system

Abstract

The system according to the embodiment aims to analyze information about a child and provide appropriate advice. [Solution] According to an embodiment, the system includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information about the child. The analysis unit analyzes the information collected by the collection unit. The provision unit provides advice based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately provide appropriate advice based on the child's information, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze information about a child and provide appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information about the child. The analysis unit analyzes the information collected by the collection unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze information about a child 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 child support system according to an embodiment of the present invention provides appropriate meal ideas, advice on home remedies for illness, and lifestyle guidance for children based on their information, health status, and school grades. This system collects information about the child (e.g., age, gender, height, weight), health status (e.g., past medical history, allergy information), and school grades (e.g., grades in each subject, test scores), and uses AI to analyze the information to provide appropriate advice. For example, the system analyzes a child's health status and grades to provide appropriate meal ideas for the child. Specifically, it suggests nutritionally balanced meal menus and meal menus that take allergies into consideration. It also provides advice on home remedies for illness. For example, in the case of a cold, it advises drinking hot drinks and getting plenty of rest. It also provides lifestyle guidance, such as advice on developing healthy lifestyle habits and stress management methods. This helps maintain a child's health and improve their quality of life. It also provides advice on home education based on academic ability and weak subjects. For example, if a child is weak in a particular subject, the system will suggest effective study methods for that subject. It will also provide information on suitable schools and cram schools as advice for entrance exams. This will support children in improving their academic ability and enable them to prepare for exams effectively. The system provides comprehensive support for children's health and academic ability, allowing parents and teachers to provide appropriate advice. For example, if a child catches a cold, the system will suggest an appropriate treatment, and parents can follow that advice. Also, if a child's grades drop, the system will suggest effective study methods, which parents and teachers can put into practice. In this way, the child support system provides comprehensive support for children's health and academic ability, allowing parents and teachers to provide appropriate advice.

[0029] A child support system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information about a child. The child's information includes, but is not limited to, age, gender, height, weight, past medical history, allergy information, grades in each subject, and test scores. The collection unit collects information input by, for example, a parent or teacher. The collection unit can also monitor a child's health using a sensor and collect the data. For example, the collection unit can measure a child's heart rate and body temperature using a wearable device and collect the data. The analysis unit analyzes the information collected by the collection unit. The analysis can be performed using, for example, data mining or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit can analyze a child's health and grades and generate data to provide meal ideas appropriate for the child. The analysis unit can also perform analysis based on the collected data using a generation AI. For example, the analysis unit can input prompts to the generation AI to generate analysis results. The provision unit provides advice based on the analysis results obtained by the analysis unit. The advice may include, but is not limited to, health advice, study advice, and lifestyle advice. For example, the providing unit may include a menu unit that provides nutritionally balanced meal menus. The menu unit may suggest meal menus that include a balanced amount of nutrients, such as vitamins, minerals, and proteins. The providing unit may also provide meal menus that take allergies into consideration. For example, the providing unit may suggest meal menus that take dairy allergies and nut allergies into consideration. The providing unit may also include a therapy unit that provides at-home therapy when the user is feeling unwell. For example, the therapy unit may provide advice such as drinking hot drinks and getting sufficient rest when the user has a cold. The providing unit may also provide advice for developing healthy lifestyle habits. For example, the providing unit may provide advice on sleep duration, meal timing, exercise habits, etc. The providing unit may also provide stress management methods. For example, the providing unit may suggest stress management methods such as relaxation techniques, counseling, and exercise. The providing unit may also include a learning unit that provides effective study methods for specific subjects.The learning unit suggests methods such as repetitive learning, visual learning, and practical learning. The provision unit includes an examination unit that provides information on suitable schools and cram schools. The examination unit selects schools and cram schools based on, for example, academic ability level, strengths in specific subjects, location, etc., and provides the information. This allows the child support system according to the embodiment to collect and analyze information about children and provide appropriate advice.

[0030] The provision unit may include a menu unit that provides nutritionally balanced meal menus. The menu unit may propose meal menus that include a balanced amount of nutrients, such as vitamins, minerals, and proteins. For example, the menu unit may propose fruits that are rich in vitamin C for breakfast and meat or fish that are rich in protein for lunch. The menu unit may also propose vegetables that are rich in minerals for dinner. The menu unit may also adjust calorie intake according to the child's age, gender, and activity level. For example, the menu unit may propose high-calorie meals for children who exercise a lot and low-calorie meals for children who exercise less. This allows for the provision of nutritionally balanced meal menus to support children's health. Some or all of the above-described processing in the menu unit may be performed using, or without, AI. For example, the menu unit may input collected information about the child into a generation AI, which then generates an optimal meal menu.

[0031] The providing unit can provide allergy-friendly meal menus. The providing unit can propose, for example, meal menus that are considerate of dairy allergies or nut allergies. For example, the providing unit can propose dairy-free meal menus to children with dairy allergies. The providing unit can also propose nut-free meal menus to children with nut allergies. Furthermore, the providing unit can propose menus that avoid foods that may cause allergic reactions based on allergy information. For example, the providing unit can analyze allergy information, list foods that may cause allergic reactions, and propose meal menus that do not include those foods. This can reduce health risks for children by providing allergy-friendly meal menus. Some or all of the above-described processing by the providing unit can be performed, for example, using AI, or can be performed without AI. For example, the providing unit can input the collected allergy information into a generation AI, which can then generate an allergy-friendly meal menu.

[0032] The provision unit may include a therapy unit that provides therapy at home when a child is feeling unwell. For example, the therapy unit may advise a child to drink warm drinks and get plenty of rest when they have a cold. For example, the therapy unit may suggest a drink rich in vitamin C and a soup to warm the body for a child with a cold. The therapy unit may also suggest improving the sleeping environment to ensure adequate rest. For example, the therapy unit may suggest sleeping in a quiet environment and maintaining an appropriate temperature and humidity. Furthermore, the therapy unit may also advise improving physical condition by incorporating light exercise. For example, the therapy unit may suggest light stretching or walking during the recovery period from a cold. This allows for the child to recover by providing appropriate therapy when feeling unwell. Some or all of the above-described processing in the therapy unit may be performed using, or without, AI. For example, the therapy unit may input collected health information into a generation AI, which then generates an optimal therapy.

[0033] The providing unit can provide advice for developing healthy lifestyle habits. The providing unit provides advice on, for example, sleep time, meal timing, exercise habits, etc. For example, the providing unit suggests to the child that they go to bed at the same time every day and eat a balanced diet. The providing unit can also suggest incorporating regular exercise. For example, the providing unit suggests walking 30 minutes every day or engaging in sports activities three times a week. The providing unit can also provide stress management methods. For example, the providing unit suggests stress management methods such as relaxation techniques, counseling, and exercise. This can improve the quality of life of children by providing advice for developing healthy lifestyle habits. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the collected lifestyle information into a generating AI, which can then generate optimal lifestyle advice.

[0034] The providing unit can provide a stress management method. The providing unit can suggest stress management methods such as relaxation techniques, counseling, and exercise. For example, the providing unit can suggest relaxation techniques such as deep breathing, meditation, and yoga to the child. The providing unit can also suggest receiving counseling. For example, the providing unit can suggest consulting a school counselor or a professional counselor. The providing unit can also suggest methods to reduce stress by incorporating exercise. For example, the providing unit can suggest exercises such as jogging, cycling, and dancing. This can support the child's mental health by providing a stress management method. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input collected stress information into a generating AI, which can then generate an optimal stress management method.

[0035] The provision unit may include a learning unit that provides effective learning methods for specific subjects. The learning unit may suggest methods such as repetitive learning, visual learning, and practical learning. For example, the learning unit may suggest to a child that they repeatedly study the content of a specific subject. The learning unit may also suggest using visual learning materials. For example, the learning unit may suggest materials using diagrams, graphs, videos, etc. Furthermore, the learning unit may also suggest practical learning methods. For example, the learning unit may suggest a method for solidifying the learning content by actually solving problems. This can support the improvement of a child's academic ability by providing an effective learning method for a specific subject. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input collected learning information into a generation AI, which may then generate an optimal learning method.

[0036] The provision unit may include an examination unit that provides information on suitable schools and cram schools. The examination unit selects schools and cram schools based on, for example, academic ability level, strengths in specific subjects, location, etc., and provides the information. For example, the examination unit suggests appropriate schools and cram schools based on a child's academic ability level. The examination unit can also suggest schools and cram schools with strengths in specific subjects. For example, the examination unit can suggest schools and cram schools that are strong in mathematics or English. Furthermore, the examination unit can select schools and cram schools based on location. For example, the examination unit suggests the optimal school or cram school taking into account the child's commute time and transportation means. This allows for support in the child's exam preparation by providing information on suitable schools and cram schools. Some or all of the above-described processing in the examination unit may be performed using, for example, AI, or may be performed without AI. For example, the examination unit can input collected exam information into a generation AI, which then generates information on optimal schools and cram schools.

[0037] The collection unit can analyze the child's past health history and select the optimal collection method. For example, the collection unit prioritizes collecting allergy information to avoid foods to which the child has had an allergic reaction in the past. For example, the collection unit lists foods to which the child has had an allergic reaction in the past and prioritizes collecting information on how to avoid those foods. The collection unit can also collect information on improving immunity if the child has frequently caught colds in the past. For example, the collection unit prioritizes collecting information on how to improve immunity if the child has frequently caught colds in the past. Furthermore, the collection unit can also collect information on exercise habits if the child has previously lacked exercise. For example, the collection unit prioritizes collecting information on how to improve exercise habits if the child has previously lacked exercise. This allows the optimal information collection method to be selected by analyzing the past health history. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the child's past health history data into a generation AI, which can select the optimal collection method.

[0038] When collecting information, the collection unit can filter the information based on the child's current living situation and areas of interest. For example, if the child is currently interested in sports, the collection unit prioritizes collecting health information related to sports. For example, if the child is currently interested in sports, the collection unit prioritizes collecting health information related to sports. Furthermore, if the child is currently concentrating on their studies, the collection unit can prioritize collecting health information related to their studies. For example, if the child is currently concentrating on their studies, the collection unit prioritizes collecting health information related to their studies. Furthermore, if the child is currently feeling stressed, the collection unit can prioritize collecting information on stress management. For example, if the child is currently feeling stressed, the collection unit prioritizes collecting information on stress management. This allows more relevant information to be collected by filtering information based on the child's current living situation and areas of interest. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the child's current living situation and areas of interest into a generation AI, which can then filter the information.

