System

Abstract

An object of a system according to an embodiment is to analyze the talents and characteristics of a child and appropriately support a future course.SOLUTION: A system according to an embodiment includes a data collection unit, an analysis unit, an advice providing unit, and a route suggestion unit. The data collection unit collects behavior patterns and learning data of the child. The analysis unit analyzes the behavior pattern and the learning data collected by the data collection unit. The advice providing unit provides individual advice on the basis of a result analyzed by the analysis unit. The route proposal unit proposes a route in consideration of a future market trend and demand prediction.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to fully understand a child's talents and characteristics and provide appropriate support for their future career paths.

[0005] The system according to the embodiment aims to analyze children's talents and characteristics and provide appropriate support for their future career paths. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, an advice provision unit, and a career suggestion unit. The data collection unit collects children's behavioral patterns and learning data. The analysis unit analyzes the behavioral patterns and learning data collected by the data collection unit. The advice provision unit provides individual advice based on the results of the analysis by the analysis unit. The career suggestion unit suggests career paths taking into account future market trends and demand forecasts. [Effects of the Invention]

[0007] The system according to the embodiment can analyze a child's talents and characteristics and provide appropriate support for their future career path. [Brief explanation of the drawings]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The career support system according to an embodiment of the present invention is a system in which a generative AI analyzes a child's talents and characteristics and suggests the optimal career path. This allows the career support system to maximize the child's talents and characteristics and support the optimal career path for future success.

[0029] A career support system according to an embodiment includes a data collection unit, an analysis unit, an advice provision unit, and a career suggestion unit. The data collection unit collects a child's behavioral patterns and learning data. For example, the data collection unit collects data such as school grades, participation in extracurricular activities, hobbies, and interests. The data collection unit can also collect detailed behavioral data (e.g., food choices and sleep patterns) from the child's daily life. The data collection unit can also collect the child's digital device usage history (e.g., app usage time and search history). The analysis unit analyzes the collected behavioral patterns and learning data. For example, the analysis unit can analyze academic trends obtained from the learning data and interests revealed from behavioral patterns. The analysis unit can also analyze data on creative activities (e.g., painting and music) to discover talents. The analysis unit can also analyze a child's sociability and leadership characteristics from group activity data. The advice provision unit provides individualized advice based on the results of the analysis by the analysis unit. For example, if a child excels in a particular subject, the advice provision unit can suggest a career path related to that subject. If the child has specific skills or interests, the system will suggest activities and learning methods that can utilize those skills. Furthermore, the advice providing unit can generate an individual learning plan based on the child's characteristics and monitor progress in real time. The career suggestion unit suggests career paths taking into account future market trends and demand forecasts. For example, it can identify fields predicted to be in high demand in the future job market and suggest career paths related to those fields. The career suggestion unit can also compare market trends in different regions and countries and suggest career paths from a global perspective. As a result, the career support system according to the embodiment can maximize the child's talents and characteristics and support the child in choosing the optimal career path for future success.

[0030] The data collection unit can collect detailed behavioral data from children's daily lives and analyze it with the generation AI. For example, the data collection unit records children's food choices and analyzes their nutritional balance and dietary preferences. For example, daily meal contents are recorded using an app, and the generation AI analyzes nutrient intake. The data collection unit also monitors children's sleep patterns using a wearable device and analyzes sleep quality and duration. For example, using a sleep tracker, the generation AI collects sleep data and suggests an optimal sleep schedule. The data collection unit also records children's activity levels in daily life and analyzes the amount and type of exercise. For example, using a pedometer or activity tracker, the generation AI collects exercise data and suggests healthy lifestyle habits. This allows for detailed analysis of children's daily behavioral data to provide more accurate advice.

[0031] The data collection unit can collect children's digital device usage history and analyze it with the generation AI. For example, the data collection unit may record the amount of time children spend using apps on their smartphones or tablets and analyze their usage trends for learning and entertainment apps. For example, the generation AI may collect app usage data and suggest ways to optimize study time. The data collection unit may also analyze children's internet search history to identify topics of interest and learning trends. For example, the generation AI may suggest related learning resources based on the search history. The data collection unit may also analyze children's digital device usage patterns to identify areas for improvement in their digital literacy and online behavior. For example, the generation AI may collect usage data and suggest safe internet usage methods. In this way, by analyzing children's digital device usage history, it is possible to identify areas for improvement in their digital literacy and online behavior.

[0032] The data collection unit can use a wearable device to collect children's physical activity data. For example, the data collection unit has the child wear a wearable device and collects daily physical activity data. For example, the number of steps and exercise time are recorded, and the generation AI analyzes the health condition. The data collection unit also uses the wearable device to monitor the child's heart rate and calorie consumption and analyze the physical activity level. For example, the generation AI collects heart rate data and evaluates the effectiveness of exercise. The data collection unit also collects children's physical activity data to support the development of exercise habits and physical fitness. For example, the generation AI suggests an appropriate exercise plan based on the activity data. In this way, detailed physical activity data can be collected by utilizing wearable devices.

[0033] The data collection unit can also collect data on activities outside of school, which can be analyzed by the generative AI. For example, the data collection unit collects activity data from a child's sports club and analyzes their athletic ability and teamwork skills. For example, the generative AI supports the improvement of athletic ability based on practice records and match results. The data collection unit also collects learning data from a child's music class and analyzes their musical talent and performance technique. For example, the generative AI discovers musical talent based on lesson records and performance videos. The data collection unit also collects activity data from a child outside of school and analyzes their interests and special skills. For example, the generative AI suggests appropriate career paths and activities based on activity records. In this way, analyzing activity data outside of school can provide a more detailed understanding of a child's talents and characteristics.

[0034] The analysis unit can analyze not only children's learning data but also data on their creative activities to discover their talents. For example, the analysis unit digitizes a child's painting, and the generative AI analyzes the work to discover their creative talent. For example, it analyzes patterns in color use and composition to evaluate artistic talent. The analysis unit also collects children's music performance data, and the generative AI analyzes their performance technique and musical talent. For example, it analyzes patterns in the rhythm and melody of their performance to discover musical talent. The analysis unit also collects data on children's creative activities (for example, handicrafts and design), and the generative AI analyzes those activities to discover their talents. For example, it evaluates the originality and technical level of the work. In this way, by analyzing data on creative activities, children's artistic talent can be discovered.

