System

Abstract

An object of a system according to an embodiment is to easily diagnose an oral health condition using a smartphone and a special reflector.SOLUTION: A system includes an imaging unit, an upload unit, a diagnosis unit, and a feedback unit. The imaging unit captures an intraoral image using a smartphone and a special reflector. The upload unit uploads the intraoral photograph captured by the imaging unit to the dedicated application. The diagnosing unit diagnoses an intraoral condition using the generated AI on the basis of the intraoral photograph uploaded by the uploading unit. The feedback unit feeds back a diagnosis result diagnosed by the diagnosis unit to the user.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 techniques have made it difficult to undergo regular dental checkups and to properly manage oral health.

[0005] The system of the embodiment aims to easily diagnose the health condition of the mouth using a smartphone and a special reflector. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an uploading unit, a diagnosing unit, and a feedback unit. The photographing unit takes intraoral photographs using a smartphone and a special reflector. The uploading unit uploads the intraoral photographs taken by the photographing unit to a dedicated app. The diagnosing unit uses a generative AI to diagnose the condition of the oral cavity based on the intraoral photographs uploaded by the uploading unit. The feedback unit feeds back the diagnosis results obtained by the diagnosing unit to the user. [Effects of the Invention]

[0007] The system of the embodiment allows users to easily diagnose oral health using a smartphone and a special reflector. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. 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 remote dental examination system according to an embodiment of the present invention uses a smartphone and a special reflector to take intraoral photographs, which are then uploaded to a dedicated app, whereupon a generative AI diagnoses the condition of the oral cavity. This remote dental examination system allows users to easily check the health status of their oral cavity at any time, helping to prevent cavities.

[0029] A remote dental examination system according to an embodiment includes a photographing unit, an uploading unit, a diagnosis unit, and a feedback unit. The photographing unit takes intraoral photographs using a smartphone and a special reflector. For example, a user can point the smartphone camera into their mouth and use the reflector to reflect light, thereby capturing detailed images of the condition of their teeth and gums. The photographing unit can also use the reflector to brightly illuminate every corner of the mouth and take clear photographs. The photographing unit can also take high-resolution intraoral photographs using the smartphone camera. The uploading unit uploads the intraoral photographs taken by the photographing unit to a dedicated app. For example, the uploading unit is designed to allow users to easily upload their photographs to the app. The uploading unit also has a function to automatically check the quality of the photographs and prompt the user to retake them if necessary. The uploading unit can also prompt the user to retake the photograph if the photograph is blurry or if important parts are not visible. The diagnosis unit uses a generation AI to diagnose the condition of the oral cavity based on the intraoral photographs uploaded by the uploading unit. For example, the generation AI diagnoses the condition of the teeth and gums and detects signs of cavities and periodontal disease. The generation AI also generates a diagnosis result based on an intraoral photograph taken by the user. The generation AI also incorporates data related to dentists, enabling a detailed diagnosis of the oral condition. The feedback unit feeds back the diagnosis result obtained by the diagnosis unit to the user. For example, if the generation AI detects signs of tooth decay, the user can search for a nearby dental clinic through the app and make an appointment. The feedback unit also displays the diagnosis result in an easy-to-understand manner to the user, allowing them to make an appointment with a dentist as needed. The feedback unit can also provide appropriate advice to the user based on the diagnosis result. As a result, the remote dental examination system according to the embodiment allows users to easily check the health status of their oral cavity and helps prevent tooth decay.

[0030] The photographing unit can measure the humidity and temperature inside the mouth using a sensor built into the reflector and upload the data along with the photo. For example, the photographing unit measures the humidity inside the mouth using a humidity sensor built into the reflector and uploads the data along with the photo to a dedicated app. For example, the humidity sensor measures the humidity inside the mouth in real time and records it as environmental data at the time of photographing. The photographing unit also measures the temperature inside the mouth using a temperature sensor built into the reflector and uploads the data along with the photo. For example, the temperature sensor measures the temperature inside the mouth and records it as temperature data at the time of photographing. The photographing unit also has both humidity and temperature sensors built into the reflector, which simultaneously measures environmental data inside the mouth and uploads it along with the photo. For example, the humidity and temperature data can be combined to perform a more detailed analysis of the oral health condition. This allows for detailed recording of environmental data inside the mouth and improves diagnostic accuracy.

[0031] The photographing unit can generate a 3D model by simultaneously capturing images of the inside of the mouth from multiple angles using a microscopic camera built into the reflector in conjunction with a smartphone camera. The photographing unit, for example, uses a microscopic camera built into the reflector in conjunction with a smartphone camera to simultaneously capture images of the inside of the mouth from multiple angles. For example, the camera in the reflector captures images of the back of the teeth, while the smartphone camera captures images of the front. The photographing unit also places multiple microscopic cameras on the reflector and, in conjunction with the smartphone camera, generates a 3D model of the inside of the mouth. For example, the reflector camera combines images captured from different angles to create a 3D model. The photographing unit also constructs a system in which the reflector camera and the smartphone camera work together to generate a detailed 3D model of the inside of the mouth. For example, the reflector camera captures detailed images of the gaps between teeth and the condition of the gums, and reflects these in the 3D model. This generates a detailed 3D model of the inside of the mouth, improving the accuracy of diagnosis.

[0032] The imaging unit is equipped with a UV light on a reflector, allowing visualization of bacteria and plaque in the mouth. For example, the imaging unit will build a system that visualizes bacteria and plaque in the mouth by equipping the reflector with a UV light. For example, the UV light will illuminate bacteria and plaque with fluorescence, allowing them to appear in photographs. The imaging unit will also use the UV light to capture detailed images of the state of bacteria and plaque in the mouth. For example, the UV light will highlight bacterial clusters, allowing them to appear clearly in photographs. The imaging unit will also develop a system that incorporates a UV light on a reflector to visualize bacteria and plaque in the mouth in real time. For example, the UV light will illuminate bacteria and plaque with fluorescence, allowing them to be photographed with a smartphone camera. This will make the bacteria and plaque in the mouth visible, improving diagnostic accuracy.

[0033] The photographing unit can make the reflector foldable to improve portability. For example, the photographing unit designs the reflector to be foldable to improve portability. For example, the reflector can be folded compactly so that it can be stored in a pocket or bag. The photographing unit also develops a foldable reflector to make it easy for users to carry. For example, the reflector is made of a lightweight and durable material. The photographing unit also makes the reflector foldable so that it can be easily stored after use. For example, the reflector can be locked in a folded state so that it does not open during transport. This improves the portability of the reflector and increases user convenience.

