System

Abstract

To provide a system which enables a user to accurately grasp the quality of his or her brushing and to reduce insufficient brushing.SOLUTION: A system according to the embodiment includes a photography part, an analysis part, a scoring part, and a feedback part. The photography part photographs a user's teeth with a camera mounted on a toothbrush. The analysis part analyzes an image photographed by the photography part and identifies a part of insufficient brushing. The scoring part calculates a brushing score on the basis of the insufficient brushing part identified by the analysis part. The feedback part notifies the user of the score calculated by the scoring part.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional techniques, it is difficult for users to accurately assess the quality of their brushing, and there is a risk that some areas will not be brushed properly.

[0005] The system of the embodiment aims to enable the user to accurately grasp the quality of their brushing and reduce areas that are not brushed properly. [Means for solving the problem]

[0006] The system according to the embodiment includes an image capturing unit, an analysis unit, a scoring unit, and a feedback unit. The image capturing unit captures an image of the user's teeth with a camera mounted on the toothbrush. The analysis unit analyzes the image captured by the image capturing unit and identifies areas that have not been brushed. The scoring unit calculates a brushing score based on the areas that have not been brushed identified by the analysis unit. The feedback unit notifies the user of the score calculated by the scoring unit. Effect of the Invention

[0007] The system according to the embodiment enables the user to accurately grasp the quality of his / her brushing and reduce areas that are not brushed properly. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The toothbrush system according to the embodiment of the present invention is a system that is equipped with an AI camera on a toothbrush and is used to discover areas that have not been brushed and score brushing. This toothbrush system photographs the user's teeth, and AI analyzes the image to identify areas that have not been brushed, scores the brushing, and provides feedback to the user. For example, an AI camera mounted on a toothbrush photographs the user's teeth. At this time, the camera photographs the surface of the teeth in detail and acquires image data. For example, it is possible to photograph the state of dirt and plaque on the surface of the teeth. Next, the AI ​​analyzes the photographed image. The AI ​​identifies areas that have not been brushed based on the image data using image recognition technology and machine learning algorithms. For example, it is possible to detect plaque and dirt remaining on the surface of the teeth and identify areas that have not been brushed. Furthermore, the AI ​​scores the brushing. The AI ​​analyzes the user's brushing movements and time and calculates a score. For example, it is possible to evaluate the brushing time, strength, range of movement, etc., and calculate a score. Finally, the AI ​​provides feedback to the user. AI notifies the user of missed brushing areas and brushing scores via a smartphone app and advises on areas for improvement. For example, it can specifically indicate missed brushing areas and advise on how to brush. This mechanism enables users to brush their teeth effectively and improve oral hygiene. For example, identifying missed brushing areas and advising on appropriate brushing methods helps prevent cavities and periodontal disease. In addition, brushing scores enable users to understand and improve the quality of their brushing. This allows the toothbrush system to effectively brush the user's teeth and improve oral hygiene.

[0029] The toothbrush system according to the embodiment includes a photographing unit, an analysis unit, a scoring unit, and a feedback unit. The photographing unit photographs the user's teeth with a camera mounted on the toothbrush. The photographing unit photographs the surface of the teeth in detail, for example, and acquires image data. For example, the photographing unit can photograph the state of dirt and plaque on the surface of the teeth. The analysis unit analyzes the image photographed by the photographing unit and identifies areas that have not been brushed. The analysis unit analyzes the image data using, for example, image recognition technology. For example, deep learning or computer vision technology can be used to detect plaque or dirt remaining on the surface of the teeth and identify areas that have not been brushed. The analysis unit can also identify areas that have not been brushed using machine learning algorithms. For example, algorithms such as convolutional neural network (CNN) or support vector machine (SVM) can be used to identify areas that have not been brushed with high accuracy. The scoring unit calculates a brushing score based on the areas that have not been brushed identified by the analysis unit. The scoring unit calculates a score using, for example, brushing time, strength of force, and range of movement as evaluation criteria. For example, the brushing time, strength of force, range of movement, etc. can be evaluated and a score can be calculated. The feedback unit notifies the user of the score calculated by the scoring unit. The feedback unit notifies the user, for example, through a smartphone app. For example, the feedback unit can notify the user of areas that have not been brushed and the brushing score through the smartphone app and provide advice on areas to be improved. In this way, the toothbrush system according to the embodiment can effectively brush the user's teeth and improve oral hygiene.

[0030] The photographing unit photographs the user's teeth with a camera mounted on the toothbrush. The photographing unit, for example, photographs the surface of the teeth in detail and acquires image data. Specifically, a small, high-resolution camera is built into the head of the toothbrush, and continuously photographs the surface of the user's teeth while brushing. This camera is equipped with an LED light, and can capture clear images even in a dark oral cavity. In addition, the camera is designed to be waterproof, and can operate without problems even if water or toothpaste is splashed on it while using the toothbrush. The photographing unit can photograph the dirt and plaque on the surface of the teeth in detail. For example, it can photograph fine scratches on the tooth enamel, food debris remaining between the teeth, and the condition of the gums. This allows the user to grasp the condition of their teeth in detail, enabling effective brushing. The photographed image data is stored in the toothbrush's built-in memory in real time and transmitted to a smartphone or tablet via a wireless communication function. This allows the user to check the condition of their teeth through a dedicated application. In addition, the photographing unit can also record the user's brushing behavior and provide data for later analysis. This allows users to review their brushing habits and receive specific advice on how to improve them.

[0031] The analysis unit analyzes the image captured by the image capture unit and identifies areas that have not been brushed. The analysis unit analyzes the image data using, for example, image recognition technology. Specifically, deep learning and computer vision technology are used to detect plaque and dirt remaining on the surface of the teeth and identify areas that have not been brushed. Deep learning technology uses a convolutional neural network (CNN) to extract features from images of teeth and identify patterns of plaque and dirt. This makes it possible to identify areas that have not been brushed with high accuracy. In addition, machine learning algorithms such as a support vector machine (SVM) can be used to classify different types of dirt and plaque and identify areas that have not been brushed. The analysis unit combines these technologies to analyze the condition of the user's teeth in detail and identify areas that have not been brushed. Furthermore, the analysis unit can also analyze tendencies of areas that have not been brushed, taking into account past brushing data and the user's brushing habits. For example, if there is a lot of unbrushed areas on certain teeth or between the teeth, the cause can be identified and specific improvement measures can be proposed to the user. This enables the analysis unit to maximize the brushing effectiveness of the user and contribute to improved oral hygiene.

