System

Abstract

An object of a system according to an embodiment is to reduce child's repulsion to homework and to effectively manage progress.SOLUTION: A system includes a homework progress confirmation unit, an interaction unit, a progress management unit, and a reminder unit. The homework progress check unit checks the progress of the homework using the generated AI. The interaction unit performs an interaction to reduce the child's repulsion based on the homework progress confirmed by the homework progress confirmation unit. A progress management part manages the progress of homework on the basis of the interaction performed by the interaction part. The reminder unit sends a reminder at an appropriate timing based on the homework progress managed by the progress management 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 techniques, children may rebel against homework, which can lead to a negative atmosphere in the home.

[0005] The system according to the embodiment aims to reduce children's resistance to homework and effectively manage their progress. [Means for solving the problem]

[0006] The system according to the embodiment includes a homework progress confirmation unit, a dialogue unit, a progress management unit, and a reminder unit. The homework progress confirmation unit uses a generation AI to confirm homework progress. The dialogue unit conducts a dialogue to reduce the child's resistance based on the homework progress confirmed by the homework progress confirmation unit. The progress management unit manages the homework progress based on the dialogue conducted by the dialogue unit. The reminder unit sends reminders at appropriate times based on the homework progress managed by the progress management unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce children's resistance to homework and effectively manage their progress. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The homework management system according to the embodiment of the present invention is a system that uses a generation AI to support children's homework management, which allows the homework management system to efficiently and smoothly manage children's homework.

[0029] A homework management system according to an embodiment includes a homework progress confirmation unit, a dialogue unit, a progress management unit, and a reminder unit. The homework progress confirmation unit uses a generation AI to confirm homework progress. For example, the generation AI uses a text generation AI, such as GPT-3 or BERT, to ask a child about their homework progress in a gentle yet encouraging manner. The generation AI can also analyze past homework history and generate questions tailored to each child's individual progress. The dialogue unit engages in dialogue to alleviate a child's resistance based on the homework progress confirmed by the homework progress confirmation unit. For example, if a child resists by saying, "I was going to do it, but I'm not motivated anymore!", the generation AI can offer encouraging words such as, "That's tough. But if you try it little by little, it might actually be fun." The generation AI can also learn patterns of children's resistance, predict resistance, and take countermeasures in advance. The progress management unit manages homework progress based on the dialogue conducted by the dialogue unit. For example, the generation AI can monitor a child's homework progress in real time and adjust reminder content according to the progress. The generation AI can also optimize the frequency and timing of reminders to match the child's learning pace. The reminder unit sends reminders at appropriate times based on the homework progress managed by the progress management unit. For example, the generation AI can send a reminder such as, "Homework time in 30 minutes. Are you ready?" The generation AI can also use an emotion estimation function to send reminders according to the child's emotional state. As a result, the homework management system according to the embodiment can efficiently and smoothly manage children's homework.

[0030] The homework progress confirmation unit can analyze homework history and generate questions based on individual progress when checking progress. For example, the generation AI retrieves a child's past homework history from a database and generates individual questions based on that history when checking progress. For example, it asks questions such as, "How was your last math homework? How far did you get this time?" The generation AI also analyzes a child's homework history to understand the child's progress in specific subjects or assignments. For example, it can generate questions such as, "Your last English homework went well. Are you progressing at the same pace this time?" The generation AI also references past homework history to provide specific advice based on the child's progress. For example, it asks questions such as, "Your last science experiment went well. Shall we try the same method for this one?" This makes it possible to generate questions based on the child's individual progress.

[0031] The homework progress check unit can check progress at the optimal time based on learning style and preferences. For example, the generation AI retrieves a child's learning style and preferences from a database and checks progress at the optimal time based on that information. For example, it may notify the child, "Now is a good time to work on your homework," during times when the child is most likely to concentrate. The generation AI can also analyze the child's learning style and check progress according to that style. For example, if a child is a night owl, it can ask, "How much progress have you made on your homework tonight?" during the evening hours. The generation AI can also adjust the timing of progress checks taking the child's preferences into account. For example, it can avoid the broadcast time of the child's favorite anime and notify the child, "Shall we start working on your homework after the anime is over?" This allows progress checks to be done at the optimal time based on the child's learning style and preferences.

