system

Abstract

The system according to this embodiment aims to recommend the most suitable school based on a child's personality and learning patterns. [Solution] The system according to this embodiment comprises a reception unit, a recommendation unit, a scheduling unit, and a provision unit. The reception unit receives information about the child's personality and learning patterns. The recommendation unit analyzes the information received by the reception unit and recommends the most suitable school for the child. The scheduling unit incorporates the dates of school visits and school festivals of the schools recommended by the recommendation unit into the schedule. The provision unit provides information such as the characteristics and curriculum of the schools recommended by the recommendation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to select an optimal school based on a child's personality and learning pattern, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend an optimal school based on a child's personality and learning pattern.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a recommendation unit, a scheduling unit, and a provision unit. The reception unit receives information about the child's personality and learning patterns. The recommendation unit analyzes the information received by the reception unit and recommends the most suitable school for the child. The scheduling unit incorporates the dates of school visits and school festivals of the schools recommended by the recommendation unit into the schedule. The provision unit provides information such as the characteristics and curriculum of the schools recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to this embodiment can recommend the most suitable school based on the child's personality and learning patterns. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The school recommendation system according to an embodiment of the present invention targets families considering junior high school entrance exams for both parents and children, and utilizes a Large-Scale Language Model (LLM) to recommend schools that match the child's personality and learning patterns. The school recommendation system works by having parents input information about their child's personality and learning patterns, and the LLM analyzes this information to recommend the most suitable schools. Furthermore, it includes an assistant function to incorporate school visits and school festivals of the recommended schools into the schedule. For example, parents input information about their child's personality and learning patterns. For instance, they input information such as what subjects the child excels at, what learning style they prefer, or what kind of school environment they desire. This information is input into the LLM. Next, the LLM analyzes the input information and recommends the most suitable school for the child. Based on past data and the success stories of other students, the LLM identifies the school that best suits the child's characteristics. For example, for a child who prefers individualized instruction, it recommends schools with strong individualized instruction programs. Furthermore, it includes an assistant function to incorporate school visits and school festivals of the recommended schools into the schedule. For example, it automatically adds school visit and school festival dates to the calendar and sets reminders. This allows parents to efficiently choose a school without forgetting the dates of school visits and school festivals. It also provides information on school characteristics and curriculum. For example, it provides detailed information on each school's educational philosophy and curriculum, as well as past success rates, enabling parents to compare schools more concretely. This application is expected to allow parents and children to approach junior high school entrance exams without stress, stimulating children's motivation to learn and leading to better learning outcomes. For instance, by finding a school that suits their child's characteristics, children can learn in an environment that suits them, improving their motivation to learn. Furthermore, by efficiently scheduling school visits and festivals, parents can use their time effectively and enjoy the school-choosing process with their children. In this way, the school recommendation system allows parents and children to efficiently choose a school together.

[0029] The school recommendation system according to this embodiment comprises a reception unit, a recommendation unit, a scheduling unit, and a provision unit. The reception unit receives information about the child's personality and learning patterns. This information includes, but is not limited to, examples of the child's favorite subjects, learning style, and desired school environment. For example, the reception unit allows parents to input their child's favorite subjects. The reception unit also allows parents to input their child's learning style. Furthermore, the reception unit allows parents to input their desired school environment. For example, the reception unit allows parents to input mathematics as their child's favorite subject. They can also input that they prefer individualized instruction as a learning style. As for the desired school environment, they can input things like school culture, facilities, and class size. The recommendation unit uses LLM to analyze the information received by the reception unit and recommends the most suitable school for the child. For example, the recommendation unit identifies the school that best suits the child's characteristics based on past data and the success stories of other students. For example, the recommendation unit recommends schools with strong individualized instruction based on past data. The recommendation unit can also identify the most suitable school for the child based on the success stories of other students. Furthermore, the recommendation department can use LLM to identify the school that best suits the child's characteristics. For example, the recommendation department uses past data such as academic performance, college placement results, and survey results. As a success story, they identify schools based on factors such as college placement, academic improvement, and satisfaction. The scheduling department incorporates the dates of school visits and school festivals recommended by the recommendation department into the schedule. For example, the scheduling department can automatically add the dates of school visits and school festivals to the calendar and set reminders. For example, the scheduling department can add the dates of school visits to the calendar and set reminders. The scheduling department can also add the dates of school festivals to the calendar and set reminders. Furthermore, the scheduling department can automatically add the dates of school visits and school festivals to the calendar and set reminders. For example, the scheduling department can add the dates of school visits to the calendar and set reminders. For example, the scheduling department can add the dates of school festivals to the calendar and set reminders. The providing department provides information such as the characteristics and curriculum of the schools recommended by the recommendation department.The information provision department provides information such as the educational policies and curriculum details of each school, as well as past admission results. For example, the information provision department provides information such as holistic education, specialized education, and individualized instruction as educational policies. The information provision department can also provide information such as subject lists, class hours, and evaluation methods as curriculum details. Furthermore, the information provision department can also provide information such as the number of successful applicants, the pass rate, and destinations of students as past admission results. For example, the information provision department provides information such as holistic education, specialized education, and individualized instruction as educational policies. It provides information such as subject lists, class hours, and evaluation methods as curriculum details. It provides information such as the number of successful applicants, the pass rate, and destinations of students as past admission results. This allows the school recommendation system according to the embodiment to efficiently proceed with school selection for parents and children. Some or all of the above processing in the reception department, recommendation department, scheduling department, and information provision department may be performed using AI, for example, or not using AI. For example, the reception department can input information entered by parents into AI and have the AI ​​perform information analysis. The recommendation department can use AI to recommend the most suitable school for a child. The scheduling function uses AI to add dates for school tours and festivals to the calendar and set reminders. The information provision function uses AI to provide information such as the school's unique features and curriculum.

[0030] The reception desk receives information about the child's personality and learning patterns. This information includes, but is not limited to, their favorite subjects, learning style, and preferred school environment. For example, parents can input their child's favorite subjects, learning style, and preferred school environment. For instance, parents can input mathematics as their child's favorite subject, prefer individualized instruction as their learning style, and specify school culture, facilities, and class size as their preferred school environment. The reception desk centrally manages and stores the information entered by parents in a database, allowing subsequent processing departments to quickly access the necessary information. Furthermore, the reception desk has a feedback function to verify the accuracy of the entered information, allowing parents to check for errors. For example, if there are inconsistencies or incomplete entries, the system will automatically display a warning and prompt correction. The reception desk can also add questions to analyze the child's personality and learning patterns in more detail based on the information entered by parents. This allows for the collection of more accurate information, enabling subsequent recommendation departments to recommend more appropriate schools. Furthermore, the application desk has a function that visually displays a child's personality and learning patterns based on the information entered by parents, allowing parents to grasp their child's characteristics at a glance. For example, it displays favorite subjects, learning styles, and desired school environments in graphs and charts, allowing parents to intuitively understand their child's characteristics. In this way, the application desk can provide support to parents in accurately understanding their child's characteristics and making appropriate school choices.

[0031] The recommendation department uses LLM to analyze information received by the reception department and recommends the most suitable school for each child. For example, the recommendation department identifies schools that best suit a child's characteristics based on past data and the success stories of other students. For instance, based on past data, the recommendation department might recommend schools with strong individualized instruction. The recommendation department can also identify the most suitable school for a child based on the success stories of other students. Furthermore, the recommendation department can use LLM to identify schools that best suit a child's characteristics. For example, the recommendation department might use past data such as academic performance, college placement results, and survey results. As success stories, they identify schools based on factors like college placement, academic improvement, and satisfaction. The recommendation department uses LLM to integrate multiple data sources and conduct a comprehensive evaluation to identify the school that best suits a child's characteristics. For example, they might analyze past data such as academic performance, college placement results, and survey results to identify schools that best suit a child's characteristics. The recommendation department can also identify schools that best suit a child based on the success stories of other students. For example, they might recommend schools where students with similar characteristics have previously succeeded. Furthermore, the recommendation department utilizes natural language processing (NLP) technology with LLM to identify the school best suited to each child's characteristics. For example, it analyzes information entered by parents and extracts keywords to identify the school best suited to the child's characteristics. This allows the recommendation department to identify the school best suited to each child's characteristics with high accuracy and recommend appropriate schools to parents. In addition, when providing recommendation results to parents, the recommendation department has a function to clearly state the reasons and basis for the recommendation, so that parents can accept the recommendation results with confidence. For example, it explains in detail the characteristics of the recommended school, past success stories, and how they match the child's characteristics. This allows the recommendation department to provide support so that parents can proceed with school selection with peace of mind.

