface can be at least partially included in the handle. Similar to the image sensor device described above, by arranging the user interface at least partially in the handle of the device, space on the device can be managed more efficiently. Furthermore, arranging the user interface at least partially in the handle rather than in the head reduces the cost of replacing parts.

[0072] The user interface may include a speaker. Therefore, an alarm may correspond to a sound, such as a short musical note. For example, an alarm may correspond to a voice alarm, which can prompt the user to stop or continue moving.

[0073] In one or more examples, the processing delivery system may correspond to a fluid delivery system. A fluid delivery system may include a fluid reservoir for storing the working fluid. Therefore, the processed material delivered to the adjacent gap may be the working fluid.

[0074] In one or more examples, the controller can operate in multiple modes. In training mode, the controller is operable to output an alarm control signal to the user interface. In automatic mode, the controller may not be operable to output an alarm control signal to the user interface. Therefore, in training mode, where an alarm is output to the user, the user can be trained to use the oral treatment device effectively, so that in automatic mode, where no alarm is output, the user can continue to use the device effectively.

[0075] According to a second aspect of this disclosure, a computer-implemented method for operating an oral cavity processing device for processing a user's oral cavity is provided.

[0076] The oral cavity processing apparatus may include an image sensor device operable to generate an image dataset representing a portion of a user's oral cavity, and a processing delivery system operable to deliver processing to the interproximal spaces in the user's oral cavity in response to receiving a processing control signal.

[0077] The computer-implemented method may include receiving a series of generated image datasets from an image sensor device, applying a gap detection algorithm to each image dataset upon receipt, the gap detection algorithm being configured to detect interproximal gaps in oral cavity portions represented in the image dataset, and outputting a processing control signal to a processing delivery system based on the continuous detection of interproximal gaps in N consecutive image datasets.

[0078] In one or more examples, the oral treatment device may also include a user interface operable to output an alarm to the user in response to receiving an alarm control signal.

[0079] In such an example, the computer-implemented method may further include: outputting an alarm control signal to the user interface based on the continuous detection of neighbor gaps for M image datasets in the image dataset, wherein N may be greater than M. Additionally or alternatively, the computer-implemented method may include: outputting an alarm control signal based on the continuous detection of neighbor gaps for fewer than N image datasets.

[0080] It should be understood that the computer-implemented method according to the second aspect may include any one or more of the optional features set forth above with respect to the first aspect.

[0081] According to a third aspect of this disclosure, a computer program including instructions is provided that, when executed by a controller, causes the controller to perform a computer-implemented method according to the second aspect. Attached Figure Description

[0082] Figure 1A and 1B This is a perspective view of the oral treatment device according to the implementation plan;

[0083] Figure 1C This is a plan view of the oral treatment device according to the implementation plan;

[0084] Figure 2 This is a schematic diagram of the oral treatment device according to the implementation plan;

[0085] Figure 3 This is a flowchart illustrating a method for operating the oral cavity treatment device according to an implementation scheme.

[0086] Figure 4 This is a flowchart illustrating a method for operating the oral cavity treatment device according to an implementation scheme. Detailed Implementation

[0087] Figure 1A and Figure 1B A perspective view of an oral treatment device 100 according to an embodiment is shown. Figure 1C A plan view of an oral treatment device 100 is shown. The oral treatment device 100 and / or its components can be used to implement the methods described herein. Figure 1A-1C In the illustrated embodiments, the oral care device 100 includes a toothbrush. In one embodiment, the oral care device 100 includes an electric toothbrush. In another embodiment, the device 100 includes an ultrasonic toothbrush. In alternative embodiments, the oral care device 100 may include other types of devices. For example, the device 100 may include a dental floss device, an oral irrigator, an interproximal cleaning device, an oral care monitoring device, or any combination thereof. The oral care monitoring device is configured to monitor the user's oral health and provide feedback to the user accordingly.

[0088] The oral treatment device 100 includes a handle 110 and a head 120. The handle 110 forms the main body of the device 100 and can be gripped by the user during use of the device 100. Figure 1A-1C In the illustrated embodiment, the handle 110 includes a user-actuated element 112. The user-actuated element 112 includes a user-operable button configured to be pressed by a user when the user holds the handle 110. In one or more embodiments, the handle 110 includes a display (not shown) positioned to be visible to the user during use of the oral cavity treatment device 100.

