system

Abstract

The system according to this embodiment aims to identify a child's potential talents and strengths and provide a learning plan based on them. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an identification unit, a provision unit, and a support unit. The collection unit collects data on children. The analysis unit analyzes the data collected by the collection unit. The identification unit identifies potential talents and strengths based on the data analyzed by the analysis unit. The provision unit provides a learning plan based on the talents and strengths identified by the identification unit. The support unit provides support to parents and educators based on the learning plan provided by the provision unit.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the potential talents and strengths of children have not been sufficiently identified efficiently, and a learning plan based on them has not been sufficiently provided, leaving room for improvement.

[0005] The system according to the embodiment aims to identify the potential talents and strengths of children and provide a learning plan based on them.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an identification unit, a provision unit, and a support unit. The data collection unit collects data on children. The analysis unit analyzes the data collected by the data collection unit. The identification unit identifies potential talents and strengths based on the data analyzed by the analysis unit. The provision unit provides a learning plan based on the talents and strengths identified by the identification unit. The support unit provides support to parents and educators based on the learning plan provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify a child's potential talents and strengths and provide a learning plan based on them. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that utilizes Generative AI to comprehensively analyze children's data and identify their potential talents and strengths. This system collects children's data, analyzes it using Generative AI, identifies their potential talents and strengths, provides expert coaching, and develops an optimal learning plan for each child. It also provides appropriate support to parents and educators. This allows children to maximize their talents and strengths and expand their future possibilities. For example, the system collects data such as a child's learning history, interests, and behavioral patterns. Next, Generative AI analyzes the collected data to identify their potential talents and strengths. Based on the identified talents and strengths, expert coaching is provided, and an optimal learning plan is developed for each child. Furthermore, appropriate support is provided to parents and educators. This allows children to maximize their talents and strengths and expand their future possibilities. This enables the system to efficiently collect, analyze, identify, provide, and support children's data.

[0029] The system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, a provision unit, and a support unit. The collection unit collects data on children. The collection unit can collect data such as learning history, interests, and behavioral patterns. For example, the collection unit can collect learning history such as test results, homework submission status, and study time. The collection unit can also collect the child's interests, such as favorite subjects, hobbies, and topics of interest. Furthermore, the collection unit can collect behavioral patterns such as daily activities, approaches to learning, and time management. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data and identify potential talents and strengths. For example, the analysis unit can identify potential talents and strengths such as academic talent, athletic talent, and creative talent. The identification unit identifies potential talents and strengths based on the data analyzed by the analysis unit. For example, the identification unit can formulate a learning plan based on the identified talents and strengths. For example, the identification unit can formulate a learning plan such as learning goals, learning materials, and a learning schedule. The provisioning department provides learning plans based on the talents and strengths identified by the identification department. The provisioning department can, for example, provide coaching by experts. The provisioning department can, for example, provide coaching by experts such as educational specialists, psychological counselors, and sports coaches. The support department provides support to parents and educators based on the learning plans provided by the provisioning department. The support department can, for example, provide appropriate support to parents and educators. The support department can, for example, provide support such as coaching, counseling, and feedback. This enables the system to efficiently collect, analyze, identify, provide, and support children's data.

[0030] The data collection unit collects data on children. For example, it can collect data such as learning history, interests, and behavioral patterns. Specifically, it collects learning history such as school report cards, test results, homework submission status, and study time. This allows for a detailed understanding of the child's learning progress and academic improvement. The data collection unit also collects information on the child's interests, such as favorite subjects, hobbies, and topics of interest. For example, by collecting information on areas of science the child is particularly interested in, or favorite sports and art, it is possible to gain a deeper understanding of the child's personality and interests. Furthermore, the data collection unit collects behavioral patterns such as daily activities, learning approaches, and time management. For example, by collecting information on when a child can concentrate best on learning and what learning methods are effective, it is possible to understand the child's learning style and daily rhythm. This data is automatically collected through sensors and applications and stored in a cloud-based database. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, by making the collected data accessible to the analysis and specific parts of the system, and by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the collected data to identify potential talents and strengths. Specifically, the analysis unit uses AI to analyze data and identify potential talents and strengths such as academic talent, athletic talent, and creative talent from a child's learning history and behavioral patterns. For example, the AI ​​analyzes a child's academic performance trends from test results and homework submission status to find outstanding talents in specific subjects. By analyzing behavioral patterns, it can identify what kind of environment a child can learn in most effectively. Furthermore, by analyzing data on interests, it can understand which areas a child has a strong interest in and propose specific approaches to develop talents in those areas. The analysis unit comprehensively analyzes this data to evaluate the child's overall talents and strengths. This allows the analysis unit to deeply understand the child's personality and characteristics and provide foundational information for formulating an optimal learning plan. In addition, the analysis unit can use past data and statistical information to predict long-term trends in talent development and growth. This allows the analysis unit to not only grasp the situation in real time, but also to support long-term talent development and growth, thereby improving the reliability and effectiveness of the entire system.

[0032] The Specialization Department identifies potential talents and strengths based on data analyzed by the Analysis Department. For example, the Specialization Department can develop learning plans based on these identified talents and strengths. Specifically, it develops learning plans, including learning goals, materials, and schedules, based on data provided by the Analysis Department. For instance, to cultivate a child's academic talents, the Specialization Department creates a learning plan focused on specific subjects and provides necessary materials and resources. For children with athletic talents, it develops a learning plan incorporating specialized training programs and coaching. Furthermore, for children with creative talents, it provides a learning plan incorporating creative activities such as art and music. The Specialization Department can individually customize these learning plans, providing the optimal plan tailored to each child's characteristics and needs. In developing learning plans, the Specialization Department considers the child's interests and behavioral patterns, creating an environment where the child can learn effectively and enjoyably. The Specialization Department also regularly monitors the progress of the learning plan and revises or adjusts it as needed. This allows the Specialization Department to maximize the child's talents and strengths and provide effective learning support.

