MACHINE LEARNING TOOTH BRUSH

BACKGROUND

Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.

Oral hygiene may be of importance for, in addition to maintaining healthy teeth and gums, an individual's overall health and well-being. The condition of one's teeth and gums may have an effect on their quality of life, which may affect everything from self-esteem and social interactions to their ability to maintain proper nutrition. Moreover, poor oral hygiene may have links to a range of systemic health issues, including cardiovascular diseases, diabetes, and respiratory illnesses. Accordingly, proper oral hygiene, which may include brushing techniques, timing, and consistency may significantly impact the effectiveness of one's dental care regimen.

Conventional toothbrushes may lack the ability to provide real-time feedback to users, which may often lead to inconsistent brushing habits. Accordingly, proper dental care has been known to have many health benefits. A part of proper dental care may include periodic teeth cleaning by dental professionals. On an individual basis, proper dental care may include tooth brushing. Commonly, the activity of tooth brushing may be performed on a daily basis by a person. A tooth brush may be utilized by the person to brush their teeth.

Commonly, in order to facilitate effective cleaning of teeth, proper utilization of the tooth brush may be a factor. For example, proper utilization of the tooth brush may include, but not limited to, an angle of brushing, a motion of brushing, an amount of pressure, and so forth. However, each set of teeth may be particular to a person (i.e., individual to a person). As such, examples of proper utilization of the tooth brush may not be tailored to each person.

All subject matter discussed in this section of this document is not necessarily prior art and may not be presumed to be prior art simply because it is presented in this section. Plus, any reference to any prior art in this description is not and should not be taken as an acknowledgement or any form of suggestion that such prior art forms parts of the common general knowledge in any art in any country. Along these lines, any recognition of problems in the prior art are discussed in this section or associated with such subject matter should not be treated as prior art, unless expressly stated to be prior art. Rather, the discussion of any subject matter in this section should be treated as part of the approach taken towards the particular problem by the inventor(s). This approach in and of itself may also be inventive. Accordingly, the foregoing summary is illustrative only and not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

SUMMARY

Described herein are various illustrative methods, systems, and apparatus for facilitating facilitate machine learning of toothbrushing habits and providing guidance.

Some example systems may include a sensor, a communication medium, a processor, and a toothbrush learning module (TLM). The example systems may include the TLM configured to receive data associated with tooth brushing from a sensor included in a toothbrush. The TLM may be configured to determine a tooth brushing habit of a person based, at least in part, on the received data. The example systems may include the TLM configured to compare the determined tooth brushing habit with a recommended habit, to proivde guidance to the person based, at least in part, on the comparison.

The foregoing summary is illustrative only and not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF DRAWINGS

Subject matter is particularly pointed out and distinctly claimed in the concluding portion of the specification. The foregoing and other features of the present disclosure will become more fully apparent from the following description and appended claims, taken in conjunction with the accompanying drawings. Understanding that these drawings depict only several embodiments in accordance with the disclosure and are, therefore, not to be considered limiting of its scope, the disclosure will be described with additional specificity and detail through use of the accompanying drawings.

FIG. 1 illustrates an example of a system for machine learning of tooth brushing in accordance with various embodiments.

FIG. 2 illustrates an operation flow of the various embodiments disclosed herein.

FIG. 3 illustrates a computer program product in accordance with various embodiments.

FIG. 4 is a block diagram illustrating an example computing device 400, arranged in accordance with at least some embodiments described herein.

DETAILED DESCRIPTION

The following description sets forth various examples along with specific details to provide a thorough understanding of claimed subject matter. It will be understood by those skilled in the art after review and understanding of the present disclosure, however, that claimed subject matter may be practiced without some or more of the specific details disclosed herein. Further, in some circumstances, well-known methods, procedures, systems, components and/or circuits have not been described in detail in order to avoid unnecessarily obscuring claimed subject matter.

In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented here. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the Figures, can be arranged, substituted, combined, and designed in a wide variety of different configurations, all of which are explicitly contemplated and make part of this disclosure.