[0089] exist Figure 1A-1C In the illustrated embodiment, the head 120 includes multiple bristles 122 for performing brushing functions. In an alternative embodiment, the head 120 does not include bristles. For example, in one or more other embodiments, the oral treatment device 100 includes a dedicated fluid delivery device, such as for cleaning the gaps between adjacent teeth, and / or for delivering a cleaning or whitening medium to the user's teeth. Figure 1A-1C In the illustrated embodiment, the oral treatment device 100 includes a lever 130 connecting a handle 110 to a head 120. The lever 130 is elongated in shape and serves to space the head 120 from the handle 110 to facilitate user operability of the oral treatment device 100. The head 120 and / or the lever 130 can be detached from the handle 110.

[0090] The oral treatment device 100 includes a treatment delivery system for delivering treatments to a user's oral cavity. Figure 1A-1C In the illustrated embodiments, the treatment delivery system includes a fluid delivery system; it should be understood that other types of dental and / or oral treatment delivery systems may be used in other embodiments. The fluid delivery system is arranged to deliver a jet of working fluid into the oral cavity. In embodiments, the working fluid includes liquids, such as water. In alternative embodiments, the working fluid includes gases and / or powders. The working fluid may be delivered to the interproximal space between adjacent teeth to remove obstructions located in the space, such as food material like ham or other cured meats. The interproximal space is the space or gap between two adjacent teeth, and / or may be the area surrounding the contact point of adjacent teeth. The interproximal space may be defined as the area bounded by a plane tangent to the lingual surfaces of two adjacent teeth and the area between the teeth.

[0091] Alternatively or in addition, the working fluid can be delivered to the user's gum line, for example, to treat inflammation or infection of the gums. In an alternative embodiment, the treatment delivery system is configured to deliver whitening fluid and / or remove plaque from the user's teeth.

[0092] exist Figure 1A-1C In the illustrated embodiment, the oral treatment device 100 includes a fluid reservoir 114 for storing working fluid. The fluid reservoir 114 is disposed in the handle 110 of the oral treatment device 100. The fluid reservoir 114 forms part of the fluid delivery system of the device 100. In an embodiment, the fluid reservoir 114 may be detached from the handle 110, for example, to facilitate replenishment of working fluid.

[0093] In one embodiment, the oral treatment device 100 also includes a nozzle 124. This is in Figure 1CAs shown in the diagram, nozzle 124 forms part of the fluid delivery system of device 100. Nozzle 124 is disposed on the head 120 of device 100. Nozzle 124 is configured to deliver working fluid to the user's oral cavity during use of oral cavity treatment device 100. Figure 1A-1C In the illustrated embodiment, the bristles 122 are arranged at least partially around the nozzle 124. The nozzle 124 extends along the nozzle axis A, as shown... Figure 1C As shown. The nozzle axis A is substantially perpendicular to the longitudinal axis Z of the handle 110.

[0094] Nozzle 124 is arranged to receive working fluid from fluid reservoir 114 and deliver a jet of working fluid to the user's oral cavity during use of device 100. In an embodiment, the tip of nozzle 124 includes a fluid outlet through which the jet of working fluid is delivered to the oral cavity. Each jet of working fluid may have a volume of less than 1 ml, and in one or more cases less than 0.5 ml. Nozzle 124 may also include a fluid inlet for receiving working fluid from fluid reservoir 114.

[0095] In one embodiment, the fluid delivery system includes a pump assembly (not shown) for drawing working fluid from a fluid reservoir 114 to a nozzle 124. The pump assembly may be disposed within a handle 110. The pump assembly may include a pump (e.g., a positive displacement pump) and a driver for driving the pump. In one embodiment, the driver includes a pump motor. The pump motor may be powered by a battery (e.g., a rechargeable battery).

[0096] In one embodiment, the fluid delivery system includes control circuitry (not shown) for controlling the actuation of a pump motor, and thus the control circuitry and the pump motor provide a driver for driving the pump. The control circuitry may include a motor controller that supplies power to the pump motor. The control circuitry of the fluid delivery system may receive signals from a controller of the oral cavity processing device 100, as will be described in more detail below.

[0097] Figure 2 A schematic block diagram of an oral treatment device 100 according to an embodiment is shown.