[0034] The uploading unit can automatically add metadata to photos, thereby improving the accuracy of diagnoses. The uploading unit, for example, builds a system that automatically adds the date and time the photo was taken, the location, and user information when the photo is uploaded. For example, GPS data and user profile information are added to the photo. The uploading unit also automatically adds metadata to uploaded photos, thereby improving the accuracy of diagnoses. For example, the diagnosis results are made more accurate based on information about the date and time the photo was taken and the location. The uploading unit also develops a system that automatically adds metadata to photos, thereby improving the accuracy of diagnoses. For example, user information and data about the shooting environment are added to the photo. In this way, the automatic addition of metadata to photos improves the accuracy of diagnoses.

[0035] The uploading unit can evaluate the quality of photos in real time and suggest optimal shooting conditions to the user. For example, the uploading unit constructs a system in which an app evaluates the quality of photos in real time and suggests optimal shooting conditions to the user. For example, if a photo is blurry, the uploading unit prompts the user to take the photo again. The uploading unit also evaluates the quality of photos in real time and suggests optimal shooting conditions to the user. For example, it displays advice on adjusting the lighting and camera angle. The uploading unit also develops a system in which an app evaluates the quality of photos and suggests optimal shooting conditions to the user. For example, it suggests using a flash if the photo is dark. In this way, the accuracy of diagnosis is improved by evaluating the quality of photos in real time and suggesting optimal shooting conditions.

[0036] The uploading unit can conduct a questionnaire regarding the user's eating habits and tooth brushing habits at the same time as uploading the photo, and perform a comprehensive diagnosis. The uploading unit, for example, builds a system that conducts a questionnaire regarding the user's eating habits and tooth brushing habits at the same time as uploading the photo. For example, the user inputs what they eat and how often they brush their teeth. The uploading unit also collects data regarding the user's eating habits and tooth brushing habits along with the uploaded photo, and performs a comprehensive diagnosis. For example, it provides advice on improving the eating habits and tooth brushing. The uploading unit also develops a system that conducts a questionnaire regarding the user's eating habits and tooth brushing habits at the time of uploading the photo, and performs a comprehensive diagnosis based on the data. For example, it provides a diagnosis result based on the data on the eating habits and tooth brushing habits. In this way, by collecting data regarding the user's eating habits and tooth brushing habits and performing a comprehensive diagnosis, the accuracy of the diagnosis is improved.

[0037] The upload unit may add a voice input function to enable the user to give verbal instructions to upload photos. For example, the upload unit may add a voice input function to an app to build a system that enables the user to give verbal instructions to upload photos. For example, the user may give a voice instruction such as "upload a photo." The upload unit may also use the voice input function to enable the user to give verbal instructions to upload photos. For example, the user may give a voice instruction such as "proceed to the next step." The upload unit may also add a voice input function to an app to develop a system that enables the user to give verbal instructions to upload photos. For example, the user may give a voice instruction such as "take another photo." This allows the user to upload photos by giving voice instructions, thereby improving the convenience of operation.

[0038] When issuing a diagnosis, the diagnostic unit refers to past diagnosis history and the user's health data, allowing for more accurate diagnoses. For example, when the generation AI issues a diagnosis, the diagnostic unit references past diagnosis history and builds a system that tracks changes in the user's mouth. For example, it compares past diagnosis results with current photos to detect abnormalities. The diagnostic unit also has the generation AI reference the user's health data to improve the accuracy of the diagnosis. For example, it adjusts the diagnosis results based on data on the user's diet and tooth-brushing habits. The diagnostic unit also develops a system in which the generation AI integrates past diagnosis history with the user's health data to provide more accurate diagnoses. For example, it predicts the risk of cavities and periodontal disease based on past diagnosis results and health data. This improves the accuracy of diagnosis by referring to past diagnosis history and health data.

[0039] When issuing a diagnosis, the diagnostic unit analyzes the color and texture inside the mouth, enabling early detection of abnormalities. For example, the diagnostic unit will use a generative AI to analyze the color inside the mouth and build a system for early detection of abnormalities. For example, it will detect changes in the color of the teeth or gums and notify the patient of any abnormalities. The diagnostic unit will also use a generative AI to analyze the texture inside the mouth and enable early detection of abnormalities. For example, it will detect unevenness on the surface of the teeth or swelling of the gums and notify the patient of any abnormalities. The diagnostic unit will also develop a system in which a generative AI will simultaneously analyze the color and texture inside the mouth and enable early detection of abnormalities. For example, it will combine changes in color and texture to provide a more accurate diagnosis. This will enable early detection of abnormalities by analyzing the color and texture inside the mouth.

[0040] When issuing a diagnostic result, the diagnostic unit can compare it with the data of other users and detect statistical anomalies. For example, when the generation AI issues a diagnostic result, the diagnostic unit builds a system that compares it with the data of other users and detects statistical anomalies. For example, it detects anomalies by comparing it with data of users of the same age. The diagnostic unit also references the data of other users, and the generation AI detects statistical anomalies. For example, it detects anomalies by comparing it with data from a specific region or age group. The diagnostic unit also develops a system that compares the generation AI with the data of other users and detects statistical anomalies. For example, it detects anomalies by comparing it with data of users with the same lifestyle habits. In this way, by comparing it with the data of other users, statistical anomalies can be detected and the accuracy of the diagnosis can be improved.

[0041] The diagnostic unit can provide comprehensive health advice by taking into account the user's lifestyle habits and stress level when issuing a diagnostic result. For example, the diagnostic unit builds a system in which the generation AI takes into account the user's lifestyle habits when issuing a diagnostic result and provides comprehensive health advice. For example, advice based on dietary habits and exercise habits is provided. The diagnostic unit also has the generation AI take into account the user's stress level and provide health advice based on the diagnostic result. For example, advice on stress management methods and relaxation advice is provided. The diagnostic unit also develops a system in which the generation AI comprehensively considers the user's lifestyle habits and stress level and provides health advice. For example, it suggests ways to improve lifestyle habits and reduce stress. In this way, comprehensive health advice is provided by taking into account the user's lifestyle habits and stress level, improving the accuracy of the diagnosis.

[0042] The feedback unit can measure the user's level of understanding when feeding back the diagnostic result, and provide detailed explanations or additional information as needed. For example, the feedback unit builds a system that measures the user's level of understanding when feeding back the diagnostic result. For example, it provides a quiz to check whether the user understands the diagnostic result. The feedback unit also provides detailed explanations or additional information of the diagnostic result according to the user's level of understanding. For example, if the user does not understand the diagnostic result, it displays a detailed explanation. The feedback unit also develops a system that measures the user's level of understanding when feeding back the diagnostic result, and provides additional information as needed. For example, if the user does not understand the diagnostic result, it provides related information. In this way, the user's level of understanding is measured and detailed explanations or additional information is provided as needed, thereby deepening the user's understanding.