[0039] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the child's geographical location information. The collection unit, for example, collects health information suitable for the climate of the area where the child lives. For example, the collection unit collects health information suitable for the climate of the area where the child lives. The collection unit can also collect health information based on the surrounding environment of the school the child attends. For example, the collection unit collects health information based on the surrounding environment of the school the child attends. Furthermore, the collection unit can also collect health information related to places the child frequently visits. For example, the collection unit collects health information related to places the child frequently visits. In this way, more relevant information can be collected by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's geographical location information to the generation AI, and the generation AI can prioritize collecting highly relevant information.

[0040] When collecting information, the collection unit can analyze the child's social media activities and collect related information. For example, the collection unit collects health information related to topics the child is interested in on social media. For example, the collection unit collects health information related to topics the child is interested in on social media. The collection unit can also collect health information based on information about accounts the child follows on social media. For example, the collection unit collects health information based on information about accounts the child follows on social media. Furthermore, the collection unit can analyze information shared by the child on social media and collect related health information. For example, the collection unit analyzes information shared by the child on social media and collects related health information. This allows for more relevant information to be collected by analyzing social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the child's social media activity data into a generation AI, which then collects related information.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit, for example, provides detailed analysis results for important health information. For example, the analysis unit provides detailed analysis results for important health information. The analysis unit can also provide concise analysis results for less important information. For example, the analysis unit provides concise analysis results for less important information. Furthermore, the analysis unit can perform a particularly detailed analysis of information directly related to children's health. For example, the analysis unit performs a particularly detailed analysis of information directly related to children's health. In this way, by adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information to a generation AI, and the generation AI can adjust the level of detail of the analysis based on the importance of the information.

[0042] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a medical data analysis algorithm to health information. For example, the analysis unit applies a medical data analysis algorithm to health information. The analysis unit can also apply an educational data analysis algorithm to learning information. For example, the analysis unit applies an educational data analysis algorithm to learning information. The analysis unit can also apply a lifestyle data analysis algorithm to lifestyle habit information. For example, the analysis unit applies a lifestyle data analysis algorithm to lifestyle habit information. In this way, by applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information to a generation AI, which can apply different analysis algorithms depending on the category of information.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was collected. For example, the analysis unit prioritizes analysis of the most recent health information. For example, the analysis unit prioritizes analysis of the most recent health information. The analysis unit can also determine the priority of analysis for past health information based on its importance. For example, the analysis unit can determine the priority of analysis for past health information based on its importance. Furthermore, the analysis unit can also prioritize analysis of information from an appropriate time period based on the child's developmental stage. For example, the analysis unit prioritizes analysis of information from an appropriate time period based on the child's developmental stage. In this way, by determining the priority of analysis based on the time when the information was collected, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information into a generation AI, and the generation AI can determine the priority of analysis based on the time when the information was collected.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analyzing information directly related to the child's health condition. For example, the analysis unit prioritizes analyzing information directly related to the child's health condition. The analysis unit can also prioritize analyzing information related to the child's learning situation. For example, the analysis unit prioritizes analyzing information related to the child's learning situation. Furthermore, the analysis unit can also prioritize analyzing information that affects the child's lifestyle habits. For example, the analysis unit prioritizes analyzing information that affects the child's lifestyle habits. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information to a generation AI, and the generation AI can adjust the order of analysis based on the relevance of the information.

[0045] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the information. The providing unit, for example, provides detailed advice for important health information. For example, the providing unit provides detailed advice for important health information. The providing unit can also provide concise advice for less important information. For example, the providing unit provides concise advice for less important information. Furthermore, the providing unit can provide particularly detailed advice for information directly related to a child's health. For example, the providing unit provides particularly detailed advice for information directly related to a child's health. In this way, by adjusting the level of detail of the advice based on the importance of the information, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the collected information to a generating AI, and the generating AI can adjust the level of detail of the advice based on the importance of the information.

[0046] When providing advice, the providing unit can apply different advice algorithms depending on the category of information. For example, the providing unit applies a medical data analysis algorithm to health information. For example, the providing unit applies a medical data analysis algorithm to health information. The providing unit can also apply an educational data analysis algorithm to learning information. For example, the providing unit applies an educational data analysis algorithm to learning information. The providing unit can also apply a lifestyle data analysis algorithm to lifestyle habit information. For example, the providing unit applies a lifestyle data analysis algorithm to lifestyle habit information. In this way, by applying different advice algorithms depending on the category of information, more appropriate advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the collected information to a generation AI, and the generation AI can apply different advice algorithms depending on the category of information.

[0047] When providing advice, the providing unit can determine the priority of advice based on the time when the information was collected. For example, the providing unit prioritizes advice based on the most recent health information. For example, the providing unit prioritizes advice based on the most recent health information. The providing unit can also determine the priority of advice for past health information based on its importance. For example, the providing unit can determine the priority of advice for past health information based on its importance. Furthermore, the providing unit can also prioritize advice for information of an appropriate time period based on the child's developmental stage. For example, the providing unit prioritizes advice for information of an appropriate time period based on the child's developmental stage. In this way, by determining the priority of advice based on the time when the information was collected, more appropriate advice can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the collected information to a generating AI, and the generating AI can determine the priority of advice based on the time when the information was collected.

[0048] When providing advice, the providing unit can adjust the order of advice based on the relevance of the information. For example, the providing unit prioritizes advice on information directly related to the child's health condition. For example, the providing unit prioritizes advice on information directly related to the child's health condition. The providing unit can also prioritize advice on information related to the child's learning situation. For example, the providing unit prioritizes advice on information related to the child's learning situation. Furthermore, the providing unit can also prioritize advice on information that will affect the child's lifestyle habits. For example, the providing unit prioritizes advice on information that will affect the child's lifestyle habits. In this way, by adjusting the order of advice based on the relevance of the information, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input collected information to a generating AI, and the generating AI can adjust the order of advice based on the relevance of the information.

[0049] When proposing a meal menu, the menu unit can analyze the child's past meal history and select an optimal menu. For example, the menu unit prioritizes suggesting menus that the child has previously enjoyed. For example, the menu unit prioritizes suggesting menus that the child has previously enjoyed. The menu unit can also suggest menus that avoid foods to which the child has previously had an allergic reaction. For example, the menu unit proposes menus that avoid foods to which the child has previously had an allergic reaction. Furthermore, the menu unit can also suggest menus that supplement nutrients that the child has previously lacked. For example, the menu unit proposes menus that supplement nutrients that the child has previously lacked. In this way, an optimal meal menu can be provided by analyzing the past meal history. Some or all of the above-described processing in the menu unit may be performed using, for example, AI, or may be performed without using AI. For example, the menu unit can input the child's past meal history data into a generation AI, which can select an optimal menu.

[0050] When proposing a meal menu, the menu unit can select an optimal menu by taking into consideration the child's geographical location information. The menu unit, for example, proposes a meal menu suitable for the climate of the area where the child lives. For example, the menu unit proposes a meal menu suitable for the climate of the area where the child lives. The menu unit can also propose a meal menu based on the surrounding environment of the school the child attends. For example, the menu unit proposes a meal menu based on the surrounding environment of the school the child attends. Furthermore, the menu unit can also propose a meal menu related to places the child frequently visits. For example, the menu unit proposes a meal menu related to places the child frequently visits. In this way, by taking the geographical location information into consideration, a more appropriate meal menu can be provided. Some or all of the above-described processing in the menu unit may be performed using, for example, AI, or may be performed without using AI. For example, the menu unit can input the child's geographical location information into a generation AI, which can select an optimal menu.

[0051] When suggesting a meal menu, the menu unit can analyze the child's social media activity to suggest the menu. For example, the menu unit can suggest a menu related to foods that the child is interested in on social media. The menu unit can also suggest a meal menu based on information about accounts the child follows on social media. For example, the menu unit can suggest a meal menu based on information about accounts the child follows on social media. The menu unit can also analyze information shared by the child on social media to suggest related meal menus. For example, the menu unit can analyze information shared by the child on social media and suggest related meal menus. This makes it possible to provide a more appropriate meal menu by analyzing social media activity. Some or all of the above-described processing in the menu unit may be performed using, for example, AI, or may be performed without using AI. For example, the menu unit can input the child's social media activity data into a generation AI and have the generation AI suggest a menu.

[0052] When proposing a therapy, the therapy unit can analyze the child's past medical history and select the optimal therapy. For example, if the child has had a cold in the past, the therapy unit can suggest a therapy to boost immunity. For example, if the child has had a cold in the past, the therapy unit can suggest a therapy to boost immunity. Furthermore, if the child has had an allergic reaction in the past, the therapy unit can suggest a therapy that takes allergies into consideration. For example, if the child has had an allergic reaction in the past, the therapy unit can suggest a therapy that takes allergies into consideration. Furthermore, if the child has had a lack of exercise in the past, the therapy unit can suggest a therapy that incorporates exercise habits. For example, if the child has had a lack of exercise in the past, the therapy unit can suggest a therapy that incorporates exercise habits. In this way, the optimal therapy can be provided by analyzing the past medical history. Some or all of the above-described processing in the therapy unit may be performed using, for example, AI, or may be performed without using AI. For example, the therapy unit can input the child's past medical history data into a generation AI, which can select the optimal therapy.

[0053] When proposing a therapy, the therapy unit can customize the therapy based on the child's current health condition. For example, if the child has a cold, the therapy unit can suggest a therapy to boost the child's immune system. For example, if the child has a cold, the therapy unit can suggest a therapy to boost the child's immune system. Furthermore, if the child is tired, the therapy unit can suggest a therapy to replenish energy. For example, if the child is tired, the therapy unit can suggest a therapy to replenish energy. Furthermore, if the child has allergies, the therapy unit can suggest a therapy that takes allergies into consideration. For example, if the child has allergies, the therapy unit can suggest a therapy that takes allergies into consideration. This allows for customizing the therapy based on the child's current health condition to provide a more appropriate therapy. Some or all of the above-described processing in the therapy unit may be performed using, or without, AI. For example, the therapy unit can input data on the child's current health condition into a generation AI, which can customize the therapy.

[0054] When proposing a therapy, the therapy unit can select the optimal therapy by taking into account the child's geographical location information. For example, the therapy unit proposes a therapy suitable for the climate of the area where the child lives. For example, the therapy unit proposes a therapy suitable for the climate of the area where the child lives. The therapy unit can also propose a therapy based on the surrounding environment of the school the child attends. For example, the therapy unit proposes a therapy based on the surrounding environment of the school the child attends. Furthermore, the therapy unit can also propose a therapy related to places the child frequently visits. For example, the therapy unit proposes a therapy related to places the child frequently visits. In this way, by taking the geographical location information into consideration, a more appropriate therapy can be provided. Some or all of the above-described processing in the therapy unit may be performed using, or without, AI. For example, the therapy unit can input the child's geographical location information into a generation AI, and the generation AI can select the optimal therapy.

