System

Abstract

An object of the system according to the embodiment is to find a distortion of a child's body at an early stage and provide appropriate advice.SOLUTION: A system according to an embodiment includes a check unit, a detection unit, an advice unit, and a sharing unit. The check part checks the state of the body of the child. The detection unit includes a specific method of detecting the distortion of the body of the child based on the information checked by the check unit. The advice unit provides advice to the child based on the result detected by the detection unit. The sharing unit shares the advice provided by the advice unit with the parent.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to detect misalignments in children's bodies early and provide appropriate advice.

[0005] The system according to the embodiment aims to detect distortions in a child's body at an early stage and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a checking unit, a detecting unit, an advising unit, and a sharing unit. The checking unit checks the child's physical condition. The detecting unit includes a specific method for detecting distortion of the child's body based on the information checked by the checking unit. The advising unit provides advice to the child based on the results detected by the detecting unit. The sharing unit shares the advice provided by the advising unit with the parent. [Effects of the Invention]

[0007] The system according to the embodiment can detect distortions in a child's body at an early stage and provide appropriate advice. [Brief explanation of the drawings]

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

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

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

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

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

[0013] In the following embodiments, the coded storage is one or more 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) A health support system according to an embodiment of the present invention detects a child's body misalignment and allows for casual conversation using a generative AI. The health support system allows a child to check their physical condition using a smartphone or tablet, and the generative AI detects the misalignment. Based on the results, the system then converses with the child, provides advice, and shares the information with the parent. For example, the health support system checks the child's physical condition by having the child answer simple questions. The generative AI then analyzes the input information and detects any misalignment. The generative AI then provides advice regarding the child's physical condition and shares the information with the parent. This allows parents to understand their child's health and take appropriate measures. For example, if a child complains of stiff shoulders or back pain, the generative AI identifies the cause and provides appropriate advice. The health support system also sends a notification to the parent's smartphone, providing information about the child's physical condition. This allows parents to take early measures and the child to take care of themselves. The health support system detects a child's body misalignment, provides appropriate advice, and shares the information with the parent, thereby supporting the child's health. For example, if a child complains of stiff shoulders or back pain, the parent can take early measures. Additionally, children can follow the advice provided by the generative AI to take care of themselves, helping to support the health of both parents and children and lead happier lives.

[0029] A health support system according to an embodiment includes a check unit, a detection unit, an advice unit, and a sharing unit. The check unit checks a child's physical condition. For example, the check unit checks the child's physical condition by having the child answer simple questions. The check unit also allows the child to check their own physical condition using a smartphone or tablet. For example, the check unit asks a question such as, "Have you had stiff shoulders or back pain recently?" to which the child answers "yes" or "no." This information is input to the generation AI. The detection unit uses the generation AI to detect distortions of the child's body based on the information checked by the check unit. For example, the detection unit identifies which part of the body is causing the problem based on the child's answers. For example, if the child has stiff shoulders or back pain, the generation AI identifies the cause and provides appropriate advice. The advice unit uses the generation AI to provide advice to the child based on the results detected by the detection unit. The advice unit provides advice such as, "If you have stiff shoulders, it would be a good idea to do some stretching." The advice unit also shares the information with the parent. The sharing unit sends a notification to the parent's smartphone, providing information about the child's physical condition. The sharing unit, for example, sends a notification to the parent's smartphone to provide information about the child's physical condition. As a result, the health support system according to the embodiment can detect the child's body distortion, provide appropriate advice, and share the information with the parent, thereby supporting the child's health. For example, if a child complains of stiff shoulders or back pain, the parent can take early action. Furthermore, the child can take care of their body by following the advice provided by the generation AI. This supports the health of both parents and children, enabling them to live happier lives.

[0030] The checking unit can check the child's physical condition by having them answer simple questions. Examples of simple questions include, but are not limited to, yes / no questions and multiple-choice questions. For example, the checking unit checks the child's physical condition by having them answer "yes" or "no" to a question such as "Have you had shoulder stiffness or back pain recently?" The checking unit can also allow the child to answer multiple-choice questions such as "How often have you been exercising recently?" Furthermore, the checking unit can also allow the child to input a specific amount of time in response to a question such as "How much time have you been sitting recently?" In this way, the child's physical condition can be checked by answering the simple questions. Some or all of the above-described processing in the checking unit may be performed using, for example, AI, or may be performed without using AI. For example, the checking unit can input the child's answers to a generating AI and cause the generating AI to check the child's physical condition.

[0031] The detection unit can detect body distortion based on the child's answers. For example, the detection unit can identify which part of the body is causing the problem based on the child's answers. For example, if the child has stiff shoulders or back pain, the detection unit can identify the cause and provide appropriate advice. The detection unit can also evaluate the degree of body distortion based on the child's answers. For example, the detection unit can quantify the degree of stiff shoulders and provide advice based on that numerical value. The detection unit can also identify the cause of body distortion based on the child's answers. For example, the detection unit can identify that the cause is long periods of sitting work and provide countermeasures. In this way, body distortion can be detected based on the child's answers. Some or all of the above-mentioned processing in the detection unit may be performed using, or without, a generation AI. For example, the detection unit can input the child's answers into a generation AI and cause the generation AI to detect body distortion.

[0032] The advice unit can provide advice on specific stretching methods for those with stiff shoulders or lower back pain. Specific stretching methods include, but are not limited to, the type of stretching, the duration of stretching, and the number of repetitions. For example, the advice unit can provide advice such as, "If you have stiff shoulders, it's a good idea to stretch." The advice unit can also provide advice such as, "If you have lower back pain, it's a good idea to stretch your lower back." The advice unit can also provide advice such as, "If you have stiff shoulders or lower back pain, it's a good idea to stretch regularly." This makes it possible to provide appropriate advice for those with stiff shoulders or lower back pain. Some or all of the above-described processing by the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input information about stiff shoulders or lower back pain into the generation AI and cause the generation AI to provide advice on specific stretching methods.

[0033] The sharing unit can send a notification to the parent's smartphone to provide information about the child's physical condition. Notifications include, but are not limited to, push notifications and email notifications. For example, the sharing unit can send a push notification to the parent's smartphone to provide information about the child's physical condition. The sharing unit can also send an email notification to the parent's smartphone to provide information about the child's physical condition. Furthermore, the sharing unit can send an SMS notification to the parent's smartphone to provide information about the child's physical condition. In this way, information about the child's physical condition can be provided by sending a notification to the parent's smartphone. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input information about the child's physical condition to a generation AI and cause the generation AI to generate notification content for the parent.

[0034] The checking unit can analyze the child's past answer history and automatically generate appropriate questions. For example, if the child has previously complained of stiff shoulders, the checking unit can prioritize questions related to stiff shoulders. For example, the checking unit can ask a question such as, "How are your shoulders feeling lately?" If the child has previously complained of lower back pain, the checking unit can also prioritize questions related to lower back pain. For example, the checking unit can ask a question such as, "How is your lower back pain feeling lately?" If the child has previously performed a specific exercise, the checking unit can also ask a question related to that exercise. For example, the checking unit can ask a question such as, "How often have you been exercising recently?" This allows for more effective questions to be posed by automatically generating optimal questions based on the child's past answer history. Some or all of the above-described processing by the checking unit may be performed using, for example, AI, or may be performed without AI. For example, the checking unit can input the child's past answer history into the generation AI and cause the generation AI to automatically generate optimal questions.