This disclosure is drawn, inter alia, to methods, apparatus, and systems related to facilitating machine learning of tooth brushing and utilization thereof. The machine learning may include a smart toothbrush (hereon, smart brush)configured to learn an arrangement of teeth of a person, which may include a topography of the teeth. The machine learning may include learning motion of the smart brush as it may be used by the person during the activity of brushing their teeth. The machine learning may include determination of a brushing technique for the person based, at least in part, on the machine learning, where the brushing technique may be communicated to the person to facilitate guidance to the person. The determined brushing technique may facilitate effective brushing of the teeth, which may be tailored to the person.

Prior to turning to the description of the figures, a non-limiting example scenario utilizing the disclosed subject matter may be described. In the non-limiting example scenario, a person may utilize a smart toothbrush (i.e., smart brush) to brush their teeth. In one example, the smart brush may include motion detection devices such as, but not limited to, sensors. For example, the smart brush may include one or more accelerometers configured to detect motion of the smart brush as the person brushes their teeth. Additionally, as part of the motion detection, the smart brush may include gyroscopes configured to detect orientation of the smart brush as the person brushes their teeth.

In another example, the smart brush may include an image capturing device such as, but not limited to, a camera. For example, the smart brush may include one or more image sensors configured to capture images, either still and/or video, as the person brushes their teeth. The images sensors may utilize one or more lenses capable of imaging close up objects (e.g., macro lens).

In yet another example, the smart brush may include a metal detector device such as, but not limited to, a device configured to utilize electromagnetic fields to detect metal. For example, the smart brush may include devices capable of utilizing various metal detection methodologies such as, but not limited to, very low frequency (VLF), pulse induction, and/or beat frequency oscillator methodologies. The smart brush may be able to detect metal, which may be included in the teeth of the person (e.g., metal based fillings, gold crowns, braces, retainers, implants, etc.).

In yet a further example, the smart brush may include a pressure sensing device such as, but not limited to, a device configured to detect a pressure of the smart brush as the person brushes their teeth. For example, the smart brush may include a sensor that may be configured to determine a pressure applied to the teeth by detection of a pressure applied to one or more bristles on the smart brush. Additionally, the detection of the pressure applied to the teeth may be determined by detection of a flexing of a body of the smart brush.

Continuing with the non-limiting scenario, a person may have newly acquired a smart brush. The person may use the newly acquired smart brush to brush their teeth in a manner that may be personal to the person. For example, the person may brush their teeth in a manner that may not be what may be recommended by a dental professional. A brushing habit of the person may include the person brushing their teeth in a back and forth manner and pressing very hard. Additionally, the person may miss their back teeth and brush for a short period of time.

In this scenario, the smart brush may detect and/or determine a wide variety of information related to the tooth brushing. Some examples may include the smart brush detecting the motion of the brushing (i.e., back and forth motion). Some examples may include the smart brush detecting the pressure used by the person during the brushing. Some examples may include the smart brush capturing video and/or still images of the teeth and inside of the mouth. Some example may include the smart brush detecting various material anomalies (e.g., rather than regular teeth enamel, metal fillings, gold crowns, porcelain implants, metal implants, etc.).

In one example, the various information detected by the smart brush of the habit of the person brushing their teeth may be communicated to a remoted device (e.g., remote server, PC, mobile device, dental office, etc.). At the remote device, the information may be processed and/or stored. The communication may be in a variety of methodologies such as, but not limited to, wireless, wired, NFC, Bluetooth, and so forth.

In another example, the various information detected by the smart brush of the habit of the person brushing their teeth may be processed and/or stored at the smart brush. The smart brush may include various processing and/or storage devices/capabilities.

After a period of time (e.g., minutes, hours, days, weeks, months, etc.), the various information may be processed to learn the tooth brushing habits of the person. Once learned, the smart brush may compare the learned habits of the person with a recommendation such as, but not limited to, a tooth brushing recommendation as may be provided by the American Dental Association. The recommendation may be stored in the smart brush and/or the remoted device. Alternatively, the recommendation may be retrieved via a communication medium such as, but not limited to, a wired and/or wireless network.