[0098] The oral cavity processing device 100 includes a controller 210. The controller 210 is operable to perform various data processing and / or control functions according to embodiments, as will be described in more detail below. The controller 210 may include one or more components. These components may be implemented in hardware and / or software. The one or more components may be co-located or remotely located within the oral cavity processing device 100. The controller 210 may be embodied as one or more software functions and / or hardware modules. In embodiments, the controller 210 includes one or more processors 210a configured to process instructions and / or data. Operations performed by the one or more processors 210a may be performed by hardware and / or software. The controller 210 may be used to implement the methods described herein. In embodiments, the controller 210 is operable to output control signals for controlling one or more components of the oral cavity processing device 100, as will be described in more detail below.

[0099] In one embodiment, the oral cavity treatment device 100 includes a fluid delivery system 220. The fluid delivery system 220 is operable to deliver a working fluid to the user's oral cavity, as referenced above. Figure 1A-1C As described above. In one embodiment, the fluid delivery system 200 includes a nozzle for spraying working fluid and a fluid reservoir for storing the working fluid in the oral treatment device, such as the nozzle 124 and fluid reservoir 114 described above. The fluid delivery system 220 is operable to receive a control signal from the controller 210, thereby allowing the controller 210 to control the delivery of the working fluid via the fluid delivery system 220. For example, the controller 210 may output a control signal, which may be referred to as a processing control signal, received by the control circuitry of the fluid delivery system 220, causing the control circuitry of the fluid delivery system 220 to activate a pump motor, which in turn causes the working fluid to be pumped from the fluid reservoir to the nozzle, where it is sprayed into the user's oral cavity. Additionally or alternatively, the controller 210 may output a control signal received by the control circuitry of the fluid delivery system 220, causing the control circuitry of the fluid delivery system 220 to prevent the working fluid from being delivered via the nozzle. In an alternative embodiment, the oral treatment device 100 does not include a fluid reservoir. In other words, the working fluid can be delivered from outside the oral treatment device 100 (e.g., via a dedicated fluid delivery channel) to be sprayed through a nozzle, rather than being stored in the oral treatment device 100.

[0100] In one embodiment, the oral cavity processing device 100 includes an image sensor device 230. The image sensor device 230 includes one or more image sensors. Examples of such image sensors include, but are not limited to, charge-coupled device (CCD) and active pixel sensors, such as complementary metal-oxide-semiconductor (CMOS) sensors. In an embodiment, the image sensor device includes an intraoral image sensor device. For example, the image sensor device may include an intraoral camera. The intraoral image sensor device (e.g., an intraoral camera) is operable to be used at least partially within a user's oral cavity to generate image data representing the user's oral cavity. For example, the image sensor device 230 may be at least partially disposed on a head 120 of the oral cavity processing device 100, the head 120 being arranged to be inserted into the user's oral cavity. In an embodiment, the image sensor device 230 includes one or more processors. A controller 210 is operable to receive image data from the image sensor device 230. The image data output from the sensor device 230 can be used to control the oral cavity processing device 100. In an embodiment, the controller 210 is operable to control the operation of the image sensor device 230.

[0101] In such Figure 2 In the illustrated embodiment, the oral processing device 100 includes an inertial measurement unit (IMU) 240. In such an embodiment, a controller 210 is operable to receive a dataset from the IMU 240, which includes data indicating the position and / or movement of the oral processing device 100. In this embodiment, the IMU 240 includes an accelerometer, a gyroscope, and a magnetometer. Each of the accelerometer, gyroscope, and magnetometer has three axes or degrees of freedom (x, y, z). Thus, the IMU 240 may include a 9-axis IMU. In an alternative embodiment, the IMU 240 includes an accelerometer and a gyroscope, but not a magnetometer. In such an embodiment, the IMU 240 includes a 6-axis IMU. Due to the additional degrees of freedom, a 9-axis IMU can produce more accurate measurements than a 6-axis IMU. However, in one or more scenarios, a 6-axis IMU may be superior to a 9-axis IMU. For example, one or more oral processing devices may induce and / or encounter magnetic disturbances during use. Heating, magnetism, and / or magnetic induction and / or other magnetic disturbances on the device can affect the behavior of the magnetometer. Therefore, in one or more cases, a 6-axis IMU is more reliable and / or accurate than a 9-axis IMU. IMU 240 is configured to output data indicating accelerometer and gyroscope signals (and magnetometer signals in some embodiments). In one embodiment, IMU 240 is disposed in the head 120 of the oral cavity processing device 100. In an alternative embodiment, IMU 240 is disposed in the handle 110 of the oral cavity processing device 100. In one embodiment, the oral cavity processing device 100 includes a plurality of IMUs 140. For example, a first IMU 240 may be disposed in the head 120, and a second IMU 240 may be disposed in the handle 110.