[0035] When asking questions, the checking unit can customize the difficulty and content of the questions according to the child's age and gender. For example, for young children, the checking unit asks simple, easy-to-understand questions, such as, "What kind of games did you play today?" For elementary school children, the checking unit can also ask questions about specific physical conditions, such as, "Have you had shoulder stiffness or back pain recently?" For junior high school students and above, the checking unit can also ask questions about detailed physical conditions, such as, "How often have you been exercising recently?" This allows the difficulty and content of questions to be customized according to the child's age and gender, making it possible to ask more appropriate questions. Some or all of the above-described processing by the checking unit may be performed using, or without, AI. For example, the checking unit can input information about the child's age and gender into the generation AI and cause the generation AI to customize the difficulty and content of the questions.

[0036] When asking a question, the checking unit can adjust the question based on the child's current activity status. For example, if the child has just exercised, the checking unit can ask a question about physical fatigue. For example, the checking unit can ask a question such as, "How tired are you after exercising?" If the child is taking a break, the checking unit can also ask a question to help the child relax. For example, the checking unit can ask a question about body posture if the child is studying. For example, the checking unit can ask a question such as, "How often do you change your posture while studying?" By adjusting the question based on the child's current activity status, more appropriate questions can be asked. Some or all of the above-described processing by the checking unit can be performed using, for example, AI, or can be performed without using AI. For example, the checking unit can input information about the child's current activity status into the generation AI and cause the generation AI to adjust the question.

[0037] When asking questions, the checking unit can prioritize relevant questions taking into account the child's learning history. The checking unit, for example, asks questions related to content the child has learned in the past. For example, it asks a question such as, "Have you been putting what you learned into practice recently?" The checking unit can also ask questions related to content the child has been interested in in the past. For example, it asks a question such as, "Tell me about something that you've been interested in recently." The checking unit can also ask questions related to content the child has previously struggled with. For example, it asks a question such as, "Have you been trying things you're not good at recently?" This allows for more effective questions to be asked by prioritizing relevant questions taking into account the child's learning history. Some or all of the above-described processing by the checking unit may be performed using, or without, AI. For example, the checking unit can input information about the child's learning history into the generating AI and cause the generating AI to prioritize the execution of related questions.

[0038] When asking questions, the checking unit can customize the questions based on the child's living environment. For example, the checking unit asks questions about home activities based on the home environment. For example, it asks a question such as, "How often do you exercise at home?" The checking unit can also ask questions about school activities based on the school environment. For example, it asks a question such as, "How often do you exercise at school?" The checking unit can also ask questions about lifestyle habits based on the child's living environment. For example, it asks a question such as, "How much time do you spend sitting each day?" By customizing the questions based on the child's living environment, more appropriate questions can be asked. Some or all of the above-described processing by the checking unit may be performed using, for example, AI, or may be performed without using AI. For example, the checking unit can input information about the child's living environment into the generation AI and cause the generation AI to customize the questions.

[0039] When asking a question, the checking unit can adjust the question content to reflect feedback from the child's parent. For example, the checking unit may ask a question about the child's physical condition based on information provided by the parent. For example, the checking unit may ask a question such as, "According to information from the parent, it seems like you've been having stiff shoulders lately. How's that going?" The checking unit may also ask a question about the child's lifestyle based on information provided by the parent. For example, the checking unit may ask a question such as, "According to information from the parent, it seems like you haven't been getting enough exercise lately. How's that going?" The checking unit may also ask a question about the child's learning status based on information provided by the parent. For example, the checking unit may ask a question such as, "According to information from the parent, it seems like you haven't been able to concentrate on your studies lately. How's that going?" In this way, by adjusting the question content to reflect feedback from the child's parent, more appropriate questions can be asked. Some or all of the above-described processing by the checking unit may be performed using, or without, AI. For example, the checking unit may input parent feedback information into the generation AI and cause the generation AI to adjust the question content.

[0040] During detection, the detection unit can appropriately adjust the detection algorithm by referring to the child's past health data. For example, the detection unit can improve the accuracy of detecting body distortion based on the child's past health data. The detection unit can also perform detection focused on specific parts based on the child's past health data. Furthermore, the detection unit can adjust the detection algorithm based on the child's past health data to obtain optimal detection results. Thus, by optimizing the detection algorithm by referring to the child's past health data, detection accuracy is improved. Some or all of the above-described processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit can input the child's past health data into the generation AI and cause the generation AI to adjust the detection algorithm.

[0041] The detection unit can customize the detection method according to the child's body shape and growth stage during detection. For example, in the case of a toddler, the detection unit identifies body distortion using a detection method tailored to the child's body shape. In addition, in the case of an elementary school student, the detection unit can also identify body distortion using a detection method tailored to the child's growth stage. Furthermore, in the case of a junior high school student or older, the detection unit can also identify body distortion using a detailed detection method tailored to the child's body shape and growth stage. This allows for more accurate detection by customizing the detection method according to the child's body shape and growth stage. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the detection unit can input information about the child's body shape and growth stage into the generation AI and cause the generation AI to customize the detection method.

[0042] The detection unit can improve detection accuracy by taking into account the child's lifestyle habits during detection. The detection unit can improve detection accuracy of body distortion by taking into account, for example, the child's exercise frequency. The detection unit can also improve detection accuracy of body distortion by taking into account the child's diet. Furthermore, the detection unit can improve detection accuracy of body distortion by taking into account the child's overall lifestyle habits. This enables more accurate detection by improving detection accuracy by taking into account the child's lifestyle habits. Some or all of the above-mentioned processing in the detection unit can be performed using, or without, the generation AI, for example. For example, the detection unit can input information about the child's lifestyle habits into the generation AI and cause the generation AI to improve detection accuracy.

[0043] The detection unit can perform detection taking into account the child's geographical environment. For example, in the case of a child living in a cold region, the detection unit can perform detection taking into account body distortion due to the cold. Furthermore, in the case of a child living in a hot and humid region, the detection unit can perform detection taking into account body distortion due to humidity. Furthermore, in the case of a child living in a mountainous region, the detection unit can perform detection taking into account body distortion due to the terrain. Thus, by performing detection taking into account the child's geographical environment, more accurate detection is possible. Some or all of the above-described processing in the detection unit may be performed using, or without, the generation AI, for example. For example, the detection unit can input information about the child's geographical environment into the generation AI and have the generation AI perform detection.

[0044] During detection, the detection unit can improve detection accuracy by referring to the health data of the child's family. For example, the detection unit can improve detection accuracy by taking into account genetic factors based on the family's health data. The detection unit can also improve detection accuracy by taking into account specific health risks based on the family's health data. Furthermore, the detection unit can improve detection accuracy by analyzing body distortion trends based on the family's health data. By improving detection accuracy by referring to the child's family's health data, more accurate detection becomes possible. Some or all of the above-described processing in the detection unit can be performed, for example, using or without the generation AI. For example, the detection unit can input the family's health data into the generation AI and have the generation AI improve detection accuracy.

[0045] The detection unit can perform detection by taking into account activity data from the child's school or sports club. The detection unit can improve the accuracy of detecting body distortion by taking into account, for example, the amount of exercise at school. The detection unit can also improve the accuracy of detecting body distortion by taking into account activity data at the sports club. Furthermore, the detection unit can also improve the accuracy of detecting body distortion by taking into account all activities at school or sports club. This enables more accurate detection by performing detection by taking into account the activity data from the child's school or sports club. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the detection unit can input activity data from the school or sports club into the generation AI and have the generation AI perform detection.