[0032] The scoring unit calculates a brushing score based on the unbrushed areas identified by the analysis unit. The scoring unit calculates the score using, for example, brushing time, strength, and range of movement as evaluation criteria. Specifically, the brushing time is measured by measuring how long the user brushed each tooth, and the score is calculated based on the appropriate time. The strength of the force is measured by using a pressure sensor built into the toothbrush to measure how much force the user brushes, and the score is calculated based on the appropriate strength. The range of movement is measured by using a gyro sensor and acceleration sensor built into the toothbrush to record the user's brushing action, and the score is calculated based on the appropriate range of movement. The scoring unit calculates the user's brushing score by comprehensively considering these evaluation criteria. Furthermore, the scoring unit can also evaluate the degree of improvement of the user's brushing habits by comparing the score with past scores and data of other users. For example, if the score is improved compared to past scores, it indicates that the user's brushing habits have improved, and conversely, if the score is decreased, it indicates that improvement is necessary. This allows the scoring unit to provide specific feedback to the user and encourage improvement of brushing habits.

[0033] The feedback unit notifies the user of the score calculated by the scoring unit. The feedback unit notifies the user, for example, through a smartphone app. Specifically, the feedback unit notifies the user of the areas that have not been brushed and the brushing score through a dedicated smartphone app, and advises the user on how to improve. The app provides an interface that allows the user to check the images taken after brushing and the analysis results, and visually indicates the areas that have not been brushed. In addition, the app displays the brushing score and the progress of the past scores in a graph, so that the user can check his / her progress. Furthermore, the feedback unit suggests specific improvement measures to the user. For example, if there is a lot of brushing left on a specific tooth or between the teeth, the feedback unit advises the user to brush that area with priority. In addition, the feedback unit suggests appropriate methods for the brushing force and range of movement, and supports the user to brush effectively. The feedback unit continuously monitors the user's brushing habits and provides regular feedback to support the user in improving oral hygiene. As a result, the feedback unit can provide specific information for the user to review and improve their brushing habits, thereby contributing to the improvement of oral hygiene.

[0034] The analysis unit can analyze the image data using image recognition technology. Image recognition technology includes, for example, deep learning and computer vision technology. For example, by using deep learning, plaque and dirt remaining on the surface of the teeth can be detected with high accuracy. In addition, by using computer vision technology, detailed image analysis of the surface of the teeth is possible. As a result, by using image recognition technology, it is possible to accurately identify areas that have not been brushed.

[0035] The analysis unit can identify areas that have not been brushed using a machine learning algorithm. Machine learning algorithms include, for example, convolutional neural networks (CNNs) and support vector machines (SVMs). For example, CNNs can be used to detect plaque and dirt remaining on the surfaces of teeth with high accuracy. Also, SVMs can be used to identify areas that have not been brushed. As a result, machine learning algorithms can be used to identify areas that have not been brushed with high accuracy.

[0036] The scoring unit can calculate a score using brushing time, strength of force, and range of movement as evaluation criteria. Brushing time includes, for example, the time it takes to brush once and a recommended brushing time. Strength of force includes, for example, the use of a pressure sensor when brushing and an appropriate range of force. Range of movement includes, for example, the trajectory of brush movement and the speed of movement. This allows accurate score calculation by using detailed brushing evaluation criteria.

[0037] The feedback unit can notify the user through a smartphone app. The smartphone app includes, for example, a notification method and a user interface. For example, the smartphone app can notify the user of areas that have not been brushed and the brushing score, and advise the user on areas to be improved. This allows the user to easily receive feedback by notifying the user through the smartphone app.

[0038] The feedback unit can specifically indicate areas that have not been brushed and provide advice on how to improve. Methods for specifically indicating areas that have not been brushed include, for example, highlighting on an image or explaining in text. Methods for providing advice on how to improve include, for example, brushing method and type of toothbrush to use. This allows the user to effectively improve their brushing by providing specific advice.

[0039] The imaging unit can simultaneously capture not only the tooth surface but also the condition of the gums. For example, the imaging unit can simultaneously capture the condition of the tooth surface and the gums to grasp the overall condition of the oral cavity. In addition, the imaging unit can also obtain detailed images of the gums to detect inflammation or swelling of the gums. Furthermore, the health conditions of the tooth surface and the gums can be compared to perform a comprehensive evaluation. In this way, the gum condition can be captured simultaneously to grasp the overall condition of the oral cavity.

[0040] The imaging unit can capture images of the internal structure of the tooth using light of different wavelengths. The imaging unit can capture images of the internal structure of the tooth using infrared light, for example, to detect hidden cavities. It can also capture images of plaque on the surface of the tooth using ultraviolet light to highlight it. Furthermore, it can also capture images of the inside and surface of the tooth at the same time by combining visible light and infrared light. This allows the internal structure of the tooth to be captured in detail by using light of different wavelengths.

[0041] The imaging unit can measure the temperature and humidity in the user's oral cavity at the same time as imaging. For example, the imaging unit can measure the temperature in the oral cavity at the same time as imaging to evaluate the health condition. Also, the imaging unit can measure the humidity in the oral cavity at the same time as imaging to evaluate the dryness condition. Furthermore, the temperature and humidity data can be combined to evaluate the overall oral health condition. In this way, the temperature and humidity in the oral cavity can be measured at the same time to evaluate the overall oral health condition.

[0042] The imaging unit can acquire voice data from inside the user's mouth during imaging and analyze the sounds of tooth brushing. For example, the imaging unit acquires voice data during tooth brushing and analyzes the strength of brushing. In addition, the imaging unit can evaluate the rhythm and speed of brushing based on the voice data. Furthermore, the imaging unit can analyze the voice data and suggest an appropriate brushing method. In this way, the strength and rhythm of brushing can be evaluated by analyzing the voice data.

[0043] During the analysis, the analysis unit can also detect minute scratches and cracks on the tooth surface. For example, the analysis unit can detect minute scratches on the tooth surface and address them early. It can also detect cracks on the tooth surface and suggest appropriate treatment. Furthermore, it can evaluate the overall health of the teeth based on data on minute scratches and cracks. This allows early action to be taken by detecting minute scratches and cracks.

[0044] During the analysis, the analysis unit can detect changes in tooth color and transparency and evaluate the health condition. For example, the analysis unit can detect changes in tooth color and evaluate the need for whitening. It can also detect changes in tooth transparency and evaluate the condition of the enamel. Furthermore, it can also evaluate the overall health condition of the teeth based on the color and transparency data. In this way, the overall health condition of the teeth can be evaluated by detecting changes in tooth color and transparency.

