System

Abstract

An object of a system according to an embodiment is to efficiently detect an unbrushed part and a decayed tooth when brushing teeth and provide appropriate advice.SOLUTION: In general, according to one embodiment, a system includes a camera apparatus, a generation AI, and a voice advice unit. The camera device is integrated into the tip of the toothbrush. The data linkage unit transmits the video acquired by the camera device to the smartphone. The generation AI analyzes the video transmitted by the data-linking module. A voice advice part performs voice advice on the basis of a result analyzed by the generation AI.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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to detect areas that have not been brushed properly or to detect cavities early on when brushing teeth.

[0005] The system according to the embodiment aims to efficiently detect areas that have not been brushed and cavities when brushing teeth and to provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera device, a data linking unit, a generation AI, and a voice advice unit. The camera device is incorporated into the tip of a toothbrush. The data linking unit transmits images captured by the camera device to a smartphone. The generation AI analyzes the images transmitted by the data linking unit. The voice advice unit provides voice advice based on the results of the analysis by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently detect areas that have not been brushed and cavities when brushing teeth and provide appropriate advice. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded 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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. 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 process 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.

[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 user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 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 device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). 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 an embodiment of the present invention incorporates a camera device at the tip of the toothbrush and connects the camera image to a smartphone, allowing users to check the condition of their teeth in real time. Furthermore, it uses a generative AI to check for cavities and areas that have not been brushed properly, and provides audio advice on areas that have not been brushed properly. This allows the toothbrush system to allow users to check the condition of their teeth in real time and brush their teeth effectively by following the generative AI's advice.

[0029] A toothbrush system according to an embodiment includes a camera device, a data linking unit, a generation AI, and a voice advice unit. The camera device is integrated into the tip of the toothbrush and captures images of the teeth. For example, the camera device can capture images of the tooth surface and capture detailed information about cavities and tooth stains. The data linking unit transmits the images captured by the camera device to a smartphone. For example, the data linking unit transmits the images to the smartphone via wireless communication, allowing the user to view the images in real time. The generation AI analyzes the images transmitted by the data linking unit and identifies cavities and areas that have not been brushed. For example, the generation AI analyzes the images using a text generation AI (e.g., LLM) to detect cavities and areas that have not been brushed. The generation AI can also analyze the content of the video using a multimodal generation AI. The voice advice unit provides voice advice based on the results of the analysis by the generation AI. For example, the voice advice unit can provide specific instructions to the user, such as, "There is a spot on the back right tooth that has not been brushed. Please brush it more carefully." As a result, the toothbrush system according to the embodiment allows users to check the condition of their teeth in real time and brush their teeth effectively according to the advice of the generated AI. For example, this helps to detect cavities early and prevent missed spots, thereby maintaining dental health.

[0030] The camera device can add a macro lens to capture minute scratches and stains on the surface of the teeth in detail. The camera device can add a macro lens to capture minute scratches and stains on the surface of the teeth in detail. For example, it can capture minute cracks in the enamel of the teeth and plaque adhesion that is difficult to see with the naked eye in high resolution. This makes it possible to capture minute scratches and stains on the surface of the teeth in detail.

[0031] The camera device can incorporate a temperature sensor to measure the surface temperature of the teeth and evaluate their health. For example, by incorporating a temperature sensor, the camera device can measure the surface temperature of the teeth in real time. For example, if the surface temperature of the teeth is abnormally high, signs of inflammation or infection can be detected early. This allows the health of the teeth to be evaluated by measuring the surface temperature of the teeth.

[0032] The camera device is equipped with a UV light, which makes it possible to visualize bacteria and plaque on the surface of the teeth. For example, when irradiated with UV light, bacteria and plaque emit fluorescence, which can be visually confirmed. This makes it possible to visualize bacteria and plaque on the surface of the teeth.

[0033] The camera device can be made detachable and applicable to other devices. For example, the camera device can be made detachable so that it can be applied to other devices. For example, the camera device can be detached from a toothbrush and attached to an ear pick or nose hair trimmer. This allows the camera device to be applied to other devices.

[0034] Video data can be stored in the cloud and compared with past data to track changes in dental health. A system will be built that stores video data in the cloud and compares it with past data to track changes in dental health. For example, periodically captured video can be stored in the cloud and compared with past video to check changes in dental health. This makes it possible to track changes in dental health.