[0046] When giving advice, the advice unit can provide appropriate advice by referring to the child's past health data. For example, the advice unit can provide advice on a specific body part based on the child's past health data. The advice unit can also identify the cause of a body distortion based on the child's past health data and provide appropriate advice. Furthermore, the advice unit can provide preventive advice based on the child's past health data. This enables more effective advice by providing optimal advice by referring to the child's past health data. Some or all of the above-mentioned processing in the advice unit can be performed using, or without, the generation AI. For example, the advice unit can input the child's past health data into the generation AI and cause the generation AI to provide appropriate advice.

[0047] When giving advice, the advice unit can customize the content of the advice according to the child's age and gender. For example, for young children, the advice unit provides simple, easy-to-understand advice. The advice unit can also provide elementary school children with advice on specific body care methods. Furthermore, the advice unit can also provide junior high school students and above with detailed advice on body care methods. In this way, by customizing the content of the advice according to the child's age and gender, more appropriate advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, the generation AI, for example. For example, the advice unit can input information on the child's age and gender into the generation AI and cause the generation AI to customize the content of the advice.

[0048] When giving advice, the advice unit can adjust the advice based on the child's current activity status. For example, if the child has just exercised, the advice unit can provide advice on how to relieve physical fatigue. Furthermore, if the child is taking a break, the advice unit can also provide advice on how to relax. Furthermore, if the child is studying, the advice unit can also provide advice on how to improve posture. In this way, by adjusting the advice based on the child's current activity status, more appropriate advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can input information on the child's current activity status into the generation AI and cause the generation AI to adjust the advice.

[0049] When providing advice, the advice unit can prioritize relevant advice by taking into account the child's learning history. The advice unit, for example, provides advice related to content that the child has learned in the past. The advice unit can also provide advice related to content that the child has been interested in in the past. Furthermore, the advice unit can also provide advice related to content that the child has previously struggled with. This enables more effective advice by prioritizing relevant advice by taking into account the child's learning history. Some or all of the above-described processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input information about the child's learning history into the generation AI and cause the generation AI to prioritize the provision of relevant advice.

[0050] When providing advice, the advice unit can customize the advice based on the child's living environment. For example, the advice unit provides advice that can be implemented at home based on the home environment. The advice unit can also provide advice that can be implemented at school based on the school environment. Furthermore, the advice unit can provide advice regarding lifestyle habits based on the child's living environment. This allows more appropriate advice to be provided by customizing the advice based on the child's living environment. Some or all of the above-described processing in the advice unit may be performed using, or without, the generation AI, for example. For example, the advice unit can input information about the child's living environment into the generation AI and cause the generation AI to customize the advice.

[0051] When giving advice, the advice unit can adjust the advice content by reflecting feedback from the child's parent. For example, the advice unit provides advice regarding the child's physical condition based on information provided by the parent. The advice unit can also provide advice regarding the child's lifestyle habits based on information provided by the parent. Furthermore, the advice unit can also provide advice regarding the child's learning situation based on information provided by the parent. In this way, by adjusting the advice content by reflecting feedback from the child's parent, more appropriate advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input parental feedback information into the generation AI and cause the generation AI to adjust the advice content.

[0052] The sharing unit can optimize the notification content by referring to the parent's past feedback when sharing. The sharing unit can, for example, adjust the notification content based on feedback provided by the parent in the past. The sharing unit can also prioritize notifying specific information based on feedback provided by the parent in the past. The sharing unit can also adjust the frequency of notifications based on feedback provided by the parent in the past. This enables more effective notifications by optimizing the notification content by referring to the parent's past feedback. Some or all of the above-described processing in the sharing unit can be performed using, for example, AI, or can be performed without using AI. For example, the sharing unit can input information about the parent's past feedback into the generation AI and cause the generation AI to optimize the notification content.

[0053] The sharing unit can adjust the timing of notifications based on the parent's living situation during sharing. For example, when the parent is at work, the sharing unit sends only important notifications. The sharing unit can also send detailed notifications when the parent is on break. Furthermore, the sharing unit can adjust the timing of notifications based on the parent's living situation. This allows for more appropriate notifications by adjusting the timing of notifications based on the parent's living situation. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input information about the parent's living situation into the generation AI and have the generation AI adjust the timing of notifications.

[0054] The sharing unit can select an appropriate notification method according to the parent's device information when sharing. For example, if the parent is using a smartphone, the sharing unit can send a push notification. Also, if the parent is using a tablet, the sharing unit can send an email notification. Furthermore, the sharing unit can select the optimal notification method based on the parent's device information. This enables more effective notification by selecting the optimal notification method according to the parent's device information. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the parent's device information into the generation AI and have the generation AI select the notification method.

[0055] The sharing unit can send notifications taking into account the parent's geographical environment when sharing. For example, the sharing unit can send a detailed notification when the parent is at home. The sharing unit can also send a brief notification when the parent is at work. Furthermore, the sharing unit can adjust the content and timing of the notification based on the parent's geographical environment. This allows for more appropriate notifications by sending notifications taking into account the parent's geographical environment. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input information about the parent's geographical environment into the generation AI and have the generation AI execute the notification.

[0056] When sharing, the sharing unit can customize the notification content by referring to the parent's family health data. For example, the sharing unit can send notifications regarding specific health risks based on the family's health data. The sharing unit can also send notifications including preventive advice based on the family's health data. Furthermore, the sharing unit can send notifications regarding body distortion trends based on the family's health data. This allows for more appropriate notifications by customizing the notification content by referring to the parent's family health data. Some or all of the above-mentioned processing in the sharing unit may be performed using, or without, AI, for example. For example, the sharing unit can input the family's health data into the generation AI and have the generation AI customize the notification content.

[0057] At the time of sharing, the sharing unit can analyze the parent's social media activity and select the optimal notification method. For example, if the parent is active on social media, the sharing unit can send a notification via social media. Also, if the parent is not very active on social media, the sharing unit can send an email notification. Furthermore, the sharing unit can select the optimal notification method based on the parent's social media activity. This enables more effective notification by analyzing the parent's social media activity and selecting the optimal notification method. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input information about the parent's social media activity into the generation AI and have the generation AI select the notification method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The health support system can further include a motion sensor unit that monitors a child's body movements in real time. The motion sensor unit detects how a child moves their body in daily life and analyzes the data. For example, the motion sensor unit can issue a warning if a child remains in the same position for a long period of time. The motion sensor unit can also analyze a child's movements while exercising and provide advice on appropriate exercise methods. Furthermore, if a child is moving unnaturally, the motion sensor unit can identify the cause and provide appropriate measures. This allows for more accurate health support by monitoring a child's body movements in real time.

[0060] The check unit may be equipped with a vital sensor that measures the child's body temperature and heart rate. The vital sensor measures the child's body temperature and heart rate in real time and analyzes the data. For example, the vital sensor can issue a warning if the child's body temperature is abnormally high. The vital sensor can also identify the cause of an abnormally high heart rate and provide appropriate measures. Furthermore, the vital sensor can evaluate the child's health condition and provide appropriate advice based on the child's body temperature and heart rate data. This allows for more accurate health support by measuring the child's body temperature and heart rate in real time.

