System

Abstract

An object of a system according to an embodiment is to identify a child's potential talent and specialty and provide customized coaching based on the identified potential talent and specialty.SOLUTION: A system according to an embodiment includes a data collection unit, an analysis unit, an identification unit, and a coaching generation unit. The data collection unit collects learning data or behavior data of the child. The analysis unit analyzes the learning data or the action data collected by the data collection unit. The specification unit specifies the potential talent or specialist field of the child on the basis of the result analyzed by the analysis unit. The coaching generation unit generates a customized coaching program based on the talent or specialist field specified by the specification unit.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 technologies are not sufficient in efficiently identifying a child's potential talents and areas of strength and providing customized coaching based on those talents, and there is room for improvement.

[0005] The system according to the embodiment aims to identify a child's potential talents and areas of expertise and provide customized coaching based on those talents and areas of expertise. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, an identification unit, and a coaching generation unit. The data collection unit collects learning data or behavioral data of a child. The analysis unit analyzes the learning data or behavioral data collected by the data collection unit. The identification unit identifies the child's potential talents or areas of expertise based on the results of the analysis by the analysis unit. The coaching generation unit generates a customized coaching program based on the talents or areas of expertise identified by the identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify a child's potential talents and areas of expertise and provide customized coaching based on them. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A coaching system according to an embodiment of the present invention is a system that uses generative AI to analyze a child's data, identify potential talents and areas of expertise, and provide customized coaching based on those areas. This allows the coaching system to identify a child's potential talents and areas of expertise and provide customized coaching according to those areas.

[0029] A coaching system according to an embodiment includes a data collection unit, an analysis unit, an identification unit, and a coaching generation unit. The data collection unit collects learning data or behavioral data of a child. For example, the data collection unit collects school grades and extracurricular activity results. The data collection unit can also collect daily behavioral patterns of a child. For example, the data collection unit collects test results and homework submission status of a child. The data collection unit can also collect participation records for club activities. The analysis unit analyzes the learning data or behavioral data collected by the data collection unit. For example, the analysis unit analyzes the data using data mining technology. The analysis unit can also analyze the data using statistical analysis. The analysis unit can also analyze the data using a machine learning algorithm. The identification unit identifies a child's potential talent or area of ​​expertise based on the results of the analysis by the analysis unit. For example, the identification unit can identify academic talent. The identification unit can also identify sports talent. The identification unit can also identify artistic talent. The coaching generation unit generates a customized coaching program based on the talents or areas of expertise identified by the identification unit. For example, the coaching generation unit generates an individual learning plan. The coaching generation unit can also generate a specific skill improvement program. The coaching generation unit can also generate a specific sports training program. As a result, the coaching system according to the embodiment can identify a child's potential talents and areas of expertise and provide customized coaching tailored to those areas. For example, the coaching system can identify individual talents and areas of expertise based on the child's learning data and behavioral data and provide a coaching program tailored to those areas. The coaching system can also manage the child's progress and provide regular feedback. For example, the coaching system can monitor the child's learning and training progress and adjust the program content as needed. The coaching system also provides regular feedback to parents and teachers to support the child's development.

[0030] The data collection unit can also collect data from IoT devices in the home and add daily behavioral patterns to the analysis. The data collection unit collects data from IoT devices in the home, such as smart speakers and smart lights, and adds daily behavioral patterns to the analysis. For example, it analyzes what time of day a child is most active. The data collection unit also collects data from IoT devices in the home and adds the child's sleep patterns and meal timings to the analysis. For example, it uses data from a smart bed or smart refrigerator to understand the child's health condition. The data collection unit also collects data from IoT devices in the home and adds the child's learning environment to the analysis. For example, it uses data from a smart desk or smart lamp to analyze the environment in which the child is studying. In this way, collecting data from IoT devices in the home and adding daily behavioral patterns to the analysis enables more comprehensive data collection.

[0031] The data collection unit collects not only school grades or extracurricular activity results, but also data on the child's social media posts or online activities, allowing for more comprehensive analysis. In the data collection unit, for example, the generation AI collects the child's social media posts and adds the child's interests and concerns to the analysis. For example, it analyzes the topics the child posts about. The data collection unit also collects data on the child's online activities and adds behavior related to learning and hobbies to the analysis. For example, it analyzes the websites the child visits. In addition, the data collection unit collects data on the child's online game play and adds in-game behavior patterns to the analysis. For example, it analyzes the strategies the child uses to progress through games. In this way, collecting data on social media and online activities allows for more comprehensive analysis.

[0032] The data collection unit can also collect children's health data and analyze the relationship between their health status and learning outcomes. For example, the data collection unit uses the generation AI to collect children's sleep patterns and analyze the relationship between their health status and learning outcomes. For example, it can analyze the relationship between sleep duration and test scores. The data collection unit can also collect children's dietary details and analyze the relationship between their health status and learning outcomes. For example, it can analyze the impact of whether or not they eat breakfast on their learning outcomes. The data collection unit can also use the generation AI to collect children's exercise data and analyze the relationship between their health status and learning outcomes. For example, it can analyze the relationship between the amount of exercise and concentration. This makes it possible to collect more comprehensive data by collecting health data and analyzing the relationship between their health status and learning outcomes.

[0033] The data collection unit can collect data on children of different age groups and grades and analyze the characteristics of each age and grade. In the data collection unit, for example, the generation AI collects data on children of different age groups and analyzes the learning characteristics of each age. For example, it analyzes the differences in learning patterns between elementary school and junior high school students. The data collection unit also collects data on children of different grades and analyzes the learning outcomes of each grade. For example, it analyzes the differences in grade performance on the same task. The data collection unit also collects data on children of different age groups and grades and analyzes the behavioral characteristics of each age and grade. For example, it analyzes differences in participation rates and interests in extracurricular activities. This enables more comprehensive data collection by collecting data on children of different age groups and grades and analyzing the characteristics of each age and grade.