[0035] By adding AR functionality to a smartphone app, it is possible to display the condition of teeth in a 3D model in real time. By adding AR functionality to a smartphone app, it is possible to build a system that displays the condition of teeth in a 3D model in real time. For example, a 3D model of teeth can be generated based on camera images and displayed on the smartphone screen. This allows the condition of teeth to be displayed in a 3D model in real time.

[0036] Data can be linked with other smart devices, such as smartwatches and smart mirrors, to enable comprehensive health management. A system can be built that links data with other smart devices, such as smartwatches and smart mirrors, to enable comprehensive health management. For example, heart rate data from a smartwatch can be integrated with dental health data for analysis. This allows for comprehensive health management.

[0037] Video data can be shared with family and dentists, enabling remote health checks. We will build a system that allows video data to be shared with family and dentists, enabling remote health checks. For example, video data can be shared via a smartphone app, allowing dentists to perform remote diagnoses. This makes remote health checks possible.

[0038] Generative AI can be used to analyze changes in tooth surface color, enabling early detection of signs of tooth decay and periodontal disease. Generative AI can be used to build a system that analyzes changes in tooth surface color and detects early signs of tooth decay and periodontal disease. For example, changes in tooth surface color can be analyzed with high accuracy to detect early signs of tooth decay and periodontal disease. This makes it possible to detect early signs of tooth decay and periodontal disease.

[0039] Generative AI can analyze the shape and arrangement of teeth and suggest the best brushing method for each individual user. We will build a system using generative AI to analyze the shape and arrangement of teeth and suggest the best brushing method for each individual user. For example, we can analyze the shape and arrangement of teeth with high precision and suggest the best brushing method for each user. This makes it possible to suggest the best brushing method for each individual user.

[0040] Generative AI can detect not only the health of teeth but also abnormalities in the oral cavity. Using generative AI, we will build a system that detects not only the health of teeth but also abnormalities in the oral cavity. For example, it can detect stomatitis and swollen gums with high accuracy and issue an alert to the user. This makes it possible to detect abnormalities in the oral cavity.

[0041] Based on the detected data, the generative AI can suggest the type of toothpaste and toothbrush that is suitable for the user. Based on the data detected by the generative AI, we will build a system that suggests the type of toothpaste and toothbrush that is suitable for the user. For example, it will suggest the optimal toothpaste based on the condition of the user's teeth. This will allow us to suggest the type of toothpaste and toothbrush that is suitable for the user.

[0042] Multilingual support will be added to voice advice, making it possible to accommodate users who speak different languages. Multilingual support will be added to voice advice, making it possible to construct a system that can accommodate users who speak different languages. For example, voice advice can be provided in multiple languages, such as English, French, and Chinese. This will make it possible to accommodate users who speak different languages.

[0043] A function to call out the user's name can be added to voice advice, allowing for a more personalized experience. A function to call out the user's name can be added to voice advice, allowing for a more personalized experience. For example, the user's name can be called out when giving voice advice. This allows for a more personalized experience for the user.

[0044] Voice advice can be linked to smart speakers and smart home devices to support health management within the home. Voice advice can be linked to smart speakers and smart home devices to build a system that supports health management within the home. For example, voice advice can be provided through a smart speaker to support tooth brushing habits within the home. This can support health management within the home.

[0045] Combining music and relaxation sounds with audio advice can make tooth brushing time more enjoyable. We will build a system that combines music and relaxation sounds with audio advice to make tooth brushing time more enjoyable. For example, playing music with a relaxing effect while brushing teeth can help users brush their teeth more enjoyably. This can make tooth brushing time more enjoyable.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The toothbrush system may further include a vibration sensor. The vibration sensor detects the movement of the toothbrush and determines whether the user is brushing their teeth with appropriate force. For example, the vibration sensor may issue an alert if the user is brushing with excessive force and instruct the user to brush with appropriate force. This allows the user to brush their teeth effectively without damaging their teeth or gums.

[0048] The toothbrush system may further include a humidity sensor. The humidity sensor measures the humidity in the oral cavity and detects dryness. For example, the humidity sensor can issue an alert to encourage hydration if the oral cavity is dry. This helps prevent dryness in the oral cavity and maintain a healthy oral environment.

