System

Abstract

An object of a system according to an embodiment is to provide content according to the interest or concentration of a child and perform education according to the intention of a guardian.SOLUTION: A system according to an embodiment includes an input unit, a monitoring unit, and a providing unit. The input unit allows a guardian to input information of a child. The monitoring unit monitors the state of the child based on the information input by the input unit. The providing unit provides the content based on the information monitored by the monitoring unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to provide content that matched children's interests and concentration levels, and there was a problem that education was not being provided in line with parents' wishes.

[0005] The system according to the embodiment aims to provide content that matches the interests and concentration levels of children and to provide education that is in line with the wishes of their parents. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a monitoring unit, and a providing unit. The input unit allows a parent or guardian to input information about the child. The monitoring unit monitors the child's condition based on the information input by the input unit. The providing unit provides content based on the information monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment provides content that matches the child's interests and level of concentration, and can provide education that meets the wishes of the parents. [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) In an embodiment of the present invention, a system allows parents to set their child's information, desired content, and time in advance, analyze the child's concentration level and interests using a camera, and provide content tailored to the parent's preferences and the child's interests using AI. The system allows parents to set their child's information, desired content, and time in advance, analyze the child's concentration level and interests using a camera, and provide content tailored to the parent's preferences and the child's interests using AI. For example, if a child becomes bored with learning, the system can provide playful content. The system also monetizes by providing learning results to parents in the form of reports and suggesting learning materials and services tailored to the child's characteristics and the parent's needs. For example, parents set their child's information, desired content, and time in advance. They input information such as the child's age, interests, and learning goals. For example, a parent may set, "I want to provide 30 minutes of English learning content to my 5-year-old child." This information is then input into AI. The camera then monitors the child's facial expressions and behavior in real time, and the AI ​​analyzes them. For example, it can determine whether the child is focused or interested. This allows the child's condition to be understood. AI provides content that matches the parents' wishes and the children's interests. For example, if a child becomes bored with studying, it can provide playful content. This helps maintain the child's interest. The system also provides parents with reports on the results of their studies. For example, it can provide information such as what content the child was interested in and how focused they were. The system also aims to generate revenue by suggesting learning materials and services that match the child's characteristics and the parents' needs. For example, if a child is interested in a particular subject, it can suggest learning materials and services related to that subject. This allows parents to select appropriate learning materials and services to support their child's learning. This allows the system to allow parents to input their child's information, monitor the child's condition, and provide appropriate content. For example, it can maintain a child's interest even if they become bored with studying.In addition, parents will be able to understand their child's learning situation, and by providing teaching materials and services that suit the child's characteristics and the needs of the parents, learning effectiveness can be improved.

[0029] The education support system according to the embodiment includes an input unit, a monitoring unit, and a providing unit. A parent inputs information about a child into the input unit. The information input by the parent includes, but is not limited to, the child's age, interests, and learning goals. For example, the parent inputs the child's age into the input unit. The parent can also input the child's interests into the input unit. The parent can also input the child's learning goals into the input unit. The monitoring unit monitors the child's condition based on the information input by the input unit. The monitoring unit analyzes the child's concentration and interest using, for example, a camera. The monitoring unit can also monitor the child's facial expressions and movements in real time and analyze them using AI. The monitoring unit can also determine whether the child is concentrating or interested. The providing unit provides content based on the information monitored by the monitoring unit. For example, if the child becomes bored with learning, the providing unit provides play content. The providing unit can also provide the parent with a report on the learning results. The providing unit can also suggest teaching materials and services that match the child's characteristics and the parent's needs. As a result, the education support system according to the embodiment allows parents to input information about their children, monitors their children's status, and provides appropriate content. For example, even if a child becomes bored with learning, the system can maintain the child's interest. Furthermore, parents can grasp the child's learning situation, and the system can provide learning materials and services that match the child's characteristics and the parents' needs, thereby improving the learning effect.

[0030] The providing unit can provide play content when a child loses interest in learning. For example, the providing unit provides play content when a child is unresponsive for a certain period of time. The providing unit can also provide play content when it is determined that a child has lost interest in learning based on the results of facial expression analysis. The providing unit can also provide an interactive story when a child becomes bored with learning. This makes it possible to maintain a child's interest even when the child becomes bored with learning.

[0031] The providing unit can provide the learning results to the guardian as a report. For example, the providing unit can provide the child's test scores to the guardian as a report. The providing unit can also provide the child's study time to the guardian as a report. The providing unit can also provide the child's achievement level to the guardian as a report. The report is provided in, for example, PDF format. The report may also include graphs and charts. This allows the guardian to understand the child's learning situation.