[0032] The homework progress confirmation unit can check not only the progress of homework, but also the progress of other daily tasks. For example, the generation AI can check not only the progress of homework, but also the progress of daily tasks such as tidying up the room and reading. For example, it can ask, "Are you making progress on tidying up your room today?" The generation AI can also check the progress of daily tasks. For example, it can check tasks other than homework by asking, "Did you have time to read today?" The generation AI can also check the progress of daily tasks other than homework, supporting the child to work on tasks in a balanced manner. For example, it can suggest, "Shall we work on tidying up your room together with your homework today?" This makes it possible to check the progress of daily tasks other than homework.

[0033] The homework progress confirmation unit imitates the voice of the guardian, allowing the child to check progress in a way that is familiar to them. For example, the generation AI imitates the voice of the guardian, allowing the child to check progress in a way that is familiar to them. For example, it may ask, "In mom's voice, how much progress have you made on your homework today?" The generation AI also imitates the voice of the guardian. For example, it may notify, "In dad's voice, you should start working on your homework now." The generation AI also imitates the voice of the guardian, allowing the child to check progress in a way that is familiar to them. For example, it may ask, "In mom's voice, what homework do you have today?" By imitating the voice of the guardian, progress can be checked in a way that is familiar to them.

[0034] The dialogue unit learns patterns of resistance, can predict resistance, and take measures in advance. For example, the dialogue unit, a generation AI, learns patterns of resistance from children, can predict resistance, and take measures in advance. For example, when a child is likely to say, "I don't want to do my homework," it can suggest, "Let's try doing it little by little." The generation AI also learns patterns of resistance from children. For example, when a child is likely to say, "I don't feel motivated," it can suggest, "Let's take a short break and then start again." The generation AI also learns patterns of resistance from children, can predict resistance, and take measures in advance. For example, when a child is likely to say, "I don't like homework," it can suggest, "Let's do it together." This makes it possible to predict children's resistance and take measures in advance.

[0035] The dialogue unit can incorporate a child's favorite characters and topics into the dialogue. For example, the generation AI can incorporate a child's favorite characters and topics that pique their interest. For example, it can say, "Your favorite character is also working hard on their homework." The generation AI can also incorporate topics that pique a child's interest. For example, it can suggest, "Let's work on your homework while talking about your favorite anime." The generation AI can also incorporate a child's favorite characters and topics into the dialogue. For example, it can suggest, "Let's work on your homework while talking about your favorite game." By incorporating a child's favorite characters and topics, the dialogue becomes more familiar.

[0036] The dialogue unit can gamify homework and encourage children to work on it while having fun. For example, the generation AI can gamify homework and encourage children to work on it while having fun. For example, it can suggest, "You'll earn points if you complete your homework." The generation AI can also gamify homework and encourage children to work on it while having fun. For example, it can suggest, "You'll level up if you complete your homework." The generation AI can also gamify homework and encourage children to work on it while having fun. For example, it can suggest, "You'll get a reward if you complete your homework." By gamifying homework in this way, children can work on it while having fun.

[0037] The dialogue unit can encourage cooperation with friends and siblings, encouraging them to work on homework together. For example, the generation AI can encourage cooperation with a child's friends and siblings, encouraging them to work on homework together. For example, it can suggest, "Doing homework with friends is fun." The generation AI can also encourage cooperation with friends and siblings, encouraging them to work on homework together. For example, it can suggest, "Doing homework with your siblings is faster." The generation AI can also encourage cooperation with a child's friends and siblings, encouraging them to work on homework together. For example, it can suggest, "Doing homework with friends is fun." This can encourage cooperation with friends and siblings, increasing a child's motivation to work on their homework.

[0038] The progress management unit can monitor homework progress in real time and adjust the content of reminders according to progress. For example, the generation AI in the progress management unit monitors a child's homework progress in real time and adjusts the content of reminders according to progress. For example, it can notify the child, "You'll be done soon, keep trying!" The generation AI also monitors homework progress in real time. For example, it can notify the child, "If you keep going at the current pace, you'll be done in 30 minutes." The generation AI also monitors a child's homework progress in real time and adjusts the content of reminders according to progress. For example, it can notify the child, "You'll be done soon, keep trying a little harder." This makes it possible to adjust the content of reminders according to homework progress.

[0039] The progress management unit can optimize the frequency and timing of reminders to match the learning pace. For example, the generation AI analyzes a child's learning pace and optimizes the frequency and timing of reminders to match that pace. For example, it can notify the child, "If you continue at your current pace, you'll be done in 30 minutes." The generation AI can also optimize reminders to match the learning pace. For example, it can notify the child, "If you continue at your current pace, you'll be done in 1 hour." The generation AI can also analyze a child's learning pace and optimize the frequency and timing of reminders to match that pace. For example, it can notify the child, "If you continue at your current pace, you'll be done in 15 minutes." This makes it possible to optimize the frequency and timing of reminders to match the learning pace.