[0055] When suggesting a therapy, the therapy unit can analyze the child's social media activity and suggest a therapy. For example, the therapy unit can suggest a therapy related to a topic the child is interested in on social media. For example, the therapy unit can suggest a therapy related to a topic the child is interested in on social media. The therapy unit can also suggest a therapy based on information about accounts the child follows on social media. For example, the therapy unit can suggest a therapy based on information about accounts the child follows on social media. Furthermore, the therapy unit can analyze information shared by the child on social media and suggest a related therapy. For example, the therapy unit can analyze information shared by the child on social media and suggest a related therapy. In this way, by analyzing social media activity, a more appropriate therapy can be provided. Some or all of the above-mentioned processing in the therapy unit may be performed using, for example, AI, or may be performed without using AI. For example, the therapy unit can input the child's social media activity data into a generation AI, and the generation AI can suggest a therapy.

[0056] When suggesting a learning method, the learning unit can analyze the child's past learning history and select the optimal method. For example, the learning unit prioritizes suggesting learning methods that have been effective for the child in the past. For example, the learning unit prioritizes suggesting learning methods that have been effective for the child in the past. The learning unit can also make suggestions to avoid learning methods that the child has had difficulty with in the past. For example, the learning unit makes suggestions to avoid learning methods that the child has had difficulty with in the past. Furthermore, the learning unit can also suggest the optimal method based on learning patterns that have been successful for the child in the past. For example, the learning unit suggests the optimal method based on learning patterns that have been successful for the child in the past. In this way, the optimal learning method can be provided by analyzing the past learning history. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data of the child's past learning history into a generation AI, and the generation AI can select the optimal method.

[0057] When proposing a learning method, the learning unit can customize the method based on the child's current academic ability. For example, if a child is weak in a particular subject, the learning unit can suggest a learning method specialized for that subject. For example, if a child is weak in a particular subject, the learning unit can suggest a learning method specialized for that subject. Furthermore, if a child has high academic ability overall, the learning unit can also suggest an applied learning method. For example, if a child has high academic ability overall, the learning unit can suggest an applied learning method. Furthermore, if a child excels in a particular area, the learning unit can suggest a learning method to further improve that area. For example, if a child excels in a particular area, the learning unit can suggest a learning method to further improve that area. In this way, by customizing the learning method based on the child's current academic ability, a more appropriate learning method can be provided. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input the child's current academic ability data into a generation AI, and the generation AI can customize the method.

[0058] When suggesting a study method, the learning unit can select the optimal method by taking into account the child's geographical location information. The learning unit, for example, suggests a study method suited to the educational environment in the area where the child lives. For example, the learning unit suggests a study method suited to the educational environment in the area where the child lives. The learning unit can also suggest a study method based on the curriculum of the school the child attends. For example, the learning unit suggests a study method based on the curriculum of the school the child attends. Furthermore, the learning unit can also suggest a study method related to places the child frequently visits. For example, the learning unit suggests a study method related to places the child frequently visits. In this way, by taking the geographical location information into consideration, a more appropriate study method can be provided. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the child's geographical location information into a generation AI, which can select the optimal method.

[0059] When suggesting a learning method, the learning unit can analyze the child's social media activity and suggest a method. For example, the learning unit can suggest a learning method related to a topic the child is interested in on social media. For example, the learning unit can suggest a learning method related to a topic the child is interested in on social media. The learning unit can also suggest a learning method based on information about accounts the child follows on social media. For example, the learning unit can suggest a learning method based on information about accounts the child follows on social media. Furthermore, the learning unit can analyze information shared by the child on social media and suggest a related learning method. For example, the learning unit can analyze information shared by the child on social media and suggest a related learning method. In this way, by analyzing social media activity, a more appropriate learning method can be provided. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the child's social media activity data into a generation AI, which can then suggest a method.

[0060] When proposing test-taking advice, the test-taking unit can analyze the child's past test-taking history and select optimal advice. The test-taking unit, for example, proposes advice based on test-taking strategies that have been successful for the child in the past. For example, the test-taking unit proposes advice based on test-taking strategies that have been successful for the child in the past. The test-taking unit can also propose advice to avoid test-taking strategies that have failed the child in the past. For example, the test-taking unit proposes advice to avoid test-taking strategies that have failed the child in the past. The test-taking unit can also propose test-taking advice based on study methods that have been effective for the child in the past. For example, the test-taking unit proposes test-taking advice based on study methods that have been effective for the child in the past. In this way, optimal test-taking advice can be provided by analyzing the past test-taking history. Some or all of the above-mentioned processing in the test-taking unit may be performed using, for example, AI, or may be performed without using AI. For example, the test-taking unit can input the child's past test-taking history data into a generation AI, which can select optimal advice.

[0061] When proposing exam advice, the test-taking unit can customize the advice based on the child's current academic ability. For example, if a child is weak in a particular subject, the test-taking unit can propose exam advice specialized for that subject. For example, if a child is weak in a particular subject, the test-taking unit can propose exam advice specialized for that subject. Furthermore, if a child has high academic ability overall, the test-taking unit can propose applied exam advice. For example, if a child has high academic ability overall, the test-taking unit can propose applied exam advice. Furthermore, if a child excels in a particular area, the test-taking unit can propose exam advice to further improve that area. For example, if a child excels in a particular area, the test-taking unit can propose exam advice to further improve that area. In this way, by customizing the exam advice based on the child's current academic ability, more appropriate exam advice can be provided. Some or all of the above-mentioned processing in the test-taking unit may be performed using, for example, AI, or may be performed without AI. For example, the test-taking unit can input the child's current academic ability data into a generation AI, and the generation AI can customize the advice.

[0062] When proposing exam advice, the examination unit can select optimal advice by taking into account the child's geographical location information. The examination unit, for example, proposes exam advice suited to the educational environment in the area where the child lives. For example, the examination unit proposes exam advice suited to the educational environment in the area where the child lives. The examination unit can also propose exam advice based on the curriculum of the school the child attends. For example, the examination unit proposes exam advice based on the curriculum of the school the child attends. Furthermore, the examination unit can also propose exam advice related to places the child frequently visits. For example, the examination unit proposes exam advice related to places the child frequently visits. In this way, more appropriate exam advice can be provided by taking geographical location information into consideration. Some or all of the above-mentioned processing in the examination unit may be performed using, for example, AI, or may be performed without using AI. For example, the examination unit can input the child's geographical location information into a generation AI, which can select optimal advice.

[0063] When proposing exam advice, the test-taking unit can analyze the child's social media activity and propose the advice. For example, the test-taking unit proposes exam advice related to schools that the child has shown interest in on social media. For example, the test-taking unit proposes exam advice related to schools that the child has shown interest in on social media. The test-taking unit can also propose exam advice based on information about education-related accounts that the child follows on social media. For example, the test-taking unit proposes exam advice based on information about education-related accounts that the child follows on social media. Furthermore, the test-taking unit can analyze information shared by the child on social media and propose related exam advice. For example, the test-taking unit analyzes information shared by the child on social media and proposes related exam advice. In this way, more appropriate exam advice can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the test-taking unit may be performed using, for example, AI, or may be performed without using AI. For example, the test-taking unit can input the child's social media activity data into a generation AI, and the generation AI can propose advice.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The child support system may further include a motivation improvement unit. The motivation improvement unit provides an approach to promote a child's motivation to learn and improve their lifestyle habits. For example, the motivation improvement unit may provide rewards for goals achieved by the child. Specifically, points may be awarded when a learning goal is achieved, and the points may be used to exchange for items of the child's choice. The motivation improvement unit may also customize learning content based on the child's interests. For example, it may provide learning materials incorporating characters or themes that the child likes. Furthermore, the motivation improvement unit may collaborate with parents and teachers to share the child's progress and provide appropriate feedback. This may increase the child's motivation to learn and support their continuous growth.

[0066] The providing unit may further include an exercise advice unit. The exercise advice unit suggests an appropriate exercise program based on the child's health condition and lifestyle habits. For example, the exercise advice unit may provide an exercise menu according to the child's age and physical fitness level. Specifically, the exercise advice unit may suggest exercises that incorporate play for young children, and activities such as sports and dance for elementary school students. The exercise advice unit may also customize an exercise program according to the child's interests and preferences. For example, the exercise advice unit may provide a program that incorporates the child's favorite sports and activities. Furthermore, the exercise advice unit may work with parents and teachers to support the child's exercise habits and provide regular feedback. This helps maintain the child's health and develop exercise habits.

[0067] The provision unit may further include a social skills improvement unit. The social skills improvement unit provides approaches to improving children's communication and interpersonal skills. For example, the social skills improvement unit may suggest ways for children to communicate smoothly with their friends. Specifically, it may teach basic communication skills such as how to greet others, how to listen, and how to express opinions. The social skills improvement unit may also provide programs for children to learn how to cooperate through group activities and teamwork. For example, it may develop cooperation and leadership skills through group projects or sports activities. Furthermore, the social skills improvement unit may collaborate with parents and teachers to share progress on children's social skills and provide appropriate feedback. This may help improve children's social skills and build healthy interpersonal relationships.

[0068] The provision unit may further include a creativity development unit. The creativity development unit provides approaches to develop children's creativity and imagination. For example, the creativity development unit may suggest activities that allow children to freely expand their ideas. Specifically, it may suggest creative activities such as drawing, writing stories, and doing crafts. The creativity development unit may also teach children methods to generate new ideas. For example, it may teach them how to organize and develop ideas using brainstorming or mind maps. Furthermore, the creativity development unit may collaborate with parents and teachers to share the progress of children's creativity and provide appropriate feedback. This helps develop children's creativity and enable them to come up with original ideas.

[0069] The provision unit can further include a time management support unit. The time management support unit provides an approach for children to effectively manage their time and act in a planned manner. For example, the time management support unit can teach children how to make a schedule. Specifically, it teaches them how to organize their daily plans and prioritize them. The time management support unit can also teach children how to set goals and make plans to achieve them. For example, it teaches them how to set learning goals or life goals and take specific steps toward them. Furthermore, the time management support unit can work with parents and teachers to share the child's time management progress and provide appropriate feedback. This helps children acquire the ability to effectively manage their time and act in a planned manner.

