system

Abstract

The system according to the embodiment aims to analyze educational data and provide appropriate support methods on a regular basis. [Solution] A system according to an embodiment includes an analysis unit, a proposal unit, and a transmission unit. The analysis unit analyzes educational data. The proposal unit proposes support methods based on the data analyzed by the analysis unit. The transmission unit periodically transmits the support methods proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology makes it difficult for parents to appropriately utilize the contents of communication notebooks and report cards, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze educational data and provide appropriate support methods on a regular basis. [Means for solving the problem]

[0006] A system according to an embodiment includes an analysis unit, a proposal unit, and a transmission unit. The analysis unit analyzes educational data. The proposal unit proposes a support method based on the data analyzed by the analysis unit. The transmission unit periodically transmits the support method proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the educational data and provide appropriate support methods periodically. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An educational support system according to an embodiment of the present invention uses a generative AI to analyze past educational data and provide parents and children with support methods to help their children grow. In this educational support system, the generative AI first analyzes past educational data. During this process, it analyzes each piece of data in detail to identify the child's strengths and weaknesses. Next, based on the analysis results, the generative AI proposes specific support methods for the child's development. Furthermore, the generative AI periodically sends the proposals to the parent and child. For example, it may send a message every weekend with support methods based on that week's educational data. This allows parents and children to continuously support their children's development. This system allows parents to learn specific support methods for their children's grades and behaviors, which is expected to lead to more effective child development. Furthermore, effective use of educational data can improve the quality of education. For example, the educational support system analyzes past academic data and teacher comments to propose support methods that take into account the child's learning style and interests. Furthermore, the educational support system can estimate the child's emotions and adjust support methods based on those emotions. This allows for appropriate support based on the child's emotions. The educational support system can also send messages at optimal times, taking into account the parent's schedule and daily routine. This allows parents and children to communicate effectively while supporting their children's development. The educational support system provides specific support methods for children's grades and behavior, allowing parents and children to continuously support their children's development.

[0029] The education support system according to the embodiment includes an analysis unit, a suggestion unit, and a transmission unit. The analysis unit analyzes educational data. The educational data includes, but is not limited to, test results, learning history, and teacher evaluations. The analysis unit analyzes the educational data using, for example, statistical analysis or a machine learning algorithm. The analysis unit can also use a generation AI to perform a detailed analysis of past educational data to identify a child's strengths and weaknesses. For example, the generation AI analyzes past test results and teacher comments to identify a child's academic ability and behavioral trends. The suggestion unit suggests support methods based on the data analyzed by the analysis unit. Support methods include, but are not limited to, study plans, provision of teaching materials, and feedback methods. The suggestion unit uses, for example, the generation AI to suggest specific support methods based on the analysis results. For example, the generation AI can suggest individual instruction or group learning methods based on a child's strengths and weaknesses. The transmission unit periodically transmits the support methods proposed by the suggestion unit. For example, the transmission unit can transmit a support method based on that week's educational data as a message every weekend. The transmission unit can also use, for example, a generation AI to send messages to the parent and child at appropriate times. This allows the education support system according to the embodiment to effectively support the child's growth. Some or all of the above-described processing in the transmission unit can be performed, for example, using AI, or can be performed without using AI. For example, the transmission unit can use a generation AI to send messages to the parent and child at appropriate times. This allows the parent and child to receive continuous support.

[0030] The analysis unit can analyze past educational data and identify a child's strengths and weaknesses. The analysis unit can, for example, perform a detailed analysis of the past year's educational data to identify a child's strengths and weaknesses. For example, the generation AI can analyze past test results and teacher comments to identify a child's academic ability and behavioral trends. The analysis unit can also analyze the past five years' educational data to identify a child's growth trends. For example, the generation AI can compare past grade data to identify trends in improvement or decline in grades. This allows a detailed analysis of past educational data to identify a child's strengths and weaknesses and propose appropriate support methods. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can use the generation AI to perform a detailed analysis of past educational data to identify a child's strengths and weaknesses.

[0031] The suggestion unit can suggest specific support methods based on the strengths and weaknesses identified by the analysis unit. The suggestion unit, for example, uses a generation AI to suggest specific support methods based on the strengths and weaknesses identified by the analysis unit. For example, the generation AI can suggest a learning plan to make the most of a child's strengths. The suggestion unit can also use the generation AI to suggest support methods to overcome a child's weaknesses. For example, the generation AI can suggest teaching materials and feedback methods to compensate for a child's weaknesses. The suggestion unit can also use the generation AI to customize support methods based on a child's interests and learning style. For example, the generation AI can suggest visual teaching materials for a child with a visual learning style. This allows for effective support of a child's development by suggesting specific support methods based on the strengths and weaknesses. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can use the generation AI to suggest specific support methods based on the strengths and weaknesses identified by the analysis unit.