[0040] The progress management unit is capable of not only managing homework progress but also sending reminders for other learning activities. For example, the generation AI not only manages homework progress but also sends reminders for other learning activities such as reading and projects. For example, it can notify, "Did you find time to read today?" The generation AI can also send reminders for other learning activities. For example, it can notify, "How is the project progressing today?" The generation AI can also send reminders for learning activities other than homework. For example, it can notify, "Did you find time to read today?" This makes it possible to send reminders for learning activities other than homework.

[0041] The progress management unit can imitate the voice of the parent or guardian to send reminders in a format that is easy for children to understand. For example, the generation AI imitates the voice of the parent or guardian to send reminders in a format that is easy for children to understand. For example, it can notify the child in "Mom's voice, it's time for homework in 30 minutes." The generation AI also imitates the voice of the parent or guardian. For example, it can notify the child in "Dad's voice, it's a good idea to start working on your homework now." The generation AI also imitates the voice of the parent or guardian to send reminders in a format that is easy for children to understand. For example, it can notify the child in "Mom's voice, it's time for homework in 30 minutes." By imitating the voice of the parent or guardian, it is possible to send reminders in a format that is easy for children to understand.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The homework management system may further include a reward management unit. The reward management unit provides rewards according to the child's progress in homework. For example, when a certain level of progress is made in homework, points may be awarded, and the points may be accumulated and exchanged for specific rewards. The reward management unit may also provide rewards according to the child's preferences. For example, a sticker of a child's favorite character or the right to participate in a specific activity may be provided as a reward. Furthermore, the reward management unit may also customize the content of rewards in cooperation with parents. For example, rewards may be provided based on specific household rules set by parents. This may increase the child's motivation for doing their homework.

[0044] The homework management system can further include a learning style analysis unit. The learning style analysis unit analyzes a child's learning style and suggests a learning method that is best suited to that style. For example, if a child prefers visual learning, it can suggest learning materials that make extensive use of diagrams and graphs. If a child prefers auditory learning, it can provide learning materials in the form of audio guides or podcasts. Furthermore, the learning style analysis unit can also suggest creating a learning environment that suits a child's learning style. For example, if a child prefers studying in a quiet environment, it can recommend the use of noise-canceling headphones. This allows children to study efficiently using a learning method that suits them best.

[0045] The homework management system can further include a health management unit. The health management unit monitors the child's health status and encourages them to take breaks at appropriate times. For example, if a child has been studying for a long period of time, it may notify the child to "take a short break." The health management unit can also detect the child's posture and eye fatigue and provide appropriate advice. For example, it can notify the child to "straighten up and rest their eyes." Furthermore, the health management unit can monitor the child's eating and sleeping habits and support a balanced lifestyle. For example, it can notify the child to "go to bed early today." This allows the child to continue their studies while maintaining their health.

[0046] A homework management system can also be equipped with a learning record section. The learning record section records a child's learning history in detail and shares it with parents and teachers. For example, it can record how much time a child spends on each subject and provide weekly or monthly learning status reports. The learning record section can also visualize a child's learning progress and results. For example, it can use graphs and charts to display a child's learning trends and grade trends. Furthermore, the learning record section can set a child's learning goals and track their progress. For example, a goal can be set such as "This week's goal is to complete 10 pages of math workbook," and the progress toward that goal can be recorded. This allows a child to understand their own learning situation and plan their studies toward their goals.

[0047] The homework management system may further include a learning content providing unit. The learning content providing unit provides appropriate learning content according to the child's learning situation. For example, it may provide supplementary videos or practice problems for subjects that the child is weak in. The learning content providing unit may also suggest learning content based on the child's interests. For example, if a child is interested in science, it may provide videos of science experiments or related articles. Furthermore, the learning content providing unit may also provide content that matches the child's learning pace. For example, if a child is learning at a fast pace, it may provide content with more advanced content. This allows the child to use the optimal learning content according to their learning situation and interests.

