System

Abstract

An object of the system according to the embodiment is to find a trouble in the oral cavity at an early stage and propose appropriate care.SOLUTION: A system according to an embodiment includes an intraoral imaging camera, a AI diagnostic unit, a care suggestion unit, and a care content providing unit. The intraoral imaging camera acquires images of the intraoral cavity. The AI diagnostic unit analyzes an image acquired by the intraoral imaging camera. The care suggestion unit suggests an appropriate care method on the basis of the result analyzed by the AI diagnostic unit. The care content providing unit provides content related to the care method proposed by the care proposal unit.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 not being able to adequately detect oral problems early and recommend appropriate care.

[0005] The system according to the embodiment aims to detect oral problems early and propose appropriate care. [Means for solving the problem]

[0006] The system according to the embodiment includes an intraoral camera, an AI diagnostic unit, a care suggestion unit, and a care content providing unit. The intraoral camera acquires images of the oral cavity. The AI ​​diagnostic unit analyzes the images acquired by the intraoral camera. The care suggestion unit suggests an appropriate care method based on the results of the analysis by the AI ​​diagnostic unit. The care content providing unit provides content related to the care method suggested by the care suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect oral problems early and suggest appropriate care. [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 non-volatile storage devices that store various programs, 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 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 oral trouble prevention system according to the embodiment of the present invention is a system that acquires images of the oral cavity, analyzes them using a generation AI, proposes appropriate care methods, and provides care content. As a result, the oral trouble prevention system can comprehensively prevent oral troubles.

[0029] An oral trouble prevention system according to an embodiment includes an intraoral camera, an AI diagnostic unit, a care suggestion unit, and a care content provider. The intraoral camera captures images of the oral cavity. For example, the intraoral camera can capture detailed images of the oral cavity. The intraoral camera is designed for easy use at home. For example, by placing the camera inside the mouth and taking pictures, the condition of the teeth and gums can be recorded in detail. The AI ​​diagnostic unit analyzes the images captured by the intraoral camera. For example, the generation AI detects early symptoms of tooth decay, signs of periodontal disease, and areas of insufficient brushing. The generation AI analyzes the images using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, by inputting a prompt to the generation AI such as "Analyze intraoral images to detect signs of tooth decay," the AI ​​analyzes the images and outputs the results. The care suggestion unit suggests appropriate care methods based on the results of the analysis by the AI ​​diagnostic unit. For example, if early symptoms of tooth decay are detected, the system may recommend the use of a specific toothpaste or toothbrush. Additionally, if there are signs of periodontal disease, the system recommends a consultation with a specialist. The generation AI suggests specific care methods based on the user's oral condition. The care content provider provides content related to the care methods suggested by the care suggestion unit. For example, it provides videos and articles including the correct way to brush your teeth and dietary advice for maintaining oral health. This allows the oral trouble prevention system to comprehensively prevent oral trouble. For example, users can easily learn about oral care at home. Users can regularly check the condition of their oral cavity and detect and deal with problems early. This allows users to maintain oral health and prevent problems such as cavities and periodontal disease.

[0030] The intraoral camera is equipped with an autofocus function, which allows it to automatically capture images of specific areas within the oral cavity with optimal focus. For example, the camera automatically adjusts the focus to record the condition of the tooth surfaces and gums in detail. This allows for detailed images of the oral cavity to be obtained.

[0031] The intraoral camera is equipped with a temperature sensor and can detect temperature changes within the oral cavity to detect abnormalities early. For example, the intraoral camera is equipped with a temperature sensor and can detect temperature changes within the oral cavity in real time. For example, the temperature sensor detects abnormal temperature changes to detect signs of gum inflammation or infection early. This allows for early detection of abnormalities within the oral cavity.

[0032] Intraoral cameras are equipped with UV light, making it possible to visualize bacteria and plaque in the oral cavity. For example, by irradiating the camera with UV light, bacteria and plaque emit fluorescence, which can be visually confirmed. This makes it possible to visualize bacteria and plaque in the oral cavity.

[0033] The intraoral camera can be linked to a smartphone and provide an app that allows you to check the captured images in real time. For example, the intraoral camera can be linked to a smartphone and provide an app that allows you to check the captured images in real time. For example, an image of the oral cavity can be displayed on the smartphone screen and the detailed condition can be confirmed. This allows you to check the captured images in real time.