[0032] The transmitting unit can periodically transmit the proposed content to the parent and child. For example, the transmitting unit transmits a support method based on that week's educational data as a message every weekend. For example, the generating AI analyzes that week's educational data on the weekend, proposes an appropriate support method, and the transmitting unit transmits the proposed content to the parent and child. The transmitting unit can also transmit a support method based on that month's educational data as a message at the beginning of each month. For example, the generating AI analyzes that month's educational data at the beginning of the month, proposes an appropriate support method, and the transmitting unit transmits the proposed content to the parent and child. The transmitting unit can also transmit support methods in conjunction with specific events or occasions. For example, the generating AI proposes special support methods at the end of a semester or before an exam, and the transmitting unit transmits the proposed content to the parent and child. In this way, by periodically transmitting the proposed content, the parent and child can receive continuous support. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can use the generating AI to send messages to the parent and child at appropriate times.

[0033] The transmitting unit can periodically transmit support methods based on that week's educational data as a message. For example, the transmitting unit transmits support methods based on that week's educational data as a message every weekend. For example, the generating AI analyzes that week's educational data on the weekend, proposes appropriate support methods, and the transmitting unit transmits the contents to the parent and child. The transmitting unit can also transmit support methods based on that month's educational data as a message at the beginning of each month. For example, the generating AI analyzes that month's educational data at the beginning of the month, proposes appropriate support methods, and the transmitting unit transmits the contents to the parent and child. The transmitting unit can also transmit support methods in conjunction with specific events or occasions. For example, the generating AI proposes special support methods at the end of a semester or before an exam, and the transmitting unit transmits the contents to the parent and child. In this way, by transmitting support methods every weekend, the parent and child can receive regular support. Some or all of the above-described processing in the transmitting unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmitting unit can use the generating AI to send messages to the parent and child at appropriate times.

[0034] When analyzing educational data, the analysis unit can improve the accuracy of the analysis based on the child's learning style and interests. The analysis unit, for example, analyzes the educational data taking into account the child's learning style and interests. For example, the generative AI can prioritize analyzing visual data for a child with a visual learning style. The generative AI can also focus on analyzing auditory data for a child with an auditory learning style. Furthermore, the generative AI can prioritize analyzing data related to subjects that the child is interested in. For example, the generative AI analyzes related data to suggest educational materials and activities that will interest the child. This improves the accuracy of the analysis by taking into account the child's learning style and interests. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use the generative AI to analyze the educational data taking into account the child's learning style and interests.

[0035] When analyzing the educational data, the analysis unit can compare past educational data with current data to identify growth trends. The analysis unit, for example, compares past educational data with current data to identify growth trends. For example, the generation AI compares past grade data with current grade data to identify trends in improvement or decline in grades. The generation AI can also compare past behavioral data with current behavioral data to identify behavioral changes. Furthermore, the generation AI can compare past emotional data with current emotional data to identify emotional changes. In this way, growth trends can be identified by comparing past and current data. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can use the generation AI to compare past educational data with current data to identify growth trends.

[0036] When analyzing the educational data, the analysis unit can perform the analysis based on the child's home environment and lifestyle habits. The analysis unit, for example, analyzes the educational data taking into account the child's home environment and lifestyle habits. For example, the generation AI analyzes appropriate support methods based on the child's home environment. The generation AI can also analyze appropriate learning methods based on the child's lifestyle habits. Furthermore, the generation AI can analyze appropriate behavioral guidance based on the child's home environment and lifestyle habits. This makes it possible to obtain more appropriate analysis results by taking the home environment and lifestyle habits into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use the generation AI to analyze the educational data taking into account the child's home environment and lifestyle habits.

[0037] When analyzing the educational data, the analysis unit can perform the analysis based on the child's friendships and social connections. The analysis unit, for example, analyzes the educational data taking into account the child's friendships and social connections. For example, the generation AI analyzes an appropriate support method based on the child's friendships. The generation AI can also analyze an appropriate learning method based on the child's social connections. Furthermore, the generation AI can analyze appropriate behavioral guidance based on the child's friendships and social connections. In this way, more appropriate analysis results can be obtained by taking into account the friendships and social connections. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use the generation AI to analyze the educational data taking into account the child's friendships and social connections.

[0038] When making a suggestion, the suggestion unit can customize the support method based on the child's learning goals and future goals. The suggestion unit customizes the support method based on, for example, the child's learning goals and future goals. For example, the generation AI can suggest an appropriate learning method based on the child's learning goals. The generation AI can also suggest an appropriate career path based on the child's future goals. Furthermore, the generation AI can also suggest appropriate behavioral guidance based on the child's learning goals and future goals. In this way, by customizing the support method based on the learning goals and future goals, more appropriate support can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can customize the support method based on the child's learning goals and future goals using the generation AI.

[0039] When making a proposal, the suggestion unit can update the support method by reflecting the child's learning progress and achievements in real time. The suggestion unit, for example, updates the support method by reflecting the child's learning progress and achievements in real time. For example, the generation AI suggests an appropriate learning method in real time based on the child's learning progress. The generation AI can also update the appropriate support method in real time based on the child's learning achievements. Furthermore, the generation AI can also suggest appropriate behavioral guidance in real time based on the child's learning progress and achievements. This makes it possible to provide a more appropriate support method by reflecting the learning progress and achievements in real time. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can use the generation AI to update the support method by reflecting the child's learning progress and achievements in real time.