[0032] The scheduling function incorporates the dates of school visits and school festivals recommended by the recommendation function into the schedule. For example, the scheduling function automatically adds the dates of recommended school visits and school festivals to the calendar and sets reminders. For example, the scheduling function adds the dates of school visits to the calendar and sets reminders. The scheduling function can also add the dates of school festivals to the calendar and set reminders. Furthermore, the scheduling function can automatically add the dates of school visits and school festivals to the calendar and set reminders. For example, the scheduling function adds the dates of school visits to the calendar and sets reminders. For example, it adds the dates of school festivals to the calendar and sets reminders. The scheduling function has a function to set reminders so that parents do not forget the dates of school visits and school festivals. For example, as the date of a school visit approaches, it sends a notification to the parent's smartphone to prompt them to prepare for the visit. The scheduling function also has a function to synchronize the dates of school visits and school festivals with the parent's calendar app, so that the date can be checked in the calendar app that the parent normally uses. Furthermore, the scheduling function provides detailed information about the dates of school visits and school festivals, allowing parents to understand the content of these events in advance. For example, it provides information such as the program for school visits, the event schedule for school festivals, and how to participate. This allows the scheduling function to efficiently support parents in preparing to participate in school visits and school festivals. The scheduling function also has a function to quickly notify parents if the dates of school visits or school festivals change, ensuring that parents are always aware of the latest information. For example, if the date of a school visit changes, a notification is sent to the parent's smartphone and the new date is automatically updated in the calendar. This allows the scheduling function to efficiently manage the schedules for parents to participate in school visits and school festivals.

[0033] The information provision department provides information such as the characteristics and curriculum of schools recommended by the recommendation department. For example, the department provides information such as the educational policies and curriculum details of each school, and past admission results. For instance, the department provides information such as holistic education, specialized education, and individualized instruction as educational policies. The department can also provide information such as subject lists, class hours, and evaluation methods as curriculum details. Furthermore, the department can provide information such as the number of successful applicants, acceptance rates, and destinations of students as past admission results. For example, the department provides information such as holistic education, specialized education, and individualized instruction as educational policies. It provides information such as subject lists, class hours, and evaluation methods as curriculum details. It provides information such as the number of successful applicants, acceptance rates, and destinations of students as past admission results. The information provision department has a function to provide information in a visually easy-to-understand format so that parents can grasp the details of the recommended schools. For example, it displays the educational policies and curriculum details of each school in graphs and charts so that parents can understand them intuitively. The department also displays past admission results in tables and graphs so that parents can grasp the school's performance at a glance. Furthermore, the service includes a function to save information on schools that parents are interested in, making it easy to access later. For example, parents can add information on schools they are interested in to a favorites list so they can check detailed information later. The service also includes a function to share information on schools that parents are interested in with other family and friends, allowing them to proceed with school selection together. For example, parents can share information on schools they are interested in via email or social media and exchange opinions with family and friends. In this way, the service can support parents in efficiently proceeding with school selection. In addition, the service includes a function to provide information to help parents plan school visits and participation in school festivals based on the information on schools recommended to them. For example, it provides information on how to make reservations for school visits, how to participate in school festivals, and transportation access, supporting parents in smoothly participating in school visits and school festivals. In this way, the service can support parents in planning concrete actions based on the information on schools recommended to them.

[0034] The reception desk can receive information such as a child's favorite subjects, learning style, and desired school environment. For example, parents can input their child's favorite subjects. For instance, the reception desk can input mathematics as a child's favorite subject. The reception desk can also input parents' learning styles. For example, the reception desk can input that a parent prefers individual tutoring as a child's learning style. Furthermore, the reception desk can input parents' desired school environment. For example, the reception desk can input school culture, facilities, class size, etc., as a desired school environment. By receiving detailed information about the child, it becomes possible to make more accurate school recommendations. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the information entered by the parents into an AI and have the AI ​​perform the analysis of the information.

[0035] The recommendation department can identify the school best suited to a child's characteristics based on past data and the success stories of other students. For example, the recommendation department might recommend schools with strong individualized instruction based on past data. For example, the recommendation department might use academic performance data, college entrance exam results, and survey results as past data. The recommendation department can also identify the best school for a child based on the success stories of other students. For example, the recommendation department might identify schools based on factors such as college destinations, academic improvement, and satisfaction levels as success stories. Furthermore, the recommendation department can use LLM (Learning Leadership Model) to identify the school best suited to a child's characteristics. For example, the recommendation department might identify the best school for a child based on past data and success stories. This allows for the recommendation of more appropriate schools by utilizing past data and success stories. Some or all of the above processes in the recommendation department may be performed using, for example, generative AI, or not. For example, the recommendation department can input past data and success stories into a generative AI and have the generative AI identify the best school for the child.

[0036] The scheduling function can automatically add the dates of recommended school visits and school festivals to the calendar and set reminders. For example, the scheduling function can add the dates of recommended school visits to the calendar and set reminders. For example, the scheduling function can add the dates of school visits to the calendar and set reminders. The scheduling function can also add the dates of school festivals to the calendar and set reminders. For example, the scheduling function can add the dates of school festivals to the calendar and set reminders. Furthermore, the scheduling function can automatically add the dates of school visits and school festivals to the calendar and set reminders. For example, the scheduling function can add the dates of school visits to the calendar and set reminders. For example, the scheduling function can add the dates of school festivals to the calendar and set reminders. This allows parents to efficiently proceed with school selection by automatically incorporating the dates of school visits and school festivals into their schedule. Some or all of the above processing in the scheduling function may be performed using AI, for example, or not. For example, the scheduling function allows users to input the dates of school tours and school festivals into the AI, which then adds them to the calendar and sets reminders.

[0037] The information provider can provide information such as the educational policies and curriculum details of each school, as well as past admission results. For example, the information provider can provide information such as holistic education, specialized education, and individualized instruction as part of each school's educational policies. The information provider can also provide information such as subject lists, class hours, and evaluation methods as part of curriculum details. Furthermore, the information provider can also provide information such as the number of successful applicants, acceptance rates, and destinations of students as part of past admission results. By providing detailed information about schools, parents can compare and consider schools more concretely. Some or all of the above processing in the information provider may be performed using AI, for example, or not. For example, the information provider can input information such as the educational policies and curriculum details of each school, as well as past admission results, into an AI and have the AI ​​provide the information.

[0038] The reception desk can analyze a child's past learning history and provide the optimal information input format. For example, the reception desk can suggest the optimal input format based on the learning style the child has used in the past. The reception desk can also analyze a child's past academic performance data and provide input fields tailored to their strengths and weaknesses. Furthermore, the reception desk can provide an input format based on a specific learning pattern derived from the child's past learning history. This improves the efficiency of information input by providing the optimal input format based on past learning history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the child's past learning history into AI and have AI provide the optimal information input format.

[0039] The reception desk can customize input fields based on the parent's current interests and the child's learning progress when information is entered. For example, the reception desk can provide relevant input fields based on the educational topics the parent is currently interested in. The reception desk can also prioritize the input of necessary information according to the child's learning progress. Furthermore, the reception desk can dynamically change input fields considering the parent's current interests and the child's learning progress. For example, the reception desk can dynamically change input fields considering the parent's current interests and the child's learning progress. This allows for the input of more appropriate information by customizing input fields according to the parent's interests and the child's learning progress. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the parent's current interests and the child's learning progress into the AI ​​and have the AI ​​perform the customization of input fields.