[0061] The health support system may further include a meal recorder that records the child's diet. The meal recorder allows the child to input daily meal information and analyzes the data. For example, the meal recorder can evaluate whether the child is eating a balanced diet. If the child is lacking in a particular nutrient, the meal recorder can also provide advice on how to supplement that nutrient. Furthermore, the meal recorder can share the child's diet with the parent and provide information for the parent to manage the child's diet. This allows for more comprehensive health support by recording the child's diet.

[0062] The health support system may further include a sleep monitor unit that monitors a child's sleep patterns. The sleep monitor unit measures the child's sleep duration and sleep quality and analyzes the data. For example, the sleep monitor unit can issue a warning if the child is not getting enough sleep. The sleep monitor unit can also identify the cause of poor sleep quality and provide appropriate measures. Furthermore, the sleep monitor unit can share the child's sleep patterns with parents and provide information for parents to manage their child's sleep. This allows for more comprehensive health support by monitoring a child's sleep patterns.

[0063] The health support system may further include an exercise recording unit that records the child's exercise history. The exercise recording unit receives the child's daily exercise input and analyzes the data. For example, the exercise recording unit may evaluate whether the child is getting enough exercise. If the child is lacking in a particular type of exercise, the exercise recording unit may provide advice on how to supplement that exercise. Furthermore, the exercise recording unit may share the child's exercise history with the parent and provide the parent with information to manage the child's exercise. In this way, recording the child's exercise history enables more comprehensive health support.

[0064] The health support system can further include a learning record unit that records a child's learning status. The learning record unit receives input from the child about their daily learning and analyzes the data. For example, the learning record unit can identify which subjects a child is weak in. The learning record unit can also evaluate a child's efforts when their grades improve in a particular subject. Furthermore, the learning record unit can share the child's learning status with parents and provide them with information to support their child's learning. By recording a child's learning status, more comprehensive support can be provided.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The check unit checks the child's physical condition. For example, the child can check their physical condition by answering simple questions. Children can also check their own physical condition using a smartphone or tablet. For example, to a question such as, "Have you had shoulder stiffness or back pain recently?" the child answers "yes" or "no." This information is input into the generation AI. Step 2: The detection unit uses the generation AI to detect distortions in the child's body based on the information checked by the checking unit. For example, it identifies which part of the body has a problem based on the child's answers. If the child has stiff shoulders or back pain, the generation AI will identify the cause and provide appropriate advice. Step 3: The advice unit uses the generation AI to provide advice to the child based on the results detected by the detection unit. For example, the advice unit may provide advice such as, "If you have stiff shoulders, it would be good to do some stretching." The advice unit also shares this information with the parent. Step 4: The sharing unit sends a notification to the parent's smartphone, providing information about the child's physical condition, allowing the parent to understand the child's physical condition and take early action.

[0067] (Example 2) A health support system according to an embodiment of the present invention detects a child's body misalignment and allows for casual conversation using a generative AI. The health support system allows a child to check their physical condition using a smartphone or tablet, and the generative AI detects the misalignment. Based on the results, the system then converses with the child, provides advice, and shares the information with the parent. For example, the health support system checks the child's physical condition by having the child answer simple questions. The generative AI then analyzes the input information and detects any misalignment. The generative AI then provides advice regarding the child's physical condition and shares the information with the parent. This allows parents to understand their child's health and take appropriate measures. For example, if a child complains of stiff shoulders or back pain, the generative AI identifies the cause and provides appropriate advice. The health support system also sends a notification to the parent's smartphone, providing information about the child's physical condition. This allows parents to take early measures and the child to take care of themselves. The health support system detects a child's body misalignment, provides appropriate advice, and shares the information with the parent, thereby supporting the child's health. For example, if a child complains of stiff shoulders or back pain, the parent can take early measures. Additionally, children can follow the advice provided by the generative AI to take care of themselves, helping to support the health of both parents and children and lead happier lives.

[0068] A health support system according to an embodiment includes a check unit, a detection unit, an advice unit, and a sharing unit. The check unit checks a child's physical condition. For example, the check unit checks the child's physical condition by having the child answer simple questions. The check unit also allows the child to check their own physical condition using a smartphone or tablet. For example, the check unit asks a question such as, "Have you had stiff shoulders or back pain recently?" to which the child answers "yes" or "no." This information is input to the generation AI. The detection unit uses the generation AI to detect distortions of the child's body based on the information checked by the check unit. For example, the detection unit identifies which part of the body is causing the problem based on the child's answers. For example, if the child has stiff shoulders or back pain, the generation AI identifies the cause and provides appropriate advice. The advice unit uses the generation AI to provide advice to the child based on the results detected by the detection unit. The advice unit provides advice such as, "If you have stiff shoulders, it would be a good idea to do some stretching." The advice unit also shares the information with the parent. The sharing unit sends a notification to the parent's smartphone, providing information about the child's physical condition. The sharing unit, for example, sends a notification to the parent's smartphone to provide information about the child's physical condition. As a result, the health support system according to the embodiment can detect the child's body distortion, provide appropriate advice, and share the information with the parent, thereby supporting the child's health. For example, if a child complains of stiff shoulders or back pain, the parent can take early action. Furthermore, the child can take care of their body by following the advice provided by the generation AI. This supports the health of both parents and children, enabling them to live happier lives.

[0069] The checking unit can check the child's physical condition by having them answer simple questions. Examples of simple questions include, but are not limited to, yes / no questions and multiple-choice questions. For example, the checking unit checks the child's physical condition by having them answer "yes" or "no" to a question such as "Have you had shoulder stiffness or back pain recently?" The checking unit can also allow the child to answer multiple-choice questions such as "How often have you been exercising recently?" Furthermore, the checking unit can also allow the child to input a specific amount of time in response to a question such as "How much time have you been sitting recently?" In this way, the child's physical condition can be checked by answering the simple questions. Some or all of the above-described processing in the checking unit may be performed using, for example, AI, or may be performed without using AI. For example, the checking unit can input the child's answers to a generating AI and cause the generating AI to check the child's physical condition.

[0070] The detection unit can detect body distortion based on the child's answers. For example, the detection unit can identify which part of the body is causing the problem based on the child's answers. For example, if the child has stiff shoulders or back pain, the detection unit can identify the cause and provide appropriate advice. The detection unit can also evaluate the degree of body distortion based on the child's answers. For example, the detection unit can quantify the degree of stiff shoulders and provide advice based on that numerical value. The detection unit can also identify the cause of body distortion based on the child's answers. For example, the detection unit can identify that the cause is long periods of sitting work and provide countermeasures. In this way, body distortion can be detected based on the child's answers. Some or all of the above-mentioned processing in the detection unit may be performed using, or without, a generation AI. For example, the detection unit can input the child's answers into a generation AI and cause the generation AI to detect body distortion.

[0071] The advice unit can provide advice on specific stretching methods for those with stiff shoulders or lower back pain. Specific stretching methods include, but are not limited to, the type of stretching, the duration of stretching, and the number of repetitions. For example, the advice unit can provide advice such as, "If you have stiff shoulders, it's a good idea to stretch." The advice unit can also provide advice such as, "If you have lower back pain, it's a good idea to stretch your lower back." The advice unit can also provide advice such as, "If you have stiff shoulders or lower back pain, it's a good idea to stretch regularly." This makes it possible to provide appropriate advice for those with stiff shoulders or lower back pain. Some or all of the above-described processing by the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input information about stiff shoulders or lower back pain into the generation AI and cause the generation AI to provide advice on specific stretching methods.