[0033] The service provider will provide learning plans based on the talents and strengths identified by the specific department. The service provider can, for example, offer expert coaching. Specifically, the service provider will provide coaching from experts such as educational specialists, psychological counselors, and sports coaches to provide specific guidance to develop children's talents and strengths. For example, educational specialists will guide children on effective learning methods and how to select learning materials based on their learning plans. Psychological counselors will support children's mental health and provide counseling to increase their motivation to learn. Sports coaches will provide training programs to develop children's athletic talents and support improvements in skills and physical fitness. Through coaching by these experts, the service provider will help maximize children's talents and strengths. Furthermore, the service provider will utilize online platforms to create an environment where children and parents can receive coaching anytime, anywhere. This allows the service provider to effectively deliver children's learning plans and support their growth. In addition, the service provider will regularly evaluate the progress of the learning plans and revise or adjust them as needed. This allows the service provider to consistently offer high-quality learning support based on the latest information, maximizing children's talents and strengths.

[0034] The Support Department provides support to parents and educators based on the learning plans provided by the Provision Department. For example, the Support Department can provide appropriate support to parents and educators. Specifically, the Support Department provides support such as coaching, counseling, and feedback. For example, it instructs parents on how to support their children at home based on their learning plans and how to communicate effectively. For educators, it proposes methods for conducting lessons and providing individualized instruction based on the children's learning plans. Furthermore, the Support Department collects feedback from parents and educators to improve and adjust learning plans. This allows the Support Department to help parents and educators effectively support their children's learning. The Support Department also utilizes online platforms to ensure that parents and educators can receive support anytime, anywhere. This enables the Support Department to provide parents and educators with prompt and effective support, comprehensively assisting children's learning. Additionally, the Support Department facilitates collaboration with parents and educators through regular communication to share information about children's learning status and progress, and to build an optimal learning environment together. This allows the support department to work in collaboration with parents and educators to provide comprehensive support for children's learning and maximize their talents and strengths.

[0035] The data collection unit can collect data such as learning history, interests, and behavioral patterns. For example, the data collection unit can collect learning history such as test results, homework submission status, and study time. It can also collect interests such as the child's favorite subjects, hobbies, and topics of interest. Furthermore, the data collection unit can collect behavioral patterns such as daily activities, learning approaches, and time management. This allows the data collection unit to collect diverse data, enabling more comprehensive data analysis. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's learning history data into AI, which can then analyze and collect the data.

[0036] The analysis unit can analyze the collected data and identify potential talents and strengths. For example, the analysis unit can identify potential talents and strengths such as academic talent, athletic talent, or creative talent. This allows the analysis unit to identify a child's potential talents and strengths by analyzing the data. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the collected data into an AI, which can then analyze the data to identify potential talents and strengths.

[0037] The specific unit can develop a learning plan based on identified talents and strengths. For example, the specific unit can develop a learning plan that includes learning objectives, learning materials, and a learning schedule. This allows the specific unit to provide a learning plan that is optimal for the child. Some or all of the above-described processes in the specific unit may be performed using AI, or not. For example, the specific unit can input identified talents and strengths into an AI, which can then develop a learning plan.

[0038] The service provider can offer coaching by experts. For example, the service provider can offer coaching by experts such as educational experts, psychological counselors, and sports coaches. This improves the learning effectiveness of children by providing coaching by experts. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the content of the expert coaching into AI, and the AI ​​can assist in providing the coaching.

[0039] The support unit can provide appropriate support to parents and educators. For example, the support unit can provide support such as coaching, counseling, and feedback. This ensures a suitable learning environment for children by providing appropriate support to parents and educators. Some or all of the above processes in the support unit may be performed using AI, or not. For example, the support unit can input the content of the support provided to parents and educators into the AI, which can then assist in providing that support.

[0040] The data collection unit can analyze a child's past learning history and select the optimal data collection method. For example, the data collection unit can identify the time of day when a child can learn most effectively based on their past learning history and collect data during that time. Furthermore, based on their past learning history, the data collection unit can prioritize collecting data related to topics that children are likely to be interested in. In addition, by analyzing past learning history, the data collection unit can focus on collecting data in areas where the child struggles. This allows the optimal data collection method to be selected by analyzing past learning history. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input past learning history data into an AI, which can then select the optimal data collection method.

[0041] The data collection unit can filter data based on the child's current interests and behavioral patterns during data collection. For example, the data collection unit can prioritize collecting data related to topics the child is currently interested in. The data collection unit can also analyze the child's behavioral patterns and collect data related to specific behaviors. Furthermore, if the child's interests change, the data collection unit can appropriately change the types of data it collects. This allows for the collection of more relevant data by filtering the data based on the child's current interests and behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's current interests into an AI, which can then filter the data.

[0042] The data collection unit can prioritize the collection of highly relevant data based on the child's geographical location information during data collection. For example, if the child is in a specific location, the data collection unit can prioritize the collection of data related to that location. Furthermore, if the child is on the move, the data collection unit can also prioritize the collection of data related to their destination. In addition, if the child is interested in a particular region, the data collection unit can prioritize the collection of data related to that region. This allows for the collection of more relevant data by collecting data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's geographical location information into an AI, which can then prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze a child's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to topics that a child frequently mentions on social media. The data collection unit can also analyze the time periods when a child is active on social media and collect data during those times. Furthermore, the data collection unit can collect data related to accounts that a child follows on social media. In this way, relevant data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the child's social media activity data into AI, and the AI ​​can collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data of moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the data, more detailed analysis can be performed on more important data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a learning pattern analysis algorithm to learning history data. It can also apply an interest analysis algorithm to interest data. Furthermore, it can apply a behavior analysis algorithm to behavior pattern data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, which can then apply different analysis algorithms.

[0046] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. It can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period. This allows for the prioritization of analysis based on the data collection period, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into the AI, and the AI ​​can determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This allows for prioritizing the analysis of more relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, which can then adjust the order of analysis.

[0048] The identification unit can improve the accuracy of identification by considering the interrelationships of data at the time of identification. For example, the identification unit can improve the accuracy of identification by combining learning history and interest data. It can also improve the accuracy of identification by combining behavior patterns and learning history data. Furthermore, it can improve the accuracy of identification by combining interest and behavior patterns data. In this way, the accuracy of identification is improved by considering the interrelationships of data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the interrelationships of data into AI, and the AI ​​can improve the accuracy of identification.