[0040] The reception desk can prioritize inputting highly relevant information by considering the parent's geographical location when information is entered. For example, the reception desk can prioritize inputting school information in the area where the parent lives. The reception desk can also automatically suggest information about nearby schools based on the parent's geographical location. For example, the reception desk automatically suggests information about nearby schools based on the parent's geographical location. Furthermore, the reception desk can prioritize inputting highly relevant information by considering the parent's geographical location. For example, the reception desk prioritizes inputting highly relevant information by considering the parent's geographical location. This allows for the priority input of highly relevant information by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the parent's geographical location information into AI and have AI perform the priority input of highly relevant information.

[0041] The reception desk can analyze parents' social media activity and input relevant information when information is entered. For example, the reception desk can provide relevant input fields based on information shared by parents on social media. The reception desk can also analyze parents' social media activity and suggest input fields based on educational topics of interest. Furthermore, the reception desk can prioritize inputting relevant information, taking into account parents' social media activity. This allows for efficient input of relevant information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input parents' social media activity into AI and have AI input relevant information.

[0042] The recommendation system can adjust the level of detail in recommendations based on the child's learning style. For example, if a child prefers individual tutoring, the recommendation system will recommend schools with strong individual tutoring programs. Similarly, if a child prefers group learning, the recommendation system can recommend schools with strong group learning programs. Furthermore, if a child prefers online learning, the recommendation system can recommend schools with strong online learning programs. This allows for the recommendation of more appropriate schools by adjusting the level of detail according to the child's learning style. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or without one. For example, the recommendation system can input the child's learning style into a generative AI and have the AI ​​adjust the level of detail in the recommendations.

[0043] The recommendation system can apply different recommendation algorithms depending on the child's characteristics during the recommendation process. For example, if a child wants to choose a school based on their strongest subjects, the recommendation system can apply a recommendation algorithm specialized in those subjects. The recommendation system can also apply a recommendation algorithm specialized in learning styles if the child wants to choose a school based on their learning style. Furthermore, if a child wants to choose a school based on their desired school environment, the recommendation system can apply a recommendation algorithm specialized in that environment. This allows for more accurate recommendations by applying recommendation algorithms tailored to each child's characteristics. Some or all of the above-described processes in the recommendation system may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation system can input the child's characteristics into a generative AI and have the generative AI apply different recommendation algorithms.

[0044] The recommendation department can determine the priority of recommendations based on past recommendation history. For example, the recommendation department can prioritize recommending schools that parents have shown interest in from among schools that have been recommended in the past. The recommendation department can also analyze the characteristics of schools that parents prefer based on past recommendation history and recommend them preferentially. Furthermore, the recommendation department can also prioritize recommending schools that parents have shown interest in from past recommendation history. For example, the recommendation department can prioritize recommending schools that parents have shown interest in from past recommendation history. In this way, by determining the priority based on past recommendation history, it is possible to recommend schools that parents are interested in preferentially. Some or all of the above processing in the recommendation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation department can input past recommendation history into a generative AI and have the generative AI perform the determination of recommendation priorities.

[0045] The recommendation system can adjust the order of recommendations based on the child's relevance. For example, the recommendation system may prioritize schools related to the child's strongest subjects. It can also prioritize schools related to the child's learning style. Furthermore, it can prioritize schools related to the child's desired school environment. By adjusting the order of recommendations based on the child's relevance, it can prioritize recommending more appropriate schools. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not. For example, the recommendation system can input the child's relevance into a generative AI and have the generative AI adjust the order of recommendations.

[0046] The scheduling unit can provide an optimal schedule by referring to the parent's past schedule history when setting a schedule. For example, the scheduling unit can suggest an optimal schedule based on the dates of school visits and school festivals that the parent has attended in the past. The scheduling unit can also analyze the parent's past schedule history and provide an efficient schedule. Furthermore, the scheduling unit can suggest schedules related to specific events based on the parent's past schedule history. In this way, by providing an optimal schedule based on past schedule history, the parent can manage their schedule efficiently. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the parent's past schedule history into AI and have the AI ​​perform the task of providing an optimal schedule.

[0047] The scheduling unit can customize the scheduling method based on the parent's current living situation when setting a schedule. For example, if the parent is busy, the scheduling unit can suggest an efficient schedule. For example, if the parent is busy, the scheduling unit can suggest an efficient schedule. For example, if the parent has free time, the scheduling unit can suggest a detailed schedule. For example, if the parent has free time, the scheduling unit can suggest a detailed schedule. Furthermore, the scheduling unit can provide an optimal schedule considering the parent's current living situation. For example, the scheduling unit can provide an optimal schedule considering the parent's current living situation. This allows for the provision of a more appropriate schedule by customizing the schedule according to the parent's living situation. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the parent's current living situation into AI and have AI perform the customization of the scheduling method.

[0048] The scheduling unit can provide an optimal schedule by considering the parent's geographical location information when setting a schedule. For example, the scheduling unit can prioritize scheduling school open days and school festivals in the parent's area. The scheduling unit can also automatically suggest events at nearby schools based on the parent's geographical location information. Furthermore, the scheduling unit can provide an efficient schedule by considering the parent's geographical location information. This allows for prioritizing the inclusion of highly relevant events in the schedule by considering geographical location information. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the parent's geographical location information into AI and have the AI ​​provide an optimal schedule.

[0049] The scheduling unit can analyze the parent's social media activity and suggest scheduling methods when setting a schedule. For example, the scheduling unit can incorporate relevant events into the schedule based on information shared by the parent on social media. The scheduling unit can also analyze the parent's social media activity and suggest events of interest. Furthermore, the scheduling unit can provide an optimal schedule considering the parent's social media activity. This allows for the efficient incorporation of events of interest to the parent into the schedule by analyzing social media activity. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the parent's social media activity into AI and have the AI ​​suggest scheduling methods.

[0050] The information provider can provide the most relevant information by referring to the parent's past information browsing history when providing information. For example, the information provider can prioritize providing relevant information based on the school information the parent has previously viewed. The information provider can also analyze the parent's past information browsing history and provide information based on educational topics of interest. Furthermore, the information provider can provide the most relevant information by considering the parent's past information browsing history. This allows for the efficient provision of information of interest to parents by providing the most relevant information based on their past information browsing history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the parent's past information browsing history into AI and have AI provide the most relevant information.

[0051] The information provider can customize the means of information delivery based on the parent's current interests. For example, the provider can provide relevant information based on educational topics that the parent is currently interested in. The provider can also customize how the information is displayed, taking into account the parent's current interests. Furthermore, the provider can select the most appropriate means of information delivery based on the parent's current interests. By customizing the means of information delivery according to the parent's interests, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the parent's current interests into AI and have AI perform the customization of the means of information delivery.

[0052] The information provider can provide optimal information by considering the parent's geographical location when providing information. For example, the provider can prioritize providing school information in the area where the parent lives. The provider can also automatically suggest information about nearby schools based on the parent's geographical location. Furthermore, the provider can provide highly relevant information by considering the parent's geographical location. This allows for the priority provision of highly relevant information by considering geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the parent's geographical location into AI and have AI provide optimal information.

[0053] The information provider can analyze parents' social media activity and suggest ways to deliver information when providing information. For example, the provider can provide relevant information based on information shared by parents on social media. The provider can also analyze parents' social media activity and provide information based on educational topics of interest. Furthermore, the provider can select the most appropriate means of information delivery, taking into account parents' social media activity. This allows for the efficient delivery of information of interest to parents by analyzing their social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input parents' social media activity into AI and have the AI ​​suggest ways to deliver information.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The school recommendation system can also include a feedback section. This section collects feedback from parents and children to improve the system's accuracy. For example, parents can input their satisfaction level with the recommended school. Children can also share their experiences at schools they have actually attended. Furthermore, the feedback section can improve the recommendation algorithm based on the opinions of parents and children. This allows for improved system accuracy through feedback, enabling more appropriate school recommendations.