[0040] When making a proposal, the proposal unit can propose a support method based on the child's home environment and the parent's educational policy. The proposal unit proposes a support method, for example, taking into account the child's home environment and the parent's educational policy. For example, the generation AI proposes an appropriate support method based on the child's home environment. The generation AI can also propose an appropriate learning method based on the parent's educational policy. Furthermore, the generation AI can also propose appropriate behavioral guidance based on the child's home environment and the parent's educational policy. This makes it possible to provide a more appropriate support method by taking into account the home environment and the parent's educational policy. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can use the generation AI to propose a support method taking into account the child's home environment and the parent's educational policy.

[0041] When making a proposal, the suggestion unit can suggest a support method based on the child's activities and hobbies outside of school. The suggestion unit proposes a support method, for example, taking into account the child's activities and hobbies outside of school. For example, the generation AI can suggest an appropriate support method based on the child's activities outside of school. The generation AI can also suggest an appropriate learning method based on the child's hobbies. Furthermore, the generation AI can also suggest appropriate behavioral guidance based on the child's activities and hobbies outside of school. This makes it possible to provide a more appropriate support method by taking into account the child's activities and hobbies outside of school. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can use the generation AI to suggest a support method taking into account the child's activities and hobbies outside of school.

[0042] When sending a message, the sending unit can select an optimal sending time based on the parent's schedule and lifestyle. The sending unit selects the optimal sending time, for example, taking into account the parent's schedule and lifestyle. For example, the generation AI selects an appropriate sending time based on the parent's work hours and family schedule. The generation AI can also select an appropriate sending time based on the parent's wake-up time and bedtime. Furthermore, the generation AI can also select an appropriate sending time based on the parent's meal times and free time. This allows a more appropriate sending time to be selected by taking into account the parent's schedule and lifestyle. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can use the generation AI to select an optimal sending time taking into account the parent's schedule and lifestyle.

[0043] When sending a message, the sending unit can optimize the content of the message by referring to past sending history. The sending unit, for example, optimizes the content of the message by referring to past sending history. For example, the generation AI selects appropriate content to send based on past sending content and sending frequency. The generation AI can also select appropriate sending timing based on past sending results. Furthermore, the generation AI can also select an appropriate sending method based on past sending history. In this way, more appropriate content can be provided by referring to past sending history. Some or all of the above-described processing in the sending unit may be performed using AI, for example, or may be performed without using AI. For example, the sending unit can use the generation AI to optimize the content of the message by referring to past sending history.

[0044] When sending a message, the sending unit can select the optimal sending method based on the parent's device information. The sending unit selects the optimal sending method, for example, by taking into account the parent's device information. For example, the generation AI selects an appropriate sending method based on the type of device used by the parent. The generation AI can also select an appropriate sending time based on the parent's device usage status. Furthermore, the generation AI can also select appropriate sending content based on the parent's device settings. In this way, a more appropriate sending method can be selected by taking into account the device information. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can use the generation AI to select the optimal sending method by taking into account the parent's device information.

[0045] When sending a message, the sending unit can customize the content to be sent by referring to the parent-child communication history. The sending unit customizes the content to be sent by referring to, for example, the parent-child communication history. For example, the generation AI selects appropriate content to be sent based on the content of past messages and the frequency of exchanges. The generation AI can also select an appropriate timing for sending based on the parent-child's reaction. Furthermore, the generation AI can also select an appropriate sending method based on the parent-child communication history. In this way, more appropriate content can be provided by referring to the communication history. Some or all of the above-described processing in the sending unit may be performed, for example, using AI, or may be performed without using AI. For example, the sending unit can customize the content to be sent by using the generation AI to refer to the parent-child communication history.

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

[0047] The analysis unit can monitor a child's learning environment in real time and adjust the analysis method according to changes in the environment. For example, if a child's learning environment is noisy, the analysis unit will prioritize analyzing data to improve concentration. On the other hand, if the learning environment is quiet, the analysis unit can also analyze data to promote deeper understanding. Furthermore, the analysis unit can select an appropriate analysis method according to changes in the temperature and lighting of the learning environment. This allows for more effective support by performing analysis according to the learning environment.

[0048] The suggestion unit can predict a child's future learning plan based on their learning history and suggest long-term support methods. For example, the suggestion unit can analyze past learning data and predict the learning content required for the next semester. The suggestion unit can also suggest an appropriate learning plan based on the child's future educational destination and career. Furthermore, the suggestion unit can set long-term goals according to the child's learning pace and suggest support methods based on those goals. This makes it possible to support a child's growth from a long-term perspective.

[0049] The sending unit can collect feedback from the parent and child and improve the suggestions based on the feedback. For example, the sending unit can periodically collect feedback from the parent and child and evaluate the effectiveness of the suggestions. The sending unit can also customize the suggestions based on the feedback. Furthermore, the sending unit can analyze the feedback and identify areas for improvement in the suggestions. This makes it possible to provide more effective support by utilizing the feedback from the parent and child.

[0050] The analysis unit can analyze the child's health data and adjust the learning support method based on the child's health condition. For example, the analysis unit can analyze the child's sleep data and suggest lighter learning content if the child is sleep deprived. The analysis unit can also analyze the child's exercise data and suggest learning methods that incorporate exercise if the child is not getting enough exercise. Furthermore, the analysis unit can analyze the child's dietary data and provide advice on improving the diet if the nutritional balance is unbalanced. This makes it possible to provide learning support according to the child's health condition.