[0035] The analysis unit can analyze group activity data to determine children's sociability and leadership characteristics. For example, the analysis unit records children's comments and actions during group activities, and the generation AI analyzes their sociability and leadership characteristics. For example, it evaluates the number of comments and the level of leadership demonstrated. The analysis unit also collects data on children's team projects, and the generation AI analyzes their cooperation and leadership characteristics. For example, it evaluates the progress of the project and the division of roles. The analysis unit also monitors children's emotional states during group activities, and the generation AI analyzes their sociability and leadership characteristics. For example, it evaluates changes in emotions and how they interact with others. In this way, by analyzing group activity data, it is possible to understand children's sociability and leadership characteristics.

[0036] The analysis unit can analyze data on different learning styles to discover children's talents. For example, the analysis unit classifies children's learning styles into visual, auditory, tactile, etc., and the generation AI analyzes the data according to each style. For example, for visual children, it suggests a learning method using diagrams and graphs. The analysis unit also collects teaching materials and resources according to children's learning styles, and the generation AI analyzes that data to suggest the optimal learning method. For example, it recommends audio materials for auditory children. The analysis unit also analyzes children's learning styles and generates individual learning plans based on that analysis. For example, it suggests a learning method that incorporates experiments and hands-on activities for tactile children. In this way, children's talents can be discovered by analyzing data on different learning styles.

[0037] The analysis unit can collect data on the home environment and parents' educational style and analyze it with the generative AI to analyze the child's characteristics. For example, the analysis unit collects data on the child's home environment (e.g., parents' occupation and educational level), and the generative AI analyzes that data to discover the child's characteristics. For example, the analysis unit can evaluate the impact of parents' educational style on the child's learning attitude. The analysis unit can also record the child's home learning environment (e.g., study space and study time), and the generative AI can analyze that data to suggest the optimal learning environment. For example, the analysis unit can evaluate the importance of a quiet study space. The generative AI can also propose an individual learning plan based on the child's home environment and parents' educational style. For example, in homes where parents are actively involved, the analysis unit can suggest increasing opportunities for collaborative learning. This allows for a more detailed understanding of a child's characteristics by analyzing data on the home environment and parents' educational style.

[0038] The advice providing unit can generate a study plan based on the child's characteristics and monitor progress. For example, the advice providing unit uses a generation AI to create an individual study plan based on the child's learning data and monitor progress in real time. For example, the learning progress is visualized and the plan is adjusted as needed. The advice providing unit also generates a study plan according to the child's characteristics, and the generation AI tracks progress in real time. For example, a study plan focusing on a specific subject is proposed and progress is evaluated. The advice providing unit also builds a system that generates a study plan for the child and the generation AI monitors progress in real time. For example, learning progress data is collected and the generation AI provides appropriate feedback. This makes it possible to generate an individual study plan and monitor progress in real time, thereby improving the child's learning efficiency.

[0039] The advice providing unit can suggest special teaching materials and resources according to a child's talents. For example, the advice providing unit analyzes a child's talents, and the generative AI automatically suggests special teaching materials and resources. For example, for a child who excels in a particular subject, specialized teaching materials and online courses are recommended. The advice providing unit also builds a system in which the generative AI suggests appropriate learning resources based on a child's characteristics. For example, it recommends books and videos related to areas of interest. The advice providing unit also discovers a child's talents, and the generative AI suggests special teaching materials and resources according to them. For example, a child with creative talents would be provided with art and design materials. This makes it possible to improve learning effectiveness by automatically suggesting special teaching materials and resources according to a child's talents.

[0040] The advice providing unit can update advice and respond flexibly according to the child's progress. For example, the advice providing unit monitors the child's learning progress in real time, and the generation AI regularly updates advice. For example, the learning plan is adjusted based on the progress data. The advice providing unit also builds a system in which the generation AI flexibly provides advice according to the child's progress. For example, if progress is lagging, additional support is suggested. The advice providing unit also analyzes the child's learning progress, and the generation AI regularly updates advice. For example, new learning resources are suggested according to the progress status. In this way, advice can be updated according to progress and flexibly responded to maximize the child's learning effectiveness.

[0041] The advice-providing unit can introduce mentors and role models based on the child's characteristics. For example, the advice-providing unit analyzes the child's characteristics and the generation AI introduces appropriate mentors and role models. For example, it may recommend experts with the same interests and talents. The advice-providing unit also builds a system in which the generation AI introduces appropriate mentors based on the child's characteristics. For example, it may introduce people who are successful in a particular field. The advice-providing unit also analyzes the child's talents and interests and the generation AI introduces appropriate role models. For example, it may recommend professionals who are active in the same field. This makes it possible to support a child's growth by introducing appropriate mentors and role models.

[0042] The career suggestion unit can develop an algorithm that evaluates a child's aptitude in the job market based on their characteristics. For example, the career suggestion unit develops an algorithm that uses a generative AI to evaluate their aptitude in the future job market based on the child's characteristic data. For example, it evaluates occupational aptitude based on specific skills and interests. The career suggestion unit also analyzes a child's talents and characteristics, and builds a system that uses a generative AI to evaluate their aptitude in the future job market. For example, it evaluates aptitude based on job market demand data. The career suggestion unit also integrates the child's characteristic data with future job market data, and develops an algorithm that uses a generative AI to evaluate occupational aptitude. For example, it evaluates the skills and characteristics required for a specific job. This makes it possible to evaluate a child's aptitude in the future job market based on their characteristics, and suggest the optimal career path.

[0043] The career suggestion unit can compare market trends in different regions and countries and suggest career paths from a global perspective. For example, the career suggestion unit collects market trend data from different regions and countries, and the generation AI compares them to suggest career paths. For example, it analyzes economic growth and job demand in each region. The career suggestion unit also analyzes market trends from a global perspective, and builds a system in which the generation AI suggests the optimal career path. For example, it suggests career paths based on demand data in the international job market. The career suggestion unit also compares market trends in different regions and countries, and the generation AI suggests career paths from a global perspective. For example, it evaluates job demand and growth fields in a specific region. This makes it possible to suggest the optimal career path from a global perspective by comparing market trends in different regions and countries.

[0044] The career suggestion unit can discover occupations and fields based on market trends and suggest them to children. For example, the career suggestion unit analyzes future market trend data, and the generative AI discovers new occupations and fields and suggests them to children. For example, it recommends occupations based on emerging industries and technological innovations. The career suggestion unit also builds a system in which the generative AI discovers new occupations and fields based on market trend data. For example, it identifies fields that are predicted to have high demand in the future. The career suggestion unit also analyzes future market trends, and the generative AI discovers new occupations and fields and suggests them to children. For example, it recommends new occupations that require specific technologies and skills. This allows the system to discover new occupations and fields based on future market trends and suggest them to children, thereby supporting their future success.