[0056] The school recommendation system can also include a communications department. This department supports communication between parents and schools. For example, it provides an interface for parents to submit questions to schools. It can also notify parents of the school's responses. Furthermore, the communications department can facilitate scheduling between parents and schools. This streamlines communication between parents and schools and supports the school selection process.

[0057] The school recommendation system can also include an alert function. This alert function notifies parents of important events and deadlines. For example, it can notify parents of school entrance exam dates and application deadlines. It can also set reminders for school visits and school festivals. Furthermore, the alert function can provide custom alerts set by parents. This allows parents to efficiently choose a school without missing important events or deadlines.

[0058] The school recommendation system can also include an analytics department. This department analyzes input data from parents and children to identify trends and patterns. For example, it can identify popular schools in a specific area. It can also suggest the most effective learning methods based on a child's learning style. Furthermore, it can identify areas for system improvement based on parental feedback. This allows for the use of data analysis to provide more appropriate school recommendations and learning method suggestions.

[0059] The school recommendation system can also include a customization section. This customization section allows users to tailor the system settings to the needs of parents and children. For example, parents can specify detailed criteria for their preferred schools. The customization section can also provide an interface that suits the child's learning style. Furthermore, the customization section can add or modify system functions based on feedback from parents and children. This enables flexible system operation that meets the needs of parents and children.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk receives information about the child's personality and learning patterns. For example, parents can enter information about their child's favorite subjects, learning style, and desired school environment. Specifically, they can enter mathematics as a favorite subject, prefer individual tutoring as a learning style, and specify the school's atmosphere, facilities, and class size as their desired school environment. Step 2: The recommendation department analyzes the information received by the reception department and recommends the best school for the child. For example, they identify the school that best suits the child's characteristics based on past data and the success stories of other students. Specifically, they recommend schools with strong individualized instruction using academic performance data, college entrance exam results, and survey results. Step 3: The scheduling department incorporates the dates of school visits and school festivals recommended by the recommendation department into the schedule. For example, it automatically adds the dates of school visits and school festivals to the calendar and sets reminders. Step 4: The providing department provides information about the characteristics and curriculum of the schools recommended by the recommendation department. For example, they provide information such as the educational policies and curriculum details of each school, and past admission results.

[0062] (Example of form 2) The school recommendation system according to an embodiment of the present invention targets families considering junior high school entrance exams for both parents and children, and utilizes a Large-Scale Language Model (LLM) to recommend schools that match the child's personality and learning patterns. The school recommendation system works by having parents input information about their child's personality and learning patterns, and the LLM analyzes this information to recommend the most suitable schools. Furthermore, it includes an assistant function to incorporate school visits and school festivals of the recommended schools into the schedule. For example, parents input information about their child's personality and learning patterns. For instance, they input information such as what subjects the child excels at, what learning style they prefer, or what kind of school environment they desire. This information is input into the LLM. Next, the LLM analyzes the input information and recommends the most suitable school for the child. Based on past data and the success stories of other students, the LLM identifies the school that best suits the child's characteristics. For example, for a child who prefers individualized instruction, it recommends schools with strong individualized instruction programs. Furthermore, it includes an assistant function to incorporate school visits and school festivals of the recommended schools into the schedule. For example, it automatically adds school visit and school festival dates to the calendar and sets reminders. This allows parents to efficiently choose a school without forgetting the dates of school visits and school festivals. It also provides information on school characteristics and curriculum. For example, it provides detailed information on each school's educational philosophy and curriculum, as well as past success rates, enabling parents to compare schools more concretely. This application is expected to allow parents and children to approach junior high school entrance exams without stress, stimulating children's motivation to learn and leading to better learning outcomes. For instance, by finding a school that suits their child's characteristics, children can learn in an environment that suits them, improving their motivation to learn. Furthermore, by efficiently scheduling school visits and festivals, parents can use their time effectively and enjoy the school-choosing process with their children. In this way, the school recommendation system allows parents and children to efficiently choose a school together.

[0063] The school recommendation system according to this embodiment comprises a reception unit, a recommendation unit, a scheduling unit, and a provision unit. The reception unit receives information about the child's personality and learning patterns. This information includes, but is not limited to, examples of the child's favorite subjects, learning style, and desired school environment. For example, the reception unit allows parents to input their child's favorite subjects. The reception unit also allows parents to input their child's learning style. Furthermore, the reception unit allows parents to input their desired school environment. For example, the reception unit allows parents to input mathematics as their child's favorite subject. They can also input that they prefer individualized instruction as a learning style. As for the desired school environment, they can input things like school culture, facilities, and class size. The recommendation unit uses LLM to analyze the information received by the reception unit and recommends the most suitable school for the child. For example, the recommendation unit identifies the school that best suits the child's characteristics based on past data and the success stories of other students. For example, the recommendation unit recommends schools with strong individualized instruction based on past data. The recommendation unit can also identify the most suitable school for the child based on the success stories of other students. Furthermore, the recommendation department can use LLM to identify the school that best suits the child's characteristics. For example, the recommendation department uses past data such as academic performance, college placement results, and survey results. As a success story, they identify schools based on factors such as college placement, academic improvement, and satisfaction. The scheduling department incorporates the dates of school visits and school festivals recommended by the recommendation department into the schedule. For example, the scheduling department can automatically add the dates of school visits and school festivals to the calendar and set reminders. For example, the scheduling department can add the dates of school visits to the calendar and set reminders. The scheduling department can also add the dates of school festivals to the calendar and set reminders. Furthermore, the scheduling department can automatically add the dates of school visits and school festivals to the calendar and set reminders. For example, the scheduling department can add the dates of school visits to the calendar and set reminders. For example, the scheduling department can add the dates of school festivals to the calendar and set reminders. The providing department provides information such as the characteristics and curriculum of the schools recommended by the recommendation department.The information provision department provides information such as the educational policies and curriculum details of each school, as well as past admission results. For example, the information provision department provides information such as holistic education, specialized education, and individualized instruction as educational policies. The information provision department can also provide information such as subject lists, class hours, and evaluation methods as curriculum details. Furthermore, the information provision department can also provide information such as the number of successful applicants, the pass rate, and destinations of students as past admission results. For example, the information provision department provides information such as holistic education, specialized education, and individualized instruction as educational policies. It provides information such as subject lists, class hours, and evaluation methods as curriculum details. It provides information such as the number of successful applicants, the pass rate, and destinations of students as past admission results. This allows the school recommendation system according to the embodiment to efficiently proceed with school selection for parents and children. Some or all of the above processing in the reception department, recommendation department, scheduling department, and information provision department may be performed using AI, for example, or not using AI. For example, the reception department can input information entered by parents into AI and have the AI ​​perform information analysis. The recommendation department can use AI to recommend the most suitable school for a child. The scheduling function uses AI to add dates for school tours and festivals to the calendar and set reminders. The information provision function uses AI to provide information such as the school's unique features and curriculum.

[0064] The reception desk receives information about the child's personality and learning patterns. This information includes, but is not limited to, their favorite subjects, learning style, and preferred school environment. For example, parents can input their child's favorite subjects, learning style, and preferred school environment. For instance, parents can input mathematics as their child's favorite subject, prefer individualized instruction as their learning style, and specify school culture, facilities, and class size as their preferred school environment. The reception desk centrally manages and stores the information entered by parents in a database, allowing subsequent processing departments to quickly access the necessary information. Furthermore, the reception desk has a feedback function to verify the accuracy of the entered information, allowing parents to check for errors. For example, if there are inconsistencies or incomplete entries, the system will automatically display a warning and prompt correction. The reception desk can also add questions to analyze the child's personality and learning patterns in more detail based on the information entered by parents. This allows for the collection of more accurate information, enabling subsequent recommendation departments to recommend more appropriate schools. Furthermore, the application desk has a function that visually displays a child's personality and learning patterns based on the information entered by parents, allowing parents to grasp their child's characteristics at a glance. For example, it displays favorite subjects, learning styles, and desired school environments in graphs and charts, allowing parents to intuitively understand their child's characteristics. In this way, the application desk can provide support to parents in accurately understanding their child's characteristics and making appropriate school choices.