From the comparison of the various information detected by the smart brush and the recommendation, the smart brush may provide guidance to the person via an interface. For example, the smart brush may have an associated application, which may be executed on a smart phone. The application on the smart phone may provide guidance to the person. The guidance may include generating an interactable three dimensional image of the teeth of the person and graphically providing guidance utilizing audio, text, and/or graphically, which may include animation.

In some examples, a topographical image of the inside of the mouth of the person may be generated from the various sensors. The topographical image may be utilized to provide guidance.

In some examples, various images, still and/or video, may be utilized by the machine learning smart brush to detect issues with soft tissue inside the mouth. For example, the various images may facilitate detection of some form of gum disease. In another example, the various images may facilitate detection of a potential lesion and/or tumor, which may be present within the mouth of the person. The soft tissue may include the tongue, tonsils, gums, etc.

In some examples, the smart brush may facilitate detection of the person following the guidance provided. For example, the smart brush may continue to detect the various information utilizing the wide variety of sensors to determine if the guidance is being followed. After providing the guidance, the smart brush may continue to compare the learned habits to the recommendations.

In some examples, the guidance may be adjusted to substantially match the habits of the person. For example, the person may override the guidance and/or parts of the guidance, which may result in a tailored guidance for the person.

Some communication methodologies may include as internet of things (loT) related methodologies, which may be utilized alone and/or in conjunction with a smart brush to facilitate machine learning of the tooth brushing habits of a person. Accordingly, for example, wireless communication methodologies may be utilized such as, but not limited to, Wi-Fi, IEEE 802 based, Bluetooth® type, Near Field Communication (NFC), radio-frequency identification (RFID), ad-hoc wireless network solutions (e.g., AirDrop), internet of things (IoT) related communication solutions, mesh local area network (LAN) type (e.g., ZigBee, Bluetooth Low Energy, Z-Wave, 6LoWPAN, Thread, etc.), and any combination thereof.

It should be appreciated by one of ordinary skill in the relevant art that a wide variety of machine learning methodologies may be employed including tooth brushing methodologies having AI capabilities to facilitate at least some of the functionality described herein such as, but not limited to, AI capable processors available from Intel Corporation of Santa Clara, California (e.g., Nervana TM type processors), available from Nvidia Corporation of Santa Clara, California (e.g., Volta TM type processors), available from Apple Company of Cupertino, California (e.g., A11 Bionic TM type processors), available from Huawei Technologies Company of Shenzen, Guangdong, China (e.g., Kirin TM type processors), available from Advanced Micro Devices, Inc. of Sunnyvale, California (e.g., Radeon Instinct TM type processors), available from Samsung of Seoul, South Korea (e.g., Exynos TM type processors), and so forth. Accordingly, the claimed subject matter is not limited in these respects. The utilization of machine learning of tooth brushing may facilitate guidance of tooth brushing for a person.

Turning now to FIG. 1, FIG. 1 illustrates an example of a system for machine learning of tooth brushing in accordance with various embodiments. In FIG. 1, a toothbrush learning system 100 may include a toothbrush, a person's mouth having one or more teeth 104, and a sensor 106 communicatively coupled with the toothbrush 102. Additionally, the system 100 may include a processor 108, a toothbrush learning module (TLM) 110, a storage medium 112, and a remote device 114. Further, the system 100 may include a communication network 116.

In FIG. 1, the processor 108 may include the TLM 110, which may include instructions configured to perform the various embodiments disclosed herein. The processor may communicatively coupled with the storage medium 112, which may include data such as, but not limited to, a toothbrushing habits, data received from the toothbrush 102, and/or recommendation of toothbrushing techniques (e.g., some guidance from a dental association/ADA). Shown in FIG. 1, the system 100 may facilitate communication with various the various components, the processor 108, remote device 114, and/or the toothbrush 102, which may be communicatively coupled with the communication network 116 via various communication mediums 118. That is, the processor 108, the toothbrush 102, and/or the remote device 114 may be communicatively coupled with the communication network 116 via the various communication mediums 118,

In accordance with various embodiments, in FIG. 1, the processor 108 may receive data associated with tooth brushing from the sensor 106, which may be communicatively coupled with the toothbrush 102. The TLM 110 may be configured to determine a tooth brushing habit of a person (i.e., brushing of the teeth of the person's mouth 104) based, at least in part, on the received data. The TLM 110 may be configured to compare the tooth brushing habit with a recommendation habit. Based, at least in part, on the comparison, the TLM 110 may provide guidance to the person.