[0070] The delivery department may further include a leadership development department. The leadership development department provides an approach to help children develop leadership skills and the ability to lead teams. For example, the leadership development department may teach children how to understand and practice their role as a leader. Specifically, it may teach them how to plan projects, coordinate teams, and solve problems. The leadership development department may also teach children how to cooperate with others and communicate effectively. For example, it may teach skills such as mediating opinions, providing feedback, and maintaining motivation. Furthermore, the leadership development department may work with parents and teachers to share the progress of children's leadership skills and provide appropriate feedback. This helps children develop leadership skills and the ability to lead teams.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The collection unit collects information about the child. The child's information includes age, gender, height, weight, past medical history, allergy information, grades in each subject, test scores, etc. The collection unit collects information entered by parents and teachers. The collection unit can also monitor the child's health using sensors and collect that data. For example, the collection unit can measure the child's heart rate and body temperature using a wearable device and collect that data. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using data mining or machine learning algorithms. For example, the analysis unit analyzes a child's health condition and grades and generates data to provide meal ideas suitable for that child. The analysis unit can also use a generation AI to perform analysis based on the collected data. For example, the analysis unit inputs prompts to the generation AI and generates analysis results. Step 3: The providing unit provides advice based on the analysis results obtained by the analyzing unit. The advice includes health advice, study advice, lifestyle advice, and the like. For example, the providing unit includes a menu unit that provides nutritionally balanced meal menus. The menu unit suggests meal menus that include a balanced amount of nutrients such as vitamins, minerals, and proteins. The providing unit can also provide meal menus that take allergies into consideration. The providing unit also includes a therapy unit that provides home therapy when the user is feeling unwell. The therapy unit advises the user to drink hot drinks and get plenty of rest if they have a cold. The providing unit can also provide advice on developing healthy lifestyle habits. For example, the providing unit advises on sleep duration, meal timing, exercise habits, etc. The providing unit can also provide stress management methods. For example, the providing unit suggests stress management methods such as relaxation techniques, counseling, and exercise. The providing unit includes a learning unit that provides effective learning methods for specific subjects. The learning unit suggests methods such as repetitive learning, visual learning, and practical learning. The information provision department has an exam preparation department that provides information on suitable schools and cram schools. The exam preparation department selects schools and cram schools based on academic ability level, strengths in specific subjects, location, etc., and provides that information.

[0073] (Example 2) A child support system according to an embodiment of the present invention provides appropriate meal ideas, advice on home remedies for illness, and lifestyle guidance for children based on their information, health status, and school grades. This system collects information about the child (e.g., age, gender, height, weight), health status (e.g., past medical history, allergy information), and school grades (e.g., grades in each subject, test scores), and uses AI to analyze the information to provide appropriate advice. For example, the system analyzes a child's health status and grades to provide appropriate meal ideas for the child. Specifically, it suggests nutritionally balanced meal menus and meal menus that take allergies into consideration. It also provides advice on home remedies for illness. For example, in the case of a cold, it advises drinking hot drinks and getting plenty of rest. It also provides lifestyle guidance, such as advice on developing healthy lifestyle habits and stress management methods. This helps maintain a child's health and improve their quality of life. It also provides advice on home education based on academic ability and weak subjects. For example, if a child is weak in a particular subject, the system will suggest effective study methods for that subject. It will also provide information on suitable schools and cram schools as advice for entrance exams. This will support children in improving their academic ability and enable them to prepare for exams effectively. The system provides comprehensive support for children's health and academic ability, allowing parents and teachers to provide appropriate advice. For example, if a child catches a cold, the system will suggest an appropriate treatment, and parents can follow that advice. Also, if a child's grades drop, the system will suggest effective study methods, which parents and teachers can put into practice. In this way, the child support system provides comprehensive support for children's health and academic ability, allowing parents and teachers to provide appropriate advice.

[0074] A child support system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information about a child. The child's information includes, but is not limited to, age, gender, height, weight, past medical history, allergy information, grades in each subject, and test scores. The collection unit collects information input by, for example, a parent or teacher. The collection unit can also monitor a child's health using a sensor and collect the data. For example, the collection unit can measure a child's heart rate and body temperature using a wearable device and collect the data. The analysis unit analyzes the information collected by the collection unit. The analysis can be performed using, for example, data mining or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit can analyze a child's health and grades and generate data to provide meal ideas appropriate for the child. The analysis unit can also perform analysis based on the collected data using a generation AI. For example, the analysis unit can input prompts to the generation AI to generate analysis results. The provision unit provides advice based on the analysis results obtained by the analysis unit. The advice may include, but is not limited to, health advice, study advice, and lifestyle advice. For example, the providing unit may include a menu unit that provides nutritionally balanced meal menus. The menu unit may suggest meal menus that include a balanced amount of nutrients, such as vitamins, minerals, and proteins. The providing unit may also provide meal menus that take allergies into consideration. For example, the providing unit may suggest meal menus that take dairy allergies and nut allergies into consideration. The providing unit may also include a therapy unit that provides at-home therapy when the user is feeling unwell. For example, the therapy unit may provide advice such as drinking hot drinks and getting sufficient rest when the user has a cold. The providing unit may also provide advice for developing healthy lifestyle habits. For example, the providing unit may provide advice on sleep duration, meal timing, exercise habits, etc. The providing unit may also provide stress management methods. For example, the providing unit may suggest stress management methods such as relaxation techniques, counseling, and exercise. The providing unit may also include a learning unit that provides effective study methods for specific subjects.The learning unit suggests methods such as repetitive learning, visual learning, and practical learning. The provision unit includes an examination unit that provides information on suitable schools and cram schools. The examination unit selects schools and cram schools based on, for example, academic ability level, strengths in specific subjects, location, etc., and provides the information. This allows the child support system according to the embodiment to collect and analyze information about children and provide appropriate advice.

[0075] The provision unit may include a menu unit that provides nutritionally balanced meal menus. The menu unit may propose meal menus that include a balanced amount of nutrients, such as vitamins, minerals, and proteins. For example, the menu unit may propose fruits that are rich in vitamin C for breakfast and meat or fish that are rich in protein for lunch. The menu unit may also propose vegetables that are rich in minerals for dinner. The menu unit may also adjust calorie intake according to the child's age, gender, and activity level. For example, the menu unit may propose high-calorie meals for children who exercise a lot and low-calorie meals for children who exercise less. This allows for the provision of nutritionally balanced meal menus to support children's health. Some or all of the above-described processing in the menu unit may be performed using, or without, AI. For example, the menu unit may input collected information about the child into a generation AI, which then generates an optimal meal menu.

[0076] The providing unit can provide allergy-friendly meal menus. The providing unit can propose, for example, meal menus that are considerate of dairy allergies or nut allergies. For example, the providing unit can propose dairy-free meal menus to children with dairy allergies. The providing unit can also propose nut-free meal menus to children with nut allergies. Furthermore, the providing unit can propose menus that avoid foods that may cause allergic reactions based on allergy information. For example, the providing unit can analyze allergy information, list foods that may cause allergic reactions, and propose meal menus that do not include those foods. This can reduce health risks for children by providing allergy-friendly meal menus. Some or all of the above-described processing by the providing unit can be performed, for example, using AI, or can be performed without AI. For example, the providing unit can input the collected allergy information into a generation AI, which can then generate an allergy-friendly meal menu.

[0077] The provision unit may include a therapy unit that provides therapy at home when a child is feeling unwell. For example, the therapy unit may advise a child to drink warm drinks and get plenty of rest when they have a cold. For example, the therapy unit may suggest a drink rich in vitamin C and a soup to warm the body for a child with a cold. The therapy unit may also suggest improving the sleeping environment to ensure adequate rest. For example, the therapy unit may suggest sleeping in a quiet environment and maintaining an appropriate temperature and humidity. Furthermore, the therapy unit may also advise improving physical condition by incorporating light exercise. For example, the therapy unit may suggest light stretching or walking during the recovery period from a cold. This allows for the child to recover by providing appropriate therapy when feeling unwell. Some or all of the above-described processing in the therapy unit may be performed using, or without, AI. For example, the therapy unit may input collected health information into a generation AI, which then generates an optimal therapy.

[0078] The providing unit can provide advice for developing healthy lifestyle habits. The providing unit provides advice on, for example, sleep time, meal timing, exercise habits, etc. For example, the providing unit suggests to the child that they go to bed at the same time every day and eat a balanced diet. The providing unit can also suggest incorporating regular exercise. For example, the providing unit suggests walking 30 minutes every day or engaging in sports activities three times a week. The providing unit can also provide stress management methods. For example, the providing unit suggests stress management methods such as relaxation techniques, counseling, and exercise. This can improve the quality of life of children by providing advice for developing healthy lifestyle habits. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the collected lifestyle information into a generating AI, which can then generate optimal lifestyle advice.

[0079] The providing unit can provide a stress management method. The providing unit can suggest stress management methods such as relaxation techniques, counseling, and exercise. For example, the providing unit can suggest relaxation techniques such as deep breathing, meditation, and yoga to the child. The providing unit can also suggest receiving counseling. For example, the providing unit can suggest consulting a school counselor or a professional counselor. The providing unit can also suggest methods to reduce stress by incorporating exercise. For example, the providing unit can suggest exercises such as jogging, cycling, and dancing. This can support the child's mental health by providing a stress management method. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input collected stress information into a generating AI, which can then generate an optimal stress management method.

[0080] The provision unit may include a learning unit that provides effective learning methods for specific subjects. The learning unit may suggest methods such as repetitive learning, visual learning, and practical learning. For example, the learning unit may suggest to a child that they repeatedly study the content of a specific subject. The learning unit may also suggest using visual learning materials. For example, the learning unit may suggest materials using diagrams, graphs, videos, etc. Furthermore, the learning unit may also suggest practical learning methods. For example, the learning unit may suggest a method for solidifying the learning content by actually solving problems. This can support the improvement of a child's academic ability by providing an effective learning method for a specific subject. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit may input collected learning information into a generation AI, which may then generate an optimal learning method.

[0081] The provision unit may include an examination unit that provides information on suitable schools and cram schools. The examination unit selects schools and cram schools based on, for example, academic ability level, strengths in specific subjects, location, etc., and provides the information. For example, the examination unit suggests appropriate schools and cram schools based on a child's academic ability level. The examination unit can also suggest schools and cram schools with strengths in specific subjects. For example, the examination unit can suggest schools and cram schools that are strong in mathematics or English. Furthermore, the examination unit can select schools and cram schools based on location. For example, the examination unit suggests the optimal school or cram school taking into account the child's commute time and transportation means. This allows for support in the child's exam preparation by providing information on suitable schools and cram schools. Some or all of the above-described processing in the examination unit may be performed using, for example, AI, or may be performed without AI. For example, the examination unit can input collected exam information into a generation AI, which then generates information on optimal schools and cram schools.