[0032] The provision unit can propose teaching materials and services that match the characteristics of the child and the needs of the parent. For example, the provision unit can propose teaching materials that match the child's learning style. The provision unit can also propose teaching materials related to the child's areas of interest. The provision unit can also propose teaching materials that match the child's learning speed. The provision unit can also propose services that match the parent's learning goals. For example, the provision unit can propose individual tutoring services to parents who are aiming to acquire a specific skill. This makes it possible to improve learning effectiveness by providing teaching materials and services that match the child's characteristics and the parent's needs.

[0033] The monitoring unit can analyze the concentration level and interest of the child using a camera. For example, the monitoring unit can use a camera to track the child's line of sight and analyze the concentration level. The monitoring unit can also use a camera to analyze the child's facial expressions and analyze the interest. The monitoring unit can also use a camera to analyze the child's behavior patterns and analyze the concentration level and interest. In this way, the use of a camera makes it possible to accurately analyze the concentration level and interest of the child.

[0034] The input unit allows a parent to input information about the child's age, interests, learning goals, and interests. For example, the input unit allows a parent to input the child's age. The input unit also allows a parent to input the child's interests. The input unit also allows a parent to input the child's learning goals. By allowing a parent to input detailed information about a child, more appropriate content can be provided.

[0035] The input unit can provide input assistance by referring to past input history to improve the accuracy of information entered by the guardian. For example, the input unit can automatically display information about the child that the guardian has previously entered as a candidate. The input unit can also preferentially suggest input methods (voice, text, etc.) that the guardian has previously used. The input unit can also predict and suggest information that will be entered during a specific time period based on the guardian's past input history. In this way, the accuracy of input can be improved by referring to past input history.

[0036] The input unit can increase the types of information that parents can input, for example, to allow parents to input their child's health condition and daily activities. For example, the input unit adds a field where parents can input their child's health condition (body temperature, diet, etc.). The input unit can also provide an option where parents can input their child's daily activities (play, study time, etc.). The input unit can also allow parents to input their child's specific events (school events, sports activities, etc.). This allows parents to input their child's health condition and daily activities, making it possible to provide more detailed information.

[0037] The input unit can accept voice input or image input of information entered by the guardian, thereby diversifying the input method. For example, the input unit can allow the guardian to enter information about the child by voice. The input unit can also allow the guardian to upload images to record the child's health condition and activity details. The input unit can also allow the guardian to combine voice input and image input to enter more detailed information. In this way, by accepting voice input and image input, the input method can be diversified.

[0038] The input unit can add a function that allows parents to share information they input with other parents, thereby forming a community. For example, the input unit provides a function that allows parents to share information about their children that they input with other parents. The input unit can also provide a forum where parents can exchange opinions with other parents based on the information they input. The input unit can also provide a function that allows parents to plan events together with other parents based on the information they input. This allows parents to share information with each other, forming a community and exchanging information.

[0039] The input unit can add a function to share information entered by parents with the child's school or educational institution. For example, the input unit provides a function that allows information about the child entered by the parent to be shared with the child's teachers at school. The input unit can also provide a function that allows the child to participate in school events and activities based on the information entered by the parent. The input unit can also provide a function that creates a learning plan for the child in cooperation with educational institutions based on the information entered by the parent. This makes it possible to support the child's learning by sharing information with schools and educational institutions.

[0040] The input unit can be added with a function to automatically complete information entered by a parent or guardian by comparing it with the child's past learning data. For example, the input unit automatically completes information entered by a parent or guardian by comparing it with the child's past learning data. The input unit can also automatically update the child's learning progress based on the information entered by the parent or guardian. The input unit can also automatically set the child's learning goals based on the information entered by the parent or guardian. This makes it possible to automatically complete the input information by comparing it with the child's past learning data.

[0041] During monitoring, the monitoring unit can analyze not only the child's facial expressions but also their voice and actions to more accurately determine the child's level of concentration and interest. For example, the monitoring unit can analyze the child's facial expressions to determine the child's level of concentration and interest. The monitoring unit can also analyze the child's voice to determine the child's level of concentration and interest. The monitoring unit can also analyze the child's actions to determine the child's level of concentration and interest. Thus, by analyzing the facial expressions, voice, and actions, the child's level of concentration and interest can be determined more accurately.

[0042] During monitoring, the monitoring unit can refer to data on the child's past concentration and interests to predict the child's current state. For example, the monitoring unit can refer to the child's past concentration data to predict the child's current concentration. The monitoring unit can also refer to the child's past interest data to predict the child's current interest. The monitoring unit can also refer to the child's past learning data to predict the child's current learning state. By referring to past data, the current state can be predicted more accurately.

[0043] The monitoring unit can analyze the child's concentration level and interest based on the child's environment during monitoring. For example, the monitoring unit can analyze the child's concentration level taking into account the brightness of the room. The monitoring unit can also analyze the child's concentration level taking into account the temperature of the room. The monitoring unit can also analyze the child's concentration level taking into account the noise level in the room. In this way, by taking the environment into account, the concentration level and interest can be analyzed more accurately.