[0043] When feeding back the diagnostic results, the feedback unit can compare them with the user's past diagnostic results and visualize the progress. For example, when feeding back the diagnostic results, the feedback unit builds a system that compares the diagnostic results with the user's past diagnostic results and visualizes the progress. For example, the feedback unit displays the past diagnostic results and the current diagnostic results in a graph. The feedback unit also compares the user's past diagnostic results with the current diagnostic results and visualizes the progress. For example, it displays the progress of tooth decay and the improvement status of the gums. Also, when feeding back the diagnostic results, the feedback unit develops a system that compares the past diagnostic results with the user's past diagnostic results and visualizes the progress. For example, it compares the past diagnostic results with the current diagnostic results and displays areas for improvement and points to note. In this way, by comparing with the user's past diagnostic results, the progress is visualized and the user's understanding is deepened.

[0044] The feedback unit can suggest specific improvement measures based on the user's lifestyle habits when feeding back the diagnostic results. For example, the feedback unit builds a system that suggests specific improvement measures based on the user's lifestyle habits when feeding back the diagnostic results. For example, it suggests areas for improvement in diet or tooth brushing habits. The feedback unit also suggests specific improvement measures for the diagnostic results based on the user's lifestyle habits. For example, it provides advice on areas for improvement in diet or tooth brushing. The feedback unit also develops a system that suggests specific improvement measures based on the user's lifestyle habits when feeding back the diagnostic results. For example, it suggests areas for improvement in the diagnostic results based on lifestyle habit data. In this way, by suggesting specific improvement measures based on the user's lifestyle habits, the user's health is supported.

[0045] The feedback unit adds a function that allows the user to share the diagnostic result when providing feedback on the diagnostic result, allowing the user to share the information with family and friends. For example, the feedback unit builds a system that adds a function that allows the user to share the diagnostic result when providing feedback on the diagnostic result. For example, a button for sharing the diagnostic result with family and friends is installed. The feedback unit also adds a function that allows the user to share the diagnostic result, allowing the user to share the information with family and friends. For example, a function for sharing the diagnostic result via social media or email is provided. The feedback unit also develops a system that adds a function that allows the user to share the diagnostic result when providing feedback on the diagnostic result. For example, a link for sharing the diagnostic result is generated, and the link is shared with family and friends. This allows the user to share the diagnostic result with family and friends, supporting health management.

[0046] The feedback unit can automatically generate an individualized prevention plan based on the user's diagnostic history and send periodic reminders. The feedback unit, for example, builds a system that automatically generates an individualized prevention plan based on the user's diagnostic history. For example, it recommends regular tooth brushing and flossing based on past diagnostic results. The feedback unit also references the diagnostic history and provides the user with an individualized prevention plan. For example, it sends reminders for dental checkups at specific intervals. The feedback unit also develops a system that automatically generates an individualized prevention plan based on the user's diagnostic history and sends periodic reminders. For example, it suggests specific preventive measures to users who are at high risk of tooth decay. In this way, the system supports the user's health management by automatically generating an individualized prevention plan based on the user's diagnostic history and sending periodic reminders.

[0047] The feedback unit analyzes the user's lifestyle data, predicts the risk of tooth decay and periodontal disease, and enables early countermeasures to be taken. The feedback unit, for example, analyzes the user's lifestyle data and builds a system that predicts the risk of tooth decay and periodontal disease. For example, the risk is assessed based on data on dietary habits and tooth brushing habits. The feedback unit also predicts the risk of tooth decay and periodontal disease based on the lifestyle data and enables early countermeasures to be taken. For example, specific preventive measures are suggested for users at high risk. The feedback unit also analyzes the user's lifestyle data and develops a system that predicts the risk of tooth decay and periodontal disease. For example, the risk is assessed based on data on dietary habits and stress levels, and early countermeasures are taken. In this way, the user's health management is supported by analyzing the user's lifestyle data, predicting the risk of tooth decay and periodontal disease, and enabling early countermeasures to be taken.

[0048] The feedback unit can provide personalized tooth brushing instructions and dietary advice based on the user's diagnosis results. The feedback unit, for example, builds a system that provides personalized tooth brushing instructions based on the user's diagnosis results. For example, it suggests a specific tooth brushing method or flossing method. The feedback unit also provides personalized dietary advice to the user based on the diagnosis results. For example, it suggests how to choose meals to reduce the risk of tooth decay. The feedback unit also develops a system that provides personalized tooth brushing instructions and dietary advice based on the user's diagnosis results. For example, it suggests tooth brushing products and meal plans that are suitable for the user based on the diagnosis results. In this way, personalized tooth brushing instructions and dietary advice are provided based on the user's diagnosis results, thereby supporting the user's health management.

[0049] The feedback unit can add a function that allows a user to search for a nearby dental clinic based on the diagnosis result and make an appointment. The feedback unit, for example, builds a system that adds a function that allows a user to search for a nearby dental clinic based on the diagnosis result and make an appointment. For example, the system suggests the most suitable dental clinic for the user based on the diagnosis result. The feedback unit also provides a function that allows a user to search for a nearby dental clinic based on the diagnosis result and make an appointment. For example, the system suggests a dental clinic that provides the most suitable treatment for the user based on the diagnosis result. The feedback unit also develops a system that adds a function that allows a user to search for a nearby dental clinic based on the diagnosis result and make an appointment. For example, the system suggests a dental clinic that provides the most suitable treatment for the user based on the diagnosis result and makes an appointment. This supports quick treatment by allowing a user to search for a nearby dental clinic based on the diagnosis result and make an appointment.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The imaging unit uses a sensor built into the reflector to measure the humidity and temperature inside the mouth and upload the data along with the photograph. For example, the humidity sensor measures the humidity inside the mouth in real time and records it as environmental data at the time of photographing. In addition, the temperature sensor measures the temperature inside the mouth and uploads that data along with the photograph. This allows detailed recording of environmental data inside the mouth, improving the accuracy of diagnosis.

[0052] The imaging unit uses a tiny camera built into the reflector that works in conjunction with a smartphone camera to simultaneously capture images of the inside of the mouth from multiple angles, allowing it to generate a 3D model. For example, the camera in the reflector captures the back of the teeth, while the smartphone camera captures the front. It is also possible to place multiple tiny cameras on the reflector and link them with the smartphone camera to generate a 3D model of the inside of the mouth. This allows for the generation of a detailed 3D model of the inside of the mouth, improving the accuracy of diagnosis.

[0053] The imaging unit is equipped with a UV light on a reflector, allowing for the visualization of bacteria and plaque in the mouth. For example, the UV light illuminates bacteria and plaque with fluorescence, making them visible in photographs. The UV light can also be used to take detailed images of the state of bacteria and plaque in the mouth. This makes the bacteria and plaque in the mouth more visible, improving the accuracy of diagnosis.

[0054] The photographing unit can improve portability by making the reflector foldable. For example, the reflector can be made compact so that it can be stored in a pocket or bag. Also, a foldable reflector can be developed so that users can easily carry it. This improves the portability of the reflector and increases user convenience.