[0034] When analyzing a child's data, the identification unit can refer to past success stories and failure stories to identify talents with higher accuracy. In the identification unit, for example, the generation AI refers to past success stories and analyzes the child's data. For example, based on the data of a child who has been successful in mathematics in the past, the current child's mathematical talent is identified. In addition, the identification unit analyzes the child's data with the generation AI referring to past failure stories. For example, based on the data of a child who has failed in sports in the past, the current child's sports strengths are identified. In addition, the identification unit analyzes the child's data with the generation AI referring to past success stories and failure stories. For example, based on the data of a child who has been successful in music in the past, the current child's musical talent is identified. In this way, by referring to past success stories and failure stories, more accurate talent identification is possible.

[0035] When analyzing a child's data, the identification unit incorporates the opinions of experts in different fields and can identify talents from multiple perspectives. For example, the generation AI incorporates the opinions of experts in different fields and analyzes the child's data. For example, talents are identified based on the opinions of educational experts and psychologists. The identification unit also incorporates the opinions of experts in different fields and analyzes the child's data. For example, areas of expertise are identified based on the opinions of sports coaches and music teachers. The identification unit also incorporates the opinions of experts in different fields and analyzes the child's data. For example, talents are identified based on the opinions of scientists and artists. In this way, by incorporating the opinions of experts in different fields, talents can be identified from multiple perspectives.

[0036] When identifying talents, the identification unit also takes into account the child's cultural background and home environment, allowing for more personalized talent identification. For example, the generation AI in the identification unit takes into account the child's cultural background to identify talents. For example, it may identify talents in traditional arts or sports in a particular culture. The identification unit also takes into account the child's home environment to identify talents. For example, it may identify talents based on the educational policy within the family or the parents' occupations. The identification unit also takes into account the child's cultural background and home environment to identify talents. For example, it may identify the language ability and adaptability of children from multicultural families. This makes it possible to identify talents more personalized by taking cultural background and home environment into account.

[0037] The identification unit can analyze combinations of different talents or areas of expertise and identify the possibility that multiple talents complement each other. For example, the identification unit allows the generative AI to analyze a child's different talents and areas of expertise and identify the possibility that they complement each other. For example, it analyzes whether talents in mathematics and music complement each other. The identification unit also analyzes combinations of different talents and areas of expertise and identify the possibility that multiple talents complement each other. For example, it analyzes whether talents in sports and leadership complement each other. The identification unit also allows the generative AI to analyze a child's different talents and areas of expertise and identify the possibility that they complement each other. For example, it analyzes whether talents in science and art complement each other. In this way, by analyzing combinations of different talents and areas of expertise, it is possible to identify the possibility that multiple talents complement each other.

[0038] The coaching generation unit can adjust the coaching program in real time according to the child's learning style and pace. In the coaching generation unit, for example, the generation AI analyzes the child's learning style and adjusts the coaching program in real time. For example, visual learning materials are provided for visual learners. The coaching generation unit also analyzes the child's learning pace and adjusts the coaching program in real time. For example, more difficult tasks are provided for children who progress quickly. In addition, the coaching generation unit analyzes the child's learning style and pace using the generation AI and adjusts the coaching program in real time. For example, repeated practice is provided for children who need repeated learning. This makes it possible to adjust the coaching program in real time according to the child's learning style and pace.

[0039] The coaching generation unit can incorporate gamification elements into the coaching program to draw out children's interests and attention. For example, the generation AI of the coaching generation unit analyzes children's interests and attention and incorporates gamification elements into the coaching program. For example, the learning content may be provided in a game format. The coaching generation unit may also introduce a point system or rewards into the coaching program to increase children's motivation to learn. For example, a system may be provided where points can be earned each time a task is completed. The coaching generation unit may also analyze children's interests and attention and incorporate storytelling elements into the coaching program. For example, the learning content may be provided as part of an adventure story. In this way, by incorporating gamification elements to draw out children's interests and attention, it is possible to increase motivation to learn.

[0040] The coaching generation unit can incorporate advice and feedback from experts in different fields into the customized coaching program. In the coaching generation unit, for example, the generation AI incorporates advice from experts in different fields to provide a customized coaching program. For example, the program is adjusted based on the opinions of educational experts and psychologists. The coaching generation unit also incorporates feedback from experts in different fields to improve the quality of the coaching program. For example, the program is improved based on feedback from sports coaches and music teachers. The coaching generation unit also incorporates advice and feedback from experts in different fields to provide a customized coaching program. For example, the program is adjusted based on the opinions of scientists and artists. In this way, the quality of the coaching program can be improved by incorporating advice and feedback from experts in different fields.

[0041] The coaching generation unit provides coaching programs in a hybrid online and offline format, allowing for flexible responses according to a child's learning environment. For example, the generation AI of the coaching generation unit provides coaching programs in a hybrid online and offline format. For example, it combines online learning and offline practice. The coaching generation unit also flexibly adjusts the online and offline coaching programs according to a child's learning environment. For example, it links home learning with learning at school. The coaching generation unit also provides coaching programs in a hybrid online and offline format, allowing for flexible responses according to a child's learning environment. For example, it combines online feedback and offline practice. As a result, by providing coaching programs in a hybrid online and offline format, it is possible to flexibly respond according to a child's learning environment.

[0042] The coaching generation unit can analyze the child's progress data and evaluate the individual degree of goal achievement in real time. In the coaching generation unit, for example, the generation AI analyzes the child's learning progress data and evaluates the individual degree of goal achievement in real time. For example, evaluation is made based on test scores and assignment submission status. The coaching generation unit also analyzes the child's training progress data and evaluates the individual degree of goal achievement in real time. For example, evaluation is made based on sports practice records and match results. In addition, the coaching generation unit can analyze the child's progress data and evaluate the individual degree of goal achievement in real time. For example, evaluation is made based on music practice records and performance results. In this way, the child's progress data can be analyzed in real time and the individual degree of goal achievement can be evaluated.