[0082] The collection unit can estimate the child's emotions and adjust the timing of information collection based on the estimated child's emotions. For example, if the child is feeling stressed, the collection unit delays information collection until the child is relaxed. For example, when the child is feeling stressed, the collection unit pauses information collection and waits until the child is relaxed. Furthermore, if the child is excited, the collection unit can pause information collection until the child calms down. For example, when the child is excited, the collection unit pauses information collection and waits until the child calms down. Furthermore, if the child is tired, the collection unit can resume information collection after the child has rested. For example, when the child is tired, the collection unit pauses information collection and resumes it after the child has rested. This allows for more appropriate information collection by adjusting the timing of information collection according to the child's emotions. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the child's emotion data into a generation AI, and the generation AI can adjust the timing of information collection.

[0083] The collection unit can analyze the child's past health history and select the optimal collection method. For example, the collection unit prioritizes collecting allergy information to avoid foods to which the child has had an allergic reaction in the past. For example, the collection unit lists foods to which the child has had an allergic reaction in the past and prioritizes collecting information on how to avoid those foods. The collection unit can also collect information on improving immunity if the child has frequently caught colds in the past. For example, the collection unit prioritizes collecting information on how to improve immunity if the child has frequently caught colds in the past. Furthermore, the collection unit can also collect information on exercise habits if the child has previously lacked exercise. For example, the collection unit prioritizes collecting information on how to improve exercise habits if the child has previously lacked exercise. This allows the optimal information collection method to be selected by analyzing the past health history. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the child's past health history data into a generation AI, which can select the optimal collection method.

[0084] When collecting information, the collection unit can filter the information based on the child's current living situation and areas of interest. For example, if the child is currently interested in sports, the collection unit prioritizes collecting health information related to sports. For example, if the child is currently interested in sports, the collection unit prioritizes collecting health information related to sports. Furthermore, if the child is currently concentrating on their studies, the collection unit can prioritize collecting health information related to their studies. For example, if the child is currently concentrating on their studies, the collection unit prioritizes collecting health information related to their studies. Furthermore, if the child is currently feeling stressed, the collection unit can prioritize collecting information on stress management. For example, if the child is currently feeling stressed, the collection unit prioritizes collecting information on stress management. This allows more relevant information to be collected by filtering information based on the child's current living situation and areas of interest. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the child's current living situation and areas of interest into a generation AI, which can then filter the information.

[0085] The collection unit can estimate the child's emotions and determine the priority of information to be collected based on the estimated child's emotions. For example, if the child is feeling anxious, the collection unit prioritizes collecting information that provides a sense of security. For example, if the child is feeling anxious, the collection unit prioritizes collecting information that provides a sense of security. Furthermore, if the child is excited, the collection unit can prioritize collecting information to calm the child's excitement. For example, if the child is excited, the collection unit prioritizes collecting information to calm the child's excitement. Furthermore, if the child is tired, the collection unit can prioritize collecting information that helps the child relax. For example, if the child is tired, the collection unit prioritizes collecting information that helps the child relax. This enables more appropriate information collection by determining the priority of information based on the child's emotions. Some or all of the above-described processing by the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can input the child's emotion data into a generation AI, and the generation AI can determine the priority of information.

[0086] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the child's geographical location information. The collection unit, for example, collects health information suitable for the climate of the area where the child lives. For example, the collection unit collects health information suitable for the climate of the area where the child lives. The collection unit can also collect health information based on the surrounding environment of the school the child attends. For example, the collection unit collects health information based on the surrounding environment of the school the child attends. Furthermore, the collection unit can also collect health information related to places the child frequently visits. For example, the collection unit collects health information related to places the child frequently visits. In this way, more relevant information can be collected by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the child's geographical location information to the generation AI, and the generation AI can prioritize collecting highly relevant information.

[0087] When collecting information, the collection unit can analyze the child's social media activities and collect related information. For example, the collection unit collects health information related to topics the child is interested in on social media. For example, the collection unit collects health information related to topics the child is interested in on social media. The collection unit can also collect health information based on information about accounts the child follows on social media. For example, the collection unit collects health information based on information about accounts the child follows on social media. Furthermore, the collection unit can analyze information shared by the child on social media and collect related health information. For example, the collection unit analyzes information shared by the child on social media and collects related health information. This allows for more relevant information to be collected by analyzing social media activities. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the child's social media activity data into a generation AI, which then collects related information.

[0088] The analysis unit can estimate the child's emotions and adjust the way the analysis is presented based on the estimated child's emotions. For example, if the child is feeling anxious, the analysis unit presents the analysis results in an expression that provides a sense of security. For example, if the child is feeling anxious, the analysis unit presents the analysis results in an expression that provides a sense of security. Furthermore, if the child is excited, the analysis unit can present the analysis results in an expression that allows the user to remain calm. For example, if the child is excited, the analysis unit presents the analysis results in an expression that allows the user to remain calm. Furthermore, if the child is tired, the analysis unit can present the analysis results in a concise and easy-to-understand manner. For example, if the child is tired, the analysis unit presents the analysis results in a concise and easy-to-understand manner. In this way, by adjusting the way the analysis is presented based on the child's emotions, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the child's emotion data into a generation AI, and the generation AI can adjust the way the analysis is presented.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit, for example, provides detailed analysis results for important health information. For example, the analysis unit provides detailed analysis results for important health information. The analysis unit can also provide concise analysis results for less important information. For example, the analysis unit provides concise analysis results for less important information. Furthermore, the analysis unit can perform a particularly detailed analysis of information directly related to children's health. For example, the analysis unit performs a particularly detailed analysis of information directly related to children's health. In this way, by adjusting the level of detail of the analysis based on the importance of the information, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information to a generation AI, and the generation AI can adjust the level of detail of the analysis based on the importance of the information.

[0090] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a medical data analysis algorithm to health information. For example, the analysis unit applies a medical data analysis algorithm to health information. The analysis unit can also apply an educational data analysis algorithm to learning information. For example, the analysis unit applies an educational data analysis algorithm to learning information. The analysis unit can also apply a lifestyle data analysis algorithm to lifestyle habit information. For example, the analysis unit applies a lifestyle data analysis algorithm to lifestyle habit information. In this way, by applying different analysis algorithms depending on the category of information, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information to a generation AI, which can apply different analysis algorithms depending on the category of information.

[0091] The analysis unit can estimate the child's emotions and adjust the length of the analysis based on the estimated child's emotions. For example, if the child is feeling anxious, the analysis unit provides a short and concise analysis result. For example, if the child is feeling anxious, the analysis unit provides a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the child is relaxed. For example, if the child is relaxed, the analysis unit provides a detailed analysis result. Furthermore, if the child is excited, the analysis unit can provide an analysis result of a length that can be taken calmly. For example, if the child is excited, the analysis unit provides an analysis result of a length that can be taken calmly. In this way, by adjusting the length of the analysis based on the child's emotions, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the child's emotion data into a generation AI, and the generation AI can adjust the length of the analysis.

[0092] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was collected. For example, the analysis unit prioritizes analysis of the most recent health information. For example, the analysis unit prioritizes analysis of the most recent health information. The analysis unit can also determine the priority of analysis for past health information based on its importance. For example, the analysis unit can determine the priority of analysis for past health information based on its importance. Furthermore, the analysis unit can also prioritize analysis of information from an appropriate time period based on the child's developmental stage. For example, the analysis unit prioritizes analysis of information from an appropriate time period based on the child's developmental stage. In this way, by determining the priority of analysis based on the time when the information was collected, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information into a generation AI, and the generation AI can determine the priority of analysis based on the time when the information was collected.

[0093] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analyzing information directly related to the child's health condition. For example, the analysis unit prioritizes analyzing information directly related to the child's health condition. The analysis unit can also prioritize analyzing information related to the child's learning situation. For example, the analysis unit prioritizes analyzing information related to the child's learning situation. Furthermore, the analysis unit can also prioritize analyzing information that affects the child's lifestyle habits. For example, the analysis unit prioritizes analyzing information that affects the child's lifestyle habits. By adjusting the order of analysis based on the relevance of the information, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected information to a generation AI, and the generation AI can adjust the order of analysis based on the relevance of the information.

[0094] The providing unit can estimate the child's emotions and adjust the way in which advice is expressed based on the estimated child's emotions. For example, if the child is feeling anxious, the providing unit provides the advice in an expression that gives a sense of security. For example, if the child is feeling anxious, the providing unit provides the advice in an expression that gives a sense of security. Furthermore, if the child is excited, the providing unit can provide the advice in an expression that can be received calmly. For example, if the child is excited, the providing unit provides the advice in an expression that can be received calmly. Furthermore, if the child is tired, the providing unit can provide the advice in a concise and easy-to-understand manner. For example, if the child is tired, the providing unit provides the advice in a concise and easy-to-understand manner. In this way, by adjusting the way in which advice is expressed based on the child's emotions, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the child's emotion data into a generating AI, and the generating AI can adjust the way in which advice is expressed.

[0095] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the information. The providing unit, for example, provides detailed advice for important health information. For example, the providing unit provides detailed advice for important health information. The providing unit can also provide concise advice for less important information. For example, the providing unit provides concise advice for less important information. Furthermore, the providing unit can provide particularly detailed advice for information directly related to a child's health. For example, the providing unit provides particularly detailed advice for information directly related to a child's health. In this way, by adjusting the level of detail of the advice based on the importance of the information, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit may be performed using, or without, AI. For example, the providing unit can input the collected information to a generating AI, and the generating AI can adjust the level of detail of the advice based on the importance of the information.

[0096] When providing advice, the providing unit can apply different advice algorithms depending on the category of information. For example, the providing unit applies a medical data analysis algorithm to health information. For example, the providing unit applies a medical data analysis algorithm to health information. The providing unit can also apply an educational data analysis algorithm to learning information. For example, the providing unit applies an educational data analysis algorithm to learning information. The providing unit can also apply a lifestyle data analysis algorithm to lifestyle habit information. For example, the providing unit applies a lifestyle data analysis algorithm to lifestyle habit information. In this way, by applying different advice algorithms depending on the category of information, more appropriate advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the collected information to a generation AI, and the generation AI can apply different advice algorithms depending on the category of information.