[0055] The upload unit can automatically add metadata to photos to improve the accuracy of diagnoses. For example, a system can be built that automatically adds the date and time the photo was taken, the location, and user information when the photo is uploaded. This allows for automatic addition of metadata to photos, thereby improving the accuracy of diagnoses.

[0056] The upload unit can evaluate the quality of the photo in real time and suggest optimal shooting conditions to the user. For example, a system can be constructed in which an app evaluates the quality of a photo in real time and suggests optimal shooting conditions to the user. This allows for real-time evaluation of photo quality and suggestions for optimal shooting conditions, thereby improving the accuracy of diagnosis.

[0057] The upload unit may be added with a voice input function, allowing the user to give verbal instructions to upload photos. For example, a system may be constructed by adding a voice input function to an app, allowing the user to give verbal instructions to upload photos. This allows the user to give voice instructions to upload photos, thereby improving the convenience of operation.

[0058] When issuing a diagnosis, the diagnostic unit can refer to past diagnostic history and the user's health data to provide a more accurate diagnosis. For example, when the generation AI issues a diagnosis, it can refer to past diagnostic history and build a system that tracks changes in the user's mouth. This allows for improved diagnostic accuracy by referencing past diagnostic history and health data.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The camera unit takes intraoral photographs using a smartphone and a special reflector. For example, the user can point the smartphone camera into the mouth and use the reflector to reflect light, allowing detailed images of the condition of the teeth and gums to be taken. The camera unit also uses the reflector to brightly illuminate every corner of the mouth, allowing for clearer photographs. High-resolution intraoral photographs can also be taken using the smartphone camera. Step 2: The upload unit uploads the intraoral photographs taken by the photographing unit to a dedicated app. For example, it is designed so that users can easily upload the photographs they have taken to the app. The upload unit also has a function to automatically check the quality of the photographs and prompt the user to take a new photograph if necessary. If the photograph is blurry or if important parts are not visible, the user can be prompted to take a new photograph. Step 3: The diagnosis unit uses the generation AI to diagnose the condition of the oral cavity based on the oral cavity photographs uploaded by the upload unit. For example, the generation AI diagnoses the condition of the teeth and gums and detects signs of cavities and periodontal disease. The generation AI also generates a diagnosis based on the oral cavity photographs taken by the user. Furthermore, the generation AI incorporates data related to dentists, allowing it to diagnose the condition of the oral cavity in detail. Step 4: The feedback unit feeds back the diagnosis results made by the diagnosis unit to the user. For example, if the generation AI detects signs of tooth decay, the user can search for a nearby dental clinic through the app and make an appointment. The feedback unit also displays the diagnosis results in an easy-to-understand manner to the user, allowing them to make an appointment with a dentist if necessary. Furthermore, the feedback unit can provide the user with appropriate advice based on the diagnosis results.

[0061] (Example 2) The remote dental examination system according to an embodiment of the present invention uses a smartphone and a special reflector to take intraoral photographs, which are then uploaded to a dedicated app, whereupon a generative AI diagnoses the condition of the oral cavity. This remote dental examination system allows users to easily check the health status of their oral cavity at any time, helping to prevent cavities.

[0062] A remote dental examination system according to an embodiment includes a photographing unit, an uploading unit, a diagnosis unit, and a feedback unit. The photographing unit takes intraoral photographs using a smartphone and a special reflector. For example, a user can point the smartphone camera into their mouth and use the reflector to reflect light, thereby capturing detailed images of the condition of their teeth and gums. The photographing unit can also use the reflector to brightly illuminate every corner of the mouth and take clear photographs. The photographing unit can also take high-resolution intraoral photographs using the smartphone camera. The uploading unit uploads the intraoral photographs taken by the photographing unit to a dedicated app. For example, the uploading unit is designed to allow users to easily upload their photographs to the app. The uploading unit also has a function to automatically check the quality of the photographs and prompt the user to retake them if necessary. The uploading unit can also prompt the user to retake the photograph if the photograph is blurry or if important parts are not visible. The diagnosis unit uses a generation AI to diagnose the condition of the oral cavity based on the intraoral photographs uploaded by the uploading unit. For example, the generation AI diagnoses the condition of the teeth and gums and detects signs of cavities and periodontal disease. The generation AI also generates a diagnosis result based on an intraoral photograph taken by the user. The generation AI also incorporates data related to dentists, enabling a detailed diagnosis of the oral condition. The feedback unit feeds back the diagnosis result obtained by the diagnosis unit to the user. For example, if the generation AI detects signs of tooth decay, the user can search for a nearby dental clinic through the app and make an appointment. The feedback unit also displays the diagnosis result in an easy-to-understand manner to the user, allowing them to make an appointment with a dentist as needed. The feedback unit can also provide appropriate advice to the user based on the diagnosis result. As a result, the remote dental examination system according to the embodiment allows users to easily check the health status of their oral cavity and helps prevent tooth decay.

[0063] The photographing unit can measure the humidity and temperature inside the mouth using a sensor built into the reflector and upload the data along with the photo. For example, the photographing unit measures the humidity inside the mouth using a humidity sensor built into the reflector and uploads the data along with the photo to a dedicated app. For example, the humidity sensor measures the humidity inside the mouth in real time and records it as environmental data at the time of photographing. The photographing unit also measures the temperature inside the mouth using a temperature sensor built into the reflector and uploads the data along with the photo. For example, the temperature sensor measures the temperature inside the mouth and records it as temperature data at the time of photographing. The photographing unit also has both humidity and temperature sensors built into the reflector, which simultaneously measures environmental data inside the mouth and uploads it along with the photo. For example, the humidity and temperature data can be combined to perform a more detailed analysis of the oral health condition. This allows for detailed recording of environmental data inside the mouth and improves diagnostic accuracy.

[0064] The photographing unit can generate a 3D model by simultaneously capturing images of the inside of the mouth from multiple angles using a microscopic camera built into the reflector in conjunction with a smartphone camera. The photographing unit, for example, uses a microscopic camera built into the reflector in conjunction with a smartphone camera to simultaneously capture images of the inside of the mouth from multiple angles. For example, the camera in the reflector captures images of the back of the teeth, while the smartphone camera captures images of the front. The photographing unit also places multiple microscopic cameras on the reflector and, in conjunction with the smartphone camera, generates a 3D model of the inside of the mouth. For example, the reflector camera combines images captured from different angles to create a 3D model. The photographing unit also constructs a system in which the reflector camera and the smartphone camera work together to generate a detailed 3D model of the inside of the mouth. For example, the reflector camera captures detailed images of the gaps between teeth and the condition of the gums, and reflects these in the 3D model. This generates a detailed 3D model of the inside of the mouth, improving the accuracy of diagnosis.