[0043] The coaching generation unit can provide comprehensive feedback by incorporating the child's self-assessment and the parent's assessment when managing progress. In the coaching generation unit, for example, the generation AI collects the child's self-assessment and incorporates it into progress management. For example, the child self-assesss their learning or training progress. The coaching generation unit also collects the parent's assessment and incorporates it into progress management. For example, the parent assesses the child's learning or training progress. In the coaching generation unit, the generation AI collects the child's self-assessment and the parent's assessment and provides comprehensive feedback. For example, feedback is provided based on the self-assessment and the parent's assessment. In this way, comprehensive feedback can be provided by incorporating the child's self-assessment and the parent's assessment.

[0044] The coaching generation unit can compare the progress management data with the data of other children and perform a relative evaluation. In the coaching generation unit, for example, the generation AI compares a child's progress data with the data of other children and performs a relative evaluation. For example, it compares with children of the same age or grade. The coaching generation unit also collects the progress data of other children and performs a relative evaluation. For example, it compares performance on the same task. In addition, the coaching generation unit compares a child's progress data with the data of other children and performs a relative evaluation. For example, it compares progress in the same sport or music training. This makes it possible to perform a relative evaluation by comparing with the data of other children.

[0045] The coaching generation unit can provide feedback in a variety of formats, not only text but also visuals and audio. For example, the generation AI of the coaching generation unit provides feedback not only text but also visuals and audio. For example, progress is visually shown using graphs and charts. The coaching generation unit also provides feedback in audio so that the child can hear and understand. For example, progress is explained using audio messages. The coaching generation unit also provides feedback by combining text, visuals, and audio. For example, in addition to explanations in text, visuals and audio are added. In this way, feedback can be provided in a variety of formats, making it easier for the child to understand.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data collection unit collects learning data or behavioral data of the child. For example, the data collection unit collects school grades and extracurricular activity results. The data collection unit may also collect the child's daily behavioral patterns. For example, the data collection unit may collect the child's test results and homework submission status. The data collection unit may also collect club activity participation records. The analysis unit analyzes the learning data or behavioral data collected by the data collection unit. For example, the analysis unit may analyze the data using data mining technology. The analysis unit may also analyze the data using statistical analysis. The analysis unit may also analyze the data using a machine learning algorithm. The identification unit identifies the child's potential talents or areas of expertise based on the results of the analysis by the analysis unit. For example, the identification unit may identify academic talent. The identification unit may also identify sports talent. The identification unit may also identify artistic talent. The coaching generation unit generates a customized coaching program based on the talents or areas of expertise identified by the identification unit. For example, the coaching generation unit generates an individualized learning plan. The coaching generation unit can also generate a specific skill improvement program. The coaching generation unit can also generate a specific sports training program. As a result, the coaching system according to the embodiment can identify a child's potential talents and areas of expertise and provide customized coaching tailored to those areas. For example, the coaching system can identify individual talents and areas of expertise based on a child's learning data and behavioral data, and provide a coaching program tailored to those areas. The coaching system can also manage a child's progress and provide regular feedback. For example, the coaching system can monitor a child's learning and training progress and adjust the program content as needed. The coaching system also provides regular feedback to parents and teachers to support the child's growth.

[0048] The data collection unit can also collect data from IoT devices in the home and add daily behavioral patterns to the analysis. For example, data from IoT devices such as smart speakers and smart lights in the home can be collected to add a child's daily behavioral patterns to the analysis. For example, it can analyze what time of day a child is most active. The data collection unit also collects data from IoT devices in the home and adds a child's sleep patterns and meal timings to the analysis. For example, data from a smart bed or smart refrigerator can be used to understand a child's health condition. The data collection unit also collects data from IoT devices in the home and adds a child's learning environment to the analysis. For example, data from a smart desk or smart lamp can be used to analyze the environment in which a child is studying. In this way, collecting data from IoT devices in the home and adding daily behavioral patterns to the analysis enables more comprehensive data collection.

[0049] The data collection unit can collect not only school grades or extracurricular activity results, but also data on the child's social media posts or online activities, allowing for more comprehensive analysis. For example, the generation AI collects the child's social media posts and adds the child's interests to the analysis. For example, it analyzes the topics the child posts about. The data collection unit also collects data on the child's online activities and adds behavior related to learning and hobbies to the analysis. For example, it analyzes the websites the child visits. The data collection unit can also collect data on the child's online game play and add in-game behavior patterns to the analysis. For example, it analyzes the strategies the child uses to progress through games. This allows for more comprehensive analysis by collecting data on social media and online activities.

[0050] The data collection unit can also collect children's health data and analyze the relationship between their health status and learning outcomes. For example, the generation AI collects children's sleep patterns and analyzes the relationship between their health status and learning outcomes. For example, it can analyze the relationship between sleep duration and test scores. The data collection unit can also collect children's dietary habits and analyze the relationship between their health status and learning outcomes. For example, it can analyze the impact of whether or not they eat breakfast on their learning outcomes. The data collection unit can also collect children's exercise data and analyze the relationship between their health status and learning outcomes. For example, it can analyze the relationship between the amount of exercise and concentration. This makes it possible to collect more comprehensive data by collecting health data and analyzing the relationship between their health status and learning outcomes.

[0051] The data collection unit can collect data on children of different age groups and grades and analyze the characteristics of each age and grade. For example, the generation AI collects data on children of different age groups and analyzes the learning characteristics of each age. For example, it can analyze the differences in learning patterns between elementary school and junior high school students. The data collection unit can also collect data on children of different grades and analyze the learning outcomes of each grade. For example, it can analyze the differences in grade performance on the same task. The data collection unit can also collect data on children of different age groups and grades and analyze the behavioral characteristics of each age and grade. For example, it can analyze differences in participation rates and interests in extracurricular activities. This allows for more comprehensive data collection by collecting data on children of different age groups and grades and analyzing the characteristics of each age and grade.