[0048] The homework management system can further include a parent-teacher collaboration unit. The parent-teacher collaboration unit collaborates with parents to share information about their children's learning status and provide appropriate support. For example, it notifies parents of their children's homework progress and learning results, allowing them to provide appropriate advice to their children. The parent-teacher collaboration unit can also receive feedback from parents and adjust system settings. For example, if a parent wants to focus on a particular subject, it can prioritize the provision of learning content related to that subject. Furthermore, the parent-teacher collaboration unit can support parents and children in creating study plans together. For example, it can set weekly learning goals and share the progress of those goals with parents. This makes it possible to collaborate with parents to support their children's learning.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The homework progress confirmation unit uses the generation AI to check the homework progress. For example, the generation AI uses text generation AI such as GPT-3 or BERT to ask the child about their homework progress using gentle and encouraging words. The generation AI can also analyze past homework history and generate questions based on individual progress. Step 2: The dialogue unit engages in dialogue to reduce the child's resistance based on the homework progress confirmed by the homework progress confirmation unit. For example, if a child resists by saying, "I was going to do it, but I don't feel like doing it anymore!", the generation AI will offer words of encouragement such as, "That's tough. But if you try it little by little, it might actually be fun." The generation AI can also learn patterns of children's resistance, predict it, and take measures in advance. Step 3: The progress management unit manages homework progress based on the dialogue carried out by the dialogue unit. For example, the generation AI monitors the child's homework progress in real time and adjusts the content of reminders according to the progress. The generation AI can also optimize the frequency and timing of reminders to match the child's learning pace. Step 4: The reminder section sends reminders at appropriate times based on the homework progress managed by the progress management section. For example, the generation AI might send a reminder such as, "Homework time in 30 minutes. Are you ready?" The generation AI can also use its emotion estimation function to send reminders based on the child's emotional state.

[0051] (Example 2) The homework management system according to the embodiment of the present invention is a system that uses a generation AI to support children's homework management, which allows the homework management system to efficiently and smoothly manage children's homework.

[0052] A homework management system according to an embodiment includes a homework progress confirmation unit, a dialogue unit, a progress management unit, and a reminder unit. The homework progress confirmation unit uses a generation AI to confirm homework progress. For example, the generation AI uses a text generation AI, such as GPT-3 or BERT, to ask a child about their homework progress in a gentle yet encouraging manner. The generation AI can also analyze past homework history and generate questions tailored to each child's individual progress. The dialogue unit engages in dialogue to alleviate a child's resistance based on the homework progress confirmed by the homework progress confirmation unit. For example, if a child resists by saying, "I was going to do it, but I'm not motivated anymore!", the generation AI can offer encouraging words such as, "That's tough. But if you try it little by little, it might actually be fun." The generation AI can also learn patterns of children's resistance, predict resistance, and take countermeasures in advance. The progress management unit manages homework progress based on the dialogue conducted by the dialogue unit. For example, the generation AI can monitor a child's homework progress in real time and adjust reminder content according to the progress. The generation AI can also optimize the frequency and timing of reminders to match the child's learning pace. The reminder unit sends reminders at appropriate times based on the homework progress managed by the progress management unit. For example, the generation AI can send a reminder such as, "Homework time in 30 minutes. Are you ready?" The generation AI can also use an emotion estimation function to send reminders according to the child's emotional state. As a result, the homework management system according to the embodiment can efficiently and smoothly manage children's homework.

[0053] The homework progress confirmation unit can analyze homework history and generate questions based on individual progress when checking progress. For example, the generation AI retrieves a child's past homework history from a database and generates individual questions based on that history when checking progress. For example, it asks questions such as, "How was your last math homework? How far did you get this time?" The generation AI also analyzes a child's homework history to understand the child's progress in specific subjects or assignments. For example, it can generate questions such as, "Your last English homework went well. Are you progressing at the same pace this time?" The generation AI also references past homework history to provide specific advice based on the child's progress. For example, it asks questions such as, "Your last science experiment went well. Shall we try the same method for this one?" This makes it possible to generate questions based on the child's individual progress.

[0054] The homework progress check unit can check progress at the optimal time based on learning style and preferences. For example, the generation AI retrieves a child's learning style and preferences from a database and checks progress at the optimal time based on that information. For example, it may notify the child, "Now is a good time to work on your homework," during times when the child is most likely to concentrate. The generation AI can also analyze the child's learning style and check progress according to that style. For example, if a child is a night owl, it can ask, "How much progress have you made on your homework tonight?" during the evening hours. The generation AI can also adjust the timing of progress checks taking the child's preferences into account. For example, it can avoid the broadcast time of the child's favorite anime and notify the child, "Shall we start working on your homework after the anime is over?" This allows progress checks to be done at the optimal time based on the child's learning style and preferences.