[0097] The providing unit can estimate the child's emotions and adjust the length of the advice based on the estimated child's emotions. For example, if the child is feeling anxious, the providing unit provides short, concise advice. For example, if the child is feeling anxious, the providing unit provides short, concise advice. Furthermore, if the child is relaxed, the providing unit can provide detailed advice. For example, if the child is relaxed, the providing unit can provide detailed advice. Furthermore, if the child is excited, the providing unit can provide advice of a length that can be received calmly. For example, if the child is excited, the providing unit provides advice of a length that can be received calmly. In this way, by adjusting the length of the advice based on the child's emotions, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the child's emotion data into a generating AI, and the generating AI can adjust the length of the advice.

[0098] When providing advice, the providing unit can determine the priority of advice based on the time when the information was collected. For example, the providing unit prioritizes advice based on the most recent health information. For example, the providing unit prioritizes advice based on the most recent health information. The providing unit can also determine the priority of advice for past health information based on its importance. For example, the providing unit can determine the priority of advice for past health information based on its importance. Furthermore, the providing unit can also prioritize advice for information of an appropriate time period based on the child's developmental stage. For example, the providing unit prioritizes advice for information of an appropriate time period based on the child's developmental stage. In this way, by determining the priority of advice based on the time when the information was collected, more appropriate advice can be provided. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the collected information to a generating AI, and the generating AI can determine the priority of advice based on the time when the information was collected.

[0099] When providing advice, the providing unit can adjust the order of advice based on the relevance of the information. For example, the providing unit prioritizes advice on information directly related to the child's health condition. For example, the providing unit prioritizes advice on information directly related to the child's health condition. The providing unit can also prioritize advice on information related to the child's learning situation. For example, the providing unit prioritizes advice on information related to the child's learning situation. Furthermore, the providing unit can also prioritize advice on information that will affect the child's lifestyle habits. For example, the providing unit prioritizes advice on information that will affect the child's lifestyle habits. In this way, by adjusting the order of advice based on the relevance of the information, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input collected information to a generating AI, and the generating AI can adjust the order of advice based on the relevance of the information.

[0100] The menu unit can estimate the child's emotions and adjust the method for suggesting a meal menu based on the estimated child's emotions. For example, if the child is feeling anxious, the menu unit can suggest a meal menu that gives a sense of security. For example, if the child is feeling anxious, the menu unit can suggest a meal menu that gives a sense of security. Furthermore, if the child is excited, the menu unit can suggest a meal menu that the child can accept calmly. For example, if the child is excited, the menu unit can suggest a meal menu that the child can accept calmly. Furthermore, if the child is tired, the menu unit can suggest a meal menu that helps the child relax. For example, if the child is tired, the menu unit can suggest a meal menu that helps the child relax. In this way, by adjusting the method for suggesting a meal menu based on the child's emotions, a more appropriate meal menu can be provided. Some or all of the above-mentioned processing in the menu unit may be performed using AI, for example, or may be performed without using AI. For example, the menu unit can input the child's emotion data into a generation AI, and the generation AI can adjust the method for suggesting a meal menu.

[0101] When proposing a meal menu, the menu unit can analyze the child's past meal history and select an optimal menu. For example, the menu unit prioritizes suggesting menus that the child has previously enjoyed. For example, the menu unit prioritizes suggesting menus that the child has previously enjoyed. The menu unit can also suggest menus that avoid foods to which the child has previously had an allergic reaction. For example, the menu unit proposes menus that avoid foods to which the child has previously had an allergic reaction. Furthermore, the menu unit can also suggest menus that supplement nutrients that the child has previously lacked. For example, the menu unit proposes menus that supplement nutrients that the child has previously lacked. In this way, an optimal meal menu can be provided by analyzing the past meal history. Some or all of the above-described processing in the menu unit may be performed using, for example, AI, or may be performed without using AI. For example, the menu unit can input the child's past meal history data into a generation AI, which can select an optimal menu.

[0102] The menu unit can estimate the child's emotions and prioritize meal menus based on the estimated child's emotions. For example, if the child is feeling anxious, the menu unit prioritizes suggesting meal menus that give the child a sense of security. For example, if the child is feeling anxious, the menu unit prioritizes suggesting meal menus that give the child a sense of security. Furthermore, if the child is excited, the menu unit can prioritize suggesting meal menus that the child can accept calmly. For example, if the child is excited, the menu unit prioritizes suggesting meal menus that the child can accept calmly. Furthermore, if the child is tired, the menu unit can prioritize suggesting meal menus that the child can relax. For example, if the child is tired, the menu unit prioritizes suggesting meal menus that the child can relax. In this way, by prioritizing meal menus based on the child's emotions, more appropriate meal menus can be provided. Some or all of the above-described processing in the menu unit may be performed using, for example, AI, or may be performed without using AI. For example, the menu unit can input the child's emotion data into a generation AI, and the generation AI can determine the priority of meal menus.

[0103] When proposing a meal menu, the menu unit can select an optimal menu by taking into consideration the child's geographical location information. The menu unit, for example, proposes a meal menu suitable for the climate of the area where the child lives. For example, the menu unit proposes a meal menu suitable for the climate of the area where the child lives. The menu unit can also propose a meal menu based on the surrounding environment of the school the child attends. For example, the menu unit proposes a meal menu based on the surrounding environment of the school the child attends. Furthermore, the menu unit can also propose a meal menu related to places the child frequently visits. For example, the menu unit proposes a meal menu related to places the child frequently visits. In this way, by taking the geographical location information into consideration, a more appropriate meal menu can be provided. Some or all of the above-described processing in the menu unit may be performed using, for example, AI, or may be performed without using AI. For example, the menu unit can input the child's geographical location information into a generation AI, which can select an optimal menu.

[0104] When suggesting a meal menu, the menu unit can analyze the child's social media activity to suggest the menu. For example, the menu unit can suggest a menu related to foods that the child is interested in on social media. The menu unit can also suggest a meal menu based on information about accounts the child follows on social media. For example, the menu unit can suggest a meal menu based on information about accounts the child follows on social media. The menu unit can also analyze information shared by the child on social media to suggest related meal menus. For example, the menu unit can analyze information shared by the child on social media and suggest related meal menus. This makes it possible to provide a more appropriate meal menu by analyzing social media activity. Some or all of the above-described processing in the menu unit may be performed using, for example, AI, or may be performed without using AI. For example, the menu unit can input the child's social media activity data into a generation AI and have the generation AI suggest a menu.

[0105] The therapy unit can estimate the child's emotions and adjust the method of suggesting a therapy based on the estimated child's emotions. For example, if the child is feeling anxious, the therapy unit can suggest a therapy that provides a sense of security. For example, if the child is feeling anxious, the therapy unit can suggest a therapy that provides a sense of security. Furthermore, if the child is excited, the therapy unit can suggest a therapy that allows the child to remain calm. For example, if the child is excited, the therapy unit can suggest a therapy that allows the child to remain calm. Furthermore, if the child is tired, the therapy unit can suggest a therapy that helps the child to relax. For example, if the child is tired, the therapy unit can suggest a therapy that helps the child to relax. In this way, by adjusting the method of suggesting a therapy based on the child's emotions, a more appropriate therapy can be provided. Some or all of the above-described processing in the therapy unit may be performed using, for example, AI, or may be performed without using AI. For example, the therapy unit can input the child's emotion data into a generation AI, and the generation AI can adjust the method of suggesting a therapy.

[0106] When proposing a therapy, the therapy unit can analyze the child's past medical history and select the optimal therapy. For example, if the child has had a cold in the past, the therapy unit can suggest a therapy to boost immunity. For example, if the child has had a cold in the past, the therapy unit can suggest a therapy to boost immunity. Furthermore, if the child has had an allergic reaction in the past, the therapy unit can suggest a therapy that takes allergies into consideration. For example, if the child has had an allergic reaction in the past, the therapy unit can suggest a therapy that takes allergies into consideration. Furthermore, if the child has had a lack of exercise in the past, the therapy unit can suggest a therapy that incorporates exercise habits. For example, if the child has had a lack of exercise in the past, the therapy unit can suggest a therapy that incorporates exercise habits. In this way, the optimal therapy can be provided by analyzing the past medical history. Some or all of the above-described processing in the therapy unit may be performed using, for example, AI, or may be performed without using AI. For example, the therapy unit can input the child's past medical history data into a generation AI, which can select the optimal therapy.

[0107] When proposing a therapy, the therapy unit can customize the therapy based on the child's current health condition. For example, if the child has a cold, the therapy unit can suggest a therapy to boost the child's immune system. For example, if the child has a cold, the therapy unit can suggest a therapy to boost the child's immune system. Furthermore, if the child is tired, the therapy unit can suggest a therapy to replenish energy. For example, if the child is tired, the therapy unit can suggest a therapy to replenish energy. Furthermore, if the child has allergies, the therapy unit can suggest a therapy that takes allergies into consideration. For example, if the child has allergies, the therapy unit can suggest a therapy that takes allergies into consideration. This allows for customizing the therapy based on the child's current health condition to provide a more appropriate therapy. Some or all of the above-described processing in the therapy unit may be performed using, or without, AI. For example, the therapy unit can input data on the child's current health condition into a generation AI, which can customize the therapy.

[0108] The therapy unit can estimate the child's emotions and prioritize therapies based on the estimated child's emotions. For example, if the child is feeling anxious, the therapy unit prioritizes suggesting therapies that provide a sense of security. For example, if the child is feeling anxious, the therapy unit prioritizes suggesting therapies that provide a sense of security. Furthermore, if the child is excited, the therapy unit can prioritize suggesting therapies that allow the child to remain calm. For example, if the child is excited, the therapy unit prioritizes suggesting therapies that allow the child to remain calm. Furthermore, if the child is tired, the therapy unit can prioritize suggesting therapies that help the child to relax. For example, if the child is tired, the therapy unit prioritizes suggesting therapies that help the child to relax. In this way, by prioritizing the therapies based on the child's emotions, more appropriate therapies can be provided. Some or all of the above-described processing in the therapy unit may be performed using, for example, AI, or may be performed without using AI. For example, the therapy unit can input the child's emotion data into a generation AI, and the generation AI can determine the priorities of the therapies.