[0034] The AI ​​diagnostic unit can explain the diagnosis results by voice and provide easy-to-understand feedback to the user. For example, the AI ​​diagnostic unit can explain the diagnosis results by voice and provide easy-to-understand feedback to the user. For example, it can explain the signs of tooth decay and the risk of periodontal disease by voice and suggest specific countermeasures. This allows the diagnosis results to be fed back in an easy-to-understand manner.

[0035] The AI ​​diagnostic unit can compare data with past diagnostic data and track long-term changes in the oral cavity. For example, the AI ​​diagnostic unit can compare data with past diagnostic data and track long-term changes in the oral cavity. For example, it can analyze images taken periodically and record changes in the condition of teeth and gums. This makes it possible to track long-term changes in the oral cavity.

[0036] The AI ​​diagnostic unit displays the diagnostic results as a 3D model, making it easier for users to understand visually. For example, the AI ​​diagnostic unit can reproduce the condition of the oral cavity in a 3D model and show the location of cavities and periodontal disease. This makes it easier for users to understand the diagnostic results visually.

[0037] The AI ​​diagnostic unit can store the diagnostic results in the cloud and make them accessible from multiple devices. The AI ​​diagnostic unit can store the diagnostic results in the cloud and make them accessible from multiple devices. For example, the diagnostic results can be checked from a smartphone, tablet, or PC. This allows the diagnostic results to be stored in the cloud and made accessible from multiple devices.

[0038] The care suggestion unit can explain the suggested care method using a video, making it easy for the user to put it into practice. For example, the care suggestion unit can explain the suggested care method using a video, making it easy for the user to put it into practice. For example, the correct way to brush your teeth and how to use floss can be explained in detail using a video. This makes it possible to provide a care method that is easy for the user to put into practice.

[0039] The care suggestion unit can individually customize the suggested care methods to suit the user's lifestyle. For example, the care suggestion unit can individually customize the suggested care methods to suit the user's lifestyle. For example, the care suggestion unit can suggest care methods that suit the user's eating habits and lifestyle rhythm. This makes it possible to provide a care method that suits the user's lifestyle.

[0040] The care suggestion unit can adjust the suggested care method based on the user's diet and lifestyle. For example, the care suggestion unit adjusts the suggested care method based on the user's diet and lifestyle. For example, the care suggestion unit suggests a tooth brushing method according to the contents of the food the user eats. This makes it possible to provide a care method based on the user's diet and lifestyle.

[0041] The care content providing unit can customize the content to be provided according to the age and health condition of the user. For example, the care content providing unit customizes the content according to the age of the user. For example, it provides a video that teaches the basics of tooth brushing to children and an article that introduces methods for preventing periodontal disease to adults. This makes it possible to provide content that is tailored to the age and health condition of the user.

[0042] The care content providing unit can add interactive quizzes and games to the content it provides to increase the user's motivation to learn. The care content providing unit can, for example, add interactive quizzes to the content to increase the user's motivation to learn. For example, it can provide a quiz about the correct way to brush your teeth and introduce a system where points can be accumulated for correct answers. This can increase the user's motivation to learn.

[0043] The care content providing unit provides the content to be provided in multiple languages, making it possible to accommodate international users. The care content providing unit, for example, provides the content in multiple languages, making it possible to accommodate international users. For example, the content is translated into major languages ​​such as English, Spanish, and Chinese. This makes it possible to accommodate international users.

[0044] The care content providing unit can periodically update the content to provide the latest information. For example, the care content providing unit can periodically update the content to provide the latest information. For example, the care content providing unit can introduce oral care methods based on new research results and technological advances. This allows the provision of the latest information.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The oral trouble prevention system can further include a nutrition advice unit. The nutrition advice unit analyzes the user's diet and provides nutrition advice to maintain oral health. For example, it may suggest meals to increase calcium and vitamin D intake, or suggest alternative foods to reduce sugar intake. It can also provide information about the effects of specific foods on oral health. This allows the user to maintain oral health through diet.

[0047] The oral trouble prevention system can further include an alert notification unit. The alert notification unit notifies the user in real time if an abnormality is detected in the user's oral condition. For example, if early symptoms of tooth decay or signs of periodontal disease are detected, a notification is sent to a smartphone or smartwatch. It is also possible to set reminders for regular oral checks. This allows the user to discover abnormalities early and take prompt action.