[0055] The homework progress confirmation unit uses the emotion estimation function to grasp the child's emotional state in real time and check progress at the appropriate time. For example, the generation AI uses the emotion estimation function to analyze the child's facial expressions and tone of voice to grasp the child's emotional state in real time. For example, when the child is relaxed, the generation AI can notify the child, "Now is a good time to work on your homework." The generation AI also uses the emotion estimation function to analyze the child's emotional state and check progress at the appropriate time. For example, when the child is feeling stressed, the generation AI can suggest, "Shall we take a short break before working on your homework?" The generation AI also checks progress according to the child's emotional state based on the emotion estimation data. For example, when the child is in a good mood, the generation AI can notify the child, "Now is a good time to work on your homework." This allows progress checks to be performed at the appropriate time according to the child's emotional state.

[0056] The homework progress confirmation unit can check not only the progress of homework, but also the progress of other daily tasks. For example, the generation AI can check not only the progress of homework, but also the progress of daily tasks such as tidying up the room and reading. For example, it can ask, "Are you making progress on tidying up your room today?" The generation AI can also check the progress of daily tasks. For example, it can check tasks other than homework by asking, "Did you have time to read today?" The generation AI can also check the progress of daily tasks other than homework, supporting the child to work on tasks in a balanced manner. For example, it can suggest, "Shall we work on tidying up your room together with your homework today?" This makes it possible to check the progress of daily tasks other than homework.

[0057] The homework progress confirmation unit imitates the voice of the guardian, allowing the child to check progress in a way that is familiar to them. For example, the generation AI imitates the voice of the guardian, allowing the child to check progress in a way that is familiar to them. For example, it may ask, "In mom's voice, how much progress have you made on your homework today?" The generation AI also imitates the voice of the guardian. For example, it may notify, "In dad's voice, you should start working on your homework now." The generation AI also imitates the voice of the guardian, allowing the child to check progress in a way that is familiar to them. For example, it may ask, "In mom's voice, what homework do you have today?" By imitating the voice of the guardian, progress can be checked in a way that is familiar to them.

[0058] The homework progress checking unit can use the emotion estimation function to check progress during times when the child is most relaxed. For example, the generation AI can use the emotion estimation function to identify times when the child is most relaxed and check progress during those times. For example, it can notify the child, "Right now is a good time to get on with your homework because you're feeling relaxed." The generation AI can also use the emotion estimation function to check progress during times when the child is relaxed. For example, it can suggest, "You should get on with your homework now." The generation AI can also check progress during times when the child is relaxed based on the emotion estimation data. For example, it can notify the child, "Right now is a good time to get on with your homework because you're feeling relaxed." This allows progress to be checked during times when the child is most relaxed.

[0059] The dialogue unit learns patterns of resistance, can predict resistance, and take measures in advance. For example, the dialogue unit, a generation AI, learns patterns of resistance from children, can predict resistance, and take measures in advance. For example, when a child is likely to say, "I don't want to do my homework," it can suggest, "Let's try doing it little by little." The generation AI also learns patterns of resistance from children. For example, when a child is likely to say, "I don't feel motivated," it can suggest, "Let's take a short break and then start again." The generation AI also learns patterns of resistance from children, can predict resistance, and take measures in advance. For example, when a child is likely to say, "I don't like homework," it can suggest, "Let's do it together." This makes it possible to predict children's resistance and take measures in advance.

[0060] The dialogue unit can incorporate a child's favorite characters and topics into the dialogue. For example, the generation AI can incorporate a child's favorite characters and topics that pique their interest. For example, it can say, "Your favorite character is also working hard on their homework." The generation AI can also incorporate topics that pique a child's interest. For example, it can suggest, "Let's work on your homework while talking about your favorite anime." The generation AI can also incorporate a child's favorite characters and topics into the dialogue. For example, it can suggest, "Let's work on your homework while talking about your favorite game." By incorporating a child's favorite characters and topics, the dialogue becomes more familiar.

[0061] The dialogue unit can use the emotion estimation function to generate optimal dialogue according to the child's emotional state. For example, the dialogue unit's generation AI uses the emotion estimation function to grasp a child's emotional state in real time and generate optimal dialogue according to that state. For example, when a child is frustrated, it can suggest, "Let's take a short break." The generation AI also uses the emotion estimation function to generate dialogue according to the child's emotional state. For example, when a child is depressed, it can encourage them by saying, "It's okay, try doing it little by little." The generation AI also generates optimal dialogue according to the child's emotional state based on the emotion estimation data. For example, when a child is relaxed, it can suggest, "Let's get on with your homework now." This makes it possible to generate optimal dialogue according to the child's emotional state.