[0045] During the analysis, the analysis unit can provide the analysis results by comparing with the user's past tooth brushing data. For example, the analysis unit can compare with the user's past tooth brushing data and evaluate the progress. Also, the analysis unit can specifically indicate areas for improvement based on the past data. Furthermore, the analysis unit can compare the user's past data with the current data and perform a comprehensive evaluation. This allows the progress to be evaluated by comparing with the past data.

[0046] During the analysis, the analysis unit can identify areas that have not been brushed based on the user's meal history. The analysis unit can identify areas that have not been brushed based on the user's meal history, for example. It can also identify areas that are likely to be left unbrushed due to specific foods from the meal history. Furthermore, it can combine the meal history and the data on unbrushed areas to perform a comprehensive evaluation. In this way, it is possible to more accurately identify areas that have not been brushed by taking the meal history into consideration.

[0047] The scoring unit can also include the user's tooth brushing frequency and habits in the evaluation criteria when scoring. For example, the scoring unit includes the user's tooth brushing frequency in the evaluation criteria and calculates a score. The scoring unit can also include the user's tooth brushing habits (time of day and method) in the evaluation criteria and calculate a score. Furthermore, the tooth brushing frequency and habit data can be combined to calculate an overall score. In this way, by including the tooth brushing frequency and habits in the evaluation criteria, a more accurate score can be calculated.

[0048] The scoring unit can calculate a score that comprehensively evaluates the dental health condition of the user during scoring. For example, the scoring unit comprehensively evaluates the dental health condition of the user and calculates a score. Also, the scoring unit can calculate a detailed score based on the dental health condition data. Furthermore, the scoring unit can calculate a comprehensive score using a health condition evaluation criterion. In this way, a more detailed score can be provided by comprehensively evaluating the dental health condition.

[0049] The scoring unit can calculate the score taking into account the age and gender of the user when scoring. The scoring unit can, for example, calculate the score using an appropriate evaluation criterion taking into account the age of the user. The scoring unit can also calculate the score using an appropriate evaluation criterion taking into account the gender of the user. Furthermore, the scoring unit can combine data on age and gender to calculate an overall score. This allows a more appropriate score to be provided by taking into account age and gender.

[0050] The scoring unit can calculate the score taking into account the user's lifestyle habits and stress level when scoring. The scoring unit can, for example, calculate the score using an appropriate evaluation criterion taking into account the user's lifestyle habits. The scoring unit can also calculate the score using an appropriate evaluation criterion taking into account the user's stress level. Furthermore, the scoring unit can combine data on the lifestyle habits and the stress level to calculate an overall score. This makes it possible to provide a more appropriate score by taking into account the lifestyle habits and stress level.

[0051] The feedback unit can provide optimal advice by referring to the user's past feedback history when providing feedback. The feedback unit can provide optimal advice based on the user's past feedback history, for example. Also, the feedback unit can specifically indicate areas for improvement from the past feedback history. Furthermore, the feedback history can be combined with current data to provide comprehensive advice. In this way, more appropriate advice can be provided by referring to the past feedback history.

[0052] The feedback unit can display the user's tooth brushing progress in real time at the time of feedback. For example, the feedback unit can display the user's tooth brushing progress in real time and provide appropriate advice. Also, the feedback unit can display the tooth brushing progress in real time and specifically indicate areas that have not been brushed. Furthermore, the progress can be displayed in real time to support the user in proper brushing. In this way, by displaying the progress in real time, it is possible to support the user in proper brushing.

[0053] The feedback unit can select the optimal display method in consideration of the device information of the user when providing feedback. For example, when the user is using a smartphone, the feedback unit can provide a display method that matches the screen size. Also, when the user is using a tablet, the feedback unit can provide a display method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the feedback unit can provide a display method that is simple and has high visibility. In this way, the user can receive feedback in the optimal display method by considering the device information.

[0054] The feedback unit may provide a function for sharing feedback with the user's family and friends. For example, the feedback unit may provide a function for sharing the user's feedback with family and friends and encouraging each other. The feedback may also be shared and advice may be received from family and friends. Furthermore, a function for sharing feedback and improving oral hygiene together with family and friends may be provided. This allows the user to improve oral hygiene together with family and friends by sharing feedback.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The toothbrush system may further include a history management unit for monitoring the user's dental health condition over the long term. The history management unit stores the user's past brushing data and analysis results, and tracks changes in the long term health condition. For example, the improvement or deterioration of the dental health condition may be evaluated based on the past data. The history management unit may also record changes in the user's brushing habits and provide appropriate advice. Furthermore, the history management unit may visually display the user's dental health condition in graphs and charts, allowing the user to easily grasp his or her progress. This allows the user's dental health condition to be comprehensively managed through long term monitoring.

[0057] The analysis unit can perform the analysis taking into account the dietary history of the user. For example, if the user frequently eats a particular food, the impact of that food on the teeth can be reflected in the analysis. Furthermore, the dietary history can also identify areas that are likely to be left unbrushed due to a particular food. By taking the dietary history into consideration, more accurate analysis results can be provided. Furthermore, the analysis unit can advise the user on an appropriate brushing method and improvements to the diet based on the user's dietary history. This can comprehensively improve the user's diet and brushing habits.

[0058] The scoring unit can also include the user's tooth brushing frequency and habits in the evaluation criteria. For example, the user's tooth brushing frequency can be included in the evaluation criteria to calculate a score. The user's tooth brushing habits (time of day and method) can also be included in the evaluation criteria to calculate a score. Furthermore, the tooth brushing frequency and habit data can be combined to calculate an overall score. In this way, by including the tooth brushing frequency and habits in the evaluation criteria, a more accurate score can be calculated.

[0059] When providing feedback, the feedback unit can provide optimal advice by referring to the user's past feedback history. For example, optimal advice can be provided based on the user's past feedback history. Also, specific points for improvement can be indicated from the past feedback history. Furthermore, comprehensive advice can be provided by combining the feedback history with current data. In this way, more appropriate advice can be provided by referring to the past feedback history.

[0060] The imaging unit can simultaneously capture not only the tooth surface but also the condition of the gums. For example, the condition of the tooth surface and gums can be captured simultaneously to grasp the overall condition of the oral cavity. In addition, detailed images of the gums can be obtained to detect inflammation or swelling of the gums. Furthermore, the health conditions of the tooth surface and gums can be compared to perform a comprehensive evaluation. This allows the gum condition to be captured simultaneously to grasp the overall condition of the oral cavity.