[0048] The oral trouble prevention system can also be equipped with a gamification function. The gamification function provides interactive games that allow users to enjoy oral care while doing so. For example, it can provide quizzes to learn the correct way to brush your teeth, or a time attack game in which users compete to see who can brush their teeth the fastest. It can also introduce a system that allows users to earn points or badges according to the level of care they achieve. This allows users to have fun while making oral care a habit.

[0049] The oral trouble prevention system can also be equipped with a community function. The community function provides a platform for users to share information and support each other. For example, it provides a forum where users can post questions and advice about oral care, and a group chat for sharing care progress. It can also host online seminars and Q&A sessions with experts. This allows users to increase their motivation through the community and practice effective oral care.

[0050] The oral trouble prevention system can further include a virtual assistant unit. The virtual assistant unit answers the user's questions in real time and provides information about oral care. For example, it responds to questions about the correct way to brush your teeth or how to use floss by text or voice. It can also suggest the next care method to be performed based on the user's care history. This allows the user to obtain the information they need at any time and practice effective oral care.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The intraoral camera captures images of the inside of the mouth. For example, intraoral cameras can capture detailed images of the inside of the mouth and are designed for easy use at home. By placing the camera inside the mouth and taking pictures, the condition of the teeth and gums can be recorded in detail. Step 2: The AI ​​diagnostic unit analyzes the images captured by the intraoral camera. For example, the generation AI detects early symptoms of tooth decay, signs of periodontal disease, and areas where teeth have not been brushed properly. The generation AI analyzes the images using text generation AI (e.g., LLM) or multimodal generation AI. For example, by inputting a prompt to the generation AI such as "Analyze the intraoral image to detect signs of tooth decay," the AI ​​analyzes the image and outputs the results. Step 3: The care suggestion unit suggests appropriate care methods based on the results of the analysis by the AI ​​diagnosis unit. For example, if early symptoms of tooth decay are found, it will recommend the use of a specific toothpaste or toothbrush. If there are signs of periodontal disease, it will recommend a consultation with a specialist. The generation AI suggests specific care methods based on the condition of the user's oral cavity. Step 4: The care content provider provides content related to the care methods suggested by the care suggestor. For example, it provides videos and articles including advice on the correct way to brush your teeth and dietary tips to maintain oral health. This allows the user to easily learn about oral care at home.

[0053] (Example 2) The oral trouble prevention system according to the embodiment of the present invention is a system that acquires images of the oral cavity, analyzes them using a generation AI, proposes appropriate care methods, and provides care content. As a result, the oral trouble prevention system can comprehensively prevent oral troubles.

[0054] An oral trouble prevention system according to an embodiment includes an intraoral camera, an AI diagnostic unit, a care suggestion unit, and a care content provider. The intraoral camera captures images of the oral cavity. For example, the intraoral camera can capture detailed images of the oral cavity. The intraoral camera is designed for easy use at home. For example, by placing the camera inside the mouth and taking pictures, the condition of the teeth and gums can be recorded in detail. The AI ​​diagnostic unit analyzes the images captured by the intraoral camera. For example, the generation AI detects early symptoms of tooth decay, signs of periodontal disease, and areas of insufficient brushing. The generation AI analyzes the images using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, by inputting a prompt to the generation AI such as "Analyze intraoral images to detect signs of tooth decay," the AI ​​analyzes the images and outputs the results. The care suggestion unit suggests appropriate care methods based on the results of the analysis by the AI ​​diagnostic unit. For example, if early symptoms of tooth decay are detected, the system may recommend the use of a specific toothpaste or toothbrush. Additionally, if there are signs of periodontal disease, the system recommends a consultation with a specialist. The generation AI suggests specific care methods based on the user's oral condition. The care content provider provides content related to the care methods suggested by the care suggestion unit. For example, it provides videos and articles including the correct way to brush your teeth and dietary advice for maintaining oral health. This allows the oral trouble prevention system to comprehensively prevent oral trouble. For example, users can easily learn about oral care at home. Users can regularly check the condition of their oral cavity and detect and deal with problems early. This allows users to maintain oral health and prevent problems such as cavities and periodontal disease.

[0055] The intraoral camera is equipped with an autofocus function, which allows it to automatically capture images of specific areas within the oral cavity with optimal focus. For example, the camera automatically adjusts the focus to record the condition of the tooth surfaces and gums in detail. This allows for detailed images of the oral cavity to be obtained.