[0072] The sharing unit can send a notification to the parent's smartphone to provide information about the child's physical condition. Notifications include, but are not limited to, push notifications and email notifications. For example, the sharing unit can send a push notification to the parent's smartphone to provide information about the child's physical condition. The sharing unit can also send an email notification to the parent's smartphone to provide information about the child's physical condition. Furthermore, the sharing unit can send an SMS notification to the parent's smartphone to provide information about the child's physical condition. In this way, information about the child's physical condition can be provided by sending a notification to the parent's smartphone. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input information about the child's physical condition to a generation AI and cause the generation AI to generate notification content for the parent.

[0073] The check unit can estimate the child's emotions and adjust the content and timing of questions based on the estimated child's emotions. For example, if the child is feeling stressed, the check unit prioritizes questions that will help the child relax. For example, it may ask a question such as, "What have you enjoyed recently?" If the child is relaxed, the check unit can also ask specific questions about the child's physical condition. For example, it may ask a question such as, "Have you had shoulder stiffness or back pain recently?" If the child is tired, the check unit can also ask short and simple questions such as, "How much exercise did you do today?" This allows the content and timing of questions to be adjusted based on the child's emotions, resulting in more appropriate questions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the check unit may be performed using, for example, AI, or without AI. For example, the checking unit can input the child's emotional data into the generation AI and have the generation AI adjust the content and timing of the questions.

[0074] The checking unit can analyze the child's past answer history and automatically generate appropriate questions. For example, if the child has previously complained of stiff shoulders, the checking unit can prioritize questions related to stiff shoulders. For example, the checking unit can ask a question such as, "How are your shoulders feeling lately?" If the child has previously complained of lower back pain, the checking unit can also prioritize questions related to lower back pain. For example, the checking unit can ask a question such as, "How is your lower back pain feeling lately?" If the child has previously performed a specific exercise, the checking unit can also ask a question related to that exercise. For example, the checking unit can ask a question such as, "How often have you been exercising recently?" This allows for more effective questions to be posed by automatically generating optimal questions based on the child's past answer history. Some or all of the above-described processing by the checking unit may be performed using, for example, AI, or may be performed without AI. For example, the checking unit can input the child's past answer history into the generation AI and cause the generation AI to automatically generate optimal questions.

[0075] When asking questions, the checking unit can customize the difficulty and content of the questions according to the child's age and gender. For example, for young children, the checking unit asks simple, easy-to-understand questions, such as, "What kind of games did you play today?" For elementary school children, the checking unit can also ask questions about specific physical conditions, such as, "Have you had shoulder stiffness or back pain recently?" For junior high school students and above, the checking unit can also ask questions about detailed physical conditions, such as, "How often have you been exercising recently?" This allows the difficulty and content of questions to be customized according to the child's age and gender, making it possible to ask more appropriate questions. Some or all of the above-described processing by the checking unit may be performed using, or without, AI. For example, the checking unit can input information about the child's age and gender into the generation AI and cause the generation AI to customize the difficulty and content of the questions.

[0076] When asking a question, the checking unit can adjust the question based on the child's current activity status. For example, if the child has just exercised, the checking unit can ask a question about physical fatigue. For example, the checking unit can ask a question such as, "How tired are you after exercising?" If the child is taking a break, the checking unit can also ask a question to help the child relax. For example, the checking unit can ask a question about body posture if the child is studying. For example, the checking unit can ask a question such as, "How often do you change your posture while studying?" By adjusting the question based on the child's current activity status, more appropriate questions can be asked. Some or all of the above-described processing by the checking unit can be performed using, for example, AI, or can be performed without using AI. For example, the checking unit can input information about the child's current activity status into the generation AI and cause the generation AI to adjust the question.

[0077] The check unit can estimate the child's emotions and adjust the order of questions based on the estimated child's emotions. For example, if the child is feeling stressed, the check unit can first ask questions that will help the child relax, followed by questions about their physical condition. For example, the check unit can first ask a question such as, "What have you enjoyed recently?", followed by a question such as, "Have you had stiff shoulders or back pain recently?" If the child is relaxed, the check unit can first ask specific questions about their physical condition, followed by questions that will help the child relax. For example, the check unit can first ask a question such as, "Have you had stiff shoulders or back pain recently?", followed by a question such as, "What have you enjoyed recently?" If the child is tired, the check unit can first ask short, simple questions, followed by more detailed questions. For example, the check unit can first ask a question such as, "How much exercise did you do today?", followed by a question such as, "Have you had stiff shoulders or back pain recently?" By adjusting the order of questions based on the child's emotions, more appropriate questions can be asked. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the check unit may be performed using AI, or may be performed without using AI. For example, the check unit may input child emotion data into the generation AI and cause the generation AI to adjust the order of questions.

[0078] When asking questions, the checking unit can prioritize relevant questions taking into account the child's learning history. The checking unit, for example, asks questions related to content the child has learned in the past. For example, it asks a question such as, "Have you been putting what you learned into practice recently?" The checking unit can also ask questions related to content the child has been interested in in the past. For example, it asks a question such as, "Tell me about something that you've been interested in recently." The checking unit can also ask questions related to content the child has previously struggled with. For example, it asks a question such as, "Have you been trying things you're not good at recently?" This allows for more effective questions to be asked by prioritizing relevant questions taking into account the child's learning history. Some or all of the above-described processing by the checking unit may be performed using, or without, AI. For example, the checking unit can input information about the child's learning history into the generating AI and cause the generating AI to prioritize the execution of related questions.

[0079] When asking questions, the checking unit can customize the questions based on the child's living environment. For example, the checking unit asks questions about home activities based on the home environment. For example, it asks a question such as, "How often do you exercise at home?" The checking unit can also ask questions about school activities based on the school environment. For example, it asks a question such as, "How often do you exercise at school?" The checking unit can also ask questions about lifestyle habits based on the child's living environment. For example, it asks a question such as, "How much time do you spend sitting each day?" By customizing the questions based on the child's living environment, more appropriate questions can be asked. Some or all of the above-described processing by the checking unit may be performed using, for example, AI, or may be performed without using AI. For example, the checking unit can input information about the child's living environment into the generation AI and cause the generation AI to customize the questions.

[0080] When asking a question, the checking unit can adjust the question content to reflect feedback from the child's parent. For example, the checking unit may ask a question about the child's physical condition based on information provided by the parent. For example, the checking unit may ask a question such as, "According to information from the parent, it seems like you've been having stiff shoulders lately. How's that going?" The checking unit may also ask a question about the child's lifestyle based on information provided by the parent. For example, the checking unit may ask a question such as, "According to information from the parent, it seems like you haven't been getting enough exercise lately. How's that going?" The checking unit may also ask a question about the child's learning status based on information provided by the parent. For example, the checking unit may ask a question such as, "According to information from the parent, it seems like you haven't been able to concentrate on your studies lately. How's that going?" In this way, by adjusting the question content to reflect feedback from the child's parent, more appropriate questions can be asked. Some or all of the above-described processing by the checking unit may be performed using, or without, AI. For example, the checking unit may input parent feedback information into the generation AI and cause the generation AI to adjust the question content.