As described above, the sensor 106 may include a wide variety of sensors such as, but not limited to, a gyroscope device, an accelerometer device, an image capture device, an ultrasound device, a spectral imaging device, a metal detector device, a contour imaging device, a magnetic resonance imaging (MRI) type device, a computerized tomography (CT) or computerized axial tomography (CAT) type device, and so forth.

The processor 108 may include a wide variety of processors such as, but not limited to, processors having the capability of machine learning including its variations (e.g., supervised learning, unsupervised learning, semi-supervised learning, neural networks and deep learning, etc.) and/or artificial intelligence (AI) including its variations (e.g., narrow or weak AI, general or strong AI, natural language processing, computer vision, autonomous AI, etc.). Additionally, the processor 108 may be configured to perform self-diagnosis of the toothbrush 102 (e.g., low battery level, bristle wear and/or damage. Further, it should be appreciated that the processor 108 may be communicatively coupled with the toothbrush 102 via the communication network 116 and/or may be included within the toothbrush 102.

The remote device 114 may include a wide variety of remote devices such as, but not limited to, a smartphone, a server, a wearable device (e.g., smartwatch, smart glasses, etc.), a smart television, a display, etc. Accordingly, the claimed subject matter is not limited in this respect.

The communication network 116 may include a wide variety of communication networks such as, but not limited to, a cloud based network, a local area network, a wide area network, a cellular network, a metropolitan area network, a personal area network, home area network, global area network, virtual private network, client server network, peer-to-peer network, cloud network, intranet/extranet, etc.

It should be appreciated that the guidance may be in a wide variety of forms such as, but not limited to, visual indications (e.g., on a display device, a smartmirror, a audio sounds, indicator lights, etc.). For example, the sounds may be variable to facilitate indication of proper brushing.

As a result, a toothbrush may learn and provide guidance to a person for tooth brushing.

FIG. 2 illustrates an operation flow of the various embodiments disclosed herein. FIG. 2 illustrates an operational flow for facilitating machine learning of toothbrushing habits and providing guidance in accordance with various embodiments as described herein. In some portions of the description, illustrative implementations of the method are described with reference to the elements depicted in FIGS. 1-4. However, the described embodiments are not limited to these depictions.

Additionally, FIG. 2 employs block diagrams to illustrate the example methods detailed therein. These block diagrams may set out various functional blocks or actions that may be described as processing steps, functional operations, events and/or acts, etc., and may be performed by hardware, software, and/or firmware. Numerous alternatives to the functional blocks detailed may be practiced in various implementations. For example, intervening actions not shown in the figures and/or additional actions not shown in the figures may be employed and/or some of the actions shown in one figure may be operated using techniques discussed with respect to another figure. Additionally, in some examples, the actions shown in these figures may be operated using parallel processing techniques. The above described, and other not described, rearrangements, substitutions, changes, modifications, etc., may be made without departing from the scope of the claimed subject matter.

In some examples, operational flow 200 may be employed as part of a system for facilitating machine learning of toothbrushing habits and providing guidance as described herein. Beginning at block 202 (“Receive Information From Sensors”), at toothbrush learning module (TLM 118), information from a sensor may be received, where the data may include a variety of data from a variety of sensors.

Continuing from block 202 to block 204 (“Learn Brushing Habit”), the TLM 110 may determine a tooth brushing habit of a person based, at least in part, on the received data.

Continuing from block 204 to block 206 (“Compare Learned Brushing Habit with Recommendation”), the TLM 110 may compare the determined tooth brushing habit with a recommended habit.

Continuing from block 206 to block 208 (“Provide Guidance Based on Comparison”), the TLM 110 may provide guidance to the person based, at least in part, on the comparison.

In general, the operational flow described with respect to FIG. 2 and elsewhere herein may be implemented as a computer program product, executable on any suitable computing system, or the like. For example, a computer program product for facilitating machine learning of toothbrushing habits and providing guidance may be provided. Example computer program products may be described with respect to FIG. 3 and elsewhere herein.