[0049] The identification unit can perform identification while considering the attribute information of the data submitter. For example, if the submitter is a parent, the identification unit can perform identification while considering the parent's perspective. Also, if the submitter is an educator, the identification unit can perform identification while considering the educator's perspective. Furthermore, if the submitter is the child themselves, the identification unit can perform identification while considering the child's perspective. This allows for more appropriate identification by considering the submitter's attribute information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input the submitter's attribute information into AI, and the AI ​​can perform the identification.

[0050] The identification unit can perform identification while considering the geographical distribution of the data. For example, the identification unit can identify talents related to the area in which a child lives. It can also identify talents related to the area in which the child attends school. Furthermore, it can identify talents related to the area in which the child is interested. This allows for more appropriate identification by considering geographical distribution. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input geographical distribution data into AI, and the AI ​​can perform the identification.

[0051] The identification unit can improve the accuracy of identification by referring to relevant literature on the data at the time of identification. For example, the identification unit can improve the accuracy of identification by referring to literature related to learning history. It can also improve the accuracy of identification by referring to literature related to interests. Furthermore, the identification unit can improve the accuracy of identification by referring to literature related to behavioral patterns. Thus, the accuracy of identification is improved by referring to relevant literature. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input relevant literature data into AI, and the AI ​​can improve the accuracy of identification.

[0052] The service provider can provide the optimal learning plan by referring to the child's past learning history. For example, the service provider can provide a plan that allows the child to learn most effectively based on their past learning history. It can also provide a plan related to topics that the child is likely to be interested in, based on their past learning history. Furthermore, the service provider can analyze their past learning history and provide a plan that strengthens areas where the child struggles. This allows the service provider to provide the optimal learning plan by referring to their past learning history. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input past learning history data into an AI, which can then provide the optimal learning plan.

[0053] The service provider can customize learning plans based on the child's current lifestyle when providing them. For example, if the child is busy, the service provider can provide a plan that allows for effective learning in a short amount of time. Conversely, if the child has more free time, the service provider can provide a more detailed and comprehensive learning plan. Furthermore, the service provider can adjust the time slots of the learning plan to match the child's daily rhythm. This allows for the provision of more appropriate learning plans by customizing them based on the child's current lifestyle. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input data on the child's lifestyle into the AI, which can then customize the plan.

[0054] The service provider can provide the most suitable learning plan based on the child's geographical location. For example, if the child is in a specific location, the service provider can provide a learning plan relevant to that location. Furthermore, if the child is on the move, the service provider can provide a learning plan relevant to their destination. Additionally, if the child is interested in a particular region, the service provider can provide a learning plan relevant to that region. This allows for the provision of more appropriate learning plans based on geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the child's geographical location information into an AI, which can then provide the most suitable plan.

[0055] The service provider can analyze a child's social media activity and propose a learning plan when providing one. For example, the service provider can provide a learning plan related to topics that the child frequently mentions on social media. It can also analyze the times of day when the child is active on social media and provide a learning plan for those times. Furthermore, the service provider can provide a learning plan related to accounts that the child follows on social media. This allows for the provision of more appropriate learning plans by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the child's social media activity data into an AI, which can then propose a plan.

[0056] The support unit can provide optimal support by referring to the past support history of parents and educators when providing support. For example, the support unit can provide parents and educators with the most effective ways to provide support based on past support history. The support unit can also provide support information that parents and educators are likely to be interested in, by referring to past support history. Furthermore, the support unit can analyze past support history and provide support information that strengthens areas where parents and educators struggle. In this way, optimal support can be provided by referring to past support history. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input past support history data into AI, and the AI ​​can provide optimal support.

[0057] The support unit can provide optimal support based on the geographical location information of parents and educators when providing support. For example, if parents or educators are in a specific location, the support unit can provide support information relevant to that location. Furthermore, if parents or educators are on the move, the support unit can provide support information relevant to their destination. In addition, if parents or educators are interested in a particular region, the support unit can provide support information relevant to that region. This allows for more appropriate support to be provided by basing support on geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or not. For example, the support unit can input the geographical location information of parents and educators into an AI, which can then provide optimal support.

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

[0059] The data collection unit can monitor a child's health status when collecting data and adjust the frequency and timing of data collection based on that status. For example, if a child is unwell, data collection can be temporarily suspended and resumed after recovery. If the child is healthy, data can be collected at the normal frequency. Furthermore, if a child has a specific health condition, data related to that condition can be prioritized for collection. This allows for the collection of more relevant data by adjusting the frequency and timing of data collection according to the child's health status.

[0060] The analysis unit can adjust the analysis method when analyzing collected data, taking into account the child's learning style. For example, if a child has a visual learning style, a visual data analysis method can be applied. Similarly, if a child has an auditory learning style, an auditory data analysis method can be applied. Furthermore, if a child has an experiential learning style, an experiential data analysis method can be applied. By adjusting the analysis method according to the child's learning style, more appropriate analysis results can be provided.

[0061] The identification function can suggest future career paths for children based on their identified talents and strengths. For example, if academic talent is identified, an academic career path can be suggested. Similarly, if athletic talent is identified, a sports-related career path can be suggested. Furthermore, if creative talent is identified, a creative career path can be suggested. This expands a child's future possibilities by suggesting career paths based on their identified talents and strengths.

[0062] The service provider can customize learning plans by taking into account the child's learning environment. For example, if a child studies at home, a plan suitable for home study can be provided. Similarly, if a child studies at school, a plan suitable for school study can be provided. Furthermore, if a child studies online, a plan suitable for online learning can be provided. By customizing the plan according to the child's learning environment, a more appropriate learning plan can be provided.