[0049] The toothbrush system may further include an optical sensor. The optical sensor measures the light reflection of the tooth surface to evaluate the whiteness of the teeth. For example, the optical sensor may analyze the light reflection of the tooth surface to confirm the effectiveness of whitening. This allows the user to take appropriate care to maintain the whiteness of their teeth.

[0050] The toothbrush system may further include a voice recognition function. The voice recognition function analyzes the user's voice commands and controls the operation of the toothbrush. For example, if the user instructs "brush harder," the vibration intensity of the toothbrush can be adjusted. This allows the user to customize the operation of the toothbrush with voice commands.

[0051] The toothbrush system may further include a position sensor that tracks the position of the toothbrush in real time and identifies which areas the user is brushing. For example, the position sensor may identify areas that are frequently missed and prompt the user to focus on those areas, thereby ensuring that the user brushes all of their teeth evenly.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: A camera device is built into the tip of the toothbrush to capture images of the teeth. For example, the camera device can capture images of the surface of the teeth and capture the state of cavities and stains in detail. Step 2: The data linking unit transmits the video captured by the camera device to the smartphone. For example, the data linking unit transmits the video to the smartphone via wireless communication so that the user can view the video in real time. Step 3: The generation AI analyzes the video sent by the data linkage unit and identifies cavities and areas that have not been brushed. For example, the generation AI can analyze the video using text generation AI (e.g., LLM) to detect cavities and areas that have not been brushed. The generation AI can also use multimodal generation AI to analyze the content of the video. Step 4: The voice advice unit provides voice advice based on the results of the analysis by the generation AI. For example, the voice advice unit can give specific instructions to the user, such as, "There are some areas on the back right tooth that are not brushed properly. Please brush them more carefully."

[0054] (Example 2) The toothbrush system according to an embodiment of the present invention incorporates a camera device at the tip of the toothbrush and connects the camera image to a smartphone, allowing users to check the condition of their teeth in real time. Furthermore, it uses a generative AI to check for cavities and areas that have not been brushed properly, and provides audio advice on areas that have not been brushed properly. This allows the toothbrush system to allow users to check the condition of their teeth in real time and brush their teeth effectively by following the generative AI's advice.

[0055] A toothbrush system according to an embodiment includes a camera device, a data linking unit, a generation AI, and a voice advice unit. The camera device is integrated into the tip of the toothbrush and captures images of the teeth. For example, the camera device can capture images of the tooth surface and capture detailed information about cavities and tooth stains. The data linking unit transmits the images captured by the camera device to a smartphone. For example, the data linking unit transmits the images to the smartphone via wireless communication, allowing the user to view the images in real time. The generation AI analyzes the images transmitted by the data linking unit and identifies cavities and areas that have not been brushed. For example, the generation AI analyzes the images using a text generation AI (e.g., LLM) to detect cavities and areas that have not been brushed. The generation AI can also analyze the content of the video using a multimodal generation AI. The voice advice unit provides voice advice based on the results of the analysis by the generation AI. For example, the voice advice unit can provide specific instructions to the user, such as, "There is a spot on the back right tooth that has not been brushed. Please brush it more carefully." As a result, the toothbrush system according to the embodiment allows users to check the condition of their teeth in real time and brush their teeth effectively according to the advice of the generated AI. For example, this helps to detect cavities early and prevent missed spots, thereby maintaining dental health.

[0056] The camera device can add a macro lens to capture minute scratches and stains on the surface of the teeth in detail. The camera device can add a macro lens to capture minute scratches and stains on the surface of the teeth in detail. For example, it can capture minute cracks in the enamel of the teeth and plaque adhesion that is difficult to see with the naked eye in high resolution. This makes it possible to capture minute scratches and stains on the surface of the teeth in detail.

[0057] The camera device can incorporate a temperature sensor to measure the surface temperature of the teeth and evaluate their health. For example, by incorporating a temperature sensor, the camera device can measure the surface temperature of the teeth in real time. For example, if the surface temperature of the teeth is abnormally high, signs of inflammation or infection can be detected early. This allows the health of the teeth to be evaluated by measuring the surface temperature of the teeth.