[0061] The imaging unit can capture images of the internal structure of the tooth using light of different wavelengths. For example, infrared light can be used to capture images of the internal structure of the tooth to detect hidden cavities. In addition, ultraviolet light can be used to highlight and capture images of plaque on the surface of the tooth. Furthermore, visible light and infrared light can be combined to simultaneously capture images of the internal and surface conditions of the tooth. This allows the internal structure of the tooth to be captured in detail by using light of different wavelengths.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The photographing unit photographs the user's teeth with a camera mounted on the toothbrush. The photographing unit, for example, photographs the surface of the teeth in detail and acquires image data. For example, it can photograph the state of dirt and plaque on the surface of the teeth. Step 2: The analysis unit analyzes the image captured by the imaging unit and identifies areas that have not been brushed. The analysis unit analyzes the image data using, for example, image recognition technology. For example, deep learning or computer vision technology can be used to detect plaque or dirt remaining on the surface of the teeth and identify areas that have not been brushed. The analysis unit can also identify areas that have not been brushed using machine learning algorithms. For example, algorithms such as convolutional neural networks (CNN) and support vector machines (SVM) can be used to identify areas that have not been brushed with high accuracy. Step 3: The scoring unit calculates a brushing score based on the areas that have not been brushed identified by the analysis unit. The scoring unit calculates a score using, for example, brushing time, strength, and range of movement as evaluation criteria. For example, the score can be calculated by evaluating brushing time, strength, and range of movement. Step 4: The feedback unit notifies the user of the score calculated by the scoring unit. The feedback unit notifies the user, for example, through a smartphone app. For example, the feedback unit can notify the user of areas that have not been brushed and the brushing score through the smartphone app and provide advice on areas for improvement.

[0064] (Example 2) The toothbrush system according to the embodiment of the present invention is a system that is equipped with an AI camera on a toothbrush and is used to discover areas that have not been brushed and score brushing. This toothbrush system photographs the user's teeth, and AI analyzes the image to identify areas that have not been brushed, scores the brushing, and provides feedback to the user. For example, an AI camera mounted on a toothbrush photographs the user's teeth. At this time, the camera photographs the surface of the teeth in detail and acquires image data. For example, it is possible to photograph the state of dirt and plaque on the surface of the teeth. Next, the AI ​​analyzes the photographed image. The AI ​​identifies areas that have not been brushed based on the image data using image recognition technology and machine learning algorithms. For example, it is possible to detect plaque and dirt remaining on the surface of the teeth and identify areas that have not been brushed. Furthermore, the AI ​​scores the brushing. The AI ​​analyzes the user's brushing movements and time and calculates a score. For example, it is possible to evaluate the brushing time, strength, range of movement, etc., and calculate a score. Finally, the AI ​​provides feedback to the user. AI notifies the user of missed brushing areas and brushing scores via a smartphone app and advises on areas for improvement. For example, it can specifically indicate missed brushing areas and advise on how to brush. This mechanism enables users to brush their teeth effectively and improve oral hygiene. For example, identifying missed brushing areas and advising on appropriate brushing methods helps prevent cavities and periodontal disease. In addition, brushing scores enable users to understand and improve the quality of their brushing. This allows the toothbrush system to effectively brush the user's teeth and improve oral hygiene.

[0065] The toothbrush system according to the embodiment includes a photographing unit, an analysis unit, a scoring unit, and a feedback unit. The photographing unit photographs the user's teeth with a camera mounted on the toothbrush. The photographing unit photographs the surface of the teeth in detail, for example, and acquires image data. For example, the photographing unit can photograph the state of dirt and plaque on the surface of the teeth. The analysis unit analyzes the image photographed by the photographing unit and identifies areas that have not been brushed. The analysis unit analyzes the image data using, for example, image recognition technology. For example, deep learning or computer vision technology can be used to detect plaque or dirt remaining on the surface of the teeth and identify areas that have not been brushed. The analysis unit can also identify areas that have not been brushed using machine learning algorithms. For example, algorithms such as convolutional neural network (CNN) or support vector machine (SVM) can be used to identify areas that have not been brushed with high accuracy. The scoring unit calculates a brushing score based on the areas that have not been brushed identified by the analysis unit. The scoring unit calculates a score using, for example, brushing time, strength of force, and range of movement as evaluation criteria. For example, the brushing time, strength of force, range of movement, etc. can be evaluated and a score can be calculated. The feedback unit notifies the user of the score calculated by the scoring unit. The feedback unit notifies the user, for example, through a smartphone app. For example, the feedback unit can notify the user of areas that have not been brushed and the brushing score through the smartphone app and provide advice on areas to be improved. In this way, the toothbrush system according to the embodiment can effectively brush the user's teeth and improve oral hygiene.

[0066] The photographing unit photographs the user's teeth with a camera mounted on the toothbrush. The photographing unit, for example, photographs the surface of the teeth in detail and acquires image data. Specifically, a small, high-resolution camera is built into the head of the toothbrush, and continuously photographs the surface of the user's teeth while brushing. This camera is equipped with an LED light, and can capture clear images even in a dark oral cavity. In addition, the camera is designed to be waterproof, and can operate without problems even if water or toothpaste is splashed on it while using the toothbrush. The photographing unit can photograph the dirt and plaque on the surface of the teeth in detail. For example, it can photograph fine scratches on the tooth enamel, food debris remaining between the teeth, and the condition of the gums. This allows the user to grasp the condition of their teeth in detail, enabling effective brushing. The photographed image data is stored in the toothbrush's built-in memory in real time and transmitted to a smartphone or tablet via a wireless communication function. This allows the user to check the condition of their teeth through a dedicated application. In addition, the photographing unit can also record the user's brushing behavior and provide data for later analysis. This allows users to review their brushing habits and receive specific advice on how to improve them.

[0067] The analysis unit analyzes the image captured by the image capture unit and identifies areas that have not been brushed. The analysis unit analyzes the image data using, for example, image recognition technology. Specifically, deep learning and computer vision technology are used to detect plaque and dirt remaining on the surface of the teeth and identify areas that have not been brushed. Deep learning technology uses a convolutional neural network (CNN) to extract features from images of teeth and identify patterns of plaque and dirt. This makes it possible to identify areas that have not been brushed with high accuracy. In addition, machine learning algorithms such as a support vector machine (SVM) can be used to classify different types of dirt and plaque and identify areas that have not been brushed. The analysis unit combines these technologies to analyze the condition of the user's teeth in detail and identify areas that have not been brushed. Furthermore, the analysis unit can also analyze tendencies of areas that have not been brushed, taking into account past brushing data and the user's brushing habits. For example, if there is a lot of unbrushed areas on certain teeth or between the teeth, the cause can be identified and specific improvement measures can be proposed to the user. This enables the analysis unit to maximize the brushing effectiveness of the user and contribute to improved oral hygiene.