[0065] The recommendation department uses LLM to analyze information received by the reception department and recommends the most suitable school for each child. For example, the recommendation department identifies schools that best suit a child's characteristics based on past data and the success stories of other students. For instance, based on past data, the recommendation department might recommend schools with strong individualized instruction. The recommendation department can also identify the most suitable school for a child based on the success stories of other students. Furthermore, the recommendation department can use LLM to identify schools that best suit a child's characteristics. For example, the recommendation department might use past data such as academic performance, college placement results, and survey results. As success stories, they identify schools based on factors like college placement, academic improvement, and satisfaction. The recommendation department uses LLM to integrate multiple data sources and conduct a comprehensive evaluation to identify the school that best suits a child's characteristics. For example, they might analyze past data such as academic performance, college placement results, and survey results to identify schools that best suit a child's characteristics. The recommendation department can also identify schools that best suit a child based on the success stories of other students. For example, they might recommend schools where students with similar characteristics have previously succeeded. Furthermore, the recommendation department utilizes natural language processing (NLP) technology with LLM to identify the school best suited to each child's characteristics. For example, it analyzes information entered by parents and extracts keywords to identify the school best suited to the child's characteristics. This allows the recommendation department to identify the school best suited to each child's characteristics with high accuracy and recommend appropriate schools to parents. In addition, when providing recommendation results to parents, the recommendation department has a function to clearly state the reasons and basis for the recommendation, so that parents can accept the recommendation results with confidence. For example, it explains in detail the characteristics of the recommended school, past success stories, and how they match the child's characteristics. This allows the recommendation department to provide support so that parents can proceed with school selection with peace of mind.

[0066] The scheduling function incorporates the dates of school visits and school festivals recommended by the recommendation function into the schedule. For example, the scheduling function automatically adds the dates of recommended school visits and school festivals to the calendar and sets reminders. For example, the scheduling function adds the dates of school visits to the calendar and sets reminders. The scheduling function can also add the dates of school festivals to the calendar and set reminders. Furthermore, the scheduling function can automatically add the dates of school visits and school festivals to the calendar and set reminders. For example, the scheduling function adds the dates of school visits to the calendar and sets reminders. For example, it adds the dates of school festivals to the calendar and sets reminders. The scheduling function has a function to set reminders so that parents do not forget the dates of school visits and school festivals. For example, as the date of a school visit approaches, it sends a notification to the parent's smartphone to prompt them to prepare for the visit. The scheduling function also has a function to synchronize the dates of school visits and school festivals with the parent's calendar app, so that the date can be checked in the calendar app that the parent normally uses. Furthermore, the scheduling function provides detailed information about the dates of school visits and school festivals, allowing parents to understand the content of these events in advance. For example, it provides information such as the program for school visits, the event schedule for school festivals, and how to participate. This allows the scheduling function to efficiently support parents in preparing to participate in school visits and school festivals. The scheduling function also has a function to quickly notify parents if the dates of school visits or school festivals change, ensuring that parents are always aware of the latest information. For example, if the date of a school visit changes, a notification is sent to the parent's smartphone and the new date is automatically updated in the calendar. This allows the scheduling function to efficiently manage the schedules for parents to participate in school visits and school festivals.

[0067] The information provision department provides information such as the characteristics and curriculum of schools recommended by the recommendation department. For example, the department provides information such as the educational policies and curriculum details of each school, and past admission results. For instance, the department provides information such as holistic education, specialized education, and individualized instruction as educational policies. The department can also provide information such as subject lists, class hours, and evaluation methods as curriculum details. Furthermore, the department can provide information such as the number of successful applicants, acceptance rates, and destinations of students as past admission results. For example, the department provides information such as holistic education, specialized education, and individualized instruction as educational policies. It provides information such as subject lists, class hours, and evaluation methods as curriculum details. It provides information such as the number of successful applicants, acceptance rates, and destinations of students as past admission results. The information provision department has a function to provide information in a visually easy-to-understand format so that parents can grasp the details of the recommended schools. For example, it displays the educational policies and curriculum details of each school in graphs and charts so that parents can understand them intuitively. The department also displays past admission results in tables and graphs so that parents can grasp the school's performance at a glance. Furthermore, the service includes a function to save information on schools that parents are interested in, making it easy to access later. For example, parents can add information on schools they are interested in to a favorites list so they can check detailed information later. The service also includes a function to share information on schools that parents are interested in with other family and friends, allowing them to proceed with school selection together. For example, parents can share information on schools they are interested in via email or social media and exchange opinions with family and friends. In this way, the service can support parents in efficiently proceeding with school selection. In addition, the service includes a function to provide information to help parents plan school visits and participation in school festivals based on the information on schools recommended to them. For example, it provides information on how to make reservations for school visits, how to participate in school festivals, and transportation access, supporting parents in smoothly participating in school visits and school festivals. In this way, the service can support parents in planning concrete actions based on the information on schools recommended to them.

[0068] The reception desk can receive information such as a child's favorite subjects, learning style, and desired school environment. For example, parents can input their child's favorite subjects. For instance, the reception desk can input mathematics as a child's favorite subject. The reception desk can also input parents' learning styles. For example, the reception desk can input that a parent prefers individual tutoring as a child's learning style. Furthermore, the reception desk can input parents' desired school environment. For example, the reception desk can input school culture, facilities, class size, etc., as a desired school environment. By receiving detailed information about the child, it becomes possible to make more accurate school recommendations. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the information entered by the parents into an AI and have the AI ​​perform the analysis of the information.

[0069] The recommendation department can identify the school best suited to a child's characteristics based on past data and the success stories of other students. For example, the recommendation department might recommend schools with strong individualized instruction based on past data. For example, the recommendation department might use academic performance data, college entrance exam results, and survey results as past data. The recommendation department can also identify the best school for a child based on the success stories of other students. For example, the recommendation department might identify schools based on factors such as college destinations, academic improvement, and satisfaction levels as success stories. Furthermore, the recommendation department can use LLM (Learning Leadership Model) to identify the school best suited to a child's characteristics. For example, the recommendation department might identify the best school for a child based on past data and success stories. This allows for the recommendation of more appropriate schools by utilizing past data and success stories. Some or all of the above processes in the recommendation department may be performed using, for example, generative AI, or not. For example, the recommendation department can input past data and success stories into a generative AI and have the generative AI identify the best school for the child.

[0070] The scheduling function can automatically add the dates of recommended school visits and school festivals to the calendar and set reminders. For example, the scheduling function can add the dates of recommended school visits to the calendar and set reminders. For example, the scheduling function can add the dates of school visits to the calendar and set reminders. The scheduling function can also add the dates of school festivals to the calendar and set reminders. For example, the scheduling function can add the dates of school festivals to the calendar and set reminders. Furthermore, the scheduling function can automatically add the dates of school visits and school festivals to the calendar and set reminders. For example, the scheduling function can add the dates of school visits to the calendar and set reminders. For example, the scheduling function can add the dates of school festivals to the calendar and set reminders. This allows parents to efficiently proceed with school selection by automatically incorporating the dates of school visits and school festivals into their schedule. Some or all of the above processing in the scheduling function may be performed using AI, for example, or not. For example, the scheduling function allows users to input the dates of school tours and school festivals into the AI, which then adds them to the calendar and sets reminders.

[0071] The information provider can provide information such as the educational policies and curriculum details of each school, as well as past admission results. For example, the information provider can provide information such as holistic education, specialized education, and individualized instruction as part of each school's educational policies. The information provider can also provide information such as subject lists, class hours, and evaluation methods as part of curriculum details. Furthermore, the information provider can also provide information such as the number of successful applicants, acceptance rates, and destinations of students as part of past admission results. By providing detailed information about schools, parents can compare and consider schools more concretely. Some or all of the above processing in the information provider may be performed using AI, for example, or not. For example, the information provider can input information such as the educational policies and curriculum details of each school, as well as past admission results, into an AI and have the AI ​​provide the information.