[0062] The dialogue unit can gamify homework and encourage children to work on it while having fun. For example, the generation AI can gamify homework and encourage children to work on it while having fun. For example, it can suggest, "You'll earn points if you complete your homework." The generation AI can also gamify homework and encourage children to work on it while having fun. For example, it can suggest, "You'll level up if you complete your homework." The generation AI can also gamify homework and encourage children to work on it while having fun. For example, it can suggest, "You'll get a reward if you complete your homework." By gamifying homework in this way, children can work on it while having fun.

[0063] The dialogue unit can encourage cooperation with friends and siblings, encouraging them to work on homework together. For example, the generation AI can encourage cooperation with a child's friends and siblings, encouraging them to work on homework together. For example, it can suggest, "Doing homework with friends is fun." The generation AI can also encourage cooperation with friends and siblings, encouraging them to work on homework together. For example, it can suggest, "Doing homework with your siblings is faster." The generation AI can also encourage cooperation with a child's friends and siblings, encouraging them to work on homework together. For example, it can suggest, "Doing homework with friends is fun." This can encourage cooperation with friends and siblings, increasing a child's motivation to work on their homework.

[0064] The dialogue unit can use the emotion estimation function to hold a dialogue at a time when the child is most relaxed. For example, the generation AI can use the emotion estimation function to identify the time when the child is most relaxed and hold a dialogue at that time. For example, it can suggest, "Right now is a good time to get on with your homework because you're feeling relaxed." The generation AI can also use the emotion estimation function to hold a dialogue at a time when the child is relaxed. For example, it can suggest, "Right now is a good time to get on with your homework." The generation AI can also use the emotion estimation data to hold a dialogue at a time when the child is relaxed. For example, it can suggest, "Right now is a good time to get on with your homework because you're feeling relaxed." This enables effective dialogue by holding a dialogue at a time when the child is most relaxed.

[0065] The progress management unit can monitor homework progress in real time and adjust the content of reminders according to progress. For example, the generation AI in the progress management unit monitors a child's homework progress in real time and adjusts the content of reminders according to progress. For example, it can notify the child, "You'll be done soon, keep trying!" The generation AI also monitors homework progress in real time. For example, it can notify the child, "If you keep going at the current pace, you'll be done in 30 minutes." The generation AI also monitors a child's homework progress in real time and adjusts the content of reminders according to progress. For example, it can notify the child, "You'll be done soon, keep trying a little harder." This makes it possible to adjust the content of reminders according to homework progress.

[0066] The progress management unit can optimize the frequency and timing of reminders to match the learning pace. For example, the generation AI analyzes a child's learning pace and optimizes the frequency and timing of reminders to match that pace. For example, it can notify the child, "If you continue at your current pace, you'll be done in 30 minutes." The generation AI can also optimize reminders to match the learning pace. For example, it can notify the child, "If you continue at your current pace, you'll be done in 1 hour." The generation AI can also analyze a child's learning pace and optimize the frequency and timing of reminders to match that pace. For example, it can notify the child, "If you continue at your current pace, you'll be done in 15 minutes." This makes it possible to optimize the frequency and timing of reminders to match the learning pace.

[0067] The progress management unit can use the emotion estimation function to send reminders according to the child's emotional state. For example, the generation AI in the progress management unit uses the emotion estimation function to grasp a child's emotional state in real time and send reminders according to that state. For example, when a child is relaxed, the generation AI can notify the child, "You should start working on your homework now." The generation AI also uses the emotion estimation function to send reminders according to the child's emotional state. For example, when a child is feeling stressed, the generation AI can suggest, "Take a short break before starting your homework." The generation AI also sends reminders according to the child's emotional state based on the emotion estimation data. For example, when a child is in a good mood, the generation AI can notify the child, "You should start working on your homework now." This makes it possible to send reminders according to the child's emotional state.

[0068] The progress management unit is capable of not only managing homework progress but also sending reminders for other learning activities. For example, the generation AI not only manages homework progress but also sends reminders for other learning activities such as reading and projects. For example, it can notify, "Did you find time to read today?" The generation AI can also send reminders for other learning activities. For example, it can notify, "How is the project progressing today?" The generation AI can also send reminders for learning activities other than homework. For example, it can notify, "Did you find time to read today?" This makes it possible to send reminders for learning activities other than homework.