[0068] The scoring unit calculates a brushing score based on the unbrushed areas identified by the analysis unit. The scoring unit calculates the score using, for example, brushing time, strength, and range of movement as evaluation criteria. Specifically, the brushing time is measured by measuring how long the user brushed each tooth, and the score is calculated based on the appropriate time. The strength of the force is measured by using a pressure sensor built into the toothbrush to measure how much force the user brushes, and the score is calculated based on the appropriate strength. The range of movement is measured by using a gyro sensor and acceleration sensor built into the toothbrush to record the user's brushing action, and the score is calculated based on the appropriate range of movement. The scoring unit calculates the user's brushing score by comprehensively considering these evaluation criteria. Furthermore, the scoring unit can also evaluate the degree of improvement of the user's brushing habits by comparing the score with past scores and data of other users. For example, if the score is improved compared to past scores, it indicates that the user's brushing habits have improved, and conversely, if the score is decreased, it indicates that improvement is necessary. This allows the scoring unit to provide specific feedback to the user and encourage improvement of brushing habits.

[0069] The feedback unit notifies the user of the score calculated by the scoring unit. The feedback unit notifies the user, for example, through a smartphone app. Specifically, the feedback unit notifies the user of the areas that have not been brushed and the brushing score through a dedicated smartphone app, and advises the user on how to improve. The app provides an interface that allows the user to check the images taken after brushing and the analysis results, and visually indicates the areas that have not been brushed. In addition, the app displays the brushing score and the progress of the past scores in a graph, so that the user can check his / her progress. Furthermore, the feedback unit suggests specific improvement measures to the user. For example, if there is a lot of brushing left on a specific tooth or between the teeth, the feedback unit advises the user to brush that area with priority. In addition, the feedback unit suggests appropriate methods for the brushing force and range of movement, and supports the user to brush effectively. The feedback unit continuously monitors the user's brushing habits and provides regular feedback to support the user in improving oral hygiene. As a result, the feedback unit can provide specific information for the user to review and improve their brushing habits, thereby contributing to the improvement of oral hygiene.

[0070] The analysis unit can analyze the image data using image recognition technology. Image recognition technology includes, for example, deep learning and computer vision technology. For example, by using deep learning, plaque and dirt remaining on the surface of the teeth can be detected with high accuracy. In addition, by using computer vision technology, detailed image analysis of the surface of the teeth is possible. As a result, by using image recognition technology, it is possible to accurately identify areas that have not been brushed.

[0071] The analysis unit can identify areas that have not been brushed using a machine learning algorithm. Machine learning algorithms include, for example, convolutional neural networks (CNNs) and support vector machines (SVMs). For example, CNNs can be used to detect plaque and dirt remaining on the surfaces of teeth with high accuracy. Also, SVMs can be used to identify areas that have not been brushed. As a result, machine learning algorithms can be used to identify areas that have not been brushed with high accuracy.

[0072] The scoring unit can calculate a score using brushing time, strength of force, and range of movement as evaluation criteria. Brushing time includes, for example, the time it takes to brush once and a recommended brushing time. Strength of force includes, for example, the use of a pressure sensor when brushing and an appropriate range of force. Range of movement includes, for example, the trajectory of brush movement and the speed of movement. This allows accurate score calculation by using detailed brushing evaluation criteria.

[0073] The feedback unit can notify the user through a smartphone app. The smartphone app includes, for example, a notification method and a user interface. For example, the smartphone app can notify the user of areas that have not been brushed and the brushing score, and advise the user on areas to be improved. This allows the user to easily receive feedback by notifying the user through the smartphone app.

[0074] The feedback unit can specifically indicate areas that have not been brushed and provide advice on how to improve. Methods for specifically indicating areas that have not been brushed include, for example, highlighting on an image or explaining in text. Methods for providing advice on how to improve include, for example, brushing method and type of toothbrush to use. This allows the user to effectively improve their brushing by providing specific advice.

[0075] The photographing unit can estimate the user's emotion and adjust the timing of photographing based on the estimated emotion of the user. For example, when the user is relaxed, the photographing unit starts photographing at a natural timing. Also, when the user is nervous, the photographing unit can wait until the user relaxes before starting photographing. Furthermore, when the user is in a hurry, photographing can be performed quickly. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows for more natural photographing by adjusting the timing of photographing according to the user's emotion.

[0076] The imaging unit can simultaneously capture not only the tooth surface but also the condition of the gums. For example, the imaging unit can simultaneously capture the condition of the tooth surface and the gums to grasp the overall condition of the oral cavity. In addition, the imaging unit can also obtain detailed images of the gums to detect inflammation or swelling of the gums. Furthermore, the health conditions of the tooth surface and the gums can be compared to perform a comprehensive evaluation. In this way, the gum condition can be captured simultaneously to grasp the overall condition of the oral cavity.

[0077] The imaging unit can capture images of the internal structure of the tooth using light of different wavelengths. The imaging unit can capture images of the internal structure of the tooth using infrared light, for example, to detect hidden cavities. It can also capture images of plaque on the surface of the tooth using ultraviolet light to highlight it. Furthermore, it can also capture images of the inside and surface of the tooth at the same time by combining visible light and infrared light. This allows the internal structure of the tooth to be captured in detail by using light of different wavelengths.

[0078] The imaging unit can estimate the user's emotion and determine the priority order of the body parts to be photographed based on the estimated user's emotion. For example, when the user is stressed, the imaging unit starts photographing from simple body parts. Also, when the user is relaxed, the imaging unit can start photographing from detailed body parts. Furthermore, when the user is in a hurry, the imaging unit can start photographing from important body parts. The estimation of emotion is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This enables more effective photography by determining the priority order of the body parts to be photographed according to the user's emotion.

[0079] The imaging unit can measure the temperature and humidity in the user's oral cavity at the same time as imaging. For example, the imaging unit can measure the temperature in the oral cavity at the same time as imaging to evaluate the health condition. Also, the imaging unit can measure the humidity in the oral cavity at the same time as imaging to evaluate the dryness condition. Furthermore, the temperature and humidity data can be combined to evaluate the overall oral health condition. In this way, the temperature and humidity in the oral cavity can be measured at the same time to evaluate the overall oral health condition.