[0065] The photographing unit can use the emotion estimation function to measure the user's stress level when taking a photograph and encourage them to take the photograph again in a relaxed state. For example, the photographing unit uses the emotion estimation function to measure the user's stress level when taking a photograph and, if stress is high, provides guidance for relaxation. For example, it suggests deep breathing or relaxing music. The photographing unit also builds a system that analyzes the user's facial expressions and voice when taking a photograph and measures the stress level in real time. For example, it displays a message encouraging the user to take the photograph again if stress is high. The photographing unit also uses the emotion estimation function to develop a system that supports the user in taking photographs in a relaxed state. For example, it suggests advice and environmental settings for relaxation. This supports the user in taking photographs in a relaxed state and improves the quality of the photographs.

[0066] The imaging unit is equipped with a UV light on a reflector, allowing visualization of bacteria and plaque in the mouth. For example, the imaging unit will build a system that visualizes bacteria and plaque in the mouth by equipping the reflector with a UV light. For example, the UV light will illuminate bacteria and plaque with fluorescence, allowing them to appear in photographs. The imaging unit will also use the UV light to capture detailed images of the state of bacteria and plaque in the mouth. For example, the UV light will highlight bacterial clusters, allowing them to appear clearly in photographs. The imaging unit will also develop a system that incorporates a UV light on a reflector to visualize bacteria and plaque in the mouth in real time. For example, the UV light will illuminate bacteria and plaque with fluorescence, allowing them to be photographed with a smartphone camera. This will make the bacteria and plaque in the mouth visible, improving diagnostic accuracy.

[0067] The photographing unit can make the reflector foldable to improve portability. For example, the photographing unit designs the reflector to be foldable to improve portability. For example, the reflector can be folded compactly so that it can be stored in a pocket or bag. The photographing unit also develops a foldable reflector to make it easy for users to carry. For example, the reflector is made of a lightweight and durable material. The photographing unit also makes the reflector foldable so that it can be easily stored after use. For example, the reflector can be locked in a folded state so that it does not open during transport. This improves the portability of the reflector and increases user convenience.

[0068] The photographing unit can use the emotion estimation function to provide relaxing music and guidance to reduce the anxiety the user feels when taking a photograph. The photographing unit, for example, uses the emotion estimation function to provide relaxing music to reduce the anxiety the user feels when taking a photograph. For example, relaxing music is played when the user's stress level is high. The photographing unit also analyzes the user's emotional state when taking a photograph in real time and provides guidance to reduce anxiety. For example, advice on deep breathing and relaxation is displayed. The photographing unit also uses the emotion estimation function to develop a system that supports the user in taking photographs in a relaxed state. For example, environmental settings and advice for relaxation are provided. This supports the user in taking photographs in a relaxed state and improves the quality of the photographs.

[0069] The uploading unit can automatically add metadata to photos, thereby improving the accuracy of diagnoses. The uploading unit, for example, builds a system that automatically adds the date and time the photo was taken, the location, and user information when the photo is uploaded. For example, GPS data and user profile information are added to the photo. The uploading unit also automatically adds metadata to uploaded photos, thereby improving the accuracy of diagnoses. For example, the diagnosis results are made more accurate based on information about the date and time the photo was taken and the location. The uploading unit also develops a system that automatically adds metadata to photos, thereby improving the accuracy of diagnoses. For example, user information and data about the shooting environment are added to the photo. In this way, the automatic addition of metadata to photos improves the accuracy of diagnoses.

[0070] The uploading unit can evaluate the quality of photos in real time and suggest optimal shooting conditions to the user. For example, the uploading unit constructs a system in which an app evaluates the quality of photos in real time and suggests optimal shooting conditions to the user. For example, if a photo is blurry, the uploading unit prompts the user to take the photo again. The uploading unit also evaluates the quality of photos in real time and suggests optimal shooting conditions to the user. For example, it displays advice on adjusting the lighting and camera angle. The uploading unit also develops a system in which an app evaluates the quality of photos and suggests optimal shooting conditions to the user. For example, it suggests using a flash if the photo is dark. In this way, the accuracy of diagnosis is improved by evaluating the quality of photos in real time and suggesting optimal shooting conditions.

[0071] The upload unit can use the emotion estimation function to analyze the user's emotional state at the time of uploading and provide positive feedback. The upload unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional state at the time of uploading and provides positive feedback. For example, an encouraging message is displayed if the user is feeling anxious. The upload unit also analyzes the user's emotional state at the time of uploading in real time and provides positive feedback. For example, advice on how to relax is displayed if the user is feeling stressed. The upload unit also uses the emotion estimation function to develop a system that analyzes the user's emotional state and provides positive feedback. For example, a compliment is displayed if the user successfully uploads. In this way, the user's emotional state is analyzed and positive feedback is provided, thereby improving user satisfaction.

[0072] The uploading unit can conduct a questionnaire regarding the user's eating habits and tooth brushing habits at the same time as uploading the photo, and perform a comprehensive diagnosis. The uploading unit, for example, builds a system that conducts a questionnaire regarding the user's eating habits and tooth brushing habits at the same time as uploading the photo. For example, the user inputs what they eat and how often they brush their teeth. The uploading unit also collects data regarding the user's eating habits and tooth brushing habits along with the uploaded photo, and performs a comprehensive diagnosis. For example, it provides advice on improving the eating habits and tooth brushing. The uploading unit also develops a system that conducts a questionnaire regarding the user's eating habits and tooth brushing habits at the time of uploading the photo, and performs a comprehensive diagnosis based on the data. For example, it provides a diagnosis result based on the data on the eating habits and tooth brushing habits. In this way, by collecting data regarding the user's eating habits and tooth brushing habits and performing a comprehensive diagnosis, the accuracy of the diagnosis is improved.

[0073] The upload unit may add a voice input function to enable the user to give verbal instructions to upload photos. For example, the upload unit may add a voice input function to an app to build a system that enables the user to give verbal instructions to upload photos. For example, the user may give a voice instruction such as "upload a photo." The upload unit may also use the voice input function to enable the user to give verbal instructions to upload photos. For example, the user may give a voice instruction such as "proceed to the next step." The upload unit may also add a voice input function to an app to develop a system that enables the user to give verbal instructions to upload photos. For example, the user may give a voice instruction such as "take another photo." This allows the user to upload photos by giving voice instructions, thereby improving the convenience of operation.

[0074] The upload unit can use the emotion estimation function to provide guidance and support to reduce the anxiety felt by the user when uploading. The upload unit, for example, uses the emotion estimation function to build a system that provides guidance to reduce the anxiety felt by the user when uploading. For example, if the user is feeling anxious, advice to help the user relax is displayed. The upload unit also analyzes the user's emotional state at the time of uploading in real time and provides support to reduce the anxiety. For example, an encouraging message is displayed if the user is feeling stressed. The upload unit also uses the emotion estimation function to develop a system that provides guidance and support to reduce the anxiety felt by the user when uploading. For example, environmental settings and advice to help the user relax are provided. This reduces the anxiety felt by the user when uploading and improves operational convenience.