[0102] In one or more embodiments, the oral treatment device 100 includes a contact member 245. The contact member 245 can come into contact with a user's teeth during use of the oral treatment device 100, as will be described in more detail below. The contact member 245 is disposed at the head 120 of the oral treatment device 100. For example, the contact member 245 may include a nozzle of a fluid delivery system 220, such as those referenced above. Figures 1A to 1C The nozzle 124.

[0103] In one or more embodiments, the oral processing device 100 includes a user interface 250. The user interface 250 may include, for example, an audio and / or visual interface. In embodiments, the user interface 250 includes a display (e.g., a touchscreen display).

[0104] In one or more embodiments, the user interface 250 includes an audio output device, such as a speaker. In one embodiment, the user interface 250 includes a haptic feedback generator configured to provide haptic feedback to a user. The controller 210 is operable to control the user interface 250, for example, to cause the user interface 250 to provide output to the user, as will be described in further detail below.

[0105] As described above, in one or more embodiments, the oral processing device 100 includes a user-actuated element 112. The controller 210 is operable to receive data via the user-actuated element 112, for example, based on user input.

[0106] The oral processing device 100 also includes a memory 260. According to an embodiment, the memory 260 is operable to store various types of data. The memory may include at least one volatile memory, at least one non-volatile memory, and / or at least one data storage unit. The volatile memory, non-volatile memory, and / or data storage unit may be configured to store computer-readable information and / or instructions for use / execution by the controller 210.

[0107] In alternative embodiments, the oral treatment device 100 may include more, fewer, and / or different components. In particular, in one or more embodiments, components may be omitted (e.g., may be unnecessary). Figure 1A-1C At least some components of the oral processing device 100 shown in Figure 2 and / or 2. For example, in one or more embodiments, at least one of the fluid delivery system 220, image sensor device 230, IMU 240, user interface 250, and memory 260 may be omitted. In embodiments, the oral processing device 100 includes additional components not shown, such as a power source, like a battery.

[0108] Figure 3A method 300 for operating an oral cavity treatment device according to one or more embodiments is shown. Method 300 can be used to operate the device described above. Figure 1A , 1B The oral treatment device 100 described in section 2. Figure 3 In one embodiment, the oral cavity processing device 100 includes a processing delivery system, an image sensor device 230, and a user interface 250. The processing delivery system is configured to deliver processing to an adjacent inter-cell space in response to receiving a processing control signal. The user interface 250 is configured to output an alarm to a user in response to receiving an alarm control signal. In an embodiment, a controller 210 is configured to perform at least a portion of method 300.

[0109] In the first step 310, an image dataset representing a portion of the user's oral cavity is received from the image sensor device 230.

[0110] Upon receiving the image dataset, in the second step 320, it is determined whether inter-neighbor gaps exist within the ROI representing a portion of the oral cavity in the image dataset. This can be achieved by applying a gap detection algorithm to the image dataset, as referenced below. Figure 4 Further detailed description. If no adjacent gap is detected, the method proceeds to reset step 330, where user interface 250 stops outputting alarms. Specifically, a stop control signal is output to user interface 250, which stops user interface 250 from outputting alarms. After reset step 330, method 300 restarts at first step 310 and receives another image dataset. On the other hand, if adjacent gap is detected, method 300 proceeds to third step 340.

[0111] In the third step 340, in response to the detection of a neighboring gap, it is identified whether the neighboring gap is the same as a previously detected neighboring gap represented in the immediately preceding image dataset. In other words, it is determined whether the same neighboring gap has been detected for two consecutive image frames. This can be achieved by applying a motion direction detection algorithm to the image dataset, as referenced below. Figure 4 Further detailed description. If it is identified that the detected adjacent gap is not the same as the previously detected adjacent gap, then method 300 proceeds to reset step 330. On the other hand, if it is identified that the detected adjacent gap is the same as the previously detected adjacent gap, then the method proceeds to fourth step 350.