[0063] The support department can adjust its support methods to parents and educators, taking into account their respective teaching styles. For example, if a parent has a strict teaching style, it can provide support methods suited to that style. Similarly, if an educator has a flexible teaching style, it can provide support methods suited to that style. Furthermore, if a parent or educator has a cooperative teaching style, it can provide support methods suited to that style. By adjusting support methods according to the teaching styles of parents and educators, more appropriate support can be provided.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects data on the child. The data collection unit can collect data such as learning history, interests, and behavioral patterns. Specifically, it collects learning history such as test results, homework submission status, and study time; interests such as the child's favorite subjects, hobbies, and topics of interest; and behavioral patterns such as daily activities, approaches to learning, and time management. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, analyze the collected data to identify potential talents and strengths. Specifically, it can identify potential talents and strengths such as academic talent, athletic talent, and creative talent. Step 3: The Identification Unit identifies potential talents and strengths based on the data analyzed by the Analysis Unit. The Identification Unit can, for example, develop a learning plan based on the identified talents and strengths. Specifically, it develops a learning plan that includes learning objectives, learning materials, and a learning schedule. Step 4: The service provider provides a learning plan based on the talents and strengths identified by the specific service provider. The service provider can, for example, provide coaching by experts. Specifically, they can provide coaching by experts such as educational specialists, psychological counselors, and sports coaches. Step 5: The support department provides support to parents and educators based on the learning plan provided by the delivery department. The support department can provide appropriate support to parents and educators, for example. Specifically, it can provide support such as coaching, counseling, and feedback.

[0066] (Example of form 2) The system according to an embodiment of the present invention is a system that utilizes Generative AI to comprehensively analyze children's data and identify their potential talents and strengths. This system collects children's data, analyzes it using Generative AI, identifies their potential talents and strengths, provides expert coaching, and develops an optimal learning plan for each child. It also provides appropriate support to parents and educators. This allows children to maximize their talents and strengths and expand their future possibilities. For example, the system collects data such as a child's learning history, interests, and behavioral patterns. Next, Generative AI analyzes the collected data to identify their potential talents and strengths. Based on the identified talents and strengths, expert coaching is provided, and an optimal learning plan is developed for each child. Furthermore, appropriate support is provided to parents and educators. This allows children to maximize their talents and strengths and expand their future possibilities. This enables the system to efficiently collect, analyze, identify, provide, and support children's data.

[0067] The system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, a provision unit, and a support unit. The collection unit collects data on children. The collection unit can collect data such as learning history, interests, and behavioral patterns. For example, the collection unit can collect learning history such as test results, homework submission status, and study time. The collection unit can also collect the child's interests, such as favorite subjects, hobbies, and topics of interest. Furthermore, the collection unit can collect behavioral patterns such as daily activities, approaches to learning, and time management. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data and identify potential talents and strengths. For example, the analysis unit can identify potential talents and strengths such as academic talent, athletic talent, and creative talent. The identification unit identifies potential talents and strengths based on the data analyzed by the analysis unit. For example, the identification unit can formulate a learning plan based on the identified talents and strengths. For example, the identification unit can formulate a learning plan such as learning goals, learning materials, and a learning schedule. The provisioning department provides learning plans based on the talents and strengths identified by the identification department. The provisioning department can, for example, provide coaching by experts. The provisioning department can, for example, provide coaching by experts such as educational specialists, psychological counselors, and sports coaches. The support department provides support to parents and educators based on the learning plans provided by the provisioning department. The support department can, for example, provide appropriate support to parents and educators. The support department can, for example, provide support such as coaching, counseling, and feedback. This enables the system to efficiently collect, analyze, identify, provide, and support children's data.

[0068] The data collection unit collects data on children. For example, it can collect data such as learning history, interests, and behavioral patterns. Specifically, it collects learning history such as school report cards, test results, homework submission status, and study time. This allows for a detailed understanding of the child's learning progress and academic improvement. The data collection unit also collects information on the child's interests, such as favorite subjects, hobbies, and topics of interest. For example, by collecting information on areas of science the child is particularly interested in, or favorite sports and art, it is possible to gain a deeper understanding of the child's personality and interests. Furthermore, the data collection unit collects behavioral patterns such as daily activities, learning approaches, and time management. For example, by collecting information on when a child can concentrate best on learning and what learning methods are effective, it is possible to understand the child's learning style and daily rhythm. This data is automatically collected through sensors and applications and stored in a cloud-based database. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, by making the collected data accessible to the analysis and specific parts of the system, and by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0069] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the collected data to identify potential talents and strengths. Specifically, the analysis unit uses AI to analyze data and identify potential talents and strengths such as academic talent, athletic talent, and creative talent from a child's learning history and behavioral patterns. For example, the AI ​​analyzes a child's academic performance trends from test results and homework submission status to find outstanding talents in specific subjects. By analyzing behavioral patterns, it can identify what kind of environment a child can learn in most effectively. Furthermore, by analyzing data on interests, it can understand which areas a child has a strong interest in and propose specific approaches to develop talents in those areas. The analysis unit comprehensively analyzes this data to evaluate the child's overall talents and strengths. This allows the analysis unit to deeply understand the child's personality and characteristics and provide foundational information for formulating an optimal learning plan. In addition, the analysis unit can use past data and statistical information to predict long-term trends in talent development and growth. This allows the analysis unit to not only grasp the situation in real time, but also to support long-term talent development and growth, thereby improving the reliability and effectiveness of the entire system.

[0070] The Specialization Department identifies potential talents and strengths based on data analyzed by the Analysis Department. For example, the Specialization Department can develop learning plans based on these identified talents and strengths. Specifically, it develops learning plans, including learning goals, materials, and schedules, based on data provided by the Analysis Department. For instance, to cultivate a child's academic talents, the Specialization Department creates a learning plan focused on specific subjects and provides necessary materials and resources. For children with athletic talents, it develops a learning plan incorporating specialized training programs and coaching. Furthermore, for children with creative talents, it provides a learning plan incorporating creative activities such as art and music. The Specialization Department can individually customize these learning plans, providing the optimal plan tailored to each child's characteristics and needs. In developing learning plans, the Specialization Department considers the child's interests and behavioral patterns, creating an environment where the child can learn effectively and enjoyably. The Specialization Department also regularly monitors the progress of the learning plan and revises or adjusts it as needed. This allows the Specialization Department to maximize the child's talents and strengths and provide effective learning support.