[0075] When issuing a diagnosis, the diagnostic unit refers to past diagnosis history and the user's health data, allowing for more accurate diagnoses. For example, when the generation AI issues a diagnosis, the diagnostic unit references past diagnosis history and builds a system that tracks changes in the user's mouth. For example, it compares past diagnosis results with current photos to detect abnormalities. The diagnostic unit also has the generation AI reference the user's health data to improve the accuracy of the diagnosis. For example, it adjusts the diagnosis results based on data on the user's diet and tooth-brushing habits. The diagnostic unit also develops a system in which the generation AI integrates past diagnosis history with the user's health data to provide more accurate diagnoses. For example, it predicts the risk of cavities and periodontal disease based on past diagnosis results and health data. This improves the accuracy of diagnosis by referring to past diagnosis history and health data.

[0076] When issuing a diagnosis, the diagnostic unit analyzes the color and texture inside the mouth, enabling early detection of abnormalities. For example, the diagnostic unit will use a generative AI to analyze the color inside the mouth and build a system for early detection of abnormalities. For example, it will detect changes in the color of the teeth or gums and notify the patient of any abnormalities. The diagnostic unit will also use a generative AI to analyze the texture inside the mouth and enable early detection of abnormalities. For example, it will detect unevenness on the surface of the teeth or swelling of the gums and notify the patient of any abnormalities. The diagnostic unit will also develop a system in which a generative AI will simultaneously analyze the color and texture inside the mouth and enable early detection of abnormalities. For example, it will combine changes in color and texture to provide a more accurate diagnosis. This will enable early detection of abnormalities by analyzing the color and texture inside the mouth.

[0077] The diagnosis unit can use the emotion estimation function to analyze the user's emotional reaction to the diagnosis result and provide appropriate feedback. The diagnosis unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional reaction to the diagnosis result in real time and provides appropriate feedback. For example, an encouraging message is displayed if the user is feeling anxious. The diagnosis unit also analyzes the user's emotional reaction to the diagnosis result and provides positive feedback. For example, advice on how to relax is displayed if the user is feeling stressed. The diagnosis unit also uses the emotion estimation function to develop a system that analyzes the user's emotional reaction and provides appropriate feedback. For example, the system supports the user to have positive emotions toward the diagnosis result. In this way, the user's emotional reaction is analyzed and appropriate feedback is provided, thereby improving user satisfaction.

[0078] When issuing a diagnostic result, the diagnostic unit can compare it with the data of other users and detect statistical anomalies. For example, when the generation AI issues a diagnostic result, the diagnostic unit builds a system that compares it with the data of other users and detects statistical anomalies. For example, it detects anomalies by comparing it with data of users of the same age. The diagnostic unit also references the data of other users, and the generation AI detects statistical anomalies. For example, it detects anomalies by comparing it with data from a specific region or age group. The diagnostic unit also develops a system that compares the generation AI with the data of other users and detects statistical anomalies. For example, it detects anomalies by comparing it with data of users with the same lifestyle habits. In this way, by comparing it with the data of other users, statistical anomalies can be detected and the accuracy of the diagnosis can be improved.

[0079] The diagnostic unit can provide comprehensive health advice by taking into account the user's lifestyle habits and stress level when issuing a diagnostic result. For example, the diagnostic unit builds a system in which the generation AI takes into account the user's lifestyle habits when issuing a diagnostic result and provides comprehensive health advice. For example, advice based on dietary habits and exercise habits is provided. The diagnostic unit also has the generation AI take into account the user's stress level and provide health advice based on the diagnostic result. For example, advice on stress management methods and relaxation advice is provided. The diagnostic unit also develops a system in which the generation AI comprehensively considers the user's lifestyle habits and stress level and provides health advice. For example, it suggests ways to improve lifestyle habits and reduce stress. In this way, comprehensive health advice is provided by taking into account the user's lifestyle habits and stress level, improving the accuracy of the diagnosis.

[0080] The diagnosis unit can use the emotion estimation function to monitor the user's emotional reaction to the diagnosis result in real time and provide appropriate support. The diagnosis unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional reaction to the diagnosis result in real time. For example, if the user is feeling anxious, an encouraging message is displayed. The diagnosis unit also analyzes the user's emotional reaction to the diagnosis result in real time and provides appropriate support. For example, if the user is feeling stressed, advice on how to relax is displayed. The diagnosis unit also uses the emotion estimation function to develop a system that monitors the user's emotional reaction in real time and provides appropriate support. For example, support is provided to help the user have positive feelings about the diagnosis result. In this way, the user's emotional reaction can be monitored in real time and appropriate support can be provided, thereby improving user satisfaction.

[0081] The feedback unit can measure the user's level of understanding when feeding back the diagnostic result, and provide detailed explanations or additional information as needed. For example, the feedback unit builds a system that measures the user's level of understanding when feeding back the diagnostic result. For example, it provides a quiz to check whether the user understands the diagnostic result. The feedback unit also provides detailed explanations or additional information of the diagnostic result according to the user's level of understanding. For example, if the user does not understand the diagnostic result, it displays a detailed explanation. The feedback unit also develops a system that measures the user's level of understanding when feeding back the diagnostic result, and provides additional information as needed. For example, if the user does not understand the diagnostic result, it provides related information. In this way, the user's level of understanding is measured and detailed explanations or additional information is provided as needed, thereby deepening the user's understanding.

[0082] When feeding back the diagnostic results, the feedback unit can compare them with the user's past diagnostic results and visualize the progress. For example, when feeding back the diagnostic results, the feedback unit builds a system that compares the diagnostic results with the user's past diagnostic results and visualizes the progress. For example, the feedback unit displays the past diagnostic results and the current diagnostic results in a graph. The feedback unit also compares the user's past diagnostic results with the current diagnostic results and visualizes the progress. For example, it displays the progress of tooth decay and the improvement status of the gums. Also, when feeding back the diagnostic results, the feedback unit develops a system that compares the past diagnostic results with the user's past diagnostic results and visualizes the progress. For example, it compares the past diagnostic results with the current diagnostic results and displays areas for improvement and points to note. In this way, by comparing with the user's past diagnostic results, the progress is visualized and the user's understanding is deepened.