[0052] When analyzing a child's data, the identification unit refers to past successes and failures, allowing for more accurate talent identification. For example, the generation AI analyzes a child's data by referring to past successes. For example, a child's current mathematical talent is identified based on the data of a child who has been successful in mathematics in the past. The identification unit also analyzes a child's data by referring to past failures. For example, a child's current sports strengths are identified based on the data of a child who has failed in sports in the past. The identification unit also analyzes a child's data by referring to past successes and failures. For example, a child's current musical talent is identified based on the data of a child who has been successful in music in the past. In this way, by referring to past successes and failures, more accurate talent identification is possible.

[0053] When analyzing a child's data, the identification unit incorporates the opinions of experts in different fields, making it possible to identify talents from multiple perspectives. For example, the generation AI incorporates the opinions of experts in different fields and analyzes the child's data. For example, talents are identified based on the opinions of educational experts and psychologists. The identification unit also incorporates the opinions of experts in different fields and analyzes the child's data. For example, areas of expertise are identified based on the opinions of sports coaches and music teachers. The identification unit also incorporates the opinions of experts in different fields and analyzes the child's data. For example, talents are identified based on the opinions of scientists and artists. In this way, by incorporating the opinions of experts in different fields, talents can be identified from multiple perspectives.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The data collection department collects learning or behavioral data about the child, such as school grades, extracurricular activity results, test results, homework submission status, and club activity participation records. Step 2: The analysis unit analyzes the learning data or behavioral data collected by the data collection unit, for example, using data mining techniques, statistical analysis, or machine learning algorithms. Step 3: The identification unit identifies the child's potential talents or areas of expertise based on the results of the analysis by the analysis unit, for example, academic talent, sports talent, or artistic talent. Step 4: The coaching generation unit generates a customized coaching program based on the talents or areas of expertise identified by the identification unit, for example, an individualized learning plan, a specific skill development program, or a specific sports training program.

[0056] (Example 2) A coaching system according to an embodiment of the present invention is a system that uses generative AI to analyze a child's data, identify potential talents and areas of expertise, and provide customized coaching based on those areas. This allows the coaching system to identify a child's potential talents and areas of expertise and provide customized coaching according to those areas.

[0057] A coaching system according to an embodiment includes a data collection unit, an analysis unit, an identification unit, and a coaching generation unit. The data collection unit collects learning data or behavioral data of a child. For example, the data collection unit collects school grades and extracurricular activity results. The data collection unit can also collect daily behavioral patterns of a child. For example, the data collection unit collects test results and homework submission status of a child. The data collection unit can also collect participation records for club activities. The analysis unit analyzes the learning data or behavioral data collected by the data collection unit. For example, the analysis unit analyzes the data using data mining technology. The analysis unit can also analyze the data using statistical analysis. The analysis unit can also analyze the data using a machine learning algorithm. The identification unit identifies a child's potential talent or area of ​​expertise based on the results of the analysis by the analysis unit. For example, the identification unit can identify academic talent. The identification unit can also identify sports talent. The identification unit can also identify artistic talent. The coaching generation unit generates a customized coaching program based on the talents or areas of expertise identified by the identification unit. For example, the coaching generation unit generates an individual learning plan. The coaching generation unit can also generate a specific skill improvement program. The coaching generation unit can also generate a specific sports training program. As a result, the coaching system according to the embodiment can identify a child's potential talents and areas of expertise and provide customized coaching tailored to those areas. For example, the coaching system can identify individual talents and areas of expertise based on the child's learning data and behavioral data and provide a coaching program tailored to those areas. The coaching system can also manage the child's progress and provide regular feedback. For example, the coaching system can monitor the child's learning and training progress and adjust the program content as needed. The coaching system also provides regular feedback to parents and teachers to support the child's development.

[0058] The data collection unit can also collect data from IoT devices in the home and add daily behavioral patterns to the analysis. The data collection unit collects data from IoT devices in the home, such as smart speakers and smart lights, and adds daily behavioral patterns to the analysis. For example, it analyzes what time of day a child is most active. The data collection unit also collects data from IoT devices in the home and adds the child's sleep patterns and meal timings to the analysis. For example, it uses data from a smart bed or smart refrigerator to understand the child's health condition. The data collection unit also collects data from IoT devices in the home and adds the child's learning environment to the analysis. For example, it uses data from a smart desk or smart lamp to analyze the environment in which the child is studying. In this way, collecting data from IoT devices in the home and adding daily behavioral patterns to the analysis enables more comprehensive data collection.

[0059] The data collection unit collects not only school grades or extracurricular activity results, but also data on the child's social media posts or online activities, allowing for more comprehensive analysis. In the data collection unit, for example, the generation AI collects the child's social media posts and adds the child's interests and concerns to the analysis. For example, it analyzes the topics the child posts about. The data collection unit also collects data on the child's online activities and adds behavior related to learning and hobbies to the analysis. For example, it analyzes the websites the child visits. In addition, the data collection unit collects data on the child's online game play and adds in-game behavior patterns to the analysis. For example, it analyzes the strategies the child uses to progress through games. In this way, collecting data on social media and online activities allows for more comprehensive analysis.

[0060] The data collection unit uses the emotion estimation function to analyze the emotions felt by children during learning or activities, and can reflect emotional fluctuations in the data. In the data collection unit, for example, the generation AI analyzes the child's facial expressions and voice to estimate the emotions felt during learning. For example, the joy or confusion felt by a child when solving a problem is reflected in the data. The data collection unit also uses the emotion estimation function to analyze the emotions felt by children during extracurricular activities. For example, excitement or fatigue during sports activities is reflected in the data. In the data collection unit, the generation AI collects child emotion estimation data and adds emotional fluctuations during learning or activities to the analysis. For example, it analyzes the emotions a child feels toward a specific task. In this way, the emotion estimation function can reflect the child's emotional fluctuations in the data.