FIG. 3 illustrates a computer program product in accordance with various embodiments. FIG. 3 illustrates an example computer program product 300, arranged in accordance with at least some embodiments described herein. Computer program product 300 may include machine readable non-transitory medium having stored therein instructions that, when executed, cause the machine to facilitate machine learning of machine learning of toothbrushing habits and providing guidance according to the processes and methods discussed herein. Computer program product 300 may include a signal bearing medium 302. Signal bearing medium 302 may include one or more machine-readable instructions 304 which, when executed by one or more processors, may operatively enable a computing device to provide the functionality described herein. In various examples, the devices discussed herein may use some or all of the machine-readable instructions.

In some examples, the machine-readable instructions 304 may include an toothbrush learning module (TLM). In some examples, the machine readable medium 304 may facilitate the TLM to receive receive data associated with tooth brushing from a sensor included in a toothbrush.

In some examples, the machine readable medium 304 may determine a tooth brushing habit of the person based, at least in part, on the received data.

In some examples, the machine readable medium 304 may compare the determined tooth brushing habit with a recommended habit.

In some examples, the machine readable medium 304 may provide guidance to the person based, at least in part, on the comparison to facilitate machine learning of toothbrushing habits and providing guidance.

In some implementations, signal bearing medium 302 may encompass a computer-readable medium 306, such as, but not limited to, a hard disk drive, a Compact Disc (CD), a Digital Versatile Disk (DVD), a Universal Serial Bus (USB) drive, a digital tape, memory, etc. In some implementations, the signal bearing medium 302 may encompass a recordable medium 308, such as, but not limited to, memory, read/write (R/W) CDs, R/W DVDs, etc. In some implementations, the signal bearing medium 302 may encompass a communications medium 310, such as, but not limited to, a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communication link, a wireless communication link, etc.). In some examples, the signal bearing medium 302 may encompass a machine readable non-transitory medium.

In general, the methods described with respect to FIG. 3 and elsewhere herein may be implemented in any suitable computing system. Example systems may be described with respect to FIG. 4 and elsewhere herein. In general, the system may be configured to facilitate a toothbrush learning module (TLM) in accordance with various embodiments.

As a result, the present claimed subject matter may be configured to provide guidance to a person for toothbrushing. In some embodiments, present claimed subject matter may be configured to learn the toothbrushing habits of the person from various sensor data. Accordingly, improvements in oral hygiene and/or health may be facilitated and provided.

FIG. 4 is a block diagram illustrating an example computing device 400, arranged in accordance with at least some embodiments described herein. In various examples, computing device 400 may be configured to learn tooth brushing and provide guidance as discussed herein. In one example of a basic configuration 401, computing device 400 may include one or more processors 410 and a system memory 420. A memory bus 430 can be used for communicating between the one or more processors 410 and the system memory 420.

Depending on the desired configuration, the one or more processors 410 may be of any type including but not limited to a microprocessor (P), a microcontroller (uC), a digital signal processor (DSP), or any combination thereof. Additionally, the microprocessors may include AI capable processors such as those previously mentioned. The one or more processors 410 may include one or more levels of caching, such as a level one cache 411 and a level two cache 412, a processor core 413, and registers 414. The processor core 413 can include an arithmetic logic unit (ALU), a floating point unit (FPU), a digital signal processing core (DSP Core), or any combination thereof. A memory controller 415 can also be used with the one or more processors 410, or in some implementations the memory controller 415 can be an internal part of the processor 410.

Depending on the desired configuration, the system memory 420 may be of any type including but not limited to volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.) or any combination thereof. The system memory 420 may include an operating system 421, one or more applications 422, and program data 424. The one or more applications 422 may include tooth brushing learning application 423 that can be arranged to perform the functions, actions, and/or operations as described herein including the functional blocks, actions, and/or operations described herein. The program data 424 may include tooth brushing habit data 425 for use with the include toothbrush learning module application 423. In some example embodiments, the one or more applications 422 may be arranged to operate with the program data 424 on the operating system 421. This described basic configuration 401 is illustrated in FIG. 4 by those components within dashed line.