[0058] The camera device is equipped with an emotion estimation function and can analyze the facial expression of the user when using the toothbrush to detect stress or discomfort. The camera device, for example, is equipped with an emotion estimation function and analyzes the facial expression of the user when using the toothbrush. For example, if the user makes an unpleasant expression while using the toothbrush, stress or discomfort is detected. This makes it possible to detect the user's stress or discomfort.

[0059] The camera device is equipped with a UV light, which makes it possible to visualize bacteria and plaque on the surface of the teeth. For example, when irradiated with UV light, bacteria and plaque emit fluorescence, which can be visually confirmed. This makes it possible to visualize bacteria and plaque on the surface of the teeth.

[0060] The camera device can be made detachable and applicable to other devices. For example, the camera device can be made detachable so that it can be applied to other devices. For example, the camera device can be detached from a toothbrush and attached to an ear pick or nose hair trimmer. This allows the camera device to be applied to other devices.

[0061] The camera device can use the emotion estimation function to analyze the tone of the voice when the user uses the toothbrush and play music that promotes a relaxed state. The camera device, for example, uses the emotion estimation function to analyze the tone of the voice when the user uses the toothbrush. For example, if the user speaks in a relaxed tone of voice, music that promotes a relaxed state is played. In this way, music that promotes a relaxed state for the user can be played.

[0062] Video data can be stored in the cloud and compared with past data to track changes in dental health. A system will be built that stores video data in the cloud and compares it with past data to track changes in dental health. For example, periodically captured video can be stored in the cloud and compared with past video to check changes in dental health. This makes it possible to track changes in dental health.

[0063] By adding AR functionality to a smartphone app, it is possible to display the condition of teeth in a 3D model in real time. By adding AR functionality to a smartphone app, it is possible to build a system that displays the condition of teeth in a 3D model in real time. For example, a 3D model of teeth can be generated based on camera images and displayed on the smartphone screen. This allows the condition of teeth to be displayed in a 3D model in real time.

[0064] Using the emotion estimation function, it is possible to analyze the emotions of users when they are viewing video and provide positive feedback. Using the emotion estimation function, we will build a system that analyzes the emotions of users when they are viewing video. For example, by analyzing the facial expressions and voice of users when viewing video, we can estimate their emotional state. This makes it possible to provide positive feedback to users.

[0065] Data can be linked with other smart devices, such as smartwatches and smart mirrors, to enable comprehensive health management. A system can be built that links data with other smart devices, such as smartwatches and smart mirrors, to enable comprehensive health management. For example, heart rate data from a smartwatch can be integrated with dental health data for analysis. This allows for comprehensive health management.

[0066] Video data can be shared with family and dentists, enabling remote health checks. We will build a system that allows video data to be shared with family and dentists, enabling remote health checks. For example, video data can be shared via a smartphone app, allowing dentists to perform remote diagnoses. This makes remote health checks possible.

[0067] Using the emotion estimation function, it is possible to analyze the emotions of users when they are viewing video and provide appropriate advice. Using the emotion estimation function, we will build a system that analyzes the emotions of users when they are viewing video. For example, by analyzing the facial expressions and voice of users when viewing video, we can estimate their emotional state. This will allow us to provide appropriate advice to users.

[0068] Generative AI can be used to analyze changes in tooth surface color, enabling early detection of signs of tooth decay and periodontal disease. Generative AI can be used to build a system that analyzes changes in tooth surface color and detects early signs of tooth decay and periodontal disease. For example, changes in tooth surface color can be analyzed with high accuracy to detect early signs of tooth decay and periodontal disease. This makes it possible to detect early signs of tooth decay and periodontal disease.

[0069] Generative AI can analyze the shape and arrangement of teeth and suggest the best brushing method for each individual user. We will build a system using generative AI to analyze the shape and arrangement of teeth and suggest the best brushing method for each individual user. For example, we can analyze the shape and arrangement of teeth with high precision and suggest the best brushing method for each user. This makes it possible to suggest the best brushing method for each individual user.

[0070] Using the emotion estimation function, it is possible to analyze the user's emotional state and suggest brushing methods that are less stressful. Using the emotion estimation function, it is possible to build a system that analyzes the user's emotional state and suggests brushing methods that are less stressful. For example, it is possible to suggest the optimal brushing method based on emotional data so that the user can brush in a relaxed state. This makes it possible to suggest brushing methods that are less stressful for the user.