[0056] The intraoral camera is equipped with a temperature sensor and can detect temperature changes within the oral cavity to detect abnormalities early. For example, the intraoral camera is equipped with a temperature sensor and can detect temperature changes within the oral cavity in real time. For example, the temperature sensor detects abnormal temperature changes to detect signs of gum inflammation or infection early. This allows for early detection of abnormalities within the oral cavity.

[0057] The intraoral camera is equipped with an emotion estimation function, which can measure the user's stress level at the time of shooting and encourage the user to be in a relaxed state when taking the photo. For example, the intraoral camera is equipped with an emotion estimation function and can measure the user's stress level in real time when taking the photo. For example, the camera can analyze the user's facial expressions and voice and quantify the stress level. This allows the user to be photographed in a relaxed state.

[0058] Intraoral cameras are equipped with UV light, making it possible to visualize bacteria and plaque in the oral cavity. For example, by irradiating the camera with UV light, bacteria and plaque emit fluorescence, which can be visually confirmed. This makes it possible to visualize bacteria and plaque in the oral cavity.

[0059] The intraoral camera can be linked to a smartphone and provide an app that allows you to check the captured images in real time. For example, the intraoral camera can be linked to a smartphone and provide an app that allows you to check the captured images in real time. For example, an image of the oral cavity can be displayed on the smartphone screen and the detailed condition can be confirmed. This allows you to check the captured images in real time.

[0060] The AI ​​diagnostic unit can explain the diagnosis results by voice and provide easy-to-understand feedback to the user. For example, the AI ​​diagnostic unit can explain the diagnosis results by voice and provide easy-to-understand feedback to the user. For example, it can explain the signs of tooth decay and the risk of periodontal disease by voice and suggest specific countermeasures. This allows the diagnosis results to be fed back in an easy-to-understand manner.

[0061] The AI ​​diagnostic unit can compare data with past diagnostic data and track long-term changes in the oral cavity. For example, the AI ​​diagnostic unit can compare data with past diagnostic data and track long-term changes in the oral cavity. For example, it can analyze images taken periodically and record changes in the condition of teeth and gums. This makes it possible to track long-term changes in the oral cavity.

[0062] The AI ​​diagnostic unit uses its emotion estimation function to analyze the user's emotional response to the diagnostic results and provide appropriate advice. For example, the AI ​​diagnostic unit analyzes the user's emotional response to the diagnostic results in real time and provides appropriate advice. For example, if the user is feeling anxious, it will provide advice to reassure them. This allows the unit to provide appropriate advice based on the user's emotions.

[0063] The AI ​​diagnostic unit displays the diagnostic results as a 3D model, making it easier for users to understand visually. For example, the AI ​​diagnostic unit can reproduce the condition of the oral cavity in a 3D model and show the location of cavities and periodontal disease. This makes it easier for users to understand the diagnostic results visually.

[0064] The AI ​​diagnostic unit can store the diagnostic results in the cloud and make them accessible from multiple devices. The AI ​​diagnostic unit can store the diagnostic results in the cloud and make them accessible from multiple devices. For example, the diagnostic results can be checked from a smartphone, tablet, or PC. This allows the diagnostic results to be stored in the cloud and made accessible from multiple devices.

[0065] The AI ​​diagnostic unit uses its emotion estimation function to monitor the user's emotions regarding the diagnostic results in real time and respond appropriately. For example, if the user is feeling anxious, the AI ​​diagnostic unit will provide advice to reassure them. This allows the unit to respond appropriately according to the user's emotions.

[0066] The care suggestion unit can explain the suggested care method using a video, making it easy for the user to put it into practice. For example, the care suggestion unit can explain the suggested care method using a video, making it easy for the user to put it into practice. For example, the correct way to brush your teeth and how to use floss can be explained in detail using a video. This makes it possible to provide a care method that is easy for the user to put into practice.

[0067] The care suggestion unit can individually customize the suggested care methods to suit the user's lifestyle. For example, the care suggestion unit can individually customize the suggested care methods to suit the user's lifestyle. For example, the care suggestion unit can suggest care methods that suit the user's eating habits and lifestyle rhythm. This makes it possible to provide a care method that suits the user's lifestyle.

[0068] The care suggestion unit can use the emotion estimation function to analyze the user's emotional response to the proposed care methods and select the optimal care method. For example, the care suggestion unit uses the emotion estimation function to analyze the user's emotional response to the proposed care methods in real time and select the optimal care method. For example, the care suggestion unit preferentially suggests care methods that the user expresses positive emotions about. This makes it possible to provide the optimal care method according to the user's emotions.