[0080] The imaging unit can acquire voice data from inside the user's mouth during imaging and analyze the sounds of tooth brushing. For example, the imaging unit acquires voice data during tooth brushing and analyzes the strength of brushing. In addition, the imaging unit can evaluate the rhythm and speed of brushing based on the voice data. Furthermore, the imaging unit can analyze the voice data and suggest an appropriate brushing method. In this way, the strength and rhythm of brushing can be evaluated by analyzing the voice data.

[0081] The analysis unit can estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit performs a detailed analysis. Also, when the user is nervous, the analysis unit can perform a simplified analysis. Furthermore, when the user is in a hurry, the analysis unit can perform a quick analysis. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows the analysis accuracy to be adjusted according to the user's emotion, thereby providing a more appropriate analysis result.

[0082] During the analysis, the analysis unit can also detect minute scratches and cracks on the tooth surface. For example, the analysis unit can detect minute scratches on the tooth surface and address them early. It can also detect cracks on the tooth surface and suggest appropriate treatment. Furthermore, it can evaluate the overall health of the teeth based on data on minute scratches and cracks. This allows early action to be taken by detecting minute scratches and cracks.

[0083] During the analysis, the analysis unit can detect changes in tooth color and transparency and evaluate the health condition. For example, the analysis unit can detect changes in tooth color and evaluate the need for whitening. It can also detect changes in tooth transparency and evaluate the condition of the enamel. Furthermore, it can also evaluate the overall health condition of the teeth based on the color and transparency data. In this way, the overall health condition of the teeth can be evaluated by detecting changes in tooth color and transparency.

[0084] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. Also, when the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. As a result, a more appropriate analysis result can be provided by adjusting the display method according to the user's emotion.

[0085] During the analysis, the analysis unit can provide the analysis results by comparing with the user's past tooth brushing data. For example, the analysis unit can compare with the user's past tooth brushing data and evaluate the progress. Also, the analysis unit can specifically indicate areas for improvement based on the past data. Furthermore, the analysis unit can compare the user's past data with the current data and perform a comprehensive evaluation. This allows the progress to be evaluated by comparing with the past data.

[0086] During the analysis, the analysis unit can identify areas that have not been brushed based on the user's meal history. The analysis unit can identify areas that have not been brushed based on the user's meal history, for example. It can also identify areas that are likely to be left unbrushed due to specific foods from the meal history. Furthermore, it can combine the meal history and the data on unbrushed areas to perform a comprehensive evaluation. In this way, it is possible to more accurately identify areas that have not been brushed by taking the meal history into consideration.

[0087] The scoring unit can estimate the user's emotion and adjust the score calculation method based on the estimated user's emotion. For example, when the user is relaxed, the scoring unit calculates the score using detailed evaluation criteria. Also, when the user is nervous, the scoring unit can calculate the score using simplified evaluation criteria. Furthermore, when the user is in a hurry, the score can be calculated quickly. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. This allows a more appropriate score to be provided by adjusting the score calculation method according to the user's emotion.

[0088] The scoring unit can also include the user's tooth brushing frequency and habits in the evaluation criteria when scoring. For example, the scoring unit includes the user's tooth brushing frequency in the evaluation criteria and calculates a score. The scoring unit can also include the user's tooth brushing habits (time of day and method) in the evaluation criteria and calculate a score. Furthermore, the tooth brushing frequency and habit data can be combined to calculate an overall score. In this way, by including the tooth brushing frequency and habits in the evaluation criteria, a more accurate score can be calculated.

[0089] The scoring unit can calculate a score that comprehensively evaluates the dental health condition of the user during scoring. For example, the scoring unit comprehensively evaluates the dental health condition of the user and calculates a score. Also, the scoring unit can calculate a detailed score based on the dental health condition data. Furthermore, the scoring unit can calculate a comprehensive score using a health condition evaluation criterion. In this way, a more detailed score can be provided by comprehensively evaluating the dental health condition.

[0090] The scoring unit can estimate the user's emotion and adjust the display method of the score based on the estimated user's emotion. For example, when the user is nervous, the scoring unit provides a simple and highly visible display method. Also, when the user is relaxed, the scoring unit can provide a display method including detailed information. Furthermore, when the user is in a hurry, the scoring unit can provide a display method that focuses on the main points. The estimation of the emotion is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows a more appropriate score to be provided by adjusting the display method according to the user's emotion.

[0091] The scoring unit can calculate the score taking into account the age and gender of the user when scoring. The scoring unit can, for example, calculate the score using an appropriate evaluation criterion taking into account the age of the user. The scoring unit can also calculate the score using an appropriate evaluation criterion taking into account the gender of the user. Furthermore, the scoring unit can combine data on age and gender to calculate an overall score. This makes it possible to provide a more appropriate score by taking into account age and gender.

[0092] The scoring unit can calculate the score taking into account the user's lifestyle habits and stress level when scoring. The scoring unit can, for example, calculate the score using an appropriate evaluation criterion taking into account the user's lifestyle habits. The scoring unit can also calculate the score using an appropriate evaluation criterion taking into account the user's stress level. Furthermore, the scoring unit can combine data on the lifestyle habits and the stress level to calculate an overall score. This makes it possible to provide a more appropriate score by taking into account the lifestyle habits and stress level.

[0093] The feedback unit can estimate the user's emotion and adjust the content of the feedback based on the estimated user's emotion. For example, the feedback unit can provide detailed feedback when the user is relaxed. Also, the feedback unit can provide simplified feedback when the user is nervous. Furthermore, the feedback unit can provide quick feedback when the user is in a hurry. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows more appropriate advice to be provided by adjusting the content of the feedback according to the user's emotion.

[0094] The feedback unit can provide optimal advice by referring to the user's past feedback history when providing feedback. The feedback unit can provide optimal advice based on the user's past feedback history, for example. Also, the feedback unit can specifically indicate areas for improvement from the past feedback history. Furthermore, the feedback history can be combined with current data to provide comprehensive advice. In this way, more appropriate advice can be provided by referring to the past feedback history.

[0095] The feedback unit can display the user's tooth brushing progress in real time at the time of feedback. For example, the feedback unit can display the user's tooth brushing progress in real time and provide appropriate advice. Also, the feedback unit can display the tooth brushing progress in real time and specifically indicate areas that have not been brushed. Furthermore, the progress can be displayed in real time to support the user in proper brushing. In this way, by displaying the progress in real time, it is possible to support the user in proper brushing.