[0112] In step 350, in response to identifying that the same neighbor gap has been detected for two consecutive image frames, it is determined whether the same neighbor gap has been detected consecutively for N neighbor gaps in the image dataset. This can be achieved by considering the counting of a counter, as referenced below. Figure 4Further detailed description. If it is identified that the same adjacent gap has been detected consecutively for N or more image datasets, method 300 proceeds to processing delivery step 360, where processing is delivered to the user regarding the adjacent gap. Specifically, a processing control signal is output to the processing delivery system, causing the processing delivery system to output processing. After processing delivery step 360, method 300 proceeds to reset step 330, and then restarts at first step 310. On the other hand, if it is identified that the same adjacent gap has been detected consecutively for fewer than N image datasets, method 300 proceeds to alarm step 370, where an alarm is output to the user via user interface 250. Specifically, an alarm control signal is output to user interface 250, causing user interface 250 to output an alarm. The alarm prompts the user to stop moving the oral cavity processing device 100. After alarm step 370, method 300 restarts from first step 310. Thus, after completing several cycles according to method 300 while the oral cavity processing device is stationary over the inter-interproximal gap, the same inter-interproximal gap will be continuously detected for N image datasets, and the processing will be accurately output to the inter-interproximal gap.

[0113] Clearly, requiring the detection of identical inter-neighbor gaps for two consecutive frames necessitates the continuous detection of inter-neighbor gaps across M image datasets, where M is 2. Therefore, in Figure 3 In the example shown, if two consecutive neighbor gaps are detected in the dataset due to step 340, only alarm step 370 is executed. However, in other examples, an alarm can be output immediately in response to the detection of a neighbor gap. That is, in other examples, an alarm can be output immediately in response to the detection of M consecutive neighbor gaps in the image dataset, where M is 1. For example, alarm step 370 can immediately follow step 320 in response to the detection of a gap. In other examples, an alarm can be output only based on the detection of consecutive neighbor gaps in M ​​image datasets, where M is, for example, greater than 2. In such an example, step 350 may also include identifying whether the same neighbor gap has been detected consecutively for M image datasets, and identifying whether the same neighbor gap has been detected consecutively for N image datasets. Then, if it is identified in step 350 that the same neighbor gap has been detected consecutively for at least M and less than N image datasets, the method can proceed to alarm step 370.

[0114] Back Figure 3The method 300 shown can be cyclical, such that after the reset step 330 and the alarm step 370, the method starts again from the first step 310. After processing the delivery step 360, the method proceeds to the reset step 330, and then starts again from the first step 310. Thus, according to method 300, an image dataset sequence is received from the image sensor device 230.

[0115] In one or more examples, method 300 may further include the following steps, which may be, for example... Figure 3 The method 300 shown is performed between the first step 310 and the second step 320: identifying whether the detected interproximal gap is a new interproximal gap that was not previously treated in a previous oral treatment procedure. Such identification can be performed using a movement direction detection algorithm to compare the detected interproximal gap with one or more previously detected interproximal gaps that have already been delivered and treated from a corresponding one or more previous image datasets. For example, the movement direction detection algorithm can be applied to an image dataset and one or more previous image datasets. The movement direction detection algorithm may include an optical flow algorithm configured to calculate a movement vector of the image dataset relative to the previous image datasets. The movement vector can then be used to identify whether the interproximal gap is the same as a previously detected interproximal gap, or whether the interproximal gap is a new interproximal gap. If the detected interproximal gap is identified as a new interproximal gap, the method can proceed to the third step 340. If the detected interproximal gap is identified as not a new interproximal gap, the method can proceed to the reset step 330.

[0116] In one or more instances, method 300 may further include logging, for each detected adjacent gap, the log including an indication of whether processing is delivered to the adjacent gap. For example, method 300 may include: whenever a new adjacent gap is identified, recording an indication of the adjacent gap, which may be included in the log. The processing delivery step 360 may include recording an indication of processing delivery, which may be included in the log. Method 300 may further include: after the processing procedure, outputting a processing profile containing information from the log, which may therefore include information about the multiple adjacent gaps.