[0051] The suggestion unit can monitor a child's learning progress in real time and adjust support methods according to the progress. For example, if a child's learning progress is falling behind, the suggestion unit can suggest supplementary lessons or additional learning materials. In addition, if a child's learning progress is going well, the suggestion unit can also suggest learning content to advance to the next step. Furthermore, the suggestion unit can adjust the learning method or the difficulty level of learning materials according to the learning progress. This makes it possible to grasp the child's learning progress in real time and provide appropriate support.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The analysis unit analyzes the educational data. This includes test results, learning history, teacher evaluations, and more. The analysis unit uses statistical analysis and machine learning algorithms to analyze the educational data, and uses the generative AI to perform detailed analysis of past educational data and identify a child's strengths and weaknesses. For example, the generative AI can analyze past test results and teacher comments to identify a child's academic ability and behavioral trends. Step 2: The suggestion unit proposes support methods based on the data analyzed by the analysis unit. Support methods include learning plans, provision of teaching materials, and feedback methods. The suggestion unit uses the generation AI to propose specific support methods based on the analysis results. For example, the generation AI can propose individual instruction or group learning methods based on a child's strengths and weaknesses. Step 3: The sending unit periodically sends the support methods proposed by the suggestion unit. The sending unit can send support methods based on that week's educational data as a message every weekend. The sending unit can also use the generation AI to send messages to the parent and child at appropriate times. This allows the parent and child to receive continuous support.

[0054] (Example 2) An educational support system according to an embodiment of the present invention uses a generative AI to analyze past educational data and provide parents and children with support methods to help their children grow. In this educational support system, the generative AI first analyzes past educational data. During this process, it analyzes each piece of data in detail to identify the child's strengths and weaknesses. Next, based on the analysis results, the generative AI proposes specific support methods for the child's development. Furthermore, the generative AI periodically sends the proposals to the parent and child. For example, it may send a message every weekend with support methods based on that week's educational data. This allows parents and children to continuously support their children's development. This system allows parents to learn specific support methods for their children's grades and behaviors, which is expected to lead to more effective child development. Furthermore, effective use of educational data can improve the quality of education. For example, the educational support system analyzes past academic data and teacher comments to propose support methods that take into account the child's learning style and interests. Furthermore, the educational support system can estimate the child's emotions and adjust support methods based on those emotions. This allows for appropriate support based on the child's emotions. The educational support system can also send messages at optimal times, taking into account the parent's schedule and daily routine. This allows parents and children to communicate effectively while supporting their children's development. The educational support system provides specific support methods for children's grades and behavior, allowing parents and children to continuously support their children's development.

[0055] The education support system according to the embodiment includes an analysis unit, a suggestion unit, and a transmission unit. The analysis unit analyzes educational data. The educational data includes, but is not limited to, test results, learning history, and teacher evaluations. The analysis unit analyzes the educational data using, for example, statistical analysis or a machine learning algorithm. The analysis unit can also use a generation AI to perform a detailed analysis of past educational data to identify a child's strengths and weaknesses. For example, the generation AI analyzes past test results and teacher comments to identify a child's academic ability and behavioral trends. The suggestion unit suggests support methods based on the data analyzed by the analysis unit. Support methods include, but are not limited to, study plans, provision of teaching materials, and feedback methods. The suggestion unit uses, for example, the generation AI to suggest specific support methods based on the analysis results. For example, the generation AI can suggest individual instruction or group learning methods based on a child's strengths and weaknesses. The transmission unit periodically transmits the support methods proposed by the suggestion unit. For example, the transmission unit can transmit a support method based on that week's educational data as a message every weekend. The transmission unit can also use, for example, a generation AI to send messages to the parent and child at appropriate times. This allows the education support system according to the embodiment to effectively support the child's growth. Some or all of the above-described processing in the transmission unit can be performed, for example, using AI, or can be performed without using AI. For example, the transmission unit can use a generation AI to send messages to the parent and child at appropriate times. This allows the parent and child to receive continuous support.

[0056] The analysis unit can analyze past educational data and identify a child's strengths and weaknesses. The analysis unit can, for example, perform a detailed analysis of the past year's educational data to identify a child's strengths and weaknesses. For example, the generation AI can analyze past test results and teacher comments to identify a child's academic ability and behavioral trends. The analysis unit can also analyze the past five years' educational data to identify a child's growth trends. For example, the generation AI can compare past grade data to identify trends in improvement or decline in grades. This allows a detailed analysis of past educational data to identify a child's strengths and weaknesses and propose appropriate support methods. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can use the generation AI to perform a detailed analysis of past educational data to identify a child's strengths and weaknesses.