[0045] The advice providing unit can generate study plans and activity plans based on the child's preferences and monitor progress. For example, the advice providing unit uses a generation AI to create an individual study plan based on the child's preference data and monitor progress. For example, the advice providing unit suggests a plan that focuses on subjects and activities that the child is interested in. The advice providing unit also creates a system in which a study plan based on the child's preferences is generated and the generation AI tracks progress in real time. For example, the advice providing unit suggests learning resources related to specific hobbies and interests. The advice providing unit also analyzes the child's preference data and the generation AI creates an individual activity plan and monitors progress. For example, the advice providing unit suggests projects and extracurricular activities in areas of interest. In this way, by generating individual study plans and activity plans based on the child's preferences and monitoring progress, it is possible to improve learning effectiveness.

[0046] The advice providing unit can suggest a training program to maximize a child's abilities. For example, the advice providing unit has the generation AI suggest a special training program based on the child's ability data. For example, it recommends specialized training to improve a specific skill. The advice providing unit also generates a training program according to the child's characteristics, and the generation AI builds a system to monitor progress. For example, it suggests individual instruction or workshops to develop talents. The advice providing unit also analyzes the child's ability data, and the generation AI suggests a special training program. For example, it provides specialized training in fields such as sports or music. In this way, by suggesting a special training program, it is possible to maximize a child's abilities.

[0047] The advice providing unit suggests activities in different fields and industries based on the child's preferences, allowing the child to consider their career path from a multifaceted perspective. For example, the advice providing unit uses the generation AI to suggest activities in different fields and industries based on the child's preference data. For example, it recommends activities in different industries related to the child's field of interest. The advice providing unit also suggests activities based on the child's preferences, building a system in which the generation AI considers their career path from a multifaceted perspective. For example, it suggests internships or volunteer activities in different fields. The advice providing unit also analyzes the child's preference data and the generation AI suggests activities in different fields and industries. For example, it recommends projects or events in different industries related to the child's field of interest. This allows the child to consider their career path from a multifaceted perspective by suggesting activities in different fields and industries.

[0048] The advice-providing unit can suggest learning methods and approaches to make the most of a child's abilities. For example, the advice-providing unit has the generating AI suggest different learning methods and approaches based on the child's ability data. For example, it can recommend methods that suit visual, auditory, or tactile learning styles. The advice-providing unit also suggests learning methods that suit the child's characteristics, and builds a system where the generating AI monitors progress. For example, it can suggest project-based learning or collaborative learning. The advice-providing unit also analyzes the child's ability data, and the generating AI suggests different learning methods and approaches. For example, it can suggest methods such as online learning or on-site training. In this way, by suggesting different learning methods and approaches, it is possible to maximize a child's abilities.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The career support system can further include a health management unit that monitors the child's health. For example, the health management unit collects the child's daily health data (e.g., body temperature, blood pressure, heart rate), and the generating AI analyzes that data to evaluate the child's health. For example, the generating AI can conduct regular health checks and issue alerts if any abnormalities are detected. The health management unit can also use the generating AI to suggest healthy lifestyle habits based on the child's diet and exercise data. For example, it can provide a balanced meal plan and an appropriate exercise program. Furthermore, the health management unit can monitor the child's stress level, and the generating AI can provide advice on relaxation methods and stress management. This allows for comprehensive management of the child's health and supports optimal career paths.

[0051] The career support system can further include a social assessment unit that evaluates a child's social skills. The social assessment unit, for example, monitors a child's friendships and communication skills, and the generation AI analyzes that data to evaluate their social skills. For example, the frequency of interactions with friends and the content of conversations can be analyzed to support the improvement of sociability. The social assessment unit can also evaluate a child's cooperativeness and leadership traits based on data from the child's group activities and team projects. For example, the generation AI can analyze the progress of a project and the division of roles to evaluate the level of leadership demonstrated. Furthermore, the social assessment unit can collect data on a child's online communication, and the generation AI can identify areas for improvement in digital literacy and online behavior. This allows for a comprehensive evaluation of a child's social skills and supports optimal career paths.

[0052] The career support system can further include a creativity evaluation unit that evaluates a child's creativity. The creativity evaluation unit, for example, collects data on a child's artwork or musical performance, and the generative AI analyzes that data to evaluate their creative talent. For example, it analyzes the color and composition of a painting, or the rhythm and melody patterns of music, to discover artistic talent. The creativity evaluation unit can also collect data on a child's creative activities (e.g., handicrafts or design), and the generative AI analyzes those activities to evaluate their talent. For example, it evaluates the originality and technical level of the work. Furthermore, the creativity evaluation unit can monitor the progress of a child's creative project, and the generative AI can provide appropriate feedback. This allows for a comprehensive evaluation of a child's creativity and supports optimal career paths.

[0053] The career support system can further include a learning style assessment unit that evaluates a child's learning style. The learning style assessment unit, for example, collects data on a child's learning method and learning environment, and the generating AI analyzes that data to evaluate the optimal learning style. For example, it can propose a method based on a child's visual, auditory, or tactile learning style. The learning style assessment unit can also create an individual learning plan based on the child's learning progress data and monitor progress in real time. For example, it can visualize learning progress and adjust the plan as needed. Furthermore, the learning style assessment unit can collect data on the child's learning environment (for example, learning space and study time), and the generating AI can propose the optimal learning environment. This allows for a comprehensive evaluation of a child's learning style and support for the optimal career path.