[0072] The reception system can estimate the parent's emotions and adjust the information input method based on the estimated emotions. For example, if the parent is stressed, the reception system can provide a simple interface and minimize the input steps. For example, if the parent is stressed, the reception system can provide a simple interface and minimize the input steps. The reception system can also provide detailed input options and suggest customizable input methods if the parent is relaxed. For example, if the parent is relaxed, the reception system can provide detailed input options and suggest customizable input methods. Furthermore, if the parent is in a hurry, the reception system can prioritize voice input to allow for quick information entry. For example, if the parent is in a hurry, the reception system can prioritize voice input to allow for quick information entry. This allows parents to enter information without stress by adjusting the information input method according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the parent's emotions into the AI ​​and have the AI ​​perform emotion estimation.

[0073] The reception desk can analyze a child's past learning history and provide the optimal information input format. For example, the reception desk can suggest the optimal input format based on the learning style the child has used in the past. The reception desk can also analyze a child's past academic performance data and provide input fields tailored to their strengths and weaknesses. Furthermore, the reception desk can provide an input format based on a specific learning pattern derived from the child's past learning history. This improves the efficiency of information input by providing the optimal input format based on past learning history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the child's past learning history into AI and have AI provide the optimal information input format.

[0074] The reception desk can customize input fields based on the parent's current interests and the child's learning progress when information is entered. For example, the reception desk can provide relevant input fields based on the educational topics the parent is currently interested in. The reception desk can also prioritize the input of necessary information according to the child's learning progress. Furthermore, the reception desk can dynamically change input fields considering the parent's current interests and the child's learning progress. For example, the reception desk can dynamically change input fields considering the parent's current interests and the child's learning progress. This allows for the input of more appropriate information by customizing input fields according to the parent's interests and the child's learning progress. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the parent's current interests and the child's learning progress into the AI ​​and have the AI ​​perform the customization of input fields.

[0075] The reception unit can estimate the parent's emotions and determine the priority of the information to be entered based on the estimated emotions. For example, if the parent is stressed, the reception unit can prioritize the input of the most important information. For example, if the parent is stressed, the reception unit can prioritize the input of the most important information. The reception unit can also provide the option to enter detailed information if the parent is relaxed. For example, if the parent is relaxed, the reception unit can provide the option to enter detailed information. Furthermore, if the parent is in a hurry, the reception unit can prioritize the input of only the essential information. For example, if the parent is in a hurry, the reception unit can prioritize the input of only the essential information. This allows for the priority of important information to be entered by determining the priority of information according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception desk can input the parents' emotions into the AI ​​and have the AI ​​perform an emotion estimation.

[0076] The reception desk can prioritize inputting highly relevant information by considering the parent's geographical location when information is entered. For example, the reception desk can prioritize inputting school information in the area where the parent lives. The reception desk can also automatically suggest information about nearby schools based on the parent's geographical location. For example, the reception desk automatically suggests information about nearby schools based on the parent's geographical location. Furthermore, the reception desk can prioritize inputting highly relevant information by considering the parent's geographical location. For example, the reception desk prioritizes inputting highly relevant information by considering the parent's geographical location. This allows for the priority input of highly relevant information by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the parent's geographical location information into AI and have AI perform the priority input of highly relevant information.

[0077] The reception desk can analyze parents' social media activity and input relevant information when information is entered. For example, the reception desk can provide relevant input fields based on information shared by parents on social media. The reception desk can also analyze parents' social media activity and suggest input fields based on educational topics of interest. Furthermore, the reception desk can prioritize inputting relevant information, taking into account parents' social media activity. This allows for efficient input of relevant information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input parents' social media activity into AI and have AI input relevant information.

[0078] The recommendation system can estimate the parent's emotions and adjust the way recommendations are presented based on those emotions. For example, if the parent is stressed, the recommendation system will provide simple and easy-to-understand recommendations. If the parent is relaxed, the recommendation system can also provide recommendations that include detailed information. Furthermore, if the parent is in a hurry, the recommendation system can provide concise and to-the-point recommendations. By adjusting the way recommendations are presented according to the parent's emotions, recommendations that are easy for the parent to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input parents' emotions into an AI and have the AI ​​perform an emotion estimation.

[0079] The recommendation system can adjust the level of detail in recommendations based on the child's learning style. For example, if a child prefers individual tutoring, the recommendation system will recommend schools with strong individual tutoring programs. Similarly, if a child prefers group learning, the recommendation system can recommend schools with strong group learning programs. Furthermore, if a child prefers online learning, the recommendation system can recommend schools with strong online learning programs. This allows for the recommendation of more appropriate schools by adjusting the level of detail according to the child's learning style. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or without one. For example, the recommendation system can input the child's learning style into a generative AI and have the AI ​​adjust the level of detail in the recommendations.

[0080] The recommendation system can apply different recommendation algorithms depending on the child's characteristics during the recommendation process. For example, if a child wants to choose a school based on their strongest subjects, the recommendation system can apply a recommendation algorithm specialized in those subjects. The recommendation system can also apply a recommendation algorithm specialized in learning styles if the child wants to choose a school based on their learning style. Furthermore, if a child wants to choose a school based on their desired school environment, the recommendation system can apply a recommendation algorithm specialized in that environment. This allows for more accurate recommendations by applying recommendation algorithms tailored to each child's characteristics. Some or all of the above-described processes in the recommendation system may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation system can input the child's characteristics into a generative AI and have the generative AI apply different recommendation algorithms.

[0081] The recommendation system can estimate the parent's emotions and adjust the length of recommendations based on the estimated emotions. For example, if the parent is stressed, the recommendation system will provide short, concise recommendations. If the parent is relaxed, the recommendation system can provide longer recommendations with more detailed information. Furthermore, if the parent is in a hurry, the recommendation system can provide quick and concise recommendations. By adjusting the length of recommendations according to the parent's emotions, recommendations can be made easier for the parent to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI, for example, or without AI. For example, the recommendation system can input parents' emotions into an AI and have the AI ​​perform an emotion estimation.

[0082] The recommendation department can determine the priority of recommendations based on past recommendation history. For example, the recommendation department can prioritize recommending schools that parents have shown interest in from among schools that have been recommended in the past. The recommendation department can also analyze the characteristics of schools that parents prefer based on past recommendation history and recommend them preferentially. Furthermore, the recommendation department can also prioritize recommending schools that parents have shown interest in from past recommendation history. For example, the recommendation department can prioritize recommending schools that parents have shown interest in from past recommendation history. In this way, by determining the priority based on past recommendation history, it is possible to recommend schools that parents are interested in preferentially. Some or all of the above processing in the recommendation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation department can input past recommendation history into a generative AI and have the generative AI perform the determination of recommendation priorities.

[0083] The recommendation system can adjust the order of recommendations based on the child's relevance. For example, the recommendation system may prioritize schools related to the child's strongest subjects. It can also prioritize schools related to the child's learning style. Furthermore, it can prioritize schools related to the child's desired school environment. By adjusting the order of recommendations based on the child's relevance, it can prioritize recommending more appropriate schools. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not. For example, the recommendation system can input the child's relevance into a generative AI and have the generative AI adjust the order of recommendations.

[0084] The scheduling unit can estimate the parent's emotions and adjust the way the schedule is displayed based on the estimated emotions. For example, if the parent is stressed, the scheduling unit can provide a simple and easy-to-read schedule display. For example, if the parent is stressed, the scheduling unit can provide a simple and easy-to-read schedule display. For example, if the parent is relaxed, the scheduling unit can provide a schedule display that includes detailed information. For example, if the parent is relaxed, the scheduling unit can provide a schedule display that includes detailed information. Furthermore, if the parent is in a hurry, the scheduling unit can provide a schedule display that gets to the point. For example, if the parent is in a hurry, the scheduling unit can provide a schedule display that gets to the point. In this way, by adjusting the way the schedule is displayed according to the parent's emotions, the parent can check the schedule without stress. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling function can input the parents' emotions into the AI ​​and have the AI ​​perform emotion estimation.