[0061] The data collection unit can also collect children's health data and analyze the relationship between their health status and learning outcomes. For example, the data collection unit uses the generation AI to collect children's sleep patterns and analyze the relationship between their health status and learning outcomes. For example, it can analyze the relationship between sleep duration and test scores. The data collection unit can also collect children's dietary details and analyze the relationship between their health status and learning outcomes. For example, it can analyze the impact of whether or not they eat breakfast on their learning outcomes. The data collection unit can also use the generation AI to collect children's exercise data and analyze the relationship between their health status and learning outcomes. For example, it can analyze the relationship between the amount of exercise and concentration. This makes it possible to collect more comprehensive data by collecting health data and analyzing the relationship between their health status and learning outcomes.

[0062] The data collection unit can collect data on children of different age groups and grades and analyze the characteristics of each age and grade. In the data collection unit, for example, the generation AI collects data on children of different age groups and analyzes the learning characteristics of each age. For example, it analyzes the differences in learning patterns between elementary school and junior high school students. The data collection unit also collects data on children of different grades and analyzes the learning outcomes of each grade. For example, it analyzes the differences in grade performance on the same task. The data collection unit also collects data on children of different age groups and grades and analyzes the behavioral characteristics of each age and grade. For example, it analyzes differences in participation rates and interests in extracurricular activities. This enables more comprehensive data collection by collecting data on children of different age groups and grades and analyzing the characteristics of each age and grade.

[0063] The data collection unit uses the emotion estimation function to analyze in real time how a child feels about a specific activity, and can collect data based on emotions. In the data collection unit, for example, the generation AI analyzes a child's facial expressions and voice in real time to estimate their emotions about a specific activity. For example, it analyzes a child's emotions when solving math problems in real time. The data collection unit also uses the emotion estimation function to analyze in real time the emotions a child feels during sports activities. For example, it analyzes excitement and tension during a game in real time. The data collection unit also uses the generation AI to collect child's emotion estimation data in real time and add emotional fluctuations toward a specific activity to the analysis. For example, it analyzes a child's emotions while practicing music in real time. As a result, using the emotion estimation function makes it possible to collect data based on a child's emotions in real time.

[0064] When analyzing a child's data, the identification unit can refer to past success stories and failure stories to identify talents with higher accuracy. In the identification unit, for example, the generation AI refers to past success stories and analyzes the child's data. For example, based on the data of a child who has been successful in mathematics in the past, the current child's mathematical talent is identified. In addition, the identification unit analyzes the child's data with the generation AI referring to past failure stories. For example, based on the data of a child who has failed in sports in the past, the current child's sports strengths are identified. In addition, the identification unit analyzes the child's data with the generation AI referring to past success stories and failure stories. For example, based on the data of a child who has been successful in music in the past, the current child's musical talent is identified. In this way, by referring to past success stories and failure stories, more accurate talent identification is possible.

[0065] When analyzing a child's data, the identification unit incorporates the opinions of experts in different fields and can identify talents from multiple perspectives. For example, the generation AI incorporates the opinions of experts in different fields and analyzes the child's data. For example, talents are identified based on the opinions of educational experts and psychologists. The identification unit also incorporates the opinions of experts in different fields and analyzes the child's data. For example, areas of expertise are identified based on the opinions of sports coaches and music teachers. The identification unit also incorporates the opinions of experts in different fields and analyzes the child's data. For example, talents are identified based on the opinions of scientists and artists. In this way, by incorporating the opinions of experts in different fields, talents can be identified from multiple perspectives.

[0066] The identification unit uses the emotion estimation function to analyze whether a child has positive emotions toward a specific activity, and can identify talents based on emotions. For example, the identification unit uses the generation AI to analyze a child's facial expressions and voice and estimate positive emotions toward a specific activity. For example, it analyzes the joy a child feels when drawing a picture. The identification unit also uses the emotion estimation function to analyze whether a child has positive emotions toward a specific sport. For example, it analyzes the excitement a child feels when playing soccer. The identification unit also uses the generation AI to collect emotion estimation data about the child and add the positive emotions toward a specific activity to the analysis. For example, it analyzes the satisfaction a child feels when playing music. As a result, the emotion estimation function makes it possible to identify talents based on a child's positive emotions.

[0067] When identifying talents, the identification unit also takes into account the child's cultural background and home environment, allowing for more personalized talent identification. For example, the generation AI in the identification unit takes into account the child's cultural background to identify talents. For example, it may identify talents in traditional arts or sports in a particular culture. The identification unit also takes into account the child's home environment to identify talents. For example, it may identify talents based on the educational policy within the family or the parents' occupations. The identification unit also takes into account the child's cultural background and home environment to identify talents. For example, it may identify the language ability and adaptability of children from multicultural families. This makes it possible to identify talents more personalized by taking cultural background and home environment into account.

[0068] The identification unit can analyze combinations of different talents or areas of expertise and identify the possibility that multiple talents complement each other. For example, the identification unit allows the generative AI to analyze a child's different talents and areas of expertise and identify the possibility that they complement each other. For example, it analyzes whether talents in mathematics and music complement each other. The identification unit also analyzes combinations of different talents and areas of expertise and identify the possibility that multiple talents complement each other. For example, it analyzes whether talents in sports and leadership complement each other. The identification unit also allows the generative AI to analyze a child's different talents and areas of expertise and identify the possibility that they complement each other. For example, it analyzes whether talents in science and art complement each other. In this way, by analyzing combinations of different talents and areas of expertise, it is possible to identify the possibility that multiple talents complement each other.