[0044] During monitoring, the monitoring unit can analyze the level of concentration and interests taking into account the influence of the child's friends and siblings. For example, the monitoring unit analyzes the level of concentration taking into account the presence of the child's friends. The monitoring unit can also analyze the level of concentration taking into account the presence of the child's siblings. The monitoring unit can also analyze the interests taking into account the child's relationships with friends and siblings. In this way, by taking into account the influence of friends and siblings, the level of concentration and interests can be analyzed more accurately.

[0045] The monitoring unit can adjust the monitoring method according to the type of device used by the child during monitoring. For example, if the child is using a tablet, the monitoring unit can provide a monitoring method optimized for the tablet. Also, if the child is using a PC, the monitoring unit can provide a monitoring method optimized for the PC. Also, if the child is using a smartphone, the monitoring unit can provide a monitoring method optimized for the smartphone. This allows for more appropriate monitoring by adjusting the monitoring method according to the device used.

[0046] The monitoring unit can apply different monitoring algorithms depending on the content of a child's learning during monitoring. For example, if a child is learning mathematics, the monitoring unit can apply a monitoring algorithm optimized for mathematics. Also, if a child is learning English, the monitoring unit can apply a monitoring algorithm optimized for English. Also, if a child is learning science, the monitoring unit can apply a monitoring algorithm optimized for science. In this way, more appropriate monitoring can be achieved by applying a monitoring algorithm depending on the content of learning.

[0047] The providing unit can automatically adjust the difficulty level of the content to be provided in accordance with the child's learning progress and level of understanding. The providing unit adjusts the difficulty level of the content based on, for example, the child's learning progress. The providing unit can also adjust the difficulty level of the content based on the child's level of understanding. The providing unit can also adjust the difficulty level of the content based on the child's past learning data. In this way, by adjusting the difficulty level of the content in accordance with the child's learning progress and level of understanding, it is possible to improve the child's learning effectiveness.

[0048] The providing unit can customize the content to be provided based on the interests and concerns of the child. The providing unit customizes the content based on, for example, the interests of the child. The providing unit can also customize the content based on the interests of the child. The providing unit can also customize the content based on the child's past learning data. In this way, by customizing the content based on the interests and concerns of the child, it is possible to improve the learning effect.

[0049] The providing unit can optimize the content to be provided by reflecting the child's past learning history. The providing unit can optimize the content based on, for example, the child's past learning history. The providing unit can also optimize the content based on the child's past learning data. The providing unit can also optimize the content based on the child's past learning progress. In this way, by reflecting the past learning history, more appropriate content can be provided.

[0050] The providing unit can improve the content to be provided based on parental feedback. For example, the providing unit improves the content based on parental feedback. The providing unit can also improve the content by reflecting parental opinions. The providing unit can also adjust the content and format of the content based on parental requests. In this way, by improving the content based on parental feedback, more appropriate content can be provided.

[0051] The providing unit can design the content to be provided to promote collaborative learning and competition with other children. For example, the providing unit provides content that allows children to learn collaboratively with other children. The providing unit can also provide game-style content that allows children to compete with other children. The providing unit can also provide content that allows children to solve problems in cooperation with other children. This can improve learning effectiveness by promoting collaborative learning and competition with other children.

[0052] The providing unit can gradually evolve the content to be provided in accordance with the child's learning goals. For example, the providing unit can gradually increase the difficulty level of the content in accordance with the child's learning goals. The providing unit can also gradually evolve the content in accordance with the child's learning progress. The providing unit can also gradually change the format of the content in accordance with the child's level of understanding. In this way, the learning effect can be improved by gradually evolving the content in accordance with the learning goals.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The provider can automatically adjust the difficulty of the learning content according to the child's learning progress. For example, if a child quickly solves a particular problem, the provider can increase the difficulty of the next problem. Also, if a child is struggling with a particular problem, the provider can lower the difficulty of that problem. Furthermore, the provider can provide an individually optimized learning plan based on the child's past learning data. This can maximize the child's learning effectiveness.

[0055] The monitoring unit can monitor the children's learning environment in real time and adjust learning content according to changes in the environment. For example, if the brightness in the room decreases, the monitoring unit can determine that the children's concentration may be decreasing and provide content encouraging them to take a break. Also, if the room temperature is too high, the monitoring unit can determine that the children's concentration may be decreasing and provide content encouraging them to cool down. Furthermore, if the noise level in the room is high, the monitoring unit can provide noise-canceling music. This allows the children's learning environment to be optimized.

[0056] The input unit can add a function that allows parents to share information they input with other parents, thereby forming a community. For example, a function is provided that allows parents to share information about their children that they input with other parents. The input unit can also provide a forum where parents can exchange opinions with other parents based on the information they input. Furthermore, the input unit can also provide a function that allows parents to plan events together with other parents based on the information they input. This allows parents to share information with each other, forming a community and exchanging information.