[0054] The career support system can further include a home environment evaluation unit that evaluates the child's home environment. The home environment evaluation unit, for example, collects data on the child's home environment (e.g., parents' occupation and educational level), and the generating AI analyzes that data to evaluate the child's characteristics. For example, it evaluates the impact of the parents' educational style on the child's learning attitude. The home environment evaluation unit can also record the child's home learning environment (e.g., study space and study time), and the generating AI can analyze that data to suggest the optimal learning environment. For example, it can evaluate the importance of a quiet study space. Furthermore, the home environment evaluation unit can suggest an individual learning plan based on the child's home environment and the parents' educational style. For example, in a home where parents are actively involved, the generating AI can suggest increasing opportunities for collaborative learning. This allows for a comprehensive evaluation of the home environment and support for the optimal career path.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The data collection unit collects children's behavioral patterns and learning data. For example, data on school grades, participation in extracurricular activities, hobbies, and interests can be collected. It can also collect detailed behavioral data on children's daily lives (e.g., food choices and sleep patterns) and digital device usage history (e.g., app usage time and search history). Step 2: The analysis unit analyzes the collected behavioral patterns and learning data. For example, it analyzes academic performance trends derived from learning data and interests revealed from behavioral patterns. It can also analyze data on creative activities (such as painting and music) to discover talents. It can also analyze children's sociability and leadership traits from data on group activities. Step 3: The advice provider provides personalized advice based on the results of the analysis by the analysis unit. For example, if a child excels in a particular subject, it will suggest a career path related to that subject. Also, if a child has specific skills or interests, it will suggest activities and learning methods that will utilize those skills. It can also generate personalized learning plans based on the child's characteristics and monitor progress in real time. Step 4: The career suggestion department proposes career paths taking into account future market trends and demand forecasts. For example, it identifies fields that are predicted to be in high demand in the future job market and proposes career paths related to those fields. It can also compare market trends in different regions and countries and propose career paths from a global perspective.

[0057] (Example 2) The career support system according to an embodiment of the present invention is a system in which a generative AI analyzes a child's talents and characteristics and suggests the optimal career path. This allows the career support system to maximize the child's talents and characteristics and support the optimal career path for future success.

[0058] A career support system according to an embodiment includes a data collection unit, an analysis unit, an advice provision unit, and a career suggestion unit. The data collection unit collects a child's behavioral patterns and learning data. For example, the data collection unit collects data such as school grades, participation in extracurricular activities, hobbies, and interests. The data collection unit can also collect detailed behavioral data (e.g., food choices and sleep patterns) from the child's daily life. The data collection unit can also collect the child's digital device usage history (e.g., app usage time and search history). The analysis unit analyzes the collected behavioral patterns and learning data. For example, the analysis unit can analyze academic trends obtained from the learning data and interests revealed from behavioral patterns. The analysis unit can also analyze data on creative activities (e.g., painting and music) to discover talents. The analysis unit can also analyze a child's sociability and leadership characteristics from group activity data. The advice provision unit provides individualized advice based on the results of the analysis by the analysis unit. For example, if a child excels in a particular subject, the advice provision unit can suggest a career path related to that subject. If the child has specific skills or interests, the system will suggest activities and learning methods that can utilize those skills. Furthermore, the advice providing unit can generate an individual learning plan based on the child's characteristics and monitor progress in real time. The career suggestion unit suggests career paths taking into account future market trends and demand forecasts. For example, it can identify fields predicted to be in high demand in the future job market and suggest career paths related to those fields. The career suggestion unit can also compare market trends in different regions and countries and suggest career paths from a global perspective. As a result, the career support system according to the embodiment can maximize the child's talents and characteristics and support the child in choosing the optimal career path for future success.

[0059] The data collection unit can collect detailed behavioral data from children's daily lives and analyze it with the generation AI. For example, the data collection unit records children's food choices and analyzes their nutritional balance and dietary preferences. For example, daily meal contents are recorded using an app, and the generation AI analyzes nutrient intake. The data collection unit also monitors children's sleep patterns using a wearable device and analyzes sleep quality and duration. For example, using a sleep tracker, the generation AI collects sleep data and suggests an optimal sleep schedule. The data collection unit also records children's activity levels in daily life and analyzes the amount and type of exercise. For example, using a pedometer or activity tracker, the generation AI collects exercise data and suggests healthy lifestyle habits. This allows for detailed analysis of children's daily behavioral data to provide more accurate advice.

[0060] The data collection unit can collect children's digital device usage history and analyze it with the generation AI. For example, the data collection unit may record the amount of time children spend using apps on their smartphones or tablets and analyze their usage trends for learning and entertainment apps. For example, the generation AI may collect app usage data and suggest ways to optimize study time. The data collection unit may also analyze children's internet search history to identify topics of interest and learning trends. For example, the generation AI may suggest related learning resources based on the search history. The data collection unit may also analyze children's digital device usage patterns to identify areas for improvement in their digital literacy and online behavior. For example, the generation AI may collect usage data and suggest safe internet usage methods. In this way, by analyzing children's digital device usage history, it is possible to identify areas for improvement in their digital literacy and online behavior.

[0061] The data collection unit can use the emotion estimation function to collect the emotional state of a child during their actions, and analyze it with the generation AI. For example, the data collection unit monitors the child's facial expressions with a camera and analyzes their emotional state in real time using an emotion estimation algorithm. For example, the generation AI collects facial expression data and records changes in emotion. The data collection unit also analyzes the child's vocal tone to estimate their emotional state. For example, the generation AI collects voice data and evaluates their stress and joy levels. The data collection unit also monitors biometric data (e.g., heart rate and electrodermal activity) during the child's actions to estimate their emotional state. For example, the generation AI collects biometric data and analyzes changes in emotion in real time. This makes it possible to analyze the child's emotional state in real time and provide emotion-based advice.

[0062] The data collection unit can use a wearable device to collect children's physical activity data. For example, the data collection unit has the child wear a wearable device and collects daily physical activity data. For example, the number of steps and exercise time are recorded, and the generation AI analyzes the health condition. The data collection unit also uses the wearable device to monitor the child's heart rate and calorie consumption and analyze the physical activity level. For example, the generation AI collects heart rate data and evaluates the effectiveness of exercise. The data collection unit also collects children's physical activity data to support the development of exercise habits and physical fitness. For example, the generation AI suggests an appropriate exercise plan based on the activity data. In this way, detailed physical activity data can be collected by utilizing wearable devices.

[0063] The data collection unit can also collect data on activities outside of school, which can be analyzed by the generative AI. For example, the data collection unit collects activity data from a child's sports club and analyzes their athletic ability and teamwork skills. For example, the generative AI supports the improvement of athletic ability based on practice records and match results. The data collection unit also collects learning data from a child's music class and analyzes their musical talent and performance technique. For example, the generative AI discovers musical talent based on lesson records and performance videos. The data collection unit also collects activity data from a child outside of school and analyzes their interests and special skills. For example, the generative AI suggests appropriate career paths and activities based on activity records. In this way, analyzing activity data outside of school can provide a more detailed understanding of a child's talents and characteristics.