[0069] The identification unit uses the emotion estimation function to analyze what emotions a child has toward a specific subject and can identify a subject of expertise based on emotions. In the identification unit, for example, the generation AI analyzes a child's facial expressions and voice to estimate emotions toward a specific subject. For example, it analyzes a child's excitement when performing a science experiment. The identification unit also uses the emotion estimation function to analyze what emotions a child has toward a specific art activity. For example, it analyzes the satisfaction felt when drawing a picture. The identification unit also collects the child's emotion estimation data and adds emotional fluctuations toward a specific subject to the analysis. For example, it analyzes the joy a child feels when playing music. In this way, the emotion estimation function makes it possible to identify a subject of expertise based on a child's emotions.

[0070] The coaching generation unit can adjust the coaching program in real time according to the child's learning style and pace. In the coaching generation unit, for example, the generation AI analyzes the child's learning style and adjusts the coaching program in real time. For example, visual learning materials are provided for visual learners. The coaching generation unit also analyzes the child's learning pace and adjusts the coaching program in real time. For example, more difficult tasks are provided for children who progress quickly. In addition, the coaching generation unit analyzes the child's learning style and pace using the generation AI and adjusts the coaching program in real time. For example, repeated practice is provided for children who need repeated learning. This makes it possible to adjust the coaching program in real time according to the child's learning style and pace.

[0071] The coaching generation unit can incorporate gamification elements into the coaching program to draw out children's interests and attention. For example, the generation AI of the coaching generation unit analyzes children's interests and attention and incorporates gamification elements into the coaching program. For example, the learning content may be provided in a game format. The coaching generation unit may also introduce a point system or rewards into the coaching program to increase children's motivation to learn. For example, a system may be provided where points can be earned each time a task is completed. The coaching generation unit may also analyze children's interests and attention and incorporate storytelling elements into the coaching program. For example, the learning content may be provided as part of an adventure story. In this way, by incorporating gamification elements to draw out children's interests and attention, it is possible to increase motivation to learn.

[0072] The coaching generation unit uses the emotion estimation function to analyze how the child feels about the coaching program and adjust the program based on that emotion. For example, the coaching generation unit uses the generation AI to analyze the child's facial expressions and voice and estimate their emotion toward the coaching program. For example, it analyzes whether the child is excited about the program. The coaching generation unit also uses the emotion estimation function to analyze how the child feels about the coaching program and adjust the program content. For example, if the child is bored, it provides a new task. The coaching generation unit also uses the generation AI to collect emotion estimation data about the child and add emotional fluctuations toward the coaching program to the analysis. For example, it analyzes whether the child has positive emotions toward the program. This makes it possible to use the emotion estimation function to adjust the program based on the child's emotion.

[0073] The coaching generation unit can incorporate advice and feedback from experts in different fields into the customized coaching program. In the coaching generation unit, for example, the generation AI incorporates advice from experts in different fields to provide a customized coaching program. For example, the program is adjusted based on the opinions of educational experts and psychologists. The coaching generation unit also incorporates feedback from experts in different fields to improve the quality of the coaching program. For example, the program is improved based on feedback from sports coaches and music teachers. The coaching generation unit also incorporates advice and feedback from experts in different fields to provide a customized coaching program. For example, the program is adjusted based on the opinions of scientists and artists. In this way, the quality of the coaching program can be improved by incorporating advice and feedback from experts in different fields.

[0074] The coaching generation unit provides coaching programs in a hybrid online and offline format, allowing for flexible responses according to a child's learning environment. For example, the generation AI of the coaching generation unit provides coaching programs in a hybrid online and offline format. For example, it combines online learning and offline practice. The coaching generation unit also flexibly adjusts the online and offline coaching programs according to a child's learning environment. For example, it links home learning with learning at school. The coaching generation unit also provides coaching programs in a hybrid online and offline format, allowing for flexible responses according to a child's learning environment. For example, it combines online feedback and offline practice. As a result, by providing coaching programs in a hybrid online and offline format, it is possible to flexibly respond according to a child's learning environment.

[0075] The coaching generation unit uses the emotion estimation function to monitor in real time how the child feels about the coaching program and optimize the program based on the emotions. In the coaching generation unit, for example, the generation AI analyzes the child's facial expressions and voice in real time to estimate the child's emotions about the coaching program. For example, it analyzes in real time whether the child is excited about the program. The coaching generation unit also uses the emotion estimation function to monitor in real time how the child feels about the coaching program and optimize the program content. For example, if the child is bored, it provides a new task. In addition, the coaching generation unit uses the generation AI to collect child emotion estimation data in real time and add emotional fluctuations about the coaching program to the analysis. For example, it analyzes in real time whether the child has positive emotions about the program. As a result, using the emotion estimation function makes it possible to optimize the program in real time based on the child's emotions.

[0076] The coaching generation unit can analyze the child's progress data and evaluate the individual degree of goal achievement in real time. In the coaching generation unit, for example, the generation AI analyzes the child's learning progress data and evaluates the individual degree of goal achievement in real time. For example, evaluation is made based on test scores and assignment submission status. The coaching generation unit also analyzes the child's training progress data and evaluates the individual degree of goal achievement in real time. For example, evaluation is made based on sports practice records and match results. In addition, the coaching generation unit can analyze the child's progress data and evaluate the individual degree of goal achievement in real time. For example, evaluation is made based on music practice records and performance results. In this way, the child's progress data can be analyzed in real time and the individual degree of goal achievement can be evaluated.

[0077] The coaching generation unit can provide comprehensive feedback by incorporating the child's self-assessment and the parent's assessment when managing progress. In the coaching generation unit, for example, the generation AI collects the child's self-assessment and incorporates it into progress management. For example, the child self-assesss their learning or training progress. The coaching generation unit also collects the parent's assessment and incorporates it into progress management. For example, the parent assesses the child's learning or training progress. In the coaching generation unit, the generation AI collects the child's self-assessment and the parent's assessment and provides comprehensive feedback. For example, feedback is provided based on the self-assessment and the parent's assessment. In this way, comprehensive feedback can be provided by incorporating the child's self-assessment and the parent's assessment.