[0096] The feedback unit can estimate the user's emotion and determine the priority of feedback based on the estimated user's emotion. For example, when the user is relaxed, the feedback unit can provide detailed feedback with priority. Also, when the user is nervous, the feedback unit can provide simplified feedback with priority. Furthermore, when the user is in a hurry, the feedback unit can provide quick feedback with priority. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows more appropriate advice to be provided by determining the priority of feedback according to the user's emotion.

[0097] The feedback unit can select the optimal display method in consideration of the device information of the user when providing feedback. For example, when the user is using a smartphone, the feedback unit can provide a display method that matches the screen size. Also, when the user is using a tablet, the feedback unit can provide a display method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the feedback unit can provide a display method that is simple and has high visibility. In this way, the user can receive feedback in the optimal display method by considering the device information.

[0098] The feedback unit may provide a function for sharing feedback with the user's family and friends. For example, the feedback unit may provide a function for sharing the user's feedback with family and friends and encouraging each other. The feedback may also be shared and advice may be received from family and friends. Furthermore, a function for sharing feedback and improving oral hygiene together with family and friends may be provided. This allows the user to improve oral hygiene together with family and friends by sharing feedback.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The toothbrush system may further include a history management unit for monitoring the user's dental health condition over the long term. The history management unit stores the user's past brushing data and analysis results, and tracks changes in the long term health condition. For example, the improvement or deterioration of the dental health condition may be evaluated based on the past data. The history management unit may also record changes in the user's brushing habits and provide appropriate advice. Furthermore, the history management unit may visually display the user's dental health condition in graphs and charts, allowing the user to easily grasp his or her progress. This allows the user's dental health condition to be comprehensively managed through long term monitoring.

[0101] The analysis unit can perform the analysis taking into account the dietary history of the user. For example, if the user frequently eats a particular food, the impact of that food on the teeth can be reflected in the analysis. Furthermore, the dietary history can also identify areas that are likely to be left unbrushed due to a particular food. By taking the dietary history into consideration, more accurate analysis results can be provided. Furthermore, the analysis unit can advise the user on an appropriate brushing method and improvements to the diet based on the user's dietary history. This can comprehensively improve the user's diet and brushing habits.

[0102] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be performed. If the user is nervous, a simplified analysis can be performed. Furthermore, if the user is in a hurry, a quick analysis can be performed. The estimation of emotions is realized, for example, using an emotion engine or a generation AI. This makes it possible to provide more appropriate analysis results by adjusting the accuracy of the analysis according to the user's emotions.

[0103] The scoring unit can also include the user's tooth brushing frequency and habits in the evaluation criteria. For example, the user's tooth brushing frequency can be included in the evaluation criteria to calculate a score. The user's tooth brushing habits (time of day and method) can also be included in the evaluation criteria to calculate a score. Furthermore, the tooth brushing frequency and habit data can be combined to calculate an overall score. In this way, by including the tooth brushing frequency and habits in the evaluation criteria, a more accurate score can be calculated.

[0104] The feedback unit can estimate the user's emotion and adjust the content of the feedback based on the estimated user's emotion. For example, if the user is relaxed, detailed feedback can be provided. Also, if the user is nervous, simplified feedback can be provided. Furthermore, if the user is in a hurry, quick feedback can be provided. The emotion estimation is realized, for example, using an emotion engine or a generation AI. As a result, more appropriate advice can be provided by adjusting the content of the feedback according to the user's emotion.

[0105] When providing feedback, the feedback unit can provide optimal advice by referring to the user's past feedback history. For example, optimal advice can be provided based on the user's past feedback history. Also, specific points for improvement can be indicated from the past feedback history. Furthermore, comprehensive advice can be provided by combining the feedback history with current data. In this way, more appropriate advice can be provided by referring to the past feedback history.

[0106] The photographing unit can estimate the user's emotion and adjust the timing of photographing based on the estimated emotion of the user. For example, if the user is relaxed, photographing can be started at a natural timing. Also, if the user is nervous, photographing can be started after waiting until the user relaxes. Furthermore, if the user is in a hurry, photographing can be performed quickly. The estimation of emotion is realized, for example, by using an emotion engine or a generation AI. As a result, more natural photographing can be achieved by adjusting the timing of photographing according to the user's emotion.

[0107] The imaging unit can simultaneously capture not only the tooth surface but also the condition of the gums. For example, the condition of the tooth surface and gums can be captured simultaneously to grasp the overall condition of the oral cavity. In addition, detailed images of the gums can be obtained to detect inflammation or swelling of the gums. Furthermore, the health conditions of the tooth surface and gums can be compared to perform a comprehensive evaluation. This allows the gum condition to be captured simultaneously to grasp the overall condition of the oral cavity.

[0108] The imaging unit can capture images of the internal structure of the tooth using light of different wavelengths. For example, infrared light can be used to capture images of the internal structure of the tooth to detect hidden cavities. In addition, ultraviolet light can be used to highlight and capture images of plaque on the surface of the tooth. Furthermore, visible light and infrared light can be combined to simultaneously capture images of the internal and surface conditions of the tooth. This allows the internal structure of the tooth to be captured in detail by using light of different wavelengths.

[0109] The photographing unit can estimate the user's emotions and determine the priority of the parts to be photographed based on the estimated user's emotions. For example, if the user is stressed, photographing can start from simple parts. Also, if the user is relaxed, photographing can start from detailed parts. Furthermore, if the user is in a hurry, photographing can start from important parts. Emotion estimation is realized, for example, using an emotion engine or generation AI. This allows more effective photographing by determining the priority of the parts to be photographed according to the user's emotions.

[0110] The process flow of the second embodiment will be briefly described below.