[0064] The data collection unit uses the emotion estimation function to collect changes in a child's emotions when performing a specific activity, and the generation AI can analyze these changes. For example, the data collection unit monitors a child's emotional state in real time when playing sports and analyzes these changes in emotion. For example, the generation AI collects facial expression data and records changes in emotion while playing sports. The data collection unit also analyzes a child's emotional state when playing music and understands changes in emotion while playing. For example, the generation AI collects audio data and evaluates changes in emotion while playing. The data collection unit also monitors a child's emotional state when performing learning activities and analyzes changes in emotion while learning. For example, the generation AI collects biometric data and analyzes changes in emotion while learning in real time. This makes it possible to understand a child's interests and stress level by analyzing changes in emotion during specific activities.

[0065] The analysis unit can analyze not only children's learning data but also data on their creative activities to discover their talents. For example, the analysis unit digitizes a child's painting, and the generative AI analyzes the work to discover their creative talent. For example, it analyzes patterns in color use and composition to evaluate artistic talent. The analysis unit also collects children's music performance data, and the generative AI analyzes their performance technique and musical talent. For example, it analyzes patterns in the rhythm and melody of their performance to discover musical talent. The analysis unit also collects data on children's creative activities (for example, handicrafts and design), and the generative AI analyzes those activities to discover their talents. For example, it evaluates the originality and technical level of the work. In this way, by analyzing data on creative activities, children's artistic talent can be discovered.

[0066] The analysis unit can analyze group activity data to determine children's sociability and leadership characteristics. For example, the analysis unit records children's comments and actions during group activities, and the generation AI analyzes their sociability and leadership characteristics. For example, it evaluates the number of comments and the level of leadership demonstrated. The analysis unit also collects data on children's team projects, and the generation AI analyzes their cooperation and leadership characteristics. For example, it evaluates the progress of the project and the division of roles. The analysis unit also monitors children's emotional states during group activities, and the generation AI analyzes their sociability and leadership characteristics. For example, it evaluates changes in emotions and how they interact with others. In this way, by analyzing group activity data, it is possible to understand children's sociability and leadership characteristics.

[0067] The analysis unit uses the emotion estimation function to analyze a child's emotional state while learning and discover characteristics that will improve learning efficiency. For example, the analysis unit monitors a child's facial expressions while learning and analyzes their emotional state using an emotion estimation algorithm. For example, the generation AI collects facial expression data and evaluates their concentration level and stress level while learning. The analysis unit also analyzes the child's vocal tone while learning to estimate their emotional state. For example, the generation AI collects voice data and evaluates their motivation and fatigue level while learning. The analysis unit also monitors a child's biometric data (for example, heart rate and electrodermal activity) while learning to estimate their emotional state. For example, the generation AI collects biometric data and analyzes changes in emotions during learning in real time. This makes it possible to discover characteristics that will improve learning efficiency by analyzing a child's emotional state while learning.

[0068] The analysis unit can analyze data on different learning styles to discover children's talents. For example, the analysis unit classifies children's learning styles into visual, auditory, tactile, etc., and the generation AI analyzes the data according to each style. For example, for visual children, it suggests a learning method using diagrams and graphs. The analysis unit also collects teaching materials and resources according to children's learning styles, and the generation AI analyzes that data to suggest the optimal learning method. For example, it recommends audio materials for auditory children. The analysis unit also analyzes children's learning styles and generates individual learning plans based on that analysis. For example, it suggests a learning method that incorporates experiments and hands-on activities for tactile children. In this way, children's talents can be discovered by analyzing data on different learning styles.

[0069] The analysis unit can collect data on the home environment and parents' educational style and analyze it with the generative AI to analyze the child's characteristics. For example, the analysis unit collects data on the child's home environment (e.g., parents' occupation and educational level), and the generative AI analyzes that data to discover the child's characteristics. For example, the analysis unit can evaluate the impact of parents' educational style on the child's learning attitude. The analysis unit can also record the child's home learning environment (e.g., study space and study time), and the generative AI can analyze that data to suggest the optimal learning environment. For example, the analysis unit can evaluate the importance of a quiet study space. The generative AI can also propose an individual learning plan based on the child's home environment and parents' educational style. For example, in homes where parents are actively involved, the analysis unit can suggest increasing opportunities for collaborative learning. This allows for a more detailed understanding of a child's characteristics by analyzing data on the home environment and parents' educational style.

[0070] The analysis unit uses the emotion estimation function to analyze changes in a child's emotions as they work on a specific task, thereby enabling the discovery of talents. For example, the analysis unit monitors a child's facial expressions as they work on a specific task and analyzes their emotional state using an emotion estimation algorithm. For example, the generation AI collects facial expression data and evaluates their interest and concentration in the task. The analysis unit also analyzes the child's vocal tone as they work on a specific task to estimate their emotional state. For example, the generation AI collects voice data and evaluates their motivation and stress level for the task. The analysis unit also monitors biometric data (e.g., heart rate and electrodermal activity) as they work on a specific task to estimate their emotional state. For example, the generation AI collects biometric data and analyzes changes in their emotions regarding the task in real time. This makes it possible to discover a child's talents by analyzing changes in emotions as they work on a specific task.

[0071] The advice providing unit can generate a study plan based on the child's characteristics and monitor progress. For example, the advice providing unit uses a generation AI to create an individual study plan based on the child's learning data and monitor progress in real time. For example, the learning progress is visualized and the plan is adjusted as needed. The advice providing unit also generates a study plan according to the child's characteristics, and the generation AI tracks progress in real time. For example, a study plan focusing on a specific subject is proposed and progress is evaluated. The advice providing unit also builds a system that generates a study plan for the child and the generation AI monitors progress in real time. For example, learning progress data is collected and the generation AI provides appropriate feedback. This makes it possible to generate an individual study plan and monitor progress in real time, thereby improving the child's learning efficiency.

[0072] The advice providing unit can suggest special teaching materials and resources according to a child's talents. For example, the advice providing unit analyzes a child's talents, and the generative AI automatically suggests special teaching materials and resources. For example, for a child who excels in a particular subject, specialized teaching materials and online courses are recommended. The advice providing unit also builds a system in which the generative AI suggests appropriate learning resources based on a child's characteristics. For example, it recommends books and videos related to areas of interest. The advice providing unit also discovers a child's talents, and the generative AI suggests special teaching materials and resources according to them. For example, a child with creative talents would be provided with art and design materials. This makes it possible to improve learning effectiveness by automatically suggesting special teaching materials and resources according to a child's talents.