[0085] The scheduling unit can provide an optimal schedule by referring to the parent's past schedule history when setting a schedule. For example, the scheduling unit can suggest an optimal schedule based on the dates of school visits and school festivals that the parent has attended in the past. The scheduling unit can also analyze the parent's past schedule history and provide an efficient schedule. Furthermore, the scheduling unit can suggest schedules related to specific events based on the parent's past schedule history. In this way, by providing an optimal schedule based on past schedule history, the parent can manage their schedule efficiently. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the parent's past schedule history into AI and have the AI ​​perform the task of providing an optimal schedule.

[0086] The scheduling unit can customize the scheduling method based on the parent's current living situation when setting a schedule. For example, if the parent is busy, the scheduling unit can suggest an efficient schedule. For example, if the parent is busy, the scheduling unit can suggest an efficient schedule. For example, if the parent has free time, the scheduling unit can suggest a detailed schedule. For example, if the parent has free time, the scheduling unit can suggest a detailed schedule. Furthermore, the scheduling unit can provide an optimal schedule considering the parent's current living situation. For example, the scheduling unit can provide an optimal schedule considering the parent's current living situation. This allows for the provision of a more appropriate schedule by customizing the schedule according to the parent's living situation. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the parent's current living situation into AI and have AI perform the customization of the scheduling method.

[0087] The scheduling unit can estimate the parent's emotions and prioritize the schedule based on those emotions. For example, if the parent is stressed, the scheduling unit will prioritize scheduling the most important events. The scheduling unit can also provide a detailed schedule if the parent is relaxed. Furthermore, if the parent is in a hurry, the scheduling unit can prioritize scheduling only the essential events. This allows important events to be prioritized in the schedule by determining the schedule priorities according to the parent's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the parents' emotions into the AI ​​and have the AI ​​perform emotion estimation.

[0088] The scheduling unit can provide an optimal schedule by considering the parent's geographical location information when setting a schedule. For example, the scheduling unit can prioritize scheduling school open days and school festivals in the parent's area. The scheduling unit can also automatically suggest events at nearby schools based on the parent's geographical location information. Furthermore, the scheduling unit can provide an efficient schedule by considering the parent's geographical location information. This allows for prioritizing the inclusion of highly relevant events in the schedule by considering geographical location information. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the parent's geographical location information into AI and have the AI ​​provide an optimal schedule.

[0089] The scheduling unit can analyze the parent's social media activity and suggest scheduling methods when setting a schedule. For example, the scheduling unit can incorporate relevant events into the schedule based on information shared by the parent on social media. The scheduling unit can also analyze the parent's social media activity and suggest events of interest. Furthermore, the scheduling unit can provide an optimal schedule considering the parent's social media activity. This allows for the efficient incorporation of events of interest to the parent into the schedule by analyzing social media activity. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input the parent's social media activity into AI and have the AI ​​suggest scheduling methods.

[0090] The information provider can estimate the parent's emotions and adjust the way information is displayed based on the estimated emotions. For example, if the parent is stressed, the information provider can provide a simple and highly visible display of information. For example, if the parent is stressed, the information provider can provide a simple and highly visible display of information. For example, if the parent is relaxed, the information provider can provide a display method that includes detailed information. For example, if the parent is relaxed, the information provider can provide a display method that includes detailed information. Furthermore, if the parent is in a hurry, the information provider can provide a concise display of information. For example, if the parent is in a hurry, the information provider can provide a concise display of information. In this way, by adjusting the way information is displayed according to the parent's emotions, the parent can review the information without stress. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the service provider can input the parents' emotions into the AI ​​and have the AI ​​perform emotion estimation.

[0091] The information provider can provide the most relevant information by referring to the parent's past information browsing history when providing information. For example, the information provider can prioritize providing relevant information based on the school information the parent has previously viewed. The information provider can also analyze the parent's past information browsing history and provide information based on educational topics of interest. Furthermore, the information provider can provide the most relevant information by considering the parent's past information browsing history. This allows for the efficient provision of information of interest to parents by providing the most relevant information based on their past information browsing history. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the parent's past information browsing history into AI and have AI provide the most relevant information.

[0092] The information provider can customize the means of information delivery based on the parent's current interests. For example, the provider can provide relevant information based on educational topics that the parent is currently interested in. The provider can also customize how the information is displayed, taking into account the parent's current interests. Furthermore, the provider can select the most appropriate means of information delivery based on the parent's current interests. By customizing the means of information delivery according to the parent's interests, more appropriate information can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or not using AI. For example, the provider can input the parent's current interests into AI and have AI perform the customization of the means of information delivery.

[0093] The information provider can estimate the parent's emotions and prioritize information based on the estimated emotions. For example, if the parent is stressed, the information provider will prioritize providing the most important information. The information provider can also provide detailed information if the parent is relaxed. Furthermore, if the parent is in a hurry, the information provider can prioritize providing only the essential information. In this way, by prioritizing information according to the parent's emotions, important information can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the information provider may be performed using AI, for example, or without AI. For example, the service provider can input the parents' emotions into the AI ​​and have the AI ​​perform emotion estimation.

[0094] The information provider can provide optimal information by considering the parent's geographical location when providing information. For example, the provider can prioritize providing school information in the area where the parent lives. The provider can also automatically suggest information about nearby schools based on the parent's geographical location. Furthermore, the provider can provide highly relevant information by considering the parent's geographical location. This allows for the priority provision of highly relevant information by considering geographical location. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input the parent's geographical location into AI and have AI provide optimal information.

[0095] The information provider can analyze parents' social media activity and suggest ways to deliver information when providing information. For example, the provider can provide relevant information based on information shared by parents on social media. The provider can also analyze parents' social media activity and provide information based on educational topics of interest. Furthermore, the provider can select the most appropriate means of information delivery, taking into account parents' social media activity. This allows for the efficient delivery of information of interest to parents by analyzing their social media activity. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the provider can input parents' social media activity into AI and have the AI ​​suggest ways to deliver information.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The school recommendation system can also include a feedback section. This section collects feedback from parents and children to improve the system's accuracy. For example, parents can input their satisfaction level with the recommended school. Children can also share their experiences at schools they have actually attended. Furthermore, the feedback section can improve the recommendation algorithm based on the opinions of parents and children. This allows for improved system accuracy through feedback, enabling more appropriate school recommendations.

[0098] The school recommendation system can also include a communications department. This department supports communication between parents and schools. For example, it provides an interface for parents to submit questions to schools. It can also notify parents of the school's responses. Furthermore, the communications department can facilitate scheduling between parents and schools. This streamlines communication between parents and schools and supports the school selection process.

[0099] The school recommendation system can also include an alert function. This alert function notifies parents of important events and deadlines. For example, it can notify parents of school entrance exam dates and application deadlines. It can also set reminders for school visits and school festivals. Furthermore, the alert function can provide custom alerts set by parents. This allows parents to efficiently choose a school without missing important events or deadlines.

[0100] The school recommendation system can also include an analytics department. This department analyzes input data from parents and children to identify trends and patterns. For example, it can identify popular schools in a specific area. It can also suggest the most effective learning methods based on a child's learning style. Furthermore, it can identify areas for system improvement based on parental feedback. This allows for the use of data analysis to provide more appropriate school recommendations and learning method suggestions.

[0101] The school recommendation system can also include a customization section. This customization section allows users to tailor the system settings to the needs of parents and children. For example, parents can specify detailed criteria for their preferred schools. The customization section can also provide an interface that suits the child's learning style. Furthermore, the customization section can add or modify system functions based on feedback from parents and children. This enables flexible system operation that meets the needs of parents and children.