[0111] Step 1: The photographing unit photographs the user's teeth with a camera mounted on the toothbrush. The photographing unit, for example, photographs the surface of the teeth in detail and acquires image data. For example, it can photograph the state of dirt and plaque on the surface of the teeth. Step 2: The analysis unit analyzes the image captured by the imaging unit and identifies areas that have not been brushed. The analysis unit analyzes the image data using, for example, image recognition technology. For example, deep learning or computer vision technology can be used to detect plaque or dirt remaining on the surface of the teeth and identify areas that have not been brushed. The analysis unit can also identify areas that have not been brushed using machine learning algorithms. For example, algorithms such as convolutional neural networks (CNN) and support vector machines (SVM) can be used to identify areas that have not been brushed with high accuracy. Step 3: The scoring unit calculates a brushing score based on the areas that have not been brushed identified by the analysis unit. The scoring unit calculates a score using, for example, brushing time, strength, and range of movement as evaluation criteria. For example, the score can be calculated by evaluating brushing time, strength, and range of movement. Step 4: The feedback unit notifies the user of the score calculated by the scoring unit. The feedback unit notifies the user, for example, through a smartphone app. For example, the feedback unit can notify the user of areas that have not been brushed and the brushing score through the smartphone app and provide advice on areas for improvement.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0114] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0115] Each of the elements including the above-mentioned photographing unit, analysis unit, scoring unit, and feedback unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the smart device 14, and takes detailed pictures of the user's teeth and acquires image data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the image data to identify areas that have not been brushed. The scoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates a brushing score. The feedback unit is realized, for example, by the control unit 46A of the smart device 14, and notifies the user through a smartphone app. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0123] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements including the above-mentioned photographing unit, analysis unit, scoring unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the smart glasses 214, and takes a detailed image of the user's teeth and acquires image data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the image data to identify areas that have not been brushed. The scoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates a brushing score. The feedback unit is realized, for example, by the control unit 46A of the smart glasses 214, and notifies the user through a smartphone app. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] Each of the elements including the above-mentioned photographing unit, analysis unit, scoring unit, and feedback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the headset type terminal 314, and takes detailed pictures of the user's teeth and acquires image data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the image data to identify areas that have not been brushed. The scoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates a brushing score. The feedback unit is realized, for example, by the control unit 46A of the headset type terminal 314, and notifies the user through a smartphone app. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] Each of the elements including the above-mentioned photographing unit, analysis unit, scoring unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit is realized by the camera 42 of the robot 414, and takes detailed pictures of the user's teeth and acquires image data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the image data to identify areas that have not been brushed. The scoring unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and calculates a brushing score. The feedback unit is realized, for example, by the control unit 46A of the robot 414, and notifies the user through a smartphone app. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0165] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0166] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0168] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0169] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0170] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0173] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0174] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0175] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0176] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0177] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0178] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0179] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0181] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0183] (Appendix 1) A photographing unit that photographs a user's teeth using a camera mounted on the toothbrush; An analysis unit that analyzes the image captured by the imaging unit and identifies areas that need to be brushed; A scoring unit that calculates a brushing score based on the unbrushed areas identified by the analysis unit; a feedback unit that notifies a user of the score calculated by the scoring unit; Equipped A system characterized by: (Appendix 2) The analysis unit is Analyze image data using image recognition technology 2. The system of claim 1, (Appendix 3) The analysis unit is Machine learning algorithms identify areas that need polishing 2. The system of claim 1, (Appendix 4) The scoring unit includes: A score is calculated based on the brushing time, pressure, and range of movement. 2. The system of claim 1, (Appendix 5) The feedback unit is Notify users via smartphone app 2. The system of claim 1, (Appendix 6) The feedback unit is Specifically indicate areas that need polishing and give advice on how to improve. 2. The system of claim 1, (Appendix 7) The imaging unit includes: Estimating a user's emotions and adjusting the timing of photographing based on the estimated user's emotions 2. The system of claim 1, (Appendix 8) The imaging unit includes: Photograph not only the surface of the teeth but also the condition of the gums at the same time 2. The system of claim 1, (Appendix 9) The imaging unit includes: Using different wavelengths of light to capture images of the internal structure of teeth 2. The system of claim 1, (Appendix 10) The imaging unit includes: The user's emotions are estimated, and the priority of the parts to be photographed is determined based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The imaging unit includes: When taking a picture, the temperature and humidity inside the user's mouth are measured at the same time. 2. The system of claim 1, (Appendix 12) The imaging unit includes: When taking a picture, audio data from inside the user's mouth is acquired and the sounds of tooth brushing are analyzed. 2. The system of claim 1, (Appendix 13) The analysis unit is Estimate user emotions and adjust analysis accuracy based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, even minute scratches and cracks on the tooth surface are detected 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, changes in tooth color and transparency are detected to assess the health status 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, the analysis results are provided by comparing the user's past tooth brushing data. 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, the system identifies areas that need brushing based on the user's eating history. 2. The system of claim 1, (Appendix 19) The scoring unit includes: Infer the user's emotions and adjust the score calculation method based on the estimated user emotions. 2. The system of claim 1, (Appendix 20) The scoring unit includes: When scoring, the frequency and habits of the user's tooth brushing are also included as evaluation criteria. 2. The system of claim 1, (Appendix 21) The scoring unit includes: When scoring, a score is calculated that comprehensively evaluates the user's dental health. 2. The system of claim 1, (Appendix 22) The scoring unit includes: Infer the user's emotions and adjust the way the score is displayed based on the estimated user emotions 2. The system of claim 1, (Appendix 23) The scoring unit includes: When scoring, the user's age and gender are taken into account when calculating the score. 2. The system of claim 1, (Appendix 24) The scoring unit includes: When calculating the score, the user's lifestyle and stress level are taken into account. 2. The system of claim 1, (Appendix 25) The feedback unit is Inferring user emotions and adjusting feedback based on the estimated user emotions 2. The system of claim 1, (Appendix 26) The feedback unit is When providing feedback, provide optimal advice by referring to the user's past feedback history 2. The system of claim 1, (Appendix 27) The feedback unit is Shows the user's brushing progress in real time when providing feedback 2. The system of claim 1, (Appendix 28) The feedback unit is Estimate user emotions and prioritize feedback based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The feedback unit is When feedback is received, the optimal display method is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 30) The feedback unit is When providing feedback, provide the ability for users to share it with their family and friends 2. The system of claim 1, [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A photographing unit that photographs a user's teeth using a camera mounted on the toothbrush; An analysis unit that analyzes the image captured by the imaging unit and identifies areas that need to be brushed; A scoring unit that calculates a brushing score based on the unbrushed areas identified by the analysis unit; a feedback unit that notifies a user of the score calculated by the scoring unit; Equipped A system characterized by:

2. The analysis unit is Analyze image data using image recognition technology 2. The system of claim 1 .

3. The analysis unit is Machine learning algorithms identify areas that need polishing 2. The system of claim 1 .

4. The scoring unit includes: A score is calculated based on the brushing time, pressure, and range of movement.

2. The system of claim 1 .

5. The feedback unit is Notify users via smartphone app 2. The system of claim 1 .

6. The feedback unit is Specifically indicate areas that need polishing and give advice on how to improve.

2. The system of claim 1 .

7. The imaging unit includes: Estimating a user's emotions and adjusting the timing of photographing based on the estimated user's emotions 2. The system of claim 1 .

8. The imaging unit includes: Photograph not only the surface of the teeth but also the condition of the gums at the same time 2. The system of claim 1 .

Images