[0073] The advice providing unit can use the emotion estimation function to provide advice to improve motivation according to the child's emotional state. For example, the advice providing unit monitors the child's emotional state in real time, and the generation AI provides advice to improve motivation. For example, it sends an encouraging message based on the emotional data. The advice providing unit also uses the emotion estimation function to build a system that provides study advice according to the child's emotional state. For example, if the child is feeling stressed, it suggests ways to relax. The advice providing unit also analyzes the child's emotional state, and the generation AI provides specific advice to improve motivation. For example, it suggests activities that will bring out positive emotions. In this way, by providing advice to improve motivation according to the child's emotional state, it is possible to increase the child's motivation to learn.

[0074] The advice providing unit can update advice and respond flexibly according to the child's progress. For example, the advice providing unit monitors the child's learning progress in real time, and the generation AI regularly updates advice. For example, the learning plan is adjusted based on the progress data. The advice providing unit also builds a system in which the generation AI flexibly provides advice according to the child's progress. For example, if progress is lagging, additional support is suggested. The advice providing unit also analyzes the child's learning progress, and the generation AI regularly updates advice. For example, new learning resources are suggested according to the progress status. In this way, advice can be updated according to progress and flexibly responded to maximize the child's learning effectiveness.

[0075] The advice-providing unit can introduce mentors and role models based on the child's characteristics. For example, the advice-providing unit analyzes the child's characteristics and the generation AI introduces appropriate mentors and role models. For example, it may recommend experts with the same interests and talents. The advice-providing unit also builds a system in which the generation AI introduces appropriate mentors based on the child's characteristics. For example, it may introduce people who are successful in a particular field. The advice-providing unit also analyzes the child's talents and interests and the generation AI introduces appropriate role models. For example, it may recommend professionals who are active in the same field. This makes it possible to support a child's growth by introducing appropriate mentors and role models.

[0076] The advice providing unit can use the emotion estimation function to suggest relaxation activities when a child feels stressed. For example, the advice providing unit monitors a child's emotional state in real time, and the generation AI suggests activities to help them relax. For example, it suggests relaxation methods based on the emotion data. The advice providing unit also uses the emotion estimation function to build a system that suggests specific activities to help a child relax when they feel stressed. For example, it suggests methods of meditation or deep breathing. The advice providing unit also analyzes a child's emotional state, and the generation AI suggests activities to help them relax. For example, it recommends relaxing music or videos based on the emotion data. In this way, it is possible to support a child's mental health by suggesting activities to help them relax when they feel stressed.

[0077] The career suggestion unit can develop an algorithm that evaluates a child's aptitude in the job market based on their characteristics. For example, the career suggestion unit develops an algorithm that uses a generative AI to evaluate their aptitude in the future job market based on the child's characteristic data. For example, it evaluates occupational aptitude based on specific skills and interests. The career suggestion unit also analyzes a child's talents and characteristics, and builds a system that uses a generative AI to evaluate their aptitude in the future job market. For example, it evaluates aptitude based on job market demand data. The career suggestion unit also integrates the child's characteristic data with future job market data, and develops an algorithm that uses a generative AI to evaluate occupational aptitude. For example, it evaluates the skills and characteristics required for a specific job. This makes it possible to evaluate a child's aptitude in the future job market based on their characteristics, and suggest the optimal career path.

[0078] The career suggestion unit can use the emotion estimation function to analyze the emotions a child has toward a career and evaluate aptitude. For example, the career suggestion unit monitors the emotions a child has toward their future career in real time, and the generation AI analyzes those emotions to evaluate career aptitude. For example, the degree of interest in a career is evaluated based on the emotion data. The career suggestion unit also uses the emotion estimation function to analyze the emotions a child has toward their future career, and the generation AI builds a system to evaluate career aptitude. For example, it recommends careers that are associated with strong positive emotions. The career suggestion unit also analyzes the emotions a child has toward their future career, and the generation AI evaluates career aptitude. For example, it evaluates career aptitude based on the emotion data and suggests the optimal career. In this way, by analyzing the emotions a child has toward their future career, it is possible to evaluate a child's career aptitude and suggest the optimal career.

[0079] The career suggestion unit can compare market trends in different regions and countries and suggest career paths from a global perspective. For example, the career suggestion unit collects market trend data from different regions and countries, and the generation AI compares them to suggest career paths. For example, it analyzes economic growth and job demand in each region. The career suggestion unit also analyzes market trends from a global perspective, and builds a system in which the generation AI suggests the optimal career path. For example, it suggests career paths based on demand data in the international job market. The career suggestion unit also compares market trends in different regions and countries, and the generation AI suggests career paths from a global perspective. For example, it evaluates job demand and growth fields in a specific region. This makes it possible to suggest the optimal career path from a global perspective by comparing market trends in different regions and countries.

[0080] The career suggestion unit can discover occupations and fields based on market trends and suggest them to children. For example, the career suggestion unit analyzes future market trend data, and the generative AI discovers new occupations and fields and suggests them to children. For example, it recommends occupations based on emerging industries and technological innovations. The career suggestion unit also builds a system in which the generative AI discovers new occupations and fields based on market trend data. For example, it identifies fields that are predicted to have high demand in the future. The career suggestion unit also analyzes future market trends, and the generative AI discovers new occupations and fields and suggests them to children. For example, it recommends new occupations that require specific technologies and skills. This allows the system to discover new occupations and fields based on future market trends and suggest them to children, thereby supporting their future success.

[0081] The career suggestion unit can use the emotion estimation function to analyze market trends in a child's field and suggest a career path. The career suggestion unit, for example, analyzes market trends in a field in which a child is interested, and the generation AI suggests the optimal career path. For example, the growth forecast for the field of interest is evaluated based on the emotion data. The career suggestion unit also uses the emotion estimation function to analyze market trends in a field in which a child is interested, and a system is built in which the generation AI suggests a career path. For example, market trends in fields in which positive emotions are strong are evaluated. The career suggestion unit also analyzes market trends in a field in which a child is interested, and the generation AI suggests a career path. For example, the job demand in the field of interest is evaluated based on the emotion data, and the optimal career path is suggested. In this way, the optimal career path can be suggested by analyzing market trends in a field in which a child is interested.