[0057] The provider can automatically adjust the difficulty of the learning content according to the child's learning progress. For example, if a child quickly solves a particular problem, the provider can increase the difficulty of the next problem. Also, if a child is struggling with a particular problem, the provider can lower the difficulty of that problem. Furthermore, the provider can provide an individually optimized learning plan based on the child's past learning data. This can maximize the child's learning effectiveness.

[0058] The monitoring unit can adjust the monitoring method depending on the type of device used by the child. For example, if the child is using a tablet, a monitoring method optimized for the tablet can be provided. If the child is using a PC, a monitoring method optimized for the PC can be provided. Furthermore, if the child is using a smartphone, a monitoring method optimized for the smartphone can be provided. This allows for more appropriate monitoring by adjusting the monitoring method depending on the device used.

[0059] The providing unit can design the content to be provided to promote collaborative learning and competition with other children. For example, content that allows children to learn collaboratively with other children can be provided. Game-style content that allows children to compete with other children can also be provided. Furthermore, content that allows children to solve problems in cooperation with other children can be provided. This can improve learning effectiveness by promoting collaborative learning and competition with other children.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The input unit allows the parent to input information about the child. The information input by the parent includes, for example, the child's age, interests, learning goals, etc. The input unit allows the parent to input the child's age, interests, learning goals, etc. Step 2: The monitoring unit monitors the child's condition based on the information input by the input unit. The monitoring unit uses a camera to analyze the child's concentration level and interest, and monitors the child's facial expressions and movements in real time, which can be analyzed by AI. It also determines whether the child is concentrating and interested. Step 3: The provision unit provides content based on the information monitored by the monitoring unit. If the child becomes bored with learning, the provision unit provides play content and reports the learning results to the parents. It also suggests teaching materials and services that suit the child's characteristics and the parents' needs.

[0062] (Example 2)In an embodiment of the present invention, a system allows parents to set their child's information, desired content, and time in advance, analyze the child's concentration level and interests using a camera, and provide content tailored to the parent's preferences and the child's interests using AI. The system allows parents to set their child's information, desired content, and time in advance, analyze the child's concentration level and interests using a camera, and provide content tailored to the parent's preferences and the child's interests using AI. For example, if a child becomes bored with learning, the system can provide playful content. The system also monetizes by providing learning results to parents in the form of reports and suggesting learning materials and services tailored to the child's characteristics and the parent's needs. For example, parents set their child's information, desired content, and time in advance. They input information such as the child's age, interests, and learning goals. For example, a parent may set, "I want to provide 30 minutes of English learning content to my 5-year-old child." This information is then input into AI. The camera then monitors the child's facial expressions and behavior in real time, and the AI ​​analyzes them. For example, it can determine whether the child is focused or interested. This allows the child's condition to be understood. AI provides content that matches the parents' wishes and the children's interests. For example, if a child becomes bored with studying, it can provide playful content. This helps maintain the child's interest. The system also provides parents with reports on the results of their studies. For example, it can provide information such as what content the child was interested in and how focused they were. The system also aims to generate revenue by suggesting learning materials and services that match the child's characteristics and the parents' needs. For example, if a child is interested in a particular subject, it can suggest learning materials and services related to that subject. This allows parents to select appropriate learning materials and services to support their child's learning. This allows the system to allow parents to input their child's information, monitor the child's condition, and provide appropriate content. For example, it can maintain a child's interest even if they become bored with studying.In addition, parents will be able to understand their child's learning situation, and by providing teaching materials and services that suit the child's characteristics and the needs of the parents, learning effectiveness can be improved.

[0063] The education support system according to the embodiment includes an input unit, a monitoring unit, and a providing unit. A parent inputs information about a child into the input unit. The information input by the parent includes, but is not limited to, the child's age, interests, and learning goals. For example, the parent inputs the child's age into the input unit. The parent can also input the child's interests into the input unit. The parent can also input the child's learning goals into the input unit. The monitoring unit monitors the child's condition based on the information input by the input unit. The monitoring unit analyzes the child's concentration and interest using, for example, a camera. The monitoring unit can also monitor the child's facial expressions and movements in real time and analyze them using AI. The monitoring unit can also determine whether the child is concentrating or interested. The providing unit provides content based on the information monitored by the monitoring unit. For example, if the child becomes bored with learning, the providing unit provides play content. The providing unit can also provide the parent with a report on the learning results. The providing unit can also suggest teaching materials and services that match the child's characteristics and the parent's needs. As a result, the education support system according to the embodiment allows parents to input information about their children, monitors their children's status, and provides appropriate content. For example, even if a child becomes bored with learning, the system can maintain the child's interest. Furthermore, parents can grasp the child's learning situation, and the system can provide learning materials and services that match the child's characteristics and the parents' needs, thereby improving the learning effect.