[0057] The suggestion unit can suggest specific support methods based on the strengths and weaknesses identified by the analysis unit. The suggestion unit, for example, uses a generation AI to suggest specific support methods based on the strengths and weaknesses identified by the analysis unit. For example, the generation AI can suggest a learning plan to make the most of a child's strengths. The suggestion unit can also use the generation AI to suggest support methods to overcome a child's weaknesses. For example, the generation AI can suggest teaching materials and feedback methods to compensate for a child's weaknesses. The suggestion unit can also use the generation AI to customize support methods based on a child's interests and learning style. For example, the generation AI can suggest visual teaching materials for a child with a visual learning style. This allows for effective support of a child's development by suggesting specific support methods based on the strengths and weaknesses. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can use the generation AI to suggest specific support methods based on the strengths and weaknesses identified by the analysis unit.

[0058] The transmitting unit can periodically transmit the proposed content to the parent and child. For example, the transmitting unit transmits a support method based on that week's educational data as a message every weekend. For example, the generating AI analyzes that week's educational data on the weekend, proposes an appropriate support method, and the transmitting unit transmits the proposed content to the parent and child. The transmitting unit can also transmit a support method based on that month's educational data as a message at the beginning of each month. For example, the generating AI analyzes that month's educational data at the beginning of the month, proposes an appropriate support method, and the transmitting unit transmits the proposed content to the parent and child. The transmitting unit can also transmit support methods in conjunction with specific events or occasions. For example, the generating AI proposes special support methods at the end of a semester or before an exam, and the transmitting unit transmits the proposed content to the parent and child. In this way, by periodically transmitting the proposed content, the parent and child can receive continuous support. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can use the generating AI to send messages to the parent and child at appropriate times.

[0059] The transmitting unit can periodically transmit support methods based on that week's educational data as a message. For example, the transmitting unit transmits support methods based on that week's educational data as a message every weekend. For example, the generating AI analyzes that week's educational data on the weekend, proposes appropriate support methods, and the transmitting unit transmits the contents to the parent and child. The transmitting unit can also transmit support methods based on that month's educational data as a message at the beginning of each month. For example, the generating AI analyzes that month's educational data at the beginning of the month, proposes appropriate support methods, and the transmitting unit transmits the contents to the parent and child. The transmitting unit can also transmit support methods in conjunction with specific events or occasions. For example, the generating AI proposes special support methods at the end of a semester or before an exam, and the transmitting unit transmits the contents to the parent and child. In this way, by transmitting support methods every weekend, the parent and child can receive regular support. Some or all of the above-described processing in the transmitting unit may be performed, for example, using AI, or may be performed without using AI. For example, the transmitting unit can use the generating AI to send messages to the parent and child at appropriate times.

[0060] The analysis unit can estimate a child's emotions and adjust the analysis method of the educational data based on the estimated child's emotions. The analysis unit, for example, estimates a child's emotions and adjusts the analysis method of the educational data based on the estimated child's emotions. For example, the generation AI can estimate emotions using facial expression recognition or voice analysis of the child, and prioritize analysis of data useful for stress reduction when the child is feeling stressed. The generation AI can also focus on analyzing data to suppress excitement when the child is excited. Furthermore, the generation AI can analyze data to maintain relaxation when the child is relaxed. This allows for more appropriate analysis results to be obtained by adjusting the analysis method based on the child's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can estimate a child's emotions using a generation AI and adjust the analysis method of the educational data based on the estimated child's emotions.

[0061] When analyzing educational data, the analysis unit can improve the accuracy of the analysis based on the child's learning style and interests. The analysis unit, for example, analyzes the educational data taking into account the child's learning style and interests. For example, the generative AI can prioritize analyzing visual data for a child with a visual learning style. The generative AI can also focus on analyzing auditory data for a child with an auditory learning style. Furthermore, the generative AI can prioritize analyzing data related to subjects that the child is interested in. For example, the generative AI analyzes related data to suggest educational materials and activities that will interest the child. This improves the accuracy of the analysis by taking into account the child's learning style and interests. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use the generative AI to analyze the educational data taking into account the child's learning style and interests.

[0062] When analyzing the educational data, the analysis unit can compare past educational data with current data to identify growth trends. The analysis unit, for example, compares past educational data with current data to identify growth trends. For example, the generation AI compares past grade data with current grade data to identify trends in improvement or decline in grades. The generation AI can also compare past behavioral data with current behavioral data to identify behavioral changes. Furthermore, the generation AI can compare past emotional data with current emotional data to identify emotional changes. In this way, growth trends can be identified by comparing past and current data. Some or all of the above-described processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can use the generation AI to compare past educational data with current data to identify growth trends.

[0063] The analysis unit can estimate the child's emotions and prioritize the analysis results based on the estimated child's emotions. For example, the analysis unit can estimate the child's emotions and prioritize the analysis results based on the estimated child's emotions. For example, the generation AI can estimate the child's emotions using facial expression recognition or voice analysis, and prioritize analysis results that help reduce stress when the child is feeling stressed. The generation AI can also prioritize analysis results to help reduce excitement when the child is excited. The generation AI can also prioritize analysis results to help maintain relaxation when the child is relaxed. This allows for more appropriate analysis results to be provided by prioritizing based on emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can estimate the child's emotions using a generation AI and prioritize analysis results based on the estimated child's emotions.