[0071] The service provider will provide learning plans based on the talents and strengths identified by the specific department. The service provider can, for example, offer expert coaching. Specifically, the service provider will provide coaching from experts such as educational specialists, psychological counselors, and sports coaches to provide specific guidance to develop children's talents and strengths. For example, educational specialists will guide children on effective learning methods and how to select learning materials based on their learning plans. Psychological counselors will support children's mental health and provide counseling to increase their motivation to learn. Sports coaches will provide training programs to develop children's athletic talents and support improvements in skills and physical fitness. Through coaching by these experts, the service provider will help maximize children's talents and strengths. Furthermore, the service provider will utilize online platforms to create an environment where children and parents can receive coaching anytime, anywhere. This allows the service provider to effectively deliver children's learning plans and support their growth. In addition, the service provider will regularly evaluate the progress of the learning plans and revise or adjust them as needed. This allows the service provider to consistently offer high-quality learning support based on the latest information, maximizing children's talents and strengths.

[0072] The Support Department provides support to parents and educators based on the learning plans provided by the Provision Department. For example, the Support Department can provide appropriate support to parents and educators. Specifically, the Support Department provides support such as coaching, counseling, and feedback. For example, it instructs parents on how to support their children at home based on their learning plans and how to communicate effectively. For educators, it proposes methods for conducting lessons and providing individualized instruction based on the children's learning plans. Furthermore, the Support Department collects feedback from parents and educators to improve and adjust learning plans. This allows the Support Department to help parents and educators effectively support their children's learning. The Support Department also utilizes online platforms to ensure that parents and educators can receive support anytime, anywhere. This enables the Support Department to provide parents and educators with prompt and effective support, comprehensively assisting children's learning. Additionally, the Support Department facilitates collaboration with parents and educators through regular communication to share information about children's learning status and progress, and to build an optimal learning environment together. This allows the support department to work in collaboration with parents and educators to provide comprehensive support for children's learning and maximize their talents and strengths.

[0073] The data collection unit can collect data such as learning history, interests, and behavioral patterns. For example, the data collection unit can collect learning history such as test results, homework submission status, and study time. It can also collect interests such as the child's favorite subjects, hobbies, and topics of interest. Furthermore, the data collection unit can collect behavioral patterns such as daily activities, learning approaches, and time management. This allows the data collection unit to collect diverse data, enabling more comprehensive data analysis. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's learning history data into AI, which can then analyze and collect the data.

[0074] The analysis unit can analyze the collected data and identify potential talents and strengths. For example, the analysis unit can identify potential talents and strengths such as academic talent, athletic talent, or creative talent. This allows the analysis unit to identify a child's potential talents and strengths by analyzing the data. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the collected data into an AI, which can then analyze the data to identify potential talents and strengths.

[0075] The specific unit can develop a learning plan based on identified talents and strengths. For example, the specific unit can develop a learning plan that includes learning objectives, learning materials, and a learning schedule. This allows the specific unit to provide a learning plan that is optimal for the child. Some or all of the above-described processes in the specific unit may be performed using AI, or not. For example, the specific unit can input identified talents and strengths into an AI, which can then develop a learning plan.

[0076] The service provider can offer coaching by experts. For example, the service provider can offer coaching by experts such as educational experts, psychological counselors, and sports coaches. This improves the learning effectiveness of children by providing coaching by experts. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the content of the expert coaching into AI, and the AI ​​can assist in providing the coaching.

[0077] The support unit can provide appropriate support to parents and educators. For example, the support unit can provide support such as coaching, counseling, and feedback. This ensures a suitable learning environment for children by providing appropriate support to parents and educators. Some or all of the above processes in the support unit may be performed using AI, or not. For example, the support unit can input the content of the support provided to parents and educators into the AI, which can then assist in providing that support.

[0078] The data collection unit can estimate a child's emotions and adjust the timing of data collection based on the estimated emotions. For example, the data collection unit can collect data on learning history and interests when the child is relaxed. It can also collect data on behavioral patterns when the child is concentrating. Furthermore, the data collection unit can temporarily suspend data collection when the child is stressed and resume it later. This allows for the collection of more appropriate data by adjusting the timing of data collection according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input the child's emotion data into an AI, which can then adjust the timing of data collection.

[0079] The data collection unit can analyze a child's past learning history and select the optimal data collection method. For example, the data collection unit can identify the time of day when a child can learn most effectively based on their past learning history and collect data during that time. Furthermore, based on their past learning history, the data collection unit can prioritize collecting data related to topics that children are likely to be interested in. In addition, by analyzing past learning history, the data collection unit can focus on collecting data in areas where the child struggles. This allows the optimal data collection method to be selected by analyzing past learning history. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input past learning history data into an AI, which can then select the optimal data collection method.

[0080] The data collection unit can filter data based on the child's current interests and behavioral patterns during data collection. For example, the data collection unit can prioritize collecting data related to topics the child is currently interested in. The data collection unit can also analyze the child's behavioral patterns and collect data related to specific behaviors. Furthermore, if the child's interests change, the data collection unit can appropriately change the types of data it collects. This allows for the collection of more relevant data by filtering the data based on the child's current interests and behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's current interests into an AI, which can then filter the data.

[0081] The data collection unit can estimate a child's emotions and prioritize the data to collect based on the estimated emotions. For example, if a child is excited, the data collection unit can prioritize the collection of data related to their interests. If a child is relaxed, the data collection unit can prioritize the collection of data related to their learning history. Furthermore, if a child is stressed, the data collection unit can prioritize the collection of data related to their behavioral patterns. This allows for the priority collection of more important data by prioritizing data according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input child emotion data into an AI, which can then determine the data prioritization.

[0082] The data collection unit can prioritize the collection of highly relevant data based on the child's geographical location information during data collection. For example, if the child is in a specific location, the data collection unit can prioritize the collection of data related to that location. Furthermore, if the child is on the move, the data collection unit can also prioritize the collection of data related to their destination. In addition, if the child is interested in a particular region, the data collection unit can prioritize the collection of data related to that region. This allows for the collection of more relevant data by collecting data based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the child's geographical location information into an AI, which can then prioritize the collection of highly relevant data.

[0083] The data collection unit can analyze a child's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to topics that a child frequently mentions on social media. The data collection unit can also analyze the time periods when a child is active on social media and collect data during those times. Furthermore, the data collection unit can collect data related to accounts that a child follows on social media. In this way, relevant data can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the child's social media activity data into AI, and the AI ​​can collect relevant data.