[0064] The providing unit can provide play content when a child loses interest in learning. For example, the providing unit provides play content when a child is unresponsive for a certain period of time. The providing unit can also provide play content when it is determined that a child has lost interest in learning based on the results of facial expression analysis. The providing unit can also provide an interactive story when a child becomes bored with learning. This makes it possible to maintain a child's interest even when the child becomes bored with learning.

[0065] The providing unit can provide the learning results to the guardian as a report. For example, the providing unit can provide the child's test scores to the guardian as a report. The providing unit can also provide the child's study time to the guardian as a report. The providing unit can also provide the child's achievement level to the guardian as a report. The report is provided in, for example, PDF format. The report may also include graphs and charts. This allows the guardian to understand the child's learning situation.

[0066] The provision unit can propose teaching materials and services that match the characteristics of the child and the needs of the parent. For example, the provision unit can propose teaching materials that match the child's learning style. The provision unit can also propose teaching materials related to the child's areas of interest. The provision unit can also propose teaching materials that match the child's learning speed. The provision unit can also propose services that match the parent's learning goals. For example, the provision unit can propose individual tutoring services to parents who are aiming to acquire a specific skill. This makes it possible to improve learning effectiveness by providing teaching materials and services that match the child's characteristics and the parent's needs.

[0067] The monitoring unit can analyze the concentration level and interest of the child using a camera. For example, the monitoring unit can use a camera to track the child's line of sight and analyze the concentration level. The monitoring unit can also use a camera to analyze the child's facial expressions and analyze the interest. The monitoring unit can also use a camera to analyze the child's behavior patterns and analyze the concentration level and interest. In this way, the use of a camera makes it possible to accurately analyze the concentration level and interest of the child.

[0068] The input unit allows a parent to input information about the child's age, interests, learning goals, and interests. For example, the input unit allows a parent to input the child's age. The input unit also allows a parent to input the child's interests. The input unit also allows a parent to input the child's learning goals. By allowing a parent to input detailed information about a child, more appropriate content can be provided.

[0069] The input unit can estimate the parent's emotions and adjust the display method of the input interface based on the estimated parent's emotions. For example, if the parent is stressed, the input unit can provide a simple interface and minimize input steps. Furthermore, if the parent is relaxed, the input unit can provide detailed input options and suggest a customizable input method. Furthermore, if the parent is in a hurry, the input unit can prioritize voice input to enable the parent to quickly enter information about the child. This reduces input stress by adjusting the input interface according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] The input unit can provide input assistance by referring to past input history to improve the accuracy of information entered by the guardian. For example, the input unit can automatically display information about the child that the guardian has previously entered as a candidate. The input unit can also preferentially suggest input methods (voice, text, etc.) that the guardian has previously used. The input unit can also predict and suggest information that will be entered during a specific time period based on the guardian's past input history. In this way, the accuracy of input can be improved by referring to past input history.

[0071] The input unit can increase the types of information that parents can input, for example, to allow parents to input their child's health condition and daily activities. For example, the input unit adds a field where parents can input their child's health condition (body temperature, diet, etc.). The input unit can also provide an option where parents can input their child's daily activities (play, study time, etc.). The input unit can also allow parents to input their child's specific events (school events, sports activities, etc.). This allows parents to input their child's health condition and daily activities, making it possible to provide more detailed information.

[0072] The input unit can accept voice input or image input of information entered by the guardian, thereby diversifying the input method. For example, the input unit can allow the guardian to enter information about the child by voice. The input unit can also allow the guardian to upload images to record the child's health condition and activity details. The input unit can also allow the guardian to combine voice input and image input to enter more detailed information. In this way, by accepting voice input and image input, the input method can be diversified.

[0073] The input unit can estimate the parent's emotions and prioritize input content based on the estimated parent's emotions. For example, if the parent is stressed, the input unit allows the parent to input only important information first. The input unit can also provide the parent with an option to input detailed information if the parent is relaxed. The input unit can also allow the parent to input the most important information first if the parent is in a hurry. This improves input efficiency by prioritizing input content according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The input unit can add a function that allows parents to share information they input with other parents, thereby forming a community. For example, the input unit provides a function that allows parents to share information about their children that they input with other parents. The input unit can also provide a forum where parents can exchange opinions with other parents based on the information they input. The input unit can also provide a function that allows parents to plan events together with other parents based on the information they input. This allows parents to share information with each other, forming a community and exchanging information.

[0075] The input unit can add a function to share information entered by parents with the child's school or educational institution. For example, the input unit provides a function that allows information about the child entered by the parent to be shared with the child's teachers at school. The input unit can also provide a function that allows the child to participate in school events and activities based on the information entered by the parent. The input unit can also provide a function that creates a learning plan for the child in cooperation with educational institutions based on the information entered by the parent. This makes it possible to support the child's learning by sharing information with schools and educational institutions.