[0083] The feedback unit can use the emotion estimation function to analyze the user's emotional reaction to the diagnosis result and provide positive feedback. The feedback unit, for example, uses the emotion estimation function to analyze the user's emotional reaction to the diagnosis result in real time and build a system that provides positive feedback. For example, an encouraging message is displayed when the user is feeling anxious. The feedback unit also analyzes the user's emotional reaction to the diagnosis result and provides positive feedback. For example, advice on how to relax is displayed when the user is feeling stressed. The feedback unit also uses the emotion estimation function to develop a system that analyzes the user's emotional reaction and provides positive feedback. For example, the system supports the user to have positive emotions toward the diagnosis result. In this way, the user's emotional reaction is analyzed and positive feedback is provided, thereby improving user satisfaction.

[0084] The feedback unit can suggest specific improvement measures based on the user's lifestyle habits when feeding back the diagnostic results. For example, the feedback unit builds a system that suggests specific improvement measures based on the user's lifestyle habits when feeding back the diagnostic results. For example, it suggests areas for improvement in diet or tooth brushing habits. The feedback unit also suggests specific improvement measures for the diagnostic results based on the user's lifestyle habits. For example, it provides advice on areas for improvement in diet or tooth brushing. The feedback unit also develops a system that suggests specific improvement measures based on the user's lifestyle habits when feeding back the diagnostic results. For example, it suggests areas for improvement in the diagnostic results based on lifestyle habit data. In this way, by suggesting specific improvement measures based on the user's lifestyle habits, the user's health is supported.

[0085] The feedback unit adds a function that allows the user to share the diagnostic result when providing feedback on the diagnostic result, allowing the user to share the information with family and friends. For example, the feedback unit builds a system that adds a function that allows the user to share the diagnostic result when providing feedback on the diagnostic result. For example, a button for sharing the diagnostic result with family and friends is installed. The feedback unit also adds a function that allows the user to share the diagnostic result, allowing the user to share the information with family and friends. For example, a function for sharing the diagnostic result via social media or email is provided. The feedback unit also develops a system that adds a function that allows the user to share the diagnostic result when providing feedback on the diagnostic result. For example, a link for sharing the diagnostic result is generated, and the link is shared with family and friends. This allows the user to share the diagnostic result with family and friends, supporting health management.

[0086] The feedback unit can use the emotion estimation function to monitor the user's emotional reaction to the diagnosis result in real time and provide appropriate support. For example, the feedback unit uses the emotion estimation function to build a system that monitors the user's emotional reaction to the diagnosis result in real time. For example, if the user is feeling anxious, an encouraging message is displayed. The feedback unit also analyzes the user's emotional reaction to the diagnosis result in real time and provides appropriate support. For example, if the user is feeling stressed, advice on how to relax is displayed. The feedback unit also uses the emotion estimation function to develop a system that monitors the user's emotional reaction in real time and provides appropriate support. For example, support is provided to help the user have positive emotions toward the diagnosis result. In this way, the user's emotional reaction is monitored in real time and appropriate support is provided, thereby improving user satisfaction.

[0087] The feedback unit can automatically generate an individualized prevention plan based on the user's diagnostic history and send periodic reminders. The feedback unit, for example, builds a system that automatically generates an individualized prevention plan based on the user's diagnostic history. For example, it recommends regular tooth brushing and flossing based on past diagnostic results. The feedback unit also references the diagnostic history and provides the user with an individualized prevention plan. For example, it sends reminders for dental checkups at specific intervals. The feedback unit also develops a system that automatically generates an individualized prevention plan based on the user's diagnostic history and sends periodic reminders. For example, it suggests specific preventive measures to users who are at high risk of tooth decay. In this way, the system supports the user's health management by automatically generating an individualized prevention plan based on the user's diagnostic history and sending periodic reminders.

[0088] The feedback unit analyzes the user's lifestyle data, predicts the risk of tooth decay and periodontal disease, and enables early countermeasures to be taken. The feedback unit, for example, analyzes the user's lifestyle data and builds a system that predicts the risk of tooth decay and periodontal disease. For example, the risk is assessed based on data on dietary habits and tooth brushing habits. The feedback unit also predicts the risk of tooth decay and periodontal disease based on the lifestyle data and enables early countermeasures to be taken. For example, specific preventive measures are suggested for users at high risk. The feedback unit also analyzes the user's lifestyle data and develops a system that predicts the risk of tooth decay and periodontal disease. For example, the risk is assessed based on data on dietary habits and stress levels, and early countermeasures are taken. In this way, the user's health management is supported by analyzing the user's lifestyle data, predicting the risk of tooth decay and periodontal disease, and enabling early countermeasures to be taken.

[0089] The feedback unit can use the emotion estimation function to provide positive messages and rewards to increase the user's motivation. For example, the feedback unit uses the emotion estimation function to build a system that provides positive messages to increase the user's motivation. For example, an encouraging message is displayed when the user achieves a goal. The feedback unit also analyzes the user's emotional state in real time and provides rewards to increase motivation. For example, badges or points are awarded to users who continue to brush their teeth and floss regularly. The feedback unit also uses the emotion estimation function to develop a system that provides positive messages and rewards to increase the user's motivation. For example, positive feedback is provided when the user performs preventive measures. In this way, the system supports the user's health management by providing positive messages and rewards to increase the user's motivation.

[0090] The feedback unit can provide personalized tooth brushing instructions and dietary advice based on the user's diagnosis results. The feedback unit, for example, builds a system that provides personalized tooth brushing instructions based on the user's diagnosis results. For example, it suggests a specific tooth brushing method or flossing method. The feedback unit also provides personalized dietary advice to the user based on the diagnosis results. For example, it suggests how to choose meals to reduce the risk of tooth decay. The feedback unit also develops a system that provides personalized tooth brushing instructions and dietary advice based on the user's diagnosis results. For example, it suggests tooth brushing products and meal plans that are suitable for the user based on the diagnosis results. In this way, personalized tooth brushing instructions and dietary advice are provided based on the user's diagnosis results, thereby supporting the user's health management.

[0091] The feedback unit can add a function that allows a user to search for a nearby dental clinic based on the diagnosis result and make an appointment. The feedback unit, for example, builds a system that adds a function that allows a user to search for a nearby dental clinic based on the diagnosis result and make an appointment. For example, the system suggests the most suitable dental clinic for the user based on the diagnosis result. The feedback unit also provides a function that allows a user to search for a nearby dental clinic based on the diagnosis result and make an appointment. For example, the system suggests a dental clinic that provides the most suitable treatment for the user based on the diagnosis result. The feedback unit also develops a system that adds a function that allows a user to search for a nearby dental clinic based on the diagnosis result and make an appointment. For example, the system suggests a dental clinic that provides the most suitable treatment for the user based on the diagnosis result and makes an appointment. This supports quick treatment by allowing a user to search for a nearby dental clinic based on the diagnosis result and make an appointment.