[0082] The advice providing unit can generate study plans and activity plans based on the child's preferences and monitor progress. For example, the advice providing unit uses a generation AI to create an individual study plan based on the child's preference data and monitor progress. For example, the advice providing unit suggests a plan that focuses on subjects and activities that the child is interested in. The advice providing unit also creates a system in which a study plan based on the child's preferences is generated and the generation AI tracks progress in real time. For example, the advice providing unit suggests learning resources related to specific hobbies and interests. The advice providing unit also analyzes the child's preference data and the generation AI creates an individual activity plan and monitors progress. For example, the advice providing unit suggests projects and extracurricular activities in areas of interest. In this way, by generating individual study plans and activity plans based on the child's preferences and monitoring progress, it is possible to improve learning effectiveness.

[0083] The advice providing unit can suggest a training program to maximize a child's abilities. For example, the advice providing unit has the generation AI suggest a special training program based on the child's ability data. For example, it recommends specialized training to improve a specific skill. The advice providing unit also generates a training program according to the child's characteristics, and the generation AI builds a system to monitor progress. For example, it suggests individual instruction or workshops to develop talents. The advice providing unit also analyzes the child's ability data, and the generation AI suggests a special training program. For example, it provides specialized training in fields such as sports or music. In this way, by suggesting a special training program, it is possible to maximize a child's abilities.

[0084] The advice providing unit can use the emotion estimation function to identify activities that evoke positive emotions in a child and suggest a career path that makes use of those emotions. For example, the advice providing unit monitors in real time the activities that evoke the child's most positive emotions, and the generation AI suggests a career path that makes use of those activities. For example, the optimal career path is identified based on the emotion data. The advice providing unit also uses the emotion estimation function to identify activities that evoke the child's most positive emotions, building a system in which the generation AI suggests a career path. For example, the advice providing unit recommends a career path related to activities that evoke strong positive emotions. The advice providing unit also analyzes the activities that evoke the child's most positive emotions, and the generation AI suggests a career path that makes use of those emotions. For example, the advice providing unit identifies fields or occupations that interest a child based on the emotion data and suggests an optimal career path. This makes it possible to maximize a child's interests and abilities by identifying the activities that evoke the child's most positive emotions and suggesting a career path that makes use of those emotions.

[0085] The advice providing unit suggests activities in different fields and industries based on the child's preferences, allowing the child to consider their career path from a multifaceted perspective. For example, the advice providing unit uses the generation AI to suggest activities in different fields and industries based on the child's preference data. For example, it recommends activities in different industries related to the child's field of interest. The advice providing unit also suggests activities based on the child's preferences, building a system in which the generation AI considers their career path from a multifaceted perspective. For example, it suggests internships or volunteer activities in different fields. The advice providing unit also analyzes the child's preference data and the generation AI suggests activities in different fields and industries. For example, it recommends projects or events in different industries related to the child's field of interest. This allows the child to consider their career path from a multifaceted perspective by suggesting activities in different fields and industries.

[0086] The advice-providing unit can suggest learning methods and approaches to make the most of a child's abilities. For example, the advice-providing unit has the generating AI suggest different learning methods and approaches based on the child's ability data. For example, it can recommend methods that suit visual, auditory, or tactile learning styles. The advice-providing unit also suggests learning methods that suit the child's characteristics, and builds a system where the generating AI monitors progress. For example, it can suggest project-based learning or collaborative learning. The advice-providing unit also analyzes the child's ability data, and the generating AI suggests different learning methods and approaches. For example, it can suggest methods such as online learning or on-site training. In this way, by suggesting different learning methods and approaches, it is possible to maximize a child's abilities.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The career support system can further include a health management unit that monitors the child's health. For example, the health management unit collects the child's daily health data (e.g., body temperature, blood pressure, heart rate), and the generating AI analyzes that data to evaluate the child's health. For example, the generating AI can conduct regular health checks and issue alerts if any abnormalities are detected. The health management unit can also use the generating AI to suggest healthy lifestyle habits based on the child's diet and exercise data. For example, it can provide a balanced meal plan and an appropriate exercise program. Furthermore, the health management unit can monitor the child's stress level, and the generating AI can provide advice on relaxation methods and stress management. This allows for comprehensive management of the child's health and supports optimal career paths.

[0089] The career support system can further include a social assessment unit that evaluates a child's social skills. The social assessment unit, for example, monitors a child's friendships and communication skills, and the generation AI analyzes that data to evaluate their social skills. For example, the frequency of interactions with friends and the content of conversations can be analyzed to support the improvement of sociability. The social assessment unit can also evaluate a child's cooperativeness and leadership traits based on data from the child's group activities and team projects. For example, the generation AI can analyze the progress of a project and the division of roles to evaluate the level of leadership demonstrated. Furthermore, the social assessment unit can collect data on a child's online communication, and the generation AI can identify areas for improvement in digital literacy and online behavior. This allows for a comprehensive evaluation of a child's social skills and supports optimal career paths.

[0090] The career support system can further include a creativity evaluation unit that evaluates a child's creativity. The creativity evaluation unit, for example, collects data on a child's artwork or musical performance, and the generative AI analyzes that data to evaluate their creative talent. For example, it analyzes the color and composition of a painting, or the rhythm and melody patterns of music, to discover artistic talent. The creativity evaluation unit can also collect data on a child's creative activities (e.g., handicrafts or design), and the generative AI analyzes those activities to evaluate their talent. For example, it evaluates the originality and technical level of the work. Furthermore, the creativity evaluation unit can monitor the progress of a child's creative project, and the generative AI can provide appropriate feedback. This allows for a comprehensive evaluation of a child's creativity and supports optimal career paths.

[0091] The career support system can further include a learning style assessment unit that evaluates a child's learning style. The learning style assessment unit, for example, collects data on a child's learning method and learning environment, and the generating AI analyzes that data to evaluate the optimal learning style. For example, it can propose a method based on a child's visual, auditory, or tactile learning style. The learning style assessment unit can also create an individual learning plan based on the child's learning progress data and monitor progress in real time. For example, it can visualize learning progress and adjust the plan as needed. Furthermore, the learning style assessment unit can collect data on the child's learning environment (for example, learning space and study time), and the generating AI can propose the optimal learning environment. This allows for a comprehensive evaluation of a child's learning style and support for the optimal career path.

[0092] The career support system can further include a home environment evaluation unit that evaluates the child's home environment. The home environment evaluation unit, for example, collects data on the child's home environment (e.g., parents' occupation and educational level), and the generating AI analyzes that data to evaluate the child's characteristics. For example, it evaluates the impact of the parents' educational style on the child's learning attitude. The home environment evaluation unit can also record the child's home learning environment (e.g., study space and study time), and the generating AI can analyze that data to suggest the optimal learning environment. For example, it can evaluate the importance of a quiet study space. Furthermore, the home environment evaluation unit can suggest an individual learning plan based on the child's home environment and the parents' educational style. For example, in a home where parents are actively involved, the generating AI can suggest increasing opportunities for collaborative learning. This allows for a comprehensive evaluation of the home environment and support for the optimal career path.