[0076] The input unit can be added with a function to automatically complete information entered by a parent or guardian by comparing it with the child's past learning data. For example, the input unit automatically completes information entered by a parent or guardian by comparing it with the child's past learning data. The input unit can also automatically update the child's learning progress based on the information entered by the parent or guardian. The input unit can also automatically set the child's learning goals based on the information entered by the parent or guardian. This makes it possible to automatically complete the input information by comparing it with the child's past learning data.

[0077] The monitoring unit can estimate the child's emotions and adjust the frequency and method of monitoring based on the estimated child's emotions. For example, if the child is concentrating, the monitoring unit can reduce the frequency of monitoring to allow the child to focus on learning. Furthermore, if the child is excited, the monitoring unit can increase the frequency of monitoring and provide appropriate content. Furthermore, if the child is tired, the monitoring unit can reduce the frequency of monitoring and encourage the child to take a break. In this way, more appropriate monitoring can be achieved by adjusting the frequency and method of monitoring according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] During monitoring, the monitoring unit can analyze not only the child's facial expressions but also their voice and actions to more accurately determine the child's level of concentration and interest. For example, the monitoring unit can analyze the child's facial expressions to determine the child's level of concentration and interest. The monitoring unit can also analyze the child's voice to determine the child's level of concentration and interest. The monitoring unit can also analyze the child's actions to determine the child's level of concentration and interest. Thus, by analyzing the facial expressions, voice, and actions, the child's level of concentration and interest can be determined more accurately.

[0079] During monitoring, the monitoring unit can refer to data on the child's past concentration and interests to predict the child's current state. For example, the monitoring unit can refer to the child's past concentration data to predict the child's current concentration. The monitoring unit can also refer to the child's past interest data to predict the child's current interest. The monitoring unit can also refer to the child's past learning data to predict the child's current learning state. By referring to past data, the current state can be predicted more accurately.

[0080] The monitoring unit can analyze the child's concentration level and interest based on the child's environment during monitoring. For example, the monitoring unit can analyze the child's concentration level taking into account the brightness of the room. The monitoring unit can also analyze the child's concentration level taking into account the temperature of the room. The monitoring unit can also analyze the child's concentration level taking into account the noise level in the room. In this way, by taking the environment into account, the concentration level and interest can be analyzed more accurately.

[0081] The monitoring unit can estimate the child's emotions and adjust the display method of the monitoring results based on the estimated child's emotions. For example, the monitoring unit can provide a simple display method when the child is concentrating. The monitoring unit can also provide a detailed display method when the child is excited. The monitoring unit can also provide a highly visible display method when the child is tired. This allows for improved visibility by adjusting the display method of the monitoring results according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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.

[0082] During monitoring, the monitoring unit can analyze the level of concentration and interests taking into account the influence of the child's friends and siblings. For example, the monitoring unit analyzes the level of concentration taking into account the presence of the child's friends. The monitoring unit can also analyze the level of concentration taking into account the presence of the child's siblings. The monitoring unit can also analyze the interests taking into account the child's relationships with friends and siblings. In this way, by taking into account the influence of friends and siblings, the level of concentration and interests can be analyzed more accurately.

[0083] The monitoring unit can adjust the monitoring method according to the type of device used by the child during monitoring. For example, if the child is using a tablet, the monitoring unit can provide a monitoring method optimized for the tablet. Also, if the child is using a PC, the monitoring unit can provide a monitoring method optimized for the PC. Also, if the child is using a smartphone, the monitoring unit can provide a monitoring method optimized for the smartphone. This allows for more appropriate monitoring by adjusting the monitoring method according to the device used.

[0084] The monitoring unit can apply different monitoring algorithms depending on the content of a child's learning during monitoring. For example, if a child is learning mathematics, the monitoring unit can apply a monitoring algorithm optimized for mathematics. Also, if a child is learning English, the monitoring unit can apply a monitoring algorithm optimized for English. Also, if a child is learning science, the monitoring unit can apply a monitoring algorithm optimized for science. In this way, more appropriate monitoring can be achieved by applying a monitoring algorithm depending on the content of learning.

[0085] The providing unit can estimate the child's emotions and adjust the type and timing of content to be provided based on the estimated child's emotions. For example, if the child is concentrating, the providing unit can provide learning content. If the child is excited, the providing unit can also provide play content. If the child is tired, the providing unit can also provide relaxing content. In this way, by adjusting the type and timing of content according to the child's emotions, more appropriate content can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0086] The providing unit can automatically adjust the difficulty level of the content to be provided in accordance with the child's learning progress and level of understanding. The providing unit adjusts the difficulty level of the content based on, for example, the child's learning progress. The providing unit can also adjust the difficulty level of the content based on the child's level of understanding. The providing unit can also adjust the difficulty level of the content based on the child's past learning data. In this way, by adjusting the difficulty level of the content in accordance with the child's learning progress and level of understanding, it is possible to improve the child's learning effectiveness.