[0081] The detection unit can estimate the child's emotions and adjust the accuracy of body distortion detection based on the estimated child's emotions. For example, when the child is relaxed, the detection unit performs detailed detection and accurately identifies the body distortion. Furthermore, when the child is stressed, the detection unit can perform simple detection and identify the general location of the body distortion. Furthermore, when the child is tired, the detection unit can perform detection in a short time and identify the main part of the body distortion. This enables more accurate detection by adjusting the body distortion detection accuracy based on the child's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, for example, the generative AI. For example, the detection unit can input the child's emotion data into the generative AI and cause the generative AI to adjust the body distortion detection accuracy.

[0082] During detection, the detection unit can appropriately adjust the detection algorithm by referring to the child's past health data. For example, the detection unit can improve the accuracy of detecting body distortion based on the child's past health data. The detection unit can also perform detection focused on specific parts based on the child's past health data. Furthermore, the detection unit can adjust the detection algorithm based on the child's past health data to obtain optimal detection results. Thus, by optimizing the detection algorithm by referring to the child's past health data, detection accuracy is improved. Some or all of the above-described processing in the detection unit may be performed using, or without, the generation AI. For example, the detection unit can input the child's past health data into the generation AI and cause the generation AI to adjust the detection algorithm.

[0083] The detection unit can customize the detection method according to the child's body shape and growth stage during detection. For example, in the case of a toddler, the detection unit identifies body distortion using a detection method tailored to the child's body shape. In addition, in the case of an elementary school student, the detection unit can also identify body distortion using a detection method tailored to the child's growth stage. Furthermore, in the case of a junior high school student or older, the detection unit can also identify body distortion using a detailed detection method tailored to the child's body shape and growth stage. This allows for more accurate detection by customizing the detection method according to the child's body shape and growth stage. Some or all of the above-described processing in the detection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the detection unit can input information about the child's body shape and growth stage into the generation AI and cause the generation AI to customize the detection method.

[0084] The detection unit can improve detection accuracy by taking into account the child's lifestyle habits during detection. The detection unit can improve detection accuracy of body distortion by taking into account, for example, the child's exercise frequency. The detection unit can also improve detection accuracy of body distortion by taking into account the child's diet. Furthermore, the detection unit can improve detection accuracy of body distortion by taking into account the child's overall lifestyle habits. This enables more accurate detection by improving detection accuracy by taking into account the child's lifestyle habits. Some or all of the above-mentioned processing in the detection unit can be performed using, or without, the generation AI, for example. For example, the detection unit can input information about the child's lifestyle habits into the generation AI and cause the generation AI to improve detection accuracy.

[0085] The detection unit can estimate the child's emotion and adjust the display method of the detection result based on the estimated emotion. For example, if the child is relaxed, the detection unit can display a detailed detection result. Furthermore, if the child is stressed, the detection unit can display a simple detection result. Furthermore, if the child is tired, the detection unit can display a detection result that can be understood in a short time. By adjusting the display method of the detection result based on the child's emotion, it is possible to display a more easily understandable display. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the detection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the detection unit can input the child's emotion data to the generation AI and cause the generation AI to adjust the display method of the detection result.

[0086] The detection unit can perform detection taking into account the child's geographical environment. For example, in the case of a child living in a cold region, the detection unit can perform detection taking into account body distortion due to the cold. Furthermore, in the case of a child living in a hot and humid region, the detection unit can perform detection taking into account body distortion due to humidity. Furthermore, in the case of a child living in a mountainous region, the detection unit can perform detection taking into account body distortion due to the terrain. Thus, by performing detection taking into account the child's geographical environment, more accurate detection is possible. Some or all of the above-described processing in the detection unit may be performed using, or without, the generation AI, for example. For example, the detection unit can input information about the child's geographical environment into the generation AI and have the generation AI perform detection.

[0087] During detection, the detection unit can improve detection accuracy by referring to the health data of the child's family. For example, the detection unit can improve detection accuracy by taking into account genetic factors based on the family's health data. The detection unit can also improve detection accuracy by taking into account specific health risks based on the family's health data. Furthermore, the detection unit can improve detection accuracy by analyzing body distortion trends based on the family's health data. By improving detection accuracy by referring to the child's family's health data, more accurate detection becomes possible. Some or all of the above-described processing in the detection unit can be performed, for example, using or without the generation AI. For example, the detection unit can input the family's health data into the generation AI and have the generation AI improve detection accuracy.

[0088] The detection unit can perform detection by taking into account activity data from the child's school or sports club. The detection unit can improve the accuracy of detecting body distortion by taking into account, for example, the amount of exercise at school. The detection unit can also improve the accuracy of detecting body distortion by taking into account activity data at the sports club. Furthermore, the detection unit can also improve the accuracy of detecting body distortion by taking into account all activities at school or sports club. This enables more accurate detection by performing detection by taking into account the activity data from the child's school or sports club. Some or all of the above-mentioned processing in the detection unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the detection unit can input activity data from the school or sports club into the generation AI and have the generation AI perform detection.

[0089] The advice unit can estimate the child's emotions and adjust the way the advice is expressed based on the estimated child's emotions. For example, if the child is relaxed, the advice unit can provide detailed advice. Furthermore, if the child is stressed, the advice unit can provide concise and easy-to-understand advice. Furthermore, if the child is tired, the advice unit can provide advice that can be implemented in a short time. This allows for more appropriate advice to be provided by adjusting the way the advice is expressed based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the advice unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the advice unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the way the advice is expressed.

[0090] When giving advice, the advice unit can provide appropriate advice by referring to the child's past health data. For example, the advice unit can provide advice on a specific body part based on the child's past health data. The advice unit can also identify the cause of a body distortion based on the child's past health data and provide appropriate advice. Furthermore, the advice unit can provide preventive advice based on the child's past health data. This enables more effective advice by providing optimal advice by referring to the child's past health data. Some or all of the above-mentioned processing in the advice unit can be performed using, or without, the generation AI. For example, the advice unit can input the child's past health data into the generation AI and cause the generation AI to provide appropriate advice.

[0091] When giving advice, the advice unit can customize the content of the advice according to the child's age and gender. For example, for young children, the advice unit provides simple, easy-to-understand advice. The advice unit can also provide elementary school children with advice on specific body care methods. Furthermore, the advice unit can also provide junior high school students and above with detailed advice on body care methods. In this way, by customizing the content of the advice according to the child's age and gender, more appropriate advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, the generation AI, for example. For example, the advice unit can input information on the child's age and gender into the generation AI and cause the generation AI to customize the content of the advice.

[0092] When giving advice, the advice unit can adjust the advice based on the child's current activity status. For example, if the child has just exercised, the advice unit can provide advice on how to relieve physical fatigue. Furthermore, if the child is taking a break, the advice unit can also provide advice on how to relax. Furthermore, if the child is studying, the advice unit can also provide advice on how to improve posture. In this way, by adjusting the advice based on the child's current activity status, more appropriate advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the advice unit can input information on the child's current activity status into the generation AI and cause the generation AI to adjust the advice.

[0093] The advice unit can estimate the child's emotions and determine the priority of advice based on the estimated child's emotions. For example, if the child is feeling stressed, the advice unit can prioritize providing advice that helps the child relax. Furthermore, if the child is relaxed, the advice unit can prioritize providing specific advice regarding body care. Furthermore, if the child is tired, the advice unit can prioritize providing advice that can be implemented in a short time. This allows more appropriate advice to be provided by determining the priority of advice based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the advice unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the advice unit can input the child's emotion data into the generation AI and have the generation AI determine the priority of advice.