[0109] When proposing a therapy, the therapy unit can select the optimal therapy by taking into account the child's geographical location information. For example, the therapy unit proposes a therapy suitable for the climate of the area where the child lives. For example, the therapy unit proposes a therapy suitable for the climate of the area where the child lives. The therapy unit can also propose a therapy based on the surrounding environment of the school the child attends. For example, the therapy unit proposes a therapy based on the surrounding environment of the school the child attends. Furthermore, the therapy unit can also propose a therapy related to places the child frequently visits. For example, the therapy unit proposes a therapy related to places the child frequently visits. In this way, by taking the geographical location information into consideration, a more appropriate therapy can be provided. Some or all of the above-described processing in the therapy unit may be performed using, or without, AI. For example, the therapy unit can input the child's geographical location information into a generation AI, and the generation AI can select the optimal therapy.

[0110] When suggesting a therapy, the therapy unit can analyze the child's social media activity and suggest a therapy. For example, the therapy unit can suggest a therapy related to a topic the child is interested in on social media. For example, the therapy unit can suggest a therapy related to a topic the child is interested in on social media. The therapy unit can also suggest a therapy based on information about accounts the child follows on social media. For example, the therapy unit can suggest a therapy based on information about accounts the child follows on social media. Furthermore, the therapy unit can analyze information shared by the child on social media and suggest a related therapy. For example, the therapy unit can analyze information shared by the child on social media and suggest a related therapy. In this way, by analyzing social media activity, a more appropriate therapy can be provided. Some or all of the above-mentioned processing in the therapy unit may be performed using, for example, AI, or may be performed without using AI. For example, the therapy unit can input the child's social media activity data into a generation AI, and the generation AI can suggest a therapy.

[0111] The learning unit can estimate the child's emotions and adjust suggested learning methods based on the estimated child's emotions. For example, if the child is feeling anxious, the learning unit can suggest learning methods that will give the child a sense of security. For example, if the child is feeling anxious, the learning unit can suggest learning methods that will give the child a sense of security. Furthermore, if the child is excited, the learning unit can suggest learning methods that will help the child stay calm. For example, if the child is excited, the learning unit can suggest learning methods that will help the child stay calm. Furthermore, if the child is tired, the learning unit can suggest learning methods that will help the child relax. For example, if the child is tired, the learning unit can suggest learning methods that will help the child relax. In this way, by adjusting the suggested learning methods based on the child's emotions, a more appropriate learning method can be provided. Some or all of the above-mentioned processing in the learning unit may be performed using AI, for example, or may be performed without using AI. For example, the learning unit can input the child's emotional data into a generation AI, and the generation AI can adjust the suggested learning methods.

[0112] When suggesting a learning method, the learning unit can analyze the child's past learning history and select the optimal method. For example, the learning unit prioritizes suggesting learning methods that have been effective for the child in the past. For example, the learning unit prioritizes suggesting learning methods that have been effective for the child in the past. The learning unit can also make suggestions to avoid learning methods that the child has had difficulty with in the past. For example, the learning unit makes suggestions to avoid learning methods that the child has had difficulty with in the past. Furthermore, the learning unit can also suggest the optimal method based on learning patterns that have been successful for the child in the past. For example, the learning unit suggests the optimal method based on learning patterns that have been successful for the child in the past. In this way, the optimal learning method can be provided by analyzing the past learning history. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input data of the child's past learning history into a generation AI, and the generation AI can select the optimal method.

[0113] When proposing a learning method, the learning unit can customize the method based on the child's current academic ability. For example, if a child is weak in a particular subject, the learning unit can suggest a learning method specialized for that subject. For example, if a child is weak in a particular subject, the learning unit can suggest a learning method specialized for that subject. Furthermore, if a child has high academic ability overall, the learning unit can also suggest an applied learning method. For example, if a child has high academic ability overall, the learning unit can suggest an applied learning method. Furthermore, if a child excels in a particular area, the learning unit can suggest a learning method to further improve that area. For example, if a child excels in a particular area, the learning unit can suggest a learning method to further improve that area. In this way, by customizing the learning method based on the child's current academic ability, a more appropriate learning method can be provided. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input the child's current academic ability data into a generation AI, and the generation AI can customize the method.

[0114] The learning unit can estimate the child's emotions and prioritize learning methods based on the estimated child's emotions. For example, if the child is feeling anxious, the learning unit prioritizes suggesting learning methods that provide a sense of security. For example, if the child is feeling anxious, the learning unit prioritizes suggesting learning methods that provide a sense of security. Furthermore, if the child is excited, the learning unit can prioritize suggesting learning methods that allow the child to remain calm. For example, if the child is excited, the learning unit prioritizes suggesting learning methods that allow the child to remain calm. Furthermore, if the child is tired, the learning unit can prioritize suggesting learning methods that allow the child to relax. For example, if the child is tired, the learning unit prioritizes suggesting learning methods that allow the child to relax. In this way, by prioritizing learning methods based on the child's emotions, more appropriate learning methods can be provided. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the child's emotional data into a generation AI, and the generation AI can determine the priority of learning methods.

[0115] When suggesting a study method, the learning unit can select the optimal method by taking into account the child's geographical location information. The learning unit, for example, suggests a study method suited to the educational environment in the area where the child lives. For example, the learning unit suggests a study method suited to the educational environment in the area where the child lives. The learning unit can also suggest a study method based on the curriculum of the school the child attends. For example, the learning unit suggests a study method based on the curriculum of the school the child attends. Furthermore, the learning unit can also suggest a study method related to places the child frequently visits. For example, the learning unit suggests a study method related to places the child frequently visits. In this way, by taking the geographical location information into consideration, a more appropriate study method can be provided. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the child's geographical location information into a generation AI, which can select the optimal method.

[0116] When suggesting a learning method, the learning unit can analyze the child's social media activity and suggest a method. For example, the learning unit can suggest a learning method related to a topic the child is interested in on social media. For example, the learning unit can suggest a learning method related to a topic the child is interested in on social media. The learning unit can also suggest a learning method based on information about accounts the child follows on social media. For example, the learning unit can suggest a learning method based on information about accounts the child follows on social media. Furthermore, the learning unit can analyze information shared by the child on social media and suggest a related learning method. For example, the learning unit can analyze information shared by the child on social media and suggest a related learning method. In this way, by analyzing social media activity, a more appropriate learning method can be provided. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input the child's social media activity data into a generation AI, which can then suggest a method.

[0117] The examination-taking unit can estimate the child's emotions and adjust the method of proposing exam advice based on the estimated child's emotions. For example, if the child is feeling anxious, the examination unit can suggest exam advice that gives a sense of security. For example, if the child is feeling anxious, the examination unit can suggest exam advice that gives a sense of security. Furthermore, if the child is excited, the examination unit can suggest exam advice that helps the child to remain calm. For example, if the child is excited, the examination unit can suggest exam advice that helps the child to remain calm. Furthermore, if the child is tired, the examination unit can suggest exam advice that helps the child to relax. In this way, by adjusting the method of proposing exam advice based on the child's emotions, more appropriate exam advice can be provided. Some or all of the above-mentioned processing in the examination unit may be performed using AI, for example, or may be performed without using AI. For example, the examination unit can input the child's emotion data into a generation AI, and the generation AI can adjust the method of proposing exam advice.

[0118] When proposing test-taking advice, the test-taking unit can analyze the child's past test-taking history and select optimal advice. The test-taking unit, for example, proposes advice based on test-taking strategies that have been successful for the child in the past. For example, the test-taking unit proposes advice based on test-taking strategies that have been successful for the child in the past. The test-taking unit can also propose advice to avoid test-taking strategies that have failed the child in the past. For example, the test-taking unit proposes advice to avoid test-taking strategies that have failed the child in the past. The test-taking unit can also propose test-taking advice based on study methods that have been effective for the child in the past. For example, the test-taking unit proposes test-taking advice based on study methods that have been effective for the child in the past. In this way, optimal test-taking advice can be provided by analyzing the past test-taking history. Some or all of the above-mentioned processing in the test-taking unit may be performed using, for example, AI, or may be performed without using AI. For example, the test-taking unit can input the child's past test-taking history data into a generation AI, which can select optimal advice.

[0119] When proposing exam advice, the test-taking unit can customize the advice based on the child's current academic ability. For example, if a child is weak in a particular subject, the test-taking unit can propose exam advice specialized for that subject. For example, if a child is weak in a particular subject, the test-taking unit can propose exam advice specialized for that subject. Furthermore, if a child has high academic ability overall, the test-taking unit can propose applied exam advice. For example, if a child has high academic ability overall, the test-taking unit can propose applied exam advice. Furthermore, if a child excels in a particular area, the test-taking unit can propose exam advice to further improve that area. For example, if a child excels in a particular area, the test-taking unit can propose exam advice to further improve that area. In this way, by customizing the exam advice based on the child's current academic ability, more appropriate exam advice can be provided. Some or all of the above-mentioned processing in the test-taking unit may be performed using, for example, AI, or may be performed without AI. For example, the test-taking unit can input the child's current academic ability data into a generation AI, and the generation AI can customize the advice.

[0120] The examination unit can estimate the child's emotions and determine the priority of exam advice based on the estimated child's emotions. For example, if the child is feeling anxious, the examination unit prioritizes offering exam advice that gives a sense of security. For example, if the child is feeling anxious, the examination unit prioritizes offering exam advice that gives a sense of security. Furthermore, if the child is excited, the examination unit can prioritize offering exam advice that helps the child to remain calm. For example, if the child is excited, the examination unit prioritizes offering exam advice that helps the child to remain calm. Furthermore, if the child is tired, the examination unit can prioritize offering exam advice that helps the child to relax. For example, if the child is tired, the examination unit prioritizes offering exam advice that helps the child to relax. In this way, by determining the priority of exam advice based on the child's emotions, more appropriate exam advice can be provided. Some or all of the above-described processing in the examination unit may be performed using, for example, AI, or may be performed without AI. For example, the examination unit can input the child's emotion data into a generation AI, and the generation AI can determine the priority of exam advice.

[0121] When proposing exam advice, the examination unit can select optimal advice by taking into account the child's geographical location information. The examination unit, for example, proposes exam advice suited to the educational environment in the area where the child lives. For example, the examination unit proposes exam advice suited to the educational environment in the area where the child lives. The examination unit can also propose exam advice based on the curriculum of the school the child attends. For example, the examination unit proposes exam advice based on the curriculum of the school the child attends. Furthermore, the examination unit can also propose exam advice related to places the child frequently visits. For example, the examination unit proposes exam advice related to places the child frequently visits. In this way, more appropriate exam advice can be provided by taking geographical location information into consideration. Some or all of the above-mentioned processing in the examination unit may be performed using, for example, AI, or may be performed without using AI. For example, the examination unit can input the child's geographical location information into a generation AI, which can select optimal advice.