[0093] The career support system can further include an emotion monitoring unit that monitors a child's emotional state. The emotion monitoring unit, for example, monitors a child's facial expressions with a camera and analyzes the child's emotional state in real time using an emotion estimation algorithm. For example, the generation AI collects facial expression data and records changes in emotion. The emotion monitoring unit can also analyze a child's vocal tone to estimate the child's emotional state. For example, the generation AI collects voice data and evaluates the child's stress and joy levels. The emotion monitoring unit can also monitor a child's biometric data (for example, heart rate and electrodermal activity) during their behavior to estimate the child's emotional state. For example, the generation AI collects biometric data and analyzes changes in emotion in real time. This enables the child's emotional state to be analyzed in real time, enabling emotion-based advice to be provided.

[0094] The career support system may further include an emotional learning advice unit that provides advice to improve learning efficiency based on the child's emotional state. The emotional learning advice unit, for example, monitors the child's facial expressions while studying and analyzes the child's emotional state using an emotion estimation algorithm. For example, the generation AI collects facial expression data and evaluates the child's concentration level and stress level while studying. The emotional learning advice unit may also analyze the child's vocal tone while studying to estimate the child's emotional state. For example, the generation AI collects voice data and evaluates the child's motivation and fatigue level while studying. The emotional learning advice unit may also monitor the child's biometric data (for example, heart rate and electrodermal activity) while studying to estimate the child's emotional state. For example, the generation AI collects biometric data and analyzes changes in emotions during studying in real time. This allows the child's emotional state during studying to be analyzed and characteristics that improve learning efficiency to be discovered.

[0095] The career support system can further include an emotion motivation advice unit that provides advice to improve motivation based on the child's emotional state. The emotion motivation advice unit, for example, monitors the child's emotional state in real time, and the generation AI provides advice to improve motivation. For example, it sends encouraging messages based on emotional data. The emotion motivation advice unit can also use an emotion estimation function to provide study advice based on the child's emotional state. For example, it can suggest relaxation methods if the child is feeling stressed. Furthermore, the emotion motivation advice unit can analyze the child's emotional state, and the generation AI can provide specific advice to improve motivation. For example, it can suggest activities that will elicit positive emotions. In this way, by providing advice to improve motivation based on the child's emotional state, it is possible to increase the child's motivation to study.

[0096] The career support system can further include an emotional relaxation advice unit that suggests relaxation activities based on the child's emotional state. The emotional relaxation advice unit, for example, monitors the child's emotional state in real time, and the generation AI suggests activities for relaxation. For example, it suggests relaxation methods based on emotional data. The emotional relaxation advice unit can also use an emotion estimation function to suggest specific activities for relaxing when the child feels stressed. For example, it can suggest methods such as meditation or deep breathing. Furthermore, the emotional relaxation advice unit can analyze the child's emotional state, and the generation AI can suggest activities for relaxation. For example, it can recommend relaxing music or videos based on emotional data. This makes it possible to support the child's mental health by suggesting activities for relaxation when feeling stressed.

[0097] The career support system can further include an emotional career suggestion unit that suggests the optimal career path based on the child's emotional state. The emotional career suggestion unit, for example, monitors the child's emotions toward their future career in real time, and the generation AI analyzes those emotions to evaluate career aptitude. For example, it evaluates the degree of interest in a career based on the emotional data. The emotional career suggestion unit can also use an emotion estimation function to analyze the child's emotions toward their future career, and the generation AI can evaluate career aptitude. For example, it can recommend a career associated with strong positive emotions. The emotional career suggestion unit can also analyze the child's emotions toward their future career, and the generation AI can evaluate career aptitude. For example, it can evaluate career aptitude based on the emotional data and suggest the optimal career path. In this way, by analyzing the child's emotions toward their future career, it is possible to evaluate their career aptitude and suggest the optimal career path.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The data collection unit collects children's behavioral patterns and learning data. For example, data on school grades, participation in extracurricular activities, hobbies, and interests can be collected. It can also collect detailed behavioral data on children's daily lives (e.g., food choices and sleep patterns) and digital device usage history (e.g., app usage time and search history). Step 2: The analysis unit analyzes the collected behavioral patterns and learning data. For example, it analyzes academic performance trends derived from learning data and interests revealed from behavioral patterns. It can also analyze data on creative activities (such as painting and music) to discover talents. It can also analyze children's sociability and leadership traits from data on group activities. Step 3: The advice provider provides personalized advice based on the results of the analysis by the analysis unit. For example, if a child excels in a particular subject, it will suggest a career path related to that subject. Also, if a child has specific skills or interests, it will suggest activities and learning methods that will utilize those skills. It can also generate personalized learning plans based on the child's characteristics and monitor progress in real time. Step 4: The career suggestion department proposes career paths taking into account future market trends and demand forecasts. For example, it identifies fields that are predicted to be in high demand in the future job market and proposes career paths related to those fields. It can also compare market trends in different regions and countries and propose career paths from a global perspective.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0150] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0152] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0153] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0154] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0156] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0157] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0158] 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.

[0159] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0160] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0161] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0162] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0163] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection unit that collects children's behavioral patterns and learning data; an analysis unit that analyzes the behavioral patterns and the learning data collected by the data collection unit; an advice providing unit that provides individual advice based on the results of the analysis by the analysis unit; a career suggestion unit that proposes a career path taking into consideration future market trends and demand forecasts; A system characterized by:

2. The data collection unit Collecting minute behavioral data from children's daily lives and analyzing it with generative AI 2. The system of claim 1.

3. The data collection unit Collecting children's digital device usage history and analyzing it with generative AI 2. The system of claim 1.

4. The data collection unit Collecting emotional states during a child's actions and analyzing them with generative AI 2. The system of claim 1.

5. The data collection unit Use wearable devices to collect children's physical activity data 2. The system of claim 1.

6. The data collection unit Data on activities outside of school will also be collected and analyzed using generative AI.

2. The system of claim 1.

7. The data collection unit Collecting emotional changes when children perform specific activities and analyzing them with generative AI 2. The system of claim 1.

8. The analysis unit Analyzing children's learning data as well as their creative activity data to discover their talents 2. The system of claim 1.

Images