[0117] Figure 4 A method 400 for operating an oral cavity treatment device according to an embodiment is shown, which can correspond to Figure 3 The specific form of method 300 shown. In Figure 4In one embodiment, the oral cavity processing device 100 further includes a counter, which may form part of the controller 210, configured to count multiple consecutive image datasets that detect the same inter-adjacent gaps. In another embodiment, the controller 210 is configured to perform at least a portion of the method 400.

[0118] In the first step 410, an image dataset representing a portion of the user's oral cavity is received from the image sensor device 230.

[0119] In the second step 420, a gap detection algorithm is applied to the image dataset. The gap detection algorithm is configured to detect adjacent gaps in a portion of the oral cavity represented in the image dataset. Therefore, the output of the gap detection algorithm indicates whether an adjacent gap exists in the ROI of the portion of the oral cavity represented in the image dataset. Therefore, in the third step 430, the output of the gap detection algorithm is used to identify whether an adjacent gap exists in the ROI of the portion of the oral cavity. If no adjacent gap is detected, method 400 proceeds to a reset step 440, where the counter is set to zero. The reset step 440 also includes stopping the user interface 250 from outputting an alarm. Specifically, a stop control signal is output to the user interface 250, which stops the user interface 250 from outputting an alarm. Therefore, if the oral cavity processing device 100 is not located on an adjacent gap, no alarm is output. After the reset step 440, method 400 again starts from the first step 410. On the other hand, if an adjacent gap is detected, the method proceeds to the fourth step 450.

[0120] In the fourth step 450, in response to detecting a neighboring gap, it is identified whether the neighboring gap is the same as a previously detected neighboring gap represented in the immediately preceding image dataset. In other words, it is determined whether the same neighboring gap has been detected for two consecutive image frames. Figure 4 As shown, this is achieved by applying a motion direction detection algorithm to the image dataset and the immediately preceding image dataset in step 460. The motion direction detection algorithm includes an optical flow algorithm and is configured to calculate a motion vector of the image dataset relative to the immediately preceding image dataset. In step 450, the motion vector is used to identify whether the adjacent gap is the same as a previously detected adjacent gap. Figure 4As shown, upon receiving an image dataset, a motion direction detection algorithm is applied to the image dataset. In one or more examples, the motion direction detection algorithm may be additionally or alternatively applied to an IMU dataset received from the IMU of the oral processing device, which is associated with the image dataset. Therefore, the IMU data may be additionally or alternatively used to calculate the motion vector of the image dataset. If the detected adjacent gap identified from the motion vector is not the same adjacent gap as a previously detected adjacent gap, method 400 proceeds to reset step 440. On the other hand, if the detected adjacent gap identified from the motion vector is the same adjacent gap as a previously detected adjacent gap, the method proceeds to a sixth step 470.

[0121] In the sixth step 470, in response to identifying that the same adjacent gap has been detected for two consecutive image frames, it is determined whether the counter has a count of N or more. In this way, it is determined whether the same adjacent gap has been detected consecutively for N adjacent gaps in the image dataset. If the count is identified as N or more, method 400 proceeds to the processing delivery step 480, where processing is delivered to the user's adjacent gap. Specifically, a processing control signal is output to the processing delivery system, causing the processing delivery system to output processing. In the processing delivery step 480, the counter count is also reset to zero. This prevents processing from being immediately delivered to the adjacent gap again. After the processing delivery step 480, method 400 resumes from the first step 410. On the other hand, if the count is identified as less than N, method 400 proceeds to the alarm step 490, where an alarm is output to the user via the user interface 250. Specifically, an alarm control signal is output to the user interface 250, causing the user interface 250 to output an alarm. The alarm prompts the user to stop moving the oral cavity processing device 100. In the alarm step 490, the counter count is also incremented by 1. After alarm step 490, method 400 resumes from the first step 410. Thus, after a certain number of cycles of method 400 while the oral cavity processing device remains stationary over the interproximal space, the counter count will become N, and the processing will be accurately output to the interproximal space.

[0122] Although not in Figure 4 As shown, however, method 400 is performed cyclically, such that after reset step 440, processing delivery step 480, and alarm step 490, the method starts again from the first step 410. Thus, according to method 400, a sequence of image datasets is received from image sensor device 230.