[0069] The progress management unit can imitate the voice of the parent or guardian to send reminders in a format that is easy for children to understand. For example, the generation AI imitates the voice of the parent or guardian to send reminders in a format that is easy for children to understand. For example, it can notify the child in "Mom's voice, it's time for homework in 30 minutes." The generation AI also imitates the voice of the parent or guardian. For example, it can notify the child in "Dad's voice, it's a good idea to start working on your homework now." The generation AI also imitates the voice of the parent or guardian to send reminders in a format that is easy for children to understand. For example, it can notify the child in "Mom's voice, it's time for homework in 30 minutes." By imitating the voice of the parent or guardian, it is possible to send reminders in a format that is easy for children to understand.

[0070] The progress management unit can use the emotion estimation function to send reminders at times when the child is most relaxed. For example, the generation AI can use the emotion estimation function to identify times when the child is most relaxed and send reminders at those times. For example, it can notify the child, "Right now is a good time to get on with your homework because you're feeling relaxed." The generation AI can also use the emotion estimation function to send reminders at times when the child is relaxed. For example, it can notify the child, "Right now is a good time to get on with your homework." The generation AI can also send reminders at times when the child is relaxed based on the emotion estimation data. For example, it can notify the child, "Right now is a good time to get on with your homework because you're feeling relaxed." This makes it possible to send reminders at times when the child is most relaxed.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The homework management system may further include a reward management unit. The reward management unit provides rewards according to the child's progress in homework. For example, when a certain level of progress is made in homework, points may be awarded, and the points may be accumulated and exchanged for specific rewards. The reward management unit may also provide rewards according to the child's preferences. For example, a sticker of a child's favorite character or the right to participate in a specific activity may be provided as a reward. Furthermore, the reward management unit may also customize the content of rewards in cooperation with parents. For example, rewards may be provided based on specific household rules set by parents. This may increase the child's motivation for doing their homework.

[0073] The homework management system can further include a learning style analysis unit. The learning style analysis unit analyzes a child's learning style and suggests a learning method that is best suited to that style. For example, if a child prefers visual learning, it can suggest learning materials that make extensive use of diagrams and graphs. If a child prefers auditory learning, it can provide learning materials in the form of audio guides or podcasts. Furthermore, the learning style analysis unit can also suggest creating a learning environment that suits a child's learning style. For example, if a child prefers studying in a quiet environment, it can recommend the use of noise-canceling headphones. This allows children to study efficiently using a learning method that suits them best.

[0074] The homework management system can further include a health management unit. The health management unit monitors the child's health status and encourages them to take breaks at appropriate times. For example, if a child has been studying for a long period of time, it may notify the child to "take a short break." The health management unit can also detect the child's posture and eye fatigue and provide appropriate advice. For example, it can notify the child to "straighten up and rest their eyes." Furthermore, the health management unit can monitor the child's eating and sleeping habits and support a balanced lifestyle. For example, it can notify the child to "go to bed early today." This allows the child to continue their studies while maintaining their health.

[0075] The homework management system can also use its emotion estimation function to provide learning advice based on a child's emotional state. For example, if a child is feeling stressed, it can suggest, "Take a deep breath to relax a bit." Or, if a child is concentrating, it can advise, "Take on a difficult problem now." Furthermore, the emotion estimation function can also be used to adjust the learning environment based on a child's emotional state. For example, if a child is relaxed, it can recommend studying in a quiet environment. This allows a child to study in the optimal learning environment based on their emotional state.

[0076] A homework management system can also be equipped with a learning record section. The learning record section records a child's learning history in detail and shares it with parents and teachers. For example, it can record how much time a child spends on each subject and provide weekly or monthly learning status reports. The learning record section can also visualize a child's learning progress and results. For example, it can use graphs and charts to display a child's learning trends and grade trends. Furthermore, the learning record section can set a child's learning goals and track their progress. For example, a goal can be set such as "This week's goal is to complete 10 pages of math workbook," and the progress toward that goal can be recorded. This allows a child to understand their own learning situation and plan their studies toward their goals.

[0077] The homework management system can also use the emotion estimation function to send reminders according to the child's emotional state. For example, if a child is relaxed, the system can notify them by saying, "It's a good idea to get started on your homework now." If a child is feeling stressed, the system can suggest, "Take a short break before you get started on your homework." Furthermore, the emotion estimation data can be used to adjust the content of reminders according to the child's emotional state. For example, if a child is in a good mood, the system can notify them by saying, "It's a good idea to get started on your homework now." This makes it possible to send reminders according to the child's emotional state.