[0071] Generative AI can detect not only the health of teeth but also abnormalities in the oral cavity. Using generative AI, we will build a system that detects not only the health of teeth but also abnormalities in the oral cavity. For example, it can detect stomatitis and swollen gums with high accuracy and issue an alert to the user. This makes it possible to detect abnormalities in the oral cavity.

[0072] Based on the detected data, the generative AI can suggest the type of toothpaste and toothbrush that is suitable for the user. Based on the data detected by the generative AI, we will build a system that suggests the type of toothpaste and toothbrush that is suitable for the user. For example, it will suggest the optimal toothpaste based on the condition of the user's teeth. This will allow us to suggest the type of toothpaste and toothbrush that is suitable for the user.

[0073] Using the emotion estimation function, it is possible to analyze the user's emotional state and provide advice that elicits positive emotions. A system is being constructed that uses the emotion estimation function to analyze the user's emotional state and provide advice that elicits positive emotions. For example, optimal advice can be provided based on emotional data so that the user can polish their teeth in a relaxed state. This makes it possible to provide advice that elicits positive emotions in the user.

[0074] Multilingual support will be added to voice advice, making it possible to accommodate users who speak different languages. Multilingual support will be added to voice advice, making it possible to construct a system that can accommodate users who speak different languages. For example, voice advice can be provided in multiple languages, such as English, French, and Chinese. This will make it possible to accommodate users who speak different languages.

[0075] A function to call out the user's name can be added to voice advice, allowing for a more personalized experience. A function to call out the user's name can be added to voice advice, allowing for a more personalized experience. For example, the user's name can be called out when giving voice advice. This allows for a more personalized experience for the user.

[0076] Using the emotion estimation function, advice can be given in a tone that corresponds to the user's emotional state. A system is constructed that uses the emotion estimation function to give advice in a tone that corresponds to the user's emotional state. For example, advice can be given in an optimal tone based on emotional data so that the user can brush their teeth in a relaxed state. This makes it possible to give advice in a tone that corresponds to the user's emotional state.

[0077] Voice advice can be linked to smart speakers and smart home devices to support health management within the home. Voice advice can be linked to smart speakers and smart home devices to build a system that supports health management within the home. For example, voice advice can be provided through a smart speaker to support tooth brushing habits within the home. This can support health management within the home.

[0078] Combining music and relaxation sounds with audio advice can make tooth brushing time more enjoyable. We will build a system that combines music and relaxation sounds with audio advice to make tooth brushing time more enjoyable. For example, playing music with a relaxing effect while brushing teeth can help users brush their teeth more enjoyably. This can make tooth brushing time more enjoyable.

[0079] Using the emotion estimation function, it is possible to provide encouragement and praise according to the user's emotional state. Using the emotion estimation function, a system is constructed that provides encouragement and praise according to the user's emotional state. For example, optimal encouragement and praise can be provided based on emotional data so that the user can practice in a relaxed state. This makes it possible to provide encouragement and praise according to the user's emotional state.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The toothbrush system may further include a vibration sensor. The vibration sensor detects the movement of the toothbrush and determines whether the user is brushing their teeth with appropriate force. For example, the vibration sensor may issue an alert if the user is brushing with excessive force and instruct the user to brush with appropriate force. This allows the user to brush their teeth effectively without damaging their teeth or gums.

[0082] The toothbrush system may further include a humidity sensor. The humidity sensor measures the humidity in the oral cavity and detects dryness. For example, the humidity sensor can issue an alert to encourage hydration if the oral cavity is dry. This helps prevent dryness in the oral cavity and maintain a healthy oral environment.

[0083] The toothbrush system may further include an optical sensor. The optical sensor measures the light reflection of the tooth surface to evaluate the whiteness of the teeth. For example, the optical sensor may analyze the light reflection of the tooth surface to confirm the effectiveness of whitening. This allows the user to take appropriate care to maintain the whiteness of their teeth.

[0084] The toothbrush system may further include a voice recognition function. The voice recognition function analyzes the user's voice commands and controls the operation of the toothbrush. For example, if the user instructs "brush harder," the vibration intensity of the toothbrush can be adjusted. This allows the user to customize the operation of the toothbrush with voice commands.

[0085] The toothbrush system may further include a position sensor that tracks the position of the toothbrush in real time and identifies which areas the user is brushing. For example, the position sensor may identify areas that are frequently missed and prompt the user to focus on those areas, thereby ensuring that the user brushes all of their teeth evenly.