[0087] The providing unit can customize the content to be provided based on the interests and concerns of the child. The providing unit customizes the content based on, for example, the interests of the child. The providing unit can also customize the content based on the interests of the child. The providing unit can also customize the content based on the child's past learning data. In this way, by customizing the content based on the interests and concerns of the child, it is possible to improve the learning effect.

[0088] The providing unit can optimize the content to be provided by reflecting the child's past learning history. The providing unit can optimize the content based on, for example, the child's past learning history. The providing unit can also optimize the content based on the child's past learning data. The providing unit can also optimize the content based on the child's past learning progress. In this way, by reflecting the past learning history, more appropriate content can be provided.

[0089] The providing unit can estimate the child's emotions and adjust the display method of the content to be provided based on the estimated child's emotions. For example, the providing unit can provide a simple display method when the child is concentrating. The providing unit can also provide a detailed display method when the child is excited. The providing unit can also provide a highly visible display method when the child is tired. In this way, visibility can be improved by adjusting the content display method according to the child's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or 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.

[0090] The providing unit can improve the content to be provided based on parental feedback. For example, the providing unit improves the content based on parental feedback. The providing unit can also improve the content by reflecting parental opinions. The providing unit can also adjust the content and format of the content based on parental requests. In this way, by improving the content based on parental feedback, more appropriate content can be provided.

[0091] The providing unit can design the content to be provided to promote collaborative learning and competition with other children. For example, the providing unit provides content that allows children to learn collaboratively with other children. The providing unit can also provide game-style content that allows children to compete with other children. The providing unit can also provide content that allows children to solve problems in cooperation with other children. This can improve learning effectiveness by promoting collaborative learning and competition with other children.

[0092] The providing unit can gradually evolve the content to be provided in accordance with the child's learning goals. For example, the providing unit can gradually increase the difficulty level of the content in accordance with the child's learning goals. The providing unit can also gradually evolve the content in accordance with the child's learning progress. The providing unit can also gradually change the format of the content in accordance with the child's level of understanding. In this way, the learning effect can be improved by gradually evolving the content in accordance with the learning goals. === Hard Collateral 1-1 === Each of the multiple elements including the input unit, monitoring unit, and providing unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart device 14, and a guardian can input information about the child. The monitoring unit uses the camera 42 of the smart device 14 to analyze the child's concentration level and interests, and the results are analyzed by the specific processing unit 290 of the data processing device 12. The providing unit is realized by the specific processing unit 290 of the data processing device 12, and provides content that matches the guardian's intentions and the child's interests. === Hard Collateral 1-2 === Each of the multiple elements including the input unit, monitoring unit, and providing unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the smart glasses 214, and a parent or guardian can input information about the child. The monitoring unit uses the camera 42 of the smart glasses 214 to analyze the child's concentration level and interests, which are then analyzed by the specific processing unit 290 of the data processing device 12. The providing unit is realized by the specific processing unit 290 of the data processing device 12, and provides content that matches the parent's intentions and the child's interests. === Hard Collateral 1-3 === Each of the multiple elements including the input unit, monitoring unit, and providing unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the headset terminal 314, and allows a guardian to input information about the child. The monitoring unit analyzes the child's concentration level and interests using the camera 42 of the headset terminal 314, and the results are analyzed by the specific processing unit 290 of the data processing device 12. The providing unit is realized by the specific processing unit 290 of the data processing device 12, and provides content that matches the guardian's intentions and the child's interests. === Hard Collateral 1-4 === Each of the multiple elements including the input unit, monitoring unit, and providing unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit is realized by the control unit 46A of the robot 414, and a guardian can input information about the child. The monitoring unit uses the camera 42 of the robot 414 to analyze the child's concentration level and interests, and the results are analyzed by the specific processing unit 290 of the data processing device 12. The providing unit is realized by the specific processing unit 290 of the data processing device 12, and provides content that matches the guardian's intentions and the child's interests.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The provider can automatically adjust the difficulty of the learning content according to the child's learning progress. For example, if a child quickly solves a particular problem, the provider can increase the difficulty of the next problem. Also, if a child is struggling with a particular problem, the provider can lower the difficulty of that problem. Furthermore, the provider can provide an individually optimized learning plan based on the child's past learning data. This can maximize the child's learning effectiveness.

[0095] The monitoring unit can monitor the children's learning environment in real time and adjust learning content according to changes in the environment. For example, if the brightness in the room decreases, the monitoring unit can determine that the children's concentration may be decreasing and provide content encouraging them to take a break. Also, if the room temperature is too high, the monitoring unit can determine that the children's concentration may be decreasing and provide content encouraging them to cool down. Furthermore, if the noise level in the room is high, the monitoring unit can provide noise-canceling music. This allows the children's learning environment to be optimized.

[0096] The providing unit can estimate the child's emotions and adjust the format of the learning content based on the estimated emotions. For example, if the child is feeling stressed, the providing unit can provide relaxing content. If the child is excited, the providing unit can provide content to improve concentration. Furthermore, if the child is tired, the providing unit can provide content encouraging the child to take a break. In this way, adjusting the format of the learning content according to the child's emotions can improve learning effectiveness.