[0069] The care suggestion unit can adjust the suggested care method based on the user's diet and lifestyle. For example, the care suggestion unit adjusts the suggested care method based on the user's diet and lifestyle. For example, the care suggestion unit suggests a tooth brushing method according to the contents of the food the user eats. This makes it possible to provide a care method based on the user's diet and lifestyle.

[0070] The care suggestion unit can use the emotion estimation function to monitor the user's emotion regarding the proposed care method in real time and provide feedback. The care suggestion unit can, for example, use the emotion estimation function to monitor the user's emotion regarding the proposed care method in real time and provide feedback. For example, if the user is feeling anxious, the care suggestion unit can provide advice to reassure the user. This makes it possible to provide feedback according to the user's emotion.

[0071] The care content providing unit can customize the content to be provided according to the age and health condition of the user. For example, the care content providing unit customizes the content according to the age of the user. For example, it provides a video that teaches the basics of tooth brushing to children and an article that introduces methods for preventing periodontal disease to adults. This makes it possible to provide content that is tailored to the age and health condition of the user.

[0072] The care content providing unit can add interactive quizzes and games to the content it provides to increase the user's motivation to learn. The care content providing unit can, for example, add interactive quizzes to the content to increase the user's motivation to learn. For example, it can provide a quiz about the correct way to brush your teeth and introduce a system where points can be accumulated for correct answers. This can increase the user's motivation to learn.

[0073] The care content providing unit can use the emotion estimation function to analyze the user's emotional response to the content and provide optimal content. The care content providing unit, for example, uses the emotion estimation function to analyze the user's emotional response to the content in real time and provide optimal content. For example, content that the user expresses positive emotions in can be preferentially provided. This makes it possible to provide optimal content according to the user's emotions.

[0074] The care content providing unit provides the content to be provided in multiple languages, making it possible to accommodate international users. The care content providing unit, for example, provides the content in multiple languages, making it possible to accommodate international users. For example, the content is translated into major languages ​​such as English, Spanish, and Chinese. This makes it possible to accommodate international users.

[0075] The care content providing unit can periodically update the content to provide the latest information. For example, the care content providing unit can periodically update the content to provide the latest information. For example, the care content providing unit can introduce oral care methods based on new research results and technological advances. This allows the provision of the latest information.

[0076] The care content providing unit can use the emotion estimation function to monitor the user's emotion toward the content in real time and provide feedback. The care content providing unit can, for example, use the emotion estimation function to monitor the user's emotion toward the content in real time and provide feedback. For example, the care content providing unit can preferentially provide content for which the user shows positive emotion. This makes it possible to provide feedback according to the user's emotion.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The oral trouble prevention system can further include a biofeedback unit. The biofeedback unit monitors the user's biological information in real time and provides advice to reduce stress and tension. For example, the biofeedback unit measures heart rate and electrodermal activity and suggests breathing techniques and stretches to help the user relax. It can also provide music and guided meditation to help the user perform oral care in a relaxed state. This allows the user to perform effective oral care while reducing stress.

[0079] The oral trouble prevention system can further include a nutrition advice unit. The nutrition advice unit analyzes the user's diet and provides nutrition advice to maintain oral health. For example, it may suggest meals to increase calcium and vitamin D intake, or suggest alternative foods to reduce sugar intake. It can also provide information about the effects of specific foods on oral health. This allows the user to maintain oral health through diet.

[0080] The oral trouble prevention system can further include an alert notification unit. The alert notification unit notifies the user in real time if an abnormality is detected in the user's oral condition. For example, if early symptoms of tooth decay or signs of periodontal disease are detected, a notification is sent to a smartphone or smartwatch. It is also possible to set reminders for regular oral checks. This allows the user to discover abnormalities early and take prompt action.

[0081] The oral trouble prevention system can also be equipped with a gamification function. The gamification function provides interactive games that allow users to enjoy oral care while doing so. For example, it can provide quizzes to learn the correct way to brush your teeth, or a time attack game in which users compete to see who can brush their teeth the fastest. It can also introduce a system that allows users to earn points or badges according to the level of care they achieve. This allows users to have fun while making oral care a habit.

[0082] The oral trouble prevention system can also be equipped with a community function. The community function provides a platform for users to share information and support each other. For example, it provides a forum where users can post questions and advice about oral care, and a group chat for sharing care progress. It can also host online seminars and Q&A sessions with experts. This allows users to increase their motivation through the community and practice effective oral care.