[0102] The school recommendation system can further adjust the timing of recommendations based on parental emotions using emotion estimation capabilities. For example, if a parent is stressed, recommendations can be withheld. Conversely, if a parent is relaxed, recommendations can be made more actively. Furthermore, if a parent is in a hurry, recommendations can be made quickly. By adjusting the timing of recommendations according to parental emotions, parents can receive recommendations at the optimal time.

[0103] The school recommendation system can further utilize emotion estimation to adjust how information is displayed based on the parent's emotions. For example, if a parent is stressed, it can provide a simple and easy-to-read information display. If the parent is relaxed, it can provide a display that includes detailed information. Furthermore, if the parent is in a hurry, it can provide a concise information display. By adjusting the way information is displayed according to the parent's emotions, it allows parents to review information without stress.

[0104] The school recommendation system can further utilize emotion estimation to prioritize schedules based on parental emotions. For example, if a parent is stressed, the most important events will be prioritized in the schedule. Conversely, if a parent is relaxed, a more detailed schedule can be provided. Furthermore, if a parent is in a hurry, only the essential events will be prioritized in the schedule. This ensures that important events are prioritized by determining schedule priorities according to parental emotions.

[0105] The school recommendation system can further utilize emotion estimation to prioritize information based on parental emotions. For example, if a parent is stressed, the most important information will be provided first. Conversely, if a parent is relaxed, more detailed information may be provided. Furthermore, if a parent is in a hurry, only the essential information may be provided first. In this way, by prioritizing information according to parental emotions, important information can be delivered preferentially.

[0106] The school recommendation system can further adjust the way recommendations are presented based on parental emotions using emotion estimation capabilities. For example, if a parent is stressed, it will provide simple and easy-to-understand recommendations. If the parent is relaxed, it can provide recommendations with more detailed information. Furthermore, if the parent is in a hurry, it can provide concise recommendations that get straight to the point. By adjusting the way recommendations are presented according to the parent's emotions, the system can provide recommendations that are easier for parents to understand.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk receives information about the child's personality and learning patterns. For example, parents can enter information about their child's favorite subjects, learning style, and desired school environment. Specifically, they can enter mathematics as a favorite subject, prefer individual tutoring as a learning style, and specify the school's atmosphere, facilities, and class size as their desired school environment. Step 2: The recommendation department analyzes the information received by the reception department and recommends the best school for the child. For example, they identify the school that best suits the child's characteristics based on past data and the success stories of other students. Specifically, they recommend schools with strong individualized instruction using academic performance data, college entrance exam results, and survey results. Step 3: The scheduling department incorporates the dates of school visits and school festivals recommended by the recommendation department into the schedule. For example, it automatically adds the dates of school visits and school festivals to the calendar and sets reminders. Step 4: The providing department provides information about the characteristics and curriculum of the schools recommended by the recommendation department. For example, they provide information such as the educational policies and curriculum details of each school, and past admission results.

[0109] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0112] Each of the multiple elements described above, including the reception unit, recommendation unit, scheduling unit, and provision unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing parents to input information about their child's personality and learning patterns. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses LLM to recommend the most suitable school for the child. The scheduling unit is implemented by, for example, the control unit 46A of the smart device 14, which adds the dates of school visits and school festivals of the recommended schools to a calendar and sets reminders. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides information such as the educational policies and curriculum details of each school and past acceptance records. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, recommendation unit, scheduling unit, and provision unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing parents to input information about their child's personality and learning patterns. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses LLM to recommend the most suitable school for the child. The scheduling unit is implemented by, for example, the control unit 46A of the smart glasses 214, which adds the dates of school visits and school festivals of the recommended schools to the calendar and sets reminders. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides information such as the educational policies and curriculum details of each school and past acceptance records. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, recommendation unit, scheduling unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing parents to input information about their child's personality and learning patterns. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses LLM to recommend the most suitable school for the child. The scheduling unit is implemented by, for example, the control unit 46A of the headset terminal 314, which adds the dates of school visits and school festivals of the recommended schools to the calendar and sets reminders. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides information such as the educational policies and curriculum details of each school and past acceptance records. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, recommendation unit, scheduling unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing parents to input information about their child's personality and learning patterns. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses LLM to recommend the most suitable school for the child. The scheduling unit is implemented by, for example, the control unit 46A of the robot 414, which adds the dates of school visits and school festivals of the recommended schools to a calendar and sets reminders. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which provides information such as the educational policies and curriculum details of each school, and past acceptance records. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A reception desk that accepts information about children's personalities and learning patterns, The information received by the aforementioned reception department is analyzed, and the recommendation department recommends the most suitable school for the child. The scheduling department incorporates the dates of school tours and school festivals recommended by the aforementioned recommendation department into the schedule, The system comprises a provision department that provides information such as the characteristics and curriculum of schools recommended by the aforementioned recommendation department. A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept information about children's favorite subjects, learning styles, and preferred school environment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned recommendation department, Based on past data and the success stories of other students, we identify the school that best suits your child's characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned scheduling unit is The system automatically adds the dates of recommended school visits and school festivals to your calendar and sets reminders. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, This provides information such as the educational policies and curriculum details of each school, as well as past admission results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the parent's emotions and adjusts the information input method based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes a child's past learning history and provides the optimal information input format. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering information, the input fields are customized based on the parent's current interests and the child's learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the parent's emotions and determines the priority of the input information based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering information, the system prioritizes inputting highly relevant information, taking into account the parent's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering information, the system analyzes the parents' social media activity and enters relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recommendation department, The system estimates the parent's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned recommendation department, When making a recommendation, adjust the level of detail based on the child's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the child's characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recommendation department, It estimates the parent's emotions and adjusts the length of the recommendation based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recommendation department, When making a recommendation, priority is determined based on past recommendation history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recommendation department, When making recommendations, the order of recommendations will be adjusted based on the child's relevance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned scheduling unit is It estimates the parent's emotions and adjusts how the schedule is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned scheduling unit is When setting a schedule, it refers to the parent's past schedule history to provide the optimal schedule. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned scheduling unit is When setting up a schedule, customize the scheduling method based on the parents' current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned scheduling unit is It estimates the parents' emotions and prioritizes the schedule based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned scheduling unit is When setting a schedule, the system takes the parent's geographical location into consideration to provide the optimal schedule. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned scheduling unit is When setting a schedule, we analyze parents' social media activity and suggest scheduling methods. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the parent's emotions and adjusts how information is displayed based on the estimated parent's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing information, we refer to the parent's past information browsing history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing information, customize the means of information based on the parents' current interests. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the parents' emotions and prioritizes information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing information, we will consider the parents' geographical location to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing information, we analyze the parents' social media activity and suggest ways to share that information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts information about children's personalities and learning patterns, The information received by the aforementioned reception department is analyzed, and the recommendation department recommends the most suitable school for the child. The scheduling department incorporates the dates of school tours and school festivals recommended by the aforementioned recommendation department into the schedule, The system comprises a provision department that provides information such as the characteristics and curriculum of schools recommended by the aforementioned recommendation department. A system characterized by the following features.

2. The aforementioned reception unit is We accept information about children's favorite subjects, learning styles, and preferred school environment. The system according to feature 1.

3. The aforementioned recommendation department, Based on past data and the success stories of other students, we identify the school that best suits your child's characteristics. The system according to feature 1.

4. The aforementioned scheduling unit is The system automatically adds the dates of recommended school visits and school festivals to your calendar and sets reminders. The system according to feature 1.

5. The aforementioned supply unit is, This provides information such as the educational policies and curriculum details of each school, as well as past admission results. The system according to feature 1.

6. The aforementioned reception unit is It estimates the parent's emotions and adjusts the information input method based on the estimated parent's emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes a child's past learning history and provides the optimal information input format. The system according to feature 1.

8. The aforementioned reception unit is When entering information, the input fields are customized based on the parent's current interests and the child's learning progress. The system according to feature 1.

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