[0122] When proposing exam advice, the test-taking unit can analyze the child's social media activity and propose the advice. For example, the test-taking unit proposes exam advice related to schools that the child has shown interest in on social media. For example, the test-taking unit proposes exam advice related to schools that the child has shown interest in on social media. The test-taking unit can also propose exam advice based on information about education-related accounts that the child follows on social media. For example, the test-taking unit proposes exam advice based on information about education-related accounts that the child follows on social media. Furthermore, the test-taking unit can analyze information shared by the child on social media and propose related exam advice. For example, the test-taking unit analyzes information shared by the child on social media and proposes related exam advice. In this way, more appropriate exam advice can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the test-taking unit may be performed using, for example, AI, or may be performed without using AI. For example, the test-taking unit can input the child's social media activity data into a generation AI, and the generation AI can propose advice. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information about the child using a sensor or input device of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The provision unit provides advice using, for example, an output device of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information about the child using a sensor or input device of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The provision unit provides advice, for example, using an output device of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects information about the child using a sensor or input device of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The provision unit provides advice using, for example, an output device of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information about the child using sensors and input devices of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data. The provision unit provides advice using, for example, an output device of the robot 414.

[0123] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0124] The child support system may further include a motivation improvement unit. The motivation improvement unit provides an approach to promote a child's motivation to learn and improve their lifestyle habits. For example, the motivation improvement unit may provide rewards for goals achieved by the child. Specifically, points may be awarded when a learning goal is achieved, and the points may be used to exchange for items of the child's choice. The motivation improvement unit may also customize learning content based on the child's interests. For example, it may provide learning materials incorporating characters or themes that the child likes. Furthermore, the motivation improvement unit may collaborate with parents and teachers to share the child's progress and provide appropriate feedback. This may increase the child's motivation to learn and support their continuous growth.

[0125] The providing unit may further include an exercise advice unit. The exercise advice unit suggests an appropriate exercise program based on the child's health condition and lifestyle habits. For example, the exercise advice unit may provide an exercise menu according to the child's age and physical fitness level. Specifically, the exercise advice unit may suggest exercises that incorporate play for young children, and activities such as sports and dance for elementary school students. The exercise advice unit may also customize an exercise program according to the child's interests and preferences. For example, the exercise advice unit may provide a program that incorporates the child's favorite sports and activities. Furthermore, the exercise advice unit may work with parents and teachers to support the child's exercise habits and provide regular feedback. This helps maintain the child's health and develop exercise habits.

[0126] The provision unit may further include a mental health support unit. The mental health support unit provides approaches to support children's mental health. For example, the mental health support unit may suggest relaxation techniques or counseling if a child is feeling stressed. Specifically, the mental health support unit may suggest relaxation techniques such as deep breathing, meditation, or yoga, and, if necessary, recommend consulting a professional counselor. The mental health support unit may also understand a child's emotions and provide appropriate coping strategies. For example, if a child is feeling anxious, the mental health support unit may provide reassuring advice, and if a child is agitated, the mental health support unit may suggest ways to calm down. Furthermore, the mental health support unit may work with parents and teachers to support children's mental health and provide regular feedback. This helps maintain children's mental health and support their healthy development.

[0127] The provision unit may further include a social skills improvement unit. The social skills improvement unit provides approaches to improving children's communication and interpersonal skills. For example, the social skills improvement unit may suggest ways for children to communicate smoothly with their friends. Specifically, it may teach basic communication skills such as how to greet others, how to listen, and how to express opinions. The social skills improvement unit may also provide programs for children to learn how to cooperate through group activities and teamwork. For example, it may develop cooperation and leadership skills through group projects or sports activities. Furthermore, the social skills improvement unit may collaborate with parents and teachers to share progress on children's social skills and provide appropriate feedback. This may help improve children's social skills and build healthy interpersonal relationships.

[0128] The provision unit may further include a creativity development unit. The creativity development unit provides approaches to develop children's creativity and imagination. For example, the creativity development unit may suggest activities that allow children to freely expand their ideas. Specifically, it may suggest creative activities such as drawing, writing stories, and doing crafts. The creativity development unit may also teach children methods to generate new ideas. For example, it may teach them how to organize and develop ideas using brainstorming or mind maps. Furthermore, the creativity development unit may collaborate with parents and teachers to share the progress of children's creativity and provide appropriate feedback. This helps develop children's creativity and enable them to come up with original ideas.

[0129] The providing unit may further include an emotional expression support unit. The emotional expression support unit provides an approach for children to appropriately express and understand their emotions. For example, the emotional expression support unit can teach children how to express their emotions in words. Specifically, the emotional expression support unit helps children learn words to express emotions and practice using them to communicate their feelings. The emotional expression support unit can also teach children how to understand and empathize with the emotions of others. For example, it can teach children how to read emotions from other people's facial expressions and actions and how to respond appropriately. Furthermore, the emotional expression support unit can work with parents and teachers to share the child's progress in emotional expression and provide appropriate feedback. This allows children to appropriately express their emotions and communicate with others in a healthy manner.

[0130] The provision unit can further include a time management support unit. The time management support unit provides an approach for children to effectively manage their time and act in a planned manner. For example, the time management support unit can teach children how to make a schedule. Specifically, it teaches them how to organize their daily plans and prioritize them. The time management support unit can also teach children how to set goals and make plans to achieve them. For example, it teaches them how to set learning goals or life goals and take specific steps toward them. Furthermore, the time management support unit can work with parents and teachers to share the child's time management progress and provide appropriate feedback. This helps children acquire the ability to effectively manage their time and act in a planned manner.

[0131] The provision unit can further include a self-esteem improvement unit. The self-esteem improvement unit provides an approach to help children gain self-confidence and improve their self-esteem. For example, the self-esteem improvement unit can teach children how to recognize their strengths and achievements. Specifically, self-esteem is improved by recording and reflecting on small daily successes and efforts. The self-esteem improvement unit can also provide support for children to achieve their goals. For example, it can support the process of setting goals and working toward them, and provide appropriate feedback when they are achieved. Furthermore, the self-esteem improvement unit can work with parents and teachers to share the progress of children's self-esteem and provide appropriate feedback. This helps children gain self-confidence and improve their self-esteem.

[0132] The delivery department may further include a leadership development department. The leadership development department provides an approach to help children develop leadership skills and the ability to lead teams. For example, the leadership development department may teach children how to understand and practice their role as a leader. Specifically, it may teach them how to plan projects, coordinate teams, and solve problems. The leadership development department may also teach children how to cooperate with others and communicate effectively. For example, it may teach skills such as mediating opinions, providing feedback, and maintaining motivation. Furthermore, the leadership development department may work with parents and teachers to share the progress of children's leadership skills and provide appropriate feedback. This helps children develop leadership skills and the ability to lead teams.

[0133] The providing unit may further include an emotion regulation support unit. The emotion regulation support unit provides approaches to help children appropriately regulate their emotions and reduce stress and anxiety. For example, the emotion regulation support unit can suggest relaxation and stress relief techniques when a child feels stressed. Specifically, it suggests methods such as deep breathing, meditation, and exercise. The emotion regulation support unit can also teach children ways to feel reassured when they feel anxious. For example, it can teach positive self-talk and relaxation techniques. Furthermore, the emotion regulation support unit can collaborate with parents and teachers to share the child's progress in emotion regulation and provide appropriate feedback. This helps children acquire the ability to appropriately regulate their emotions and reduce stress and anxiety.

[0134] The processing flow of the second embodiment will be briefly explained below.

[0135] Step 1: The collection unit collects information about the child. The child's information includes age, gender, height, weight, past medical history, allergy information, grades in each subject, test scores, etc. The collection unit collects information entered by parents and teachers. The collection unit can also monitor the child's health using sensors and collect that data. For example, the collection unit can measure the child's heart rate and body temperature using a wearable device and collect that data. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using data mining or machine learning algorithms. For example, the analysis unit analyzes a child's health condition and grades and generates data to provide meal ideas suitable for that child. The analysis unit can also use a generation AI to perform analysis based on the collected data. For example, the analysis unit inputs prompts to the generation AI and generates analysis results. Step 3: The providing unit provides advice based on the analysis results obtained by the analyzing unit. The advice includes health advice, study advice, lifestyle advice, and the like. For example, the providing unit includes a menu unit that provides nutritionally balanced meal menus. The menu unit suggests meal menus that include a balanced amount of nutrients such as vitamins, minerals, and proteins. The providing unit can also provide meal menus that take allergies into consideration. The providing unit also includes a therapy unit that provides home therapy when the user is feeling unwell. The therapy unit advises the user to drink hot drinks and get plenty of rest if they have a cold. The providing unit can also provide advice on developing healthy lifestyle habits. For example, the providing unit advises on sleep duration, meal timing, exercise habits, etc. The providing unit can also provide stress management methods. For example, the providing unit suggests stress management methods such as relaxation techniques, counseling, and exercise. The providing unit includes a learning unit that provides effective learning methods for specific subjects. The learning unit suggests methods such as repetitive learning, visual learning, and practical learning. The information provision department has an exam preparation department that provides information on suitable schools and cram schools. The exam preparation department selects schools and cram schools based on academic ability level, strengths in specific subjects, location, etc., and provides that information.

[0136] 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.

[0137] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the 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.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0173] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0187] 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.

[0188] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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."

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0206] 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.

[0207] [Explanation of symbols]

[0208] 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 collection department that collects information about children; an analysis unit that analyzes the information collected by the collection unit; a providing unit that provides advice based on the analysis result obtained by the analyzing unit; Equipped with A system characterized by:

2. The providing unit Equipped with a menu section that offers nutritionally balanced meal menus 2. The system of claim 1.

3. The providing unit Providing allergy-friendly meal menus 2. The system of claim 1.

4. The providing unit A therapy department is available to provide in-home therapy when you are unwell.

2. The system of claim 1.

5. The providing unit Providing advice on how to develop healthy lifestyle habits 2. The system of claim 1.

6. The providing unit Providing stress management strategies 2. The system of claim 1.

7. The providing unit Equipping a learning department that provides effective learning methods for specific subjects 2. The system of claim 1.

8. The providing unit Have an entrance exam department that provides information on suitable schools and cram schools 2. The system of claim 1.

9. The collecting unit Estimate the child's emotions and adjust the timing of information collection based on the estimated emotions of the child.

2. The system of claim 1.

Images