[0078] The coaching generation unit uses the emotion estimation function to analyze how the child feels about the progress of coaching and can provide feedback based on the emotions. For example, the coaching generation unit uses the generation AI to analyze the child's facial expressions and voice and estimate how the child feels about the progress of coaching. For example, it analyzes whether the child is satisfied with the progress. The coaching generation unit also uses the emotion estimation function to analyze how the child feels about the progress of coaching and provides feedback. For example, if the child feels anxious about the progress, it provides encouraging feedback. The coaching generation unit also uses the generation AI to collect emotion estimation data about the child and add emotional fluctuations about the progress of coaching to the analysis. For example, it analyzes whether the child feels positive about the progress. In this way, by using the emotion estimation function, it is possible to provide feedback based on the child's emotions.

[0079] The coaching generation unit can compare the progress management data with the data of other children and perform a relative evaluation. In the coaching generation unit, for example, the generation AI compares a child's progress data with the data of other children and performs a relative evaluation. For example, it compares with children of the same age or grade. The coaching generation unit also collects the progress data of other children and performs a relative evaluation. For example, it compares performance on the same task. In addition, the coaching generation unit compares a child's progress data with the data of other children and performs a relative evaluation. For example, it compares progress in the same sport or music training. This makes it possible to perform a relative evaluation by comparing with the data of other children.

[0080] The coaching generation unit can provide feedback in a variety of formats, not only text but also visuals and audio. For example, the generation AI of the coaching generation unit provides feedback not only text but also visuals and audio. For example, progress is visually shown using graphs and charts. The coaching generation unit also provides feedback in audio so that the child can hear and understand. For example, progress is explained using audio messages. The coaching generation unit also provides feedback by combining text, visuals, and audio. For example, in addition to explanations in text, visuals and audio are added. In this way, feedback can be provided in a variety of formats, making it easier for the child to understand.

[0081] The coaching generation unit uses the emotion estimation function to monitor in real time how the child feels about the feedback and optimize the feedback based on the emotions. In the coaching generation unit, for example, the generation AI analyzes the child's facial expressions and voice in real time to estimate the child's emotions about the feedback. For example, it analyzes in real time whether the child is satisfied with the feedback. The coaching generation unit also uses the emotion estimation function to monitor in real time how the child feels about the feedback and optimize the content of the feedback. For example, if the child feels anxious, it provides encouraging feedback. In addition, the coaching generation unit uses the generation AI to collect child emotion estimation data in real time and add emotional fluctuations in response to the feedback to the analysis. For example, it analyzes in real time whether the child feels positive about the feedback. As a result, by using the emotion estimation function, it is possible to optimize feedback based on the child's emotions in real time.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The data collection unit collects learning data or behavioral data of the child. For example, the data collection unit collects school grades and extracurricular activity results. The data collection unit may also collect the child's daily behavioral patterns. For example, the data collection unit may collect the child's test results and homework submission status. The data collection unit may also collect club activity participation records. The analysis unit analyzes the learning data or behavioral data collected by the data collection unit. For example, the analysis unit may analyze the data using data mining technology. The analysis unit may also analyze the data using statistical analysis. The analysis unit may also analyze the data using a machine learning algorithm. The identification unit identifies the child's potential talents or areas of expertise based on the results of the analysis by the analysis unit. For example, the identification unit may identify academic talent. The identification unit may also identify sports talent. The identification unit may also identify artistic talent. The coaching generation unit generates a customized coaching program based on the talents or areas of expertise identified by the identification unit. For example, the coaching generation unit generates an individualized learning plan. The coaching generation unit can also generate a specific skill improvement program. The coaching generation unit can also generate a specific sports training program. As a result, the coaching system according to the embodiment can identify a child's potential talents and areas of expertise and provide customized coaching tailored to those areas. For example, the coaching system can identify individual talents and areas of expertise based on a child's learning data and behavioral data, and provide a coaching program tailored to those areas. The coaching system can also manage a child's progress and provide regular feedback. For example, the coaching system can monitor a child's learning and training progress and adjust the program content as needed. The coaching system also provides regular feedback to parents and teachers to support the child's growth.

[0084] The data collection unit can also collect data from IoT devices in the home and add daily behavioral patterns to the analysis. For example, data from IoT devices such as smart speakers and smart lights in the home can be collected to add a child's daily behavioral patterns to the analysis. For example, it can analyze what time of day a child is most active. The data collection unit also collects data from IoT devices in the home and adds a child's sleep patterns and meal timings to the analysis. For example, data from a smart bed or smart refrigerator can be used to understand a child's health condition. The data collection unit also collects data from IoT devices in the home and adds a child's learning environment to the analysis. For example, data from a smart desk or smart lamp can be used to analyze the environment in which a child is studying. In this way, collecting data from IoT devices in the home and adding daily behavioral patterns to the analysis enables more comprehensive data collection.

[0085] The data collection unit can collect not only school grades or extracurricular activity results, but also data on the child's social media posts or online activities, allowing for more comprehensive analysis. For example, the generation AI collects the child's social media posts and adds the child's interests to the analysis. For example, it analyzes the topics the child posts about. The data collection unit also collects data on the child's online activities and adds behavior related to learning and hobbies to the analysis. For example, it analyzes the websites the child visits. The data collection unit can also collect data on the child's online game play and add in-game behavior patterns to the analysis. For example, it analyzes the strategies the child uses to progress through games. This allows for more comprehensive analysis by collecting data on social media and online activities.

[0086] The data collection unit uses the emotion estimation function to analyze the emotions felt by children during learning or activities, and can reflect emotional fluctuations in the data. For example, the generation AI analyzes a child's facial expressions and voice to estimate their emotions during learning. For example, the joy or confusion a child feels when solving a problem can be reflected in the data. The data collection unit also uses the emotion estimation function to analyze the emotions a child feels during extracurricular activities. For example, excitement or fatigue during sports activities can be reflected in the data. The data collection unit also uses the generation AI to collect child emotion estimation data and add emotional fluctuations during learning or activities to the analysis. For example, it analyzes how a child feels about a particular task. In this way, the emotion estimation function can reflect the child's emotional fluctuations in the data.