[0086] The toothbrush system uses its emotion estimation function to analyze facial expressions while the user is using the toothbrush and detect stress or discomfort. For example, if the user makes an unpleasant facial expression while using the toothbrush, the system can detect stress or discomfort and provide advice on how to relax, allowing the user to brush their teeth comfortably.

[0087] The toothbrush system can use the emotion estimation function to analyze the tone of a user's voice when using the toothbrush and play music that promotes a relaxed state. For example, if a user speaks in a relaxed tone, music that promotes a relaxed state can be played, allowing the user to brush their teeth in a relaxed state.

[0088] The toothbrush system uses its emotion estimation function to analyze the user's emotions when viewing the video and provide positive feedback. For example, the system can analyze the user's facial expressions and voice when viewing the video to estimate their emotional state. This allows the system to provide positive feedback to the user and increase their motivation.

[0089] The toothbrush system can use its emotion estimation function to analyze the user's emotional state and suggest brushing methods that reduce stress. For example, it can suggest optimal brushing methods based on emotional data so that the user can brush their teeth in a relaxed state. This allows the user to brush their teeth in a less stressful state.

[0090] The toothbrush system can use its emotion estimation function to provide encouragement and praise according to the user's emotional state. For example, it can provide optimal encouragement and praise based on emotional data so that the user can brush their teeth in a relaxed state. This allows the user to brush their teeth with positive emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: A camera device is built into the tip of the toothbrush to capture images of the teeth. For example, the camera device can capture images of the surface of the teeth and capture the state of cavities and stains in detail. Step 2: The data linking unit transmits the video captured by the camera device to the smartphone. For example, the data linking unit transmits the video to the smartphone via wireless communication so that the user can view the video in real time. Step 3: The generation AI analyzes the video sent by the data linkage unit and identifies cavities and areas that have not been brushed. For example, the generation AI can analyze the video using text generation AI (e.g., LLM) to detect cavities and areas that have not been brushed. The generation AI can also use multimodal generation AI to analyze the content of the video. Step 4: The voice advice unit provides voice advice based on the results of the analysis by the generation AI. For example, the voice advice unit can give specific instructions to the user, such as, "There are some areas on the back right tooth that are not brushed properly. Please brush them more carefully."

[0093] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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, RAM 30, and 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.

[0099] 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.

[0100] 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 audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] 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 carried out in a secure state.

[0103] Fig. 4 shows an example of the 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.

[0104] 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.

[0105] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0109] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] 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, RAM 30, and 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.

[0114] 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.

[0115] 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 audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] 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 carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0119] 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.

[0120] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0124] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the 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.

[0128] 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, RAM 30, and 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.

[0129] 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.

[0130] 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 audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] 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 carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. 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.

[0134] 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.

[0135] 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.

[0136] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] 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 voice indicating a user input regarding 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.

[0140] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] 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.

[0150] 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 USB (Universal Serial Bus) 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.

[0151] Alternatively, 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 in response to a request from the data processing device 12.

[0152] 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.

[0153] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0155] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 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 camera device is built into the tip of the toothbrush. a data linking unit that transmits the image captured by the camera device to a smartphone; A generation AI that analyzes the video transmitted by the data linking unit; and a voice advice unit that provides voice advice based on the analysis results by the generation AI. A system characterized by:

2. The camera device Add a macro lens to capture minute scratches and stains on the surface of the tooth in detail 2. The system of claim 1.

3. The camera device Equipped with a UV light, it visualizes bacteria and plaque on the surface of the teeth.

2. The system of claim 1.

4. The video data is stored in the cloud and compared with past data to track changes in the health of the tooth.

2. The system of claim 1.

5. The generative AI analyzes changes in the color tone of the tooth surface and detects early signs of tooth decay and periodontal disease.

2. The system of claim 1.

6. Multilingual support has been added to the voice advice to accommodate users who speak different languages.

2. The system of claim 1.

7. The camera device Equipped with an emotion estimation function, it analyzes facial expressions when the user uses the toothbrush to detect stress or discomfort.

2. The system of claim 1.

8. Using emotion estimation function, analyze the emotions of the user when viewing the video and provide positive feedback.

2. The system of claim 1.

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