[0064] When analyzing the educational data, the analysis unit can perform the analysis based on the child's home environment and lifestyle habits. The analysis unit, for example, analyzes the educational data taking into account the child's home environment and lifestyle habits. For example, the generation AI analyzes appropriate support methods based on the child's home environment. The generation AI can also analyze appropriate learning methods based on the child's lifestyle habits. Furthermore, the generation AI can analyze appropriate behavioral guidance based on the child's home environment and lifestyle habits. This makes it possible to obtain more appropriate analysis results by taking the home environment and lifestyle habits into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use the generation AI to analyze the educational data taking into account the child's home environment and lifestyle habits.

[0065] When analyzing the educational data, the analysis unit can perform the analysis based on the child's friendships and social connections. The analysis unit, for example, analyzes the educational data taking into account the child's friendships and social connections. For example, the generation AI analyzes an appropriate support method based on the child's friendships. The generation AI can also analyze an appropriate learning method based on the child's social connections. Furthermore, the generation AI can analyze appropriate behavioral guidance based on the child's friendships and social connections. In this way, more appropriate analysis results can be obtained by taking into account the friendships and social connections. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use the generation AI to analyze the educational data taking into account the child's friendships and social connections.

[0066] The suggestion unit can estimate the child's emotions and adjust the suggested support methods based on the estimated child's emotions. The suggestion unit, for example, estimates the child's emotions and adjusts the suggested support methods based on the estimated child's emotions. For example, the generation AI can estimate the child's emotions using facial expression recognition or voice analysis, and if the child is feeling stressed, suggest a support method to reduce stress. The generation AI can also suggest a support method to reduce the child's excitement if the child is excited. Furthermore, the generation AI can also suggest a support method to maintain relaxation if the child is relaxed. By adjusting the suggested support methods based on the emotions, more appropriate support methods can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can estimate the child's emotions using a generation AI, and adjust the suggested support methods based on the estimated child's emotions.

[0067] When making a suggestion, the suggestion unit can customize the support method based on the child's learning goals and future goals. The suggestion unit customizes the support method based on, for example, the child's learning goals and future goals. For example, the generation AI can suggest an appropriate learning method based on the child's learning goals. The generation AI can also suggest an appropriate career path based on the child's future goals. Furthermore, the generation AI can also suggest appropriate behavioral guidance based on the child's learning goals and future goals. In this way, by customizing the support method based on the learning goals and future goals, more appropriate support can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can customize the support method based on the child's learning goals and future goals using the generation AI.

[0068] When making a proposal, the suggestion unit can update the support method by reflecting the child's learning progress and achievements in real time. The suggestion unit, for example, updates the support method by reflecting the child's learning progress and achievements in real time. For example, the generation AI suggests an appropriate learning method in real time based on the child's learning progress. The generation AI can also update the appropriate support method in real time based on the child's learning achievements. Furthermore, the generation AI can also suggest appropriate behavioral guidance in real time based on the child's learning progress and achievements. This makes it possible to provide a more appropriate support method by reflecting the learning progress and achievements in real time. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can use the generation AI to update the support method by reflecting the child's learning progress and achievements in real time.

[0069] The suggestion unit can estimate the child's emotions and prioritize the suggestions based on the estimated emotions. The suggestion unit, for example, estimates the child's emotions and prioritizes the suggestions based on the estimated emotions. For example, the generation AI can estimate the child's emotions using facial expression recognition or voice analysis, and if the child is feeling stressed, prioritize providing suggestions for reducing stress. Furthermore, if the child is excited, the generation AI can prioritize providing suggestions for reducing excitement. Furthermore, if the child is relaxed, the generation AI can prioritize providing suggestions for maintaining relaxation. In this way, by determining the priorities based on emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can estimate the child's emotions using a generation AI and prioritize the suggestions based on the estimated emotions.

[0070] When making a proposal, the proposal unit can propose a support method based on the child's home environment and the parent's educational policy. The proposal unit proposes a support method, for example, taking into account the child's home environment and the parent's educational policy. For example, the generation AI proposes an appropriate support method based on the child's home environment. The generation AI can also propose an appropriate learning method based on the parent's educational policy. Furthermore, the generation AI can also propose appropriate behavioral guidance based on the child's home environment and the parent's educational policy. This makes it possible to provide a more appropriate support method by taking into account the home environment and the parent's educational policy. Some or all of the above-mentioned processing in the proposal unit may be performed, for example, using AI, or may be performed without using AI. For example, the proposal unit can use the generation AI to propose a support method taking into account the child's home environment and the parent's educational policy.

[0071] When making a proposal, the suggestion unit can suggest a support method based on the child's activities and hobbies outside of school. The suggestion unit proposes a support method, for example, taking into account the child's activities and hobbies outside of school. For example, the generation AI can suggest an appropriate support method based on the child's activities outside of school. The generation AI can also suggest an appropriate learning method based on the child's hobbies. Furthermore, the generation AI can also suggest appropriate behavioral guidance based on the child's activities and hobbies outside of school. This makes it possible to provide a more appropriate support method by taking into account the child's activities and hobbies outside of school. Some or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can use the generation AI to suggest a support method taking into account the child's activities and hobbies outside of school.