[0087] The data collection unit can also collect children's health data and analyze the relationship between their health status and learning outcomes. For example, the generation AI collects children's sleep patterns and analyzes the relationship between their health status and learning outcomes. For example, it can analyze the relationship between sleep duration and test scores. The data collection unit can also collect children's dietary habits and analyze the relationship between their health status and learning outcomes. For example, it can analyze the impact of whether or not they eat breakfast on their learning outcomes. The data collection unit can also collect children's exercise data and analyze the relationship between their health status and learning outcomes. For example, it can analyze the relationship between the amount of exercise and concentration. This makes it possible to collect more comprehensive data by collecting health data and analyzing the relationship between their health status and learning outcomes.

[0088] The data collection unit can collect data on children of different age groups and grades and analyze the characteristics of each age and grade. For example, the generation AI collects data on children of different age groups and analyzes the learning characteristics of each age. For example, it can analyze the differences in learning patterns between elementary school and junior high school students. The data collection unit can also collect data on children of different grades and analyze the learning outcomes of each grade. For example, it can analyze the differences in grade performance on the same task. The data collection unit can also collect data on children of different age groups and grades and analyze the behavioral characteristics of each age and grade. For example, it can analyze differences in participation rates and interests in extracurricular activities. This allows for more comprehensive data collection by collecting data on children of different age groups and grades and analyzing the characteristics of each age and grade.

[0089] The data collection unit uses the emotion estimation function to analyze in real time how a child feels about a specific activity, and can collect data based on emotions. For example, the generation AI analyzes a child's facial expressions and voice in real time to estimate their emotions about a specific activity. For example, it analyzes a child's emotions when solving math problems in real time. The data collection unit also uses the emotion estimation function to analyze in real time the emotions a child feels during sports activities. For example, it analyzes excitement and tension during a game in real time. The data collection unit also uses the generation AI to collect child emotion estimation data in real time and add emotional fluctuations toward a specific activity to the analysis. For example, it analyzes a child's emotions while practicing music in real time. As a result, using the emotion estimation function makes it possible to collect data based on a child's emotions in real time.

[0090] When analyzing a child's data, the identification unit refers to past successes and failures, allowing for more accurate talent identification. For example, the generation AI analyzes a child's data by referring to past successes. For example, a child's current mathematical talent is identified based on the data of a child who has been successful in mathematics in the past. The identification unit also analyzes a child's data by referring to past failures. For example, a child's current sports strengths are identified based on the data of a child who has failed in sports in the past. The identification unit also analyzes a child's data by referring to past successes and failures. For example, a child's current musical talent is identified based on the data of a child who has been successful in music in the past. In this way, by referring to past successes and failures, more accurate talent identification is possible.

[0091] When analyzing a child's data, the identification unit incorporates the opinions of experts in different fields, making it possible to identify talents from multiple perspectives. For example, the generation AI incorporates the opinions of experts in different fields and analyzes the child's data. For example, talents are identified based on the opinions of educational experts and psychologists. The identification unit also incorporates the opinions of experts in different fields and analyzes the child's data. For example, areas of expertise are identified based on the opinions of sports coaches and music teachers. The identification unit also incorporates the opinions of experts in different fields and analyzes the child's data. For example, talents are identified based on the opinions of scientists and artists. In this way, by incorporating the opinions of experts in different fields, talents can be identified from multiple perspectives.

[0092] The identification unit uses the emotion estimation function to analyze whether a child has positive emotions toward a specific activity, and can identify talents based on emotions. For example, the generation AI analyzes a child's facial expressions and voice to estimate positive emotions toward a specific activity. For example, it analyzes the joy a child feels when drawing a picture. The identification unit also uses the emotion estimation function to analyze whether a child has positive emotions toward a specific sport. For example, it analyzes the excitement a child feels when playing soccer. The identification unit also collects the child's emotion estimation data and adds the positive emotions toward a specific activity to the analysis. For example, it analyzes the satisfaction a child feels when playing music. As a result, the emotion estimation function makes it possible to identify talents based on a child's positive emotions.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The data collection department collects learning or behavioral data about the child, such as school grades, extracurricular activity results, test results, homework submission status, and club activity participation records. Step 2: The analysis unit analyzes the learning data or behavioral data collected by the data collection unit, for example, using data mining techniques, statistical analysis, or machine learning algorithms. Step 3: The identification unit identifies the child's potential talents or areas of expertise based on the results of the analysis by the analysis unit, for example, academic talent, sports talent, or artistic talent. Step 4: The coaching generation unit generates a customized coaching program based on the talents or areas of expertise identified by the identification unit, for example, an individualized learning plan, a specific skill development program, or a specific sports training program.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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 learning data or behavioral data of children; an analysis unit that analyzes the learning data or the behavioral data collected by the data collection unit; an identification unit that identifies the child's potential talent or area of ​​expertise based on the analysis result by the analysis unit; a coaching generation unit that generates a customized coaching program based on the talent or the area of ​​expertise identified by the identification unit. A system characterized by:

2. The data collection unit Data from IoT devices in the home will also be collected and daily behavioral patterns will be added to the analysis.

2. The system of claim 1.

3. The data collection unit Collect data on children's social media posts or online activities, as well as school grades or extracurricular achievements, to conduct more comprehensive analysis.

2. The system of claim 1.

4. The data collection unit Analyze the emotions children feel during learning and activities, and reflect emotional fluctuations in the data 2. The system of claim 1.

5. The data collection unit Children's health data will also be collected and analyzed to determine the relationship between their health status and learning outcomes.

2. The system of claim 1.

6. The data collection unit Collect data on children of different age groups and grade levels and analyze their characteristics by age and grade level 2. The system of claim 1.

Images