Computing device 400 may have additional features or functionality, and additional interfaces to facilitate communications between the basic configuration 401 and any required devices and interfaces. For example, a bus/interface controller 440 may be used to facilitate communications between the basic configuration 401 and one or more data storage devices 450 via a storage interface bus 441. The one or more data storage devices 450 may be removable storage devices 451, non-removable storage devices 452, or a combination thereof. Examples of removable storage and non-removable storage devices include magnetic disk devices such as flexible disk drives and hard-disk drives (HDD), optical disk drives such as compact disk (CD) drives or digital versatile disk (DVD) drives, solid state drives (SSD), and tape drives to name a few. Example computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data.

The system memory 420, the removable storage 451 and the non-removable storage 452 are all examples of computer storage media. The computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store the desired information and which may be accessed by the computing device 400. Any such computer storage media may be part of the computing device 400.

The computing device 400 may also include an interface bus 442 for facilitating communication from various interface devices (e.g., output interfaces, peripheral interfaces, and communication interfaces) to the basic configuration 401 via the bus/interface controller 440. Example output interfaces 460 may include a graphics processing unit 461 and an audio processing unit 462, which may be configured to communicate to various external devices such as a display or speakers via one or more A/V ports 463. Example peripheral interfaces 470 may include a serial interface controller 471 or a parallel interface controller 472, which may be configured to communicate with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device, etc.) or other peripheral devices (e.g., printer, scanner, etc.) via one or more I/O ports 473. An example communication interface 480 includes a network controller 481, which may be arranged to facilitate communications with one or more other computing devices 483 over a network communication via one or more communication ports 482. A communication connection is one example of a communication media. The communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and may include any information delivery media. A “modulated data signal” may be a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared (IR) and other wireless media. The term computer readable media as used herein may include both storage media and communication media.

The computing device 400 may be implemented as a portion of a small-form factor portable (or mobile) electronic device such as a cell phone, a mobile phone, a tablet device, a laptop computer, a personal data assistant (PDA), a personal media player device, a wireless web-watch device, a personal headset device, an application specific device, or a hybrid device that includes any of the above functions. The computing device 400 may also be implemented as a personal computer including both laptop computer and non-laptop computer configurations. In addition, the computing device 400 may be implemented as part of a wireless base station or other wireless system or device.

Some portions of the foregoing detailed description are presented in terms of algorithms or symbolic representations of operations on data bits or binary digital signals stored within a computing system memory, such as a computer memory. These algorithmic descriptions or representations are examples of techniques used by those of ordinary skill in the data processing arts to convey the substance of their work to others skilled in the art. An algorithm is here, and generally, considered to be a self-consistent sequence of operations or similar processing leading to a desired result. In this context, operations or processing involve physical manipulation of physical quantities. Typically, although not necessarily, such quantities may take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared or otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to such signals as bits, data, values, elements, symbols, characters, terms, numbers, numerals or the like. It should be understood, however, that all of these and similar terms are to be associated with appropriate physical quantities and are merely convenient labels. Unless specifically stated otherwise, as apparent from the following discussion, it is appreciated that throughout this specification discussion utilizing terms such as “processing,” “computing,” “calculating,” “determining” or the like refer to actions or processes of a computing device that manipulates or transforms data represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the computing device.

Claimed subject matter is not limited in scope to the particular implementations described herein. For example, some implementations may be in hardware, such as those employed to operate on a device or combination of devices, for example, whereas other implementations may be in software and/or firmware. Likewise, although claimed subject matter is not limited in scope in this respect, some implementations may include one or more articles, such as a signal bearing medium, a storage medium and/or storage media. This storage media, such as CD-ROMs, computer disks, flash memory, or the like, for example, may have instructions stored thereon that, when executed by a computing device such as a computing system, computing platform, or other system, for example, may result in execution of a processor in accordance with claimed subject matter, such as one of the implementations previously described, for example. As one possibility, a computing device may include one or more processing units or processors, one or more input/output devices, such as a display, a keyboard and/or a mouse, and one or more memories, such as static random access memory, dynamic random access memory, flash memory, and/or a hard drive.