[0094] When providing advice, the advice unit can prioritize relevant advice by taking into account the child's learning history. The advice unit, for example, provides advice related to content that the child has learned in the past. The advice unit can also provide advice related to content that the child has been interested in in the past. Furthermore, the advice unit can also provide advice related to content that the child has previously struggled with. This enables more effective advice by prioritizing relevant advice by taking into account the child's learning history. Some or all of the above-described processing in the advice unit may be performed using, or without, the generation AI. For example, the advice unit can input information about the child's learning history into the generation AI and cause the generation AI to prioritize the provision of relevant advice.

[0095] When providing advice, the advice unit can customize the advice based on the child's living environment. For example, the advice unit provides advice that can be implemented at home based on the home environment. The advice unit can also provide advice that can be implemented at school based on the school environment. Furthermore, the advice unit can provide advice regarding lifestyle habits based on the child's living environment. This allows more appropriate advice to be provided by customizing the advice based on the child's living environment. Some or all of the above-described processing in the advice unit may be performed using, or without, the generation AI, for example. For example, the advice unit can input information about the child's living environment into the generation AI and cause the generation AI to customize the advice.

[0096] When giving advice, the advice unit can adjust the advice content by reflecting feedback from the child's parent. For example, the advice unit provides advice regarding the child's physical condition based on information provided by the parent. The advice unit can also provide advice regarding the child's lifestyle habits based on information provided by the parent. Furthermore, the advice unit can also provide advice regarding the child's learning situation based on information provided by the parent. In this way, by adjusting the advice content by reflecting feedback from the child's parent, more appropriate advice can be provided. Some or all of the above-mentioned processing in the advice unit may be performed using, or without, a generation AI. For example, the advice unit can input parental feedback information into the generation AI and cause the generation AI to adjust the advice content.

[0097] The sharing unit can estimate the child's emotions and adjust the content of the notification to the parent based on the estimated child's emotions. For example, if the child is relaxed, the sharing unit can send detailed notification content to the parent. Furthermore, if the child is stressed, the sharing unit can also send concise and easy-to-understand notification content to the parent. Furthermore, if the child is tired, the sharing unit can also send notification content that can be quickly understood to the parent. This allows for more appropriate notification by adjusting the content of the notification to the parent based on the child's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the sharing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sharing unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the content of the notification to the parent.

[0098] The sharing unit can optimize the notification content by referring to the parent's past feedback when sharing. The sharing unit can, for example, adjust the notification content based on feedback provided by the parent in the past. The sharing unit can also prioritize notifying specific information based on feedback provided by the parent in the past. The sharing unit can also adjust the frequency of notifications based on feedback provided by the parent in the past. This enables more effective notifications by optimizing the notification content by referring to the parent's past feedback. Some or all of the above-described processing in the sharing unit can be performed using, for example, AI, or can be performed without using AI. For example, the sharing unit can input information about the parent's past feedback into the generation AI and cause the generation AI to optimize the notification content.

[0099] The sharing unit can adjust the timing of notifications based on the parent's living situation during sharing. For example, when the parent is at work, the sharing unit sends only important notifications. The sharing unit can also send detailed notifications when the parent is on break. Furthermore, the sharing unit can adjust the timing of notifications based on the parent's living situation. This allows for more appropriate notifications by adjusting the timing of notifications based on the parent's living situation. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input information about the parent's living situation into the generation AI and have the generation AI adjust the timing of notifications.

[0100] The sharing unit can select an appropriate notification method according to the parent's device information when sharing. For example, if the parent is using a smartphone, the sharing unit can send a push notification. Also, if the parent is using a tablet, the sharing unit can send an email notification. Furthermore, the sharing unit can select the optimal notification method based on the parent's device information. This enables more effective notification by selecting the optimal notification method according to the parent's device information. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the parent's device information into the generation AI and have the generation AI select the notification method.

[0101] The sharing unit can estimate the child's emotions and determine the priority of notifications to the parent based on the estimated child's emotions. For example, if the child is stressed, the sharing unit can prioritize sending important notifications. Furthermore, if the child is relaxed, the sharing unit can prioritize sending detailed notifications. Furthermore, if the child is tired, the sharing unit can prioritize sending notifications that can be understood in a short time. This enables more appropriate notifications by determining the priority of notifications to the parent based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the sharing unit can be performed using, for example, AI, or without AI. For example, the sharing unit can input the child's emotion data into the generation AI and have the generation AI determine the priority of notifications.

[0102] The sharing unit can send notifications taking into account the parent's geographical environment when sharing. For example, the sharing unit can send a detailed notification when the parent is at home. The sharing unit can also send a brief notification when the parent is at work. Furthermore, the sharing unit can adjust the content and timing of the notification based on the parent's geographical environment. This allows for more appropriate notifications by sending notifications taking into account the parent's geographical environment. Some or all of the above-mentioned processing in the sharing unit may be performed using AI, for example, or may be performed without using AI. For example, the sharing unit can input information about the parent's geographical environment into the generation AI and have the generation AI execute the notification.

[0103] When sharing, the sharing unit can customize the notification content by referring to the parent's family health data. For example, the sharing unit can send notifications regarding specific health risks based on the family's health data. The sharing unit can also send notifications including preventive advice based on the family's health data. Furthermore, the sharing unit can send notifications regarding body distortion trends based on the family's health data. This allows for more appropriate notifications by customizing the notification content by referring to the parent's family health data. Some or all of the above-mentioned processing in the sharing unit may be performed using, or without, AI, for example. For example, the sharing unit can input the family's health data into the generation AI and have the generation AI customize the notification content.

[0104] At the time of sharing, the sharing unit can analyze the parent's social media activity and select the optimal notification method. For example, if the parent is active on social media, the sharing unit can send a notification via social media. Also, if the parent is not very active on social media, the sharing unit can send an email notification. Furthermore, the sharing unit can select the optimal notification method based on the parent's social media activity. This enables more effective notification by analyzing the parent's social media activity and selecting the optimal notification method. Some or all of the above-described processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input information about the parent's social media activity into the generation AI and have the generation AI select the notification method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned checking unit, detecting unit, advising unit, and sharing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the checking unit checks the child's physical condition using the reception device 38 of the smart device 14. The detecting unit detects body distortion using a generated AI by the specific processing unit 290 of the data processing device 12. The advising unit provides advice based on the detection result by the specific processing unit 290 of the data processing device 12. The sharing unit sends a notification to the parent's smartphone using the communication I / F 44 of the smart device 14, providing information regarding the child's physical condition. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned checking unit, detecting unit, advising unit, and sharing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the checking unit checks the child's physical condition using the microphone 238 of the smart glasses 214. The detecting unit detects body distortion using AI generated by the specific processing unit 290 of the data processing device 12. The advising unit provides advice based on the detection result by the specific processing unit 290 of the data processing device 12. The sharing unit sends a notification to the parent's smartphone using the communication I / F 44 of the smart glasses 214 and provides information regarding the child's physical condition. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned checking unit, detecting unit, advising unit, and sharing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the checking unit checks the child's physical condition using the microphone 238 of the headset type terminal 314. The detecting unit detects body distortion using AI generated by the specific processing unit 290 of the data processing device 12. The advising unit provides advice based on the detection results by the specific processing unit 290 of the data processing device 12. The sharing unit sends a notification to the parent's smartphone using the communication I / F 44 of the headset type terminal 314, and provides information regarding the child's physical condition. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned checking unit, detecting unit, advising unit, and sharing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the checking unit checks the child's physical condition using the microphone 238 of the robot 414. The detecting unit detects body distortion using AI generated by the specific processing unit 290 of the data processing device 12. The advising unit provides advice based on the detection results by the specific processing unit 290 of the data processing device 12. The sharing unit sends a notification to the parent's smartphone using the communication I / F 44 of the robot 414, providing information regarding the child's physical condition.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The health support system can further include a motion sensor unit that monitors a child's body movements in real time. The motion sensor unit detects how a child moves their body in daily life and analyzes the data. For example, the motion sensor unit can issue a warning if a child remains in the same position for a long period of time. The motion sensor unit can also analyze a child's movements while exercising and provide advice on appropriate exercise methods. Furthermore, if a child is moving unnaturally, the motion sensor unit can identify the cause and provide appropriate measures. This allows for more accurate health support by monitoring a child's body movements in real time.