[0083] The oral trouble prevention system can also use its emotion estimation function to provide reminders based on the user's emotions. For example, if the user is feeling stressed, it can send advice and reminders to relax. Also, if the user is showing positive emotions, it can send encouraging messages to encourage continued care. This allows the system to provide appropriate reminders based on the user's emotions and support the continuation of oral care.

[0084] The oral trouble prevention system can also use its emotion estimation function to provide care content based on the user's emotions. For example, if the user is feeling anxious, it can provide relaxing videos or music to reassure them. If the user is excited, it can also provide a meditation guide to increase concentration. This makes it possible to provide optimal care content according to the user's emotions and support effective oral care.

[0085] The oral trouble prevention system can also use the emotion estimation function to provide feedback based on the user's emotions. For example, if the user is dissatisfied with a care method, the system can suggest an alternative care method. Also, if the user is satisfied, the system can send an encouraging message to encourage the user to continue the care method. This allows the system to provide appropriate feedback according to the user's emotions and maximize the effectiveness of oral care.

[0086] The oral trouble prevention system can also use its emotion estimation function to make care suggestions based on the user's emotions. For example, if the user is feeling stressed, it can suggest care methods to help them relax. Also, if the user is showing positive emotions, it can suggest more proactive care methods. This makes it possible to make optimal care suggestions based on the user's emotions and support effective oral care.

[0087] The oral trouble prevention system can further include a virtual assistant unit. The virtual assistant unit answers the user's questions in real time and provides information about oral care. For example, it responds to questions about the correct way to brush your teeth or how to use floss by text or voice. It can also suggest the next care method to be performed based on the user's care history. This allows the user to obtain the information they need at any time and practice effective oral care.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The intraoral camera captures images of the inside of the mouth. For example, intraoral cameras can capture detailed images of the inside of the mouth and are designed for easy use at home. By placing the camera inside the mouth and taking pictures, the condition of the teeth and gums can be recorded in detail. Step 2: The AI ​​diagnostic unit analyzes the images captured by the intraoral camera. For example, the generation AI detects early symptoms of tooth decay, signs of periodontal disease, and areas where teeth have not been brushed properly. The generation AI analyzes the images using text generation AI (e.g., LLM) or multimodal generation AI. For example, by inputting a prompt to the generation AI such as "Analyze the intraoral image to detect signs of tooth decay," the AI ​​analyzes the image and outputs the results. Step 3: The care suggestion unit suggests appropriate care methods based on the results of the analysis by the AI ​​diagnosis unit. For example, if early symptoms of tooth decay are found, it will recommend the use of a specific toothpaste or toothbrush. If there are signs of periodontal disease, it will recommend a consultation with a specialist. The generation AI suggests specific care methods based on the condition of the user's oral cavity. Step 4: The care content provider provides content related to the care methods suggested by the care suggestor. For example, it provides videos and articles including advice on the correct way to brush your teeth and dietary tips to maintain oral health. This allows the user to easily learn about oral care at home.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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, in order to avoid confusion and to 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.

[0156] 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]

[0157] 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. an intraoral camera for capturing images of the oral cavity; An AI diagnostic unit that analyzes images acquired by the intraoral camera; a care suggestion unit that suggests an appropriate care method based on the results of analysis by the AI ​​diagnosis unit; a care content providing unit that provides content related to the care method suggested by the care suggestion unit. A system characterized by:

2. The intraoral photography camera is Equipped with an autofocus function, it automatically captures specific areas in the oral cavity with optimal focus.

2. The system of claim 1.

3. The AI ​​diagnostic unit Diagnostic results are explained in audio, providing easy-to-understand feedback to the user 2. The system of claim 1.

4. The care suggestion unit Explain the suggested care methods through videos to make it easier for users to put them into practice.

2. The system of claim 1.

5. The care content providing unit Customize the content you provide based on your age and health status 2. The system of claim 1.

6. The intraoral photography camera is Equipped with an emotion estimation function, it measures the user's stress level when taking a photo and encourages them to take photos in a relaxed state.

2. The system of claim 1.

7. The AI ​​diagnostic unit Analyze the user's emotional response to the diagnosis results and provide appropriate advice 2. The system of claim 1.

8. The care suggestion unit Analyze the user's emotional response to the proposed care methods and select the optimal care method 2. The system of claim 1.

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