[0084] The analysis unit can estimate the child's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the child is relaxed, the analysis unit can provide detailed analysis results. If the child is tense, the analysis unit can provide concise and to-the-point analysis results. Furthermore, if the child is excited, the analysis unit can provide visually appealing analysis results. By adjusting the presentation of the analysis according to the child's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's emotion data into the AI, and the AI ​​can adjust the presentation of the analysis.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data of moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the data, more detailed analysis can be performed on more important data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a learning pattern analysis algorithm to learning history data. It can also apply an interest analysis algorithm to interest data. Furthermore, it can apply a behavior analysis algorithm to behavior pattern data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, which can then apply different analysis algorithms.

[0087] The analysis unit can estimate the child's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the child is relaxed, the analysis unit can perform a detailed analysis. If the child is in a hurry, the analysis unit can perform a concise analysis. Furthermore, if the child is excited, the analysis unit can perform a visually appealing analysis. By adjusting the length of the analysis according to the child's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's emotion data into the AI, and the AI ​​can adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit can prioritize the analysis of the most recent data. It can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period. This allows for the prioritization of analysis based on the data collection period, thereby prioritizing the analysis of the most recent data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into the AI, and the AI ​​can determine the priority of analysis.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This allows for prioritizing the analysis of more relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, which can then adjust the order of analysis.

[0090] The identification unit can estimate a child's emotions and, based on the estimated emotions, determine the priority of talents and strengths to identify. For example, if the child is relaxed, the identification unit can prioritize identifying talents related to learning. If the child is excited, the identification unit can also prioritize identifying talents related to creativity. Furthermore, if the child is stressed, the identification unit can also prioritize identifying strengths related to stress tolerance. This allows for the identification of more appropriate talents and strengths by prioritizing them according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the identification unit may be performed using AI, or not using AI. For example, the identification unit can input the child's emotion data into an AI, which can then determine the priority of talents and strengths.

[0091] The identification unit can improve the accuracy of identification by considering the interrelationships of data at the time of identification. For example, the identification unit can improve the accuracy of identification by combining learning history and interest data. It can also improve the accuracy of identification by combining behavior patterns and learning history data. Furthermore, it can improve the accuracy of identification by combining interest and behavior patterns data. In this way, the accuracy of identification is improved by considering the interrelationships of data. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the interrelationships of data into AI, and the AI ​​can improve the accuracy of identification.

[0092] The identification unit can perform identification while considering the attribute information of the data submitter. For example, if the submitter is a parent, the identification unit can perform identification while considering the parent's perspective. Also, if the submitter is an educator, the identification unit can perform identification while considering the educator's perspective. Furthermore, if the submitter is the child themselves, the identification unit can perform identification while considering the child's perspective. This allows for more appropriate identification by considering the submitter's attribute information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without using AI. For example, the identification unit can input the submitter's attribute information into AI, and the AI ​​can perform the identification.

[0093] The identification unit can estimate a child's emotions and adjust the display method of identified talents and strengths based on the estimated emotions. For example, if the child is relaxed, the identification unit can provide a display method that includes a detailed explanation. If the child is excited, the identification unit can also provide a visually appealing display method. Furthermore, if the child is nervous, the identification unit can provide a concise and to-the-point display method. This allows for more appropriate display by adjusting the display method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input the child's emotion data into an AI, which can then adjust the display method.

[0094] The identification unit can perform identification while considering the geographical distribution of the data. For example, the identification unit can identify talents related to the area in which a child lives. It can also identify talents related to the area in which the child attends school. Furthermore, it can identify talents related to the area in which the child is interested. This allows for more appropriate identification by considering geographical distribution. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input geographical distribution data into AI, and the AI ​​can perform the identification.

[0095] The identification unit can improve the accuracy of identification by referring to relevant literature on the data at the time of identification. For example, the identification unit can improve the accuracy of identification by referring to literature related to learning history. It can also improve the accuracy of identification by referring to literature related to interests. Furthermore, the identification unit can improve the accuracy of identification by referring to literature related to behavioral patterns. Thus, the accuracy of identification is improved by referring to relevant literature. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input relevant literature data into AI, and the AI ​​can improve the accuracy of identification.

[0096] The service provider can estimate a child's emotions and adjust the way the learning plan is delivered based on the estimated emotions. For example, if the child is relaxed, the service provider can provide a detailed learning plan. If the child is excited, the service provider can also provide a visually appealing learning plan. Furthermore, if the child is nervous, the service provider can provide a concise and to-the-point learning plan. This allows for the provision of a more appropriate learning plan by adjusting the delivery method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input child emotion data into AI, and the AI ​​can adjust the way the learning plan is delivered.

[0097] The service provider can provide the optimal learning plan by referring to the child's past learning history. For example, the service provider can provide a plan that allows the child to learn most effectively based on their past learning history. It can also provide a plan related to topics that the child is likely to be interested in, based on their past learning history. Furthermore, the service provider can analyze their past learning history and provide a plan that strengthens areas where the child struggles. This allows the service provider to provide the optimal learning plan by referring to their past learning history. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input past learning history data into an AI, which can then provide the optimal learning plan.

[0098] The service provider can customize learning plans based on the child's current lifestyle when providing them. For example, if the child is busy, the service provider can provide a plan that allows for effective learning in a short amount of time. Conversely, if the child has more free time, the service provider can provide a more detailed and comprehensive learning plan. Furthermore, the service provider can adjust the time slots of the learning plan to match the child's daily rhythm. This allows for the provision of more appropriate learning plans by customizing them based on the child's current lifestyle. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can input data on the child's lifestyle into the AI, which can then customize the plan.

[0099] The service provider can estimate a child's emotions and prioritize learning plans based on those emotions. For example, if a child is relaxed, the service provider can prioritize providing a detailed learning plan. If a child is excited, the service provider can prioritize providing a visually appealing learning plan. Furthermore, if a child is stressed, the service provider can prioritize providing a concise and to-the-point learning plan. This allows for the provision of more appropriate learning plans by prioritizing them according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input child emotion data into an AI, which can then determine the priority of learning plans.