[0107] The check unit may be equipped with a vital sensor that measures the child's body temperature and heart rate. The vital sensor measures the child's body temperature and heart rate in real time and analyzes the data. For example, the vital sensor can issue a warning if the child's body temperature is abnormally high. The vital sensor can also identify the cause of an abnormally high heart rate and provide appropriate measures. Furthermore, the vital sensor can evaluate the child's health condition and provide appropriate advice based on the child's body temperature and heart rate data. This allows for more accurate health support by measuring the child's body temperature and heart rate in real time.

[0108] The detection unit can estimate the child's emotions and adjust the accuracy of body distortion detection based on the estimated child's emotions. For example, if the child is relaxed, detailed detection is performed to accurately identify the body distortion. In addition, if the child is feeling stressed, the detection unit can perform simple detection to identify the general location of the body distortion. Furthermore, if the child is tired, the detection unit can perform detection in a short time to identify the main part of the body distortion. This allows for more accurate detection by adjusting the accuracy of body distortion detection based on the child's emotions.

[0109] The advice unit can estimate the child's emotions and adjust the way the advice is expressed based on the estimated child's emotions. For example, if the child is relaxed, detailed advice can be provided. If the child is stressed, the advice unit can also provide concise and easy-to-understand advice. Furthermore, if the child is tired, the advice unit can also provide advice that can be implemented in a short time. In this way, more appropriate advice can be provided by adjusting the way the advice is expressed based on the child's emotions.

[0110] The sharing unit can estimate the child's emotions and adjust the content of the notification to the parent based on the estimated child's emotions. For example, if the child is relaxed, detailed notification content is sent to the parent. In addition, if the child is stressed, the sharing unit can also send concise and easy-to-understand notification content to the parent. Furthermore, if the child is tired, the sharing unit can also send notification content that can be quickly understood to the parent. In this way, by adjusting the content of the notification to the parent based on the child's emotions, more appropriate notifications can be provided.

[0111] The health support system may further include a meal recorder that records the child's diet. The meal recorder allows the child to input daily meal information and analyzes the data. For example, the meal recorder can evaluate whether the child is eating a balanced diet. If the child is lacking in a particular nutrient, the meal recorder can also provide advice on how to supplement that nutrient. Furthermore, the meal recorder can share the child's diet with the parent and provide information for the parent to manage the child's diet. This allows for more comprehensive health support by recording the child's diet.

[0112] The health support system may further include a sleep monitor unit that monitors a child's sleep patterns. The sleep monitor unit measures the child's sleep duration and sleep quality and analyzes the data. For example, the sleep monitor unit can issue a warning if the child is not getting enough sleep. The sleep monitor unit can also identify the cause of poor sleep quality and provide appropriate measures. Furthermore, the sleep monitor unit can share the child's sleep patterns with parents and provide information for parents to manage their child's sleep. This allows for more comprehensive health support by monitoring a child's sleep patterns.

[0113] The health support system may further include an exercise recording unit that records the child's exercise history. The exercise recording unit receives the child's daily exercise input and analyzes the data. For example, the exercise recording unit may evaluate whether the child is getting enough exercise. If the child is lacking in a particular type of exercise, the exercise recording unit may provide advice on how to supplement that exercise. Furthermore, the exercise recording unit may share the child's exercise history with the parent and provide the parent with information to manage the child's exercise. In this way, recording the child's exercise history enables more comprehensive health support.

[0114] The health support system can further include a learning record unit that records a child's learning status. The learning record unit receives input from the child about their daily learning and analyzes the data. For example, the learning record unit can identify which subjects a child is weak in. The learning record unit can also evaluate a child's efforts when their grades improve in a particular subject. Furthermore, the learning record unit can share the child's learning status with parents and provide them with information to support their child's learning. By recording a child's learning status, more comprehensive support can be provided.

[0115] The health support system may further include a stress measurement unit that measures the child's stress level. The stress measurement unit measures the child's stress level in real time and analyzes the data. For example, the stress measurement unit can issue a warning if the child's stress level is high. The stress measurement unit can also identify the cause of the child's stress and provide appropriate countermeasures. Furthermore, the stress measurement unit can share the child's stress level with the parent and provide the parent with information to manage the child's stress. In this way, measuring the child's stress level enables more comprehensive health support.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The check unit checks the child's physical condition. For example, the child can check their physical condition by answering simple questions. Children can also check their own physical condition using a smartphone or tablet. For example, to a question such as, "Have you had shoulder stiffness or back pain recently?" the child answers "yes" or "no." This information is input into the generation AI. Step 2: The detection unit uses the generation AI to detect distortions in the child's body based on the information checked by the checking unit. For example, it identifies which part of the body has a problem based on the child's answers. If the child has stiff shoulders or back pain, the generation AI will identify the cause and provide appropriate advice. Step 3: The advice unit uses the generation AI to provide advice to the child based on the results detected by the detection unit. For example, the advice unit may provide advice such as, "If you have stiff shoulders, it would be good to do some stretching." The advice unit also shares this information with the parent. Step 4: The sharing unit sends a notification to the parent's smartphone, providing information about the child's physical condition, allowing the parent to understand the child's physical condition and take early action.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 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 check section to check the child's physical condition, a detection unit including a specific method for detecting distortion of the child's body based on the information checked by the checking unit; an advice unit that provides advice to the child based on the result detected by the detection unit; a sharing unit that shares the advice provided by the advising unit with a parent. A system characterized by:

2. The checking unit Children can check their physical condition by answering simple questions 2. The system of claim 1.

3. The detection unit Detect body distortion based on the child's answers 2. The system of claim 1.

4. The advice unit Providing advice on specific stretching methods for those with stiff shoulders or back pain 2. The system of claim 1.

5. The common part is Sends notifications to parents' smartphones and provides information about their child's physical condition 2. The system of claim 1.

6. The checking unit This includes methods for estimating a child's emotions and adjusting the content and timing of specific questions based on the child's estimated emotions.

2. The system of claim 1.

7. The checking unit Analyzes children's past answer history and automatically generates appropriate questions 2. The system of claim 1.

8. The checking unit When asking questions, customize the difficulty and content of the questions according to the child's age and gender.

2. The system of claim 1.

Images