[0092] The feedback unit can use the emotion estimation function to monitor the user's emotional state and provide appropriate support for improving their health. For example, the feedback unit uses the emotion estimation function to build a system that monitors the user's emotional state in real time. For example, if the user is feeling anxious, an encouraging message is displayed. The feedback unit also analyzes the user's emotional state in real time and provides appropriate support for improving their health. For example, if the user is feeling stressed, advice on how to relax is displayed. The feedback unit also uses the emotion estimation function to develop a system that monitors the user's emotional state and provides appropriate support for improving their health. For example, positive feedback is provided when the user performs an action to improve their health. In this way, the system monitors the user's emotional state and provides appropriate support for improving their health, thereby supporting the user's health management.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The imaging unit uses a sensor built into the reflector to measure the humidity and temperature inside the mouth and upload the data along with the photograph. For example, the humidity sensor measures the humidity inside the mouth in real time and records it as environmental data at the time of photographing. In addition, the temperature sensor measures the temperature inside the mouth and uploads that data along with the photograph. This allows detailed recording of environmental data inside the mouth, improving the accuracy of diagnosis.

[0095] The imaging unit uses a tiny camera built into the reflector that works in conjunction with a smartphone camera to simultaneously capture images of the inside of the mouth from multiple angles, allowing it to generate a 3D model. For example, the camera in the reflector captures the back of the teeth, while the smartphone camera captures the front. It is also possible to place multiple tiny cameras on the reflector and link them with the smartphone camera to generate a 3D model of the inside of the mouth. This allows for the generation of a detailed 3D model of the inside of the mouth, improving the accuracy of diagnosis.

[0096] The photographing unit can use the emotion estimation function to measure the user's stress level when taking a photograph and encourage the user to take the photograph again in a more relaxed state. For example, the emotion estimation function can be used to measure the user's stress level when taking a photograph and provide guidance to help the user relax if the user is highly stressed. This helps the user take photographs in a relaxed state and improves the quality of the photographs.

[0097] The imaging unit is equipped with a UV light on a reflector, allowing for the visualization of bacteria and plaque in the mouth. For example, the UV light illuminates bacteria and plaque with fluorescence, making them visible in photographs. The UV light can also be used to take detailed images of the state of bacteria and plaque in the mouth. This makes the bacteria and plaque in the mouth more visible, improving the accuracy of diagnosis.

[0098] The photographing unit can improve portability by making the reflector foldable. For example, the reflector can be made compact so that it can be stored in a pocket or bag. Also, a foldable reflector can be developed so that users can easily carry it. This improves the portability of the reflector and increases user convenience.

[0099] The upload unit can automatically add metadata to photos to improve the accuracy of diagnoses. For example, a system can be built that automatically adds the date and time the photo was taken, the location, and user information when the photo is uploaded. This allows for automatic addition of metadata to photos, thereby improving the accuracy of diagnoses.

[0100] The upload unit can evaluate the quality of the photo in real time and suggest optimal shooting conditions to the user. For example, a system can be constructed in which an app evaluates the quality of a photo in real time and suggests optimal shooting conditions to the user. This allows for real-time evaluation of photo quality and suggestions for optimal shooting conditions, thereby improving the accuracy of diagnosis.

[0101] The upload unit can use the emotion estimation function to analyze the emotional state of the user at the time of uploading and provide positive feedback. For example, a system can be constructed that uses the emotion estimation function to analyze the emotional state of the user at the time of uploading and provide positive feedback. This makes it possible to improve user satisfaction by analyzing the user's emotional state and providing positive feedback.

[0102] The upload unit may be added with a voice input function, allowing the user to give verbal instructions to upload photos. For example, a system may be constructed by adding a voice input function to an app, allowing the user to give verbal instructions to upload photos. This allows the user to give voice instructions to upload photos, thereby improving the convenience of operation.

[0103] When issuing a diagnosis, the diagnostic unit can refer to past diagnostic history and the user's health data to provide a more accurate diagnosis. For example, when the generation AI issues a diagnosis, it can refer to past diagnostic history and build a system that tracks changes in the user's mouth. This allows for improved diagnostic accuracy by referencing past diagnostic history and health data.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The camera unit takes intraoral photographs using a smartphone and a special reflector. For example, the user can point the smartphone camera into the mouth and use the reflector to reflect light, allowing detailed images of the condition of the teeth and gums to be taken. The camera unit also uses the reflector to brightly illuminate every corner of the mouth, allowing for clearer photographs. High-resolution intraoral photographs can also be taken using the smartphone camera. Step 2: The upload unit uploads the intraoral photographs taken by the photographing unit to a dedicated app. For example, it is designed so that users can easily upload the photographs they have taken to the app. The upload unit also has a function to automatically check the quality of the photographs and prompt the user to take a new photograph if necessary. If the photograph is blurry or if important parts are not visible, the user can be prompted to take a new photograph. Step 3: The diagnosis unit uses the generation AI to diagnose the condition of the oral cavity based on the oral cavity photographs uploaded by the upload unit. For example, the generation AI diagnoses the condition of the teeth and gums and detects signs of cavities and periodontal disease. The generation AI also generates a diagnosis based on the oral cavity photographs taken by the user. Furthermore, the generation AI incorporates data related to dentists, allowing it to diagnose the condition of the oral cavity in detail. Step 4: The feedback unit feeds back the diagnosis results made by the diagnosis unit to the user. For example, if the generation AI detects signs of tooth decay, the user can search for a nearby dental clinic through the app and make an appointment. The feedback unit also displays the diagnosis results in an easy-to-understand manner to the user, allowing them to make an appointment with a dentist if necessary. Furthermore, the feedback unit can provide the user with appropriate advice based on the diagnosis results.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, 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. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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]

[0173] 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 photography unit that takes intraoral photographs using a smartphone and a special reflector, an upload unit that uploads the intraoral photograph taken by the photographing unit to a dedicated application; a diagnosis unit that diagnoses the condition of the oral cavity using a generation AI based on the oral cavity photograph uploaded by the upload unit; a feedback unit that feeds back to a user the diagnosis result obtained by the diagnosis unit. A system characterized by:

2. The imaging unit is The reflector has a built-in sensor that measures the humidity and temperature inside the mouth and uploads the data along with a photo.

2. The system of claim 1.

3. The imaging unit is The 3D model is generated by using a minute camera built into the reflector in conjunction with the smartphone camera to simultaneously capture images of the inside of the mouth from multiple angles.

2. The system of claim 1.

4. The imaging unit is Measure the user's stress level at the time of shooting and encourage them to take the photo again in a more relaxed state 2. The system of claim 1.

5. The imaging unit is The reflector is equipped with a UV light to visualize bacteria and plaque in the mouth.

2. The system of claim 1.

6. The imaging unit is The reflector is made foldable to improve portability.

2. The system of claim 1.

7. The imaging unit is Provide the user with relaxing music and guidance to reduce anxiety felt during photography.

2. The system of claim 1.

8. The upload unit Automatically add metadata to photos to improve the accuracy of the diagnosis 2. The system of claim 1.

Images