[0100] The service provider can provide the most suitable learning plan based on the child's geographical location. For example, if the child is in a specific location, the service provider can provide a learning plan relevant to that location. Furthermore, if the child is on the move, the service provider can provide a learning plan relevant to their destination. Additionally, if the child is interested in a particular region, the service provider can provide a learning plan relevant to that region. This allows for the provision of more appropriate learning plans based on geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the child's geographical location information into an AI, which can then provide the most suitable plan.

[0101] The service provider can analyze a child's social media activity and propose a learning plan when providing one. For example, the service provider can provide a learning plan related to topics that the child frequently mentions on social media. It can also analyze the times of day when the child is active on social media and provide a learning plan for those times. Furthermore, the service provider can provide a learning plan related to accounts that the child follows on social media. This allows for the provision of more appropriate learning plans by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the child's social media activity data into an AI, which can then propose a plan.

[0102] The support unit can estimate a child's emotions and adjust its support methods for parents and educators based on the estimated emotions. For example, if a child is relaxed, the support unit can provide detailed support information. If a child is excited, the support unit can provide visually appealing support information. Furthermore, if a child is anxious, the support unit can provide concise and to-the-point support information. This allows for more appropriate support to be provided by adjusting the support method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input child emotion data into an AI, which can then adjust the support method.

[0103] The support unit can provide optimal support by referring to the past support history of parents and educators when providing support. For example, the support unit can provide parents and educators with the most effective ways to provide support based on past support history. The support unit can also provide support information that parents and educators are likely to be interested in, by referring to past support history. Furthermore, the support unit can analyze past support history and provide support information that strengthens areas where parents and educators struggle. In this way, optimal support can be provided by referring to past support history. Some or all of the above processes in the support unit may be performed using AI, for example, or not. For example, the support unit can input past support history data into AI, and the AI ​​can provide optimal support.

[0104] The support unit can estimate a child's emotions and determine the priority of support based on the estimated emotions. For example, if a child is relaxed, the support unit can prioritize providing detailed support information. If a child is excited, the support unit can prioritize providing visually appealing support information. Furthermore, if a child is anxious, the support unit can prioritize providing concise and to-the-point support information. This allows for more appropriate support to be provided by prioritizing support according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, or not using AI. For example, the support unit can input child emotion data into an AI, which can then determine the priority of support.

[0105] The support unit can provide optimal support based on the geographical location information of parents and educators when providing support. For example, if parents or educators are in a specific location, the support unit can provide support information relevant to that location. Furthermore, if parents or educators are on the move, the support unit can provide support information relevant to their destination. In addition, if parents or educators are interested in a particular region, the support unit can provide support information relevant to that region. This allows for more appropriate support to be provided by basing support on geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or not. For example, the support unit can input the geographical location information of parents and educators into an AI, which can then provide optimal support.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The data collection unit can monitor a child's health status when collecting data and adjust the frequency and timing of data collection based on that status. For example, if a child is unwell, data collection can be temporarily suspended and resumed after recovery. If the child is healthy, data can be collected at the normal frequency. Furthermore, if a child has a specific health condition, data related to that condition can be prioritized for collection. This allows for the collection of more relevant data by adjusting the frequency and timing of data collection according to the child's health status.

[0108] The analysis unit can adjust the analysis method when analyzing collected data, taking into account the child's learning style. For example, if a child has a visual learning style, a visual data analysis method can be applied. Similarly, if a child has an auditory learning style, an auditory data analysis method can be applied. Furthermore, if a child has an experiential learning style, an experiential data analysis method can be applied. By adjusting the analysis method according to the child's learning style, more appropriate analysis results can be provided.

[0109] The identification function can suggest future career paths for children based on their identified talents and strengths. For example, if academic talent is identified, an academic career path can be suggested. Similarly, if athletic talent is identified, a sports-related career path can be suggested. Furthermore, if creative talent is identified, a creative career path can be suggested. This expands a child's future possibilities by suggesting career paths based on their identified talents and strengths.

[0110] The service provider can customize learning plans by taking into account the child's learning environment. For example, if a child studies at home, a plan suitable for home study can be provided. Similarly, if a child studies at school, a plan suitable for school study can be provided. Furthermore, if a child studies online, a plan suitable for online learning can be provided. By customizing the plan according to the child's learning environment, a more appropriate learning plan can be provided.

[0111] The support department can adjust its support methods to parents and educators, taking into account their respective teaching styles. For example, if a parent has a strict teaching style, it can provide support methods suited to that style. Similarly, if an educator has a flexible teaching style, it can provide support methods suited to that style. Furthermore, if a parent or educator has a cooperative teaching style, it can provide support methods suited to that style. By adjusting support methods according to the teaching styles of parents and educators, more appropriate support can be provided.

[0112] The data collection unit can estimate a child's emotions and adjust the data collection method based on the estimated emotions. For example, if a child is relaxed, data can be collected in an interview format. If a child is excited, data can be collected in a game format. Furthermore, if a child is anxious, data can be collected in an observational format. By adjusting the data collection method according to the child's emotions, more appropriate data can be collected.

[0113] The analysis unit can estimate the child's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the child is relaxed, a detailed analysis can be performed. If the child is excited, a concise analysis can be performed. Furthermore, if the child is tense, the analysis can be temporarily suspended and resumed later. By adjusting the timing of the analysis according to the child's emotions, more appropriate analysis results can be provided.

[0114] The specific unit can estimate a child's emotions and adjust the feedback method for identifying talents and strengths based on those emotions. For example, if a child is relaxed, it can provide detailed feedback. If a child is excited, it can provide visually engaging feedback. Furthermore, if a child is stressed, it can provide concise and to-the-point feedback. This allows for more appropriate feedback by adjusting the feedback method according to the child's emotions.

[0115] The system can estimate a child's emotions and adjust the content of the learning plan based on those estimates. For example, if a child is relaxed, it can provide a detailed learning plan. If a child is excited, it can provide a visually appealing learning plan. Furthermore, if a child is nervous, it can provide a concise and to-the-point learning plan. By adjusting the content of the learning plan according to the child's emotions, a more appropriate learning plan can be provided.