[0072] The transmission unit can estimate the child's emotions and adjust the timing of message transmission based on the estimated child's emotions. The transmission unit, for example, estimates the child's emotions and adjusts the timing of message transmission based on the estimated child's emotions. For example, the generation AI estimates the child's emotions using facial expression recognition or voice analysis, and if the child is feeling stressed, sends a message to reduce stress at an appropriate time. The generation AI can also send a message to calm the child at an appropriate time if the child is excited. Furthermore, the generation AI can also send a message to maintain relaxation at an appropriate time if the child is relaxed. This allows messages to be sent at more appropriate times by adjusting the transmission timing based on emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can estimate the child's emotions using the generation AI and adjust the timing of message transmission based on the estimated child's emotions.

[0073] When sending a message, the sending unit can select an optimal sending time based on the parent's schedule and lifestyle. The sending unit selects the optimal sending time, for example, taking into account the parent's schedule and lifestyle. For example, the generation AI selects an appropriate sending time based on the parent's work hours and family schedule. The generation AI can also select an appropriate sending time based on the parent's wake-up time and bedtime. Furthermore, the generation AI can also select an appropriate sending time based on the parent's meal times and free time. This allows a more appropriate sending time to be selected by taking into account the parent's schedule and lifestyle. Some or all of the above-described processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can use the generation AI to select an optimal sending time taking into account the parent's schedule and lifestyle.

[0074] When sending a message, the sending unit can optimize the content of the message by referring to past sending history. The sending unit, for example, optimizes the content of the message by referring to past sending history. For example, the generation AI selects appropriate content to send based on past sending content and sending frequency. The generation AI can also select appropriate sending timing based on past sending results. Furthermore, the generation AI can also select an appropriate sending method based on past sending history. In this way, more appropriate content can be provided by referring to past sending history. Some or all of the above-described processing in the sending unit may be performed using AI, for example, or may be performed without using AI. For example, the sending unit can use the generation AI to optimize the content of the message by referring to past sending history.

[0075] When sending a message, the sending unit can select the optimal sending method based on the parent's device information. The sending unit selects the optimal sending method, for example, by taking into account the parent's device information. For example, the generation AI selects an appropriate sending method based on the type of device used by the parent. The generation AI can also select an appropriate sending time based on the parent's device usage status. Furthermore, the generation AI can also select appropriate sending content based on the parent's device settings. In this way, a more appropriate sending method can be selected by taking into account the device information. Some or all of the above-mentioned processing in the sending unit may be performed using, for example, AI, or may be performed without using AI. For example, the sending unit can use the generation AI to select the optimal sending method by taking into account the parent's device information.

[0076] When sending a message, the sending unit can customize the content to be sent by referring to the parent-child communication history. The sending unit customizes the content to be sent by referring to, for example, the parent-child communication history. For example, the generation AI selects appropriate content to be sent based on the content of past messages and the frequency of exchanges. The generation AI can also select an appropriate timing for sending based on the parent-child's reaction. Furthermore, the generation AI can also select an appropriate sending method based on the parent-child communication history. In this way, more appropriate content can be provided by referring to the communication history. Some or all of the above-described processing in the sending unit may be performed, for example, using AI, or may be performed without using AI. For example, the sending unit can customize the content to be sent by using the generation AI to refer to the parent-child communication history. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, and transmission unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the educational data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a support method based on the analysis results. The transmission unit is realized, for example, by the control unit 46A of the smart device 14 and transmits the suggested support method to the parent and child. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, and transmission unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the educational data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a support method based on the analysis results. The transmission unit is realized, for example, by the control unit 46A of the smart glasses 214 and transmits the suggested support method to the parent and child. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, and transmission unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the training data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a support method based on the analysis results. The transmission unit is realized, for example, by the control unit 46A of the headset type terminal 314 and transmits the suggested support method to the parent and child. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, suggestion unit, and transmission unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the training data. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests a support method based on the analysis results. The transmission unit is realized, for example, by the control unit 46A of the robot 414 and transmits the suggested support method to the parent and child.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The analysis unit can monitor a child's learning environment in real time and adjust the analysis method according to changes in the environment. For example, if a child's learning environment is noisy, the analysis unit will prioritize analyzing data to improve concentration. On the other hand, if the learning environment is quiet, the analysis unit can also analyze data to promote deeper understanding. Furthermore, the analysis unit can select an appropriate analysis method according to changes in the temperature and lighting of the learning environment. This allows for more effective support by performing analysis according to the learning environment.

[0079] The suggestion unit can predict a child's future learning plan based on their learning history and suggest long-term support methods. For example, the suggestion unit can analyze past learning data and predict the learning content required for the next semester. The suggestion unit can also suggest an appropriate learning plan based on the child's future educational destination and career. Furthermore, the suggestion unit can set long-term goals according to the child's learning pace and suggest support methods based on those goals. This makes it possible to support a child's growth from a long-term perspective.

[0080] The sending unit can collect feedback from the parent and child and improve the suggestions based on the feedback. For example, the sending unit can periodically collect feedback from the parent and child and evaluate the effectiveness of the suggestions. The sending unit can also customize the suggestions based on the feedback. Furthermore, the sending unit can analyze the feedback and identify areas for improvement in the suggestions. This makes it possible to provide more effective support by utilizing the feedback from the parent and child.