Claims

1. An oral cavity treatment device for treating a user's oral cavity, the oral cavity treatment device comprising: An image sensor device operable to generate an image dataset representing a portion of a user's oral cavity; A processing delivery system operable to deliver processing to the interproximal spaces in a user's oral cavity in response to receiving a processing control signal; The controller is configured as follows: Receive a sequence of generated image datasets from the image sensor device; Upon receiving each image dataset, a gap detection algorithm is applied to the image dataset, the gap detection algorithm being configured to detect inter-adjacent gaps in the portion of the oral cavity represented in the image dataset; as well as, Based on the continuous detection of neighbor gaps for N image datasets in the image dataset, a processing control signal is output to the processing delivery system.

2. The oral cavity treatment device according to claim 1, wherein the oral cavity treatment device further comprises: A user interface operable to output an alarm to the user in response to receiving an alarm control signal; Furthermore, the controller is also configured as follows: Based on the continuous detection of neighbor gaps in M ​​image datasets, an alarm control signal is output to the user interface, where N is greater than M.

3. The oral cavity treatment device according to claim 2, wherein M is greater than 1, and the controller is further configured to: The alarm control signal is output based on the continuous detection of neighbor gaps in at least M and less than N image datasets in the image dataset.

4. The oral cavity treatment device according to claim 2 or 3, wherein the controller is further configured to output the alarm control signal more than once before outputting the treatment control signal.

5. The oral cavity treatment device according to any one of the preceding claims, wherein the controller is further configured to: Based on the detected neighboring gap, the counter count is increased, and among them, The alarm control signal and the processing control signal are output based on the count of the counter.

6. The oral cavity treatment device according to claim 5, wherein the controller is further configured to: In response to the absence of detected inter-neighbor gaps in the image dataset, the counter is reset to zero.

7. The oral cavity treatment device according to claim 5 or 6, wherein the controller is further configured to: In response to the counter counting to N, the counter count is reset to zero.

8. The oral cavity treatment device according to any one of the preceding claims, wherein the controller is further configured to: In response to the detection of a neighbor gap for an image dataset, it is identified whether the detected neighbor gap is the same as the previously detected neighbor gap from a previous image dataset.

9. The oral cavity treatment device according to claim 8, wherein, The previously detected neighbor gaps were detected from the immediately preceding image dataset.

10. The oral cavity treatment device according to claim 9, wherein the controller is further configured to: Based on the identification that the same neighbor gap is detected consecutively in N image datasets in the image dataset, the processing control signal is output.

11. The oral treatment device according to claim 9 or claim 10, wherein, when subordinate to any one of claims 5 to 7, the controller is further configured to: In response to the recognition that the detected neighbor gap corresponds to the previously detected neighbor gap, the counter is incremented.

12. The oral cavity treatment device according to any one of claims 8 to 11, wherein the controller is configured to: Upon receiving each image dataset, a motion direction detection algorithm is applied to the image dataset, the motion direction detection algorithm being configured to calculate motion parameters of the image dataset relative to the previous image dataset.

13. The oral cavity treatment device according to claim 12, wherein, Identifying whether a detected neighbor gap corresponds to a previously detected neighbor gap includes: identifying whether a detected neighbor gap corresponds to a previously detected neighbor gap based on the movement parameters.

14. The oral cavity treatment device according to any one of claims 2 to 13, wherein the controller is further configured to: A log is recorded, and for each detected neighbor gap, the log includes an indication of whether the processing was delivered to the neighbor gap; and, The output contains a processing profile of the information from the logs.

15. A computer-implemented method of operating an oral processing device for processing a user's oral cavity, the oral processing device comprising: An image sensor device operable to generate an image dataset representing a portion of a user's oral cavity; and A processing delivery system operable to deliver processing to the interproximal spaces in a user's oral cavity in response to receiving a processing control signal; The method implemented by the computer includes: Receive a sequence of generated image datasets from the image sensor device; Upon receiving each image dataset, a gap detection algorithm is applied to the image dataset, the gap detection algorithm being configured to detect interproximal gaps in the portion of the oral cavity represented in the image dataset; and, Based on the continuous detection of neighbor gaps for N image datasets in the image dataset, a processing control signal is output to the processing delivery system.

16. A computer program comprising instructions that, when executed by a controller, cause the controller to perform the computer-implemented method according to claim 15.

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