[0097] The input unit can add a function that allows parents to share information they input with other parents, thereby forming a community. For example, a function is provided that allows parents to share information about their children that they input with other parents. The input unit can also provide a forum where parents can exchange opinions with other parents based on the information they input. Furthermore, the input unit can also provide a function that allows parents to plan events together with other parents based on the information they input. This allows parents to share information with each other, forming a community and exchanging information.

[0098] The monitoring unit can estimate the child's emotions and adjust the frequency and method of monitoring based on the estimated emotions. For example, if a child is concentrating, the frequency of monitoring can be reduced to allow them to focus on their studies. Also, if a child is excited, the frequency of monitoring can be increased and appropriate content can be provided. Furthermore, if a child is tired, the frequency of monitoring can be reduced to encourage them to take a break. In this way, more appropriate monitoring can be achieved by adjusting the frequency and method of monitoring according to the child's emotions.

[0099] The provider can automatically adjust the difficulty of the learning content according to the child's learning progress. For example, if a child quickly solves a particular problem, the provider can increase the difficulty of the next problem. Also, if a child is struggling with a particular problem, the provider can lower the difficulty of that problem. Furthermore, the provider can provide an individually optimized learning plan based on the child's past learning data. This can maximize the child's learning effectiveness.

[0100] The input unit can estimate the parent's emotions and adjust the display method of the input interface based on the estimated emotions. For example, if the parent is feeling stressed, a simple interface can be provided to minimize input steps. Alternatively, if the parent is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the parent is in a hurry, voice input can be prioritized to allow the parent to quickly enter information about the child. In this way, input stress can be reduced by adjusting the input interface according to the parent's emotions.

[0101] The providing unit can estimate the child's emotions and adjust the type and timing of the content to be provided based on the estimated emotions. For example, if the child is concentrating, learning content can be provided. If the child is excited, play content can be provided. Furthermore, if the child is tired, content that helps the child relax can be provided. In this way, more appropriate content can be provided by adjusting the type and timing of content according to the child's emotions.

[0102] The monitoring unit can adjust the monitoring method depending on the type of device used by the child. For example, if the child is using a tablet, a monitoring method optimized for the tablet can be provided. If the child is using a PC, a monitoring method optimized for the PC can be provided. Furthermore, if the child is using a smartphone, a monitoring method optimized for the smartphone can be provided. This allows for more appropriate monitoring by adjusting the monitoring method depending on the device used.

[0103] The providing unit can design the content to be provided to promote collaborative learning and competition with other children. For example, content that allows children to learn collaboratively with other children can be provided. Game-style content that allows children to compete with other children can also be provided. Furthermore, content that allows children to solve problems in cooperation with other children can be provided. This can improve learning effectiveness by promoting collaborative learning and competition with other children.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The input unit allows the parent to input information about the child. The information input by the parent includes, for example, the child's age, interests, learning goals, etc. The input unit allows the parent to input the child's age, interests, learning goals, etc. Step 2: The monitoring unit monitors the child's condition based on the information input by the input unit. The monitoring unit uses a camera to analyze the child's concentration level and interest, and monitors the child's facial expressions and movements in real time, which can be analyzed by AI. It also determines whether the child is concentrating and interested. Step 3: The provision unit provides content based on the information monitored by the monitoring unit. If the child becomes bored with learning, the provision unit provides play content and reports the learning results to the parents. It also suggests teaching materials and services that suit the child's characteristics and the parents' needs.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

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

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

[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 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.

[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 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.

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

[0124] 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.

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

[0127] 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.

[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 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.

[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 (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).

[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] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 input unit where a parent or guardian inputs information about a child; a monitoring unit that monitors the condition of the child based on the information input by the input unit; a providing unit that provides content based on the information monitored by the monitoring unit; Equipped with A system characterized by:

2. The providing unit If children lose interest in learning, provide them with playful content.

2. The system of claim 1.

3. The providing unit Providing learning results to parents as a report 2. The system of claim 1.

4. The providing unit Proposing educational materials and services that match the characteristics of children and the needs of parents 2. The system of claim 1.

5. The monitoring unit Using cameras to analyze children's concentration and interests 2. The system of claim 1.

6. The input unit Parents enter information about their child's age, interests, learning goals and interests 2. The system of claim 1.

7. The input unit The parent's emotions are estimated, and the display method of the input interface is adjusted based on the estimated parent's emotions.

2. The system of claim 1.

8. The input unit To improve the accuracy of the information entered by parents, past input history is referenced to provide input assistance.

2. The system of claim 1.

9. The input unit Increase the types of information parents can enter, for example, by allowing them to enter information about their child's health and daily activities.

2. The system of claim 1.

10. The input unit Diversify input methods by accepting voice and image input of information entered by parents.

2. The system of claim 1.

Images