[0081] The analysis unit can analyze the child's health data and adjust the learning support method based on the child's health condition. For example, the analysis unit can analyze the child's sleep data and suggest lighter learning content if the child is sleep deprived. The analysis unit can also analyze the child's exercise data and suggest learning methods that incorporate exercise if the child is not getting enough exercise. Furthermore, the analysis unit can analyze the child's dietary data and provide advice on improving the diet if the nutritional balance is unbalanced. This makes it possible to provide learning support according to the child's health condition.

[0082] The suggestion unit can monitor a child's learning progress in real time and adjust support methods according to the progress. For example, if a child's learning progress is falling behind, the suggestion unit can suggest supplementary lessons or additional learning materials. In addition, if a child's learning progress is going well, the suggestion unit can also suggest learning content to advance to the next step. Furthermore, the suggestion unit can adjust the learning method or the difficulty level of learning materials according to the learning progress. This makes it possible to grasp the child's learning progress in real time and provide appropriate support.

[0083] The analysis unit can estimate the child's emotions and adjust the learning environment based on the estimated emotions. For example, if the child is feeling stressed, the analysis unit can suggest an environment where the child can relax. Also, if the child is concentrating, the analysis unit can suggest an environment that helps the child maintain concentration. Furthermore, if the child is tired, the analysis unit can suggest an environment that encourages the child to take a break. In this way, by providing a learning environment that corresponds to the child's emotions, it is possible to improve the child's learning effectiveness.

[0084] The suggestion unit can estimate the child's emotions and suggest a method to improve the child's motivation to learn based on the estimated emotions. For example, if the child is unmotivated, the suggestion unit can suggest an activity to increase the child's motivation. If the child is excited, the suggestion unit can also suggest a relaxation method to reduce the child's excitement. Furthermore, if the child is relaxed, the suggestion unit can also suggest a study method to maintain the child's relaxed state. In this way, it is possible to improve the child's motivation to learn based on emotions.

[0085] The transmitting unit can estimate the child's emotions and adjust the content of the message based on the estimated emotions. For example, if the child is feeling stressed, the transmitting unit can send an encouraging message. If the child is excited, the transmitting unit can also send a message to calm the child. Furthermore, if the child is relaxed, the transmitting unit can also send a message to maintain that state. In this way, by adjusting the content of the message based on the child's emotions, more effective communication can be achieved.

[0086] The suggestion unit can estimate the child's emotions and set learning goals based on the estimated emotions. For example, if the child feels motivated, the suggestion unit can set challenging learning goals. If the child feels anxious, the suggestion unit can also set learning goals that are easy to achieve. Furthermore, if the child feels relaxed, the suggestion unit can also set learning goals to help the child maintain that relaxed state. In this way, by setting learning goals based on emotions, it is possible to increase the child's motivation to learn.

[0087] The sending unit can estimate the child's emotions and adjust the frequency of message sending based on the estimated emotions. For example, if the child is feeling stressed, the sending unit can frequently send encouraging messages. If the child is excited, the sending unit can also send calming messages at an appropriate frequency. Furthermore, if the child is relaxed, the sending unit can also send messages to maintain that state at an appropriate frequency. In this way, more effective support can be provided by adjusting the frequency of message sending based on the child's emotions.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The analysis unit analyzes the educational data. This includes test results, learning history, teacher evaluations, and more. The analysis unit uses statistical analysis and machine learning algorithms to analyze the educational data, and uses the generative AI to perform detailed analysis of past educational data and identify a child's strengths and weaknesses. For example, the generative AI can analyze past test results and teacher comments to identify a child's academic ability and behavioral trends. Step 2: The suggestion unit proposes support methods based on the data analyzed by the analysis unit. Support methods include learning plans, provision of teaching materials, and feedback methods. The suggestion unit uses the generation AI to propose specific support methods based on the analysis results. For example, the generation AI can propose individual instruction or group learning methods based on a child's strengths and weaknesses. Step 3: The sending unit periodically sends the support methods proposed by the suggestion unit. The sending unit can send support methods based on that week's educational data as a message every weekend. The sending unit can also use the generation AI to send messages to the parent and child at appropriate times. This allows the parent and child to receive continuous support.

[0090] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0092] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0093] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0104] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0105] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0108] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0161] [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the educational data; a suggestion unit that proposes a support method based on the data analyzed by the analysis unit; a transmitting unit that periodically transmits the support method proposed by the proposing unit; Equipped with A system characterized by:

2. The analysis unit Analyze past educational data to identify your child's strengths and weaknesses The system of claim 1 .

3. The proposal unit Proposing specific support methods based on the strengths and weaknesses identified by the analysis unit The system of claim 1 .

4. The transmission unit Send suggestions to parents and children periodically The system of claim 1 .

5. The transmission unit Periodically send messages with support methods based on that week's training data The system of claim 1 .

6. The analysis unit Estimate children's emotions and adjust the analysis method of educational data based on the estimated emotions. The system of claim 1 .

7. The analysis unit Improve the accuracy of educational data analysis based on children's learning styles and interests The system of claim 1 .

8. The analysis unit When analyzing education data, compare past education data with current data to identify growth trends The system of claim 1 .

Images