[0078] The homework management system may further include a learning content providing unit. The learning content providing unit provides appropriate learning content according to the child's learning situation. For example, it may provide supplementary videos or practice problems for subjects that the child is weak in. The learning content providing unit may also suggest learning content based on the child's interests. For example, if a child is interested in science, it may provide videos of science experiments or related articles. Furthermore, the learning content providing unit may also provide content that matches the child's learning pace. For example, if a child is learning at a fast pace, it may provide content with more advanced content. This allows the child to use the optimal learning content according to their learning situation and interests.

[0079] The homework management system can also use its emotion estimation function to provide learning advice based on a child's emotional state. For example, if a child is feeling stressed, it can suggest, "Take a deep breath to relax a bit." Or, if a child is concentrating, it can advise, "Take on a difficult problem now." Furthermore, the emotion estimation function can also be used to adjust the learning environment based on a child's emotional state. For example, if a child is relaxed, it can recommend studying in a quiet environment. This allows a child to study in the optimal learning environment based on their emotional state.

[0080] The homework management system can further include a parent-teacher collaboration unit. The parent-teacher collaboration unit collaborates with parents to share information about their children's learning status and provide appropriate support. For example, it notifies parents of their children's homework progress and learning results, allowing them to provide appropriate advice to their children. The parent-teacher collaboration unit can also receive feedback from parents and adjust system settings. For example, if a parent wants to focus on a particular subject, it can prioritize the provision of learning content related to that subject. Furthermore, the parent-teacher collaboration unit can support parents and children in creating study plans together. For example, it can set weekly learning goals and share the progress of those goals with parents. This makes it possible to collaborate with parents to support their children's learning.

[0081] The homework management system can also use its emotion estimation function to provide learning advice based on a child's emotional state. For example, if a child is feeling stressed, it can suggest, "Take a deep breath to relax a bit." Or, if a child is concentrating, it can advise, "Take on a difficult problem now." Furthermore, the emotion estimation function can also be used to adjust the learning environment based on a child's emotional state. For example, if a child is relaxed, it can recommend studying in a quiet environment. This allows a child to study in the optimal learning environment based on their emotional state.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The homework progress confirmation unit uses the generation AI to check the homework progress. For example, the generation AI uses text generation AI such as GPT-3 or BERT to ask the child about their homework progress using gentle and encouraging words. The generation AI can also analyze past homework history and generate questions based on individual progress. Step 2: The dialogue unit engages in dialogue to reduce the child's resistance based on the homework progress confirmed by the homework progress confirmation unit. For example, if a child resists by saying, "I was going to do it, but I don't feel like doing it anymore!", the generation AI will offer words of encouragement such as, "That's tough. But if you try it little by little, it might actually be fun." The generation AI can also learn patterns of children's resistance, predict it, and take measures in advance. Step 3: The progress management unit manages homework progress based on the dialogue carried out by the dialogue unit. For example, the generation AI monitors the child's homework progress in real time and adjusts the content of reminders according to the progress. The generation AI can also optimize the frequency and timing of reminders to match the child's learning pace. Step 4: The reminder section sends reminders at appropriate times based on the homework progress managed by the progress management section. For example, the generation AI might send a reminder such as, "Homework time in 30 minutes. Are you ready?" The generation AI can also use its emotion estimation function to send reminders based on the child's emotional state.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

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

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

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0130] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a homework progress confirmation unit that uses the generation AI to check the progress of homework; a dialogue unit that conducts dialogue to reduce the child's resistance based on the homework progress confirmed by the homework progress confirmation unit; a progress management unit that manages the progress of homework based on the dialogue performed by the dialogue unit; a reminder unit that sends reminders at appropriate times based on the progress of homework managed by the progress management unit. A system characterized by:

2. The homework progress confirmation unit Analyze homework history and generate questions tailored to individual progress when checking progress 2. The system of claim 1.

3. The homework progress confirmation unit Check your progress at the most convenient time based on your learning style and preferences 2. The system of claim 1.

4. The homework progress confirmation unit Understand emotional states in real time and check progress at the appropriate time 2. The system of claim 1.

5. The homework progress confirmation unit Check progress not only on homework but also on other daily tasks.

2. The system of claim 1.

6. The homework progress confirmation unit Imitate the parent's voice and check progress in a way that is easy for the child to understand 2. The system of claim 1.

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