There is little distinction left between hardware and software implementations of aspects of systems; the use of hardware or software is generally (but not always, in that in certain contexts the choice between hardware and software can become significant) a design choice representing cost vs. efficiency tradeoffs. There are various vehicles by which processes and/or systems and/or other technologies described herein can be affected (e.g., hardware, software, and/or firmware), and that the preferred vehicle will vary with the context in which the processes and/or systems and/or other technologies are deployed. For example, if an implementer determines that speed and accuracy are paramount, the implementer may opt for a mainly hardware and/or firmware vehicle; if flexibility is paramount, the implementer may opt for a mainly software implementation; or, yet again alternatively, the implementer may opt for some combination of hardware, software, and/or firmware.

The foregoing detailed description has set forth various embodiments of the devices and/or processes via the use of block diagrams, flowcharts, and/or examples. Insofar as such block diagrams, flowcharts, and/or examples contain one or more functions and/or operations, it will be understood by those within the art that each function and/or operation within such block diagrams, flowcharts, or examples can be implemented, individually and/or collectively, by a wide range of hardware, software, firmware, or virtually any combination thereof. In one embodiment, several portions of the subject matter described herein may be implemented via Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs), digital signal processors (DSPs), or other integrated formats. However, those skilled in the art will recognize that some aspects of the embodiments disclosed herein, in whole or in part, can be equivalently implemented in integrated circuits, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more microprocessors), as firmware, or as virtually any combination thereof, and that designing the circuitry and/or writing the code for the software and/or firmware would be well within the skill of one of skilled in the art in light of this disclosure. In addition, those skilled in the art will appreciate that the mechanisms of the subject matter described herein are capable of being distributed as a product in a variety of forms, and that an illustrative embodiment of the subject matter described herein applies regardless of the particular type of signal bearing medium used to actually carry out the distribution. Examples of a signal bearing medium include, but are not limited to, the following: a recordable type medium such as a flexible disk, a hard disk drive (HDD), a Compact Disc (CD), a Digital Versatile Disk (DVD), a digital tape, a computer memory, etc.; and a transmission type medium such as a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link, etc.).

Those skilled in the art will recognize that it is common within the art to describe devices and/or processes in the fashion set forth herein, and thereafter use engineering practices to integrate such described devices and/or processes into data processing systems. That is, at least a portion of the devices and/or processes described herein can be integrated into a data processing system via a reasonable amount of experimentation. Those having skill in the art will recognize that a typical data processing system generally includes one or more of a system unit housing, a video display device, a memory such as volatile and non-volatile memory, processors such as microprocessors and digital signal processors, computational entities such as operating systems, drivers, graphical user interfaces, and applications programs, one or more interaction devices, such as a touch pad or screen, and/or control systems including feedback loops and control motors (e.g., feedback for sensing position and/or velocity; control motors for moving and/or adjusting components and/or quantities). A typical data processing system may be implemented utilizing any suitable commercially available components, such as those typically found in data computing/communication and/or network computing/communication systems.

The herein described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that such depicted architectures are merely exemplary, and that in fact many other architectures can be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as “associated with” each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being “operably connected”, or “operably coupled”, to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being “operably couplable”, to each other to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interacting and/or logically interactable components.

With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity.

It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to inventions containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B”.

Reference in the specification to “an implementation,” “one implementation,” “some implementations,” or “other implementations” may mean that a particular feature, structure, or characteristic described in connection with one or more implementations may be included in at least some implementations, but not necessarily in all implementations. The various appearances of “an implementation,” “one implementation,” or “some implementations” in the preceding description are not necessarily all referring to the same implementations.

While certain exemplary techniques have been described and shown herein using various methods and systems, it should be understood by those skilled in the art that various other modifications may be made, and equivalents may be substituted, without departing from claimed subject matter. Additionally, many modifications may be made to adapt a particular situation to the teachings of claimed subject matter without departing from the central concept described herein. Therefore, it is intended that claimed subject matter is not limited to the particular examples disclosed, but that such claimed subject matter also may include all implementations falling within the scope of the appended claims, and equivalents thereof.

MACHINE LEARNING TOOTH BRUSH

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