[0116] The support unit can estimate a child's emotions and adjust the support provided to parents and educators based on that estimation. For example, if a child is relaxed, it can provide detailed support information. If a child is excited, it can provide visually appealing support information. Furthermore, if a child is anxious, it can provide concise and to-the-point support information. By adjusting the support content according to the child's emotions, more appropriate support can be provided.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The data collection unit collects data on the child. The data collection unit can collect data such as learning history, interests, and behavioral patterns. Specifically, it collects learning history such as test results, homework submission status, and study time; interests such as the child's favorite subjects, hobbies, and topics of interest; and behavioral patterns such as daily activities, approaches to learning, and time management. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, analyze the collected data to identify potential talents and strengths. Specifically, it can identify potential talents and strengths such as academic talent, athletic talent, and creative talent. Step 3: The Identification Unit identifies potential talents and strengths based on the data analyzed by the Analysis Unit. The Identification Unit can, for example, develop a learning plan based on the identified talents and strengths. Specifically, it develops a learning plan that includes learning objectives, learning materials, and a learning schedule. Step 4: The service provider provides a learning plan based on the talents and strengths identified by the specific service provider. The service provider can, for example, provide coaching by experts. Specifically, they can provide coaching by experts such as educational specialists, psychological counselors, and sports coaches. Step 5: The support department provides support to parents and educators based on the learning plan provided by the delivery department. The support department can provide appropriate support to parents and educators, for example. Specifically, it can provide support such as coaching, counseling, and feedback.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, provision unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data on the child using the camera 42 and microphone 38B of the smart device 14 and processes the data with the control unit 46A. The analysis unit is implemented in the identification unit 290 of the data processing unit 12 and analyzes the collected data. The identification unit is implemented in the identification unit 290 of the data processing unit 12 and identifies potential talents and strengths based on the analysis results. The provision unit is implemented in the identification unit 290 of the data processing unit 12 and provides a learning plan based on the identified talents and strengths. The support unit is implemented in the control unit 46A of the smart device 14 and provides appropriate support to parents and educators. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, provision unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data on the child using the camera 42 and microphone 238 of the smart glasses 214 and processes the data with the control unit 46A. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The identification unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and identifies potential talents and strengths based on the analysis results. The provision unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and provides a learning plan based on the identified talents and strengths. The support unit is implemented, for example, in the control unit 46A of the smart glasses 214 and provides appropriate support to parents and educators. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, provision unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data on children using the camera 42 and microphone 238 of the headset terminal 314 and processes the data with the control unit 46A. The analysis unit is implemented in the identification unit 290 of the data processing unit 12 and analyzes the collected data. The identification unit is implemented in the identification unit 290 of the data processing unit 12 and identifies potential talents and strengths based on the analysis results. The provision unit is implemented in the identification unit 290 of the data processing unit 12 and provides a learning plan based on the identified talents and strengths. The support unit is implemented in the control unit 46A of the headset terminal 314 and provides appropriate support to parents and educators. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, provision unit, and support unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data on children using the camera 42 and microphone 238 of the robot 414 and processes the data with the control unit 46A. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The identification unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and identifies potential talents and strengths based on the analysis results. The provision unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12 and provides a learning plan based on the identified talents and strengths. The support unit is implemented, for example, in the control unit 46A of the robot 414 and provides appropriate support to parents and educators. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0172] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0181] 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.

[0182] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) A data collection unit that collects children's data, An analysis unit analyzes the data collected by the aforementioned collection unit, An identification unit identifies potential talents and strengths based on the data analyzed by the aforementioned analysis unit, A provisioning unit that provides a learning plan based on the talents and strengths identified by the aforementioned specific unit, The system includes a support unit that provides support to parents and educators based on the learning plan provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as learning history, interests, and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the collected data to identify potential talents and strengths. The system described in Appendix 1, characterized by the features described herein. (Note 4) The specified part is, Develop a learning plan based on identified talents and strengths. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide coaching by experts. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned support unit is Providing appropriate support to parents and educators The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the child's emotions and adjust the timing of data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze children's past learning history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the child's current interests and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the child's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, analyze children's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the child's emotions and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the child's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, Estimate a child's emotions and, based on those estimated emotions, prioritize identifying their talents and strengths. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, At specific times, improve specific accuracy by considering the interrelationships between data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The specified part is, When identifying data, the attribute information of the data submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The specified part is, We estimate children's emotions and adjust how talents and strengths identified based on those estimated emotions are displayed. The system described in Appendix 1, characterized by the features described herein. (Note 23) The specified part is, When identifying data, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The specified part is, At specific times, we refer to relevant literature for data to improve specific accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the child's emotions and adjusts the way learning plans are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing a learning plan, we refer to the child's past learning history to provide the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing a learning plan, customize the plan based on the child's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the child's emotions and prioritizes the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing a learning plan, we will provide the most suitable plan based on the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing a learning plan, we analyze the child's social media activity and propose a plan based on that analysis. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit is The system estimates a child's emotions and adjusts the support provided to parents and educators based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned support unit is When providing support, we refer to the past support history of parents and educators to provide the most appropriate support. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned support unit is Estimate the child's emotions and determine the priority of support based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned support unit is When providing support, we will provide optimal support based on the geographical location information of parents and educators. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects children's data, An analysis unit analyzes the data collected by the aforementioned collection unit, An identification unit identifies potential talents and strengths based on the data analyzed by the aforementioned analysis unit, A provisioning unit that provides a learning plan based on the talents and strengths identified by the aforementioned specific unit, The system includes a support unit that provides support to parents and educators based on the learning plan provided by the aforementioned provision unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as learning history, interests, and behavioral patterns. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the collected data to identify potential talents and strengths. The system according to feature 1.

4. The specified part is, Develop a learning plan based on identified talents and strengths. The system according to feature 1.

5. The aforementioned supply unit is, We provide coaching by experts. The system according to feature 1.

6. The aforementioned support unit is Providing appropriate support to parents and educators The system according to feature 1.

7. The aforementioned collection unit is We estimate the child's emotions and adjust the timing of data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze children's past learning history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on the child's current interests and behavioral patterns. The system according to feature 1.

10. The aforementioned collection unit is The system estimates the child's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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